From 7b02009823d7928ed9e492ca2ffc91b02f0eb2af Mon Sep 17 00:00:00 2001 From: Edward Byers Date: Thu, 9 Nov 2023 17:08:49 +0100 Subject: [PATCH] prepare notebook --- rime/Untitled.ipynb | 1313 ++++++++++ rime/emissions_temp_AR6_small.xlsx | Bin 0 -> 4769911 bytes rime/generate_aggregated_inputs.py | 122 +- rime/indicator_params.yml | 372 +++ rime/plot_dashboards.py | 5 + rime/pp_combined example.ipynb | 3779 ++++++++++++++++++++++++++++ rime/pp_process_maps.ipynb | 411 ++- rime/pp_process_tabledata.ipynb | 1415 +++++------ rime/process_config.py | 40 +- rime/process_maps.py | 4 +- rime/process_tabledata.py | 4 +- rime/rime_functions.py | 152 +- rime/test_map.html | 71 + rime/utils.py | 27 +- setup.cfg | 8 +- 15 files changed, 6764 insertions(+), 959 deletions(-) create mode 100644 rime/Untitled.ipynb create mode 100644 rime/emissions_temp_AR6_small.xlsx create mode 100644 rime/indicator_params.yml create mode 100644 rime/pp_combined example.ipynb create mode 100644 rime/test_map.html diff --git a/rime/Untitled.ipynb b/rime/Untitled.ipynb new file mode 100644 index 0000000..2f8a6a2 --- /dev/null +++ b/rime/Untitled.ipynb @@ -0,0 +1,1313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cb10c199-e9bf-4846-bf38-fd943a13f611", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\byers\\Anaconda3\\envs\\py310\\lib\\site-packages\\pyproj\\__init__.py:89: UserWarning: pyproj unable to set database path.\n", + " _pyproj_global_context_initialize()\n" + ] + }, + { + "data": { + "application/javascript": [ + "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pyam" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f694b80e-6fb1-4efe-b107-8d52fdb6dc09", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pyam.core - INFO: Reading file C:\\Users\\byers\\IIASA\\ECE.prog - Documents\\Research Theme - NEXUS\\Hotspots_Explorer_2p0\\rcre_testing\\testing_2\\emissions_temp_AR6_small.xlsx\n", + "pyam.core - INFO: Found column 'exclude' in sheet 'meta', moved to attribute `IamDataFrame.exclude`.\n" + ] + } + ], + "source": [ + "df = pyam.IamDataFrame('C:\\\\Users\\\\byers\\\\IIASA\\\\ECE.prog - Documents\\\\Research Theme - NEXUS\\\\Hotspots_Explorer_2p0\\\\rcre_testing\\\\testing_2\\\\emissions_temp_AR6_small.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "959b3cdb-0c91-4b78-8cce-70e43a83f10e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Index:\n", + " * model : AIM/CGE 2.0, AIM/CGE 2.1, AIM/CGE 2.2, ... WITCH-GLOBIOM 4.4 (44)\n", + " * scenario : 1.5C, 2.5C, 2C, 2CNow_Gradual, ... WB2C (559)\n", + "Timeseries data coordinates:\n", + " region : World (1)\n", + " variable : AR6 climate diagnostics|Infilled|Emissions|CO2, ... (3)\n", + " unit : K, Mt CO2/yr (2)\n", + " year : 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, ... 2100 (86)\n", + "Meta indicators:\n", + " Category (object) C3, C5, C6, C7, C4, C1, C2, C8 (8)\n", + " Category_name (object) C3: limit warming to 2°C (>67%), ... (8)\n", + " Category_subset (object) C3y_+veGHGs, C5, C6, C7, C4, C1a_NZGHGs, ... C8 (10)\n", + " Subset_Ch4 (object) Limit to 2C (>67%) immediate 2020 action, ... (6)\n", + " Category_Vetting_historical (object) C3, C5, C6, C7, C4, C1, C2, C8 (8)\n", + " ..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0533c3bd-2689-4d9d-a2d8-754aac00ee52", + "metadata": {}, + "outputs": [], + "source": [ + "dft = df.timeseries(iamc_index=False)\n", + "dft.join(" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "744f336d-d924-4a80-8f56-1d8cf8ae416c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2015201620172018201920202021202220232024...209220932094209520962097209820992100Ssp_family
modelscenario
AIM/CGE 2.0SSP1-2639152.72630038814.58550038476.50914438138.49723337800.54976737462.66674636683.89231135905.86547935128.58624934352.054622...-138.597038-188.421413-238.245789-288.070164-337.894539-387.718914-437.543289-487.367665-537.1920401.0
SSP1-261.1156981.1350781.1553811.1767371.2001061.2252771.2564031.2921581.3247281.353267...1.6369291.6378521.6350121.6319041.6251391.6177981.6135841.6117861.6095741.0
SSP1-26NaNNaNNaNNaNNaN37181.33850036442.25551035703.17252034964.08953034225.006540...505.121540444.198560383.275580322.352600261.429620200.506640139.58366078.66068017.7377001.0
SSP1-3439152.72630038814.58550038476.50914438138.49723337800.54976737462.66674637246.80758337030.95746136815.11638036599.284340...9869.5919429696.2494579522.9069719349.5644869176.2220019002.8795168829.5370318656.1945458482.8520601.0
SSP1-341.1156981.1350781.1554031.1768341.2002721.2252681.2556911.2911991.3224281.352320...2.0850472.0886222.0890802.0890552.0889862.0887092.0864822.0874862.0902441.0
.....................................................................
WITCH-GLOBIOM 4.4CD-LINKS_NPi2020_4001.1154941.1308551.1492481.1662581.1864031.2108101.2450771.2820831.3170861.344960...1.0838681.0781641.0689211.0574991.0492961.0392941.0310901.0244441.0182602.0
CD-LINKS_NPi2020_40037811.00745538380.11127238949.21508939518.31890640087.42272340656.52654037575.64347034494.76039931413.87732828332.994258...-14623.033715-14573.025928-14523.018140-14473.010353-14423.002565-14372.994777-14322.986990-14272.979202-14222.9714152.0
CD-LINKS_NoPolicy39152.72630040112.72729041072.22146142031.20881442989.68934743947.66306144954.23450945960.29268346965.83758447970.869211...83737.81993083361.66693282985.51393582609.36093782233.20794081857.05494281480.90194481104.74894780728.5959492.0
CD-LINKS_NoPolicy1.1154851.1315151.1502051.1684731.1904671.2126121.2391861.2690671.2989151.327518...3.7086403.7485623.7856193.8194063.8485183.8803853.9154873.9530173.9913282.0
CD-LINKS_NoPolicy37866.23507038824.19625939782.15744940740.11863841698.07982742656.04101643662.91634744669.79167745676.66700846683.542338...83435.47839083064.53817782693.59796582322.65775281951.71754081580.77732881209.83711580838.89690380467.9566902.0
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3606 rows × 87 columns

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" + ], + "text/plain": [ + " 2015 2016 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 39152.726300 38814.585500 \n", + " SSP1-26 1.115698 1.135078 \n", + " SSP1-26 NaN NaN \n", + " SSP1-34 39152.726300 38814.585500 \n", + " SSP1-34 1.115698 1.135078 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.115494 1.130855 \n", + " CD-LINKS_NPi2020_400 37811.007455 38380.111272 \n", + " CD-LINKS_NoPolicy 39152.726300 40112.727290 \n", + " CD-LINKS_NoPolicy 1.115485 1.131515 \n", + " CD-LINKS_NoPolicy 37866.235070 38824.196259 \n", + "\n", + " 2017 2018 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 38476.509144 38138.497233 \n", + " SSP1-26 1.155381 1.176737 \n", + " SSP1-26 NaN NaN \n", + " SSP1-34 38476.509144 38138.497233 \n", + " SSP1-34 1.155403 1.176834 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.149248 1.166258 \n", + " CD-LINKS_NPi2020_400 38949.215089 39518.318906 \n", + " CD-LINKS_NoPolicy 41072.221461 42031.208814 \n", + " CD-LINKS_NoPolicy 1.150205 1.168473 \n", + " CD-LINKS_NoPolicy 39782.157449 40740.118638 \n", + "\n", + " 2019 2020 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 37800.549767 37462.666746 \n", + " SSP1-26 1.200106 1.225277 \n", + " SSP1-26 NaN 37181.338500 \n", + " SSP1-34 37800.549767 37462.666746 \n", + " SSP1-34 1.200272 1.225268 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.186403 1.210810 \n", + " CD-LINKS_NPi2020_400 40087.422723 40656.526540 \n", + " CD-LINKS_NoPolicy 42989.689347 43947.663061 \n", + " CD-LINKS_NoPolicy 1.190467 1.212612 \n", + " CD-LINKS_NoPolicy 41698.079827 42656.041016 \n", + "\n", + " 2021 2022 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 36683.892311 35905.865479 \n", + " SSP1-26 1.256403 1.292158 \n", + " SSP1-26 36442.255510 35703.172520 \n", + " SSP1-34 37246.807583 37030.957461 \n", + " SSP1-34 1.255691 1.291199 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.245077 1.282083 \n", + " CD-LINKS_NPi2020_400 37575.643470 34494.760399 \n", + " CD-LINKS_NoPolicy 44954.234509 45960.292683 \n", + " CD-LINKS_NoPolicy 1.239186 1.269067 \n", + " CD-LINKS_NoPolicy 43662.916347 44669.791677 \n", + "\n", + " 2023 2024 ... \\\n", + "model scenario ... \n", + "AIM/CGE 2.0 SSP1-26 35128.586249 34352.054622 ... \n", + " SSP1-26 1.324728 1.353267 ... \n", + " SSP1-26 34964.089530 34225.006540 ... \n", + " SSP1-34 36815.116380 36599.284340 ... \n", + " SSP1-34 1.322428 1.352320 ... \n", + "... ... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.317086 1.344960 ... \n", + " CD-LINKS_NPi2020_400 31413.877328 28332.994258 ... \n", + " CD-LINKS_NoPolicy 46965.837584 47970.869211 ... \n", + " CD-LINKS_NoPolicy 1.298915 1.327518 ... \n", + " CD-LINKS_NoPolicy 45676.667008 46683.542338 ... \n", + "\n", + " 2092 2093 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 -138.597038 -188.421413 \n", + " SSP1-26 1.636929 1.637852 \n", + " SSP1-26 505.121540 444.198560 \n", + " SSP1-34 9869.591942 9696.249457 \n", + " SSP1-34 2.085047 2.088622 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.083868 1.078164 \n", + " CD-LINKS_NPi2020_400 -14623.033715 -14573.025928 \n", + " CD-LINKS_NoPolicy 83737.819930 83361.666932 \n", + " CD-LINKS_NoPolicy 3.708640 3.748562 \n", + " CD-LINKS_NoPolicy 83435.478390 83064.538177 \n", + "\n", + " 2094 2095 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 -238.245789 -288.070164 \n", + " SSP1-26 1.635012 1.631904 \n", + " SSP1-26 383.275580 322.352600 \n", + " SSP1-34 9522.906971 9349.564486 \n", + " SSP1-34 2.089080 2.089055 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.068921 1.057499 \n", + " CD-LINKS_NPi2020_400 -14523.018140 -14473.010353 \n", + " CD-LINKS_NoPolicy 82985.513935 82609.360937 \n", + " CD-LINKS_NoPolicy 3.785619 3.819406 \n", + " CD-LINKS_NoPolicy 82693.597965 82322.657752 \n", + "\n", + " 2096 2097 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 -337.894539 -387.718914 \n", + " SSP1-26 1.625139 1.617798 \n", + " SSP1-26 261.429620 200.506640 \n", + " SSP1-34 9176.222001 9002.879516 \n", + " SSP1-34 2.088986 2.088709 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.049296 1.039294 \n", + " CD-LINKS_NPi2020_400 -14423.002565 -14372.994777 \n", + " CD-LINKS_NoPolicy 82233.207940 81857.054942 \n", + " CD-LINKS_NoPolicy 3.848518 3.880385 \n", + " CD-LINKS_NoPolicy 81951.717540 81580.777328 \n", + "\n", + " 2098 2099 \\\n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 -437.543289 -487.367665 \n", + " SSP1-26 1.613584 1.611786 \n", + " SSP1-26 139.583660 78.660680 \n", + " SSP1-34 8829.537031 8656.194545 \n", + " SSP1-34 2.086482 2.087486 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.031090 1.024444 \n", + " CD-LINKS_NPi2020_400 -14322.986990 -14272.979202 \n", + " CD-LINKS_NoPolicy 81480.901944 81104.748947 \n", + " CD-LINKS_NoPolicy 3.915487 3.953017 \n", + " CD-LINKS_NoPolicy 81209.837115 80838.896903 \n", + "\n", + " 2100 Ssp_family \n", + "model scenario \n", + "AIM/CGE 2.0 SSP1-26 -537.192040 1.0 \n", + " SSP1-26 1.609574 1.0 \n", + " SSP1-26 17.737700 1.0 \n", + " SSP1-34 8482.852060 1.0 \n", + " SSP1-34 2.090244 1.0 \n", + "... ... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 1.018260 2.0 \n", + " CD-LINKS_NPi2020_400 -14222.971415 2.0 \n", + " CD-LINKS_NoPolicy 80728.595949 2.0 \n", + " CD-LINKS_NoPolicy 3.991328 2.0 \n", + " CD-LINKS_NoPolicy 80467.956690 2.0 \n", + "\n", + "[3606 rows x 87 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dft.merge(df.meta['Ssp_family'], left_on=['model','scenario'], right_on=['model','scenario'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8be13116-01bf-4450-9445-fcd907ae42ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2015201620172018201920202021202220232024...209220932094209520962097209820992100Ssp_family
modelscenarioregionvariableunit
AIM/CGE 2.0SSP1-26WorldAR6 climate diagnostics|Infilled|Emissions|CO2Mt CO2/yr39152.72630038814.58550038476.50914438138.49723337800.54976737462.66674636683.89231135905.86547935128.58624934352.054622...-138.597038-188.421413-238.245789-288.070164-337.894539-387.718914-437.543289-487.367665-537.1920401.0
AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th PercentileK1.1156981.1350781.1553811.1767371.2001061.2252771.2564031.2921581.3247281.353267...1.6369291.6378521.6350121.6319041.6251391.6177981.6135841.6117861.6095741.0
Emissions|CO2Mt CO2/yrNaNNaNNaNNaNNaN37181.33850036442.25551035703.17252034964.08953034225.006540...505.121540444.198560383.275580322.352600261.429620200.506640139.58366078.66068017.7377001.0
SSP1-34WorldAR6 climate diagnostics|Infilled|Emissions|CO2Mt CO2/yr39152.72630038814.58550038476.50914438138.49723337800.54976737462.66674637246.80758337030.95746136815.11638036599.284340...9869.5919429696.2494579522.9069719349.5644869176.2220019002.8795168829.5370318656.1945458482.8520601.0
AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th PercentileK1.1156981.1350781.1554031.1768341.2002721.2252681.2556911.2911991.3224281.352320...2.0850472.0886222.0890802.0890552.0889862.0887092.0864822.0874862.0902441.0
..............................................................................
WITCH-GLOBIOM 4.4CD-LINKS_NPi2020_400WorldAR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th PercentileK1.1154941.1308551.1492481.1662581.1864031.2108101.2450771.2820831.3170861.344960...1.0838681.0781641.0689211.0574991.0492961.0392941.0310901.0244441.0182602.0
Emissions|CO2Mt CO2/yr37811.00745538380.11127238949.21508939518.31890640087.42272340656.52654037575.64347034494.76039931413.87732828332.994258...-14623.033715-14573.025928-14523.018140-14473.010353-14423.002565-14372.994777-14322.986990-14272.979202-14222.9714152.0
CD-LINKS_NoPolicyWorldAR6 climate diagnostics|Infilled|Emissions|CO2Mt CO2/yr39152.72630040112.72729041072.22146142031.20881442989.68934743947.66306144954.23450945960.29268346965.83758447970.869211...83737.81993083361.66693282985.51393582609.36093782233.20794081857.05494281480.90194481104.74894780728.5959492.0
AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th PercentileK1.1154851.1315151.1502051.1684731.1904671.2126121.2391861.2690671.2989151.327518...3.7086403.7485623.7856193.8194063.8485183.8803853.9154873.9530173.9913282.0
Emissions|CO2Mt CO2/yr37866.23507038824.19625939782.15744940740.11863841698.07982742656.04101643662.91634744669.79167745676.66700846683.542338...83435.47839083064.53817782693.59796582322.65775281951.71754081580.77732881209.83711580838.89690380467.9566902.0
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3606 rows × 87 columns

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" + ], + "text/plain": [ + " 2015 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 39152.726300 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.115698 \n", + " Emissions|CO2 Mt CO2/yr NaN \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 39152.726300 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.115698 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.115494 \n", + " Emissions|CO2 Mt CO2/yr 37811.007455 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 39152.726300 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.115485 \n", + " Emissions|CO2 Mt CO2/yr 37866.235070 \n", + "\n", + " 2016 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38814.585500 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.135078 \n", + " Emissions|CO2 Mt CO2/yr NaN \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38814.585500 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.135078 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.130855 \n", + " Emissions|CO2 Mt CO2/yr 38380.111272 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 40112.727290 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.131515 \n", + " Emissions|CO2 Mt CO2/yr 38824.196259 \n", + "\n", + " 2017 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38476.509144 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.155381 \n", + " Emissions|CO2 Mt CO2/yr NaN \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38476.509144 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.155403 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.149248 \n", + " Emissions|CO2 Mt CO2/yr 38949.215089 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 41072.221461 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.150205 \n", + " Emissions|CO2 Mt CO2/yr 39782.157449 \n", + "\n", + " 2018 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38138.497233 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.176737 \n", + " Emissions|CO2 Mt CO2/yr NaN \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 38138.497233 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.176834 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.166258 \n", + " Emissions|CO2 Mt CO2/yr 39518.318906 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 42031.208814 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.168473 \n", + " Emissions|CO2 Mt CO2/yr 40740.118638 \n", + "\n", + " 2019 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37800.549767 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.200106 \n", + " Emissions|CO2 Mt CO2/yr NaN \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37800.549767 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.200272 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.186403 \n", + " Emissions|CO2 Mt CO2/yr 40087.422723 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 42989.689347 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.190467 \n", + " Emissions|CO2 Mt CO2/yr 41698.079827 \n", + "\n", + " 2020 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37462.666746 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.225277 \n", + " Emissions|CO2 Mt CO2/yr 37181.338500 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37462.666746 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.225268 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.210810 \n", + " Emissions|CO2 Mt CO2/yr 40656.526540 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 43947.663061 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.212612 \n", + " Emissions|CO2 Mt CO2/yr 42656.041016 \n", + "\n", + " 2021 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 36683.892311 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.256403 \n", + " Emissions|CO2 Mt CO2/yr 36442.255510 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37246.807583 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.255691 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.245077 \n", + " Emissions|CO2 Mt CO2/yr 37575.643470 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 44954.234509 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.239186 \n", + " Emissions|CO2 Mt CO2/yr 43662.916347 \n", + "\n", + " 2022 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 35905.865479 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.292158 \n", + " Emissions|CO2 Mt CO2/yr 35703.172520 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 37030.957461 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.291199 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.282083 \n", + " Emissions|CO2 Mt CO2/yr 34494.760399 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 45960.292683 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.269067 \n", + " Emissions|CO2 Mt CO2/yr 44669.791677 \n", + "\n", + " 2023 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 35128.586249 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.324728 \n", + " Emissions|CO2 Mt CO2/yr 34964.089530 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 36815.116380 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.322428 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.317086 \n", + " Emissions|CO2 Mt CO2/yr 31413.877328 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 46965.837584 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.298915 \n", + " Emissions|CO2 Mt CO2/yr 45676.667008 \n", + "\n", + " 2024 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 34352.054622 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.353267 \n", + " Emissions|CO2 Mt CO2/yr 34225.006540 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 36599.284340 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.352320 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.344960 \n", + " Emissions|CO2 Mt CO2/yr 28332.994258 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 47970.869211 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.327518 \n", + " Emissions|CO2 Mt CO2/yr 46683.542338 \n", + "\n", + " ... \\\n", + "model scenario region variable unit ... \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr ... \n", + " AR6 climate diagnostics|Surface Temperature (GS... K ... \n", + " Emissions|CO2 Mt CO2/yr ... \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr ... \n", + " AR6 climate diagnostics|Surface Temperature (GS... K ... \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K ... \n", + " Emissions|CO2 Mt CO2/yr ... \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr ... \n", + " AR6 climate diagnostics|Surface Temperature (GS... K ... \n", + " Emissions|CO2 Mt CO2/yr ... \n", + "\n", + " 2092 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -138.597038 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.636929 \n", + " Emissions|CO2 Mt CO2/yr 505.121540 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9869.591942 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.085047 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.083868 \n", + " Emissions|CO2 Mt CO2/yr -14623.033715 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 83737.819930 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.708640 \n", + " Emissions|CO2 Mt CO2/yr 83435.478390 \n", + "\n", + " 2093 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -188.421413 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.637852 \n", + " Emissions|CO2 Mt CO2/yr 444.198560 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9696.249457 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.088622 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.078164 \n", + " Emissions|CO2 Mt CO2/yr -14573.025928 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 83361.666932 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.748562 \n", + " Emissions|CO2 Mt CO2/yr 83064.538177 \n", + "\n", + " 2094 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -238.245789 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.635012 \n", + " Emissions|CO2 Mt CO2/yr 383.275580 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9522.906971 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.089080 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.068921 \n", + " Emissions|CO2 Mt CO2/yr -14523.018140 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 82985.513935 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.785619 \n", + " Emissions|CO2 Mt CO2/yr 82693.597965 \n", + "\n", + " 2095 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -288.070164 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.631904 \n", + " Emissions|CO2 Mt CO2/yr 322.352600 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9349.564486 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.089055 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.057499 \n", + " Emissions|CO2 Mt CO2/yr -14473.010353 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 82609.360937 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.819406 \n", + " Emissions|CO2 Mt CO2/yr 82322.657752 \n", + "\n", + " 2096 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -337.894539 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.625139 \n", + " Emissions|CO2 Mt CO2/yr 261.429620 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9176.222001 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.088986 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.049296 \n", + " Emissions|CO2 Mt CO2/yr -14423.002565 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 82233.207940 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.848518 \n", + " Emissions|CO2 Mt CO2/yr 81951.717540 \n", + "\n", + " 2097 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -387.718914 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.617798 \n", + " Emissions|CO2 Mt CO2/yr 200.506640 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 9002.879516 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.088709 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.039294 \n", + " Emissions|CO2 Mt CO2/yr -14372.994777 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 81857.054942 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.880385 \n", + " Emissions|CO2 Mt CO2/yr 81580.777328 \n", + "\n", + " 2098 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -437.543289 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.613584 \n", + " Emissions|CO2 Mt CO2/yr 139.583660 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 8829.537031 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.086482 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.031090 \n", + " Emissions|CO2 Mt CO2/yr -14322.986990 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 81480.901944 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.915487 \n", + " Emissions|CO2 Mt CO2/yr 81209.837115 \n", + "\n", + " 2099 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -487.367665 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.611786 \n", + " Emissions|CO2 Mt CO2/yr 78.660680 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 8656.194545 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.087486 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.024444 \n", + " Emissions|CO2 Mt CO2/yr -14272.979202 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 81104.748947 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.953017 \n", + " Emissions|CO2 Mt CO2/yr 80838.896903 \n", + "\n", + " 2100 \\\n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr -537.192040 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.609574 \n", + " Emissions|CO2 Mt CO2/yr 17.737700 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 8482.852060 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.090244 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 1.018260 \n", + " Emissions|CO2 Mt CO2/yr -14222.971415 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 80728.595949 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 3.991328 \n", + " Emissions|CO2 Mt CO2/yr 80467.956690 \n", + "\n", + " Ssp_family \n", + "model scenario region variable unit \n", + "AIM/CGE 2.0 SSP1-26 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 1.0 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.0 \n", + " Emissions|CO2 Mt CO2/yr 1.0 \n", + " SSP1-34 World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 1.0 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 1.0 \n", + "... ... \n", + "WITCH-GLOBIOM 4.4 CD-LINKS_NPi2020_400 World AR6 climate diagnostics|Surface Temperature (GS... K 2.0 \n", + " Emissions|CO2 Mt CO2/yr 2.0 \n", + " CD-LINKS_NoPolicy World AR6 climate diagnostics|Infilled|Emissions|CO2 Mt CO2/yr 2.0 \n", + " AR6 climate diagnostics|Surface Temperature (GS... K 2.0 \n", + " Emissions|CO2 Mt CO2/yr 2.0 \n", + "\n", + "[3606 rows x 87 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "669eba10-c925-493b-8aa8-898064999aa7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rime/emissions_temp_AR6_small.xlsx b/rime/emissions_temp_AR6_small.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..0ca51a92fa598d0b3d0ffe78b8f4d5c719e1e2ce GIT binary patch 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ze@>}?kOcvmix>YPx%PJn_P^@-IWhZ3UCW6->iRiN`&TVLXEOh&WTCwKl^%g-6LKWaHm`BBRQFc0~)zcbN~X_*h&uF`%o(f1_H-%0BETQCJ$>`z!TgI3|5f}~ zw)a8Yu}b_$?)O*0U&+r0!SrgtlK-Sae^u}+G54TAp!WM9^ 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 2;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/gridstack/gridstack@4.2.5/dist/gridstack-h5.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) >= 0) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) >= 0) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://unpkg.com/@holoviz/panel@0.14.4/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var css_urls = [\"https://cdn.holoviz.org/panel/0.14.4/dist/css/alerts.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/card.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/dataframe.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/debugger.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/json.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/loading.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/markdown.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/widgets.css\"];\n", + " var inline_js = [ function(Bokeh) {\n", + " inject_raw_css(\"\\n .bk.pn-loading.arc:before {\\n background-image: url(\\\"data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHN0eWxlPSJtYXJnaW46IGF1dG87IGJhY2tncm91bmQ6IG5vbmU7IGRpc3BsYXk6IGJsb2NrOyBzaGFwZS1yZW5kZXJpbmc6IGF1dG87IiB2aWV3Qm94PSIwIDAgMTAwIDEwMCIgcHJlc2VydmVBc3BlY3RSYXRpbz0ieE1pZFlNaWQiPiAgPGNpcmNsZSBjeD0iNTAiIGN5PSI1MCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSIjYzNjM2MzIiBzdHJva2Utd2lkdGg9IjEwIiByPSIzNSIgc3Ryb2tlLWRhc2hhcnJheT0iMTY0LjkzMzYxNDMxMzQ2NDE1IDU2Ljk3Nzg3MTQzNzgyMTM4Ij4gICAgPGFuaW1hdGVUcmFuc2Zvcm0gYXR0cmlidXRlTmFtZT0idHJhbnNmb3JtIiB0eXBlPSJyb3RhdGUiIHJlcGVhdENvdW50PSJpbmRlZmluaXRlIiBkdXI9IjFzIiB2YWx1ZXM9IjAgNTAgNTA7MzYwIDUwIDUwIiBrZXlUaW1lcz0iMDsxIj48L2FuaW1hdGVUcmFuc2Zvcm0+ICA8L2NpcmNsZT48L3N2Zz4=\\\");\\n background-size: auto calc(min(50%, 400px));\\n }\\n \");\n", + " }, function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, js_modules, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 2;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/gridstack/gridstack@4.2.5/dist/gridstack-h5.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://unpkg.com/@holoviz/panel@0.14.4/dist/panel.min.js\"];\n var js_modules = [];\n var css_urls = [\"https://cdn.holoviz.org/panel/0.14.4/dist/css/alerts.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/card.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/dataframe.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/debugger.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/json.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/loading.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/markdown.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/widgets.css\"];\n var inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n .bk.pn-loading.arc:before {\\n background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n background-size: auto calc(min(50%, 400px));\\n }\\n \");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) 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handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\byers\\Anaconda3\\envs\\py310\\lib\\site-packages\\pyproj\\__init__.py:89: UserWarning: pyproj unable to set database path.\n", + " _pyproj_global_context_initialize()\n" + ] + }, + { + "data": { + "application/javascript": [ + "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from rime.process_config import *\n", + "from rime.rime_functions import *\n", + "from rime.utils import *\n", + "\n", + "import dask\n", + "import dask.dataframe as dd\n", + "from dask.diagnostics import ProgressBar\n", + "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n", + "# from dask.distributed import Client # uncomment this to open Dask client to see performance\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyam\n", + "import re\n", + "import time\n", + "import xarray as xr\n", + "import yaml\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f7bd8af", + "metadata": {}, + "outputs": [], + "source": [ + "# Load a yaml file that will help select multiple climate indicator files (not necessarily needed)\n", + "with open('indicator_params.yml', \"r\") as f:\n", + " params = yaml.full_load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0da81a1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dask.config.set(scheduler=\"processes\")\n", + "dask.config.set(num_workers=num_workers)\n", + "# client = Client() # uncomment this to open Dask client" + ] + }, + { + "cell_type": "markdown", + "id": "7c0318d1", + "metadata": {}, + "source": [ + "To see the Dask dasboard, open http://localhost:8787/status in your browser" + ] + }, + { + "cell_type": "markdown", + "id": "f17db9b7", + "metadata": {}, + "source": [ + "### Import scenarios data\n", + "\n", + "Load an IAMC scenarios dataset. \n", + "Decide whether using global mean temperature or CO2 mode. \n", + "Assign SSPs if missing and fix duplicate temperatures. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4b0e602b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pyam.core - INFO: Reading file emissions_temp_AR6_small.xlsx\n", + "pyam.core - INFO: Found column 'exclude' in sheet 'meta', moved to attribute `IamDataFrame.exclude`.\n" + ] + } + ], + "source": [ + "df_scens_in = pyam.IamDataFrame(fname_input_scenarios)\n", + "dft = df_scens_in.filter(variable=temp_variable)\n", + "\n", + "# Replace & fill missing SSP scenario allocation\n", + "dft = ssp_helper(dft, ssp_meta_col=\"Ssp_family\", default_ssp=\"SSP2\")" + ] + }, + { + "cell_type": "markdown", + "id": "cfa7b099-a70c-4d72-953a-4467e8e8ffcc", + "metadata": {}, + "source": [ + "### Build a dataset to compare SSPs and IMPs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0f6cae87-9717-4304-b841-5d61e87b55d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Index:\n", + " * model : COFFEE 1.1, GCAM 5.3, IMAGE 3.0, IMAGE 3.0.1, ... WITCH 5.0 (6)\n", + " * scenario : CO_Bridge, EN_INDCi2030_3000f, ... SusDev_SDP-PkBudg1000 (9)\n", + "Timeseries data coordinates:\n", + " region : World (1)\n", + " variable : ... (1)\n", + " unit : K (1)\n", + " year : 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, ... 2100 (86)\n", + "Meta indicators:\n", + " Category (object) C3, C7, C6, C8, C1 (5)\n", + " Category_name (object) C3: limit warming to 2°C (>67%), ... (5)\n", + " Category_subset (object) C3x_NZGHGs, C7, C6, C8, C1a_NZGHGs (5)\n", + " Subset_Ch4 (object) Limit to 2C (>67%) immediate 2020 action, ... (3)\n", + " Category_Vetting_historical (object) C3, C7, C6, C8, C1 (5)\n", + " ..." + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenarios = ['SSP1-26', 'SSP2-45','SSP3-Baseline','SSP5-Baseline']\n", + "variable = 'AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile'\n", + "ssps = df_scens_in.filter(scenario=scenarios, model='IMAGE*', variable=variable)\n", + "imps = df_scens_in.filter(IMP_marker=['CurPol','ModAct','SP','GS','Neg'], variable=variable) #,'Ren-2.0','Neg-2.0'\n", + "ssps_imp = ssps.append(imps)\n", + "ssps_imp = ssp_helper(ssps_imp, ssp_meta_col=\"Ssp_family\", default_ssp=\"SSP2\")\n", + "ssps_imp" + ] + }, + { + "cell_type": "markdown", + "id": "f6c1301d", + "metadata": {}, + "source": [ + "# Generate some maps" + ] + }, + { + "cell_type": "markdown", + "id": "04aec0eb", + "metadata": {}, + "source": [ + "## Example 1: multiple IAM scenarios, 1 climate indicator" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4c31c82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test multiple IAM scenarios, 1 indicator\n", + "Single indicator mode\n", + "COFFEE_1_1_EN_NPi2020_400f_lowBECCS\n", + "GCAM_5_3_NGFS2_Current Policies\n", + "IMAGE_3_0_EN_INDCi2030_3000f\n", + "IMAGE_3_0_1_SSP1-26\n", + "IMAGE_3_0_1_SSP2-45\n", + "IMAGE_3_0_1_SSP3-Baseline\n", + "IMAGE_3_0_1_SSP5-Baseline\n", + "REMIND-MAgPIE_2_1-4_2_SusDev_SDP-PkBudg1000\n", + "WITCH_5_0_CO_Bridge\n" + ] + }, + { + "ename": "ValueError", + "evalue": "'threshold' is not present in all datasets.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15228\\3901077037.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m map_out_MS = map_transform_gmt_multi_dask(\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[0mssps_imp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mmapdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\github\\rime\\rime\\rime_functions.py\u001b[0m in \u001b[0;36mmap_transform_gmt_multi_dask\u001b[1;34m(df, mapdata, years, gmt_name, use_dask, temp_min, temp_max, drawdown_max, interpolation)\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0muse_dask\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 361\u001b[0m \u001b[1;31m# Compute delayed tasks concurrently\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m \u001b[0mcomputed_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdelayed_tasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 364\u001b[0m \u001b[1;31m# Merge the computed results into map_array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m 626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mshorten_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 628\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 629\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\github\\rime\\rime\\rime_functions.py\u001b[0m in \u001b[0;36mmap_transform_gmt\u001b[1;34m()\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;31m# last_slice = data_interp.isel(gmt=-1)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[0mnew_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataArray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m999\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"gmt\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"gmt\"\u001b[0m\u001b[1;33m:\u001b[0m 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\u001b[0mgmt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_interp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"gmt\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\xarray\\core\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m()\u001b[0m\n\u001b[0;32m 229\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 230\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m return _dataarray_concat(\n\u001b[0m\u001b[0;32m 232\u001b[0m \u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 233\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\envs\\py310\\lib\\site-packages\\xarray\\core\\concat.py\u001b[0m in \u001b[0;36m_dataset_concat\u001b[1;34m()\u001b[0m\n\u001b[0;32m 556\u001b[0m \u001b[0mvars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_common_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mds\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 557\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 558\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{name!r} is not present in all datasets.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 559\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 560\u001b[0m \u001b[1;31m# Try concatenate the indexes, concatenate the variables when no index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: 'threshold' is not present in all datasets." + ] + } + ], + "source": [ + "# Doesn't work -0 \n", + "# ValueError: 'threshold' is not present in all datasets.\n", + "\n", + "print(\"Test multiple IAM scenarios, 1 indicator\")\n", + "start = time.time()\n", + "\n", + "ind = \"precip\"\n", + "var = \"sdii\"\n", + "short = 'sdii' #params[\"indicators\"][ind][var][\"short_name\"]\n", + "ssp = \"ssp2\"\n", + "\n", + "\n", + "\n", + "files = glob.glob(f\"{impact_data_dir}\\\\{ind}\\\\*{short}_{ssp}*{ftype}.nc4\")\n", + "mapdata = xr.open_mfdataset(\n", + " files, preprocess=remove_ssp_from_ds, combine=\"nested\", concat_dim=\"gmt\"\n", + ")\n", + "\n", + "\n", + "map_out_MS = map_transform_gmt_multi_dask(\n", + " ssps_imp,\n", + " mapdata,\n", + " years,\n", + " use_dask=True,\n", + " gmt_name=\"gmt\",\n", + " temp_min=1.2,\n", + " temp_max=3.5,\n", + " drawdown_max=0.15,\n", + " interpolation=interpolation,\n", + ")\n", + "\n", + "comp = dict(zlib=True, complevel=5)\n", + "encoding = {var: comp for var in map_out_MS.data_vars}\n", + "filename = f\"{output_folder_maps}scenario_maps_multiscenario_{ftype}_test_notebook.nc\"\n", + "map_out_MS.to_netcdf(filename, encoding=encoding)\n", + "\n", + "print(\"FINISHED Test multiple scenarios, 1 indicator\")\n", + "print(f\"{time.time()-start}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d596a52f-3304-4cbb-b0cb-c86e83e39ecf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
      <xarray.Dataset>\n",
      +       "Dimensions:     (lon: 720, lat: 360, gmt: 6)\n",
      +       "Coordinates:\n",
      +       "  * lon         (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
      +       "  * lat         (lat) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
      +       "    stats       object 'mean'\n",
      +       "    gmt         (gmt) float64 1.2 1.5 2.0 2.5 3.0 3.5\n",
      +       "    quantile    float64 0.95\n",
      +       "Data variables:\n",
      +       "    cdd         (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    pr_r10      (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    pr_r20      (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    pr_r95p     (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    pr_r99p     (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    sdii        (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    dri         (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    sdd_c_18p3  (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    sdd_c_24p0  (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    seas        (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n",
      +       "    wsi         (gmt, lat, lon) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>
      " + ], + "text/plain": [ + "\n", + "Dimensions: (lon: 720, lat: 360, gmt: 6)\n", + "Coordinates:\n", + " * lon (lon) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n", + " * lat (lat) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n", + " stats object 'mean'\n", + " gmt (gmt) float64 1.2 1.5 2.0 2.5 3.0 3.5\n", + " quantile float64 0.95\n", + "Data variables:\n", + " cdd (gmt, lat, lon) float64 dask.array\n", + " pr_r10 (gmt, lat, lon) float64 dask.array\n", + " pr_r20 (gmt, lat, lon) float64 dask.array\n", + " pr_r95p (gmt, lat, lon) float64 dask.array\n", + " pr_r99p (gmt, lat, lon) float64 dask.array\n", + " sdii (gmt, lat, lon) float64 dask.array\n", + " dri (gmt, lat, lon) float64 dask.array\n", + " sdd_c_18p3 (gmt, lat, lon) float64 dask.array\n", + " sdd_c_24p0 (gmt, lat, lon) float64 dask.array\n", + " seas (gmt, lat, lon) float64 dask.array\n", + " wsi (gmt, lat, lon) float64 dask.array" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mapdata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "794bd1c3", + "metadata": {}, + "outputs": [], + "source": [ + "map_out_MS" + ] + }, + { + "cell_type": "markdown", + "id": "f31bf98d", + "metadata": {}, + "source": [ + "## Example 2: 1 scenario, multiple indicators\n" + ] + }, + { + "cell_type": "markdown", + "id": "07f26175", + "metadata": {}, + "source": [ + "### Build combined dataset with multiple indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "85bab5fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test 1 scenario, multiple indicators\n", + "cdd\n", + "pr_r10\n", + "pr_r20\n", + "pr_r95p\n", + "pr_r99p\n", + "sdii\n", + "dri\n", + "sdd_c_18p3\n", + "sdd_c_24p0\n", + "seas\n", + "wsi\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\byers\\AppData\\Local\\Temp\\ipykernel_15228\\3574393669.py:33: UserWarning: rename 'threshold' to 'gmt' does not create an index anymore. Try using swap_dims instead or use set_index after rename to create an indexed coordinate.\n", + " mapdata = mapdata.rename({'threshold':'gmt'})\n" + ] + } + ], + "source": [ + "# Doesn't work -0 \n", + "# AttributeError: 'DataArray' object has no attribute 'rename_vars'\n", + "# lines 230-233 in map_transform_gmt\n", + "\n", + "gmt_name=\"gmt\"\n", + "print(\"Test 1 scenario, multiple indicators\")\n", + "start = time.time()\n", + "ssp = \"ssp2\"\n", + "mapdata = xr.Dataset()\n", + "indicators = [\n", + " \"cdd\",\n", + " \"precip\",\n", + " \"dri\",\n", + " # \"dri_qtot\",\n", + " # \"ia_var\",\n", + " # \"ia_var_qtot\",\n", + " \"sdd_18p3\",\n", + " \"sdd_24p0\",\n", + " \"seas\",\n", + " \"wsi\",\n", + "] #'heatwave']\n", + "\n", + "for ind in indicators:\n", + " for var in params[\"indicators\"][ind]:\n", + " short = params[\"indicators\"][ind][var][\"short_name\"]\n", + " print(short)\n", + " files = glob.glob(\n", + " os.path.join(impact_data_dir, ind, f\"*{short}_{ssp}*{ftype}.nc4\")\n", + " )\n", + " mapdata[short] = xr.open_mfdataset(\n", + " files, preprocess=remove_ssp_from_ds, combine=\"nested\", concat_dim=\"gmt\"\n", + " )[short]\n", + "mapdata = mapdata.rename({'threshold':'gmt'})\n", + "# mapdata = mapdata.isel({gmt_name:0}).reset_coords(drop=True)" + ] + }, + { + "cell_type": "markdown", + "id": "cc24e437", + "metadata": {}, + "source": [ + "### Run transformation (12 impact indicators) (faster with dask=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bd26a126", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "NDFrame.filter() got an unexpected keyword argument 'model'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15228\\3762043090.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mscenario\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"SSP2-34\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m map_out_MI = map_transform_gmt_multi_dask(\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscenario\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscenario\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mmapdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0myears\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: NDFrame.filter() got an unexpected keyword argument 'model'" + ] + } + ], + "source": [ + "model = \"AIM*\"\n", + "scenario = \"SSP2-34\"\n", + "map_out_MI = map_transform_gmt_multi_dask(\n", + " dft.filter(model=model, scenario=scenario),\n", + " mapdata,\n", + " years,\n", + " use_dask=False,\n", + " gmt_name=gmt_name,\n", + ")\n", + "\n", + "comp = dict(zlib=True, complevel=5)\n", + "encoding = {var: comp for var in map_out_MI.data_vars}\n", + "filename = f\"{output_folder_maps}scenario_maps_multiindicator_{ftype}_test_notebook.nc\"\n", + "# map_out_MI.to_netcdf(filename, encoding=encoding)\n", + "\n", + "print(\"FINISHED 1 scenario, multiple indicators\")\n", + "print(f\"{time.time()-start}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "8c7c007c", + "metadata": {}, + "source": [ + "### Data is now transformed to 10 year interval starting 2015" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "169f89a5", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'map_out_MI' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15228\\3522842798.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmap_out_MI\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'map_out_MI' is not defined" + ] + } + ], + "source": [ + "map_out_MI" + ] + }, + { + "cell_type": "markdown", + "id": "b4ff7baa", + "metadata": {}, + "source": [ + "## Plot dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b407883b", + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'test_map.html'\n", + "plot_maps_dashboard(map_out_MI, \n", + " filename=filename, year=2063, cmap='magma_r', \n", + " shared_axes=True, clim=None,)\n", + "os.startfile(filename)" + ] + }, + { + "cell_type": "markdown", + "id": "53155084", + "metadata": {}, + "source": [ + "## Process corresponding table data, aggregated by country" + ] + }, + { + "cell_type": "markdown", + "id": "534c8807", + "metadata": {}, + "source": [ + "### Import the climate impacts database files\n", + "Here is an example for sdii - standard precipitation index, and multiple variables, e.g. \n", + "Exposure of land area \n", + "Exposure of population \n", + "Hazard value \n", + "... \n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a9ee85cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
      <xarray.Dataset>\n",
      +       "Dimensions:                                    (gmt: 251, year: 18, ssp: 3,\n",
      +       "                                                region: 226)\n",
      +       "Coordinates:\n",
      +       "  * gmt                                        (gmt) float64 1.2 1.206 ... 3.5\n",
      +       "  * year                                       (year) int64 2015 2020 ... 2100\n",
      +       "  * ssp                                        (ssp) object 'SSP1' 'SSP2' 'SSP3'\n",
      +       "  * region                                     (region) object 'AFG' ... 'world'\n",
      +       "Data variables:\n",
      +       "    cdd|Exposure|Land area                     (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>\n",
      +       "    cdd|Exposure|Population                    (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>\n",
      +       "    cdd|Exposure|Population|%                  (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>\n",
      +       "    cdd|Hazard|Absolute|Land area weighted     (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>\n",
      +       "    cdd|Hazard|Absolute|Population weighted    (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>\n",
      +       "    cdd|Hazard|Risk score|Population weighted  (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 18, 3, 226), meta=np.ndarray>
      " + ], + "text/plain": [ + "\n", + "Dimensions: (gmt: 251, year: 18, ssp: 3,\n", + " region: 226)\n", + "Coordinates:\n", + " * gmt (gmt) float64 1.2 1.206 ... 3.5\n", + " * year (year) int64 2015 2020 ... 2100\n", + " * ssp (ssp) object 'SSP1' 'SSP2' 'SSP3'\n", + " * region (region) object 'AFG' ... 'world'\n", + "Data variables:\n", + " cdd|Exposure|Land area (gmt, year, ssp, region) float64 dask.array\n", + " cdd|Exposure|Population (gmt, year, ssp, region) float64 dask.array\n", + " cdd|Exposure|Population|% (gmt, year, ssp, region) float64 dask.array\n", + " cdd|Hazard|Absolute|Land area weighted (gmt, year, ssp, region) float64 dask.array\n", + " cdd|Hazard|Absolute|Population weighted (gmt, year, ssp, region) float64 dask.array\n", + " cdd|Hazard|Risk score|Population weighted (gmt, year, ssp, region) float64 dask.array" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filesall = glob.glob(fname_input_climate)\n", + "files = filesall#[:6]\n", + "f = files[-0]\n", + "# load input climate impacts data file\n", + "ds = xr.open_mfdataset(f)\n", + "ds = ds.sel(year=years)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d1e3f825", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# of variables = 6\n" + ] + } + ], + "source": [ + "varis = list(ds.data_vars.keys())[:lvaris]\n", + "dsi = ds[varis]\n", + "print(f\"# of variables = {len(varis)}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a597a72f", + "metadata": {}, + "source": [ + "### Select scenarios data\n", + "Load an IAMC scenarios dataset (done previously at the beginning). \n", + "Decide whether using global mean temperature or CO2 mode. \n", + "Assign SSPs if missing and fix duplicate temperatures. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e3551368", + "metadata": {}, + "outputs": [], + "source": [ + "mode = 'GMT'\n", + "if mode == \"GMT\":\n", + " dfp = df_scens_in.filter(variable=temp_variable)\n", + "elif mode == \"bypass\":\n", + " dfp = prepare_cumCO2(df_scens_in, years=years, use_dask=True)\n", + " ts = dfp.timeseries().apply(co2togmt_simple)\n", + " ts = pyam.IamDataFrame(ts)\n", + " ts.rename(\n", + " {\n", + " \"variable\": {ts.variable[0]: \"RIME|GSAT_tcre\"},\n", + " \"unit\": {ts.unit[0]: \"°C\"},\n", + " },\n", + " inplace=True,\n", + " )\n", + " # Export data to check error and relationships\n", + " # ts.append(dfp).to_csv('c://users//byers//downloads//tcre_gmt_output.csv')\n", + " dfp = ts\n", + " dfp.meta = df_scens_in.meta.copy()\n", + "dfp = dfp.filter(year=years)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "7f799e2a", + "metadata": {}, + "outputs": [], + "source": [ + "# For testing on C1 scenarios\n", + "# few_scenarios = True\n", + "# very_few_scenarios = False\n", + "\n", + "# if few_scenarios:\n", + "# dfp = dfp.filter(Category=[\"C1*\"])\n", + "# if very_few_scenarios:\n", + "# dfp = dfp.filter(model=\"REMIND 2.1*\", scenario=\"*\") \n", + " \n", + "dfp = ssps_imp\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ebe93e79-94ce-4127-9ef6-5aa1f789766f", + "metadata": {}, + "outputs": [], + "source": [ + "# pre-prepare the dataset into correct format\n", + "# Assign SSP to meta and select SSP2 in case SSP not present in name\n", + "dfp = ssp_helper(dfp, ssp_meta_col=\"Ssp_family\", default_ssp=\"SSP2\", keep_meta=False)\n", + "\n", + "dft = dfp.timeseries()\n", + "dft = dft.join(dfp.meta['Ssp_family']).reset_index()\n", + "# dft = dft.apply(fix_duplicate_temps, years=years, axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "9fad6daf", + "metadata": {}, + "source": [ + "### START PROCESSING " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "d4d94aa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ ] | 0% Completed | 114.62 ms" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\byers\\AppData\\Roaming\\Python\\Python310\\site-packages\\dask\\dataframe\\utils.py:312: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " return pd.Series([], dtype=dtype, name=name, index=index)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[########################################] | 100% Completed | 21.77 s\n", + " Done: 22.688447952270508\n", + " Saved: COUNTRIES yrs=10\n", + " 22.688447952270508\n", + "6 variables, 9 scenarios\n" + ] + } + ], + "source": [ + "start = time.time()\n", + "year_res = 10\n", + "parallel = True\n", + "if parallel:\n", + " \"\"\"\n", + " For parallel processing, convert dft as a wide IAMC pd.Dataframe\n", + " into a dask.DataFrame.\n", + " \"\"\"\n", + " ddf = dd.from_pandas(dft, npartitions=1000)\n", + "\n", + " # dfx = dft.iloc[0].squeeze() # FOR DEBUIGGING THE FUNCTION\n", + " outd = ddf.apply(\n", + " calculate_impacts_gmt, dsi=dsi, ssp_meta_col=\"Ssp_family\", axis=1, meta=(\"result\", None)\n", + " )\n", + "\n", + " with ProgressBar():\n", + " # try:\n", + " df_new = outd.compute(num_workers=num_workers)\n", + "else:\n", + " df_new = dft.apply(calculate_impacts_gmt, dsi=dsi, axis=1)\n", + "\n", + "expandeddGMT = pd.concat([df_new[x] for x in df_new.index])\n", + "print(f\" Done: {time.time()-start}\")\n", + "\n", + "filename = f\"RIME_output_{region}_{year_res}yr.csv\"\n", + "\n", + "# expandedd.to_csv(filename, encoding=\"utf-8\", index=False)\n", + "print(f\" Saved: {region} yrs={year_res}\\n {time.time()-start}\")\n", + "print(f\"{len(dsi.data_vars)} variables, {len(dfp.meta)} scenarios\")" + ] + }, + { + "cell_type": "markdown", + "id": "86b7950f", + "metadata": {}, + "source": [ + "To see the Dask dasboard, open http://localhost:8787/status in your browser" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "d1cc8871", + "metadata": {}, + "outputs": [], + "source": [ + "expandeddGMT = pyam.IamDataFrame(expandeddGMT)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "4d08042a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['RIME|cdd|Exposure|Land area',\n", + " 'RIME|cdd|Exposure|Population',\n", + " 'RIME|cdd|Exposure|Population|%',\n", + " 'RIME|cdd|Hazard|Absolute|Land area weighted',\n", + " 'RIME|cdd|Hazard|Absolute|Population weighted',\n", + " 'RIME|cdd|Hazard|Risk score|Population weighted']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expandeddGMT.variable" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "0457fa1e-09db-4557-83e5-7f4e13064d84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# model = \"IMAG*\"\n", + "scenario = ['CO_Bridge',\n", + " 'EN_INDCi2030_3000f',\n", + " 'EN_NPi2020_400f_lowBECCS',\n", + " # 'NGFS2_Current Policies',\n", + " 'SSP1-26',\n", + " 'SSP2-45',\n", + " # 'SSP3-Baseline',\n", + " # 'SSP5-Baseline',\n", + " 'SusDev_SDP-PkBudg1000']\n", + "variable = 'RIME|sdii|Hazard|Risk score|Population weighted'\n", + "# variable = 'RIME|wsi|Exposure|Population|%'\n", + "variable = 'RIME|cdd|Exposure|Population|%'\n", + "expandeddGMT.filter(variable=variable,\n", + " scenario=scenario, region='GBR').plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "614d21e6-345c-43d5-9e9c-06606ce3a3d2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pyam.core - WARNING: Filtered IamDataFrame is empty!\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "expandeddGMT.filter(variable= variable,\n", + " scenario=scenario, region='PAK').plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "def35b50-a552-4a79-b481-08bceeca1918", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['RIME|cdd|Exposure|Land area',\n", + " 'RIME|cdd|Exposure|Population',\n", + " 'RIME|cdd|Exposure|Population|%',\n", + " 'RIME|cdd|Hazard|Absolute|Land area weighted',\n", + " 'RIME|cdd|Hazard|Absolute|Population weighted',\n", + " 'RIME|cdd|Hazard|Risk score|Population weighted']" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expandeddGMT.variable" + ] + }, + { + "cell_type": "markdown", + "id": "d7a88cf3-0636-4965-8d3c-59041766d70c", + "metadata": {}, + "source": [ + "### Make SSP & IMP plots for presentation" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "d8f60f4b-7154-44fe-92b8-49f0027e2e81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['EN_NPi2020_400f_lowBECCS',\n", + " 'NGFS2_Current Policies',\n", + " 'EN_INDCi2030_3000f',\n", + " 'SusDev_SDP-PkBudg1000',\n", + " 'CO_Bridge']" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imps.scenario" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "151e35c4-6772-4b6c-bf27-f728e5b9d9f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_map = {\n", + " 'SSP1-26': 'AR6-SSP1-2.6',\n", + " 'SSP2-45': 'AR6-SSP2-4.5',\n", + " 'SSP3-Baseline': 'AR6-SSP3-7.0',\n", + " 'SSP5-Baseline': 'AR6-SSP5-8.5',\n", + " 'NGFS2_Current Policies': 'AR6-IP-CurPol',\n", + " # 'EN_NPi2020_900f': 'AR6-IMP-Neg2.0',\n", + " 'EN_INDCi2030_3000f': 'AR6-IP-ModAct',\n", + " # 'SSP2_openres_lc_50': 'AR6-IMP-Ren2.0',\n", + " # AR6-IMP-GS',\n", + " 'CO_Bridge': 'AR6-IMP-GS',\n", + " 'EN_NPi2020_400f_lowBECCS': 'AR6-IMP-Neg',\n", + " 'SusDev_SDP-PkBudg1000': 'AR6-IMP-SP',\n", + "}\n", + "\n", + "pyam.run_control().update({'color': {'scenario': color_map}})\n", + "\n", + "ssps_imp.filter(variable='AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile',\n", + " scenario='SSP*').plot(\n", + " legend=False, fill_between=True, color='scenario', title='')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bcea971-06e9-426c-8aca-71fc96d27e13", + "metadata": {}, + "outputs": [], + "source": [ + "ssps_imp.filter(variable='AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile').plot(\n", + " legend=False, fill_between=True, color='scenario', title='')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rime/pp_process_maps.ipynb b/rime/pp_process_maps.ipynb index 4133af9..c838f8c 100644 --- a/rime/pp_process_maps.ipynb +++ b/rime/pp_process_maps.ipynb @@ -508,12 +508,44 @@ " params = yaml.full_load(f)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4c8cb305", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Github\\\\climate_impacts_processing\\\\hotspots.yml'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yaml_path" + ] + }, { "cell_type": "code", "execution_count": 3, "id": "e10671eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\byers\\Anaconda3\\envs\\py310\\lib\\site-packages\\distributed\\node.py:179: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 60903 instead\n", + " warnings.warn(\n" + ] + } + ], "source": [ "dask.config.set(scheduler=\"processes\")\n", "dask.config.set(num_workers=num_workers)\n", @@ -540,11 +572,341 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d1b29ceb", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "{'protocol': {'cdd': '3b',\n", + " 'dri_qtot': '2b',\n", + " 'dri': '2b',\n", + " 'heatwave': '3b',\n", + " 'ia_var': '2b',\n", + " 'ia_var_qtot': '2b',\n", + " 'lc': '3b',\n", + " 'precip': '3b',\n", + " 'seas': '2b',\n", + " 'seas_qtot': '2b',\n", + " 'sdd': '3b',\n", + " 'sdd_24p0': '3b',\n", + " 'sdd_20p0': '3b',\n", + " 'sdd_18p3': '3b',\n", + " 'tr20': '3b',\n", + " 'yields_cereals': '3b',\n", + " 'yields_sugarc': '3b',\n", + " 'yields_oilseeds': '3b',\n", + " 'yields_roots': '3b',\n", + " 'yields_food': '3b',\n", + " 'wsi': '2b'},\n", + " 'indicators': {'cdd': {'cdd': {'short_name': 'cdd',\n", + " 'long_name': 'Consecutive dry days',\n", + " 'unit': 'days/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 180,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -20,\n", + " 'diff_max': 20,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [24, 13, 8, 0]}},\n", + " 'dri': {'drought_intensity': {'short_name': 'dri',\n", + " 'long_name': 'Drought intensity',\n", + " 'unit': '-',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1730,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -20,\n", + " 'diff_max': 20,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [27, 12, 8, 0]}},\n", + " 'dri_qtot': {'drought_intensity': {'short_name': 'dri_qtot',\n", + " 'long_name': 'Drought intensity (Runoff)',\n", + " 'unit': '-',\n", + " 'ind_min': 0,\n", + " 'ind_max': 210,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -20,\n", + " 'diff_max': 20,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [27, 10, 6, 0]}},\n", + " 'heatwave': {'dot': {'short_name': 'hw',\n", + " 'long_name': 'Heatwave intensity',\n", + " 'unit': 'days/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 260,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': 0,\n", + " 'diff_max': 7200,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [271, 102, 56, 0]}},\n", + " 'ia_var': {'ia_var': {'short_name': 'iavar',\n", + " 'long_name': 'Inter-annual variability',\n", + " 'unit': '-',\n", + " 'ind_min': -1,\n", + " 'ind_max': 1,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -50,\n", + " 'diff_max': 50,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [75, 28, 17, 0]}},\n", + " 'ia_var_qtot': {'ia_var': {'short_name': 'iavar_qtot',\n", + " 'long_name': 'Inter-annual variability (Runoff)',\n", + " 'unit': '-',\n", + " 'ind_min': -1,\n", + " 'ind_max': 1,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -50,\n", + " 'diff_max': 50,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [77, 29, 18, 0]}},\n", + " 'lc': {'LC_area_share': {'short_name': 'lc',\n", + " 'long_name': 'Land cover data',\n", + " 'unit': '-',\n", + " 'ind_min': -1,\n", + " 'ind_max': 1,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -50,\n", + " 'diff_max': 50,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [-33, -6, -2, 0]}},\n", + " 'pm2p5_2030': {'pm2p5': {'short_name': 'pm2p5',\n", + " 'long_name': 'Ambient PM2.5 concentrations',\n", + " 'unit': 'μg/m3',\n", + " 'ind_min': 0,\n", + " 'ind_max': 100,\n", + " 'ind_cmap': 'pink_r',\n", + " 'diff': 'relative',\n", + " 'diff_min': -50,\n", + " 'diff_max': 50,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [25, 10, 5, 0]}},\n", + " 'pm2p5_2050': {'pm2p5': {'short_name': 'pm2p5',\n", + " 'long_name': 'Ambient PM2.5 concentrations',\n", + " 'unit': 'μg/m3',\n", + " 'ind_min': 0,\n", + " 'ind_max': 100,\n", + " 'ind_cmap': 'pink_r',\n", + " 'diff': 'relative',\n", + " 'diff_min': -50,\n", + " 'diff_max': 50,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [25, 10, 5, 0]}},\n", + " 'precip': {'r10': {'short_name': 'pr_r10',\n", + " 'long_name': 'Heavy precipitation days',\n", + " 'unit': 'days/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 70,\n", + " 'ind_cmap': 'YlGnBu',\n", + " 'diff': 'relative',\n", + " 'diff_min': -90,\n", + " 'diff_max': 90,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [97, 46, 26, 0]},\n", + " 'r20': {'short_name': 'pr_r20',\n", + " 'long_name': 'Very heavy precipitation days',\n", + " 'unit': 'days/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 30,\n", + " 'ind_cmap': 'YlGnBu',\n", + " 'diff': 'relative',\n", + " 'diff_min': -180,\n", + " 'diff_max': 180,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [189, 71, 38, 0]},\n", + " 'r95p': {'short_name': 'pr_r95p',\n", + " 'long_name': 'Wet days',\n", + " 'unit': 'mm/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 820,\n", + " 'ind_cmap': 'YlGnBu',\n", + " 'diff': 'relative',\n", + " 'diff_min': -60,\n", + " 'diff_max': 60,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [66, 39, 26, 0]},\n", + " 'r99p': {'short_name': 'pr_r99p',\n", + " 'long_name': 'Very wet days',\n", + " 'unit': 'mm/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 330,\n", + " 'ind_cmap': 'YlGnBu',\n", + " 'diff': 'relative',\n", + " 'diff_min': -100,\n", + " 'diff_max': 100,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [106, 63, 43, 0]},\n", + " 'sdii': {'short_name': 'sdii',\n", + " 'long_name': 'Precipitation intensity index',\n", + " 'unit': 'mm/day',\n", + " 'ind_min': 0,\n", + " 'ind_max': 10,\n", + " 'ind_cmap': 'YlGnBu',\n", + " 'diff': 'relative',\n", + " 'diff_min': -20,\n", + " 'diff_max': 20,\n", + " 'diff_cmap': 'BrBG',\n", + " 'score_range': [16, 11, 8, 0]}},\n", + " 'seas': {'seasonality': {'short_name': 'seas',\n", + " 'long_name': 'Seasonality',\n", + " 'unit': '-',\n", + " 'ind_min': 0,\n", + " 'ind_max': 2,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -40,\n", + " 'diff_max': 40,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [57, 23, 13, 0]}},\n", + " 'seas_qtot': {'seasonality': {'short_name': 'seas_qtot',\n", + " 'long_name': 'Seasonality (Runoff)',\n", + " 'unit': '-',\n", + " 'ind_min': 0,\n", + " 'ind_max': 2,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -40,\n", + " 'diff_max': 40,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [60, 22, 13, 0]}},\n", + " 'sdd': {'sdd_c': {'short_name': 'sdd_c',\n", + " 'long_name': 'Cooling degree days (26C)',\n", + " 'unit': 'CDD/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1830,\n", + " 'ind_cmap': 'viridis',\n", + " 'diff': 'relative',\n", + " 'diff_min': -4990,\n", + " 'diff_max': 4990,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [1071, 643, 228, 0]}},\n", + " 'sdd_24p0': {'sdd_c': {'short_name': 'sdd_c_24p0',\n", + " 'long_name': 'Cooling degree days (24C)',\n", + " 'unit': 'CDD/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1830,\n", + " 'ind_cmap': 'viridis',\n", + " 'diff': 'relative',\n", + " 'diff_min': -4990,\n", + " 'diff_max': 4990,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [1071, 643, 228, 0]}},\n", + " 'sdd_20p0': {'sdd_c': {'short_name': 'sdd_c_20p0',\n", + " 'long_name': 'Cooling degree days (20C)',\n", + " 'unit': 'CDD/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1830,\n", + " 'ind_cmap': 'viridis',\n", + " 'diff': 'relative',\n", + " 'diff_min': -4990,\n", + " 'diff_max': 4990,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [1071, 643, 228, 0]}},\n", + " 'sdd_18p3': {'sdd_c': {'short_name': 'sdd_c_18p3',\n", + " 'long_name': 'Cooling degree days (18.3C)',\n", + " 'unit': 'CDD/yr',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1830,\n", + " 'ind_cmap': 'viridis',\n", + " 'diff': 'relative',\n", + " 'diff_min': -4990,\n", + " 'diff_max': 4990,\n", + " 'diff_cmap': 'RdBu_r',\n", + " 'score_range': [1071, 643, 228, 0]}},\n", + " 'tr20': {'max_dur': {'short_name': 'tr20',\n", + " 'long_name': 'Tropical nights',\n", + " 'unit': 'consecutive days',\n", + " 'ind_min': 0,\n", + " 'ind_max': 350,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -140,\n", + " 'diff_max': 140,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'yields_cereals': {'SimU_yields': {'short_name': 'yld_crls',\n", + " 'long_name': 'Crop yields',\n", + " 'unit': 'ton/ha',\n", + " 'ind_min': 0,\n", + " 'ind_max': 30,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -260,\n", + " 'diff_max': 260,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'yields_sugarc': {'SimU_yields': {'short_name': 'yld_sgrc',\n", + " 'long_name': 'Crop yields',\n", + " 'unit': 'ton/ha',\n", + " 'ind_min': 0,\n", + " 'ind_max': 30,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -110,\n", + " 'diff_max': 110,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'yields_oilseeds': {'SimU_yields': {'short_name': 'yld_osds',\n", + " 'long_name': 'Crop yields',\n", + " 'unit': 'ton/ha',\n", + " 'ind_min': 0,\n", + " 'ind_max': 30,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -290,\n", + " 'diff_max': 290,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'yields_roots': {'SimU_yields': {'short_name': 'yld_roots',\n", + " 'long_name': 'Crop yields',\n", + " 'unit': 'ton/ha',\n", + " 'ind_min': 0,\n", + " 'ind_max': 30,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -190,\n", + " 'diff_max': 190,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'yields_food': {'SimU_yields': {'short_name': 'yld_food',\n", + " 'long_name': 'Crop yields',\n", + " 'unit': 'ton/ha',\n", + " 'ind_min': 0,\n", + " 'ind_max': 60,\n", + " 'ind_cmap': 'YlOrRd',\n", + " 'diff': 'relative',\n", + " 'diff_min': -220,\n", + " 'diff_max': 220,\n", + " 'diff_cmap': 'Reds',\n", + " 'score_range': [7676, 1638, 561, 0]}},\n", + " 'wsi': {'wsi': {'short_name': 'wsi',\n", + " 'long_name': 'Water stress index',\n", + " 'unit': '-',\n", + " 'ind_min': 0,\n", + " 'ind_max': 1,\n", + " 'ind_cmap': 'gist_earth',\n", + " 'diff': 'relative',\n", + " 'diff_min': -1910,\n", + " 'diff_max': 1910,\n", + " 'diff_cmap': 'PuRd',\n", + " 'score_range': [0.4, 0.3, 0.2, 0.1]}}}}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params" + ] }, { "cell_type": "markdown", @@ -572,6 +934,17 @@ "pyam.core - INFO: Reading file C:\\Users\\byers\\IIASA\\ECE.prog - Documents\\Research Theme - NEXUS\\Hotspots_Explorer_2p0\\rcre_testing\\testing_2\\emissions_temp_AR6_small.xlsx\n", "pyam.core - INFO: Reading meta indicators\n" ] + }, + { + "ename": "NameError", + "evalue": "name 'ssp_helper' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_21140\\2011608686.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# dft = pyam.IamDataFrame(dft)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Replace & fill missing SSP scenario allocation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mssp_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mssp_meta_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Ssp_family\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault_ssp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"SSP2\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'ssp_helper' is not defined" + ] } ], "source": [ @@ -585,32 +958,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6f3c225d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Index:\n", - " * model : AIM/CGE 2.0, AIM/CGE 2.1, AIM/CGE 2.2, ... WITCH-GLOBIOM 4.4 (44)\n", - " * scenario : 1.5C, 2.5C, 2C, 2CNow_Gradual, ... WB2C (559)\n", - "Timeseries data coordinates:\n", - " region : World (1)\n", - " variable : ... (1)\n", - " unit : K (1)\n", - " year : 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, ... 2100 (86)\n", - " ssp_family : SSP1, SSP2, SSP3, SSP4, SSP5 (5)\n", - "Meta indicators:\n", - " exclude (bool) False (1)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dft" ] @@ -620,7 +971,7 @@ "id": "4b3ecfc0", "metadata": {}, "source": [ - "## Example multiple scenarios, 1 climate indicator" + "## Example: multiple IAM scenarios, 1 climate indicator" ] }, { @@ -1300,7 +1651,7 @@ "id": "3ce5dffd", "metadata": {}, "source": [ - "## Example: 1 scenario, multiple indicators Faster with non dask\n" + "## Example: 1 scenario, multiple indicators (faster with dask=False)\n" ] }, { diff --git a/rime/pp_process_tabledata.ipynb b/rime/pp_process_tabledata.ipynb index a22bbe6..a632541 100644 --- a/rime/pp_process_tabledata.ipynb +++ b/rime/pp_process_tabledata.ipynb @@ -2,463 +2,10 @@ "cells": [ { "cell_type": "code", - 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return comm_promise.then((comm) => {\n", - " comm.send(data, metadata, buffers, disposeOnDone);\n", - " });\n", - " };\n", - " var comm = {\n", - " send: sendClosure\n", - " };\n", - " }\n", - " window.PyViz.comms[comm_id] = comm;\n", - " return comm;\n", - " }\n", - " window.PyViz.comm_manager = new JupyterCommManager();\n", - " \n", - "\n", - "\n", - "var JS_MIME_TYPE = 'application/javascript';\n", - "var HTML_MIME_TYPE = 'text/html';\n", - "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", - "var CLASS_NAME = 'output';\n", - "\n", - "/**\n", - " * Render data to the DOM node\n", - " */\n", - "function render(props, node) {\n", - " var div = document.createElement(\"div\");\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(div);\n", - " node.appendChild(script);\n", - "}\n", - "\n", - "/**\n", - " * Handle when a new output is added\n", - " */\n", - "function handle_add_output(event, handle) {\n", - " var output_area = 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metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[0]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " events.on('output_added.OutputArea', handle_add_output);\n", - " events.on('output_updated.OutputArea', handle_update_output);\n", - " events.on('clear_output.CodeCell', handle_clear_output);\n", - " events.on('delete.Cell', handle_clear_output);\n", - " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", - "\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " safe: true,\n", - " index: 0\n", - " });\n", - "}\n", - "\n", - "if (window.Jupyter !== undefined) {\n", - " try {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " } catch(err) {\n", - " }\n", - "}\n" - ], - "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof IPython !== 'undefined') { IPython.OutputArea.prototype._should_scroll = function(lines){ return false; }}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from process_config import *\n", "from rime_functions import *\n", @@ -507,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "id": "bfee2567", "metadata": { "scrolled": true @@ -893,7 +440,7 @@ " sdii|Exposure|Population|% (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 9, 3, 226), meta=np.ndarray>\n", " sdii|Hazard|Absolute|Land area weighted (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 9, 3, 226), meta=np.ndarray>\n", " sdii|Hazard|Absolute|Population weighted (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 9, 3, 226), meta=np.ndarray>\n", - " sdii|Hazard|Risk score|Population weighted (gmt, year, ssp, region) float64 dask.array<chunksize=(251, 9, 3, 226), meta=np.ndarray>

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