diff --git a/src/ETL_cotier_wp5.ipynb b/src/ETL_cotier_wp5.ipynb
index 03c47d0d48a71e59034a374daf3bba1b9e0406a4..fb7e54782bf56d1a5b69e12d454e520b492c53a1 100644
--- a/src/ETL_cotier_wp5.ipynb
+++ b/src/ETL_cotier_wp5.ipynb
@@ -62,7 +62,12 @@
"id": "c16fc1d0a614563e"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:53.628161Z",
+ "start_time": "2024-07-26T16:06:53.596898Z"
+ }
+ },
"cell_type": "code",
"source": [
"from io import BytesIO\n",
@@ -85,7 +90,7 @@
],
"id": "1c7e2a949ba37f25",
"outputs": [],
- "execution_count": null
+ "execution_count": 619
},
{
"metadata": {},
@@ -94,7 +99,12 @@
"id": "d5e1523665ff0d0e"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.186443Z",
+ "start_time": "2024-07-26T16:06:55.167095Z"
+ }
+ },
"cell_type": "code",
"source": [
"OUTPUT_COLUMNS=['SITE','DATE','LATITUDE','LONGITUDE']\n",
@@ -116,7 +126,7 @@
],
"id": "98afebbd30de2843",
"outputs": [],
- "execution_count": null
+ "execution_count": 620
},
{
"metadata": {},
@@ -129,15 +139,37 @@
"id": "c256cecaa11bdb01"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.327799Z",
+ "start_time": "2024-07-26T16:06:55.285065Z"
+ }
+ },
"cell_type": "code",
"source": [
"hydromed = pd.DataFrame(columns=OUTPUT_COLUMNS)\n",
"hydromed.columns"
],
"id": "a4c195908e7c379b",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['SITE', 'DATE', 'LATITUDE', 'LONGITUDE', 'T', 'T_QUALITY', 'S',\n",
+ " 'S_QUALITY', 'NO2', 'NO2_QUALITY', 'NO3', 'NO3_QUALITY', 'NO2NO3',\n",
+ " 'NO2NO3_QUALITY', 'NH4', 'NH4_QUALITY', 'PO4', 'PO4_QUALITY', 'SIOH4',\n",
+ " 'SIOH4_QUALITY', 'CHLA', 'CHLA_QUALITY', 'CHLASPECTRO',\n",
+ " 'CHLASPECTRO_QUALITY', 'CHLAHPLC', 'CHLAHPLC_QUALITY', 'CHLAFLUO',\n",
+ " 'CHLAFLUO_QUALITY', 'DVCHLA', 'DVCHLA_QUALITY', 'SOURCE', 'DOI'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 621,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 621
},
{
"metadata": {},
@@ -152,12 +184,17 @@
"id": "abfc4256b72478ab"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.458281Z",
+ "start_time": "2024-07-26T16:06:55.442678Z"
+ }
+ },
"cell_type": "code",
"source": "HYDROMED_OUTPUT_FILE='../data/hydromed.csv'",
"id": "37205855d2a6dfa8",
"outputs": [],
- "execution_count": null
+ "execution_count": 622
},
{
"metadata": {},
@@ -174,12 +211,17 @@
"id": "fa0a6e347d89a79d"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.671965Z",
+ "start_time": "2024-07-26T16:06:55.640678Z"
+ }
+ },
"cell_type": "code",
"source": "HYDROMED_START_DATE='2000-01-01'",
"id": "34780eca1f57588c",
"outputs": [],
- "execution_count": null
+ "execution_count": 623
},
{
"metadata": {},
@@ -191,12 +233,17 @@
"id": "5fb0f28fdc31af33"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.742758Z",
+ "start_time": "2024-07-26T16:06:55.727174Z"
+ }
+ },
"cell_type": "code",
"source": "PICKLE_DIR='data'\n",
"id": "bec6dba2445b3022",
"outputs": [],
- "execution_count": null
+ "execution_count": 624
},
{
"metadata": {},
@@ -253,7 +300,12 @@
{
"cell_type": "code",
"id": "d17553a8",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:55.884289Z",
+ "start_time": "2024-07-26T16:06:55.868215Z"
+ }
+ },
"source": [
"SOMLIT_CSV_URL = \"https://www.seanoe.org/data/00891/100323/data/110694.csv\"\n",
"SOMLIT_CSV_SEPARATOR = \";\"\n",
@@ -263,7 +315,7 @@
"SOMLIT_PICKLED_DF = os.path.join(PICKLE_DIR,\"somlit.pickle\")"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 625
},
{
"cell_type": "markdown",
@@ -276,7 +328,12 @@
{
"cell_type": "code",
"id": "8c55b154",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.093282Z",
+ "start_time": "2024-07-26T16:06:56.014687Z"
+ }
+ },
"source": [
"somlit_df = None\n",
"if os.path.exists(SOMLIT_PICKLED_DF) :\n",
@@ -287,7 +344,7 @@
" pd.to_pickle(somlit_df,SOMLIT_PICKLED_DF)"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 626
},
{
"cell_type": "markdown",
@@ -310,7 +367,12 @@
{
"cell_type": "code",
"id": "fc327a12",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.123178Z",
+ "start_time": "2024-07-26T16:06:56.107515Z"
+ }
+ },
"source": [
"SOMLIT_COLUMNS_GENERIC_TYPES={'ID_SITE': 'string', 'DATE': 'datetime64[ns]','HEURE': 'string','PROF_TEXT': 'string','PROF_NUM': 'float'}\n",
"SOMLIT_COLUMNS_GENERIC_NAMES=list(SOMLIT_COLUMNS_GENERIC_TYPES.keys())\n",
@@ -326,7 +388,7 @@
"SOMLIT_COLUMNS_HYDRO_QUALITY_TYPES=dict(map(lambda n : (n,'float'),SOMLIT_COLUMNS_HYDRO_QUALITY_NAMES))"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 627
},
{
"cell_type": "markdown",
@@ -337,10 +399,15 @@
{
"cell_type": "code",
"id": "a76bd487",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.311721Z",
+ "start_time": "2024-07-26T16:06:56.282045Z"
+ }
+ },
"source": "somlit = somlit_df[SOMLIT_COLUMNS_GENERIC_NAMES+SOMLIT_COLUMNS_LOCATION+SOMLIT_COLUMNS_HYDRO_NAMES+SOMLIT_COLUMNS_HYDRO_QUALITY_NAMES]",
"outputs": [],
- "execution_count": null
+ "execution_count": 628
},
{
"cell_type": "markdown",
@@ -351,13 +418,18 @@
{
"cell_type": "code",
"id": "ae8b752eeb3b80aa",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.421620Z",
+ "start_time": "2024-07-26T16:06:56.358596Z"
+ }
+ },
"source": [
"somlit= somlit.astype(SOMLIT_COLUMNS_GENERIC_TYPES)\n",
"somlit= somlit.astype(SOMLIT_COLUMNS_HYDRO_TYPES)"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 629
},
{
"cell_type": "markdown",
@@ -368,12 +440,17 @@
{
"cell_type": "code",
"id": "bb949d89bcb915c2",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.547121Z",
+ "start_time": "2024-07-26T16:06:56.515847Z"
+ }
+ },
"source": [
"somlit = somlit.rename(columns=SOMLIT_COLUMNS_LOCATION_SANITIZED)"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 630
},
{
"cell_type": "markdown",
@@ -384,12 +461,379 @@
{
"cell_type": "code",
"id": "bef98bd0",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.767892Z",
+ "start_time": "2024-07-26T16:06:56.689703Z"
+ }
+ },
"source": [
"somlit"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "1 6 1997-01-15 14:40:00 S 1.0 Eyrac 44.6641 \n",
+ "2 6 1997-01-15 09:12:00 S 1.0 Eyrac 44.6641 \n",
+ "3 6 1997-01-15 14:40:00 F 6.0 Eyrac 44.6641 \n",
+ "4 6 1997-01-15 09:12:00 F 6.0 Eyrac 44.6641 \n",
+ "5 6 1997-02-19 08:55:00 S 1.0 Eyrac 44.6641 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17630 2 2023-05-05 13:19:00 S 1.0 Point L 50.6875 \n",
+ "17631 2 2023-10-16 14:40:00 F 50.0 Point L 50.6875 \n",
+ "17632 2 2023-10-16 14:40:00 S 1.0 Point L 50.6875 \n",
+ "17633 2 2023-11-28 13:14:00 F 50.0 Point L 50.6875 \n",
+ "17634 2 2023-11-28 13:14:00 S 1.0 Point L 50.6875 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 \\\n",
+ "1 -1.14313 7.3 24.500 ... 999999 1.42 7.0 7.0 7.0 7.0 \n",
+ "2 -1.14313 8.1 27.400 ... 999999 999999 2.0 2.0 2.0 9.0 \n",
+ "3 -1.14313 7.4 24.300 ... 999999 1.91 7.0 7.0 7.0 7.0 \n",
+ "4 -1.14313 8.3 27.700 ... 999999 999999 2.0 2.0 2.0 9.0 \n",
+ "5 -1.14313 9.9 26.000 ... 999999 999999 7.0 7.0 9.0 9.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... \n",
+ "17630 1.41667 999999.0 999999 ... 999999 1.11 9.0 9.0 9.0 9.0 \n",
+ "17631 1.41667 999999.0 999999 ... 999999 0.40 9.0 9.0 9.0 9.0 \n",
+ "17632 1.41667 999999.0 999999 ... 999999 0.69 9.0 9.0 9.0 9.0 \n",
+ "17633 1.41667 999999.0 999999 ... 999999 0.59 9.0 9.0 9.0 9.0 \n",
+ "17634 1.41667 999999.0 999999 ... 999999 0.53 9.0 9.0 9.0 9.0 \n",
+ "\n",
+ " qNH4 qPO4 qSIOH4 qCHLA \n",
+ "1 9.0 7.0 9.0 7.0 \n",
+ "2 9.0 2.0 9.0 9.0 \n",
+ "3 9.0 7.0 9.0 7.0 \n",
+ "4 9.0 2.0 9.0 9.0 \n",
+ "5 9.0 9.0 9.0 9.0 \n",
+ "... ... ... ... ... \n",
+ "17630 6.0 9.0 9.0 6.0 \n",
+ "17631 6.0 9.0 9.0 6.0 \n",
+ "17632 6.0 9.0 9.0 6.0 \n",
+ "17633 6.0 9.0 9.0 6.0 \n",
+ "17634 6.0 9.0 9.0 6.0 \n",
+ "\n",
+ "[17634 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6</td>\n",
+ " <td>1997-01-15</td>\n",
+ " <td>14:40:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Eyrac</td>\n",
+ " <td>44.6641</td>\n",
+ " <td>-1.14313</td>\n",
+ " <td>7.3</td>\n",
+ " <td>24.500</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>1.42</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>7.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6</td>\n",
+ " <td>1997-01-15</td>\n",
+ " <td>09:12:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Eyrac</td>\n",
+ " <td>44.6641</td>\n",
+ " <td>-1.14313</td>\n",
+ " <td>8.1</td>\n",
+ " <td>27.400</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>999999</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>6</td>\n",
+ " <td>1997-01-15</td>\n",
+ " <td>14:40:00</td>\n",
+ " <td>F</td>\n",
+ " <td>6.0</td>\n",
+ " <td>Eyrac</td>\n",
+ " <td>44.6641</td>\n",
+ " <td>-1.14313</td>\n",
+ " <td>7.4</td>\n",
+ " <td>24.300</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>1.91</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>7.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>6</td>\n",
+ " <td>1997-01-15</td>\n",
+ " <td>09:12:00</td>\n",
+ " <td>F</td>\n",
+ " <td>6.0</td>\n",
+ " <td>Eyrac</td>\n",
+ " <td>44.6641</td>\n",
+ " <td>-1.14313</td>\n",
+ " <td>8.3</td>\n",
+ " <td>27.700</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>999999</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>6</td>\n",
+ " <td>1997-02-19</td>\n",
+ " <td>08:55:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Eyrac</td>\n",
+ " <td>44.6641</td>\n",
+ " <td>-1.14313</td>\n",
+ " <td>9.9</td>\n",
+ " <td>26.000</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>999999</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17630</th>\n",
+ " <td>2</td>\n",
+ " <td>2023-05-05</td>\n",
+ " <td>13:19:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point L</td>\n",
+ " <td>50.6875</td>\n",
+ " <td>1.41667</td>\n",
+ " <td>999999.0</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>1.11</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17631</th>\n",
+ " <td>2</td>\n",
+ " <td>2023-10-16</td>\n",
+ " <td>14:40:00</td>\n",
+ " <td>F</td>\n",
+ " <td>50.0</td>\n",
+ " <td>Point L</td>\n",
+ " <td>50.6875</td>\n",
+ " <td>1.41667</td>\n",
+ " <td>999999.0</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.40</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17632</th>\n",
+ " <td>2</td>\n",
+ " <td>2023-10-16</td>\n",
+ " <td>14:40:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point L</td>\n",
+ " <td>50.6875</td>\n",
+ " <td>1.41667</td>\n",
+ " <td>999999.0</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.69</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17633</th>\n",
+ " <td>2</td>\n",
+ " <td>2023-11-28</td>\n",
+ " <td>13:14:00</td>\n",
+ " <td>F</td>\n",
+ " <td>50.0</td>\n",
+ " <td>Point L</td>\n",
+ " <td>50.6875</td>\n",
+ " <td>1.41667</td>\n",
+ " <td>999999.0</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.59</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17634</th>\n",
+ " <td>2</td>\n",
+ " <td>2023-11-28</td>\n",
+ " <td>13:14:00</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point L</td>\n",
+ " <td>50.6875</td>\n",
+ " <td>1.41667</td>\n",
+ " <td>999999.0</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.53</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>17634 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 631,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 631
},
{
"cell_type": "markdown",
@@ -407,7 +851,12 @@
{
"cell_type": "code",
"id": "6c1eb937",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:56.971495Z",
+ "start_time": "2024-07-26T16:06:56.783475Z"
+ }
+ },
"source": [
"somlit_map = Map(center=(48, 5), zoom=5, basemap=basemaps.Esri.OceanBasemap)\n",
"sites = somlit.groupby(['nomSite','gpsLat','gpsLong']).size().to_frame(name='size').reset_index()\n",
@@ -420,8 +869,24 @@
" somlit_map.add(marker) \n",
"somlit_map"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Map(center=[48, 5], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c70e9f7af1844e529cfa11681f3acc2b"
+ }
+ },
+ "execution_count": 632,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 632
},
{
"cell_type": "markdown",
@@ -432,12 +897,28 @@
{
"cell_type": "code",
"id": "0f5f4da9",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:57.503849Z",
+ "start_time": "2024-07-26T16:06:56.971495Z"
+ }
+ },
"source": [
"ax = sites.plot.bar(x='nomSite',y='samples', xlabel='Sampling Station', ylabel='Sample Size', title='Total Samples per Station')"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 633
},
{
"cell_type": "markdown",
@@ -460,7 +941,12 @@
{
"cell_type": "code",
"id": "68535534",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:57.535107Z",
+ "start_time": "2024-07-26T16:06:57.503849Z"
+ }
+ },
"source": [
"SOMLIT_SITE_FILTER={'fields' : ['nomSite'], 'values' :['Point B', 'Sola', 'Frioul', 'Sete']}\n",
"SOMLIT_DEPTH_CODE_FILTER={'fields' :['PROF_TEXT'], 'values': ['S']}\n",
@@ -472,7 +958,7 @@
"SOMLIT_CLEANING_FILTERS = [SOMLIT_QUALITY_FILTER, SOMLIT_IGNORE_VALUE_FILTER]"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 634
},
{
"cell_type": "markdown",
@@ -490,7 +976,12 @@
{
"cell_type": "code",
"id": "279951d5",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:57.550741Z",
+ "start_time": "2024-07-26T16:06:57.535107Z"
+ }
+ },
"source": [
"def apply_filters(df_in,filters):\n",
" df_out = df_in\n",
@@ -510,7 +1001,7 @@
" return df_out"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 635
},
{
"cell_type": "markdown",
@@ -526,7 +1017,12 @@
{
"cell_type": "code",
"id": "bfc368857bb35fcd",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:57.880535Z",
+ "start_time": "2024-07-26T16:06:57.864965Z"
+ }
+ },
"source": [
"def make_composite_labels(values):\n",
" def make_composite_label(pct):\n",
@@ -536,7 +1032,7 @@
" return make_composite_label\n"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 636
},
{
"cell_type": "markdown",
@@ -553,13 +1049,380 @@
{
"cell_type": "code",
"id": "4506ee5c",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:58.077459Z",
+ "start_time": "2024-07-26T16:06:57.986224Z"
+ }
+ },
"source": [
"somlit_filtered = apply_filters(somlit,[SOMLIT_SITE_FILTER])\n",
"somlit_filtered"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "57 10 1997-03-13 08:30:00 F 24.0 Sola 42.4883 \n",
+ "58 10 1997-03-13 08:30:00 S 3.0 Sola 42.4883 \n",
+ "59 10 1997-03-20 08:30:00 F 24.0 Sola 42.4883 \n",
+ "60 10 1997-03-20 08:30:00 S 3.0 Sola 42.4883 \n",
+ "61 10 1997-03-25 08:30:00 F 24.0 Sola 42.4883 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17600 12 2023-12-05 08:09:39 F 50.0 Point B 43.6833 \n",
+ "17601 12 2023-12-12 08:10:48 S 1.0 Point B 43.6833 \n",
+ "17602 12 2023-12-12 08:12:26 F 50.0 Point B 43.6833 \n",
+ "17603 12 2023-12-20 08:23:17 S 1.0 Point B 43.6833 \n",
+ "17604 12 2023-12-20 08:24:57 F 50.0 Point B 43.6833 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 qNH4 \\\n",
+ "57 3.145 13.210 37.390 ... 999999 0.64 2.0 2.0 2.0 9.0 2.0 \n",
+ "58 3.145 13.240 37.340 ... 999999 0.61 2.0 2.0 2.0 9.0 2.0 \n",
+ "59 3.145 13.380 37.400 ... 999999 0.99 2.0 2.0 2.0 9.0 2.0 \n",
+ "60 3.145 13.510 37.380 ... 999999 0.99 2.0 2.0 2.0 9.0 2.0 \n",
+ "61 3.145 13.420 37.440 ... 999999 0.61 2.0 2.0 2.0 9.0 2.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
+ "17600 7.31667 16.284 38.173 ... 0.71 0.42 2.0 2.0 8.0 0.0 6.0 \n",
+ "17601 7.31667 16.113 38.183 ... 0.77 0.39 2.0 2.0 8.0 8.0 6.0 \n",
+ "17602 7.31667 16.116 38.184 ... 0.72 0.36 2.0 2.0 8.0 0.0 6.0 \n",
+ "17603 7.31667 15.791 38.175 ... 0.86 0.36 2.0 2.0 8.0 8.0 0.0 \n",
+ "17604 7.31667 15.804 38.183 ... 0.89 0.32 2.0 2.0 8.0 8.0 0.0 \n",
+ "\n",
+ " qPO4 qSIOH4 qCHLA \n",
+ "57 9.0 9.0 2.0 \n",
+ "58 9.0 9.0 2.0 \n",
+ "59 9.0 9.0 2.0 \n",
+ "60 9.0 9.0 2.0 \n",
+ "61 9.0 9.0 2.0 \n",
+ "... ... ... ... \n",
+ "17600 8.0 8.0 2.0 \n",
+ "17601 8.0 8.0 2.0 \n",
+ "17602 8.0 8.0 2.0 \n",
+ "17603 0.0 8.0 2.0 \n",
+ "17604 8.0 8.0 2.0 \n",
+ "\n",
+ "[6921 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>57</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-13</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>F</td>\n",
+ " <td>24.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.210</td>\n",
+ " <td>37.390</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.64</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>58</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-13</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.240</td>\n",
+ " <td>37.340</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.61</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-20</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>F</td>\n",
+ " <td>24.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.380</td>\n",
+ " <td>37.400</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.99</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-20</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.510</td>\n",
+ " <td>37.380</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.99</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-25</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>F</td>\n",
+ " <td>24.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.420</td>\n",
+ " <td>37.440</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.61</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17600</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-05</td>\n",
+ " <td>08:09:39</td>\n",
+ " <td>F</td>\n",
+ " <td>50.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.284</td>\n",
+ " <td>38.173</td>\n",
+ " <td>...</td>\n",
+ " <td>0.71</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17601</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-12</td>\n",
+ " <td>08:10:48</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.113</td>\n",
+ " <td>38.183</td>\n",
+ " <td>...</td>\n",
+ " <td>0.77</td>\n",
+ " <td>0.39</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17602</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-12</td>\n",
+ " <td>08:12:26</td>\n",
+ " <td>F</td>\n",
+ " <td>50.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.116</td>\n",
+ " <td>38.184</td>\n",
+ " <td>...</td>\n",
+ " <td>0.72</td>\n",
+ " <td>0.36</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17603</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-20</td>\n",
+ " <td>08:23:17</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>15.791</td>\n",
+ " <td>38.175</td>\n",
+ " <td>...</td>\n",
+ " <td>0.86</td>\n",
+ " <td>0.36</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17604</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-20</td>\n",
+ " <td>08:24:57</td>\n",
+ " <td>F</td>\n",
+ " <td>50.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>15.804</td>\n",
+ " <td>38.183</td>\n",
+ " <td>...</td>\n",
+ " <td>0.89</td>\n",
+ " <td>0.32</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>6921 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 637,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 637
},
{
"cell_type": "markdown",
@@ -572,7 +1435,12 @@
{
"cell_type": "code",
"id": "1810e075",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:58.438733Z",
+ "start_time": "2024-07-26T16:06:58.391317Z"
+ }
+ },
"source": [
"somlit_map = Map(center=(48, 5), zoom=5, basemap=basemaps.OpenStreetMap.Mapnik)\n",
"\n",
@@ -581,8 +1449,24 @@
"[ somlit_map.add(Marker(title=f\"{sites['nomSite'][row]} - {sites['samples'][row]}\",location=(sites['gpsLat'][row],sites['gpsLong'][row]))) for row in sites.index] \n",
"somlit_map"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Map(center=[48, 5], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "477da9eeffa6413d90191ae39c5fc34a"
+ }
+ },
+ "execution_count": 638,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 638
},
{
"cell_type": "markdown",
@@ -595,13 +1479,29 @@
{
"cell_type": "code",
"id": "b6e2a1c3",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:58.734244Z",
+ "start_time": "2024-07-26T16:06:58.577110Z"
+ }
+ },
"source": [
"pie_sites=sites.set_index('nomSite',inplace=False)\n",
"ax = pie_sites.plot.pie(x=\"nomSite\", y='samples', title='Samples per Selected Station', xlabel='nomSite', autopct=make_composite_labels(pie_sites['samples']), legend = False)\n"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 639
},
{
"cell_type": "markdown",
@@ -612,13 +1512,380 @@
{
"cell_type": "code",
"id": "74bfa690",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:06:59.129893Z",
+ "start_time": "2024-07-26T16:06:58.991111Z"
+ }
+ },
"source": [
"somlit_filtered = apply_filters(somlit_filtered,[SOMLIT_DEPTH_CODE_FILTER])\n",
"somlit_filtered"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "58 10 1997-03-13 08:30:00 S 3.0 Sola 42.4883 \n",
+ "60 10 1997-03-20 08:30:00 S 3.0 Sola 42.4883 \n",
+ "62 10 1997-03-25 08:30:00 S 3.0 Sola 42.4883 \n",
+ "64 10 1997-04-01 08:30:00 S 3.0 Sola 42.4883 \n",
+ "66 10 1997-04-08 08:30:00 S 3.0 Sola 42.4883 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17595 12 2023-11-21 07:50:30 S 1.0 Point B 43.6833 \n",
+ "17597 12 2023-11-28 07:07:39 S 1.0 Point B 43.6833 \n",
+ "17599 12 2023-12-05 08:08:29 S 1.0 Point B 43.6833 \n",
+ "17601 12 2023-12-12 08:10:48 S 1.0 Point B 43.6833 \n",
+ "17603 12 2023-12-20 08:23:17 S 1.0 Point B 43.6833 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 qNH4 \\\n",
+ "58 3.145 13.240 37.340 ... 999999 0.61 2.0 2.0 2.0 9.0 2.0 \n",
+ "60 3.145 13.510 37.380 ... 999999 0.99 2.0 2.0 2.0 9.0 2.0 \n",
+ "62 3.145 13.420 37.470 ... 999999 0.61 2.0 2.0 2.0 9.0 2.0 \n",
+ "64 3.145 13.650 37.500 ... 999999 1.11 2.0 2.0 2.0 9.0 2.0 \n",
+ "66 3.145 14.110 37.480 ... 999999 0.85 2.0 2.0 2.0 9.0 2.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
+ "17595 7.31667 18.056 38.065 ... 0.64 0.74 2.0 2.0 0.0 0.0 6.0 \n",
+ "17597 7.31667 16.976 38.178 ... 0.49 0.51 2.0 2.0 0.0 0.0 6.0 \n",
+ "17599 7.31667 16.287 38.171 ... 0.65 0.42 2.0 2.0 8.0 0.0 6.0 \n",
+ "17601 7.31667 16.113 38.183 ... 0.77 0.39 2.0 2.0 8.0 8.0 6.0 \n",
+ "17603 7.31667 15.791 38.175 ... 0.86 0.36 2.0 2.0 8.0 8.0 0.0 \n",
+ "\n",
+ " qPO4 qSIOH4 qCHLA \n",
+ "58 9.0 9.0 2.0 \n",
+ "60 9.0 9.0 2.0 \n",
+ "62 9.0 9.0 2.0 \n",
+ "64 9.0 9.0 2.0 \n",
+ "66 9.0 9.0 2.0 \n",
+ "... ... ... ... \n",
+ "17595 8.0 8.0 3.0 \n",
+ "17597 0.0 8.0 2.0 \n",
+ "17599 8.0 8.0 2.0 \n",
+ "17601 8.0 8.0 2.0 \n",
+ "17603 0.0 8.0 2.0 \n",
+ "\n",
+ "[3422 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>58</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-13</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.240</td>\n",
+ " <td>37.340</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.61</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-20</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.510</td>\n",
+ " <td>37.380</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.99</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>62</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-03-25</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.420</td>\n",
+ " <td>37.470</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.61</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-04-01</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>13.650</td>\n",
+ " <td>37.500</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>1.11</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66</th>\n",
+ " <td>10</td>\n",
+ " <td>1997-04-08</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>14.110</td>\n",
+ " <td>37.480</td>\n",
+ " <td>...</td>\n",
+ " <td>999999</td>\n",
+ " <td>0.85</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17595</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-21</td>\n",
+ " <td>07:50:30</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>18.056</td>\n",
+ " <td>38.065</td>\n",
+ " <td>...</td>\n",
+ " <td>0.64</td>\n",
+ " <td>0.74</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17597</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-28</td>\n",
+ " <td>07:07:39</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.976</td>\n",
+ " <td>38.178</td>\n",
+ " <td>...</td>\n",
+ " <td>0.49</td>\n",
+ " <td>0.51</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17599</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-05</td>\n",
+ " <td>08:08:29</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.287</td>\n",
+ " <td>38.171</td>\n",
+ " <td>...</td>\n",
+ " <td>0.65</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17601</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-12</td>\n",
+ " <td>08:10:48</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.113</td>\n",
+ " <td>38.183</td>\n",
+ " <td>...</td>\n",
+ " <td>0.77</td>\n",
+ " <td>0.39</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17603</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-20</td>\n",
+ " <td>08:23:17</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>15.791</td>\n",
+ " <td>38.175</td>\n",
+ " <td>...</td>\n",
+ " <td>0.86</td>\n",
+ " <td>0.36</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>3422 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 640,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 640
},
{
"cell_type": "markdown",
@@ -631,24 +1898,45 @@
{
"cell_type": "code",
"id": "110ed006",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:00.030024Z",
+ "start_time": "2024-07-26T16:06:59.998711Z"
+ }
+ },
"source": [
"sites = somlit_filtered.groupby(['nomSite','gpsLat','gpsLong']).size().to_frame(name='size').reset_index()\n",
"sites.rename(columns={'size':'samples'}, inplace=True)"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 641
},
{
"cell_type": "code",
"id": "5ca6338a",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:00.547625Z",
+ "start_time": "2024-07-26T16:07:00.359524Z"
+ }
+ },
"source": [
"pie_sites=sites.set_index('nomSite',inplace=False)\n",
"ax = pie_sites.plot.pie(x=\"nomSite\", y='samples', title='Samples per Selected Station and Selected Depth', xlabel='nomSite', autopct=make_composite_labels(pie_sites['samples']), legend = False)\n"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 642
},
{
"cell_type": "markdown",
@@ -659,13 +1947,380 @@
{
"cell_type": "code",
"id": "a9ca16ba",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:00.793355Z",
+ "start_time": "2024-07-26T16:07:00.695698Z"
+ }
+ },
"source": [
"somlit_filtered = apply_filters(somlit_filtered,[SOMLIT_DATE_FILTER])\n",
"somlit_filtered"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "1558 10 2000-01-11 07:30:00 S 3.0 Sola 42.4883 \n",
+ "1560 10 2000-01-20 07:30:00 S 3.0 Sola 42.4883 \n",
+ "1562 10 2000-01-25 07:30:00 S 3.0 Sola 42.4883 \n",
+ "1564 10 2000-02-02 07:30:00 S 3.0 Sola 42.4883 \n",
+ "1566 10 2000-02-10 07:30:00 S 3.0 Sola 42.4883 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17595 12 2023-11-21 07:50:30 S 1.0 Point B 43.6833 \n",
+ "17597 12 2023-11-28 07:07:39 S 1.0 Point B 43.6833 \n",
+ "17599 12 2023-12-05 08:08:29 S 1.0 Point B 43.6833 \n",
+ "17601 12 2023-12-12 08:10:48 S 1.0 Point B 43.6833 \n",
+ "17603 12 2023-12-20 08:23:17 S 1.0 Point B 43.6833 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 qNH4 \\\n",
+ "1558 3.145 999999.000 999999 ... 1.94 0.34 9.0 9.0 2.0 9.0 2.0 \n",
+ "1560 3.145 10.989 37.539 ... 2.48 0.39 2.0 2.0 2.0 9.0 2.0 \n",
+ "1562 3.145 9.901 37.437 ... 2.63 0.47 2.0 2.0 2.0 9.0 2.0 \n",
+ "1564 3.145 999999.000 999999 ... 2.80 0.63 9.0 9.0 2.0 9.0 2.0 \n",
+ "1566 3.145 999999.000 999999 ... 2.25 0.68 9.0 9.0 2.0 9.0 2.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
+ "17595 7.31667 18.056 38.065 ... 0.64 0.74 2.0 2.0 0.0 0.0 6.0 \n",
+ "17597 7.31667 16.976 38.178 ... 0.49 0.51 2.0 2.0 0.0 0.0 6.0 \n",
+ "17599 7.31667 16.287 38.171 ... 0.65 0.42 2.0 2.0 8.0 0.0 6.0 \n",
+ "17601 7.31667 16.113 38.183 ... 0.77 0.39 2.0 2.0 8.0 8.0 6.0 \n",
+ "17603 7.31667 15.791 38.175 ... 0.86 0.36 2.0 2.0 8.0 8.0 0.0 \n",
+ "\n",
+ " qPO4 qSIOH4 qCHLA \n",
+ "1558 2.0 2.0 2.0 \n",
+ "1560 2.0 2.0 2.0 \n",
+ "1562 2.0 2.0 2.0 \n",
+ "1564 2.0 2.0 2.0 \n",
+ "1566 2.0 2.0 2.0 \n",
+ "... ... ... ... \n",
+ "17595 8.0 8.0 3.0 \n",
+ "17597 0.0 8.0 2.0 \n",
+ "17599 8.0 8.0 2.0 \n",
+ "17601 8.0 8.0 2.0 \n",
+ "17603 0.0 8.0 2.0 \n",
+ "\n",
+ "[3068 rows x 24 columns]"
+ ],
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1558</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-01-11</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>999999.000</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>1.94</td>\n",
+ " <td>0.34</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1560</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-01-20</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>10.989</td>\n",
+ " <td>37.539</td>\n",
+ " <td>...</td>\n",
+ " <td>2.48</td>\n",
+ " <td>0.39</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1562</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-01-25</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>9.901</td>\n",
+ " <td>37.437</td>\n",
+ " <td>...</td>\n",
+ " <td>2.63</td>\n",
+ " <td>0.47</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1564</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-02-02</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>999999.000</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>2.80</td>\n",
+ " <td>0.63</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1566</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-02-10</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>999999.000</td>\n",
+ " <td>999999</td>\n",
+ " <td>...</td>\n",
+ " <td>2.25</td>\n",
+ " <td>0.68</td>\n",
+ " <td>9.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17595</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-21</td>\n",
+ " <td>07:50:30</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>18.056</td>\n",
+ " <td>38.065</td>\n",
+ " <td>...</td>\n",
+ " <td>0.64</td>\n",
+ " <td>0.74</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17597</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-28</td>\n",
+ " <td>07:07:39</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.976</td>\n",
+ " <td>38.178</td>\n",
+ " <td>...</td>\n",
+ " <td>0.49</td>\n",
+ " <td>0.51</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17599</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-05</td>\n",
+ " <td>08:08:29</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.287</td>\n",
+ " <td>38.171</td>\n",
+ " <td>...</td>\n",
+ " <td>0.65</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17601</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-12</td>\n",
+ " <td>08:10:48</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.113</td>\n",
+ " <td>38.183</td>\n",
+ " <td>...</td>\n",
+ " <td>0.77</td>\n",
+ " <td>0.39</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17603</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-20</td>\n",
+ " <td>08:23:17</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>15.791</td>\n",
+ " <td>38.175</td>\n",
+ " <td>...</td>\n",
+ " <td>0.86</td>\n",
+ " <td>0.36</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>3068 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 643,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 643
},
{
"cell_type": "markdown",
@@ -678,15 +2333,31 @@
{
"cell_type": "code",
"id": "4f3ff13d",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:01.278198Z",
+ "start_time": "2024-07-26T16:07:01.152737Z"
+ }
+ },
"source": [
"sites = somlit_filtered.groupby(['nomSite','gpsLat','gpsLong']).size().to_frame(name='size').reset_index()\n",
"sites.rename(columns={'size':'samples'}, inplace=True)\n",
"pie_sites=sites.set_index('nomSite',inplace=False)\n",
"ax = pie_sites.plot.pie(x=\"nomSite\", y='samples', title='Samples per Selected Station, Depth and Date Range', xlabel='nomSite', autopct=make_composite_labels(pie_sites['samples']), legend = False)\n"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 644
},
{
"cell_type": "markdown",
@@ -697,13 +2368,380 @@
{
"cell_type": "code",
"id": "4f4ef9f6",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:01.693201Z",
+ "start_time": "2024-07-26T16:07:01.615061Z"
+ }
+ },
"source": [
"somlit_filtered = apply_filters(somlit_filtered,[SOMLIT_IGNORE_VALUE_FILTER])\n",
"somlit_filtered"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "1604 10 2000-06-27 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1607 10 2000-07-11 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1609 10 2000-07-18 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1615 10 2000-08-08 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1619 10 2000-08-23 06:30:00 S 3.0 Sola 42.4883 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17595 12 2023-11-21 07:50:30 S 1.0 Point B 43.6833 \n",
+ "17597 12 2023-11-28 07:07:39 S 1.0 Point B 43.6833 \n",
+ "17599 12 2023-12-05 08:08:29 S 1.0 Point B 43.6833 \n",
+ "17601 12 2023-12-12 08:10:48 S 1.0 Point B 43.6833 \n",
+ "17603 12 2023-12-20 08:23:17 S 1.0 Point B 43.6833 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 qNH4 \\\n",
+ "1604 3.145 20.489 37.264 ... 1.88 0.37 2.0 2.0 2.0 2.0 2.0 \n",
+ "1607 3.145 20.123 37.693 ... 1.34 0.19 2.0 2.0 2.0 2.0 2.0 \n",
+ "1609 3.145 19.062 37.931 ... 0.88 0.40 2.0 2.0 0.0 2.0 2.0 \n",
+ "1615 3.145 20.614 37.806 ... 0.58 0.14 2.0 2.0 2.0 2.0 2.0 \n",
+ "1619 3.145 22.577 37.266 ... 0.18 0.42 2.0 2.0 2.0 2.0 2.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
+ "17595 7.31667 18.056 38.065 ... 0.64 0.74 2.0 2.0 0.0 0.0 6.0 \n",
+ "17597 7.31667 16.976 38.178 ... 0.49 0.51 2.0 2.0 0.0 0.0 6.0 \n",
+ "17599 7.31667 16.287 38.171 ... 0.65 0.42 2.0 2.0 8.0 0.0 6.0 \n",
+ "17601 7.31667 16.113 38.183 ... 0.77 0.39 2.0 2.0 8.0 8.0 6.0 \n",
+ "17603 7.31667 15.791 38.175 ... 0.86 0.36 2.0 2.0 8.0 8.0 0.0 \n",
+ "\n",
+ " qPO4 qSIOH4 qCHLA \n",
+ "1604 2.0 2.0 2.0 \n",
+ "1607 2.0 2.0 2.0 \n",
+ "1609 2.0 2.0 2.0 \n",
+ "1615 2.0 2.0 2.0 \n",
+ "1619 2.0 2.0 2.0 \n",
+ "... ... ... ... \n",
+ "17595 8.0 8.0 3.0 \n",
+ "17597 0.0 8.0 2.0 \n",
+ "17599 8.0 8.0 2.0 \n",
+ "17601 8.0 8.0 2.0 \n",
+ "17603 0.0 8.0 2.0 \n",
+ "\n",
+ "[2078 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1604</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-06-27</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.489</td>\n",
+ " <td>37.264</td>\n",
+ " <td>...</td>\n",
+ " <td>1.88</td>\n",
+ " <td>0.37</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1607</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-07-11</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.123</td>\n",
+ " <td>37.693</td>\n",
+ " <td>...</td>\n",
+ " <td>1.34</td>\n",
+ " <td>0.19</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1609</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-07-18</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>19.062</td>\n",
+ " <td>37.931</td>\n",
+ " <td>...</td>\n",
+ " <td>0.88</td>\n",
+ " <td>0.40</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1615</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-08-08</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.614</td>\n",
+ " <td>37.806</td>\n",
+ " <td>...</td>\n",
+ " <td>0.58</td>\n",
+ " <td>0.14</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1619</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-08-23</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>22.577</td>\n",
+ " <td>37.266</td>\n",
+ " <td>...</td>\n",
+ " <td>0.18</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17595</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-21</td>\n",
+ " <td>07:50:30</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>18.056</td>\n",
+ " <td>38.065</td>\n",
+ " <td>...</td>\n",
+ " <td>0.64</td>\n",
+ " <td>0.74</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17597</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-11-28</td>\n",
+ " <td>07:07:39</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.976</td>\n",
+ " <td>38.178</td>\n",
+ " <td>...</td>\n",
+ " <td>0.49</td>\n",
+ " <td>0.51</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17599</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-05</td>\n",
+ " <td>08:08:29</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.287</td>\n",
+ " <td>38.171</td>\n",
+ " <td>...</td>\n",
+ " <td>0.65</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17601</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-12</td>\n",
+ " <td>08:10:48</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>16.113</td>\n",
+ " <td>38.183</td>\n",
+ " <td>...</td>\n",
+ " <td>0.77</td>\n",
+ " <td>0.39</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17603</th>\n",
+ " <td>12</td>\n",
+ " <td>2023-12-20</td>\n",
+ " <td>08:23:17</td>\n",
+ " <td>S</td>\n",
+ " <td>1.0</td>\n",
+ " <td>Point B</td>\n",
+ " <td>43.6833</td>\n",
+ " <td>7.31667</td>\n",
+ " <td>15.791</td>\n",
+ " <td>38.175</td>\n",
+ " <td>...</td>\n",
+ " <td>0.86</td>\n",
+ " <td>0.36</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2078 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 645,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 645
},
{
"cell_type": "markdown",
@@ -716,15 +2754,31 @@
{
"cell_type": "code",
"id": "da73416f",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:01.980682Z",
+ "start_time": "2024-07-26T16:07:01.855198Z"
+ }
+ },
"source": [
"sites = somlit_filtered.groupby(['nomSite','gpsLat','gpsLong']).size().to_frame(name='size').reset_index()\n",
"sites.rename(columns={'size':'samples'}, inplace=True)\n",
"pie_sites=sites.set_index('nomSite',inplace=False)\n",
"ax = pie_sites.plot.pie(x=\"nomSite\", y='samples', title='Remaining Samples per Station', xlabel='nomSite', autopct=make_composite_labels(pie_sites['samples']), legend = False)\n"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 646
},
{
"cell_type": "markdown",
@@ -735,13 +2789,380 @@
{
"cell_type": "code",
"id": "1c549e28",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:02.464098Z",
+ "start_time": "2024-07-26T16:07:02.397290Z"
+ }
+ },
"source": [
"somlit_filtered = apply_filters(somlit_filtered,[SOMLIT_QUALITY_FILTER])\n",
"somlit_filtered"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " ID_SITE DATE HEURE PROF_TEXT PROF_NUM nomSite gpsLat \\\n",
+ "1604 10 2000-06-27 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1607 10 2000-07-11 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1609 10 2000-07-18 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1615 10 2000-08-08 06:30:00 S 3.0 Sola 42.4883 \n",
+ "1619 10 2000-08-23 06:30:00 S 3.0 Sola 42.4883 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "17490 22 2023-03-13 08:30:00 S 2.0 Sete 43.3267 \n",
+ "17491 22 2023-03-28 08:00:00 S 2.0 Sete 43.3267 \n",
+ "17492 22 2023-04-12 07:30:00 S 2.0 Sete 43.3267 \n",
+ "17493 22 2023-04-25 07:45:00 S 2.0 Sete 43.3267 \n",
+ "17495 22 2023-05-24 07:30:00 S 2.0 Sete 43.3267 \n",
+ "\n",
+ " gpsLong T S ... SIOH4 CHLA qT qS qNO3 qNO2 qNH4 \\\n",
+ "1604 3.145 20.489 37.264 ... 1.88 0.37 2.0 2.0 2.0 2.0 2.0 \n",
+ "1607 3.145 20.123 37.693 ... 1.34 0.19 2.0 2.0 2.0 2.0 2.0 \n",
+ "1609 3.145 19.062 37.931 ... 0.88 0.40 2.0 2.0 0.0 2.0 2.0 \n",
+ "1615 3.145 20.614 37.806 ... 0.58 0.14 2.0 2.0 2.0 2.0 2.0 \n",
+ "1619 3.145 22.577 37.266 ... 0.18 0.42 2.0 2.0 2.0 2.0 2.0 \n",
+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
+ "17490 3.66167 12.848 38.084 ... 1.23 0.78 2.0 2.0 6.0 6.0 6.0 \n",
+ "17491 3.66167 12.681 37.921 ... 1.89 0.86 2.0 2.0 6.0 6.0 6.0 \n",
+ "17492 3.66167 13.737 37.924 ... 0.40 0.64 2.0 2.0 0.0 6.0 6.0 \n",
+ "17493 3.66167 14.954 36.346 ... 1.29 0.79 2.0 2.0 6.0 6.0 6.0 \n",
+ "17495 3.66167 16.275 37.423 ... 0.62 0.46 2.0 2.0 6.0 6.0 6.0 \n",
+ "\n",
+ " qPO4 qSIOH4 qCHLA \n",
+ "1604 2.0 2.0 2.0 \n",
+ "1607 2.0 2.0 2.0 \n",
+ "1609 2.0 2.0 2.0 \n",
+ "1615 2.0 2.0 2.0 \n",
+ "1619 2.0 2.0 2.0 \n",
+ "... ... ... ... \n",
+ "17490 6.0 6.0 2.0 \n",
+ "17491 6.0 6.0 2.0 \n",
+ "17492 6.0 6.0 2.0 \n",
+ "17493 6.0 6.0 2.0 \n",
+ "17495 6.0 6.0 2.0 \n",
+ "\n",
+ "[1465 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID_SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>HEURE</th>\n",
+ " <th>PROF_TEXT</th>\n",
+ " <th>PROF_NUM</th>\n",
+ " <th>nomSite</th>\n",
+ " <th>gpsLat</th>\n",
+ " <th>gpsLong</th>\n",
+ " <th>T</th>\n",
+ " <th>S</th>\n",
+ " <th>...</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>CHLA</th>\n",
+ " <th>qT</th>\n",
+ " <th>qS</th>\n",
+ " <th>qNO3</th>\n",
+ " <th>qNO2</th>\n",
+ " <th>qNH4</th>\n",
+ " <th>qPO4</th>\n",
+ " <th>qSIOH4</th>\n",
+ " <th>qCHLA</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1604</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-06-27</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.489</td>\n",
+ " <td>37.264</td>\n",
+ " <td>...</td>\n",
+ " <td>1.88</td>\n",
+ " <td>0.37</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1607</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-07-11</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.123</td>\n",
+ " <td>37.693</td>\n",
+ " <td>...</td>\n",
+ " <td>1.34</td>\n",
+ " <td>0.19</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1609</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-07-18</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>19.062</td>\n",
+ " <td>37.931</td>\n",
+ " <td>...</td>\n",
+ " <td>0.88</td>\n",
+ " <td>0.40</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1615</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-08-08</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.614</td>\n",
+ " <td>37.806</td>\n",
+ " <td>...</td>\n",
+ " <td>0.58</td>\n",
+ " <td>0.14</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1619</th>\n",
+ " <td>10</td>\n",
+ " <td>2000-08-23</td>\n",
+ " <td>06:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>3.0</td>\n",
+ " <td>Sola</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>22.577</td>\n",
+ " <td>37.266</td>\n",
+ " <td>...</td>\n",
+ " <td>0.18</td>\n",
+ " <td>0.42</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17490</th>\n",
+ " <td>22</td>\n",
+ " <td>2023-03-13</td>\n",
+ " <td>08:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Sete</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>12.848</td>\n",
+ " <td>38.084</td>\n",
+ " <td>...</td>\n",
+ " <td>1.23</td>\n",
+ " <td>0.78</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17491</th>\n",
+ " <td>22</td>\n",
+ " <td>2023-03-28</td>\n",
+ " <td>08:00:00</td>\n",
+ " <td>S</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Sete</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>12.681</td>\n",
+ " <td>37.921</td>\n",
+ " <td>...</td>\n",
+ " <td>1.89</td>\n",
+ " <td>0.86</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17492</th>\n",
+ " <td>22</td>\n",
+ " <td>2023-04-12</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Sete</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>13.737</td>\n",
+ " <td>37.924</td>\n",
+ " <td>...</td>\n",
+ " <td>0.40</td>\n",
+ " <td>0.64</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17493</th>\n",
+ " <td>22</td>\n",
+ " <td>2023-04-25</td>\n",
+ " <td>07:45:00</td>\n",
+ " <td>S</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Sete</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>14.954</td>\n",
+ " <td>36.346</td>\n",
+ " <td>...</td>\n",
+ " <td>1.29</td>\n",
+ " <td>0.79</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17495</th>\n",
+ " <td>22</td>\n",
+ " <td>2023-05-24</td>\n",
+ " <td>07:30:00</td>\n",
+ " <td>S</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Sete</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>16.275</td>\n",
+ " <td>37.423</td>\n",
+ " <td>...</td>\n",
+ " <td>0.62</td>\n",
+ " <td>0.46</td>\n",
+ " <td>2.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1465 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 647,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 647
},
{
"cell_type": "markdown",
@@ -754,15 +3175,31 @@
{
"cell_type": "code",
"id": "d02c6bcf",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:02.960222Z",
+ "start_time": "2024-07-26T16:07:02.818064Z"
+ }
+ },
"source": [
"sites = somlit_filtered.groupby(['nomSite','gpsLat','gpsLong']).size().to_frame(name='size').reset_index()\n",
"sites.rename(columns={'size':'samples'}, inplace=True)\n",
"pie_sites=sites.set_index('nomSite',inplace=False)\n",
"ax = pie_sites.plot.pie(x=\"nomSite\", y='samples', title='Samples per Selected Station', xlabel='nomSite', autopct=make_composite_labels(pie_sites['samples']), legend = False)\n"
],
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 648
},
{
"cell_type": "markdown",
@@ -777,13 +3214,18 @@
{
"cell_type": "code",
"id": "d88b6752b8c62a1",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:03.038984Z",
+ "start_time": "2024-07-26T16:07:03.023412Z"
+ }
+ },
"source": [
"somlit_filtered['SOURCE'] = 'SOMLIT'\n",
"somlit_filtered['DOI'] = 'https://doi.org/10.17882/100323'"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 649
},
{
"metadata": {},
@@ -799,7 +3241,12 @@
"id": "892e7ea606d01634"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:03.689321Z",
+ "start_time": "2024-07-26T16:07:03.456946Z"
+ }
+ },
"cell_type": "code",
"source": [
"SOMLIT_COLUMN_MAPPER={\n",
@@ -855,8 +3302,396 @@
"hydromed_somlit"
],
"id": "6967a38d6d648068",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " SITE DATE LATITUDE LONGITUDE T T_QUALITY \\\n",
+ "1604 Sola 2000-06-27 42.4883 3.145 20.489 Probably Good Value \n",
+ "1607 Sola 2000-07-11 42.4883 3.145 20.123 Probably Good Value \n",
+ "1609 Sola 2000-07-18 42.4883 3.145 19.062 Probably Good Value \n",
+ "1615 Sola 2000-08-08 42.4883 3.145 20.614 Probably Good Value \n",
+ "1619 Sola 2000-08-23 42.4883 3.145 22.577 Probably Good Value \n",
+ "... ... ... ... ... ... ... \n",
+ "17490 Sete 2023-03-13 43.3267 3.66167 12.848 Probably Good Value \n",
+ "17491 Sete 2023-03-28 43.3267 3.66167 12.681 Probably Good Value \n",
+ "17492 Sete 2023-04-12 43.3267 3.66167 13.737 Probably Good Value \n",
+ "17493 Sete 2023-04-25 43.3267 3.66167 14.954 Probably Good Value \n",
+ "17495 Sete 2023-05-24 43.3267 3.66167 16.275 Probably Good Value \n",
+ "\n",
+ " S S_QUALITY NO2 NO2_QUALITY ... \\\n",
+ "1604 37.264 Probably Good Value 0.01 Probably Good Value ... \n",
+ "1607 37.693 Probably Good Value 0.04 Probably Good Value ... \n",
+ "1609 37.931 Probably Good Value 0.05 Probably Good Value ... \n",
+ "1615 37.806 Probably Good Value 0.06 Probably Good Value ... \n",
+ "1619 37.266 Probably Good Value 0.02 Probably Good Value ... \n",
+ "... ... ... ... ... ... \n",
+ "17490 38.084 Probably Good Value 0.39 Value Below Limit of Detection ... \n",
+ "17491 37.921 Probably Good Value 0.61 Value Below Limit of Detection ... \n",
+ "17492 37.924 Probably Good Value 0.15 Value Below Limit of Detection ... \n",
+ "17493 36.346 Probably Good Value 0.27 Value Below Limit of Detection ... \n",
+ "17495 37.423 Probably Good Value 0.21 Value Below Limit of Detection ... \n",
+ "\n",
+ " NH4 NH4_QUALITY PO4 \\\n",
+ "1604 0.140 Probably Good Value 0.02 \n",
+ "1607 0.160 Probably Good Value 0.02 \n",
+ "1609 0.230 Probably Good Value 0.01 \n",
+ "1615 0.150 Probably Good Value 0.02 \n",
+ "1619 0.130 Probably Good Value 0.02 \n",
+ "... ... ... ... \n",
+ "17490 0.049 Value Below Limit of Detection 0.11 \n",
+ "17491 0.238 Value Below Limit of Detection 0.06 \n",
+ "17492 0.088 Value Below Limit of Detection 0.07 \n",
+ "17493 0.075 Value Below Limit of Detection 0.11 \n",
+ "17495 0.096 Value Below Limit of Detection 0.11 \n",
+ "\n",
+ " PO4_QUALITY SIOH4 SIOH4_QUALITY \\\n",
+ "1604 Probably Good Value 1.88 Probably Good Value \n",
+ "1607 Probably Good Value 1.34 Probably Good Value \n",
+ "1609 Probably Good Value 0.88 Probably Good Value \n",
+ "1615 Probably Good Value 0.58 Probably Good Value \n",
+ "1619 Probably Good Value 0.18 Probably Good Value \n",
+ "... ... ... ... \n",
+ "17490 Value Below Limit of Detection 1.23 Value Below Limit of Detection \n",
+ "17491 Value Below Limit of Detection 1.89 Value Below Limit of Detection \n",
+ "17492 Value Below Limit of Detection 0.40 Value Below Limit of Detection \n",
+ "17493 Value Below Limit of Detection 1.29 Value Below Limit of Detection \n",
+ "17495 Value Below Limit of Detection 0.62 Value Below Limit of Detection \n",
+ "\n",
+ " CHLAFLUO CHLAFLUO_QUALITY SOURCE DOI \n",
+ "1604 0.37 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "1607 0.19 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "1609 0.40 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "1615 0.14 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "1619 0.42 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "... ... ... ... ... \n",
+ "17490 0.78 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "17491 0.86 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "17492 0.64 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "17493 0.79 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "17495 0.46 Probably Good Value SOMLIT https://doi.org/10.17882/100323 \n",
+ "\n",
+ "[1465 rows x 24 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " <th>T</th>\n",
+ " <th>T_QUALITY</th>\n",
+ " <th>S</th>\n",
+ " <th>S_QUALITY</th>\n",
+ " <th>NO2</th>\n",
+ " <th>NO2_QUALITY</th>\n",
+ " <th>...</th>\n",
+ " <th>NH4</th>\n",
+ " <th>NH4_QUALITY</th>\n",
+ " <th>PO4</th>\n",
+ " <th>PO4_QUALITY</th>\n",
+ " <th>SIOH4</th>\n",
+ " <th>SIOH4_QUALITY</th>\n",
+ " <th>CHLAFLUO</th>\n",
+ " <th>CHLAFLUO_QUALITY</th>\n",
+ " <th>SOURCE</th>\n",
+ " <th>DOI</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1604</th>\n",
+ " <td>Sola</td>\n",
+ " <td>2000-06-27</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.489</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.264</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.01</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>...</td>\n",
+ " <td>0.140</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.02</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>1.88</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.37</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1607</th>\n",
+ " <td>Sola</td>\n",
+ " <td>2000-07-11</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.123</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.693</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.04</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>...</td>\n",
+ " <td>0.160</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.02</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>1.34</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.19</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1609</th>\n",
+ " <td>Sola</td>\n",
+ " <td>2000-07-18</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>19.062</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.931</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.05</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>...</td>\n",
+ " <td>0.230</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.01</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.88</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.40</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1615</th>\n",
+ " <td>Sola</td>\n",
+ " <td>2000-08-08</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>20.614</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.806</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.06</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>...</td>\n",
+ " <td>0.150</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.02</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.58</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.14</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1619</th>\n",
+ " <td>Sola</td>\n",
+ " <td>2000-08-23</td>\n",
+ " <td>42.4883</td>\n",
+ " <td>3.145</td>\n",
+ " <td>22.577</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.266</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.02</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>...</td>\n",
+ " <td>0.130</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.02</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.18</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.42</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17490</th>\n",
+ " <td>Sete</td>\n",
+ " <td>2023-03-13</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>12.848</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>38.084</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.39</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>...</td>\n",
+ " <td>0.049</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.11</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>1.23</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.78</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17491</th>\n",
+ " <td>Sete</td>\n",
+ " <td>2023-03-28</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>12.681</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.921</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.61</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>...</td>\n",
+ " <td>0.238</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.06</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>1.89</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.86</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17492</th>\n",
+ " <td>Sete</td>\n",
+ " <td>2023-04-12</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>13.737</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.924</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.15</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>...</td>\n",
+ " <td>0.088</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.07</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.40</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.64</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17493</th>\n",
+ " <td>Sete</td>\n",
+ " <td>2023-04-25</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>14.954</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>36.346</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.27</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>...</td>\n",
+ " <td>0.075</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.11</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>1.29</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.79</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17495</th>\n",
+ " <td>Sete</td>\n",
+ " <td>2023-05-24</td>\n",
+ " <td>43.3267</td>\n",
+ " <td>3.66167</td>\n",
+ " <td>16.275</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>37.423</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>0.21</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>...</td>\n",
+ " <td>0.096</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.11</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.62</td>\n",
+ " <td>Value Below Limit of Detection</td>\n",
+ " <td>0.46</td>\n",
+ " <td>Probably Good Value</td>\n",
+ " <td>SOMLIT</td>\n",
+ " <td>https://doi.org/10.17882/100323</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1465 rows × 24 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 650,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 650
},
{
"cell_type": "markdown",
@@ -893,13 +3728,18 @@
{
"cell_type": "code",
"id": "a58089d4",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:03.895205Z",
+ "start_time": "2024-07-26T16:07:03.879575Z"
+ }
+ },
"source": [
"SURVAL_URL='https://www.data.gouv.fr/fr/datasets/r/c14c8f0c-73c0-4138-8bce-df7f84f9f04c'\n",
"SURVAL_CSV='surval.csv'"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 651
},
{
"cell_type": "markdown",
@@ -912,13 +3752,18 @@
{
"cell_type": "code",
"id": "dfd438aa1993d540",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:04.148494Z",
+ "start_time": "2024-07-26T16:07:04.132988Z"
+ }
+ },
"source": [
"SURVAL_CSV_CHUNKSIZE=200000\n",
"SURVAL_PICKLED_DF=os.path.join(PICKLE_DIR,'surval.pickle')"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 652
},
{
"cell_type": "markdown",
@@ -931,12 +3776,17 @@
{
"cell_type": "code",
"id": "fcf9b02d",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:04.469990Z",
+ "start_time": "2024-07-26T16:07:04.454415Z"
+ }
+ },
"source": [
"SURVAL_PROGRAMME_FILTER='REPHY'"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 653
},
{
"metadata": {},
@@ -945,12 +3795,17 @@
"id": "b0054d968adc0af6"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:04.534430Z",
+ "start_time": "2024-07-26T16:07:04.519049Z"
+ }
+ },
"cell_type": "code",
"source": "REPHY_COLUMNS_KEEP=['SOUS_REGION_MARINE_DCSMM','PARAMETRE_CODE','DATE','SUPPORT_NIVEAU_PRELEVEMENT','RESULTAT','RESULTAT_COMMENTAIRE', 'RESULTAT_DESCRIPTION','LIEU_LIBELLE','PASSAGE_COORDONNEES']",
"id": "f32690eb254d703a",
"outputs": [],
- "execution_count": null
+ "execution_count": 654
},
{
"metadata": {},
@@ -973,7 +3828,12 @@
{
"cell_type": "code",
"id": "d7ce8d7c",
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:13.934738Z",
+ "start_time": "2024-07-26T16:07:05.023711Z"
+ }
+ },
"source": [
"surval_df = None\n",
"if os.path.exists(SURVAL_PICKLED_DF) :\n",
@@ -994,7 +3854,7 @@
"rephy = surval_df[REPHY_COLUMNS_KEEP]"
],
"outputs": [],
- "execution_count": null
+ "execution_count": 655
},
{
"metadata": {},
@@ -1013,7 +3873,12 @@
"id": "572797791e597a25"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:16.953284Z",
+ "start_time": "2024-07-26T16:07:13.934738Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy_res=rephy['RESULTAT'].astype(str)\n",
@@ -1022,11 +3887,33 @@
"rephy_res_unique"
],
"id": "c1c984da92c14fc9",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['', '<(<valeur)', '<(InfLD)', '<(InfLQ)', 'E-', '<E-(InfLQ)',\n",
+ " '<E-(InfLD)', '>(>valeur)', 'E', '>E(>valeur)', 'Absence',\n",
+ " 'typiquesavecdiarrhée', 'typiquessansdiarrhée',\n",
+ " 'atypiques:neurolconvulsion', 'typiquesansdiarrhée',\n",
+ " 'nonobservée:mortnocturne', 'survieaffaiblie',\n",
+ " 'typiqueavecdiarrhée', 'atypique:neurologique,convulsion', '<µm',\n",
+ " '>µm', '-', '>(>seuildesaturation)'], dtype=object)"
+ ]
+ },
+ "execution_count": 656,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 656
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:20.878529Z",
+ "start_time": "2024-07-26T16:07:16.953284Z"
+ }
+ },
"cell_type": "code",
"source": [
"if 'RESULTAT_QUALITE' not in REPHY_COLUMNS_KEEP:\n",
@@ -1040,7 +3927,7 @@
],
"id": "5b99e0777fc9ba6f",
"outputs": [],
- "execution_count": null
+ "execution_count": 657
},
{
"metadata": {},
@@ -1052,7 +3939,12 @@
"id": "29284b35088e0c69"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:22.385296Z",
+ "start_time": "2024-07-26T16:07:20.878529Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy=rephy[pd.to_numeric(rephy['RESULTAT'], errors='coerce').notnull()]\n",
@@ -1060,7 +3952,7 @@
],
"id": "869ba047713d5a31",
"outputs": [],
- "execution_count": null
+ "execution_count": 658
},
{
"metadata": {},
@@ -1074,7 +3966,12 @@
"id": "140f7faf9d87451a"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:23.935525Z",
+ "start_time": "2024-07-26T16:07:22.385296Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy['RESULTAT_COMMENTAIRE']=rephy['RESULTAT_COMMENTAIRE'].astype(str)\n",
@@ -1084,7 +3981,7 @@
],
"id": "693028b47b33b88a",
"outputs": [],
- "execution_count": null
+ "execution_count": 659
},
{
"metadata": {},
@@ -1099,7 +3996,12 @@
"id": "d64f7b0dca8f9408"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:23.951576Z",
+ "start_time": "2024-07-26T16:07:23.937032Z"
+ }
+ },
"cell_type": "code",
"source": [
"with open('../config/rephy_config.yml', 'r', encoding='UTF-8') as yamlfile:\n",
@@ -1108,10 +4010,15 @@
],
"id": "18869c9e91753cf9",
"outputs": [],
- "execution_count": null
+ "execution_count": 660
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:52.730200Z",
+ "start_time": "2024-07-26T16:07:23.951576Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy['METHOD']=rephy['RESULTAT_DESCRIPTION'].str.replace(\"^.*Méthode : (?P<method>.+) - Unité.*$\", lambda m : m.group(\"method\"),regex=True)\n",
@@ -1127,8 +4034,31 @@
"rephy['METHOD'].value_counts()"
],
"id": "46689b4dde461acb",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "METHOD\n",
+ "[FLORTOT: Comptage cellules au microscope - eau] 719645\n",
+ "[FLORPAR: Comptage cellules au microscope - eau] 223130\n",
+ "[TEMP: Capteur de température] 130167\n",
+ "[SALI: Capteur de conductivité] 120266\n",
+ "[FLORIND: Comptage cellules au microscope - eau] 102462\n",
+ " ... \n",
+ "[PSP_SEMI_QUANTI: PSP par CLHP-DFL] 1\n",
+ "[NO3+NO2: Méthode non définie] 1\n",
+ "[42-OH-PLTX: CL-SM/SM Palytoxines-like 2010] 1\n",
+ "[NO2: Colorimétrie selon Bend. / Rob.] 1\n",
+ "[Ostreo-D: CL-SM/SM Palytoxines-like 2010] 1\n",
+ "Name: count, Length: 224, dtype: int64"
+ ]
+ },
+ "execution_count": 661,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 661
},
{
"metadata": {},
@@ -1141,7 +4071,12 @@
"id": "9650ba0a1141d743"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:07:59.328130Z",
+ "start_time": "2024-07-26T16:07:52.737703Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy['PARAMETRE_CODE']=np.where(rephy['METHOD'].str.contains(\"\\[CHLOROA: Fluorimétrie\"),'CHLOROAFLUO',rephy['PARAMETRE_CODE'])\n",
@@ -1153,7 +4088,7 @@
],
"id": "c0693bb49c0c5596",
"outputs": [],
- "execution_count": null
+ "execution_count": 662
},
{
"metadata": {},
@@ -1168,7 +4103,12 @@
"id": "93cd990d4399657e"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:05.238600Z",
+ "start_time": "2024-07-26T16:07:59.328130Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy['LATITUDE']=pd.to_numeric(rephy['PASSAGE_COORDONNEES'].str.replace(\"^.*latitude\\s*(?P<coord>-?[0-9.]+).*$\",lambda r : r.group(\"coord\"), regex=True))\n",
@@ -1182,15 +4122,314 @@
],
"id": "ca9a10b34d6d148",
"outputs": [],
- "execution_count": null
+ "execution_count": 663
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:05.269881Z",
+ "start_time": "2024-07-26T16:08:05.238600Z"
+ }
+ },
"cell_type": "code",
"source": "rephy",
"id": "428fbddab9e3e2b8",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " SOUS_REGION_MARINE_DCSMM PARAMETRE_CODE \\\n",
+ "161735 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161736 NaN AO-total \n",
+ "161737 NaN AO-total \n",
+ "161738 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161739 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "... ... ... \n",
+ "13771691 DCSMM sous-région Golfe-de-Gascogne nord 45-OH-YTX \n",
+ "13771693 DCSMM sous-région Golfe-de-Gascogne nord 45-OH-YTX \n",
+ "13771694 DCSMM sous-région Golfe-de-Gascogne nord 45-OH-YTX \n",
+ "13771697 DCSMM sous-région Golfe-de-Gascogne nord 45-OH-YTX \n",
+ "13771701 DCSMM sous-région Golfe-de-Gascogne nord 45-OH-YTX \n",
+ "\n",
+ " DATE \\\n",
+ "161735 2023-02-14T10:00:00 \n",
+ "161736 2019-02-19T10:40:00 \n",
+ "161737 2019-02-25T10:10:00 \n",
+ "161738 2023-01-03T10:00:00 \n",
+ "161739 2023-01-09T11:00:00 \n",
+ "... ... \n",
+ "13771691 2023-09-18T10:30:00 \n",
+ "13771693 2023-09-25T14:30:00 \n",
+ "13771694 2023-10-02T13:44:00 \n",
+ "13771697 2024-05-27T10:05:00 \n",
+ "13771701 2024-05-30T12:30:00 \n",
+ "\n",
+ " SUPPORT_NIVEAU_PRELEVEMENT RESULTAT \\\n",
+ "161735 Support : Bivalve - Donax trunculus (donace tr... 165.8 \n",
+ "161736 Support : Bivalve - Mytilus galloprovincialis ... 75.9 \n",
+ "161737 Support : Bivalve - Mytilus galloprovincialis ... 82.9 \n",
+ "161738 Support : Bivalve - Donax trunculus (donace tr... 257.0 \n",
+ "161739 Support : Bivalve - Donax trunculus (donace tr... 336.9 \n",
+ "... ... ... \n",
+ "13771691 Support : Bivalve - Mytilus (moule) - Niveau :... 8.5 \n",
+ "13771693 Support : Bivalve - Mytilus (moule) - Niveau :... 9.0 \n",
+ "13771694 Support : Bivalve - Mytilus (moule) - Niveau :... 4.9 \n",
+ "13771697 Support : Bivalve - Mytilus (moule) - Niveau :... 766.2 \n",
+ "13771701 Support : Bivalve - Mytilus (moule) - Niveau :... 135.0 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE \\\n",
+ "161735 \n",
+ "161736 \n",
+ "161737 \n",
+ "161738 \n",
+ "161739 \n",
+ "... ... \n",
+ "13771691 \n",
+ "13771693 \n",
+ "13771694 \n",
+ "13771697 \n",
+ "13771701 \n",
+ "\n",
+ " LIEU_LIBELLE \\\n",
+ "161735 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161736 097-P-002 - Parc Leucate 2 \n",
+ "161737 097-P-002 - Parc Leucate 2 \n",
+ "161738 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161739 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "... ... \n",
+ "13771691 044-S-031 - Filières Sainte Marine \n",
+ "13771693 044-S-031 - Filières Sainte Marine \n",
+ "13771694 044-S-031 - Filières Sainte Marine \n",
+ "13771697 044-S-031 - Filières Sainte Marine \n",
+ "13771701 044-S-031 - Filières Sainte Marine \n",
+ "\n",
+ " RESULTAT_QUALITE METHOD \\\n",
+ "161735 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161736 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161737 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161738 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161739 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "... ... ... \n",
+ "13771691 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13771693 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13771694 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13771697 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13771701 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "\n",
+ " LATITUDE LONGITUDE \n",
+ "161735 43.005600 3.057470 \n",
+ "161736 42.872787 3.014369 \n",
+ "161737 42.872787 3.014369 \n",
+ "161738 43.005650 3.057510 \n",
+ "161739 43.005620 3.057330 \n",
+ "... ... ... \n",
+ "13771691 47.858050 -4.122250 \n",
+ "13771693 47.857767 -4.122650 \n",
+ "13771694 47.857033 -4.120833 \n",
+ "13771697 47.857300 -4.122162 \n",
+ "13771701 47.857050 -4.119700 \n",
+ "\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SOUS_REGION_MARINE_DCSMM</th>\n",
+ " <th>PARAMETRE_CODE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>SUPPORT_NIVEAU_PRELEVEMENT</th>\n",
+ " <th>RESULTAT</th>\n",
+ " <th>RESULTAT_COMMENTAIRE</th>\n",
+ " <th>LIEU_LIBELLE</th>\n",
+ " <th>RESULTAT_QUALITE</th>\n",
+ " <th>METHOD</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>161735</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-02-14T10:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>165.8</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005600</td>\n",
+ " <td>3.057470</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161736</th>\n",
+ " <td>NaN</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2019-02-19T10:40:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>75.9</td>\n",
+ " <td></td>\n",
+ " <td>097-P-002 - Parc Leucate 2</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>42.872787</td>\n",
+ " <td>3.014369</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161737</th>\n",
+ " <td>NaN</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2019-02-25T10:10:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>82.9</td>\n",
+ " <td></td>\n",
+ " <td>097-P-002 - Parc Leucate 2</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>42.872787</td>\n",
+ " <td>3.014369</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161738</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-03T10:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>257.0</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005650</td>\n",
+ " <td>3.057510</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161739</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-09T11:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>336.9</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005620</td>\n",
+ " <td>3.057330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13771691</th>\n",
+ " <td>DCSMM sous-région Golfe-de-Gascogne nord</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2023-09-18T10:30:00</td>\n",
+ " <td>Support : Bivalve - Mytilus (moule) - Niveau :...</td>\n",
+ " <td>8.5</td>\n",
+ " <td></td>\n",
+ " <td>044-S-031 - Filières Sainte Marine</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>47.858050</td>\n",
+ " <td>-4.122250</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13771693</th>\n",
+ " <td>DCSMM sous-région Golfe-de-Gascogne nord</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2023-09-25T14:30:00</td>\n",
+ " <td>Support : Bivalve - Mytilus (moule) - Niveau :...</td>\n",
+ " <td>9.0</td>\n",
+ " <td></td>\n",
+ " <td>044-S-031 - Filières Sainte Marine</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>47.857767</td>\n",
+ " <td>-4.122650</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13771694</th>\n",
+ " <td>DCSMM sous-région Golfe-de-Gascogne nord</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2023-10-02T13:44:00</td>\n",
+ " <td>Support : Bivalve - Mytilus (moule) - Niveau :...</td>\n",
+ " <td>4.9</td>\n",
+ " <td></td>\n",
+ " <td>044-S-031 - Filières Sainte Marine</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>47.857033</td>\n",
+ " <td>-4.120833</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13771697</th>\n",
+ " <td>DCSMM sous-région Golfe-de-Gascogne nord</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2024-05-27T10:05:00</td>\n",
+ " <td>Support : Bivalve - Mytilus (moule) - Niveau :...</td>\n",
+ " <td>766.2</td>\n",
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+ " <td>044-S-031 - Filières Sainte Marine</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
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+ " <td>-4.122162</td>\n",
+ " </tr>\n",
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+ " <th>13771701</th>\n",
+ " <td>DCSMM sous-région Golfe-de-Gascogne nord</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2024-05-30T12:30:00</td>\n",
+ " <td>Support : Bivalve - Mytilus (moule) - Niveau :...</td>\n",
+ " <td>135.0</td>\n",
+ " <td></td>\n",
+ " <td>044-S-031 - Filières Sainte Marine</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>47.857050</td>\n",
+ " <td>-4.119700</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1899201 rows × 11 columns</p>\n",
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+ ]
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+ "execution_count": 664,
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{
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@@ -1203,7 +4442,12 @@
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@@ -1212,8 +4456,140 @@
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+ " LIEU_LIBELLE LATITUDE LONGITUDE SAMPLES\n",
+ "0 001-P-015 - Point 1 Dunkerque 51.068650 2.333499 610\n",
+ "1 001-P-166 - Strouanne 50.905971 1.676990 5\n",
+ "2 002-P-002 - Bouchots Tardinghen 50.876817 1.614833 4\n",
+ "3 002-P-007 - Point 1 Boulogne 50.753132 1.548658 744\n",
+ "4 002-P-024 - Parc 10 n 50.763466 1.598659 49\n",
+ "... ... ... ... ...\n",
+ "1385 179-P-003 - Etang d'Ayguades - Ciné 43.132943 3.136297 1\n",
+ "1386 179-P-003 - Etang d'Ayguades - Ciné 43.132984 3.136304 1\n",
+ "1387 179-P-003 - Etang d'Ayguades - Ciné 43.133012 3.136398 1\n",
+ "1388 179-P-003 - Etang d'Ayguades - Ciné 43.133016 3.136458 1\n",
+ "1389 179-P-003 - Etang d'Ayguades - Ciné 43.133043 3.136495 1\n",
+ "\n",
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+ " <th>0</th>\n",
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+ " <td>1.676990</td>\n",
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@@ -1222,7 +4598,12 @@
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@@ -1266,7 +4663,7 @@
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@@ -1288,8 +4685,302 @@
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+ " SOUS_REGION_MARINE_DCSMM PARAMETRE_CODE \\\n",
+ "161735 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161738 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161739 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161740 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "161741 DCSMM sous-région Méditerranée-Occidentale AO-total \n",
+ "... ... ... \n",
+ "13762104 DCSMM sous-région Méditerranée-Occidentale 45-OH-YTX \n",
+ "13762105 DCSMM sous-région Méditerranée-Occidentale 45-OH-YTX \n",
+ "13762106 DCSMM sous-région Méditerranée-Occidentale 45-OH-YTX \n",
+ "13762167 DCSMM sous-région Méditerranée-Occidentale 45-OH-YTX \n",
+ "13764148 DCSMM sous-région Méditerranée-Occidentale 45-OH-YTX \n",
+ "\n",
+ " DATE \\\n",
+ "161735 2023-02-14T10:00:00 \n",
+ "161738 2023-01-03T10:00:00 \n",
+ "161739 2023-01-09T11:00:00 \n",
+ "161740 2023-01-17T10:30:00 \n",
+ "161741 2023-01-23T10:00:00 \n",
+ "... ... \n",
+ "13762104 2015-05-26T12:00:00 \n",
+ "13762105 2015-07-06T12:15:00 \n",
+ "13762106 2015-09-14T15:00:00 \n",
+ "13762167 2018-08-27T08:00:00 \n",
+ "13764148 2021-08-03T11:30:00 \n",
+ "\n",
+ " SUPPORT_NIVEAU_PRELEVEMENT RESULTAT \\\n",
+ "161735 Support : Bivalve - Donax trunculus (donace tr... 165.8 \n",
+ "161738 Support : Bivalve - Donax trunculus (donace tr... 257.0 \n",
+ "161739 Support : Bivalve - Donax trunculus (donace tr... 336.9 \n",
+ "161740 Support : Bivalve - Donax trunculus (donace tr... 309.1 \n",
+ "161741 Support : Bivalve - Donax trunculus (donace tr... 310.9 \n",
+ "... ... ... \n",
+ "13762104 Support : Bivalve - Mytilus galloprovincialis ... 59.6 \n",
+ "13762105 Support : Bivalve - Mytilus galloprovincialis ... 58.9 \n",
+ "13762106 Support : Bivalve - Mytilus galloprovincialis ... 14.6 \n",
+ "13762167 Support : Bivalve - Mytilus galloprovincialis ... 10.8 \n",
+ "13764148 Support : Bivalve - Donax trunculus (donace tr... 34.3 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE \\\n",
+ "161735 \n",
+ "161738 \n",
+ "161739 \n",
+ "161740 \n",
+ "161741 \n",
+ "... ... \n",
+ "13762104 \n",
+ "13762105 \n",
+ "13762106 \n",
+ "13762167 \n",
+ "13764148 \n",
+ "\n",
+ " LIEU_LIBELLE \\\n",
+ "161735 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161738 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161739 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161740 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "161741 095-P-022 - Bande Littorale - Port La Nouvelle... \n",
+ "... ... \n",
+ "13762104 112-P-001 - Lazaret (a) \n",
+ "13762105 112-P-001 - Lazaret (a) \n",
+ "13762106 112-P-001 - Lazaret (a) \n",
+ "13762167 117-P-001 - Diana mer \n",
+ "13764148 102-P-121 - Le Grand Travers Ouest \n",
+ "\n",
+ " RESULTAT_QUALITE METHOD \\\n",
+ "161735 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161738 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161739 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161740 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "161741 [AO-total: Good Value] [AO-total: ANSES PBM BM LSA_INS-0147] \n",
+ "... ... ... \n",
+ "13762104 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13762105 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13762106 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13762167 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "13764148 [45-OH-YTX: Good Value] [45-OH-YTX: ANSES PBM BM LSA_INS-0147] \n",
+ "\n",
+ " LATITUDE LONGITUDE \n",
+ "161735 43.005600 3.057470 \n",
+ "161738 43.005650 3.057510 \n",
+ "161739 43.005620 3.057330 \n",
+ "161740 43.005620 3.057360 \n",
+ "161741 43.005160 3.057200 \n",
+ "... ... ... \n",
+ "13762104 43.087319 5.906421 \n",
+ "13762105 43.087319 5.906421 \n",
+ "13762106 43.087319 5.906421 \n",
+ "13762167 42.148999 9.572345 \n",
+ "13764148 43.555949 4.034321 \n",
+ "\n",
+ "[218090 rows x 11 columns]"
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SOUS_REGION_MARINE_DCSMM</th>\n",
+ " <th>PARAMETRE_CODE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>SUPPORT_NIVEAU_PRELEVEMENT</th>\n",
+ " <th>RESULTAT</th>\n",
+ " <th>RESULTAT_COMMENTAIRE</th>\n",
+ " <th>LIEU_LIBELLE</th>\n",
+ " <th>RESULTAT_QUALITE</th>\n",
+ " <th>METHOD</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>161735</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-02-14T10:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>165.8</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005600</td>\n",
+ " <td>3.057470</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161738</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-03T10:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>257.0</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005650</td>\n",
+ " <td>3.057510</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161739</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-09T11:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>336.9</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005620</td>\n",
+ " <td>3.057330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161740</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-17T10:30:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>309.1</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005620</td>\n",
+ " <td>3.057360</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>161741</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>AO-total</td>\n",
+ " <td>2023-01-23T10:00:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>310.9</td>\n",
+ " <td></td>\n",
+ " <td>095-P-022 - Bande Littorale - Port La Nouvelle...</td>\n",
+ " <td>[AO-total: Good Value]</td>\n",
+ " <td>[AO-total: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.005160</td>\n",
+ " <td>3.057200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13762104</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2015-05-26T12:00:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>59.6</td>\n",
+ " <td></td>\n",
+ " <td>112-P-001 - Lazaret (a)</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.087319</td>\n",
+ " <td>5.906421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13762105</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2015-07-06T12:15:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>58.9</td>\n",
+ " <td></td>\n",
+ " <td>112-P-001 - Lazaret (a)</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.087319</td>\n",
+ " <td>5.906421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13762106</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2015-09-14T15:00:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>14.6</td>\n",
+ " <td></td>\n",
+ " <td>112-P-001 - Lazaret (a)</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.087319</td>\n",
+ " <td>5.906421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13762167</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2018-08-27T08:00:00</td>\n",
+ " <td>Support : Bivalve - Mytilus galloprovincialis ...</td>\n",
+ " <td>10.8</td>\n",
+ " <td></td>\n",
+ " <td>117-P-001 - Diana mer</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>42.148999</td>\n",
+ " <td>9.572345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13764148</th>\n",
+ " <td>DCSMM sous-région Méditerranée-Occidentale</td>\n",
+ " <td>45-OH-YTX</td>\n",
+ " <td>2021-08-03T11:30:00</td>\n",
+ " <td>Support : Bivalve - Donax trunculus (donace tr...</td>\n",
+ " <td>34.3</td>\n",
+ " <td></td>\n",
+ " <td>102-P-121 - Le Grand Travers Ouest</td>\n",
+ " <td>[45-OH-YTX: Good Value]</td>\n",
+ " <td>[45-OH-YTX: ANSES PBM BM LSA_INS-0147]</td>\n",
+ " <td>43.555949</td>\n",
+ " <td>4.034321</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>218090 rows × 11 columns</p>\n",
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+ },
+ "execution_count": 668,
+ "metadata": {},
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+ "execution_count": 668
},
{
"metadata": {},
@@ -1298,7 +4989,12 @@
"id": "16d4fd550eb79a0c"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
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"cell_type": "code",
"source": [
"rephy_map = Map(center=(48, 5), zoom=5, basemap=basemaps.OpenStreetMap.Mapnik)\n",
@@ -1309,8 +5005,24 @@
"rephy_map"
],
"id": "4b0a73bfee746dec",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
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+ "Map(center=[48, 5], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_text…"
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+ "execution_count": 669,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 669
},
{
"metadata": {},
@@ -1328,7 +5040,7 @@
],
"id": "8ae274fbed402bdc",
"outputs": [],
- "execution_count": null
+ "execution_count": 670
},
{
"metadata": {},
@@ -1348,8 +5060,264 @@
"rephy_filtered"
],
"id": "8f53407ebc6d7969",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
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+ " PARAMETRE_CODE DATE \\\n",
+ "1189593 CHLOROAFLUO 2006-04-18T09:00:00 \n",
+ "1189599 CHLOROAFLUO 2006-03-22T09:00:00 \n",
+ "1189600 CHLOROAFLUO 2006-04-10T09:00:00 \n",
+ "1189605 CHLOROAFLUO 2006-05-02T09:00:00 \n",
+ "1189606 CHLOROAFLUO 2006-06-07T09:00:00 \n",
+ "... ... ... \n",
+ "12283496 TEMP 2016-08-16T10:10:00 \n",
+ "12283501 TEMP 2016-02-01T08:45:00 \n",
+ "12283502 TEMP 2016-05-11T09:15:00 \n",
+ "12283503 TEMP 2016-05-23T09:10:00 \n",
+ "12283504 TEMP 2016-08-29T10:15:00 \n",
+ "\n",
+ " SUPPORT_NIVEAU_PRELEVEMENT RESULTAT \\\n",
+ "1189593 Support : Masse d'eau, eau brute - Niveau : Su... 0.99 \n",
+ "1189599 Support : Masse d'eau, eau brute - Niveau : Su... 2.56 \n",
+ "1189600 Support : Masse d'eau, eau brute - Niveau : Su... 0.93 \n",
+ "1189605 Support : Masse d'eau, eau brute - Niveau : Su... 0.39 \n",
+ "1189606 Support : Masse d'eau, eau brute - Niveau : Su... 0.40 \n",
+ "... ... ... \n",
+ "12283496 Niveau : Surface (0-1m) 23.60 \n",
+ "12283501 Niveau : Surface (0-1m) 11.40 \n",
+ "12283502 Niveau : Surface (0-1m) 16.30 \n",
+ "12283503 Niveau : Surface (0-1m) 17.40 \n",
+ "12283504 Niveau : Surface (0-1m) 24.30 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE LIEU_LIBELLE \\\n",
+ "1189593 094-P-002 - Banyuls-Sola \n",
+ "1189599 094-P-002 - Banyuls-Sola \n",
+ "1189600 094-P-002 - Banyuls-Sola \n",
+ "1189605 094-P-002 - Banyuls-Sola \n",
+ "1189606 094-P-002 - Banyuls-Sola \n",
+ "... ... ... \n",
+ "12283496 107-P-025 - Etang du Ponant - VVF \n",
+ "12283501 107-P-025 - Etang du Ponant - VVF \n",
+ "12283502 107-P-025 - Etang du Ponant - VVF \n",
+ "12283503 107-P-025 - Etang du Ponant - VVF \n",
+ "12283504 107-P-025 - Etang du Ponant - VVF \n",
+ "\n",
+ " RESULTAT_QUALITE METHOD \\\n",
+ "1189593 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189599 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189600 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189605 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189606 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "... ... ... \n",
+ "12283496 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283501 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283502 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283503 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283504 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "\n",
+ " LATITUDE LONGITUDE \n",
+ "1189593 42.488333 3.145000 \n",
+ "1189599 42.488333 3.145000 \n",
+ "1189600 42.488333 3.145000 \n",
+ "1189605 42.488333 3.145000 \n",
+ "1189606 42.488333 3.145000 \n",
+ "... ... ... \n",
+ "12283496 43.559067 4.101667 \n",
+ "12283501 43.559067 4.101667 \n",
+ "12283502 43.559067 4.101667 \n",
+ "12283503 43.559067 4.101667 \n",
+ "12283504 43.559067 4.101667 \n",
+ "\n",
+ "[37854 rows x 10 columns]"
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+ " <th></th>\n",
+ " <th>PARAMETRE_CODE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>SUPPORT_NIVEAU_PRELEVEMENT</th>\n",
+ " <th>RESULTAT</th>\n",
+ " <th>RESULTAT_COMMENTAIRE</th>\n",
+ " <th>LIEU_LIBELLE</th>\n",
+ " <th>RESULTAT_QUALITE</th>\n",
+ " <th>METHOD</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1189593</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-04-18T09:00:00</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.99</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
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+ " <tr>\n",
+ " <th>1189599</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-03-22T09:00:00</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>2.56</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189600</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-04-10T09:00:00</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.93</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189605</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-05-02T09:00:00</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.39</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189606</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-06-07T09:00:00</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.40</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
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+ " <tr>\n",
+ " <th>12283496</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-08-16T10:10:00</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>23.60</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
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+ " <tr>\n",
+ " <th>12283501</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-02-01T08:45:00</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>11.40</td>\n",
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+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
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+ " <th>12283502</th>\n",
+ " <td>TEMP</td>\n",
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+ " <td>TEMP</td>\n",
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+ " <td>17.40</td>\n",
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+ " <td>4.101667</td>\n",
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+ " <td>24.30</td>\n",
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+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
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@@ -1371,8 +5339,264 @@
"rephy_filtered "
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+ " PARAMETRE_CODE DATE \\\n",
+ "1189593 CHLOROAFLUO 2006-04-18 \n",
+ "1189599 CHLOROAFLUO 2006-03-22 \n",
+ "1189600 CHLOROAFLUO 2006-04-10 \n",
+ "1189605 CHLOROAFLUO 2006-05-02 \n",
+ "1189606 CHLOROAFLUO 2006-06-07 \n",
+ "... ... ... \n",
+ "12283496 TEMP 2016-08-16 \n",
+ "12283501 TEMP 2016-02-01 \n",
+ "12283502 TEMP 2016-05-11 \n",
+ "12283503 TEMP 2016-05-23 \n",
+ "12283504 TEMP 2016-08-29 \n",
+ "\n",
+ " SUPPORT_NIVEAU_PRELEVEMENT RESULTAT \\\n",
+ "1189593 Support : Masse d'eau, eau brute - Niveau : Su... 0.99 \n",
+ "1189599 Support : Masse d'eau, eau brute - Niveau : Su... 2.56 \n",
+ "1189600 Support : Masse d'eau, eau brute - Niveau : Su... 0.93 \n",
+ "1189605 Support : Masse d'eau, eau brute - Niveau : Su... 0.39 \n",
+ "1189606 Support : Masse d'eau, eau brute - Niveau : Su... 0.40 \n",
+ "... ... ... \n",
+ "12283496 Niveau : Surface (0-1m) 23.60 \n",
+ "12283501 Niveau : Surface (0-1m) 11.40 \n",
+ "12283502 Niveau : Surface (0-1m) 16.30 \n",
+ "12283503 Niveau : Surface (0-1m) 17.40 \n",
+ "12283504 Niveau : Surface (0-1m) 24.30 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE LIEU_LIBELLE \\\n",
+ "1189593 094-P-002 - Banyuls-Sola \n",
+ "1189599 094-P-002 - Banyuls-Sola \n",
+ "1189600 094-P-002 - Banyuls-Sola \n",
+ "1189605 094-P-002 - Banyuls-Sola \n",
+ "1189606 094-P-002 - Banyuls-Sola \n",
+ "... ... ... \n",
+ "12283496 107-P-025 - Etang du Ponant - VVF \n",
+ "12283501 107-P-025 - Etang du Ponant - VVF \n",
+ "12283502 107-P-025 - Etang du Ponant - VVF \n",
+ "12283503 107-P-025 - Etang du Ponant - VVF \n",
+ "12283504 107-P-025 - Etang du Ponant - VVF \n",
+ "\n",
+ " RESULTAT_QUALITE METHOD \\\n",
+ "1189593 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189599 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189600 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189605 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189606 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "... ... ... \n",
+ "12283496 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283501 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283502 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283503 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283504 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "\n",
+ " LATITUDE LONGITUDE \n",
+ "1189593 42.488333 3.145000 \n",
+ "1189599 42.488333 3.145000 \n",
+ "1189600 42.488333 3.145000 \n",
+ "1189605 42.488333 3.145000 \n",
+ "1189606 42.488333 3.145000 \n",
+ "... ... ... \n",
+ "12283496 43.559067 4.101667 \n",
+ "12283501 43.559067 4.101667 \n",
+ "12283502 43.559067 4.101667 \n",
+ "12283503 43.559067 4.101667 \n",
+ "12283504 43.559067 4.101667 \n",
+ "\n",
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+ " <th>1189593</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-04-18</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.99</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
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+ " <th>1189599</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-03-22</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>2.56</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
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+ " <tr>\n",
+ " <th>1189600</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-04-10</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.93</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189605</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-05-02</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.39</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
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+ " <tr>\n",
+ " <th>1189606</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-06-07</td>\n",
+ " <td>Support : Masse d'eau, eau brute - Niveau : Su...</td>\n",
+ " <td>0.40</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
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+ " <th>...</th>\n",
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+ " <td>...</td>\n",
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+ " <tr>\n",
+ " <th>12283496</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-08-16</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>23.60</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
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+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
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+ " <th>12283501</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-02-01</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>11.40</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
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+ " <th>12283502</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-05-11</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>16.30</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283503</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-05-23</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>17.40</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
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+ " <td>4.101667</td>\n",
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+ " <th>12283504</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-08-29</td>\n",
+ " <td>Niveau : Surface (0-1m)</td>\n",
+ " <td>24.30</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
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@@ -1391,8 +5615,226 @@
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+ " PARAMETRE_CODE DATE RESULTAT RESULTAT_COMMENTAIRE \\\n",
+ "1189593 CHLOROAFLUO 2006-04-18 0.99 \n",
+ "1189599 CHLOROAFLUO 2006-03-22 2.56 \n",
+ "1189600 CHLOROAFLUO 2006-04-10 0.93 \n",
+ "1189605 CHLOROAFLUO 2006-05-02 0.39 \n",
+ "1189606 CHLOROAFLUO 2006-06-07 0.40 \n",
+ "... ... ... ... ... \n",
+ "12283496 TEMP 2016-08-16 23.60 \n",
+ "12283501 TEMP 2016-02-01 11.40 \n",
+ "12283502 TEMP 2016-05-11 16.30 \n",
+ "12283503 TEMP 2016-05-23 17.40 \n",
+ "12283504 TEMP 2016-08-29 24.30 \n",
+ "\n",
+ " LIEU_LIBELLE RESULTAT_QUALITE \\\n",
+ "1189593 094-P-002 - Banyuls-Sola [CHLOROAFLUO: Good Value] \n",
+ "1189599 094-P-002 - Banyuls-Sola [CHLOROAFLUO: Good Value] \n",
+ "1189600 094-P-002 - Banyuls-Sola [CHLOROAFLUO: Good Value] \n",
+ "1189605 094-P-002 - Banyuls-Sola [CHLOROAFLUO: Good Value] \n",
+ "1189606 094-P-002 - Banyuls-Sola [CHLOROAFLUO: Good Value] \n",
+ "... ... ... \n",
+ "12283496 107-P-025 - Etang du Ponant - VVF [TEMP: Good Value] \n",
+ "12283501 107-P-025 - Etang du Ponant - VVF [TEMP: Good Value] \n",
+ "12283502 107-P-025 - Etang du Ponant - VVF [TEMP: Good Value] \n",
+ "12283503 107-P-025 - Etang du Ponant - VVF [TEMP: Good Value] \n",
+ "12283504 107-P-025 - Etang du Ponant - VVF [TEMP: Good Value] \n",
+ "\n",
+ " METHOD LATITUDE LONGITUDE \n",
+ "1189593 [CHLOROA: Fluorimétrie] 42.488333 3.145000 \n",
+ "1189599 [CHLOROA: Fluorimétrie] 42.488333 3.145000 \n",
+ "1189600 [CHLOROA: Fluorimétrie] 42.488333 3.145000 \n",
+ "1189605 [CHLOROA: Fluorimétrie] 42.488333 3.145000 \n",
+ "1189606 [CHLOROA: Fluorimétrie] 42.488333 3.145000 \n",
+ "... ... ... ... \n",
+ "12283496 [TEMP: Capteur de température] 43.559067 4.101667 \n",
+ "12283501 [TEMP: Capteur de température] 43.559067 4.101667 \n",
+ "12283502 [TEMP: Capteur de température] 43.559067 4.101667 \n",
+ "12283503 [TEMP: Capteur de température] 43.559067 4.101667 \n",
+ "12283504 [TEMP: Capteur de température] 43.559067 4.101667 \n",
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+ " <td>094-P-002 - Banyuls-Sola</td>\n",
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+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
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+ " <td>CHLOROAFLUO</td>\n",
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+ " <td>0.93</td>\n",
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+ " <td>3.145000</td>\n",
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+ " <th>1189605</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-05-02</td>\n",
+ " <td>0.39</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189606</th>\n",
+ " <td>CHLOROAFLUO</td>\n",
+ " <td>2006-06-07</td>\n",
+ " <td>0.40</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283496</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-08-16</td>\n",
+ " <td>23.60</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283501</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-02-01</td>\n",
+ " <td>11.40</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283502</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-05-11</td>\n",
+ " <td>16.30</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283503</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-05-23</td>\n",
+ " <td>17.40</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283504</th>\n",
+ " <td>TEMP</td>\n",
+ " <td>2016-08-29</td>\n",
+ " <td>24.30</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>30635 rows × 9 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 673,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 673
},
{
"metadata": {},
@@ -1412,7 +5854,12 @@
"id": "3635d0e0679261ef"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:28.404567Z",
+ "start_time": "2024-07-26T16:08:28.184770Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy_filtered_columns = rephy_filtered.copy()\n",
@@ -1434,8 +5881,335 @@
"rephy_filtered_columns"
],
"id": "3be6e5d74cc56662",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " DATE RESULTAT_COMMENTAIRE LIEU_LIBELLE \\\n",
+ "1189593 2006-04-18 094-P-002 - Banyuls-Sola \n",
+ "1189599 2006-03-22 094-P-002 - Banyuls-Sola \n",
+ "1189600 2006-04-10 094-P-002 - Banyuls-Sola \n",
+ "1189605 2006-05-02 094-P-002 - Banyuls-Sola \n",
+ "1189606 2006-06-07 094-P-002 - Banyuls-Sola \n",
+ "... ... ... ... \n",
+ "12283496 2016-08-16 107-P-025 - Etang du Ponant - VVF \n",
+ "12283501 2016-02-01 107-P-025 - Etang du Ponant - VVF \n",
+ "12283502 2016-05-11 107-P-025 - Etang du Ponant - VVF \n",
+ "12283503 2016-05-23 107-P-025 - Etang du Ponant - VVF \n",
+ "12283504 2016-08-29 107-P-025 - Etang du Ponant - VVF \n",
+ "\n",
+ " RESULTAT_QUALITE METHOD \\\n",
+ "1189593 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189599 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189600 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189605 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "1189606 [CHLOROAFLUO: Good Value] [CHLOROA: Fluorimétrie] \n",
+ "... ... ... \n",
+ "12283496 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283501 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283502 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283503 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "12283504 [TEMP: Good Value] [TEMP: Capteur de température] \n",
+ "\n",
+ " LATITUDE LONGITUDE CHLOROAFLUO CHLOROAHPLC CHLOROASPECTRO \\\n",
+ "1189593 42.488333 3.145000 0.99 NaN NaN \n",
+ "1189599 42.488333 3.145000 2.56 NaN NaN \n",
+ "1189600 42.488333 3.145000 0.93 NaN NaN \n",
+ "1189605 42.488333 3.145000 0.39 NaN NaN \n",
+ "1189606 42.488333 3.145000 0.40 NaN NaN \n",
+ "... ... ... ... ... ... \n",
+ "12283496 43.559067 4.101667 NaN NaN NaN \n",
+ "12283501 43.559067 4.101667 NaN NaN NaN \n",
+ "12283502 43.559067 4.101667 NaN NaN NaN \n",
+ "12283503 43.559067 4.101667 NaN NaN NaN \n",
+ "12283504 43.559067 4.101667 NaN NaN NaN \n",
+ "\n",
+ " DVCHLOROA NH4 NO3+NO2 PO4 SALI SIOH TEMP \n",
+ "1189593 NaN NaN NaN NaN NaN NaN NaN \n",
+ "1189599 NaN NaN NaN NaN NaN NaN NaN \n",
+ "1189600 NaN NaN NaN NaN NaN NaN NaN \n",
+ "1189605 NaN NaN NaN NaN NaN NaN NaN \n",
+ "1189606 NaN NaN NaN NaN NaN NaN NaN \n",
+ "... ... ... ... ... ... ... ... \n",
+ "12283496 NaN NaN NaN NaN NaN NaN 23.6 \n",
+ "12283501 NaN NaN NaN NaN NaN NaN 11.4 \n",
+ "12283502 NaN NaN NaN NaN NaN NaN 16.3 \n",
+ "12283503 NaN NaN NaN NaN NaN NaN 17.4 \n",
+ "12283504 NaN NaN NaN NaN NaN NaN 24.3 \n",
+ "\n",
+ "[30635 rows x 17 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>DATE</th>\n",
+ " <th>RESULTAT_COMMENTAIRE</th>\n",
+ " <th>LIEU_LIBELLE</th>\n",
+ " <th>RESULTAT_QUALITE</th>\n",
+ " <th>METHOD</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " <th>CHLOROAFLUO</th>\n",
+ " <th>CHLOROAHPLC</th>\n",
+ " <th>CHLOROASPECTRO</th>\n",
+ " <th>DVCHLOROA</th>\n",
+ " <th>NH4</th>\n",
+ " <th>NO3+NO2</th>\n",
+ " <th>PO4</th>\n",
+ " <th>SALI</th>\n",
+ " <th>SIOH</th>\n",
+ " <th>TEMP</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1189593</th>\n",
+ " <td>2006-04-18</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " <td>0.99</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189599</th>\n",
+ " <td>2006-03-22</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " <td>2.56</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189600</th>\n",
+ " <td>2006-04-10</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " <td>0.93</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189605</th>\n",
+ " <td>2006-05-02</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " <td>0.39</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1189606</th>\n",
+ " <td>2006-06-07</td>\n",
+ " <td></td>\n",
+ " <td>094-P-002 - Banyuls-Sola</td>\n",
+ " <td>[CHLOROAFLUO: Good Value]</td>\n",
+ " <td>[CHLOROA: Fluorimétrie]</td>\n",
+ " <td>42.488333</td>\n",
+ " <td>3.145000</td>\n",
+ " <td>0.40</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283496</th>\n",
+ " <td>2016-08-16</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>23.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283501</th>\n",
+ " <td>2016-02-01</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>11.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283502</th>\n",
+ " <td>2016-05-11</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283503</th>\n",
+ " <td>2016-05-23</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>17.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12283504</th>\n",
+ " <td>2016-08-29</td>\n",
+ " <td></td>\n",
+ " <td>107-P-025 - Etang du Ponant - VVF</td>\n",
+ " <td>[TEMP: Good Value]</td>\n",
+ " <td>[TEMP: Capteur de température]</td>\n",
+ " <td>43.559067</td>\n",
+ " <td>4.101667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>24.3</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>30635 rows × 17 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 674,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 674
},
{
"metadata": {},
@@ -1448,7 +6222,12 @@
"id": "594d1a932d5e021"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:29.399649Z",
+ "start_time": "2024-07-26T16:08:28.404567Z"
+ }
+ },
"cell_type": "code",
"source": [
"rephy_columns_groupby=REPHY_COLUMNS_KEEP[:]\n",
@@ -1463,16 +6242,397 @@
"rephy_filtered_columns"
],
"id": "aa21a7c0ead3664a",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " DATE LIEU_LIBELLE LATITUDE LONGITUDE \\\n",
+ "0 2000-01-03 102-P-016 - Espiguette 43.498852 4.115340 \n",
+ "1 2000-01-03 105-P-151 - Etang du Prévost 43.520838 3.909521 \n",
+ "2 2000-01-03 105-P-152 - Ingril sud 43.436139 3.773873 \n",
+ "3 2000-01-03 107-P-002 - Ponant embouchure 43.556978 4.100710 \n",
+ "4 2000-01-03 112-P-001 - Lazaret (a) 43.087319 5.906421 \n",
+ "... ... ... ... ... \n",
+ "11335 2024-06-03 106-P-011 - Rousty 43.457109 4.494486 \n",
+ "11336 2024-06-03 109-P-027 - Anse de Carteau 2 43.376780 4.884290 \n",
+ "11337 2024-06-03 109-S-155 - Piémanson 43.346551 4.783033 \n",
+ "11338 2024-06-04 112-P-001 - Lazaret (a) 43.087319 5.906421 \n",
+ "11339 2024-06-04 112-P-010 - 22B - Toulon gde rade 43.078999 5.954667 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE \\\n",
+ "0 [TEMP: Résultat ressaisi au bon niveau par la ... \n",
+ "1 [SALI: Résultat ressaisi au bon niveau par la ... \n",
+ "2 \n",
+ "3 [SALI: Résultat ressaisi au bon niveau par la ... \n",
+ "4 \n",
+ "... ... \n",
+ "11335 \n",
+ "11336 \n",
+ "11337 \n",
+ "11338 \n",
+ "11339 \n",
+ "\n",
+ " RESULTAT_QUALITE \\\n",
+ "0 [SALI: Good Value] [TEMP: Good Value] \n",
+ "1 [SALI: Good Value] [TEMP: Good Value] \n",
+ "2 [SALI: Good Value] [TEMP: Good Value] \n",
+ "3 [SALI: Good Value] [TEMP: Good Value] \n",
+ "4 [CHLOROASPECTRO: Good Value] [SALI: Good Value... \n",
+ "... ... \n",
+ "11335 [SALI: Good Value] [TEMP: Good Value] \n",
+ "11336 [SALI: Good Value] [TEMP: Good Value] \n",
+ "11337 [SALI: Good Value] [TEMP: Good Value] \n",
+ "11338 [SALI: Good Value] [TEMP: Good Value] \n",
+ "11339 [SALI: Good Value] [TEMP: Good Value] \n",
+ "\n",
+ " METHOD CHLOROAFLUO \\\n",
+ "0 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "1 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "2 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "3 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "4 [CHLOROA: Spectrophotométrie] [SALI: Capteur d... NaN \n",
+ "... ... ... \n",
+ "11335 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "11336 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "11337 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "11338 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "11339 [SALI: Capteur de conductivité] [TEMP: Capteur... NaN \n",
+ "\n",
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+ "11338 NaN NaN NaN NaN NaN NaN 37.41 NaN \n",
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@@ -1495,8 +6660,373 @@
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+ " DATE LIEU_LIBELLE LATITUDE LONGITUDE \\\n",
+ "0 2000-01-03 102-P-016 - Espiguette 43.498852 4.115340 \n",
+ "1 2000-01-03 105-P-151 - Etang du Prévost 43.520838 3.909521 \n",
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+ "4 2000-01-03 112-P-001 - Lazaret (a) 43.087319 5.906421 \n",
+ "... ... ... ... ... \n",
+ "11335 2024-06-03 106-P-011 - Rousty 43.457109 4.494486 \n",
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+ "11339 2024-06-04 112-P-010 - 22B - Toulon gde rade 43.078999 5.954667 \n",
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+ " RESULTAT_COMMENTAIRE \\\n",
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+ " RESULTAT_QUALITE \\\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>30.50</td>\n",
+ " <td>NaN</td>\n",
+ " <td>7.6</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2000-01-03</td>\n",
+ " <td>105-P-152 - Ingril sud</td>\n",
+ " <td>43.436139</td>\n",
+ " <td>3.773873</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>35.20</td>\n",
+ " <td>NaN</td>\n",
+ " <td>7.5</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2000-01-03</td>\n",
+ " <td>107-P-002 - Ponant embouchure</td>\n",
+ " <td>43.556978</td>\n",
+ " <td>4.100710</td>\n",
+ " <td>[SALI: Résultat ressaisi au bon niveau par la ...</td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13.20</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.1</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2000-01-03</td>\n",
+ " <td>112-P-001 - Lazaret (a)</td>\n",
+ " <td>43.087319</td>\n",
+ " <td>5.906421</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [SALI: Good Value...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [SALI: Capteur d...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.93</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>36.50</td>\n",
+ " <td>NaN</td>\n",
+ " <td>12.6</td>\n",
+ " <td>CHLOROASPECTRO,SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11335</th>\n",
+ " <td>2024-06-03</td>\n",
+ " <td>106-P-011 - Rousty</td>\n",
+ " <td>43.457109</td>\n",
+ " <td>4.494486</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>36.70</td>\n",
+ " <td>NaN</td>\n",
+ " <td>18.5</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11336</th>\n",
+ " <td>2024-06-03</td>\n",
+ " <td>109-P-027 - Anse de Carteau 2</td>\n",
+ " <td>43.376780</td>\n",
+ " <td>4.884290</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>36.60</td>\n",
+ " <td>NaN</td>\n",
+ " <td>18.4</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11337</th>\n",
+ " <td>2024-06-03</td>\n",
+ " <td>109-S-155 - Piémanson</td>\n",
+ " <td>43.346551</td>\n",
+ " <td>4.783033</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>37.60</td>\n",
+ " <td>NaN</td>\n",
+ " <td>18.5</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11338</th>\n",
+ " <td>2024-06-04</td>\n",
+ " <td>112-P-001 - Lazaret (a)</td>\n",
+ " <td>43.087319</td>\n",
+ " <td>5.906421</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>37.41</td>\n",
+ " <td>NaN</td>\n",
+ " <td>19.2</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11339</th>\n",
+ " <td>2024-06-04</td>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td></td>\n",
+ " <td>[SALI: Good Value] [TEMP: Good Value]</td>\n",
+ " <td>[SALI: Capteur de conductivité] [TEMP: Capteur...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>38.60</td>\n",
+ " <td>NaN</td>\n",
+ " <td>18.7</td>\n",
+ " <td>SALI,TEMP</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>11340 rows × 18 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 677,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 677
},
{
"metadata": {},
@@ -1505,15 +7035,43 @@
"id": "e4fc8b6308deacfe"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:29.587734Z",
+ "start_time": "2024-07-26T16:08:29.556433Z"
+ }
+ },
"cell_type": "code",
"source": [
"uniques=rephy_filtered_columns['VALID_PARAMETERS'].value_counts()\n",
"uniques.sort_values()"
],
"id": "441c42f6e2714b98",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "VALID_PARAMETERS\n",
+ "NH4,NO3+NO2,PO4,SIOH,TEMP 1\n",
+ "CHLOROAFLUO,NO3+NO2,SALI,TEMP 1\n",
+ "CHLOROASPECTRO,NH4,NO3+NO2,PO4,SIOH 1\n",
+ "CHLOROAFLUO,NH4,NO3+NO2,PO4,SALI,SIOH 1\n",
+ "CHLOROASPECTRO,NO3+NO2,PO4,SIOH 1\n",
+ " ... \n",
+ "CHLOROAFLUO,CHLOROAHPLC,DVCHLOROA,SALI,TEMP 178\n",
+ "CHLOROAFLUO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP 422\n",
+ "CHLOROAFLUO,SALI,TEMP 1020\n",
+ "CHLOROASPECTRO,SALI,TEMP 1329\n",
+ "SALI,TEMP 7195\n",
+ "Name: count, Length: 82, dtype: int64"
+ ]
+ },
+ "execution_count": 678,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 678
},
{
"metadata": {},
@@ -1528,7 +7086,12 @@
"id": "a6e08664c82c4e51"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:29.619007Z",
+ "start_time": "2024-07-26T16:08:29.587734Z"
+ }
+ },
"cell_type": "code",
"source": [
"min_parameter_list=REPHY_PARAMETER_LIST_FILTER[:]\n",
@@ -1561,11 +7124,41 @@
"valid_parameter_lists"
],
"id": "59996620192acc30",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAHPLC,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAHPLC,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROASPECTRO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROASPECTRO,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROAHPLC,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROAHPLC,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROASPECTRO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROASPECTRO,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAHPLC,CHLOROASPECTRO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAHPLC,CHLOROASPECTRO,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROAHPLC,CHLOROASPECTRO,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP',\n",
+ " 'CHLOROAFLUO,CHLOROAHPLC,CHLOROASPECTRO,DVCHLOROA,NH4,NO3+NO2,PO4,SALI,SIOH,TEMP']"
+ ]
+ },
+ "execution_count": 679,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 679
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:29.681996Z",
+ "start_time": "2024-07-26T16:08:29.619007Z"
+ }
+ },
"cell_type": "code",
"source": [
"\n",
@@ -1574,8 +7167,361 @@
"rephy_filtered_valid\n"
],
"id": "18fb48ea86ea1ce9",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " DATE LIEU_LIBELLE LATITUDE LONGITUDE \\\n",
+ "3616 2009-03-02 102-P-007 - Sète mer 43.389233 3.700686 \n",
+ "3632 2009-03-16 095-P-002 - Barcares 42.874567 3.057233 \n",
+ "3637 2009-03-16 106-P-011 - Rousty 43.457109 4.494486 \n",
+ "3638 2009-03-16 106-P-064 - Courbe 43.348639 4.695969 \n",
+ "3646 2009-03-18 113-P-010 - Ile du soleil 43.014832 6.419668 \n",
+ "... ... ... ... ... \n",
+ "11237 2024-01-15 112-P-010 - 22B - Toulon gde rade 43.078999 5.954667 \n",
+ "11248 2024-01-29 109-P-027 - Anse de Carteau 2 43.376780 4.884290 \n",
+ "11250 2024-01-29 112-P-010 - 22B - Toulon gde rade 43.078999 5.954667 \n",
+ "11251 2024-01-30 106-P-011 - Rousty 43.457109 4.494486 \n",
+ "11271 2024-02-13 112-P-010 - 22B - Toulon gde rade 43.078999 5.954667 \n",
+ "\n",
+ " RESULTAT_COMMENTAIRE \\\n",
+ "3616 \n",
+ "3632 \n",
+ "3637 \n",
+ "3638 \n",
+ "3646 \n",
+ "... ... \n",
+ "11237 [NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ... \n",
+ "11248 [NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ... \n",
+ "11250 [NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ... \n",
+ "11251 [NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ... \n",
+ "11271 [NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ... \n",
+ "\n",
+ " RESULTAT_QUALITE \\\n",
+ "3616 [CHLOROASPECTRO: Good Value] [NH4: Good Value]... \n",
+ "3632 [CHLOROASPECTRO: Good Value] [NH4: Good Value]... \n",
+ "3637 [CHLOROASPECTRO: Good Value] [NH4: Good Value]... \n",
+ "3638 [CHLOROASPECTRO: Good Value] [NH4: Good Value]... \n",
+ "3646 [CHLOROASPECTRO: Good Value] [NH4: Good Value]... \n",
+ "... ... \n",
+ "11237 [CHLOROAFLUO: Good Value] [NH4: Good Value] [N... \n",
+ "11248 [CHLOROAFLUO: Good Value] [NH4: Good Value] [N... \n",
+ "11250 [CHLOROAFLUO: Good Value] [NH4: Good Value] [N... \n",
+ "11251 [CHLOROAFLUO: Good Value] [NH4: Good Value] [N... \n",
+ "11271 [CHLOROAFLUO: Good Value] [NH4: Good Value] [N... \n",
+ "\n",
+ " METHOD CHLOROAFLUO \\\n",
+ "3616 [CHLOROA: Spectrophotométrie] [NH4: Fluorimétr... NaN \n",
+ "3632 [CHLOROA: Spectrophotométrie] [NH4: Fluorimétr... NaN \n",
+ "3637 [CHLOROA: Spectrophotométrie] [NH4: Fluorimétr... NaN \n",
+ "3638 [CHLOROA: Spectrophotométrie] [NH4: Fluorimétr... NaN \n",
+ "3646 [CHLOROA: Spectrophotométrie] [NH4: Fluorimétr... NaN \n",
+ "... ... ... \n",
+ "11237 [CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N... 0.24 \n",
+ "11248 [CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N... 2.17 \n",
+ "11250 [CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N... 0.71 \n",
+ "11251 [CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N... 1.33 \n",
+ "11271 [CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N... 0.51 \n",
+ "\n",
+ " CHLOROAHPLC CHLOROASPECTRO DVCHLOROA NH4 NO3+NO2 PO4 SALI \\\n",
+ "3616 NaN 0.658 NaN 0.260 2.600 0.060 37.40 \n",
+ "3632 NaN 0.762 NaN 0.100 1.290 0.000 36.50 \n",
+ "3637 NaN 1.110 NaN 0.409 1.770 0.090 33.80 \n",
+ "3638 NaN 0.830 NaN 0.073 1.370 0.032 33.70 \n",
+ "3646 NaN 0.420 NaN 0.076 0.384 0.031 37.50 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "11237 NaN NaN NaN 0.160 1.350 0.040 38.70 \n",
+ "11248 NaN NaN NaN 1.270 27.900 0.450 29.75 \n",
+ "11250 NaN NaN NaN 0.160 0.710 0.060 36.53 \n",
+ "11251 NaN NaN NaN 0.670 50.600 0.290 22.50 \n",
+ "11271 NaN NaN NaN 0.130 1.090 0.040 38.50 \n",
+ "\n",
+ " SIOH TEMP \n",
+ "3616 2.80 10.6 \n",
+ "3632 2.43 11.9 \n",
+ "3637 3.11 13.0 \n",
+ "3638 2.34 12.0 \n",
+ "3646 1.04 14.2 \n",
+ "... ... ... \n",
+ "11237 1.32 14.1 \n",
+ "11248 20.10 12.0 \n",
+ "11250 1.27 14.1 \n",
+ "11251 33.70 11.2 \n",
+ "11271 1.47 13.6 \n",
+ "\n",
+ "[560 rows x 17 columns]"
+ ],
+ "text/html": [
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>DATE</th>\n",
+ " <th>LIEU_LIBELLE</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " <th>RESULTAT_COMMENTAIRE</th>\n",
+ " <th>RESULTAT_QUALITE</th>\n",
+ " <th>METHOD</th>\n",
+ " <th>CHLOROAFLUO</th>\n",
+ " <th>CHLOROAHPLC</th>\n",
+ " <th>CHLOROASPECTRO</th>\n",
+ " <th>DVCHLOROA</th>\n",
+ " <th>NH4</th>\n",
+ " <th>NO3+NO2</th>\n",
+ " <th>PO4</th>\n",
+ " <th>SALI</th>\n",
+ " <th>SIOH</th>\n",
+ " <th>TEMP</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3616</th>\n",
+ " <td>2009-03-02</td>\n",
+ " <td>102-P-007 - Sète mer</td>\n",
+ " <td>43.389233</td>\n",
+ " <td>3.700686</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [NH4: Good Value]...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [NH4: Fluorimétr...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.658</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.260</td>\n",
+ " <td>2.600</td>\n",
+ " <td>0.060</td>\n",
+ " <td>37.40</td>\n",
+ " <td>2.80</td>\n",
+ " <td>10.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3632</th>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>095-P-002 - Barcares</td>\n",
+ " <td>42.874567</td>\n",
+ " <td>3.057233</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [NH4: Good Value]...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [NH4: Fluorimétr...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.762</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.100</td>\n",
+ " <td>1.290</td>\n",
+ " <td>0.000</td>\n",
+ " <td>36.50</td>\n",
+ " <td>2.43</td>\n",
+ " <td>11.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3637</th>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>106-P-011 - Rousty</td>\n",
+ " <td>43.457109</td>\n",
+ " <td>4.494486</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [NH4: Good Value]...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [NH4: Fluorimétr...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.110</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.409</td>\n",
+ " <td>1.770</td>\n",
+ " <td>0.090</td>\n",
+ " <td>33.80</td>\n",
+ " <td>3.11</td>\n",
+ " <td>13.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3638</th>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>106-P-064 - Courbe</td>\n",
+ " <td>43.348639</td>\n",
+ " <td>4.695969</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [NH4: Good Value]...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [NH4: Fluorimétr...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.830</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.073</td>\n",
+ " <td>1.370</td>\n",
+ " <td>0.032</td>\n",
+ " <td>33.70</td>\n",
+ " <td>2.34</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3646</th>\n",
+ " <td>2009-03-18</td>\n",
+ " <td>113-P-010 - Ile du soleil</td>\n",
+ " <td>43.014832</td>\n",
+ " <td>6.419668</td>\n",
+ " <td></td>\n",
+ " <td>[CHLOROASPECTRO: Good Value] [NH4: Good Value]...</td>\n",
+ " <td>[CHLOROA: Spectrophotométrie] [NH4: Fluorimétr...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.420</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.076</td>\n",
+ " <td>0.384</td>\n",
+ " <td>0.031</td>\n",
+ " <td>37.50</td>\n",
+ " <td>1.04</td>\n",
+ " <td>14.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11237</th>\n",
+ " <td>2024-01-15</td>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>[NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ...</td>\n",
+ " <td>[CHLOROAFLUO: Good Value] [NH4: Good Value] [N...</td>\n",
+ " <td>[CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N...</td>\n",
+ " <td>0.24</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.160</td>\n",
+ " <td>1.350</td>\n",
+ " <td>0.040</td>\n",
+ " <td>38.70</td>\n",
+ " <td>1.32</td>\n",
+ " <td>14.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11248</th>\n",
+ " <td>2024-01-29</td>\n",
+ " <td>109-P-027 - Anse de Carteau 2</td>\n",
+ " <td>43.376780</td>\n",
+ " <td>4.884290</td>\n",
+ " <td>[NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ...</td>\n",
+ " <td>[CHLOROAFLUO: Good Value] [NH4: Good Value] [N...</td>\n",
+ " <td>[CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N...</td>\n",
+ " <td>2.17</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.270</td>\n",
+ " <td>27.900</td>\n",
+ " <td>0.450</td>\n",
+ " <td>29.75</td>\n",
+ " <td>20.10</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11250</th>\n",
+ " <td>2024-01-29</td>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>[NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ...</td>\n",
+ " <td>[CHLOROAFLUO: Good Value] [NH4: Good Value] [N...</td>\n",
+ " <td>[CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N...</td>\n",
+ " <td>0.71</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.160</td>\n",
+ " <td>0.710</td>\n",
+ " <td>0.060</td>\n",
+ " <td>36.53</td>\n",
+ " <td>1.27</td>\n",
+ " <td>14.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11251</th>\n",
+ " <td>2024-01-30</td>\n",
+ " <td>106-P-011 - Rousty</td>\n",
+ " <td>43.457109</td>\n",
+ " <td>4.494486</td>\n",
+ " <td>[NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ...</td>\n",
+ " <td>[CHLOROAFLUO: Good Value] [NH4: Good Value] [N...</td>\n",
+ " <td>[CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N...</td>\n",
+ " <td>1.33</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.670</td>\n",
+ " <td>50.600</td>\n",
+ " <td>0.290</td>\n",
+ " <td>22.50</td>\n",
+ " <td>33.70</td>\n",
+ " <td>11.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11271</th>\n",
+ " <td>2024-02-13</td>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>[NH4: Cofrac] [NO3+NO2: Cofrac] [PO4: Cofrac] ...</td>\n",
+ " <td>[CHLOROAFLUO: Good Value] [NH4: Good Value] [N...</td>\n",
+ " <td>[CHLOROA: Fluorimétrie] [NH4: Fluorimétrie] [N...</td>\n",
+ " <td>0.51</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.130</td>\n",
+ " <td>1.090</td>\n",
+ " <td>0.040</td>\n",
+ " <td>38.50</td>\n",
+ " <td>1.47</td>\n",
+ " <td>13.6</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>560 rows × 17 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 680,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 680
},
{
"metadata": {},
@@ -1584,7 +7530,12 @@
"id": "c940b12ba4e2e9f"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:29.838633Z",
+ "start_time": "2024-07-26T16:08:29.681996Z"
+ }
+ },
"cell_type": "code",
"source": [
"hydromed_rephy = pd.DataFrame(columns=OUTPUT_COLUMNS)\n",
@@ -1647,8 +7598,396 @@
"hydromed_rephy"
],
"id": "2067d4ea2d47cdda",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " SITE DATE LATITUDE LONGITUDE \\\n",
+ "3616 102-P-007 - Sète mer 2009-03-02 43.389233 3.700686 \n",
+ "3632 095-P-002 - Barcares 2009-03-16 42.874567 3.057233 \n",
+ "3637 106-P-011 - Rousty 2009-03-16 43.457109 4.494486 \n",
+ "3638 106-P-064 - Courbe 2009-03-16 43.348639 4.695969 \n",
+ "3646 113-P-010 - Ile du soleil 2009-03-18 43.014832 6.419668 \n",
+ "... ... ... ... ... \n",
+ "11237 112-P-010 - 22B - Toulon gde rade 2024-01-15 43.078999 5.954667 \n",
+ "11248 109-P-027 - Anse de Carteau 2 2024-01-29 43.376780 4.884290 \n",
+ "11250 112-P-010 - 22B - Toulon gde rade 2024-01-29 43.078999 5.954667 \n",
+ "11251 106-P-011 - Rousty 2024-01-30 43.457109 4.494486 \n",
+ "11271 112-P-010 - 22B - Toulon gde rade 2024-02-13 43.078999 5.954667 \n",
+ "\n",
+ " T T_QUALITY S S_QUALITY NO2 NO2_QUALITY ... CHLASPECTRO \\\n",
+ "3616 10.6 Good Value 37.40 Good Value ... 0.658 \n",
+ "3632 11.9 Good Value 36.50 Good Value ... 0.762 \n",
+ "3637 13.0 Good Value 33.80 Good Value ... 1.11 \n",
+ "3638 12.0 Good Value 33.70 Good Value ... 0.83 \n",
+ "3646 14.2 Good Value 37.50 Good Value ... 0.42 \n",
+ "... ... ... ... ... .. ... ... ... \n",
+ "11237 14.1 Good Value 38.70 Good Value ... \n",
+ "11248 12.0 Good Value 29.75 Good Value ... \n",
+ "11250 14.1 Good Value 36.53 Good Value ... \n",
+ "11251 11.2 Good Value 22.50 Good Value ... \n",
+ "11271 13.6 Good Value 38.50 Good Value ... \n",
+ "\n",
+ " CHLASPECTRO_QUALITY CHLAHPLC CHLAHPLC_QUALITY CHLAFLUO \\\n",
+ "3616 Good Value \n",
+ "3632 Good Value \n",
+ "3637 Good Value \n",
+ "3638 Good Value \n",
+ "3646 Good Value \n",
+ "... ... ... ... ... \n",
+ "11237 0.24 \n",
+ "11248 2.17 \n",
+ "11250 0.71 \n",
+ "11251 1.33 \n",
+ "11271 0.51 \n",
+ "\n",
+ " CHLAFLUO_QUALITY DVCHLA DVCHLA_QUALITY SOURCE \\\n",
+ "3616 REPHY \n",
+ "3632 REPHY \n",
+ "3637 REPHY \n",
+ "3638 REPHY \n",
+ "3646 REPHY \n",
+ "... ... ... ... ... \n",
+ "11237 Good Value REPHY \n",
+ "11248 Good Value REPHY \n",
+ "11250 Good Value REPHY \n",
+ "11251 Good Value REPHY \n",
+ "11271 Good Value REPHY \n",
+ "\n",
+ " DOI \n",
+ "3616 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "3632 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "3637 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "3638 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "3646 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "... ... \n",
+ "11237 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "11248 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "11250 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "11251 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "11271 https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba... \n",
+ "\n",
+ "[560 rows x 32 columns]"
+ ],
+ "text/html": [
+ "<div>\n",
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+ " vertical-align: middle;\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SITE</th>\n",
+ " <th>DATE</th>\n",
+ " <th>LATITUDE</th>\n",
+ " <th>LONGITUDE</th>\n",
+ " <th>T</th>\n",
+ " <th>T_QUALITY</th>\n",
+ " <th>S</th>\n",
+ " <th>S_QUALITY</th>\n",
+ " <th>NO2</th>\n",
+ " <th>NO2_QUALITY</th>\n",
+ " <th>...</th>\n",
+ " <th>CHLASPECTRO</th>\n",
+ " <th>CHLASPECTRO_QUALITY</th>\n",
+ " <th>CHLAHPLC</th>\n",
+ " <th>CHLAHPLC_QUALITY</th>\n",
+ " <th>CHLAFLUO</th>\n",
+ " <th>CHLAFLUO_QUALITY</th>\n",
+ " <th>DVCHLA</th>\n",
+ " <th>DVCHLA_QUALITY</th>\n",
+ " <th>SOURCE</th>\n",
+ " <th>DOI</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3616</th>\n",
+ " <td>102-P-007 - Sète mer</td>\n",
+ " <td>2009-03-02</td>\n",
+ " <td>43.389233</td>\n",
+ " <td>3.700686</td>\n",
+ " <td>10.6</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>37.40</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td>0.658</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3632</th>\n",
+ " <td>095-P-002 - Barcares</td>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>42.874567</td>\n",
+ " <td>3.057233</td>\n",
+ " <td>11.9</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>36.50</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td>0.762</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3637</th>\n",
+ " <td>106-P-011 - Rousty</td>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>43.457109</td>\n",
+ " <td>4.494486</td>\n",
+ " <td>13.0</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>33.80</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td>1.11</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3638</th>\n",
+ " <td>106-P-064 - Courbe</td>\n",
+ " <td>2009-03-16</td>\n",
+ " <td>43.348639</td>\n",
+ " <td>4.695969</td>\n",
+ " <td>12.0</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>33.70</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td>0.83</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3646</th>\n",
+ " <td>113-P-010 - Ile du soleil</td>\n",
+ " <td>2009-03-18</td>\n",
+ " <td>43.014832</td>\n",
+ " <td>6.419668</td>\n",
+ " <td>14.2</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>37.50</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td>0.42</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11237</th>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>2024-01-15</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>14.1</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>38.70</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>0.24</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11248</th>\n",
+ " <td>109-P-027 - Anse de Carteau 2</td>\n",
+ " <td>2024-01-29</td>\n",
+ " <td>43.376780</td>\n",
+ " <td>4.884290</td>\n",
+ " <td>12.0</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>29.75</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>2.17</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11250</th>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>2024-01-29</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>14.1</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>36.53</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>0.71</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11251</th>\n",
+ " <td>106-P-011 - Rousty</td>\n",
+ " <td>2024-01-30</td>\n",
+ " <td>43.457109</td>\n",
+ " <td>4.494486</td>\n",
+ " <td>11.2</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>22.50</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>1.33</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11271</th>\n",
+ " <td>112-P-010 - 22B - Toulon gde rade</td>\n",
+ " <td>2024-02-13</td>\n",
+ " <td>43.078999</td>\n",
+ " <td>5.954667</td>\n",
+ " <td>13.6</td>\n",
+ " <td>Good Value</td>\n",
+ " <td>38.50</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>...</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>0.51</td>\n",
+ " <td>Good Value</td>\n",
+ " <td></td>\n",
+ " <td></td>\n",
+ " <td>REPHY</td>\n",
+ " <td>https://doi.org/10.12770/cf5048f6-5bbf-4e44-ba...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>560 rows × 32 columns</p>\n",
+ "</div>"
+ ]
+ },
+ "execution_count": 681,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 681
},
{
"metadata": {},
@@ -1657,7 +7996,12 @@
"id": "6e46bfb0ca8bb743"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:30.783111Z",
+ "start_time": "2024-07-26T16:08:29.845357Z"
+ }
+ },
"cell_type": "code",
"source": [
"sites=hydromed_rephy[['SITE','LATITUDE','LONGITUDE','DATE']]\n",
@@ -1665,8 +8009,19 @@
"ax = sites.plot.bar(x='SITE',y='SAMPLES', xlabel='Sampling Station', ylabel='Number of Samples', title='Total Samples per Station')\n"
],
"id": "df0fb950127f5fd8",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 682
},
{
"metadata": {},
@@ -1675,7 +8030,12 @@
"id": "4ef732a15225deec"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:31.191597Z",
+ "start_time": "2024-07-26T16:08:30.783111Z"
+ }
+ },
"cell_type": "code",
"source": [
"time_series=hydromed_rephy[['DATE','SITE']]\n",
@@ -1684,8 +8044,29 @@
"time_series.plot()"
],
"id": "2514151b066026fc",
- "outputs": [],
- "execution_count": null
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='DATE'>"
+ ]
+ },
+ "execution_count": 683,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ],
+ "image/png": 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4js89dlmPEYt2oVGgDw6+OcRFFRIREdHN6vP32xEkfXYVAMRWz+Tkl1SixFglcjVERETkKpIPOcG+KoRVbx1nXw4REZH3kHzIAW48qJN9OURERN7CO0IOz7AiIiLyOt4RcriNnIiIyOt4RciJb8SZHCIiIm/jFSEnNowzOURERN7GK0JOfHXj8RVDBSpMZpGrISIiIlfwipATFuCDQLUSggBcusYlKyIiIm/gFSFHJpPxoE4iIiIv4xUhBwDibTuseIYVERGRV/CakBNrvyAgm4+JiIi8gdeEHFvz8QVuIyciIvIKXhNybNvIsziTQ0RE5BW8JuTYLgh46Vo5qswWkashIiIiZ/OakBMZ5Au1Uo4qi4CcogqxyyEiIiIn85qQI5fLEBtm68vhkhUREZHUeU3IAXhQJxERkTfxspDDgzqJiIi8hVeFHG4jJyIi8h5eFXJsy1VZhVyuIiIikjovCznXl6ssFkHkaoiIiMiZvCrkNAnxg1Iug7HKgtxibiMnIiKSMq8KOUqFHE1C/QCw+ZiIiEjqvCrkANxGTkRE5C28LuTEcxs5ERGRV/C6kGO76jFDDhERkbR5XciJr16u4tEORERE0uZ1Ice2jTyroAyCwG3kREREUuV1IScmzB8yGVBsrEJhaaXY5RAREZGTeF3I8VUpEBXsCwC4WMi+HCIiIqnyupADALH2HVbsyyEiIpIqrww59ubjfM7kEBERSZVXhhzbTE4Wl6uIiIgkyytDDreRExERSZ9Xhpwbt5ETERGRNHlpyLHO5BSUVsJQYRK5GiIiInIGrww5gWolGgX6AOBsDhERkVR5ZcgBeIYVERGR1HltyGHzMRERkbR5bcix9eVwuYqIiEiaHB5yzGYz5syZg4SEBPj5+aF58+b429/+VuMwTEEQ8NZbbyEqKgp+fn4YPHgwzpw54+hS7si2w4ozOURERNLk8JDz97//HZ9++ikWL16MkydP4u9//zvmz5+PRYsW2Z8zf/58LFy4EEuWLMG+ffsQEBCAoUOHoqKiwtHl3FZcOHtyiIiIpEzp6Bfcs2cPRo0aheHDhwMA4uPjsXr1auzfvx+AdRZnwYIFePPNNzFq1CgAwJdffonIyEisW7cO48aNu+U1jUYjjEaj/WuDwXDPddqWq3SGClSYzPBVKe75NYmIiMh9OHwm57777sPmzZtx+vRpAMCRI0ewa9cuDBs2DACQmZkJnU6HwYMH279Ho9Ggd+/eSElJqfU1582bB41GY7/FxMTcc52h/ioE+VozHo93ICIikh6Hh5w33ngD48aNQ5s2baBSqdC1a1fMnDkTEyZMAADodDoAQGRkZI3vi4yMtD92s9mzZ0Ov19tv2dnZ91ynTCaz77DikhUREZH0OHy56ttvv8XKlSuxatUqtG/fHmlpaZg5cyaio6MxefLkBr2mWq2GWq12cKXWgzrTL+txkc3HREREkuPwkPPqq6/aZ3MAoGPHjrh48SLmzZuHyZMnQ6vVAgByc3MRFRVl/77c3Fx06dLF0eXcUTx3WBEREUmWw5erysrKIJfXfFmFQgGLxQIASEhIgFarxebNm+2PGwwG7Nu3D4mJiY4u547iwrhcRUREJFUOn8kZOXIk3n33XcTGxqJ9+/Y4fPgwPvroIzzzzDMArL0wM2fOxDvvvIOWLVsiISEBc+bMQXR0NEaPHu3ocu6I28iJiIiky+EhZ9GiRZgzZw7++Mc/Ii8vD9HR0Xj++efx1ltv2Z/z2muvobS0FNOmTUNRURH69euHDRs2wNfX19Hl3FF8I+tMzuWicpjMFqgUXnsBaCIiIsmRCTdeithDGAwGaDQa6PV6BAcHN/h1BEFA27c2oMJkwbb/e8AeeoiIiMjxHPX3u668eupCJpPZ+3LYfExERCQtXh1yAOs2coAXBCQiIpIarw859m3k+Qw5REREUuL1Icd2hlVWIZeriIiIpIQhx35BQM7kEBFJicUi4Pu0yxj04TaM+zwFGbpisUsiF3P4FnJPE2+fySmDxSJALpeJXBEREd2rPefyMe/nU0i/rAcAnLtaiuELd2LqgGZ48cGW8PNRiFwhuYLXz+REaXyhlMtQWWWBzlAhdjlERHQPMnTFmLJ8P576Yh/SL+sRqFbi5cGtMLR9JKosAj7ddg4PLdiO7aevil0quYDXz+QoFXLEhPkjM78UFwpKER3iJ3ZJRERUTzp9BT7alIH/pl6CRQCUchkm9onDCw+2QHig9YDnX4/rMPeH48guLMfkZfsxsnM05oxoi4gg116IllzH60MOYO3LycwvRVZBGe5rLnY1RERUV8UVJny2/Tz+tes8KkzWMxIf6ajFa0Pb3HKB14faa3Ffi0b4eNNpLN+diR+P5GBbRh7eGNYG43vGsl1BghhyAMSFsfmYiMiTVFZZsHp/Fj7ZfAaFpZUAgJ7xoZj9SFt0iw297fcFqpWYM6IdHuvaBLPXpiP9sh5//u4Y1h66jPce64jW2iBXfQRyAYYcXN9GfpFXPSYicmuCIOCXYzrM33DK/g/TZo0D8MbDbTCkXSRksrrNxnRoosG6pL74MuUCPtiYgdSL1zB84U5MG9AML7AxWTIYcsDTyImIPMH+zEK89/NJpGUXAQAaBarx8pCWGNsjBsoGHLCskMswpW8CHu6gxV9+OI6Nx3Pxz23nsP7oFfxtdAfc36qxgz8BuRpDDmrO5AiCUOd/CRARkfOdzSvB3zecwqYTuQAAfx8Fpg1ohqn9myFAfe9/xqI0fvjs9z3sjclZhWWYvGw/Hu0cjTkj2qFxkPqe34PEwZADICbMDzIZUFppRkFpJRoF8geaiEhsecUVWPDbGXxzIBtmiwCFXIaxPWMwc3BLp+yIsjUmf/TraazYk4kf7I3JbTGuZwwbkz0QQw4AtVKBaI0fLheV42JBKUMOEZGISo1V+HzHeXyx8zzKKs0AgCHtIvH6w63RIsK5jcGBaiXeGlndmPzdURy7bMCfvkvH/w5dYmOyB/L6iwHaxPGgTiIiUZnMFny19yLu/8c2fLL5DMoqzegSE4Jvn0/EF5N6OD3g3KhjUw3W/bEv3hrRDgE+Cntj8j82nkKFyeyyOujecCanWly4P/acK8DFQoYcIiJXEgQBv57Ixd83nML5q9ZdrvHh/njt4TYY1kErWp+kUiHHM/2uNyb/eiIXyVvP4ccjV/DO6A4YwMZkt8eQU43byImIXC/14jXM+/kkDl68BgAIC/DBS4NaYnyvWPgo3WOxITrED59P6oGNx3X4S3Vj8qRl+zGqSzTeHM7GZHfGkFMtntvIiYhcJjO/FPM3nMIvx3QAAF+VHM/1a4bn72+GIF+VyNXVbmh7Lfre0Jj8fVoOtp7Kw+xH2mJsDzYmuyOGnGqxYZzJISJytvwSIxZuPoNV+7JQZREglwFPdG+KWUNaQ6tx/zOkamtMnr02Hf9LvYT3Hu+IVpFsTHYnDDnVbI3H18pM0JeboPFzz39JEBF5orLKKizdmYkl28+htHrH1MDWjfH6sDZoow0Wubr6szUmf5lyER/+moGDF6/hkU924vn7rVdM9lXxisnugCGnWoBaiUaBauSXGJFVUIaOTTVil0RE5PGqzBb8N/USPtp0GnnFRgBAxyYazB7WBve1aCRydffmxsbkuT8cx6bqxuT1R62Nyf1bsjFZbAw5N4gP90d+iREXCkoZcoiI7tHp3GK8uPowTumKAQBNQ/3w6tDWGNkpWlL9K9EhfviiujF57vfHcbGgDL9fuh+ju0TjzRHteO01EblH67qbsO2wyuI2ciKie/LjkRyMTt6NU7piaPxUeHN4W2x+5X6M6tJEUgHnRkPba/HbK/djSt94yGXAurQcDPpwO77enwWLRRC7PK/EkHOD6xcEZPMxEVFDmMwW/G39Cbyw+jDKKs24r3k4Nr9yP57r3wxqpfT7VALVSswd2R7rkvqiQ5Ng6MtNeGNtOsZ+noIzucVil+d1GHJuwNPIiYgaLq+4AhO+2IeluzIBANMfaI4vn+nllcs1nZqGYN0f+2LOiHbw91HgwIVrGLFoF/6Xekns0rwKQ84N7BcELORMDhFRfRy8UIgRC3dh/4VCBKqVWDKxO15/uA2UCu/9M6NUyPFsvwT8Nut+3N+qMYxVFryy5gjeXJcOYxWPhnAF7/3pq4XtgoC5BiPKK/kDSER0N4IgYPnuTIz7fC/yio1oGRGI72f0xcMdtGKX5jaiQ/yw/OmeeHlwK8hkwFd7szD2s724oi8XuzTJY8i5QYi/j/36OGw+JiK6s7LKKsz8Jg1v/3gCVRYBIzpFYV1SXzRvHCh2aW5HLpfhpcEtsezpntD4qZCWXYQRC3dhz9l8sUuTNIacm9ibj3nlYyKi28rML8VjyXvwfVoOFHIZ5oxoh0XjuyJAzSuT3MnA1hFY/0I/tI8ORkFpJSYu3Ycl289BELj7yhkYcm7CgzqJiO7s1+M6PLpoFzJyi9EoUI3VU/vg2X4Jop0W7mliwvzxv+n34YnuTWERgPd/OYXpXx1CcYVJ7NIkhyHnJnFh3GFFRFQbs0XAPzaewrT/pKLYWIUecaH46cV+6JUQJnZpHsdXpcA/nuiE9x7rCB+FHBuO6zBq8W5uM3cwhpybcBs5EdGtCksrMXnZfiRvPQcAmNI3Hqun9UFksPsfqumuZDIZnuodi2//kIhojS/O55diVPJurD+aI3ZpksGQc5P4RtxGTkR0oyPZRRi5aBd2nc2Hn0qBT8Z1wdyR7aHy4u3hjtQlJgQ/vtAPfVuEo6zSjBmrDuNv60/AZLaIXZrH40/oTWzLVZevlaOyij9gROTdVu/PwpNLUnC5qBwJjQKwLqkvRnVpInZZkhMeqMa/p/TC9AeaAwCW7srEhH/tQ15xhciVeTaGnJs0DlLDT6WARQAuXeOSFRF5pwqTGa/99whmr01HpdmCIe0i8f2MvmitDRK7NMlSKuR4/eE2WDKxOwLVSuzPtF5g8eCFQrFL81gMOTeRyWTX+3J4rRwi8kLZhWV4YskefHvwEuQy4NWhrfHZxO4I9lWJXZpXeLiDFj/M6ItWkYHIKzZi3Od7sWJ3JreZNwBDTi3sIYcHdRKRl9l++ipGLt6FY5cNCPVX4ctneiNpYAvJnhzurpo1DsR3f+yLEZ2iUGUR8JcfT2DmN2koq6wSuzSPwpBTi3j7GVacySEi72CxCFi0+QyeXr4fRWUmdGqqwfoX+6Nfy0Zil+a1AtRKLBrfFXNGtINCLsP3aTl4LHkPMvkP8DpjyKlFLLeRE5EX0ZebMPXLg/hw02kIAjC+Vyy+fT4RTUL8xC7N68lkMjzbLwGrp/ZB4yA1MnKL8eiiXdh0Ilfs0jyCU0LO5cuXMXHiRISHh8PPzw8dO3bEwYMH7Y8LgoC33noLUVFR8PPzw+DBg3HmzBlnlNIgtpkcHu1ARFJ38ooBjy7ehc2n8uCjlGP+E50w7/GO8FUpxC6NbtArIQw/vdAPPeJCUWyswtQvD+KDjRkwW9incycODznXrl1D3759oVKp8Msvv+DEiRP48MMPERoaan/O/PnzsXDhQixZsgT79u1DQEAAhg4diooK99gqF1u9jfxSYTl/gIhIsr47fAmP/XM3LhaUoWmoH9ZOvw+/6xEjdll0GxHBvlg9rQ+m9I0HACzeehZPL9+PwtJKcQtzYzLBwe3ab7zxBnbv3o2dO3fW+rggCIiOjsYrr7yC//u//wMA6PV6REZGYsWKFRg3btxd38NgMECj0UCv1yM4ONiR5QOwXrq8zZxfYDIL2PX6QDQN9Xf4exARiaWyyoJ3fjqBL1MuAgAGtGqMT8Z2QWiAj8iVUV19n3YZb/wvHeUmM5qE+OGfE7qhc0yI2GXdlbP/ft/M4TM5P/zwA3r06IEnn3wSERER6Nq1K7744gv745mZmdDpdBg8eLD9Po1Gg969eyMlJaXW1zQajTAYDDVuzqSQyxBTPZuTxb4cIpIQnb4C4z5PsQecFwe1xPKnezLgeJhRXZpgXVJfJDQKwOWicjy5JAVf788Suyy34/CQc/78eXz66ado2bIlNm7ciOnTp+PFF1/Ev//9bwCATqcDAERGRtb4vsjISPtjN5s3bx40Go39FhPj/OlU25WPLzDkEJFEpJwrwIhFO3EoqwjBvkosndwDs4a0goLbwz1Sa20Qvp/RF0PaRaLSbMEba9Px+n+PosJkFrs0t+HwkGOxWNCtWze899576Nq1K6ZNm4apU6diyZIlDX7N2bNnQ6/X22/Z2dkOrLh2cbZt5Gw+JiIPJwgCvthxHhOX7kN+SSXaRgXjxxf6YVDbyLt/M7m1YF8VPpvYHa8ObQ25DPjmYDaeXJKCbF4CBYATQk5UVBTatWtX4762bdsiK8s6jabVagEAubk1t7/l5ubaH7uZWq1GcHBwjZuz8TRyIpKCEmMVklYdwrs/n4TZIuDxrk2wdvp99n/IkeeTy2VIGtgCXz7TG6H+KqRf1mPk4l3Yfvqq2KWJzuEhp2/fvsjIyKhx3+nTpxEXFwcASEhIgFarxebNm+2PGwwG7Nu3D4mJiY4up8G4jZyIPN3ZvGKMWrwLP6froFLI8LfRHfDh7zrDz4fbw6WoX8tGWP9if3RqqkFRmQlPL9+PRZvPwOLFu4QdHnJefvll7N27F++99x7Onj2LVatW4fPPP0dSUhIA64WNZs6ciXfeeQc//PAD0tPTMWnSJERHR2P06NGOLqfBbDM5WYVlPC+EiDzOsct6jE7eg3NXS6EN9sU3zyfi933iIJOx/0bKmoT44dvnEzG+VywEAfhw02lM/fIg9OUmsUsThcNDTs+ePfHdd99h9erV6NChA/72t79hwYIFmDBhgv05r732Gl544QVMmzYNPXv2RElJCTZs2ABfX19Hl9NgTUP9IZcBZZVmXC0xil0OEVGdFVeYkLTqEEqMVegZH4ofX+iHbrGhd/9GkgRflQLzHu+I+WM6wUcpx+ZTeXh08S6cvOLcncnuyOHXyXEFV+2z7/f3Lbh0rRxr/pCInvFhTnsfIiJHEQQBM1Ydxk/pV9AkxA8/vdgPIf7cHu6tjl3W4/n/pOJyUTl8VXLMf6IzHu0cLVo9Hn+dHClh8zEReZr/7L2In9KvQCmXYdFTXRlwvFyHJhqsf6EfBrRqjAqTBa98m4arxd6zOsGQcwfcRk5EniT9kh7vrD8JAHhjWBsuUREAIDTAB8uf7ok22iCYzAJ2n80XuySXYci5g/hwXhCQiDyDoboPp9JswZB2kXi2X4LYJZEbUchluL91YwDAzjMMOQQgNsw6k5PFmRwicmOCIOC1NUeRVWg9aPODJzpzFxXdYkBLW8i56jW7hhly7iC+EWdyiMj9/XvPBWw4br0WTvJT3aDxV4ldErmh7nGh8FXJkVdsxOncErHLcQmGnDuIrT6/Sl9uQlEZj7InIvdzJLsI7/5s7cP50yNtPeIkahKHr0qB3gnhAKyzOd6AIecO/H2UiAhSA+AOKyJyP/pyax+OySzg4fZaPH1fvNglkZvr37IRAO/py2HIuQse70BE7kgQBLz23yO4dK0cMWF++PsTndiHQ3fVv7ovZ19mgVecVs6QcxextuMdOJNDRG5k2e4L2Hg8Fz4KOf75VHdo/NiHQ3fXKjIQEUFqVJgsOHTxmtjlOB1Dzl1wGzkRuZu07CK8/4u1D+fPw9uiY1ONyBWRp5DJZOhXvWS1wwuWrBhy7iK2erkqq5DLVUQkvqKySiSttPbhPNJRi0mJcWKXRB7mxq3kUseQcxecySEidyEIAv5vzVFcLipHXLg/3h/DPhyqv74trDM5x3MMKJD4AdQMOXcRV31BwKvFRpQaq0Suhoi82dJdmfjtpLUPJ/mpbgj2ZR8O1V/jIDXaRlkPx9wl8SMeGHLuQuOvQkj1hbWyCjmbQ0TiOJR1De//cgoAMGdkO3Rowj4cargBXrKVnCGnDnhQJxGJ6VppJWasPIQqi4ARnaIwsXes2CWRh7NtJd91Jl/SRzww5NRBXPWVj3lBQCJyNYtFwCtrjiBHX4GERgGY93hH9uHQPesRHwq1Ug6doQJn86R7xANDTh2w+ZiIxPLFzvPYcioPPko5Fj/VFUHswyEH8FUp0CshDIC0t5Iz5NQBl6uISAwHLxRi/sYMAMDcke3QPpp9OOQ43rCVnCGnDuLCuVxFRK5VWFqJF1Yfhtki4NHO0XiqF/twyLFsFwXcd74QxippHvHAkFMHtpmcHH25ZH8QiMh9WCwCZn2bhiv6CjRrFID32IdDTtBGG4RGgWqUm8xIlegRDww5ddAo0Af+PgoIAnDpWrnY5RCRxH224zy2ZVyFWilH8oRuCFQrxS6JJEgmk0l+KzlDTh3IZDL25RCRSxy4UIgPfrX24bz9aHv7RduInMG2ZLWLIce72XdY5bMvh4ico6DEiBmrDsFsEfBY1yYY2zNG7JJI4vpVH/FwLEcvySMeGHLqKLY65PCqx0TkDBaLgJe/PYJcgxHNGwfgndEd2IdDThcR7Is22iAIArD7XIHY5TgcQ04dxVcvV13gchUROcGn289hx+mr8FXJ8c8J3RHAPhxykQGtbFc/lt5WcoacOrJd9TiL28iJyMH2ni/Ah9V9OH8d1QGttUEiV0TexLZktVOCRzww5NRRXCPrTE72tTJUmS0iV0NEUpFfYsSLqw/DIgCPd2uCJ7s3Fbsk8jK9EsLgo5Tjir4C565K64gHhpw6igr2hY9SDpNZwBV9hdjlEJEEmC0CXv4mDXnFRrSMCGQfDonCV6VA7+ojHqS2lZwhp47kchliQv0A8MrHROQYyVvPYueZfPipFPjnhG7w92EfDonjxiUrKWHIqQc2HxORo+w5l48Fv50GAPxtdAe0jGQfDomnf/U5VnvPF6CySjotGQw59cBt5ETkCFeLjXjp6zRYBODJ7k3xBPtwSGS2Ix7KKs04lCWdIx4YcurBPpOTz5kcImoYs0XAS18fxtViI1pFBuKvozqIXRIR5HIZ+rUIByCtU8kZcuqBp5ET0b1atOUM9pwrgL+PtQ/Hz0chdklEAK4vWUmpL4chpx7s51cVlkruWgJE5Hy7z+bjk81nAADvPtYBLSLYh0Puo3/1OVbpl/W4VlopcjWOwZBTD01C/KCQy1BhsiCvWHpnfBCR8+QVV+Clr9MgCMDYHjF4rCv7cMi9RAT7onWk7YgHaczmMOTUg49SjugQXwBcsiKiujNbBLy0Og35JUa00Qbh7VHtxS6JqFa22ZydpxlyvBK3kRNRfX3y22mknLf24SRP6AZfFftwyD31b2Xry7kqibYMhpx6ut58zJBDRHe388xVLNp6FgAw7/GOaN44UOSKiG6vV3wYfBRy5OgrcF4CO4kZcuopLqy6+ZjLVUR0F7mGCsys7sMZ3ysWo7o0Ebskojvy81GgZ0IoAGDnac/fSs6QU0/cRk5EdVFltuCF1YdRUFqJtlHBmDuyndglEdWJlLaSM+TUU9wNPTlSWK8kIudY8NsZ7M8sRICPAslPdWUfDnkMW/OxFI54cHrIef/99yGTyTBz5kz7fRUVFUhKSkJ4eDgCAwMxZswY5ObmOrsUh4gNs87kFFdUoajMJHI1ROSODlwoRPI2ax/O+2M6oRn7cMiDtNUGIzzAB6WVZhz28CMenBpyDhw4gM8++wydOnWqcf/LL7+MH3/8EWvWrMH27duRk5ODxx9/3JmlOIyfjwLaYOs2cu6wIqKbCYKAf2zIgFB9LtXIztFil0RUL3K5DP1aSuNUcqeFnJKSEkyYMAFffPEFQkND7ffr9XosXboUH330ER588EF0794dy5cvx549e7B3715nleNQPKiTiG5n19l87L9QCB+lHK881FrscogaxN6Xc5Yhp1ZJSUkYPnw4Bg8eXOP+1NRUmEymGve3adMGsbGxSElJqfW1jEYjDAZDjZuY4qtDzoV8hhwiuk4QBHz462kAwITesdBqfEWuiKhh+rWwzuQcvVSEojLPPeLBKSHn66+/xqFDhzBv3rxbHtPpdPDx8UFISEiN+yMjI6HT6Wp9vXnz5kGj0dhvMTExzii7zm48w4qIyGZrRh7Ssovgq5Jj+gPNxS6HqMG0Gl+0igy0HvFwtkDschrM4SEnOzsbL730ElauXAlfX8f8K2b27NnQ6/X2W3Z2tkNet6G4jZyIbiYIAj7aZJ3FmZwYj4ggzuKQZ7MtWe0667nXy3F4yElNTUVeXh66desGpVIJpVKJ7du3Y+HChVAqlYiMjERlZSWKiopqfF9ubi60Wm2tr6lWqxEcHFzjJibb0Q686jER2Ww8notjlw0I8FHg+fs5i0Oez9Z8vON0vsdeMsXhIWfQoEFIT09HWlqa/dajRw9MmDDB/t8qlQqbN2+2f09GRgaysrKQmJjo6HKcwtZ4nF9SiRJjlcjVEJHYLBYBH1fP4kzpm4CwAB+RKyK6d70TrEc8XC4qR6aHHvGgdPQLBgUFoUOHDjXuCwgIQHh4uP3+Z599FrNmzUJYWBiCg4PxwgsvIDExEX369HF0OU4R7KtCWIAPCksrcSa3GF1jQ+/+TUQkWT+lX0FGbjGCfJWY2r+Z2OUQOYS/jxI94kOx51wBdp3N98jrPYlyxeOPP/4YI0aMwJgxYzBgwABotVqsXbtWjFIarHdCGADg1xOecRFDInIOs0XAgt+sszjP9WsGjb9K5IqIHOfGJStP5JKQs23bNixYsMD+ta+vL5KTk1FYWIjS0lKsXbv2tv047mp4pygAwPqjOR67VklE9+77tMs4d7UUIf4qPNMvXuxyiBxqQHXz8d7zBTCZPe+IB55d1UAPtomAn0qB7MJypF/Wi10OEYnAZLbgk81nAADTBjRDkC9ncUha2kUFIyzAByXGKqRlF4ldTr0x5DSQv48SD7aNAAD8dPSKyNUQkRjWHrqEiwVlCA/wweTEeLHLIXI4uVyGvtUXBtx52vO2kjPk3IMRHW1LVle4ZEXkZSqrLFi42XoI5/QHmiNA7fB9HERuwXYq+Q4PPMeKIecePNA6Av4+ClwuKseRS1yyIvIm3xzMxuWickQEqTGxT5zY5RA5jS3kHL1UBH2ZSeRq6och5x74+SgwqG0kAGD9kRyRqyEiV6kwmZG8xTqLkzSwBXxVCpErInKeKI0fWkQEwiIAe8551mwOQ849Gl69ZPVz+hVYLFyyIvIGq/ZlQWeoQLTGF+N6iXuWHpEreOqSFUPOPXqgdWME+CiQo6/AYQ/sPCei+imvNOOf284BAGY82BJqJWdxSPpsW8l3nrnqUT2oDDn3yFelwJB21iUr7rIikr4vUy4gv8SImDA/PNmjqdjlELlE72ZhUClkuHSt3KMOp2bIcYDhnaIBcMmKSOpKjFVYst06i/Pigy2hUvBXKHkHfx8lusdZjzDaecZztpLz/1AH6N+yEYLUSugMFUjNuiZ2OUTkJCt2Z+JamQnNGgXgsa5NxC6HyKX6Vy9ZeVJfDkOOA3DJikj69OUmfL7jPADgpcEtoeQsDnkZ+xEP5zzniAf+X+ogIzpf32Vl5pIVkeQs3ZUJQ0UVWkYEYkT1EjWRN2kfHYxQfxWKjVU44iEbbRhyHKRfi8YI8lUir9iIgxcKxS6HiBzoWmkllu3KBAC8PKQVFHKZyBURud6NRzx4ypIVQ46D+CjlGNreepL6T+lcsiKSks93nkeJsQpto4LxcPX/50TeyLZktctDmo8ZchxoeCfbkpWOS1ZEEpFfYsSK3RcAALOGtIKcszjkxfpVXxQwLbsI+nL3P+KBIceB+jZvBI2fCvklRuzLLBC7HCJygCXbzqHcZEbnphoMbhshdjlEoooO8UPzxgGwCECKBxzxwJDjQD5KuX0qm7usiDxfrqEC/9l7EYC1F0cm4ywOUX/71Y8ZcryObclqwzEdqjxkix0R1e6fW8/CWGVB97hQ3N+qsdjlELkF2zlWDDleKLF5OEL9VSgorcS+TO6yIvJUl4vKsXp/NgDgFc7iENn1aRYOlUKGrMIyXCwoFbucO2LIcTCVQo6HO1iXrNYfzRG5GiJqqMVbzqLSbEGfZmG4r3rbLBEBAWolusXajnhw79kchhwnGN7ReqGwDcd0HnNVSCK6LqugDGsOVs/iPNRa5GqI3M/1JSv33krOkOMEfZqFITzAB9fKTEg5x11WRJ5m4ZYzqLII6N+yEXrGh4ldDpHbsTUf7zlb4Nb9pww5TqC8YcmKu6yIPMv5qyVYe+gSAM7iEN1OhyYahNiOeLhUJHY5t8WQ4yT2XVbHuWRF5Ek+2XwGFgEY1CYCXWJCxC6HyC0pbjjiwZ37chhynKR3QjgaBfpAX27C7rPu+wNARNedzi3GD0esGwZeHtJK5GqI3Ft/hhzvpZDLMKyDdTZnPZesiDzCgt9OQxCAh9tr0aGJRuxyiNzajUc8GCrc84gHhhwnsi1ZbTyuQ2UVl6yI3NnxHD1+TtdBJuMsDlFdNA31R7PGATBbBLfdZMOQ40Q948MQEaRGcUUVdp117212RN7u401nAAAjOkWjtTZI5GqIPMP1JSv3/BvHkONECrkMj3TkkhWRuzuSXYTfTuZCLgNmDm4pdjlEHsPdz7FiyHEy25LVpuO5MFaZRa6GiGrz0abTAIDRXZugeeNAkash8hx9modDKZfhYkEZsgrKxC7nFgw5TtY9NhSRwWoUG6uw47R7Jl0ib5Z6sRDbT1+FQi7DS4M4i0NUH4E3HvHghm0ZDDlOJr9hyeonnmVF5HY+/NU6i/Nk96aICw8QuRoiz2M/4sEN/yHPkOMCIzpZz7LadCIXFSYuWRG5i5RzBdhzrgAqhQwzHmwhdjlEHql/q+ojHs7lu90RDww5LtA1JgTRGl+UVpqx/bT7TecReSNBEPDRpgwAwLiesWga6i9yRUSeqWMTDTR+KhgqqnD0sl7scmpgyHGBmktW3GVF5A52nsnHgQvX4KOUI2kgZ3GIGsp6xEM4APdbsmLIcRHbLqvfTuaivJJLVkRiEgQBH1bvqJrYOw5aja/IFRF5NttWcne7JhxDjot0iQlBkxA/lFWasS0jT+xyiLzallN5OJJdBD+VAtMfaC52OUQer1/1RQEPZRWh2I2OeGDIcRGZTGafzVmfziUrIrFYe3GssziT7otD4yC1yBUReb6YMH8kNHK/Ix4YclxoRHXI2XIyD2WVVSJXQ+SdNh7X4XiOAQE+Cjw/gLM4RI5i20q+66z79OUw5LhQxyYaxIT5odxkxtZT7rVuSeQNLBbBfkbVM/0SEBbgI3JFRNLRz36OFUOOV5LJZBje0XrNnPW8MCCRy61Pv4KM3GIE+SrxXL9mYpdDJCmJzcOhkMuQmV+K7EL3OOLB4SFn3rx56NmzJ4KCghAREYHRo0cjIyOjxnMqKiqQlJSE8PBwBAYGYsyYMcjNzXV0KW7JvmR1Kg+lRi5ZEblKldmCBb9Ze3Gm9m8Gjb9K5IqIpCXIV4VusSEA3Gc2x+EhZ/v27UhKSsLevXuxadMmmEwmPPTQQygtLbU/5+WXX8aPP/6INWvWYPv27cjJycHjjz/u6FLcUvvoYMSF+8NYZcHmU9xlReQq36fl4PzVUoT4qzClb7zY5RBJkrttJVc6+gU3bNhQ4+sVK1YgIiICqampGDBgAPR6PZYuXYpVq1bhwQcfBAAsX74cbdu2xd69e9GnT59bXtNoNMJoNNq/NhgMji7bZWQyGUZ0ikLy1nP46WgOHu0cLXZJRJJnMlvwyWZrL87zA5ojyJezOETO0K9lI3y06TR2ncmH2SJAIZeJWo/Te3L0euslnsPCwgAAqampMJlMGDx4sP05bdq0QWxsLFJSUmp9jXnz5kGj0dhvMTExzi7bqWx9OVszrqKES1ZETve/1EvIKixDo0AfTL4vTuxyiCSrUxMNgn2V1iMeLhWJXY5zQ47FYsHMmTPRt29fdOjQAQCg0+ng4+ODkJCQGs+NjIyETqer9XVmz54NvV5vv2VnZzuzbKdrGxWEZo0CUFllwW8nvKMXiUgsxiozFm05CwD4w/3N4e/j8AlsIqqmVMjRt3qX1S436MtxashJSkrCsWPH8PXXX9/T66jVagQHB9e4ebIaFwbkWVZETvXtgWxcLipHZLAaE/twFofI2fq1dJ+t5E4LOTNmzMD69euxdetWNG3a1H6/VqtFZWUlioqKajw/NzcXWq3WWeW4HVvI2XH6KgxudAlsIimpMJmxeKt1FidpYAv4qhQiV0QkfQOqm48PZV0TvSXD4SFHEATMmDED3333HbZs2YKEhIQaj3fv3h0qlQqbN2+235eRkYGsrCwkJiY6uhy31ToyCC0iAlFp5pIVkbOs3JeFXIMR0RpfjO3p2b18RJ4iJswf8eH+qLII2CvyEQ8ODzlJSUn46quvsGrVKgQFBUGn00Gn06G8vBwAoNFo8Oyzz2LWrFnYunUrUlNTMWXKFCQmJta6s0qqrBcGtM7m/MQlKyKHK6uswqfbrLM4LwxqCbWSszhErnJ9yUrcreQODzmffvop9Ho9HnjgAURFRdlv33zzjf05H3/8MUaMGIExY8ZgwIAB0Gq1WLt2raNLcXv2JaszV6Ev55IVkSN9mXIR+SWViA3zxxPdm979G4jIYWzXyxG7L8fh2wwEQbjrc3x9fZGcnIzk5GRHv71HaRUZhFaRgTidW4Jfj+vwZA9OpxM5QomxCp9tPwcAeHFQS6gUPMGGyJVsRzyczy/FpWtlaBrqL0od/D9fZLZr5vyUziUrIkdZvisT18pMaNYoAKO78IKbRK4W7KtCl5gQAOJuJWfIEZltyWrXmXwUlVWKXA2R59OXm/DFzvMAgJcGt4SSszhEoujvBlvJ+X+/yFpEBKKNNghVFgG/HucuK6J7IQgC3lx3DIaKKrSKDMTITpzFIRKLrS9n9znrEQ9iYMhxA7aTyddzyYronnyx8zx+PJIDpVyGd0Z3hFzkc3OIvFnnphoE+SpRVGbCsct6UWpgyHEDj1RvJd99Nh+FpVyyImqInWeu4v1fTgEA5oxoh14JYSJXROTdlAo57mseDkC8reQMOW6gWeNAtIsKhtkiYOPx2s/vIqLbyy4swwurD8MiAE90b4pJiTy+gcgdiL2VnCHHTdgakHlhQKL6Ka80Y9p/UlFUZkKnphq8M7oDZDIuUxG5A7GPeGDIcRO2vpw95/JRUGIUuRoizyAIAl7731GcvGJAo0AfLJnYnedTEbmR2HB/xIb5w2QWsO+86494YMhxE3HhAejYRAOLAGzgkhVRnfxrZ6a90Tj5qW6IDvETuyQiuomYW8kZctyIbclq/REuWRHdza4z+Zj3y0kA1kbj3s3CRa6IiGpzvS/H9c3HDDluxHZg577MAuQVV4hcDZH7yi4sw4zVh9hoTOQBEpuHQy4Dzl0tRU5RuUvfmyHHjcSE+aNzU+uS1cZjXLIiqg0bjYk8i8bv+hEPKedcu2TFkONmRlRfoXU9d1kR3UIQBLzORmMij2Nbsko559rmY4YcNzOsoxYAsP9CIfIMXLIiutG/dmbiBzYaE3kcW/Nxiot3WDHkuJmmof7oGhsCQQB+5jEPRHY3Nhq/ObwtG42JPEjnmBAEqZXQl7v2WjkMOW7I1oD8E0MOEYCajcZjujXF5PvixS6JiOpBpZAjsbnr/2HCkOOGbGdZHbhwDTo9l6zIu5VXmvH8DY3G7z7GRmMiT2RbsnIlhhw3FB3ihx5xoQC4ZEXezdZofOKKAeEBbDQm8mS25mNXYshxU/azrBhyyIst3XVDo/EENhoTebK4cH80CfV16Xsy5LipYR2iIJMBqRevufziSUTuYPfZfLz38/VG4z5sNCbyaDKZDB//rotL35Mhx01pNb7oGRcGgEtW5H2yC8swY5W10fjxbk3YaEwkEe2iNS59P4YcN2Y/y4oXBiQvYms0vlZmQscmGrz3WEc2GhNRgzDkuLFhHbSQyYC07CJkF5aJXQ6R0wmCgDfWXm80/uz3bDQmooZjyHFjEcG+6J1gXbL65Rhnc0j6lu7KxPdpOVDIZVjMKxoT0T1iyHFzw6vPsvqJS1YkcTc3Gotx4TAikhaGHDf3cHst5DLgyCU9sgq4ZEXSdHOj8dNsNCYiB2DIcXONg9T2rbO8Zg5JERuNichZGHI8wPULA+aIXAmRY93caLyEjcZE5EAMOR7g4fZaKOQyHLtswIX8UrHLIXKYmxuNm7DRmIgciCHHA4QHqnFfcy5ZkbTsOZuPeb+cAsBGYyJyDoYcDzG8Iy8MSNKRXViGpFWHYLYIbDQmIqdhyPEQQ6uXrE5eMeDc1RKxyyFqMDYaE5GrMOR4iNAAH/Rt0QgA8DNnc8hDCYKA2dWNxmFsNCYiJ2PI8SAj7LusGHLIMy3dlYl11Y3GyWw0JiInY8jxIEPbaaFSyHBKV4yzecVil0NULzc2Gv/5ETYaE5HzMeR4EI2/Cv2ql6zYgEye5NK1GxqNuzbBlL7xYpdERF6AIcfD8Cwr8jQ3Nhp3aBKM9x5nozERuQZDjocZ0i4SKoUMZ/JKcDqXS1bk3myNxsdzrI3Gn/2+BxuNichlGHI8jMZPhQEtGwPgkhW5v2W7L7DRmIhEw5DjgUZ0rt5ldTQHgiCIXA1R7facy8d7P58EwEZjIhKHqCEnOTkZ8fHx8PX1Re/evbF//34xy/EYg9tGwkcpx7mrpTil45IVuZ9L18owY9VhmC0CHmOjMRGJRLSQ880332DWrFmYO3cuDh06hM6dO2Po0KHIy8sTqySPEeSrwv2trEtWbEAmd2NrNC4srUSHJsGYx0ZjIhKJTBBpvaN3797o2bMnFi9eDACwWCyIiYnBCy+8gDfeeOOO32swGKDRaKDX6xEcHOyKct3O92mX8dLXaYgP98dXz/UWuxwiuw82ZmBdWg7CAnzww4y+aBrqL3ZJROQmXP33W+n0d6hFZWUlUlNTMXv2bPt9crkcgwcPRkpKyi3PNxqNMBqN9q8NBoNL6nRng9pGQq2U40JBGfr9favY5RDVoJDLsPiprgw4RCQqUUJOfn4+zGYzIiMja9wfGRmJU6dO3fL8efPm4e2333ZVeR4hUK3Ec/0TsGzXBVjYfExuxN9HgTeGtcF9zRuJXQoReTlRQk59zZ49G7NmzbJ/bTAYEBMTI2JF7uHVoW3w6tA2YpdBRETklkQJOY0aNYJCoUBubm6N+3Nzc6HVam95vlqthlqtdlV5REREJAGi7K7y8fFB9+7dsXnzZvt9FosFmzdvRmJiohglERERkcSItlw1a9YsTJ48GT169ECvXr2wYMEClJaWYsqUKWKVRERERBIiWsgZO3Ysrl69irfeegs6nQ5dunTBhg0bbmlGJiIiImoI0a6Tcy94nRwiIiLP4+q/3zy7ioiIiCSJIYeIiIgkiSGHiIiIJIkhh4iIiCSJIYeIiIgkiSGHiIiIJIkhh4iIiCSJIYeIiIgkiSGHiIiIJEm0Yx3uhe0izQaDQeRKiIiIqK5sf7ddddiCR4ac4uJiAEBMTIzIlRAREVF9FRQUQKPROP19PPLsKovFglatWiE1NRUymUyUGgwGA2JiYpCdnS3q+Vk9e/bEgQMHRHt/joMVx+E6joUVx8GK42DFcbDS6/WIjY3FtWvXEBIS4vT388iZHLlcDh8fH5ekwLsJDg4W9QdWoVC4xSGlHAcrjsN1HAsrjoMVx8GK42All7umJdhjG4+TkpLELsEtcBysOA5WHIfrOBZWHAcrjoOVt42DRy5XuQNXHxfvrjgOVhyH6zgWVhwHK46DFcfBytXj4LEzOWJTq9WYO3cu1Gq12KWIiuNgxXG4jmNhxXGw4jhYcRysXD0OnMkhIiIiSeJMDhEREUkSQw4RERFJEkMOERERSRJDDhEREUmSV4ecefPmoWfPnggKCkJERARGjx6NjIyMGs+pqKhAUlISwsPDERgYiDFjxiA3N7fGc1588UV0794darUaXbp0qfW9jh49iv79+8PX1xcxMTGYP3++sz5WvblqHLZt24ZRo0YhKioKAQEB6NKlC1auXOnMj1Yvrvx5sDl79iyCgoJccuXPunLlOAiCgA8++ACtWrWCWq1GkyZN8O677zrro9WLK8dh48aN6NOnD4KCgtC4cWOMGTMGFy5ccNInqx9HjMORI0cwfvx4xMTEwM/PD23btsUnn3xyy3tt27YN3bp1g1qtRosWLbBixQpnf7w6c9U4rF27FkOGDEHjxo0RHByMxMREbNy40SWfsa5c+TNhs3v3biiVyrv+Tr2ZV4ec7du3IykpCXv37sWmTZtgMpnw0EMPobS01P6cl19+GT/++CPWrFmD7du3IycnB48//vgtr/XMM89g7Nixtb6PwWDAQw89hLi4OKSmpuIf//gH/vKXv+Dzzz932merD1eNw549e9CpUyf873//w9GjRzFlyhRMmjQJ69evd9pnqw9XjYONyWTC+PHj0b9/f4d/lnvhynF46aWX8K9//QsffPABTp06hR9++AG9evVyyueqL1eNQ2ZmJkaNGoUHH3wQaWlp2LhxI/Lz82t9HTE4YhxSU1MRERGBr776CsePH8ef//xnzJ49G4sXL7Y/JzMzE8OHD8fAgQORlpaGmTNn4rnnnnObP/CuGocdO3ZgyJAh+Pnnn5GamoqBAwdi5MiROHz4sEs/7524aixsioqKMGnSJAwaNKj+xQpkl5eXJwAQtm/fLgiCIBQVFQkqlUpYs2aN/TknT54UAAgpKSm3fP/cuXOFzp0733L/P//5TyE0NFQwGo32+15//XWhdevWjv8QDuCscajNI488IkyZMsUhdTuas8fhtddeEyZOnCgsX75c0Gg0ji7fYZw1DidOnBCUSqVw6tQpp9XuSM4ahzVr1ghKpVIwm832+3744QdBJpMJlZWVjv8g9+hex8Hmj3/8ozBw4ED716+99prQvn37Gs8ZO3asMHToUAd/Asdw1jjUpl27dsLbb7/tmMKdwNljMXbsWOHNN9+s198WG6+eybmZXq8HAISFhQGwJk2TyYTBgwfbn9OmTRvExsYiJSWlzq+bkpKCAQMGwMfHx37f0KFDkZGRgWvXrjmoesdx1jjc7r1s7+NunDkOW7ZswZo1a5CcnOy4gp3EWePw448/olmzZli/fj0SEhIQHx+P5557DoWFhY79AA7irHHo3r075HI5li9fDrPZDL1ej//85z8YPHgwVCqVYz+EAzhqHG7+fz8lJaXGawDW35P3+jvGWZw1DjezWCwoLi5229+TgHPHYvny5Th//jzmzp3boNoYcqpZLBbMnDkTffv2RYcOHQAAOp0OPj4+t/RLREZGQqfT1fm1dTodIiMjb3kN22PuxJnjcLNvv/0WBw4cwJQpU+6lZKdw5jgUFBTg6aefxooVK9z+8u7OHIfz58/j4sWLWLNmDb788kusWLECqampeOKJJxz5ERzCmeOQkJCAX3/9FX/605+gVqsREhKCS5cu4dtvv3XkR3AIR43Dnj178M0332DatGn2+273e9JgMKC8vNyxH+QeOXMcbvbBBx+gpKQEv/vd7xxWvyM5cyzOnDmDN954A1999RWUyoadJ+6Rp5A7Q1JSEo4dO4Zdu3aJXYqoXDUOW7duxZQpU/DFF1+gffv2Tn2vhnDmOEydOhVPPfUUBgwY4PDXdjRnjoPFYoHRaMSXX36JVq1aAQCWLl2K7t27IyMjA61bt3b4ezaUM8dBp9Nh6tSpmDx5MsaPH4/i4mK89dZbeOKJJ7Bp0ybIZDKHv2dDOWIcjh07hlGjRmHu3Ll46KGHHFid67hqHFatWoW3334b33//PSIiIhr8Xs7krLEwm8146qmn8Pbbb9t/PzQEZ3IAzJgxA+vXr8fWrVvRtGlT+/1arRaVlZUoKiqq8fzc3Fxotdo6v75Wq71lx4Xt6/q8jrM5exxstm/fjpEjR+Ljjz/GpEmT7rVsh3P2OGzZsgUffPABlEollEolnn32Wej1eiiVSixbtsxRH+OeOXscoqKioFQqa/wCa9u2LQAgKyvr3op3IGePQ3JyMjQaDebPn4+uXbtiwIAB+Oqrr7B582bs27fPUR/jnjliHE6cOIFBgwZh2rRpePPNN2s8drvfk8HBwfDz83Psh7kHzh4Hm6+//hrPPfccvv3221uW8dyFM8eiuLgYBw8exIwZM+y/K//617/iyJEjUCqV2LJlS92KrFcHj8RYLBYhKSlJiI6OFk6fPn3L47bmqf/+97/2+06dOtXgxuMbmwhnz57tNo3HrhoHQRCErVu3CgEBAcLixYsdVr+juGocTpw4IaSnp9tv77zzjhAUFCSkp6cLhYWFDv1MDeGqcdi4caMAQDh79qz9vrS0NAGAkJGR4ZgPcw9cNQ6zZs0SevXqVeO+nJwcAYCwe/fue/8g98hR43Ds2DEhIiJCePXVV2t9n9dee03o0KFDjfvGjx/vNo3HrhoHQRCEVatWCb6+vsK6desc+yEcxBVjYTaba/yeTE9PF6ZPny60bt1aSE9PF0pKSupUq1eHnOnTpwsajUbYtm2bcOXKFfutrKzM/pw//OEPQmxsrLBlyxbh4MGDQmJiopCYmFjjdc6cOSMcPnxYeP7554VWrVoJhw8fFg4fPmzfTVVUVCRERkYKv//974Vjx44JX3/9teDv7y989tlnLv28t+OqcdiyZYvg7+8vzJ49u8b7FBQUuPTz3o6rxuFm7ra7ylXjYDabhW7dugkDBgwQDh06JBw8eFDo3bu3MGTIEJd+3ttx1Ths3rxZkMlkwttvvy2cPn1aSE1NFYYOHSrExcXVeC+xOGIc0tPThcaNGwsTJ06s8Rp5eXn255w/f17w9/cXXn31VeHkyZNCcnKyoFAohA0bNrj0896Oq8Zh5cqVglKpFJKTk2s8p6ioyKWf905cNRY3a8juKq8OOQBqvS1fvtz+nPLycuGPf/yjEBoaKvj7+wuPPfaYcOXKlRqvc//999f6OpmZmfbnHDlyROjXr5+gVquFJk2aCO+//76LPuXduWocJk+eXOvj999/v+s+7B248ufhRu4Wclw5DpcvXxYef/xxITAwUIiMjBSefvpptwm9rhyH1atXC127dhUCAgKExo0bC48++qhw8uRJF33SO3PEOMydO7fW14iLi6vxXlu3bhW6dOki+Pj4CM2aNavxHmJz1Tjc7udl8uTJrvuwd+HKn4kbNSTkyKoLJiIiIpIUNh4TERGRJDHkEBERkSQx5BAREZEkMeQQERGRJDHkEBERkSQx5BAREZEkMeQQERGRJDHkEBERkSQx5BAREZEkMeQQkUM9/fTTkMlkkMlkUKlUiIyMxJAhQ7Bs2TJYLJZbnj906FAoFAocOHAAAHDhwgX799/utmLFCmzbtu22j+t0Old/bCJyQww5RORwDz/8MK5cuYILFy7gl19+wcCBA/HSSy9hxIgRqKqqsj8vKysLe/bswYwZM7Bs2TIAQExMDK5cuWK/vfLKK2jfvn2N+8aOHWt/jYyMjBqPXblyBRERES7/zETkfpRiF0BE0qNWq6HVagEATZo0Qbdu3dCnTx8MGjQIK1aswHPPPQcAWL58OUaMGIHp06ejT58++Oijj+Dn52f/XgAIDAyEUqmscd+NIiIiEBIS4vTPRESehzM5ROQSDz74IDp37oy1a9cCAARBwPLlyzFx4kS0adMGLVq0wH//+1+RqyQiKWHIISKXadOmDS5cuAAA+O2331BWVoahQ4cCACZOnIilS5fW+zWbNm2KwMBA+619+/aOLJmIPBiXq4jIZQRBgEwmAwAsW7YMY8eOhVJp/TU0fvx4vPrqqzh37hyaN29e59fcuXMngoKC7F+rVCrHFk1EHoshh4hc5uTJk0hISEBhYSG+++47mEwmfPrpp/bHzWYzli1bhnfffbfOr5mQkMCeHCKqFZeriMgltmzZgvT0dIwZMwYrV65E06ZNceTIEaSlpdlvH374IVasWAGz2Sx2uUQkAZzJISKHMxqN0Ol0MJvNyM3NxYYNGzBv3jyMGDECkyZNQvfu3fHEE0+gQ4cONb4vJiYGs2fPxoYNGzB8+PA6vVdeXh4qKipq3BceHs5lKyJiyCEix9uwYQOioqKgVCoRGhqKzp07Y+HChZg8eTIOHz6MI0eO4Isvvrjl+zQaDQYNGoSlS5fWOeS0bt36lvtSUlLQp0+fe/4cROTZZIIgCGIXQURERORo7MkhIiIiSWLIISIiIkliyCEiIiJJYsghIiIiSWLIISIiIkliyCEiIiJJYsghIiIiSWLIISIiIkliyCEiIiJJYsghIiIiSWLIISIiIkn6f3tbQMhvQ0I9AAAAAElFTkSuQmCC"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 683
},
{
"metadata": {},
@@ -1698,7 +8079,12 @@
"id": "89c4e53a8c37bc49"
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:31.350462Z",
+ "start_time": "2024-07-26T16:08:31.191597Z"
+ }
+ },
"cell_type": "code",
"source": [
"hydromed=pd.concat([hydromed_somlit,hydromed_rephy])\n",
@@ -1708,15 +8094,20 @@
],
"id": "f8e243cb153a1d9b",
"outputs": [],
- "execution_count": null
+ "execution_count": 684
},
{
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-07-26T16:08:31.366535Z",
+ "start_time": "2024-07-26T16:08:31.350462Z"
+ }
+ },
"cell_type": "code",
"source": "",
"id": "2ca70a84e455a29f",
"outputs": [],
- "execution_count": null
+ "execution_count": 684
}
],
"metadata": {