update for compatibility with pyam v0.5 (#31)

iiasa · Apr 6, 2020 · 93b922f · 93b922f
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diff --git a/README.md b/README.md
@@ -37,8 +37,10 @@ and the [data release notes](DATA_RELEASE_NOTES.md) in this repository.
 
 ## Scenario ensemble download
 
-The scenario ensemble used for this assessment is available for download
-at [data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).
+The scenario ensemble used for this assessment 
+and the metadata table of scenario categorisation and quantitative indicators
+are available for download via
+the [IAMC 1.5°C Scenario Explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).
 
 The scenario data is released under a custom license.
 If appropriate reference is made to the data source, it is permitted to use

diff --git a/assessment/spm_sr15_figure_3a_global_emissions_pathways.ipynb b/assessment/spm_sr15_figure_3a_global_emissions_pathways.ipynb
@@ -39,7 +39,6 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import warnings\n",
     "import io\n",
     "import itertools\n",
     "import yaml\n",
@@ -56,8 +55,8 @@
    "source": [
     "## Import scenario data, categorization and specifications files\n",
     "\n",
-    "The metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository.  \n",
-    "If the snapshot file has been updated, make sure that you rerun the categorization notebook.\n",
+    "The metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.\n",
     "\n",
     "The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook."
    ]
@@ -68,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -77,7 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5.load_metadata('sr15_metadata_indicators.xlsx')"
+    "sr1p5.load_meta('sr15_metadata_indicators.xlsx')"
    ]
   },
   {
@@ -177,7 +176,7 @@
    "source": [
     "co2 = (\n",
     "    df.filter(variable='Emissions|CO2')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     ")\n",
     "data.append(('Net carbon dioxide', co2))"
    ]
@@ -270,7 +269,7 @@
    "outputs": [],
    "source": [
     "n2o = df.filter(variable='Emissions|N2O')\n",
-    "n2o.convert_unit({'kt N2O/yr': ('Mt N2O/yr', 0.001)}, inplace=True)\n",
+    "n2o.convert_unit('kt N2O/yr', 'Mt N2O/yr', factor=1/1000, inplace=True)\n",
     "data.append(('Nitrous oxide', n2o))"
    ]
   },

diff --git a/assessment/spm_sr15_figure_3b_illustrative_pathways.ipynb b/assessment/spm_sr15_figure_3b_illustrative_pathways.ipynb
@@ -33,7 +33,6 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import warnings\n",
     "import io\n",
     "import itertools\n",
     "import yaml\n",
@@ -50,8 +49,8 @@
    "source": [
     "## Import scenario data, categorization and specifications files\n",
     "\n",
-    "The metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository.  \n",
-    "If the snapshot file has been updated, make sure that you rerun the categorization notebook.\n",
+    "The metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.\n",
     "\n",
     "The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook."
    ]
@@ -62,7 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -71,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5.load_metadata('sr15_metadata_indicators.xlsx')"
+    "sr1p5.load_meta('sr15_metadata_indicators.xlsx')"
    ]
   },
   {
@@ -163,7 +162,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pw.convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)}, inplace=True)"
+    "pw.convert_unit('Mt CO2/yr', 'Gt CO2/yr', inplace=True)"
    ]
   },
   {
@@ -390,7 +389,7 @@
    "source": [
     "co2 = (\n",
     "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|CO2', year=years)\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -415,7 +414,8 @@
    "source": [
     "kyoto_ghg = (\n",
     "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|Kyoto Gases (SAR-GWP100)', year=years)\n",
-    "    .convert_unit({'Mt CO2-equiv/yr': ('Gt CO2-equiv/yr', 0.001)})\n",
+    "    .rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})\n",
+    "    .convert_unit('Mt CO2e/yr','Gt CO2e/yr')\n",
     "    .timeseries()\n",
     ")\n",
     "for y in compare_years:\n",
@@ -553,7 +553,7 @@
     "\n",
     "    data = (\n",
     "        df.filter(variable=variable)\n",
-    "        .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "        .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "        .timeseries()\n",
     "    )\n",
     "    \n",
@@ -598,7 +598,7 @@
    "source": [
     "energy_crops = (\n",
     "    df.filter(variable='Land Cover|Cropland|Energy Crops', year=2050)\n",
-    "    .convert_unit({'million ha': ('million km2', 0.01)})\n",
+    "    .convert_unit('million ha', 'million km2', factor=0.01)\n",
     "    .timeseries()\n",
     ")"
    ]

diff --git a/assessment/spm_sr15_statements.ipynb b/assessment/spm_sr15_statements.ipynb
@@ -32,7 +32,6 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import warnings\n",
     "import io\n",
     "import itertools\n",
     "import yaml\n",
@@ -49,8 +48,8 @@
    "source": [
     "## Import scenario data, categorization and specifications files\n",
     "\n",
-    "The metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository.  \n",
-    "If the snapshot file has been updated, make sure that you rerun the categorization notebook.\n",
+    "The metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.\n",
     "\n",
     "The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook."
    ]
@@ -61,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -70,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5.load_metadata('sr15_metadata_indicators.xlsx')"
+    "sr1p5.load_meta('sr15_metadata_indicators.xlsx')"
    ]
   },
   {
@@ -186,7 +185,7 @@
     "co2 = (\n",
     "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|CO2')\n",
     "    .filter(**filter_args_aim)\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -595,7 +594,8 @@
    "source": [
     "ghg_ar4_sar = (\n",
     "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|Kyoto Gases (AR4-GWP100)')\n",
-    "    .convert_unit({'Mt CO2-equiv/yr': ('Gt CO2-equiv/yr', 0.001)})\n",
+    "    .rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})\n",
+    "    .convert_unit('Mt CO2e/yr','Gt CO2e/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -644,7 +644,8 @@
    "source": [
     "ghg_ar4 = (\n",
     "    df.filter(variable='Emissions|Kyoto Gases (AR4-GWP100)')\n",
-    "    .convert_unit({'Mt CO2-equiv/yr': ('Gt CO2-equiv/yr', 0.001)})\n",
+    "    .rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})\n",
+    "    .convert_unit('Mt CO2e/yr','Gt CO2e/yr')\n",
     "    .timeseries()\n",
     ")"
    ]

diff --git a/assessment/sr15_2.0_categories_indicators.ipynb b/assessment/sr15_2.0_categories_indicators.ipynb
@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "import pyam\n",
-    "logger = pyam.logger()"
+    "logger = pyam.logger"
    ]
   },
   {
@@ -127,7 +127,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -1461,7 +1461,7 @@
     "def filter_and_convert(variable):\n",
     "    return (sr1p5\n",
     "            .filter(variable=variable)\n",
-    "            .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "            .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "            .timeseries()\n",
     "           )\n",
     "\n",

diff --git a/assessment/sr15_2.3.1_range_of_assumptions.ipynb b/assessment/sr15_2.3.1_range_of_assumptions.ipynb
@@ -49,8 +49,8 @@
    "source": [
     "## Import scenario data, categorization and specifications files\n",
     "\n",
-    "The metadata file must have been generated from the notebook `sr15_2.0_categories_indicators` included in this repository.  \n",
-    "If the snapshot file has been updated, make sure that you rerun the categorization notebook.\n",
+    "The metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.\n",
     "\n",
     "The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook."
    ]
@@ -61,7 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -70,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5.load_metadata('sr15_metadata_indicators.xlsx')"
+    "sr1p5.load_meta('sr15_metadata_indicators.xlsx')"
    ]
   },
   {
@@ -191,11 +191,11 @@
     "fig, ax = plt.subplots(2, 2, figsize=(8, 6))\n",
     "\n",
     "pop = df.filter(variable='Population')\n",
-    "pop.convert_unit({'million': ['billion', 1/1000]}, inplace=True)\n",
+    "pop.convert_unit('million', 'billion', factor=1/1000, inplace=True)\n",
     "line_plot_with_markers(ax[0][0], pop, 'Population', 'a')\n",
     "\n",
     "gdp = df.filter(variable='GDP|PPP')\n",
-    "gdp.convert_unit({'billion US$2010/yr': ['trillion US$2010/yr', 1/1000]}, inplace=True)\n",
+    "gdp.convert_unit('billion US$2010/yr', 'trillion US$2010/yr', factor=1/1000, inplace=True)\n",
     "line_plot_with_markers(ax[0][1], gdp, 'Gross World Product', 'b')\n",
     "\n",
     "final = df.filter(variable='Final Energy')\n",

diff --git a/assessment/sr15_2.3.3_global_emissions_characteristics.ipynb b/assessment/sr15_2.3.3_global_emissions_characteristics.ipynb
@@ -32,7 +32,6 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import warnings\n",
     "import io\n",
     "import itertools\n",
     "import yaml\n",
@@ -51,8 +50,8 @@
    "source": [
     "## Import scenario data, categorization and specifications files\n",
     "\n",
-    "The metadata file must be generated from the notebook `sr15_2.0_categories_indicators` included in this repository.  \n",
-    "If the snapshot file has been updated, make sure that you rerun the categorization notebook.\n",
+    "The metadata file with scenario categorisation and quantitative indicators can be downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "Alternatively, it can be re-created using the notebook `sr15_2.0_categories_indicators`.\n",
     "\n",
     "The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook."
    ]
@@ -63,7 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')"
    ]
   },
   {
@@ -72,7 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5.load_metadata('sr15_metadata_indicators.xlsx')"
+    "sr1p5.load_meta('sr15_metadata_indicators.xlsx')"
    ]
   },
   {
@@ -198,7 +197,8 @@
    "source": [
     "ghg = (\n",
     "    df.filter(variable='Emissions|Kyoto Gases (AR4-GWP100)')\n",
-    "    .convert_unit({'Mt CO2-equiv/yr': ('Gt CO2-equiv/yr', 0.001)})\n",
+    "    .rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})\n",
+    "    .convert_unit('Mt CO2e/yr','Gt CO2e/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -225,7 +225,7 @@
    "source": [
     "co2 = (\n",
     "    df.filter(variable='Emissions|CO2')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -252,7 +252,7 @@
    "source": [
     "co2_afolu = (\n",
     "    df.filter(variable='Emissions|CO2|AFOLU')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -279,7 +279,7 @@
    "source": [
     "n2o = (\n",
     "    df.filter(variable='Emissions|N2O')\n",
-    "    .convert_unit({'kt N2O/yr': ('Mt N2O/yr', 0.001)})   \n",
+    "    .convert_unit('kt N2O/yr', 'Mt N2O/yr', factor=1/1000)\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -307,7 +307,7 @@
    "source": [
     "co2_ene = (\n",
     "    df.filter(variable='Emissions|CO2|Energy and Industrial Processes')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -334,7 +334,7 @@
    "source": [
     "co2_supply = (\n",
     "    df.filter(variable='Emissions|CO2|Energy|Supply')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]
@@ -361,7 +361,7 @@
    "source": [
     "co2_demand = (\n",
     "    df.filter(variable='Emissions|CO2|Energy|Demand')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    "    .convert_unit('Mt CO2/yr', 'Gt CO2/yr')\n",
     "    .timeseries()\n",
     ")"
    ]