diff --git a/notebooks/download-stats.ipynb b/notebooks/download-stats.ipynb index 0ee300f..3e466a1 100644 --- a/notebooks/download-stats.ipynb +++ b/notebooks/download-stats.ipynb @@ -18,7 +18,8 @@ "from rook.usage import Downloads\n", "from datetime import datetime\n", "import os\n", - "import pandas as pd" + "import pandas as pd\n", + "import time" ] }, { @@ -81,18 +82,21 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'./downloads.csv'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 31.28702998161316 secs\n" + ] } ], "source": [ - "d.collect(outdir=os.curdir)" + "start = time.time()\n", + "\n", + "d.collect(outdir=os.curdir)\n", + "\n", + "duration = time.time() - start\n", + "\n", + "print(f\"duration: {duration} secs\")" ] }, { @@ -387,6 +391,11 @@ " referer\n", " user_agent\n", " filename\n", + " collection\n", + " name\n", + " t\n", + " r\n", + " experiment\n", " model\n", " freq\n", " var\n", @@ -407,6 +416,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " ta_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20220816...\n", + " cmip6\n", + " ta_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", " GFDL-ESM4\n", " Amon\n", " ta\n", @@ -425,6 +439,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " uas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081...\n", + " cmip6\n", + " uas_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", " GFDL-ESM4\n", " Amon\n", " uas\n", @@ -443,6 +462,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " tas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081...\n", + " cmip6\n", + " tas_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", " GFDL-ESM4\n", " Amon\n", " tas\n", @@ -461,6 +485,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " hur_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081...\n", + " cmip6\n", + " hur_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", " GFDL-ESM4\n", " Amon\n", " hur\n", @@ -479,6 +508,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " psl_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081...\n", + " cmip6\n", + " psl_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", " GFDL-ESM4\n", " Amon\n", " psl\n", @@ -500,6 +534,11 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 221072\n", @@ -515,6 +554,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " pr_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20150101-...\n", + " cmip6\n", + " pr_day_ACCESS-CM2_ssp245\n", + " 20150101-20640705.nc\n", + " r1i1p1f1\n", + " ssp245\n", " ACCESS-CM2\n", " day\n", " pr\n", @@ -533,6 +577,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " pr_day_MIROC6_historical_r1i1p1f1_gn_19810101-...\n", + " cmip6\n", + " pr_day_MIROC6_historical\n", + " 19810101-20101231.nc\n", + " r1i1p1f1\n", + " historical\n", " MIROC6\n", " day\n", " pr\n", @@ -551,6 +600,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " tos_Omon_CanESM5-CanOE_ssp585_r1i1p2f1_gn_2050...\n", + " cmip6\n", + " tos_Omon_CanESM5-CanOE_ssp585\n", + " 20500116-20991216.nc\n", + " r1i1p2f1\n", + " ssp585\n", " CanESM5-CanOE\n", " Omon\n", " tos\n", @@ -569,6 +623,11 @@ " -\n", " Python-urllib/3.6\" \"-\n", " pr_day_NorESM2-MM_historical_r1i1p1f1_gn_19810...\n", + " cmip6\n", + " pr_day_NorESM2-MM_historical\n", + " 19810101-20101231.nc\n", + " r1i1p1f1\n", + " historical\n", " NorESM2-MM\n", " day\n", " pr\n", @@ -587,13 +646,18 @@ " -\n", " Python-urllib/3.6\" \"-\n", " pr_day_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_...\n", + " cmip6\n", + " pr_day_HadGEM3-GC31-LL_historical\n", + " 19810101-20101230.nc\n", + " r1i1p1f3\n", + " historical\n", " HadGEM3-GC31-LL\n", " day\n", " pr\n", " \n", " \n", "\n", - "

221077 rows × 15 columns

\n", + "

221077 rows × 20 columns

\n", "" ], "text/plain": [ @@ -636,33 +700,46 @@ "221075 HTTP/1.1 200 127257297 - Python-urllib/3.6\" \"- \n", "221076 HTTP/1.1 200 61905737 - Python-urllib/3.6\" \"- \n", "\n", - " filename model \\\n", - "0 ta_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20220816... GFDL-ESM4 \n", - "1 uas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... GFDL-ESM4 \n", - "2 tas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... GFDL-ESM4 \n", - "3 hur_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... GFDL-ESM4 \n", - "4 psl_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... GFDL-ESM4 \n", - "... ... ... \n", - "221072 pr_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20150101-... ACCESS-CM2 \n", - "221073 pr_day_MIROC6_historical_r1i1p1f1_gn_19810101-... MIROC6 \n", - "221074 tos_Omon_CanESM5-CanOE_ssp585_r1i1p2f1_gn_2050... CanESM5-CanOE \n", - "221075 pr_day_NorESM2-MM_historical_r1i1p1f1_gn_19810... NorESM2-MM \n", - "221076 pr_day_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_... HadGEM3-GC31-LL \n", - "\n", - " freq var \n", - "0 Amon ta \n", - "1 Amon uas \n", - "2 Amon tas \n", - "3 Amon hur \n", - "4 Amon psl \n", - "... ... ... \n", - "221072 day pr \n", - "221073 day pr \n", - "221074 Omon tos \n", - "221075 day pr \n", - "221076 day pr \n", - "\n", - "[221077 rows x 15 columns]" + " filename collection \\\n", + "0 ta_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20220816... cmip6 \n", + "1 uas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... cmip6 \n", + "2 tas_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... cmip6 \n", + "3 hur_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... cmip6 \n", + "4 psl_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_2022081... cmip6 \n", + "... ... ... \n", + "221072 pr_day_ACCESS-CM2_ssp245_r1i1p1f1_gn_20150101-... cmip6 \n", + "221073 pr_day_MIROC6_historical_r1i1p1f1_gn_19810101-... cmip6 \n", + "221074 tos_Omon_CanESM5-CanOE_ssp585_r1i1p2f1_gn_2050... cmip6 \n", + "221075 pr_day_NorESM2-MM_historical_r1i1p1f1_gn_19810... cmip6 \n", + "221076 pr_day_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_... cmip6 \n", + "\n", + " name t r \\\n", + "0 ta_Amon_GFDL-ESM4_ssp585 20220816-20220816.nc r1i1p1f1 \n", + "1 uas_Amon_GFDL-ESM4_ssp585 20220816-20220816.nc r1i1p1f1 \n", + "2 tas_Amon_GFDL-ESM4_ssp585 20220816-20220816.nc r1i1p1f1 \n", + "3 hur_Amon_GFDL-ESM4_ssp585 20220816-20220816.nc r1i1p1f1 \n", + "4 psl_Amon_GFDL-ESM4_ssp585 20220816-20220816.nc r1i1p1f1 \n", + "... ... ... ... \n", + "221072 pr_day_ACCESS-CM2_ssp245 20150101-20640705.nc r1i1p1f1 \n", + "221073 pr_day_MIROC6_historical 19810101-20101231.nc r1i1p1f1 \n", + "221074 tos_Omon_CanESM5-CanOE_ssp585 20500116-20991216.nc r1i1p2f1 \n", + "221075 pr_day_NorESM2-MM_historical 19810101-20101231.nc r1i1p1f1 \n", + "221076 pr_day_HadGEM3-GC31-LL_historical 19810101-20101230.nc r1i1p1f3 \n", + "\n", + " experiment model freq var \n", + "0 ssp585 GFDL-ESM4 Amon ta \n", + "1 ssp585 GFDL-ESM4 Amon uas \n", + "2 ssp585 GFDL-ESM4 Amon tas \n", + "3 ssp585 GFDL-ESM4 Amon hur \n", + "4 ssp585 GFDL-ESM4 Amon psl \n", + "... ... ... ... ... \n", + "221072 ssp245 ACCESS-CM2 day pr \n", + "221073 historical MIROC6 day pr \n", + "221074 ssp585 CanESM5-CanOE Omon tos \n", + "221075 historical NorESM2-MM day pr \n", + "221076 historical HadGEM3-GC31-LL day pr \n", + "\n", + "[221077 rows x 20 columns]" ] }, "execution_count": 7, @@ -671,7 +748,26 @@ } ], "source": [ + "def classify(name):\n", + " count = name.count(\"_\")\n", + " if count == 6:\n", + " val = \"cmip6\"\n", + " elif count == 5:\n", + " val = \"cmip6_fx\"\n", + " elif count == 4:\n", + " val = \"atlas\"\n", + " elif count > 6:\n", + " val = \"cordex\"\n", + " else:\n", + " val = \"other\"\n", + " return val\n", + "\n", "df[\"filename\"] = df[\"request\"].apply(lambda x: x.split('/')[-1])\n", + "df[\"collection\"] = df[\"filename\"].apply(lambda x: classify(x))\n", + "df[\"name\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[0:4]))\n", + "df[\"t\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[6:7]))\n", + "df[\"r\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[4:5]))\n", + "df[\"experiment\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[3:4]))\n", "df[\"model\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[2:3]))\n", "df[\"freq\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[1:2]))\n", "df[\"var\"] = df[\"filename\"].apply(lambda x: \"_\".join(x.split('_')[0:1]))\n", @@ -681,27 +777,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "5390a351-649a-4559-a8a9-3486ee5d300b", + "id": "62c28699-a0a5-4484-b20a-6e84db7d4091", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['GFDL-ESM4', 'CNRM-CM6-1-HR', 'CanESM5', 'CMCC-ESM2', 'FGOALS-g3',\n", - " 'MPI-ESM1-2-LR', 'INM-CM4-8', 'ACCESS-CM2', 'AWI-CM-1-1-MR',\n", - " 'BCC-CSM2-MR', 'CAMS-CSM1-0', 'CanESM5-CanOE', 'EC-Earth3-AerChem',\n", - " 'INM-CM5-0', 'IPSL-CM6A-LR', 'MIROC6', 'MIROC-ES2L', 'FGOALS-f3-L',\n", - " 'NorESM2-MM', 'TaiESM1', 'CESM2-WACCM', 'CESM2', 'CMCC-CM2-SR5',\n", - " 'EC-Earth3-Veg', 'CNRM-CM6-1', 'CNRM-ESM2-1', 'EC-Earth3-Veg-LR',\n", - " 'IPSL-CM5A2-INCA', 'KACE-1-0-G', 'MCM-UA-1-0', 'UKESM1-0-LL',\n", - " 'NorESM2-LM', 'EC-Earth3-CC', 'FIO-ESM-2-0', 'NESM3',\n", - " 'ACCESS-ESM1-5', 'MRI-ESM2-0', 'EC-Earth3', 'CMCC-CM2-HR4',\n", - " 'HadGEM3-GC31-MM', 'AWI-ESM-1-1-LR', 'HadGEM3-GC31-LL', 'IITM-ESM',\n", - " 'KIOST-ESM', 'MPI-ESM-1-2-HAM', 'E3SM-1-1', 'CIESM', 'CESM2-FV2',\n", - " 'MPI-ESM1-2-HR', 'CESM2-WACCM-FV2', 'E3SM-1-0', 'E3SM-1-1-ECA',\n", - " 'NorCPM1', 'BCC-ESM1', 'MIROC-ES2H', 'SAM0-UNICON', 'GISS-E2-1-G',\n", - " 'GISS-E2-1-H', 'IPSL-CM5B-LR', 'MOHC-HadGEM2-ES', 'no-expt',\n", - " 'rcp26'], dtype=object)" + "array(['cmip6', 'cmip6_fx', 'cordex', 'atlas'], dtype=object)" ] }, "execution_count": 8, @@ -710,25 +792,71 @@ } ], "source": [ - "df[\"model\"].unique()" + "df[\"collection\"].unique()" ] }, { "cell_type": "code", "execution_count": 9, - "id": "20eb375e-aee5-4195-b82a-fae9b00c7c67", + "id": "253ea766-64ec-49fd-b45a-9c4085eeb690", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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collectioncount
0cmip6221016
1cmip6_fx44
2atlas13
3cordex4
\n", + "
" + ], "text/plain": [ - "array(['ta', 'uas', 'tas', 'hur', 'psl', 'hus', 'huss', 'ts', 'ua', 'zg',\n", - " 'mrsos', 'sfcWind', 'va', 'ps', 'clt', 'pr', 'zos', 'hurs', 'mrro',\n", - " 'tasmax', 'tasmin', 'evspsbl', 'rlut', 'rlds', 'rsds', 'tos',\n", - " 'vas', 'sos', 'orog', 'prsn', 'rsdt', 'snd', 'snw', 'siconc',\n", - " 'rlus', 'rsus', 'sftof', 'sithick', 'hfls', 'hfss', 'zg500',\n", - " 'sftlf', 'simass', 'rsut', 'tauu', 'tauv', 'mrsofc', 'cdd', 't',\n", - " 'sfcwind', 'tx35', 'txx', 'tn', 'tx', 'area'], dtype=object)" + " collection count\n", + "0 cmip6 221016\n", + "1 cmip6_fx 44\n", + "2 atlas 13\n", + "3 cordex 4" ] }, "execution_count": 9, @@ -737,35 +865,15 @@ } ], "source": [ - "df[\"var\"].unique()" + "col_counts = df['collection'].value_counts().reset_index()\n", + "col_counts.columns = ['collection', 'count']\n", + "col_counts" ] }, { "cell_type": "code", "execution_count": 10, - "id": "93d4cd72-6bf8-4c9a-b092-5592c7d2de8e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Amon', 'Lmon', 'Omon', 'day', 'fx', 'LImon', 'SImon', 'Ofx',\n", - " 'EUR-11', 'AERday', 'E-OBS', 'CMIP5'], dtype=object)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[\"freq\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b3066814-9872-4125-84d5-116c3d1092d6", + "id": "75850eaa-7c02-488d-9ad2-b4226090d540", "metadata": {}, "outputs": [ { @@ -789,103 +897,438 @@ " \n", " \n", " \n", + " remote_host_ip\n", + " ip_number\n", + " datetime\n", + " timezone\n", + " request_type\n", + " request\n", + " protocol\n", + " status_code\n", + " size\n", + " referer\n", + " user_agent\n", + " filename\n", + " collection\n", + " name\n", + " t\n", + " r\n", + " experiment\n", " model\n", - " count\n", + " freq\n", + " var\n", " \n", " \n", " \n", " \n", - " 0\n", - " MPI-ESM1-2-LR\n", - " 11491\n", + " 123871\n", + " 136.156.139.146\n", + " 2291960722\n", + " 2024-08-01 16:35:35\n", + " 200\n", + " GET\n", + " /outputs/rook/4f8d2838-5013-11ef-ac87-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 64586\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " cdd_E-OBS_no-expt_yr_19500101-20210101.nc\n", + " atlas\n", + " cdd_E-OBS_no-expt_yr\n", + " \n", + " 19500101-20210101.nc\n", + " yr\n", + " no-expt\n", + " E-OBS\n", + " cdd\n", " \n", " \n", - " 1\n", - " ACCESS-CM2\n", - " 10339\n", + " 123901\n", + " 136.156.137.114\n", + " 2291960178\n", + " 2024-08-01 16:41:25\n", + " 200\n", + " GET\n", + " /outputs/rook/2039b1ea-5014-11ef-ac55-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 965094\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " t_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " t_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " t\n", " \n", " \n", - " 2\n", - " CNRM-ESM2-1\n", - " 9546\n", + " 128575\n", + " 136.156.138.207\n", + " 2291960527\n", + " 2024-08-02 16:55:15\n", + " 200\n", + " GET\n", + " /outputs/rook/375fefc6-50df-11ef-9a31-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 665905\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " rsds_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " rsds_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " rsds\n", " \n", " \n", - " 3\n", - " MIROC-ES2L\n", - " 9480\n", + " 128576\n", + " 136.156.140.238\n", + " 2291961070\n", + " 2024-08-02 16:58:53\n", + " 200\n", + " GET\n", + " /outputs/rook/bb34e8f6-50df-11ef-b6b5-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 540626\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " sfcwind_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " sfcwind_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " sfcwind\n", " \n", " \n", - " 4\n", - " MRI-ESM2-0\n", - " 9422\n", + " 128577\n", + " 136.156.139.188\n", + " 2291960764\n", + " 2024-08-02 17:06:07\n", + " 200\n", + " GET\n", + " /outputs/rook/bd85f07c-50e0-11ef-b422-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 965094\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " t_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " t_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " t\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", + " 128578\n", + " 136.156.138.149\n", + " 2291960469\n", + " 2024-08-02 17:07:12\n", + " 200\n", + " GET\n", + " /outputs/rook/e4c15fd2-50e0-11ef-aa72-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 102286\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " tx35_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " tx35_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " tx35\n", " \n", " \n", - " 57\n", - " E3SM-1-0\n", - " 46\n", + " 128579\n", + " 136.156.139.146\n", + " 2291960722\n", + " 2024-08-02 17:10:29\n", + " 200\n", + " GET\n", + " /outputs/rook/59e4e4b4-50e1-11ef-8859-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 957903\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " txx_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " txx_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " txx\n", " \n", " \n", - " 58\n", + " 128581\n", + " 136.156.138.199\n", + " 2291960519\n", + " 2024-08-02 17:17:34\n", + " 200\n", + " GET\n", + " /outputs/rook/575dd3bc-50e2-11ef-8ef3-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 912057\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " pr_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " pr_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", " no-expt\n", - " 11\n", + " E-OBS\n", + " pr\n", " \n", " \n", - " 59\n", - " IPSL-CM5B-LR\n", - " 4\n", + " 128582\n", + " 136.156.140.220\n", + " 2291961052\n", + " 2024-08-02 17:18:32\n", + " 200\n", + " GET\n", + " /outputs/rook/79bd31aa-50e2-11ef-b6bd-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 64586\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " cdd_E-OBS_no-expt_yr_19500101-20210101.nc\n", + " atlas\n", + " cdd_E-OBS_no-expt_yr\n", + " \n", + " 19500101-20210101.nc\n", + " yr\n", + " no-expt\n", + " E-OBS\n", + " cdd\n", " \n", " \n", - " 60\n", - " MOHC-HadGEM2-ES\n", - " 4\n", + " 128583\n", + " 136.156.140.238\n", + " 2291961070\n", + " 2024-08-02 17:19:29\n", + " 200\n", + " GET\n", + " /outputs/rook/9c00f0d0-50e2-11ef-b64b-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 963043\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " tn_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " tn_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " tn\n", " \n", " \n", - " 61\n", + " 128584\n", + " 136.156.139.146\n", + " 2291960722\n", + " 2024-08-02 17:20:19\n", + " 200\n", + " GET\n", + " /outputs/rook/b9ce1ff2-50e2-11ef-8917-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 971325\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " tx_E-OBS_no-expt_mon_19500101-20211201.nc\n", + " atlas\n", + " tx_E-OBS_no-expt_mon\n", + " \n", + " 19500101-20211201.nc\n", + " mon\n", + " no-expt\n", + " E-OBS\n", + " tx\n", + " \n", + " \n", + " 208574\n", + " 136.156.138.53\n", + " 2291960373\n", + " 2024-08-09 12:05:05\n", + " 200\n", + " GET\n", + " /outputs/rook/492e11f4-5636-11ef-910e-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 920848260\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " txx_CMIP5_rcp26_mon_20060101-20730801.nc\n", + " atlas\n", + " txx_CMIP5_rcp26_mon\n", + " \n", + " 20060101-20730801.nc\n", + " mon\n", " rcp26\n", - " 2\n", + " CMIP5\n", + " txx\n", + " \n", + " \n", + " 208575\n", + " 136.156.138.53\n", + " 2291960373\n", + " 2024-08-09 12:05:14\n", + " 200\n", + " GET\n", + " /outputs/rook/492e2572-5636-11ef-910e-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 371556139\n", + " -\n", + " python-requests/2.32.3\" \"-\n", + " txx_CMIP5_rcp26_mon_20730901-21001201.nc\n", + " atlas\n", + " txx_CMIP5_rcp26_mon\n", + " \n", + " 20730901-21001201.nc\n", + " mon\n", + " rcp26\n", + " CMIP5\n", + " txx\n", " \n", " \n", "\n", - "

62 rows × 2 columns

\n", "" ], "text/plain": [ - " model count\n", - "0 MPI-ESM1-2-LR 11491\n", - "1 ACCESS-CM2 10339\n", - "2 CNRM-ESM2-1 9546\n", - "3 MIROC-ES2L 9480\n", - "4 MRI-ESM2-0 9422\n", - ".. ... ...\n", - "57 E3SM-1-0 46\n", - "58 no-expt 11\n", - "59 IPSL-CM5B-LR 4\n", - "60 MOHC-HadGEM2-ES 4\n", - "61 rcp26 2\n", - "\n", - "[62 rows x 2 columns]" + " remote_host_ip ip_number datetime timezone \\\n", + "123871 136.156.139.146 2291960722 2024-08-01 16:35:35 200 \n", + "123901 136.156.137.114 2291960178 2024-08-01 16:41:25 200 \n", + "128575 136.156.138.207 2291960527 2024-08-02 16:55:15 200 \n", + "128576 136.156.140.238 2291961070 2024-08-02 16:58:53 200 \n", + "128577 136.156.139.188 2291960764 2024-08-02 17:06:07 200 \n", + "128578 136.156.138.149 2291960469 2024-08-02 17:07:12 200 \n", + "128579 136.156.139.146 2291960722 2024-08-02 17:10:29 200 \n", + "128581 136.156.138.199 2291960519 2024-08-02 17:17:34 200 \n", + "128582 136.156.140.220 2291961052 2024-08-02 17:18:32 200 \n", + "128583 136.156.140.238 2291961070 2024-08-02 17:19:29 200 \n", + "128584 136.156.139.146 2291960722 2024-08-02 17:20:19 200 \n", + "208574 136.156.138.53 2291960373 2024-08-09 12:05:05 200 \n", + "208575 136.156.138.53 2291960373 2024-08-09 12:05:14 200 \n", + "\n", + " request_type request \\\n", + "123871 GET /outputs/rook/4f8d2838-5013-11ef-ac87-fa163e93... \n", + "123901 GET /outputs/rook/2039b1ea-5014-11ef-ac55-fa163e93... \n", + "128575 GET /outputs/rook/375fefc6-50df-11ef-9a31-fa163e93... \n", + "128576 GET /outputs/rook/bb34e8f6-50df-11ef-b6b5-fa163e93... \n", + "128577 GET /outputs/rook/bd85f07c-50e0-11ef-b422-fa163e93... \n", + "128578 GET /outputs/rook/e4c15fd2-50e0-11ef-aa72-fa163e93... \n", + "128579 GET /outputs/rook/59e4e4b4-50e1-11ef-8859-fa163e93... \n", + "128581 GET /outputs/rook/575dd3bc-50e2-11ef-8ef3-fa163e93... \n", + "128582 GET /outputs/rook/79bd31aa-50e2-11ef-b6bd-fa163e93... \n", + "128583 GET /outputs/rook/9c00f0d0-50e2-11ef-b64b-fa163e93... \n", + "128584 GET /outputs/rook/b9ce1ff2-50e2-11ef-8917-fa163e93... \n", + "208574 GET /outputs/rook/492e11f4-5636-11ef-910e-fa163e93... \n", + "208575 GET /outputs/rook/492e2572-5636-11ef-910e-fa163e93... \n", + "\n", + " protocol status_code size referer user_agent \\\n", + "123871 HTTP/1.1 200 64586 - python-requests/2.32.3\" \"- \n", + "123901 HTTP/1.1 200 965094 - python-requests/2.32.3\" \"- \n", + "128575 HTTP/1.1 200 665905 - python-requests/2.32.3\" \"- \n", + "128576 HTTP/1.1 200 540626 - python-requests/2.32.3\" \"- \n", + "128577 HTTP/1.1 200 965094 - python-requests/2.32.3\" \"- \n", + "128578 HTTP/1.1 200 102286 - python-requests/2.32.3\" \"- \n", + "128579 HTTP/1.1 200 957903 - python-requests/2.32.3\" \"- \n", + "128581 HTTP/1.1 200 912057 - python-requests/2.32.3\" \"- \n", + "128582 HTTP/1.1 200 64586 - python-requests/2.32.3\" \"- \n", + "128583 HTTP/1.1 200 963043 - python-requests/2.32.3\" \"- \n", + "128584 HTTP/1.1 200 971325 - python-requests/2.32.3\" \"- \n", + "208574 HTTP/1.1 200 920848260 - python-requests/2.32.3\" \"- \n", + "208575 HTTP/1.1 200 371556139 - python-requests/2.32.3\" \"- \n", + "\n", + " filename collection \\\n", + "123871 cdd_E-OBS_no-expt_yr_19500101-20210101.nc atlas \n", + "123901 t_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128575 rsds_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128576 sfcwind_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128577 t_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128578 tx35_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128579 txx_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128581 pr_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128582 cdd_E-OBS_no-expt_yr_19500101-20210101.nc atlas \n", + "128583 tn_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "128584 tx_E-OBS_no-expt_mon_19500101-20211201.nc atlas \n", + "208574 txx_CMIP5_rcp26_mon_20060101-20730801.nc atlas \n", + "208575 txx_CMIP5_rcp26_mon_20730901-21001201.nc atlas \n", + "\n", + " name t r experiment model \\\n", + "123871 cdd_E-OBS_no-expt_yr 19500101-20210101.nc yr no-expt \n", + "123901 t_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128575 rsds_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128576 sfcwind_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128577 t_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128578 tx35_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128579 txx_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128581 pr_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128582 cdd_E-OBS_no-expt_yr 19500101-20210101.nc yr no-expt \n", + "128583 tn_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "128584 tx_E-OBS_no-expt_mon 19500101-20211201.nc mon no-expt \n", + "208574 txx_CMIP5_rcp26_mon 20060101-20730801.nc mon rcp26 \n", + "208575 txx_CMIP5_rcp26_mon 20730901-21001201.nc mon rcp26 \n", + "\n", + " freq var \n", + "123871 E-OBS cdd \n", + "123901 E-OBS t \n", + "128575 E-OBS rsds \n", + "128576 E-OBS sfcwind \n", + "128577 E-OBS t \n", + "128578 E-OBS tx35 \n", + "128579 E-OBS txx \n", + "128581 E-OBS pr \n", + "128582 E-OBS cdd \n", + "128583 E-OBS tn \n", + "128584 E-OBS tx \n", + "208574 CMIP5 txx \n", + "208575 CMIP5 txx " ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model_counts = df['model'].value_counts().reset_index()\n", - "model_counts.columns = ['model', 'count']\n", - "model_counts" + "df_atlas = df[df['collection'] == 'atlas']\n", + "df_atlas" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "f1f908d6-4845-44b3-b73b-7487879c462f", + "execution_count": 11, + "id": "cd988e05-7239-4443-a78b-5c8674a9f632", "metadata": {}, "outputs": [ { @@ -909,20 +1352,2693 @@ " \n", " \n", " \n", - " model\n", - " count\n", - " \n", - " \n", - " \n", + " remote_host_ip\n", + " ip_number\n", + " datetime\n", + " timezone\n", + " request_type\n", + " request\n", + " protocol\n", + " status_code\n", + " size\n", + " referer\n", + " user_agent\n", + " filename\n", + " collection\n", + " name\n", + " t\n", + " r\n", + " experiment\n", + " model\n", + " freq\n", + " var\n", + " \n", + " \n", + " \n", + " \n", + " 0\n", + " 136.156.155.149\n", + " 2291964821\n", + " 2024-08-14 00:13:10\n", + " 200\n", + " GET\n", + " /outputs/rook/3769991e-59c1-11ef-b5c0-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 1984894\n", + " -\n", + " Python-urllib/3.6\" \"-\n", + " ta_Amon_GFDL-ESM4_ssp585_r1i1p1f1_gr1_20220816...\n", + " cmip6\n", + " ta_Amon_GFDL-ESM4_ssp585\n", + " 20220816-20220816.nc\n", + " r1i1p1f1\n", + " ssp585\n", + " GFDL-ESM4\n", + " Amon\n", + " ta\n", + " \n", + " \n", + " 1\n", + " 136.156.153.235\n", + " 2291964395\n", + " 2024-08-14 00:13:32\n", + " 200\n", + " GET\n", + " /outputs/rook/46226da0-59c1-11ef-84a3-fa163e93...\n", + " HTTP/1.1\n", + " 200\n", + " 215882\n", + " -\n", + " Python-urllib/3.6\" \"-\n", + " 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221016 rows × 20 columns

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rcount
0r1i1p1f1159146
1r1i1p1f238640
2r4i1p1f17504
3r1i1p1f36851
4r2i1p1f13807
5r1i1p2f12453
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12r1i1p5f27
13r0i0p0f27
14r5i1p1f13
15r3i1p1f12
16r2i1p1f22
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rcount
0r1i1p1f1159146
1r1i1p1f238640
2r4i1p1f17504
3r1i1p1f36851
4r2i1p1f13807
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" + ], + "text/plain": [ + " r count\n", + "0 r1i1p1f1 159146\n", + "1 r1i1p1f2 38640\n", + "2 r4i1p1f1 7504\n", + "3 r1i1p1f3 6851\n", + "4 r2i1p1f1 3807" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_counts.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "99db9042-a84f-40da-afc5-b833553c3acf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " experiment count\n", + "0 ssp585 70566\n", + "1 ssp245 46204\n", + "2 historical 35541\n", + "3 ssp370 34477\n", + "4 ssp126 31618\n", + "5 ssp119 1951\n", + "6 ssp460 263\n", + "7 ssp434 234\n", + "8 dcppA-hindcast 132\n", + "9 ssp534-over 22\n", + "10 dcppB-forecast 8" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp_counts = df['experiment'].value_counts().reset_index()\n", + "exp_counts.columns = ['experiment', 'count']\n", + "exp_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b3f0c2a9-88a4-4545-aff2-946526fffbbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " experiment count\n", + "0 ssp585 70566\n", + "1 ssp245 46204\n", + "2 historical 35541\n", + "3 ssp370 34477\n", + "4 ssp126 31618\n", + "5 ssp119 1951\n", + "6 ssp460 263" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp_counts.head(7)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2d1fe476-924a-4bde-ba2e-9f1310e91aef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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modelcount
0MPI-ESM1-2-LR11488
1ACCESS-CM210331
2CNRM-ESM2-19546
3MIROC-ES2L9479
4MRI-ESM2-09421
5GFDL-ESM49174
6UKESM1-0-LL9024
7MIROC68766
8CESM28493
9NorESM2-MM8130
10INM-CM5-07985
11EC-Earth3-Veg-LR7759
12IPSL-CM6A-LR7263
13CNRM-CM6-17109
14INM-CM4-87003
15BCC-CSM2-MR6802
16CMCC-ESM25768
17AWI-CM-1-1-MR5561
18CNRM-CM6-1-HR5545
19CMCC-CM2-SR55429
20CanESM55142
21EC-Earth3-CC5067
22KACE-1-0-G4375
23FGOALS-g34304
24HadGEM3-GC31-LL4167
25IITM-ESM4083
26TaiESM13358
27CAMS-CSM1-03348
28NorESM2-LM3190
29NESM33118
30KIOST-ESM2972
31HadGEM3-GC31-MM2697
32FGOALS-f3-L2612
33CanESM5-CanOE2452
34CESM2-WACCM2369
35IPSL-CM5A2-INCA1713
36EC-Earth3-AerChem1075
37MPI-ESM1-2-HR521
38MPI-ESM-1-2-HAM518
39ACCESS-ESM1-5383
40AWI-ESM-1-1-LR380
41EC-Earth3375
42FIO-ESM-2-0326
43BCC-ESM1301
44CMCC-CM2-HR4289
45CESM2-FV2285
46SAM0-UNICON262
47CIESM217
48NorCPM1185
49CESM2-WACCM-FV2172
50MCM-UA-1-0146
51GISS-E2-1-G119
52EC-Earth3-Veg110
53GISS-E2-1-H94
54E3SM-1-168
55MIROC-ES2H51
56E3SM-1-1-ECA50
57E3SM-1-046
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" + ], + "text/plain": [ + " model count\n", + "0 MPI-ESM1-2-LR 11488\n", + "1 ACCESS-CM2 10331\n", + "2 CNRM-ESM2-1 9546\n", + "3 MIROC-ES2L 9479\n", + "4 MRI-ESM2-0 9421\n", + "5 GFDL-ESM4 9174\n", + "6 UKESM1-0-LL 9024\n", + "7 MIROC6 8766\n", + "8 CESM2 8493\n", + "9 NorESM2-MM 8130\n", + "10 INM-CM5-0 7985\n", + "11 EC-Earth3-Veg-LR 7759\n", + "12 IPSL-CM6A-LR 7263\n", + "13 CNRM-CM6-1 7109\n", + "14 INM-CM4-8 7003\n", + "15 BCC-CSM2-MR 6802\n", + "16 CMCC-ESM2 5768\n", + "17 AWI-CM-1-1-MR 5561\n", + "18 CNRM-CM6-1-HR 5545\n", + "19 CMCC-CM2-SR5 5429\n", + "20 CanESM5 5142\n", + "21 EC-Earth3-CC 5067\n", + "22 KACE-1-0-G 4375\n", + "23 FGOALS-g3 4304\n", + "24 HadGEM3-GC31-LL 4167\n", + "25 IITM-ESM 4083\n", + "26 TaiESM1 3358\n", + "27 CAMS-CSM1-0 3348\n", + "28 NorESM2-LM 3190\n", + "29 NESM3 3118\n", + "30 KIOST-ESM 2972\n", + "31 HadGEM3-GC31-MM 2697\n", + "32 FGOALS-f3-L 2612\n", + "33 CanESM5-CanOE 2452\n", + "34 CESM2-WACCM 2369\n", + "35 IPSL-CM5A2-INCA 1713\n", + "36 EC-Earth3-AerChem 1075\n", + "37 MPI-ESM1-2-HR 521\n", + "38 MPI-ESM-1-2-HAM 518\n", + "39 ACCESS-ESM1-5 383\n", + "40 AWI-ESM-1-1-LR 380\n", + "41 EC-Earth3 375\n", + "42 FIO-ESM-2-0 326\n", + "43 BCC-ESM1 301\n", + "44 CMCC-CM2-HR4 289\n", + "45 CESM2-FV2 285\n", + "46 SAM0-UNICON 262\n", + "47 CIESM 217\n", + "48 NorCPM1 185\n", + "49 CESM2-WACCM-FV2 172\n", + "50 MCM-UA-1-0 146\n", + "51 GISS-E2-1-G 119\n", + "52 EC-Earth3-Veg 110\n", + "53 GISS-E2-1-H 94\n", + "54 E3SM-1-1 68\n", + "55 MIROC-ES2H 51\n", + "56 E3SM-1-1-ECA 50\n", + "57 E3SM-1-0 46" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_counts = df['model'].value_counts().reset_index()\n", + "model_counts.columns = ['model', 'count']\n", + "model_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "f1f908d6-4845-44b3-b73b-7487879c462f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelcount
0MPI-ESM1-2-LR1149111488
1ACCESS-CM21033910331
2
3MIROC-ES2L94809479
4MRI-ESM2-094229421
5GFDL-ESM491759174
6
7MIROC687688766
8CESM284968493
9NorESM2-MM81328130
10INM-CM5-07985
11EC-Earth3-Veg-LR7759
12IPSL-CM6A-LR7263
13CNRM-CM6-17109
14INM-CM4-87003
15BCC-CSM2-MR6802
16CMCC-ESM25768
17AWI-CM-1-1-MR5561
18CNRM-CM6-1-HR5545
19CMCC-CM2-SR55429
20CanESM55142
21EC-Earth3-CC5067
22KACE-1-0-G4375
23FGOALS-g34304
24HadGEM3-GC31-LL4167
25IITM-ESM4083
26TaiESM13358
27CAMS-CSM1-03348
28NorESM2-LM3190
29NESM33118
30KIOST-ESM2972
31HadGEM3-GC31-MM2697
32FGOALS-f3-L2612
33CanESM5-CanOE2452
34CESM2-WACCM2369
35IPSL-CM5A2-INCA1713
36EC-Earth3-AerChem1075
37MPI-ESM1-2-HR521
38MPI-ESM-1-2-HAM518
39ACCESS-ESM1-5383
\n", "
" ], "text/plain": [ - " model count\n", - "0 MPI-ESM1-2-LR 11491\n", - "1 ACCESS-CM2 10339\n", - "2 CNRM-ESM2-1 9546\n", - "3 MIROC-ES2L 9480\n", - "4 MRI-ESM2-0 9422\n", - "5 GFDL-ESM4 9175\n", - "6 UKESM1-0-LL 9024\n", - "7 MIROC6 8768\n", - "8 CESM2 8496\n", - "9 NorESM2-MM 8132" + " model count\n", + "0 MPI-ESM1-2-LR 11488\n", + "1 ACCESS-CM2 10331\n", + "2 CNRM-ESM2-1 9546\n", + "3 MIROC-ES2L 9479\n", + "4 MRI-ESM2-0 9421\n", + "5 GFDL-ESM4 9174\n", + "6 UKESM1-0-LL 9024\n", + "7 MIROC6 8766\n", + "8 CESM2 8493\n", + "9 NorESM2-MM 8130\n", + "10 INM-CM5-0 7985\n", + "11 EC-Earth3-Veg-LR 7759\n", + "12 IPSL-CM6A-LR 7263\n", + "13 CNRM-CM6-1 7109\n", + "14 INM-CM4-8 7003\n", + "15 BCC-CSM2-MR 6802\n", + "16 CMCC-ESM2 5768\n", + "17 AWI-CM-1-1-MR 5561\n", + "18 CNRM-CM6-1-HR 5545\n", + "19 CMCC-CM2-SR5 5429\n", + "20 CanESM5 5142\n", + "21 EC-Earth3-CC 5067\n", + "22 KACE-1-0-G 4375\n", + "23 FGOALS-g3 4304\n", + "24 HadGEM3-GC31-LL 4167\n", + "25 IITM-ESM 4083\n", + "26 TaiESM1 3358\n", + "27 CAMS-CSM1-0 3348\n", + "28 NorESM2-LM 3190\n", + "29 NESM3 3118\n", + "30 KIOST-ESM 2972\n", + "31 HadGEM3-GC31-MM 2697\n", + "32 FGOALS-f3-L 2612\n", + "33 CanESM5-CanOE 2452\n", + "34 CESM2-WACCM 2369\n", + "35 IPSL-CM5A2-INCA 1713\n", + "36 EC-Earth3-AerChem 1075\n", + "37 MPI-ESM1-2-HR 521\n", + "38 MPI-ESM-1-2-HAM 518\n", + "39 ACCESS-ESM1-5 383" ] }, - "execution_count": 12, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model_counts.head(10)" + "model_counts.head(40)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, + "id": "ec23a75f-c6ac-4125-8499-9f4eafd0b129", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_counts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, "id": "9ece3642-c87f-42b9-81a6-1ce1df75e98a", "metadata": {}, "outputs": [ @@ -1026,12 +4353,12 @@ " \n", " 0\n", " pr\n", - " 155026\n", + " 155025\n", " \n", " \n", " 1\n", " tas\n", - " 16675\n", + " 16667\n", " \n", " \n", " 2\n", @@ -1066,7 +4393,7 @@ " \n", " 8\n", " rsds\n", - " 1546\n", + " 1545\n", " \n", " \n", " 9\n", @@ -1155,12 +4482,12 @@ " \n", " \n", " 26\n", - " rsus\n", + " uas\n", " 83\n", " \n", " \n", " 27\n", - " uas\n", + " rsus\n", " 83\n", " \n", " \n", @@ -1210,109 +4537,54 @@ " \n", " \n", " 37\n", - " orog\n", - " 20\n", - " \n", - " \n", - " 38\n", " snw\n", " 17\n", " \n", " \n", - " 39\n", - " sftof\n", - " 15\n", - " \n", - " \n", - " 40\n", + " 38\n", " rsut\n", " 8\n", " \n", " \n", - " 41\n", + " 39\n", " zg500\n", " 5\n", " \n", " \n", - " 42\n", - " tauv\n", - " 3\n", - " \n", - " \n", - " 43\n", - " txx\n", - " 3\n", - " \n", - " \n", - " 44\n", - " mrsofc\n", - " 3\n", - " \n", - " \n", - " 45\n", + " 40\n", " simass\n", " 3\n", " \n", " \n", - " 46\n", + " 41\n", " tauu\n", " 3\n", " \n", " \n", - " 47\n", - " cdd\n", - " 2\n", - " \n", - " \n", - " 48\n", - " t\n", - " 2\n", - " \n", - " \n", - " 49\n", - " sftlf\n", - " 2\n", + " 42\n", + " tauv\n", + " 3\n", " \n", " \n", - " 50\n", + " 43\n", " area\n", " 2\n", " \n", - " \n", - " 51\n", - " sfcwind\n", - " 1\n", - " \n", - " \n", - " 52\n", - " tx35\n", - " 1\n", - " \n", - " \n", - " 53\n", - " tn\n", - " 1\n", - " \n", - " \n", - " 54\n", - " tx\n", - " 1\n", - " \n", " \n", "\n", "" ], "text/plain": [ " var count\n", - "0 pr 155026\n", - "1 tas 16675\n", + "0 pr 155025\n", + "1 tas 16667\n", "2 tasmax 15014\n", "3 tasmin 8276\n", "4 huss 5469\n", "5 sfcWind 5129\n", "6 ta 4362\n", "7 psl 2978\n", - "8 rsds 1546\n", + "8 rsds 1545\n", "9 zg 862\n", "10 tos 769\n", "11 ua 614\n", @@ -1330,8 +4602,8 @@ "23 rsdt 111\n", "24 vas 96\n", "25 clt 86\n", - "26 rsus 83\n", - "27 uas 83\n", + "26 uas 83\n", + "27 rsus 83\n", "28 rlus 62\n", "29 sos 60\n", "30 hfss 58\n", @@ -1341,27 +4613,16 @@ "34 sithick 38\n", "35 prsn 31\n", "36 rlut 24\n", - "37 orog 20\n", - "38 snw 17\n", - "39 sftof 15\n", - "40 rsut 8\n", - "41 zg500 5\n", + "37 snw 17\n", + "38 rsut 8\n", + "39 zg500 5\n", + "40 simass 3\n", + "41 tauu 3\n", "42 tauv 3\n", - "43 txx 3\n", - "44 mrsofc 3\n", - "45 simass 3\n", - "46 tauu 3\n", - "47 cdd 2\n", - "48 t 2\n", - "49 sftlf 2\n", - "50 area 2\n", - "51 sfcwind 1\n", - "52 tx35 1\n", - "53 tn 1\n", - "54 tx 1" + "43 area 2" ] }, - "execution_count": 13, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1374,7 +4635,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 43, "id": "3355a644-710c-4d35-92e1-777a81d577a1", "metadata": {}, "outputs": [ @@ -1407,12 +4668,12 @@ " \n", " 0\n", " pr\n", - " 155026\n", + " 155025\n", " \n", " \n", " 1\n", " tas\n", - " 16675\n", + " 16667\n", " \n", " \n", " 2\n", @@ -1447,7 +4708,7 @@ " \n", " 8\n", " rsds\n", - " 1546\n", + " 1545\n", " \n", " \n", " 9\n", @@ -1460,19 +4721,19 @@ ], "text/plain": [ " var count\n", - "0 pr 155026\n", - "1 tas 16675\n", + "0 pr 155025\n", + "1 tas 16667\n", "2 tasmax 15014\n", "3 tasmin 8276\n", "4 huss 5469\n", "5 sfcWind 5129\n", "6 ta 4362\n", "7 psl 2978\n", - "8 rsds 1546\n", + "8 rsds 1545\n", "9 zg 862" ] }, - "execution_count": 14, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1483,7 +4744,38 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 44, + "id": "61e59b72-1aed-47ce-a1c7-75b1512eb4db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "var_counts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, "id": "98454c89-cb91-42b2-bd0e-0a46f678b2a5", "metadata": {}, "outputs": [ @@ -1516,7 +4808,7 @@ " \n", " 0\n", " day\n", - " 158421\n", + " 158417\n", " \n", " \n", " 1\n", @@ -1545,55 +4837,25 @@ " \n", " \n", " 6\n", - " fx\n", - " 25\n", - " \n", - " \n", - " 7\n", - " Ofx\n", - " 15\n", - " \n", - " \n", - " 8\n", - " E-OBS\n", - " 11\n", - " \n", - " \n", - " 9\n", " AERday\n", " 5\n", " \n", - " \n", - " 10\n", - " EUR-11\n", - " 4\n", - " \n", - " \n", - " 11\n", - " CMIP5\n", - " 2\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " freq count\n", - "0 day 158421\n", - "1 Amon 60992\n", - "2 Omon 944\n", - "3 Lmon 502\n", - "4 SImon 91\n", - "5 LImon 65\n", - "6 fx 25\n", - "7 Ofx 15\n", - "8 E-OBS 11\n", - "9 AERday 5\n", - "10 EUR-11 4\n", - "11 CMIP5 2" + " freq count\n", + "0 day 158417\n", + "1 Amon 60992\n", + "2 Omon 944\n", + "3 Lmon 502\n", + "4 SImon 91\n", + "5 LImon 65\n", + "6 AERday 5" ] }, - "execution_count": 15, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1603,6 +4865,37 @@ "freq_counts.columns = ['freq', 'count']\n", "freq_counts" ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "64d8bb08-71b9-4a92-8943-96b945e69292", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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