From 9e9f6fc1e024951deefb314868aefe7de208a680 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 15 Jun 2022 12:51:58 -0600 Subject: [PATCH 01/80] Add DOI values for epacems_unitid_eia_plant_crosswalk file to the datastore.py module so that you can download them to the datastore. --- src/pudl/workspace/datastore.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index 9230445b5c..88ff9b4bc4 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -152,6 +152,7 @@ class ZenodoFetcher: "eia861": "10.5072/zenodo.687052", "eia923": "10.5072/zenodo.926301", "epacems": "10.5072/zenodo.672963", + "epacems_unitid_eia_plant_crosswalk": "10.5072/zenodo.1072001", "ferc1": "10.5072/zenodo.926302", "ferc714": "10.5072/zenodo.926660", }, @@ -162,6 +163,7 @@ class ZenodoFetcher: "eia861": "10.5281/zenodo.5602102", "eia923": "10.5281/zenodo.5596977", "epacems": "10.5281/zenodo.4660268", + "epacems_unitid_eia_plant_crosswalk": "10.5281/zenodo.6633770", "ferc1": "10.5281/zenodo.5534788", "ferc714": "10.5281/zenodo.5076672", }, From 312075ff0c16bfb340b0ec4aee881cc9938dd94e Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 15 Jun 2022 16:35:18 -0600 Subject: [PATCH 02/80] Update Crosswalk Glue Extractor - Add an extract function to the current eia_epacems glue module to grab the official crosswalk from the datastore (rather than just a static csv saved as part of the repo). I still need to remove the old code but this is a start. - Add a shell for a transform function in the eia_epacems glue module. - Fix a typo in the metadata.classes module - Add a notebook for experimenting with the table transformations. --- .../play_with_avg_num_employees_agg.ipynb | 545 ++++++++++++++++++ src/pudl/glue/eia_epacems.py | 17 + src/pudl/metadata/classes.py | 2 +- 3 files changed, 563 insertions(+), 1 deletion(-) create mode 100644 notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb diff --git a/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb b/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb new file mode 100644 index 0000000000..3035467d1d --- /dev/null +++ b/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb @@ -0,0 +1,545 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2441744b-1695-4d8d-a13e-6136c6271a72", + "metadata": {}, + "source": [ + "# Play around with `avg_num_employees` agg" + ] + }, + { + "cell_type": "markdown", + "id": "81315513-812a-48cd-8a40-6f947f2ee2a9", + "metadata": {}, + "source": [ + "This notebook reviews two files: \n", + "- **agg_df:** aggregated by year, utility, and plant type\n", + "- **full_df:** un-aggregated but with a column `avg_num_employees_agg` for aggergated year, utility, plant, and plant-type employee values. I included this one so you can play around and make sure the totals flags are working properly / change them if you don't like them. I'll show you how below. \n", + "\n", + "It's important to remeber that the `avg_num_employees_agg` values in the `agg_df` are calculated at the PLANT/PLANT-TYPE level not the UTILITY level. There is another round of aggregation that occurs before that. This is to make it easier to see what assumptions were made in the process of creating the final utility-aggregated employee number value." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d0caf1e2-068e-4a08-a386-13d234b4a5a6", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d3c182dd-486b-436f-9bca-14ae6361f4dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Path to agg and full files; UPDATE as needed\n", + "agg_path = '/Users/aesharpe/Desktop/num_employees_agg.xlsx'\n", + "full_path = '/Users/aesharpe/Desktop/num_employees.xlsx'\n", + "\n", + "# Load excel files into pandas\n", + "agg_df = pd.read_excel(agg_path).drop(columns=['Unnamed: 0'])\n", + "full_df = pd.read_excel(full_path).drop(columns=['Unnamed: 0'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d5d73933-63ab-49d1-8254-35aa8b919def", + "metadata": {}, + "outputs": [], + "source": [ + "def get_random_group(df):\n", + " \"\"\"Show random year/utility groups that have multiple rows and at least one total.\n", + " \n", + " Use this function to see how the aggregation chose to allocate the avg_num_employees.\n", + " You can compare the avg_num_employees column with the avg_num_employees_agg column.\n", + " \n", + " Args: \n", + " df (pandas.DataFrame): The num_employees.xlsx dataframe (i.e., the non \n", + " aggregated one).\n", + " Returns:\n", + " df (pandas.DataFrame): A random subset of the full dataframe that shows\n", + " the records for a specific year and utility that has more than one\n", + " record and at least one flagged total row in total_types. \n", + " \"\"\"\n", + " groups = df.groupby(['report_year', 'utility_id_ferc1']) # add plant_id_pudl if you want to narrow the groups\n", + " while True:\n", + " random_key = random.choice(list(groups.groups.keys()))\n", + " random_group = groups.get_group(random_key)\n", + " more_than_one_row = len(random_group) > 1\n", + " has_total = random_group.total_type.notna().any()\n", + " if more_than_one_row & has_total:\n", + " break\n", + " return random_group[[\n", + " 'record_id', 'report_year', 'utility_id_ferc1', 'utility_name_ferc1', \n", + " 'plant_id_pudl', 'plant_name_ferc1', 'total_type', 'avg_num_employees', 'avg_num_employees_agg',\n", + " 'avg_num_employees_flag', 'capacity_mw', 'installation_year', 'plant_type']]" + ] + }, + { + "cell_type": "markdown", + "id": "4f41f8a6-e90c-41f6-8b52-8d375c8c6df7", + "metadata": {}, + "source": [ + "Every time you run this you'll get a different subset of the full df\n", + "You can use this to look at the way that employee numbers were allocated and decide whether you agree\n", + "Remember, all aggregation allocation decisions are being made at the year, utility, and plant level so\n", + "all of the values in avg_num_employees_agg represent the summary value for that plant, that's why they\n", + "are repeated for multipe records in a plant. The utility level aggregation is calculated (shown below)\n", + "by adding up the designated employee count for each year, utility, and plant id. In other words, you\n", + "can't just sum the column." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "be598c5d-8bbd-4746-b20a-6eb1a93bea81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['combustion_turbine', 'steam', 'nuclear', 'unknown', 'storage',\n", + " 'run-of-river'], dtype=object)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate a random year/utility group\n", + "peek = get_random_group(full_df)\n", + "\n", + "# Show what fuel types appear in that group\n", + "peek.plant_type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ffa607a4-86ee-4c93-b76c-ea2384a4b381", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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record_idreport_yearutility_id_ferc1utility_name_ferc1plant_id_pudlplant_name_ferc1total_typeavg_num_employeesavg_num_employees_aggavg_num_employees_flagcapacity_mwinstallation_yearplant_type
3782f1_steam_1996_12_57_0_5199657Georgia Power Company73bowenNaN423.0423actual values provided3499.01975.0steam
3783f1_steam_1996_12_57_1_5199657Georgia Power Company246hammondNaN213.0213actual values provided953.01970.0steam
3784f1_steam_1996_12_57_1_4199657Georgia Power Company250harllee branchNaN347.0347actual values provided1746.01969.0steam
3788f1_steam_1996_12_57_1_1199657Georgia Power Company383mcdonoughNaN177.0177actual values provided598.01964.0steam
3791f1_steam_1996_12_57_2_3199657Georgia Power Company398mcmanusNaN43.043actual values provided144.01959.0steam
3793f1_steam_1996_12_57_2_5199657Georgia Power Company412mitchellNaN64.064actual values provided218.01964.0steam
3794f1_steam_1996_12_57_3_1199657Georgia Power Company526schererNaN399.0399actual values provided818.01988.0steam
3801f1_steam_1996_12_57_2_1199657Georgia Power Company656yatesNaN317.0317actual values provided1488.01974.0steam
3803f1_steam_1996_12_57_3_4199657Georgia Power Company658wansleyNaN249.0249actual values provided1019.01978.0steam
3817f1_steam_1996_12_57_0_1199657Georgia Power Company9611arkwrightNaN80.080actual values provided181.01948.0steam
3819f1_steam_1996_12_57_0_3199657Georgia Power Company9612atkinsonNaNNaN0no total rows198.01948.0steam
\n", + "
" + ], + "text/plain": [ + " record_id report_year utility_id_ferc1 \\\n", + "3782 f1_steam_1996_12_57_0_5 1996 57 \n", + "3783 f1_steam_1996_12_57_1_5 1996 57 \n", + "3784 f1_steam_1996_12_57_1_4 1996 57 \n", + "3788 f1_steam_1996_12_57_1_1 1996 57 \n", + "3791 f1_steam_1996_12_57_2_3 1996 57 \n", + "3793 f1_steam_1996_12_57_2_5 1996 57 \n", + "3794 f1_steam_1996_12_57_3_1 1996 57 \n", + "3801 f1_steam_1996_12_57_2_1 1996 57 \n", + "3803 f1_steam_1996_12_57_3_4 1996 57 \n", + "3817 f1_steam_1996_12_57_0_1 1996 57 \n", + "3819 f1_steam_1996_12_57_0_3 1996 57 \n", + "\n", + " utility_name_ferc1 plant_id_pudl plant_name_ferc1 total_type \\\n", + "3782 Georgia Power Company 73 bowen NaN \n", + "3783 Georgia Power Company 246 hammond NaN \n", + "3784 Georgia Power Company 250 harllee branch NaN \n", + "3788 Georgia Power Company 383 mcdonough NaN \n", + "3791 Georgia Power Company 398 mcmanus NaN \n", + "3793 Georgia Power Company 412 mitchell NaN \n", + "3794 Georgia Power Company 526 scherer NaN \n", + "3801 Georgia Power Company 656 yates NaN \n", + "3803 Georgia Power Company 658 wansley NaN \n", + "3817 Georgia Power Company 9611 arkwright NaN \n", + "3819 Georgia Power Company 9612 atkinson NaN \n", + "\n", + " avg_num_employees avg_num_employees_agg avg_num_employees_flag \\\n", + "3782 423.0 423 actual values provided \n", + "3783 213.0 213 actual values provided \n", + "3784 347.0 347 actual values provided \n", + "3788 177.0 177 actual values provided \n", + "3791 43.0 43 actual values provided \n", + "3793 64.0 64 actual values provided \n", + "3794 399.0 399 actual values provided \n", + "3801 317.0 317 actual values provided \n", + "3803 249.0 249 actual values provided \n", + "3817 80.0 80 actual values provided \n", + "3819 NaN 0 no total rows \n", + "\n", + " capacity_mw installation_year plant_type \n", + "3782 3499.0 1975.0 steam \n", + "3783 953.0 1970.0 steam \n", + "3784 1746.0 1969.0 steam \n", + "3788 598.0 1964.0 steam \n", + "3791 144.0 1959.0 steam \n", + "3793 218.0 1964.0 steam \n", + "3794 818.0 1988.0 steam \n", + "3801 1488.0 1974.0 steam \n", + "3803 1019.0 1978.0 steam \n", + "3817 181.0 1948.0 steam \n", + "3819 198.0 1948.0 steam " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at one of those fuel types for a snapshot of what's going on\n", + "peek[peek['plant_type']=='steam'].sort_values('plant_id_pudl')" + ] + }, + { + "cell_type": "markdown", + "id": "78877a95-4c94-4df8-821a-6851546cee53", + "metadata": {}, + "source": [ + "## Recreate the agg_df from the full_df" + ] + }, + { + "cell_type": "markdown", + "id": "eb486e1e-3527-4c6a-946a-d245fbefb9a9", + "metadata": {}, + "source": [ + "If you see any values for `avg_num_employees_agg` that you do not think are representative of that year, utility, plant, and plant type, then you can change them. Just make sure you change the `avg_num_employees_agg` value in the `full_df` spreadsheet (num_employees_full.xlsx) for **ALL** records in the year, utility, plant, and plant type group. Then, you can run these next cells which will recreate the aggregated spreadsheet as well as show you the difference between the original aggregated spreadsheet and the version with changes. If you *don't* change the spreadsheet, this should output a blank df." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9d42e01b-824b-4dfe-a8ad-4786c3714e6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Group by relevant columns. We include plant_id_pudl here because many of the totals are plant-level totals\n", + "groups = full_df.groupby(['report_year', 'utility_id_ferc1', 'plant_id_pudl', 'plant_type'])\n", + "\n", + "# Test that the groups we've defined above all have the same values for the column avg_num_employees_agg\n", + "# This will spit out an error if that's not true\n", + "assert (groups.avg_num_employees_agg.nunique() > 1).any() == False, \"groups don't have the same avg_num_employees_agg\" \n", + "\n", + "# Group by plant and grab the first value in each avg_num_employees group because we know they are all the same\n", + "plant_groups_df = groups.agg('first').reset_index()\n", + "\n", + "# Now we'll aggregate up to the utility plant-type level which is what we want for the final version.\n", + "util_groups_df = (\n", + " plant_groups_df\n", + " .groupby(['report_year', 'utility_id_ferc1', 'plant_type'])\n", + " .agg('sum')\n", + " .assign(avg_num_employees=lambda x: x.avg_num_employees_agg.astype('Int64'))\n", + " .drop(columns=['plant_id_pudl'])\n", + " .reset_index())[['report_year', 'utility_id_ferc1', 'plant_type', 'avg_num_employees']]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5bdf845b-1354-4c6a-98f6-e888bdfe073b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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report_yearutility_id_ferc1plant_typeavg_num_employees
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [report_year, utility_id_ferc1, plant_type, avg_num_employees]\n", + "Index: []" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show the differences between the original agg_df and your newly aggregated full_df\n", + "# If you don't change anything, this should be empty\n", + "agg_no_flag = agg_df.drop(columns=['avg_num_employees_flag'])\n", + "pd.concat([agg_no_flag, util_groups_df]).drop_duplicates(keep=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42a73048-fec1-4b09-8c80-d40a232129be", + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/pudl/glue/eia_epacems.py b/src/pudl/glue/eia_epacems.py index 2f0037da5d..804bdff229 100644 --- a/src/pudl/glue/eia_epacems.py +++ b/src/pudl/glue/eia_epacems.py @@ -21,10 +21,27 @@ import pudl from pudl.metadata.fields import apply_pudl_dtypes +from pudl.workspace.datastore import Datastore logger = logging.getLogger(__name__) +def extract(ds: Datastore) -> pd.DataFrame: + """Extract the EPACEMS-EIA Crosswalk from the Datastore.""" + with ds.get_zipfile_resource( + "epacems_unitid_eia_plant_crosswalk", + name="epacems_unitid_eia_plant_crosswalk.zip", + ).open("camd-eia-crosswalk-master/epa_eia_crosswalk.csv") as f: + return pd.read_csv(f) + + +def transform(epa_eia_crosswalk: pd.DataFrame) -> pd.DataFrame: + """Clean up the EPACEMS-EIA Crosswalk file.""" + epa_eia_crosswalk_clean = epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) + + return epa_eia_crosswalk_clean + + def grab_n_clean_epa_orignal(): """Retrieve and clean column names for the original EPA-EIA crosswalk file. diff --git a/src/pudl/metadata/classes.py b/src/pudl/metadata/classes.py index ec89d08e49..8825c37e5b 100644 --- a/src/pudl/metadata/classes.py +++ b/src/pudl/metadata/classes.py @@ -910,7 +910,7 @@ class DataSource(Base): email: Email = None def get_resource_ids(self) -> list[str]: - """Compile list of resoruce IDs associated with this data source.""" + """Compile list of resource IDs associated with this data source.""" # Temporary check to use eia861.RESOURCE_METADATA directly # eia861 is not currently included in the general RESOURCE_METADATA dict resources = RESOURCE_METADATA From a9eebc4c643017c7b2ae9f86a43ca5025c76fc1c Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 16 Jun 2022 13:38:15 -0600 Subject: [PATCH 03/80] Update eia_epacems.py module glue functions to extract and transform and split the epa crosswalk file. --- src/pudl/glue/eia_epacems.py | 94 +++++++++++------------------------- 1 file changed, 29 insertions(+), 65 deletions(-) diff --git a/src/pudl/glue/eia_epacems.py b/src/pudl/glue/eia_epacems.py index 804bdff229..74c76261e3 100644 --- a/src/pudl/glue/eia_epacems.py +++ b/src/pudl/glue/eia_epacems.py @@ -2,19 +2,11 @@ This module defines functions that read the raw EPA-EIA crosswalk file, clean up the column names, and separate it into three distinctive normalize tables -for integration in the database. There are many gaps in the mapping of EIA -plant and generator ids to EPA plant and unit ids, so, for the time being these -tables are sparse. +for integration in the database. -The EPA, in conjunction with the EIA, plans to relase an crosswalk with fewer -gaps at the beginning of 2021. Until then, this module reads and cleans the -currently available crosswalk. - -The raw crosswalk file was obtained from Greg Schivley. His methods for filling -in some of the gaps are not included in this version of the module. -https://github.com/grgmiller/EPA-EIA-Unit-Crosswalk +The crosswalk file was a joint effort on behalf on EPA and EIA and is published on the +EPA's github account at www.github.com/USEPA". """ -import importlib import logging import pandas as pd @@ -37,47 +29,21 @@ def extract(ds: Datastore) -> pd.DataFrame: def transform(epa_eia_crosswalk: pd.DataFrame) -> pd.DataFrame: """Clean up the EPACEMS-EIA Crosswalk file.""" - epa_eia_crosswalk_clean = epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) - - return epa_eia_crosswalk_clean - - -def grab_n_clean_epa_orignal(): - """Retrieve and clean column names for the original EPA-EIA crosswalk file. - - Returns: - pandas.DataFrame: a version of the EPA-EIA crosswalk containing only - relevant columns. Columns names are clear and programatically - accessible. - """ - logger.info("grabbing original crosswalk") - eia_epacems_crosswalk_csv = importlib.resources.open_text( - "pudl.package_data.glue", "epa_eia_crosswalk_from_epa.csv" - ) - eia_epacems_crosswalk = ( - pd.read_csv(eia_epacems_crosswalk_csv) - .pipe(pudl.helpers.simplify_columns) - .rename( - columns={ - "oris_code": "plant_id_epa", - "eia_oris": "plant_id_eia", - "unit_id": "unit_id_epa", - "facility_name": "plant_name_eia", - } - ) - .filter( - [ - "plant_name_eia", - "plant_id_eia", - "plant_id_epa", - "unit_id_epa", - "generator_id", - "boiler_id", - ] - ) + logger.info("Cleaning up the epacems-eia crosswalk") + column_rename = { + "camd_unit_id": "unit_id_epa", + "camd_plant_id": "plant_id_epa", + "eia_plant_name": "plant_name_eia", + "eia_plant_id": "plant_id_eia", + "eia_generator_id": "generator_id_eia", + } + epa_eia_crosswalk_clean = ( + epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) + .rename(columns=column_rename) + .filter(list(column_rename.values())) .pipe(apply_pudl_dtypes, "eia") ) - return eia_epacems_crosswalk + return epa_eia_crosswalk_clean def split_tables(df: pd.DataFrame) -> dict[str, pd.DataFrame]: @@ -85,7 +51,7 @@ def split_tables(df: pd.DataFrame) -> dict[str, pd.DataFrame]: Args: df: a DataFrame of relevant, readible columns from the - EIA-EPA crosswalk. Output of grab_n_clean_epa_original(). + EIA-EPA crosswalk. Output of transform() defined above. Returns: A dictionary of three normalized DataFrames comprised of the data @@ -94,18 +60,13 @@ def split_tables(df: pd.DataFrame) -> dict[str, pd.DataFrame]: id. Includes no nan values. """ logger.info("splitting crosswalk into three normalized tables") - epa_df = ( - df.filter(["plant_id_epa", "unit_id_epa"]).copy().drop_duplicates().dropna() - ) - plants_eia_epa = ( - df.filter(["plant_id_eia", "plant_id_epa"]).copy().drop_duplicates().dropna() - ) - gen_unit_df = ( - df.filter(["plant_id_eia", "generator_id", "unit_id_epa"]) - .copy() - .drop_duplicates() - .dropna() - ) + + def drop_n_reset(df, cols): + return df.filter(cols).copy().dropna() + + epa_df = drop_n_reset(df, ["plant_id_epa", "unit_id_epa"]) + plants_eia_epa = drop_n_reset(df, ["plant_id_eia", "plant_id_epa"]) + gen_unit_df = drop_n_reset(df, ["plant_id_eia", "generator_id_eia", "unit_id_epa"]) return { "plant_unit_epa": epa_df, @@ -114,15 +75,18 @@ def split_tables(df: pd.DataFrame) -> dict[str, pd.DataFrame]: } -def grab_clean_split() -> dict[str, pd.DataFrame]: +def grab_clean_split(ds: Datastore) -> dict[str, pd.DataFrame]: """Clean raw crosswalk data, drop nans, and return split tables. + Args: + ds (:class:datastore.Datastore): Initialized datastore. + Returns: A dictionary of three normalized DataFrames comprised of the data in the original crosswalk file. EPA plant id to EPA unit id; EPA plant id to EIA plant id; and EIA plant id to EIA generator id to EPA unit id. """ - crosswalk = grab_n_clean_epa_orignal().reset_index().dropna() + crosswalk = transform(extract(ds)) return split_tables(crosswalk) From 5391249c043233abd7155ff89f4c3646bcf0c872 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 16 Jun 2022 13:42:55 -0600 Subject: [PATCH 04/80] Update name of eia_epacems glue module to be more specific and reflect the name used in the scraper and archiver modules. --- src/pudl/__init__.py | 2 +- src/pudl/etl.py | 2 +- .../{eia_epacems.py => epacems_unitid_eia_plant_crosswalk.py} | 0 3 files changed, 2 insertions(+), 2 deletions(-) rename src/pudl/glue/{eia_epacems.py => epacems_unitid_eia_plant_crosswalk.py} (100%) diff --git a/src/pudl/__init__.py b/src/pudl/__init__.py index 5cd6d1e560..548769ee2d 100644 --- a/src/pudl/__init__.py +++ b/src/pudl/__init__.py @@ -26,7 +26,7 @@ import pudl.extract.excel import pudl.extract.ferc1 import pudl.extract.ferc714 -import pudl.glue.eia_epacems +import pudl.glue.epacems_unitid_eia_plant_crosswalk import pudl.glue.ferc1_eia import pudl.helpers import pudl.load diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 5c9d68c5de..b52b07554c 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -361,7 +361,7 @@ def _etl_glue(glue_settings: GlueSettings) -> dict[str, pd.DataFrame]: # Add the EPA to EIA crosswalk, but only if the eia data is being processed. # Otherwise the foreign key references will have nothing to point at: if glue_settings.eia: - glue_dfs.update(pudl.glue.eia_epacems.grab_clean_split()) + glue_dfs.update(pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split()) return glue_dfs diff --git a/src/pudl/glue/eia_epacems.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py similarity index 100% rename from src/pudl/glue/eia_epacems.py rename to src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py From a396fd9fd60853b1f915504f1af88380bd014154 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Fri, 17 Jun 2022 12:53:24 -0600 Subject: [PATCH 05/80] Add Datastore argument to crosswalk glue function when called in the etl module--was breaking the etl without it --- src/pudl/etl.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index b52b07554c..5b68fdf2c3 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -341,7 +341,9 @@ def etl_epacems( ############################################################################### # GLUE EXPORT FUNCTIONS ############################################################################### -def _etl_glue(glue_settings: GlueSettings) -> dict[str, pd.DataFrame]: +def _etl_glue( + glue_settings: GlueSettings, ds_kwargs: dict[str, Any] +) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. Args: @@ -360,8 +362,11 @@ def _etl_glue(glue_settings: GlueSettings) -> dict[str, pd.DataFrame]: # Add the EPA to EIA crosswalk, but only if the eia data is being processed. # Otherwise the foreign key references will have nothing to point at: + ds = Datastore(**ds_kwargs) if glue_settings.eia: - glue_dfs.update(pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split()) + glue_dfs.update( + pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split(ds) + ) return glue_dfs @@ -435,7 +440,7 @@ def etl( # noqa: C901 if datasets.get("eia", False): sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) if datasets.get("glue", False): - sqlite_dfs.update(_etl_glue(datasets["glue"])) + sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs)) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) From ccaba0dba3734f33931fb9ba464c2b16e954553d Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Fri, 17 Jun 2022 17:57:48 -0600 Subject: [PATCH 06/80] - Change generator_id_eia to generator_id in crosswalk glue transform module so that it matches other eia data - Put split_tables function into transform function in crosswalk glue module to consolidate code. --- .../epacems_unitid_eia_plant_crosswalk.py | 50 +++++++++---------- 1 file changed, 23 insertions(+), 27 deletions(-) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 74c76261e3..2f0d931a5f 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -27,50 +27,48 @@ def extract(ds: Datastore) -> pd.DataFrame: return pd.read_csv(f) -def transform(epa_eia_crosswalk: pd.DataFrame) -> pd.DataFrame: - """Clean up the EPACEMS-EIA Crosswalk file.""" +def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: + """Clean up the EPACEMS-EIA Crosswalk file and split it into normalized tables. + + Args: + epa_eia_crosswalk: The result of running this module's extract() function. + + Returns: + A dictionary of three normalized DataFrames comprised of the data + in the original crosswalk file. EPA plant id to EPA unit id; EPA plant + id to EIA plant id; and EIA plant id to EIA generator id to EPA unit + id. Includes no nan values. + """ logger.info("Cleaning up the epacems-eia crosswalk") + column_rename = { "camd_unit_id": "unit_id_epa", "camd_plant_id": "plant_id_epa", "eia_plant_name": "plant_name_eia", "eia_plant_id": "plant_id_eia", - "eia_generator_id": "generator_id_eia", + "eia_generator_id": "generator_id", } - epa_eia_crosswalk_clean = ( + crosswalk_clean = ( epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) .pipe(apply_pudl_dtypes, "eia") ) - return epa_eia_crosswalk_clean - -def split_tables(df: pd.DataFrame) -> dict[str, pd.DataFrame]: - """Split the cleaned EIA-EPA crosswalk table into three normalized tables. - - Args: - df: a DataFrame of relevant, readible columns from the - EIA-EPA crosswalk. Output of transform() defined above. - - Returns: - A dictionary of three normalized DataFrames comprised of the data - in the original crosswalk file. EPA plant id to EPA unit id; EPA plant - id to EIA plant id; and EIA plant id to EIA generator id to EPA unit - id. Includes no nan values. - """ - logger.info("splitting crosswalk into three normalized tables") + logger.info("Splitting crosswalk into three normalized tables") def drop_n_reset(df, cols): return df.filter(cols).copy().dropna() - epa_df = drop_n_reset(df, ["plant_id_epa", "unit_id_epa"]) - plants_eia_epa = drop_n_reset(df, ["plant_id_eia", "plant_id_epa"]) - gen_unit_df = drop_n_reset(df, ["plant_id_eia", "generator_id_eia", "unit_id_epa"]) + epa_df = drop_n_reset(crosswalk_clean, ["plant_id_epa", "unit_id_epa"]) + plants_eia_epa_df = drop_n_reset(crosswalk_clean, ["plant_id_eia", "plant_id_epa"]) + gen_unit_df = drop_n_reset( + crosswalk_clean, ["plant_id_eia", "generator_id", "unit_id_epa"] + ) return { "plant_unit_epa": epa_df, - "assn_plant_id_eia_epa": plants_eia_epa, + "assn_plant_id_eia_epa": plants_eia_epa_df, "assn_gen_eia_unit_epa": gen_unit_df, } @@ -87,6 +85,4 @@ def grab_clean_split(ds: Datastore) -> dict[str, pd.DataFrame]: id to EIA plant id; and EIA plant id to EIA generator id to EPA unit id. """ - crosswalk = transform(extract(ds)) - - return split_tables(crosswalk) + return transform(extract(ds)) From a4230bf90327437ab81bf4b77fe8825d2a53bc51 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 20 Jun 2022 21:49:35 -0600 Subject: [PATCH 07/80] - Add drop_duplicates to drop_n_reset() function in the crosswalk glue etl module - Clean the generator ids so that numeric generator id values have no preceeding zeros. Tested this against the eia data and it matches. --- src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 2f0d931a5f..351090257e 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -48,17 +48,26 @@ def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: "eia_plant_id": "plant_id_eia", "eia_generator_id": "generator_id", } + # Basic column rename, selection, and dtype alignment. crosswalk_clean = ( epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) .pipe(apply_pudl_dtypes, "eia") ) + # There are some eia generator_id values in the crosswalk that don't match the eia + # generator_id values in the generators_eia860 table where the foreign keys are + # stored. All of them appear to have preceeding zeros. I.e.: 0010 should be 10. + # This makes sure to nix preceeding zeros on crosswalk generator ids that are all + # numeric. I.e.: 00A10 will stay 00A10 but 0010 will become 10. + crosswalk_clean.loc[ + crosswalk_clean.generator_id.str.contains(r"^0+\d+$"), "generator_id" + ] = crosswalk_clean.generator_id.replace({r"^0+": ""}, regex=True) logger.info("Splitting crosswalk into three normalized tables") def drop_n_reset(df, cols): - return df.filter(cols).copy().dropna() + return df.filter(cols).copy().dropna().drop_duplicates() epa_df = drop_n_reset(crosswalk_clean, ["plant_id_epa", "unit_id_epa"]) plants_eia_epa_df = drop_n_reset(crosswalk_clean, ["plant_id_eia", "plant_id_epa"]) From 3692d9b3a2b33fc93aeb6ab6f9826a5d7254fef3 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 21 Jun 2022 00:00:23 -0600 Subject: [PATCH 08/80] Pass the generators_entity_eia table to the EPACEMS crosswalk dquote> The epacems crosswalk file does not have a data field. It does, however, use foreign keys from eia tables that are restricted by date when the ETL is run for a subset of years. Without this addition, the integration tests will fail because the tests, based on the fast etl (aka one year of data), formulate entity tables (used to map foreign keys onto the crosswalk) that contain less data than the crosswalk (based on all years). One solution is to restrict the crosswalk data to only show the plant and generator ids from the selected subset of eia data. That's what I've implemented here. This isn't ideal, because there are some cases in which the crosswalk has erronious generator_id data. I fixed this in the previous commit (before selecting only the plant-gen records that show up in the eia subset). If I hadn't checked these generator records they would have been excluded from the crosswalk for not matching. By only including the values that are inherently in the eia subset we guarantee to only get the data pertaining to the years we want BUT we run the risk of excluding data that was erroniously reported (and could be fixed) without warning. --- src/pudl/etl.py | 17 +++++++++-- .../epacems_unitid_eia_plant_crosswalk.py | 30 ++++++++++++++++--- 2 files changed, 40 insertions(+), 7 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 5b68fdf2c3..b94d5c6527 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -342,12 +342,19 @@ def etl_epacems( # GLUE EXPORT FUNCTIONS ############################################################################### def _etl_glue( - glue_settings: GlueSettings, ds_kwargs: dict[str, Any] + glue_settings: GlueSettings, + ds_kwargs: dict[str, Any], + generators_entity_eia: pd.DataFrame, ) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. Args: glue_settings: Validated ETL parameters required by this data source. + ds_kwargs: Keyword arguments for instantiating a PUDL datastore, so that the ETL + can access the raw input data. + generators_entity_eia: the EIA generators entity table. Used to create subsets + of the crosswalk for use with specific year subsets of eia data. Necessary + to pass the tests. Returns: A dictionary of DataFrames whose keys are the names of the corresponding @@ -365,7 +372,9 @@ def _etl_glue( ds = Datastore(**ds_kwargs) if glue_settings.eia: glue_dfs.update( - pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split(ds) + pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split( + ds, generators_entity_eia + ) ) return glue_dfs @@ -440,7 +449,9 @@ def etl( # noqa: C901 if datasets.get("eia", False): sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) if datasets.get("glue", False): - sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs)) + sqlite_dfs.update( + _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs["generators_entity_eia"]) + ) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 351090257e..47492a404f 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -27,11 +27,14 @@ def extract(ds: Datastore) -> pd.DataFrame: return pd.read_csv(f) -def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: +def transform( + epa_eia_crosswalk: pd.DataFrame, generators_entity_eia: pd.DataFrame +) -> dict[str, pd.DataFrame]: """Clean up the EPACEMS-EIA Crosswalk file and split it into normalized tables. Args: epa_eia_crosswalk: The result of running this module's extract() function. + generators_entity_eia: The generators_entity_eia table. Returns: A dictionary of three normalized DataFrames comprised of the data @@ -48,6 +51,7 @@ def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: "eia_plant_id": "plant_id_eia", "eia_generator_id": "generator_id", } + # Basic column rename, selection, and dtype alignment. crosswalk_clean = ( epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) @@ -55,6 +59,19 @@ def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: .filter(list(column_rename.values())) .pipe(apply_pudl_dtypes, "eia") ) + + # The crosswalk is a static file: there is no year field. The plant_id_eia and + # generator_id fields, however, are foreign keys from an annualized table. If the + # fast ETL is run (on one year of data) the test will break because the crosswalk + # tables with plant_id_eia and generator_id contain values from various years. To + # keep the crosswalk in alignment with the available eia data, we'll restrict it + # based on the generator entity table which has plant id and generator id. + crosswalk_clean = crosswalk_clean.merge( + generators_entity_eia[["plant_id_eia", "generator_id"]], + on=["plant_id_eia", "generator_id"], + how="inner", + ) + # There are some eia generator_id values in the crosswalk that don't match the eia # generator_id values in the generators_eia860 table where the foreign keys are # stored. All of them appear to have preceeding zeros. I.e.: 0010 should be 10. @@ -64,6 +81,8 @@ def transform(epa_eia_crosswalk: pd.DataFrame) -> dict[str, pd.DataFrame]: crosswalk_clean.generator_id.str.contains(r"^0+\d+$"), "generator_id" ] = crosswalk_clean.generator_id.replace({r"^0+": ""}, regex=True) + # NOTE: still need to see whether unit_id matches up with the values in EPA well! + logger.info("Splitting crosswalk into three normalized tables") def drop_n_reset(df, cols): @@ -82,11 +101,14 @@ def drop_n_reset(df, cols): } -def grab_clean_split(ds: Datastore) -> dict[str, pd.DataFrame]: +def grab_clean_split( + ds: Datastore, generators_entity_eia: pd.DataFrame +) -> dict[str, pd.DataFrame]: """Clean raw crosswalk data, drop nans, and return split tables. Args: - ds (:class:datastore.Datastore): Initialized datastore. + ds: Initialized datastore. + generators_entity_eia: The generators_entity_eia table. Returns: A dictionary of three normalized DataFrames comprised of the data @@ -94,4 +116,4 @@ def grab_clean_split(ds: Datastore) -> dict[str, pd.DataFrame]: id to EIA plant id; and EIA plant id to EIA generator id to EPA unit id. """ - return transform(extract(ds)) + return transform(extract(ds), generators_entity_eia) From 9aa2eeee4979c635e739ae356c007cb8e5818443 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 21 Jun 2022 14:25:12 -0600 Subject: [PATCH 09/80] Modify _etl_glue() function - Previously I was passing the sqlite_dfs[generators_entitiy_eia] table strait through to the etl_glue function. This was failing the tests due to the ferc1_solo test that would not have any eia tables in the sqlite_dfs dict. Instead of passing in the specific table, I passed the whole dictionary to the etl_glue function which determines which glue tables to include based on whether there are eia or ferc table there. This we only call the sqlite_dfs[generators_entity_eia] table when we know the table will be there. --- src/pudl/etl.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index b94d5c6527..9459b442f3 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -344,7 +344,7 @@ def etl_epacems( def _etl_glue( glue_settings: GlueSettings, ds_kwargs: dict[str, Any], - generators_entity_eia: pd.DataFrame, + sqlite_dfs: dict[str, pd.DataFrame], ) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. @@ -373,7 +373,7 @@ def _etl_glue( if glue_settings.eia: glue_dfs.update( pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split( - ds, generators_entity_eia + ds, sqlite_dfs["generators_entity_eia"] ) ) @@ -448,10 +448,9 @@ def etl( # noqa: C901 sqlite_dfs.update(_etl_ferc1(datasets["ferc1"], pudl_settings)) if datasets.get("eia", False): sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) + logger.info(sqlite_dfs.keys()) if datasets.get("glue", False): - sqlite_dfs.update( - _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs["generators_entity_eia"]) - ) + sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs)) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) From ca00656b425c60600c0a7e3c23f707518544c7ad Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 22 Jun 2022 12:57:20 -0600 Subject: [PATCH 10/80] Fix etl_glue args to match args doc string. --- src/pudl/etl.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 9459b442f3..2cbfd25888 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -352,9 +352,14 @@ def _etl_glue( glue_settings: Validated ETL parameters required by this data source. ds_kwargs: Keyword arguments for instantiating a PUDL datastore, so that the ETL can access the raw input data. - generators_entity_eia: the EIA generators entity table. Used to create subsets - of the crosswalk for use with specific year subsets of eia data. Necessary - to pass the tests. + sqlite_dfs: The dictionary of dataframes to be loaded into the pudl database. + We pass the dictionary though because the EPACEMS-EIA crosswalk needs to + know which EIA plants and generators are being loaded into the database + (based on whether we run the full or fast etl). The tests will break if we + pass the generators_entity_eia table as an argument because of the + ferc1_solo test (where no eia tables are in the sqlite_dfs dict). Passing + the whole dict avoids this because the crosswalk will only load if there + are eia tables in the dict, but the dict will always be there. Returns: A dictionary of DataFrames whose keys are the names of the corresponding From 7b7d7262e93ee01ad73bee985c589660db4cf035 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 28 Jun 2022 10:20:45 -0600 Subject: [PATCH 11/80] Only restrict crosswalk if not processing all years - Pull in the working partitions and settings years from eia860 to see if they are the same. If they are, then the ETL is processing all available years and the crosswalk should not be restricted. This will enable the foreign key restraints to work properly. If they are not equal then the ETL is not processing all the years (likely the fast etl) and the crosswalk must be restricted so as not to fail the tests due to foreign key restraints. - I also renamed the function in the epacems_unitid_....py module from grab_clean_split() to crosswalk_et(). Not sure if this is more or less intuitive, but the idea is that it accomplishes the E and the T parts of the ETL. - While not necessary, I kept the extract and transform functions seperate in the glue epacems_unitid....py module so as to mimic the format used for the rest of the non-glue data. --- src/pudl/etl.py | 19 +++++++-- .../epacems_unitid_eia_plant_crosswalk.py | 41 ++++++++++++++----- 2 files changed, 47 insertions(+), 13 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 2cbfd25888..729b577427 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -345,6 +345,7 @@ def _etl_glue( glue_settings: GlueSettings, ds_kwargs: dict[str, Any], sqlite_dfs: dict[str, pd.DataFrame], + processing_all_eia_years: bool, ) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. @@ -360,6 +361,10 @@ def _etl_glue( ferc1_solo test (where no eia tables are in the sqlite_dfs dict). Passing the whole dict avoids this because the crosswalk will only load if there are eia tables in the dict, but the dict will always be there. + processing_all_eia_years: A boolean indicating whether the settings file has + prompted the etl to process all years of available eia data or not. If not, + the EPACEMS-EIA crosswalk will get restricted via th generators_entity_eia + table accessed above. Returns: A dictionary of DataFrames whose keys are the names of the corresponding @@ -377,8 +382,8 @@ def _etl_glue( ds = Datastore(**ds_kwargs) if glue_settings.eia: glue_dfs.update( - pudl.glue.epacems_unitid_eia_plant_crosswalk.grab_clean_split( - ds, sqlite_dfs["generators_entity_eia"] + pudl.glue.epacems_unitid_eia_plant_crosswalk.crosswalk_et( + ds, sqlite_dfs["generators_entity_eia"], processing_all_eia_years ) ) @@ -455,7 +460,15 @@ def etl( # noqa: C901 sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) logger.info(sqlite_dfs.keys()) if datasets.get("glue", False): - sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs)) + # Check to see whether the settings file indicates the processing of all + # available EIA years. + processing_all_eia_years = ( + datasets["eia"].eia860.years + == datasets["eia"].eia860.data_source.working_partitions["years"] + ) + sqlite_dfs.update( + _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, processing_all_eia_years) + ) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 47492a404f..24e99c8b9a 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -12,6 +12,8 @@ import pandas as pd import pudl + +# from typing import Boolean from pudl.metadata.fields import apply_pudl_dtypes from pudl.workspace.datastore import Datastore @@ -28,13 +30,19 @@ def extract(ds: Datastore) -> pd.DataFrame: def transform( - epa_eia_crosswalk: pd.DataFrame, generators_entity_eia: pd.DataFrame + epa_eia_crosswalk: pd.DataFrame, + generators_entity_eia: pd.DataFrame, + processing_all_eia_years: bool, ) -> dict[str, pd.DataFrame]: """Clean up the EPACEMS-EIA Crosswalk file and split it into normalized tables. Args: epa_eia_crosswalk: The result of running this module's extract() function. generators_entity_eia: The generators_entity_eia table. + processing_all_years: A boolean indicating whether the years from the + Eia860Settings object match the EIA860 working partitions. This indicates + whether or not to restrict the crosswalk data so the tests don't fail on + foreign key restraints. Returns: A dictionary of three normalized DataFrames comprised of the data @@ -65,12 +73,21 @@ def transform( # fast ETL is run (on one year of data) the test will break because the crosswalk # tables with plant_id_eia and generator_id contain values from various years. To # keep the crosswalk in alignment with the available eia data, we'll restrict it - # based on the generator entity table which has plant id and generator id. - crosswalk_clean = crosswalk_clean.merge( - generators_entity_eia[["plant_id_eia", "generator_id"]], - on=["plant_id_eia", "generator_id"], - how="inner", - ) + # based on the generator entity table which has plant id and generator id so long + # as it's not using the full suite of avilable years. If it is, we don't want to + # restrict the crosswalk so we can get warnings and errors from any foreign key + # discrepancies. + if not processing_all_eia_years: + logger.info( + "Selected subset of avilable EIA years--restricting EIA-EPA Crosswalk to \ + chosen subset of EIA years" + ) + crosswalk_clean = pd.merge( + crosswalk_clean.dropna(subset=["plant_id_eia"]), + generators_entity_eia[["plant_id_eia", "generator_id"]].drop_duplicates(), + on=["plant_id_eia", "generator_id"], + how="inner", + ) # There are some eia generator_id values in the crosswalk that don't match the eia # generator_id values in the generators_eia860 table where the foreign keys are @@ -101,14 +118,18 @@ def drop_n_reset(df, cols): } -def grab_clean_split( - ds: Datastore, generators_entity_eia: pd.DataFrame +def crosswalk_et( + ds: Datastore, generators_entity_eia: pd.DataFrame, processing_all_eia_years: bool ) -> dict[str, pd.DataFrame]: """Clean raw crosswalk data, drop nans, and return split tables. Args: ds: Initialized datastore. generators_entity_eia: The generators_entity_eia table. + processing_all_eia_years: A boolean indicating whether the years from the + Eia860Settings object match the EIA860 working partitions. This tell the + function whether to restrict the crosswalk data so the tests don't fail on + foreign key restraints. Returns: A dictionary of three normalized DataFrames comprised of the data @@ -116,4 +137,4 @@ def grab_clean_split( id to EIA plant id; and EIA plant id to EIA generator id to EPA unit id. """ - return transform(extract(ds), generators_entity_eia) + return transform(extract(ds), generators_entity_eia, processing_all_eia_years) From a5fcc71e127b529d5c512b0e64c827c7ce7df236 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 28 Jun 2022 10:35:40 -0600 Subject: [PATCH 12/80] Use pudl helper function to remove leading zeros from EPACEMS-EIA crosswalk --- src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 24e99c8b9a..0706ca9188 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -93,10 +93,9 @@ def transform( # generator_id values in the generators_eia860 table where the foreign keys are # stored. All of them appear to have preceeding zeros. I.e.: 0010 should be 10. # This makes sure to nix preceeding zeros on crosswalk generator ids that are all - # numeric. I.e.: 00A10 will stay 00A10 but 0010 will become 10. - crosswalk_clean.loc[ - crosswalk_clean.generator_id.str.contains(r"^0+\d+$"), "generator_id" - ] = crosswalk_clean.generator_id.replace({r"^0+": ""}, regex=True) + # numeric. I.e.: 00A10 will stay 00A10 but 0010 will become 10. This same method + # is applied to the EIA data. + crosswalk_clean = pudl.helpers.fix_leading_zero_gen_ids(crosswalk_clean) # NOTE: still need to see whether unit_id matches up with the values in EPA well! From afb4327acee10ab06ebab55a968c50e16c574f8c Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 28 Jun 2022 11:30:41 -0600 Subject: [PATCH 13/80] Pass the datasets object through to the _etl_glue() function because there is already an if statement checking to see if the eia tables are there. Only then can we reliably access the datasets[eia].eia860 tables used to get the settings years and working partitions used to restrict the epacems crosswalk in certain circumstances. --- src/pudl/etl.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index e065a357d7..0f0c6c1058 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -34,6 +34,7 @@ from pudl.metadata.dfs import FERC_ACCOUNTS, FERC_DEPRECIATION_LINES from pudl.metadata.fields import apply_pudl_dtypes from pudl.settings import ( + DatasetsSettings, EiaSettings, EpaCemsSettings, EtlSettings, @@ -356,7 +357,7 @@ def _etl_glue( glue_settings: GlueSettings, ds_kwargs: dict[str, Any], sqlite_dfs: dict[str, pd.DataFrame], - processing_all_eia_years: bool, + datasets: DatasetsSettings, ) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. @@ -372,10 +373,9 @@ def _etl_glue( ferc1_solo test (where no eia tables are in the sqlite_dfs dict). Passing the whole dict avoids this because the crosswalk will only load if there are eia tables in the dict, but the dict will always be there. - processing_all_eia_years: A boolean indicating whether the settings file has - prompted the etl to process all years of available eia data or not. If not, - the EPACEMS-EIA crosswalk will get restricted via th generators_entity_eia - table accessed above. + datasets: An immutable pydantic model to validate PUDL Dataset settings. This is + used to acess the eia settings years and working partitions used to restrict + the crosswalk in the case of ETL runs that aren't using all available years. Returns: A dictionary of DataFrames whose keys are the names of the corresponding @@ -392,6 +392,12 @@ def _etl_glue( # Otherwise the foreign key references will have nothing to point at: ds = Datastore(**ds_kwargs) if glue_settings.eia: + # Check to see whether the settings file indicates the processing of all + # available EIA years. + processing_all_eia_years = ( + datasets["eia"].eia860.years + == datasets["eia"].eia860.data_source.working_partitions["years"] + ) glue_dfs.update( pudl.glue.epacems_unitid_eia_plant_crosswalk.crosswalk_et( ds, sqlite_dfs["generators_entity_eia"], processing_all_eia_years @@ -471,15 +477,7 @@ def etl( # noqa: C901 sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) logger.info(sqlite_dfs.keys()) if datasets.get("glue", False): - # Check to see whether the settings file indicates the processing of all - # available EIA years. - processing_all_eia_years = ( - datasets["eia"].eia860.years - == datasets["eia"].eia860.data_source.working_partitions["years"] - ) - sqlite_dfs.update( - _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, processing_all_eia_years) - ) + sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, datasets)) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) From e46031bef4ec209787410fce6b1339eab3844cb7 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 12 Jul 2022 12:48:05 -0600 Subject: [PATCH 14/80] Update fix_leading_zero_gen_ids function First, make it faster by taking away the apply() function. Second, make it applicable to any column you want (not just generator_id) Third, change the function name to remove_leading_zeros_from_numeric_strings() --- src/pudl/extract/eia860.py | 4 +-- src/pudl/extract/eia860m.py | 4 +-- src/pudl/extract/eia861.py | 4 +-- src/pudl/extract/eia923.py | 4 +-- src/pudl/helpers.py | 50 +++++++++++++++++++------------------ test/unit/helpers_test.py | 8 +++--- 6 files changed, 39 insertions(+), 35 deletions(-) diff --git a/src/pudl/extract/eia860.py b/src/pudl/extract/eia860.py index 46669c9ac4..05f9687f80 100644 --- a/src/pudl/extract/eia860.py +++ b/src/pudl/extract/eia860.py @@ -9,7 +9,7 @@ import pandas as pd from pudl.extract import excel -from pudl.helpers import fix_leading_zero_gen_ids +from pudl.helpers import remove_leading_zeros_from_numeric_strings from pudl.settings import Eia860Settings logger = logging.getLogger(__name__) @@ -47,7 +47,7 @@ def process_raw(self, df, page, **partition): if page in pages_eia860m: df = df.assign(data_source="eia860") self.cols_added.append("data_source") - df = fix_leading_zero_gen_ids(df) + df = remove_leading_zeros_from_numeric_strings(df, "generator_id") return df def extract(self, settings: Eia860Settings = Eia860Settings()): diff --git a/src/pudl/extract/eia860m.py b/src/pudl/extract/eia860m.py index 47fb16d542..f686e9eb7d 100644 --- a/src/pudl/extract/eia860m.py +++ b/src/pudl/extract/eia860m.py @@ -19,7 +19,7 @@ import pandas as pd from pudl.extract import excel -from pudl.helpers import fix_leading_zero_gen_ids +from pudl.helpers import remove_leading_zeros_from_numeric_strings from pudl.settings import Eia860Settings logger = logging.getLogger(__name__) @@ -47,7 +47,7 @@ def process_raw(self, df, page, **partition): ).year df = df.assign(data_source="eia860m") self.cols_added = ["data_source", "report_year"] - df = fix_leading_zero_gen_ids(df) + df = remove_leading_zeros_from_numeric_strings(df, "generator_id") return df def extract(self, settings: Eia860Settings = Eia860Settings()): diff --git a/src/pudl/extract/eia861.py b/src/pudl/extract/eia861.py index 7b1d3377b0..b6aada361b 100644 --- a/src/pudl/extract/eia861.py +++ b/src/pudl/extract/eia861.py @@ -11,7 +11,7 @@ import pandas as pd from pudl.extract import excel -from pudl.helpers import fix_leading_zero_gen_ids +from pudl.helpers import remove_leading_zeros_from_numeric_strings from pudl.settings import Eia861Settings logger = logging.getLogger(__name__) @@ -46,7 +46,7 @@ def process_raw(self, df, page, **partition): ) ) self.cols_added = [] - df = fix_leading_zero_gen_ids(df) + df = remove_leading_zeros_from_numeric_strings(df, "generator_id") return df def extract(self, settings: Eia861Settings = Eia861Settings()): diff --git a/src/pudl/extract/eia923.py b/src/pudl/extract/eia923.py index 8f9a606a48..807f19b80b 100644 --- a/src/pudl/extract/eia923.py +++ b/src/pudl/extract/eia923.py @@ -10,7 +10,7 @@ import pandas as pd from pudl.extract import excel -from pudl.helpers import fix_leading_zero_gen_ids +from pudl.helpers import remove_leading_zeros_from_numeric_strings from pudl.settings import Eia923Settings logger = logging.getLogger(__name__) @@ -41,7 +41,7 @@ def process_raw(self, df, page, **partition): df.drop(to_drop, axis=1, inplace=True) df = df.rename(columns=self._metadata.get_column_map(page, **partition)) self.cols_added = [] - df = fix_leading_zero_gen_ids(df) + df = remove_leading_zeros_from_numeric_strings(df, "generator_id") return df def extract(self, settings: Eia923Settings = Eia923Settings()): diff --git a/src/pudl/helpers.py b/src/pudl/helpers.py index edd6780fdc..cc449aac6f 100644 --- a/src/pudl/helpers.py +++ b/src/pudl/helpers.py @@ -867,39 +867,41 @@ def month_year_to_date(df): return df -def fix_leading_zero_gen_ids(df): - """Remove leading zeros from EIA generator IDs which are numeric strings. +def remove_leading_zeros_from_numeric_strings( + df: pd.DataFrame, col_name: str +) -> pd.DataFrame: + """Remove leading zeros frame column values that are numeric strings. - If the DataFrame contains a column named ``generator_id`` then that column - will be cast to a string, and any all numeric value with leading zeroes - will have the leading zeroes removed. This is necessary because in some - but not all years of data, some of the generator IDs are treated as integers - in the Excel spreadsheets published by EIA, so the same generator may show - up with the ID "0001" and "1" in different years. + Sometimes an ID column (like generator_id or unit_id) will be reported with leading + zeros and sometimes it won't. For example, in the Excel spreadsheets published by + EIA, the same generator may show up with the ID "0001" and "1" in different years + This function strips the leading zeros from those numeric strings so the data can + be mapped accross years and datasets more reliably. Alphanumeric generator IDs with leadings zeroes are not affected, as we - found no instances in which an alphanumeric generator ID appeared both with - and without leading zeroes. + found no instances in which an alphanumeric ID appeared both with + and without leading zeroes. The ID "0A1" will stay "0A1". Args: - df (pandas.DataFrame): DataFrame, presumably containing a column named - generator_id (otherwise no action will be taken.) + df: A DataFrame containing the column you'd like to remove numeric leading zeros + from. + col_name: The name of the column you'd like to remove numeric leading zeros + from. Returns: - pandas.DataFrame + A DataFrame without leading zeros for numeric string values in the desired + column. """ - if "generator_id" in df.columns: - fixed_generator_id = ( - df["generator_id"] - .astype(str) - .apply(lambda x: re.sub(r"^0+(\d+$)", r"\1", x)) - ) - num_fixes = len(df.loc[df["generator_id"].astype(str) != fixed_generator_id]) - logger.debug("Fixed %s EIA generator IDs with leading zeros.", num_fixes) - df = df.drop("generator_id", axis="columns").assign( - generator_id=fixed_generator_id - ) + if col_name in df.columns: + number_with_leading_zeros = r"^0+(\d+$)" + if df[col_name].str.contains(number_with_leading_zeros).any(): + logger.debug(f"Fixing leading zeros in {col_name} column") + df.loc[df[col_name].str.contains(number_with_leading_zeros), col_name] = df[ + col_name + ].str.replace(r"^0+", "", regex=True) + else: + logger.debug(f"Found no numeric leading zeros in {col_name}") return df diff --git a/test/unit/helpers_test.py b/test/unit/helpers_test.py index a5de61b027..6eb882becf 100644 --- a/test/unit/helpers_test.py +++ b/test/unit/helpers_test.py @@ -12,7 +12,7 @@ date_merge, expand_timeseries, fix_eia_na, - fix_leading_zero_gen_ids, + remove_leading_zeros_from_numeric_strings, zero_pad_numeric_string, ) @@ -490,7 +490,7 @@ def test_fix_eia_na(): assert_frame_equal(out_df, expected_df) -def test_fix_leading_zero_gen_ids(): +def test_remove_leading_zeros_from_numeric_strings(): """Test removal of leading zeroes from EIA generator IDs.""" in_df = pd.DataFrame( { @@ -516,7 +516,9 @@ def test_fix_leading_zero_gen_ids(): ] } ) - out_df = fix_leading_zero_gen_ids(in_df) + out_df = remove_leading_zeros_from_numeric_strings( + in_df.astype(str), "generator_id" + ) assert_frame_equal(out_df, expected_df) From d9c62caa02b449223ef7d2bb025ea60767f80857 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 12 Jul 2022 16:02:28 -0600 Subject: [PATCH 15/80] Fix typo in field description and add field for generator_id_epa from epa-eia crosswalk --- src/pudl/metadata/fields.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py index 55a0fa2698..eb62cf0f19 100644 --- a/src/pudl/metadata/fields.py +++ b/src/pudl/metadata/fields.py @@ -741,6 +741,10 @@ "type": "string", "description": "Generator ID is usually numeric, but sometimes includes letters. Make sure you treat it as a string!", }, + "generator_id_epa": { + "type": "string", + "description": "Generator ID used by the EPA.", + }, "generators_num_less_1_mw": {"type": "number", "unit": "MW"}, "generators_number": {"type": "number"}, "green_pricing_revenue": {"type": "number", "unit": "USD"}, @@ -1911,7 +1915,7 @@ }, "unit_id_epa": { "type": "string", - "description": "Emissions (smokestake) unit monitored by EPA CEMS.", + "description": "Emissions (smokestack) unit monitored by EPA CEMS.", }, "unit_id_pudl": { "type": "integer", From c77c065f12119791a9cfa8cd43e66523eaab8809 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 14 Jul 2022 14:43:51 -0600 Subject: [PATCH 16/80] Fix fix to helper function The remove_leading_zeros_from_numeric_strings() function was faster than the old one, but there were some issues with it. First, there was a capture group in the regex causing it to throw an error. I removed the parenthesis from the regex to fix this. Second, there was an error grabbing string rows from a column with NA values. I added a df[col_name].isna() to the specifications and the warning went away. --- src/pudl/helpers.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/pudl/helpers.py b/src/pudl/helpers.py index cc449aac6f..6c52c310f3 100644 --- a/src/pudl/helpers.py +++ b/src/pudl/helpers.py @@ -894,12 +894,14 @@ def remove_leading_zeros_from_numeric_strings( """ if col_name in df.columns: - number_with_leading_zeros = r"^0+(\d+$)" + number_with_leading_zeros = r"^0+\d+$" if df[col_name].str.contains(number_with_leading_zeros).any(): logger.debug(f"Fixing leading zeros in {col_name} column") - df.loc[df[col_name].str.contains(number_with_leading_zeros), col_name] = df[ - col_name - ].str.replace(r"^0+", "", regex=True) + df.loc[ + df[col_name].notna() + & df[col_name].str.contains(number_with_leading_zeros), + col_name, + ] = df[col_name].str.replace(r"^0+", "", regex=True) else: logger.debug(f"Found no numeric leading zeros in {col_name}") return df From a8c50342a384404bd058bf108bfd421e053732a2 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 18 Jul 2022 17:09:52 -0600 Subject: [PATCH 17/80] Update EPA-EIA crosswalk and add to CEMS transform First, I updated the crosswalk from three tables to one. The previous tables figured that there was a 1:1 relationship between plant_id_epa and plant_id_eia. All the fields are interconnected, and it makes most sense for them to be in one table .I also added in the boiler_id_eia and generator_id_eia fields for mapping onto more granular eia data. Next, I fixed some of the column names in CEMS. What we previously called was a close but not perfect approximation of ORISPL code that the crosswalk intended to fix. I changed to . There were also two unit columns in the CEMS data, one called and one called . referred to the smokestack units and referred to...well...I still don't know. So I dropped the column and renamed to . Lastly, I removed the column because it didn't seem to refer to anything useful. After editing the column names, I updated the metadata, etl functions, and tests to reflect the changes. The CEMS transform module was relying on the old column to correct timezones in the data. While the values were in the right timezones, it still wasn't technically the right , so I pulled the crosswalk into the CEMS transform module to replace the old column (now ) with the correct . I did this by filling in the long empty harmonize_eia_epa_orispl() function. I also removed the add_facility_id_unit_id_epa() function because I removed those columns from the CEMS data. --- src/pudl/etl.py | 6 ++ src/pudl/extract/epacems.py | 10 ++- .../epacems_unitid_eia_plant_crosswalk.py | 76 ++++++------------ src/pudl/metadata/fields.py | 15 ---- src/pudl/metadata/resources/epacems.py | 6 +- src/pudl/metadata/resources/glue.py | 33 ++------ src/pudl/transform/epacems.py | 78 ++++++++++++------- test/integration/epacems_test.py | 2 +- 8 files changed, 98 insertions(+), 128 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 0f0c6c1058..baf4202b17 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -278,6 +278,12 @@ def etl_epacems( "No plants_eia860 available in the PUDL DB! Have you run the ETL? " f"Trying to access PUDL DB: {pudl_engine}" ) + # Verify that we have a PUDL DB with crosswalk data + if "epacamd_eia_crosswalk" not in inspector.get_table_names(): + raise RuntimeError( + "No EPA-EIA Crosswalk available in the PUDL DB! Have you run the ETL? " + f"Trying to access PUDL DB: {pudl_engine}" + ) eia_plant_years = pd.read_sql( """ diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index d244efe8ff..a6516d6e80 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -17,8 +17,8 @@ RENAME_DICT = { "STATE": "state", # "FACILITY_NAME": "plant_name", # Not reading from CSV - "ORISPL_CODE": "plant_id_eia", - "UNITID": "unitid", + "ORISPL_CODE": "plant_id_epa", # Not quite the same as plant_id_eia + "UNITID": "unit_id_epa", # The smokestake unit # These op_date, op_hour, and op_time variables get converted to # operating_date, operating_datetime and operating_time_interval in # transform/epacems.py @@ -50,8 +50,8 @@ # "CO2_RATE_MEASURE_FLG": "co2_rate_measure_flg", # Not reading from CSV "HEAT_INPUT (mmBtu)": "heat_content_mmbtu", "HEAT_INPUT": "heat_content_mmbtu", - "FAC_ID": "facility_id", - "UNIT_ID": "unit_id_epa", + # "FAC_ID": "facility_id", # IDK what this is, but it isn't helpful + # "UNIT_ID": "unit_id_epa", # IDK what this is, but it isn't helpful } """dict: A dictionary containing EPA CEMS column names (keys) and replacement names to use when reading those columns into PUDL (values). @@ -66,6 +66,8 @@ "CO2_RATE (tons/mmBtu)", "CO2_RATE", "CO2_RATE_MEASURE_FLG", + "FAC_ID", + "UNIT_ID", } """set: The set of EPA CEMS columns to ignore when reading data.""" diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 0706ca9188..25d121254e 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -1,19 +1,16 @@ -"""Extract, clean, and normalize the EPA-EIA crosswalk. +"""Extract, clean, and normalize the EPACAMD-EIA crosswalk. -This module defines functions that read the raw EPA-EIA crosswalk file, clean -up the column names, and separate it into three distinctive normalize tables -for integration in the database. +This module defines functions that read the raw EPACAMD-EIA crosswalk file and cleans +up the column names. The crosswalk file was a joint effort on behalf on EPA and EIA and is published on the -EPA's github account at www.github.com/USEPA". +EPA's github account at www.github.com/USEPA/camd-eia-crosswalk". """ import logging import pandas as pd -import pudl - -# from typing import Boolean +from pudl.helpers import remove_leading_zeros_from_numeric_strings, simplify_columns from pudl.metadata.fields import apply_pudl_dtypes from pudl.workspace.datastore import Datastore @@ -21,23 +18,23 @@ def extract(ds: Datastore) -> pd.DataFrame: - """Extract the EPACEMS-EIA Crosswalk from the Datastore.""" + """Extract the EPACAMD-EIA Crosswalk from the Datastore.""" with ds.get_zipfile_resource( - "epacems_unitid_eia_plant_crosswalk", - name="epacems_unitid_eia_plant_crosswalk.zip", + "epacems_unitid_eia_plant_crosswalk", # eventually change these names? + name="epacems_unitid_eia_plant_crosswalk.zip", # eventually change these names? ).open("camd-eia-crosswalk-master/epa_eia_crosswalk.csv") as f: return pd.read_csv(f) def transform( - epa_eia_crosswalk: pd.DataFrame, + epacamd_eia_crosswalk: pd.DataFrame, generators_entity_eia: pd.DataFrame, processing_all_eia_years: bool, ) -> dict[str, pd.DataFrame]: - """Clean up the EPACEMS-EIA Crosswalk file and split it into normalized tables. + """Clean up the EPACAMD-EIA Crosswalk file. Args: - epa_eia_crosswalk: The result of running this module's extract() function. + epacamd_eia_crosswalk: The result of running this module's extract() function. generators_entity_eia: The generators_entity_eia table. processing_all_years: A boolean indicating whether the years from the Eia860Settings object match the EIA860 working partitions. This indicates @@ -45,24 +42,22 @@ def transform( foreign key restraints. Returns: - A dictionary of three normalized DataFrames comprised of the data - in the original crosswalk file. EPA plant id to EPA unit id; EPA plant - id to EIA plant id; and EIA plant id to EIA generator id to EPA unit - id. Includes no nan values. + A dictionary containing the cleaned EPACAMD-EIA crosswalk DataFrame. """ logger.info("Cleaning up the epacems-eia crosswalk") column_rename = { - "camd_unit_id": "unit_id_epa", "camd_plant_id": "plant_id_epa", - "eia_plant_name": "plant_name_eia", + "camd_unit_id": "unit_id_epa", + "camd_generator_id": "generator_id_epa", "eia_plant_id": "plant_id_eia", - "eia_generator_id": "generator_id", + # "eia_boiler_id": "boiler_id", # Eventually change to boiler_id_eia + "eia_generator_id": "generator_id", # Eventually change to generator_id_eia } # Basic column rename, selection, and dtype alignment. crosswalk_clean = ( - epa_eia_crosswalk.pipe(pudl.helpers.simplify_columns) + epacamd_eia_crosswalk.pipe(simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) .pipe(apply_pudl_dtypes, "eia") @@ -89,38 +84,20 @@ def transform( how="inner", ) - # There are some eia generator_id values in the crosswalk that don't match the eia - # generator_id values in the generators_eia860 table where the foreign keys are - # stored. All of them appear to have preceeding zeros. I.e.: 0010 should be 10. - # This makes sure to nix preceeding zeros on crosswalk generator ids that are all - # numeric. I.e.: 00A10 will stay 00A10 but 0010 will become 10. This same method - # is applied to the EIA data. - crosswalk_clean = pudl.helpers.fix_leading_zero_gen_ids(crosswalk_clean) - - # NOTE: still need to see whether unit_id matches up with the values in EPA well! - - logger.info("Splitting crosswalk into three normalized tables") - - def drop_n_reset(df, cols): - return df.filter(cols).copy().dropna().drop_duplicates() - - epa_df = drop_n_reset(crosswalk_clean, ["plant_id_epa", "unit_id_epa"]) - plants_eia_epa_df = drop_n_reset(crosswalk_clean, ["plant_id_eia", "plant_id_epa"]) - gen_unit_df = drop_n_reset( - crosswalk_clean, ["plant_id_eia", "generator_id", "unit_id_epa"] + # More indepth cleaning and droping rows with no plant_id_eia match. + crosswalk_clean = ( + crosswalk_clean.pipe(remove_leading_zeros_from_numeric_strings, "generator_id") + .pipe(remove_leading_zeros_from_numeric_strings, "unit_id_epa") + .dropna(subset="plant_id_eia") ) - return { - "plant_unit_epa": epa_df, - "assn_plant_id_eia_epa": plants_eia_epa_df, - "assn_gen_eia_unit_epa": gen_unit_df, - } + return {"epacamd_eia_crosswalk": crosswalk_clean} def crosswalk_et( ds: Datastore, generators_entity_eia: pd.DataFrame, processing_all_eia_years: bool ) -> dict[str, pd.DataFrame]: - """Clean raw crosswalk data, drop nans, and return split tables. + """Clean raw crosswalk data. Args: ds: Initialized datastore. @@ -131,9 +108,6 @@ def crosswalk_et( foreign key restraints. Returns: - A dictionary of three normalized DataFrames comprised of the data - in the original crosswalk file. EPA plant id to EPA unit id; EPA plant - id to EIA plant id; and EIA plant id to EIA generator id to EPA unit - id. + A dictionary containing the cleaned EPACAMD-EIA crosswalk DataFrame. """ return transform(extract(ds), generators_entity_eia, processing_all_eia_years) diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py index e8d624f38a..c06bb919dc 100644 --- a/src/pudl/metadata/fields.py +++ b/src/pudl/metadata/fields.py @@ -526,7 +526,6 @@ "type": "number", "description": "FERC Account 103: Experimental Plant Unclassified.", }, - "facility_id": {"type": "integer", "description": "New EPA plant ID."}, "ferc_account_id": { "type": "string", "description": "Account number, from FERC's Uniform System of Accounts for Electric Plant. Also includes higher level labeled categories.", @@ -1921,10 +1920,6 @@ "type": "integer", "description": "Dynamically assigned PUDL unit id. WARNING: This ID is not guaranteed to be static long term as the input data and algorithm may evolve over time.", }, - "unitid": { - "type": "string", - "description": "Facility-specific unit id (e.g. Unit 4)", - }, "uprate_derate_completed_date": { "type": "date", "description": "The date when the uprate or derate was completed.", @@ -2038,16 +2033,6 @@ "required": True, } }, - "plant_id_eia": { - "constraints": { - "required": True, - } - }, - "unitid": { - "constraints": { - "required": True, - } - }, "year": { "constraints": { "required": True, diff --git a/src/pudl/metadata/resources/epacems.py b/src/pudl/metadata/resources/epacems.py index 1c7105dfec..b637459410 100644 --- a/src/pudl/metadata/resources/epacems.py +++ b/src/pudl/metadata/resources/epacems.py @@ -7,12 +7,10 @@ "schema": { "fields": [ "plant_id_eia", - "unitid", + "unit_id_epa", "operating_datetime_utc", "year", "state", - "facility_id", - "unit_id_epa", "operating_time_hours", "gross_load_mw", "heat_content_mmbtu", @@ -26,7 +24,7 @@ "co2_mass_tons", "co2_mass_measurement_code", ], - "primary_key": ["plant_id_eia", "unitid", "operating_datetime_utc"], + "primary_key": ["plant_id_eia", "unit_id_epa", "operating_datetime_utc"], }, "sources": ["eia860", "epacems"], "field_namespace": "epacems", diff --git a/src/pudl/metadata/resources/glue.py b/src/pudl/metadata/resources/glue.py index 8d6eb47861..6e89652434 100644 --- a/src/pudl/metadata/resources/glue.py +++ b/src/pudl/metadata/resources/glue.py @@ -2,39 +2,22 @@ from typing import Any RESOURCE_METADATA: dict[str, dict[str, Any]] = { - "assn_gen_eia_unit_epa": { - "schema": { - "fields": [ - "generator_id", - "plant_id_eia", - "unit_id_epa", - ], - }, - "field_namespace": "glue", - "etl_group": "glue", - "sources": ["eia_epa_crosswalk"], - }, - "assn_plant_id_eia_epa": { - "schema": { - "fields": [ - "plant_id_eia", - "plant_id_epa", - ], - }, - "field_namespace": "glue", - "etl_group": "glue", - "sources": ["eia_epa_crosswalk"], - }, - "plant_unit_epa": { + "epacamd_eia_crosswalk": { "schema": { "fields": [ "plant_id_epa", "unit_id_epa", + "generator_id_epa", + "plant_id_eia", + "boiler_id", + "generator_id", ], }, "field_namespace": "glue", "etl_group": "glue", - "sources": ["eia_epa_crosswalk"], + "sources": [ + "epacamd_eia_crosswalk" + ], # eia_epa_crosswalk --> what is this anyways }, } """ diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index e65f458e97..564e2ed8c2 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -8,6 +8,7 @@ import pytz import sqlalchemy as sa +from pudl.helpers import remove_leading_zeros_from_numeric_strings from pudl.metadata.fields import apply_pudl_dtypes logger = logging.getLogger(__name__) @@ -18,6 +19,53 @@ ############################################################################### +def harmonize_eia_epa_orispl( + df: pd.DataFrame, + pudl_engine: sa.engine.Engine, +) -> pd.DataFrame: + """Harmonize the ORISPL code to match the EIA data. + + The EIA plant IDs and CEMS ORISPL codes almost match, but not quite. EPA has + compiled a crosswalk that maps one set of IDs to the other. The crosswalk is + integrated into the PUDL db. + + EIA IDs are more correct so use the crosswalk to fix any erronious EPA IDs and get + rid of that column to avoid confusion. + + https://github.com/USEPA/camd-eia-crosswalk + + Note that this transformation needs to be run *before* fix_up_dates, because + fix_up_dates uses the plant ID to look up timezones. + + Args: + pudl_engine: SQLAlchemy connection engine for connecting to an existing PUDL DB. + This is used to access the crosswalk file for conversion. The crosswalk must + be processed prior to running this function or it won't work. + df: A CEMS hourly dataframe for one year-month-state. + + Returns: + The same data, with the ORISPL plant codes corrected to match the EIA plant IDs. + + """ + # Already ran a test to make sure this works. When you group the crosswalk by + # plant_id_epa and unit_id_epa then calculate .nunique() for plant_id_eia, none of + # the values are greater than one meaning that this drop/merge is ok. Might want to + # make that an official test somwwhere. + crosswalk_df = pd.read_sql("epacamd_eia_crosswalk", pudl_engine)[ + ["plant_id_eia", "plant_id_epa", "unit_id_epa"] + ].drop_duplicates() + + # I wonder if there is a faster way to do this by checking if the id needs to be + # fixed rather than just merging it all together (as done below). + + # Merge CEMS with Crosswalk to get correct EIA ORISPL code. Remove incorrect + # plant_id_epa column to avoid confusion. + df_merged = pd.merge( + df, crosswalk_df, on=["plant_id_epa", "unit_id_epa"], how="left" + ).drop(columns=["plant_id_epa"]) + return df_merged + + def fix_up_dates(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFrame: """Fix the dates for the CEMS data. @@ -95,32 +143,6 @@ def _load_plant_utc_offset(pudl_engine): return timezones -def harmonize_eia_epa_orispl(df): - """Harmonize the ORISPL code to match the EIA data -- NOT YET IMPLEMENTED. - - The EIA plant IDs and CEMS ORISPL codes almost match, but not quite. EPA has - compiled a crosswalk that maps one set of IDs to the other, but we haven't - integrated it yet. It can be found at: - - https://github.com/USEPA/camd-eia-crosswalk - - Note that this transformation needs to be run *before* fix_up_dates, because - fix_up_dates uses the plant ID to look up timezones. - - Args: - df (pandas.DataFrame): A CEMS hourly dataframe for one year-month-state. - - Returns: - pandas.DataFrame: The same data, with the ORISPL plant codes corrected to match - the EIA plant IDs. - - Todo: - Actually implement the function... - - """ - return df - - def add_facility_id_unit_id_epa(df): """Harmonize columns that are added later. @@ -208,9 +230,9 @@ def transform(raw_df: pd.DataFrame, pudl_engine: sa.engine.Engine) -> pd.DataFra """ return ( raw_df.fillna({"gross_load_mw": 0.0, "heat_content_mmbtu": 0.0}) - .pipe(harmonize_eia_epa_orispl) + .pipe(remove_leading_zeros_from_numeric_strings, "unit_id_epa") + .pipe(harmonize_eia_epa_orispl, pudl_engine) .pipe(fix_up_dates, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) - .pipe(add_facility_id_unit_id_epa) .pipe(correct_gross_load_mw) .pipe(apply_pudl_dtypes, group="epacems") ) diff --git a/test/integration/epacems_test.py b/test/integration/epacems_test.py index 030701d2ab..b33d562c4b 100644 --- a/test/integration/epacems_test.py +++ b/test/integration/epacems_test.py @@ -88,4 +88,4 @@ def test_epacems_parallel(pudl_settings_fixture, pudl_ds_kwargs, tmpdir_factory) engine="pyarrow", split_row_groups=True, ).compute() - assert df.shape == (96_360, 19) # nosec: B101 + assert df.shape == (96_360, 17) # nosec: B101 From 4d49fad716c0a90ebd000ac10024aaa0941ddbc4 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 18 Jul 2022 17:26:35 -0600 Subject: [PATCH 18/80] Update the remove_leading_zeros_from_numeric_strings() function so that it reuses variables instead of calling portions of the dataframe repeatedly --- src/pudl/helpers.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/pudl/helpers.py b/src/pudl/helpers.py index 6c52c310f3..d44d74a6fd 100644 --- a/src/pudl/helpers.py +++ b/src/pudl/helpers.py @@ -894,14 +894,12 @@ def remove_leading_zeros_from_numeric_strings( """ if col_name in df.columns: - number_with_leading_zeros = r"^0+\d+$" - if df[col_name].str.contains(number_with_leading_zeros).any(): + leading_zeros = df[col_name].str.contains(r"^0+\d+$") + if leading_zeros.any(): logger.debug(f"Fixing leading zeros in {col_name} column") - df.loc[ - df[col_name].notna() - & df[col_name].str.contains(number_with_leading_zeros), - col_name, - ] = df[col_name].str.replace(r"^0+", "", regex=True) + df.loc[df[col_name].notna() & leading_zeros, col_name] = df[ + col_name + ].str.replace(r"^0+", "", regex=True) else: logger.debug(f"Found no numeric leading zeros in {col_name}") return df From 5cf6e89834e89b9c58af0be270a51b318434db71 Mon Sep 17 00:00:00 2001 From: Austen Sharpe <49878195+aesharpe@users.noreply.github.com> Date: Mon, 18 Jul 2022 17:35:00 -0600 Subject: [PATCH 19/80] Delete play_with_avg_num_employees_agg.ipynb --- .../play_with_avg_num_employees_agg.ipynb | 545 ------------------ 1 file changed, 545 deletions(-) delete mode 100644 notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb diff --git a/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb b/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb deleted file mode 100644 index 3035467d1d..0000000000 --- a/notebooks/work-in-progress/play_with_avg_num_employees_agg.ipynb +++ /dev/null @@ -1,545 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2441744b-1695-4d8d-a13e-6136c6271a72", - "metadata": {}, - "source": [ - "# Play around with `avg_num_employees` agg" - ] - }, - { - "cell_type": "markdown", - "id": "81315513-812a-48cd-8a40-6f947f2ee2a9", - "metadata": {}, - "source": [ - "This notebook reviews two files: \n", - "- **agg_df:** aggregated by year, utility, and plant type\n", - "- **full_df:** un-aggregated but with a column `avg_num_employees_agg` for aggergated year, utility, plant, and plant-type employee values. I included this one so you can play around and make sure the totals flags are working properly / change them if you don't like them. I'll show you how below. \n", - "\n", - "It's important to remeber that the `avg_num_employees_agg` values in the `agg_df` are calculated at the PLANT/PLANT-TYPE level not the UTILITY level. There is another round of aggregation that occurs before that. This is to make it easier to see what assumptions were made in the process of creating the final utility-aggregated employee number value." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d0caf1e2-068e-4a08-a386-13d234b4a5a6", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d3c182dd-486b-436f-9bca-14ae6361f4dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Path to agg and full files; UPDATE as needed\n", - "agg_path = '/Users/aesharpe/Desktop/num_employees_agg.xlsx'\n", - "full_path = '/Users/aesharpe/Desktop/num_employees.xlsx'\n", - "\n", - "# Load excel files into pandas\n", - "agg_df = pd.read_excel(agg_path).drop(columns=['Unnamed: 0'])\n", - "full_df = pd.read_excel(full_path).drop(columns=['Unnamed: 0'])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d5d73933-63ab-49d1-8254-35aa8b919def", - "metadata": {}, - "outputs": [], - "source": [ - "def get_random_group(df):\n", - " \"\"\"Show random year/utility groups that have multiple rows and at least one total.\n", - " \n", - " Use this function to see how the aggregation chose to allocate the avg_num_employees.\n", - " You can compare the avg_num_employees column with the avg_num_employees_agg column.\n", - " \n", - " Args: \n", - " df (pandas.DataFrame): The num_employees.xlsx dataframe (i.e., the non \n", - " aggregated one).\n", - " Returns:\n", - " df (pandas.DataFrame): A random subset of the full dataframe that shows\n", - " the records for a specific year and utility that has more than one\n", - " record and at least one flagged total row in total_types. \n", - " \"\"\"\n", - " groups = df.groupby(['report_year', 'utility_id_ferc1']) # add plant_id_pudl if you want to narrow the groups\n", - " while True:\n", - " random_key = random.choice(list(groups.groups.keys()))\n", - " random_group = groups.get_group(random_key)\n", - " more_than_one_row = len(random_group) > 1\n", - " has_total = random_group.total_type.notna().any()\n", - " if more_than_one_row & has_total:\n", - " break\n", - " return random_group[[\n", - " 'record_id', 'report_year', 'utility_id_ferc1', 'utility_name_ferc1', \n", - " 'plant_id_pudl', 'plant_name_ferc1', 'total_type', 'avg_num_employees', 'avg_num_employees_agg',\n", - " 'avg_num_employees_flag', 'capacity_mw', 'installation_year', 'plant_type']]" - ] - }, - { - "cell_type": "markdown", - "id": "4f41f8a6-e90c-41f6-8b52-8d375c8c6df7", - "metadata": {}, - "source": [ - "Every time you run this you'll get a different subset of the full df\n", - "You can use this to look at the way that employee numbers were allocated and decide whether you agree\n", - "Remember, all aggregation allocation decisions are being made at the year, utility, and plant level so\n", - "all of the values in avg_num_employees_agg represent the summary value for that plant, that's why they\n", - "are repeated for multipe records in a plant. The utility level aggregation is calculated (shown below)\n", - "by adding up the designated employee count for each year, utility, and plant id. In other words, you\n", - "can't just sum the column." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "be598c5d-8bbd-4746-b20a-6eb1a93bea81", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['combustion_turbine', 'steam', 'nuclear', 'unknown', 'storage',\n", - " 'run-of-river'], dtype=object)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate a random year/utility group\n", - "peek = get_random_group(full_df)\n", - "\n", - "# Show what fuel types appear in that group\n", - "peek.plant_type.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ffa607a4-86ee-4c93-b76c-ea2384a4b381", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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record_idreport_yearutility_id_ferc1utility_name_ferc1plant_id_pudlplant_name_ferc1total_typeavg_num_employeesavg_num_employees_aggavg_num_employees_flagcapacity_mwinstallation_yearplant_type
3782f1_steam_1996_12_57_0_5199657Georgia Power Company73bowenNaN423.0423actual values provided3499.01975.0steam
3783f1_steam_1996_12_57_1_5199657Georgia Power Company246hammondNaN213.0213actual values provided953.01970.0steam
3784f1_steam_1996_12_57_1_4199657Georgia Power Company250harllee branchNaN347.0347actual values provided1746.01969.0steam
3788f1_steam_1996_12_57_1_1199657Georgia Power Company383mcdonoughNaN177.0177actual values provided598.01964.0steam
3791f1_steam_1996_12_57_2_3199657Georgia Power Company398mcmanusNaN43.043actual values provided144.01959.0steam
3793f1_steam_1996_12_57_2_5199657Georgia Power Company412mitchellNaN64.064actual values provided218.01964.0steam
3794f1_steam_1996_12_57_3_1199657Georgia Power Company526schererNaN399.0399actual values provided818.01988.0steam
3801f1_steam_1996_12_57_2_1199657Georgia Power Company656yatesNaN317.0317actual values provided1488.01974.0steam
3803f1_steam_1996_12_57_3_4199657Georgia Power Company658wansleyNaN249.0249actual values provided1019.01978.0steam
3817f1_steam_1996_12_57_0_1199657Georgia Power Company9611arkwrightNaN80.080actual values provided181.01948.0steam
3819f1_steam_1996_12_57_0_3199657Georgia Power Company9612atkinsonNaNNaN0no total rows198.01948.0steam
\n", - "
" - ], - "text/plain": [ - " record_id report_year utility_id_ferc1 \\\n", - "3782 f1_steam_1996_12_57_0_5 1996 57 \n", - "3783 f1_steam_1996_12_57_1_5 1996 57 \n", - "3784 f1_steam_1996_12_57_1_4 1996 57 \n", - "3788 f1_steam_1996_12_57_1_1 1996 57 \n", - "3791 f1_steam_1996_12_57_2_3 1996 57 \n", - "3793 f1_steam_1996_12_57_2_5 1996 57 \n", - "3794 f1_steam_1996_12_57_3_1 1996 57 \n", - "3801 f1_steam_1996_12_57_2_1 1996 57 \n", - "3803 f1_steam_1996_12_57_3_4 1996 57 \n", - "3817 f1_steam_1996_12_57_0_1 1996 57 \n", - "3819 f1_steam_1996_12_57_0_3 1996 57 \n", - "\n", - " utility_name_ferc1 plant_id_pudl plant_name_ferc1 total_type \\\n", - "3782 Georgia Power Company 73 bowen NaN \n", - "3783 Georgia Power Company 246 hammond NaN \n", - "3784 Georgia Power Company 250 harllee branch NaN \n", - "3788 Georgia Power Company 383 mcdonough NaN \n", - "3791 Georgia Power Company 398 mcmanus NaN \n", - "3793 Georgia Power Company 412 mitchell NaN \n", - "3794 Georgia Power Company 526 scherer NaN \n", - "3801 Georgia Power Company 656 yates NaN \n", - "3803 Georgia Power Company 658 wansley NaN \n", - "3817 Georgia Power Company 9611 arkwright NaN \n", - "3819 Georgia Power Company 9612 atkinson NaN \n", - "\n", - " avg_num_employees avg_num_employees_agg avg_num_employees_flag \\\n", - "3782 423.0 423 actual values provided \n", - "3783 213.0 213 actual values provided \n", - "3784 347.0 347 actual values provided \n", - "3788 177.0 177 actual values provided \n", - "3791 43.0 43 actual values provided \n", - "3793 64.0 64 actual values provided \n", - "3794 399.0 399 actual values provided \n", - "3801 317.0 317 actual values provided \n", - "3803 249.0 249 actual values provided \n", - "3817 80.0 80 actual values provided \n", - "3819 NaN 0 no total rows \n", - "\n", - " capacity_mw installation_year plant_type \n", - "3782 3499.0 1975.0 steam \n", - "3783 953.0 1970.0 steam \n", - "3784 1746.0 1969.0 steam \n", - "3788 598.0 1964.0 steam \n", - "3791 144.0 1959.0 steam \n", - "3793 218.0 1964.0 steam \n", - "3794 818.0 1988.0 steam \n", - "3801 1488.0 1974.0 steam \n", - "3803 1019.0 1978.0 steam \n", - "3817 181.0 1948.0 steam \n", - "3819 198.0 1948.0 steam " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Look at one of those fuel types for a snapshot of what's going on\n", - "peek[peek['plant_type']=='steam'].sort_values('plant_id_pudl')" - ] - }, - { - "cell_type": "markdown", - "id": "78877a95-4c94-4df8-821a-6851546cee53", - "metadata": {}, - "source": [ - "## Recreate the agg_df from the full_df" - ] - }, - { - "cell_type": "markdown", - "id": "eb486e1e-3527-4c6a-946a-d245fbefb9a9", - "metadata": {}, - "source": [ - "If you see any values for `avg_num_employees_agg` that you do not think are representative of that year, utility, plant, and plant type, then you can change them. Just make sure you change the `avg_num_employees_agg` value in the `full_df` spreadsheet (num_employees_full.xlsx) for **ALL** records in the year, utility, plant, and plant type group. Then, you can run these next cells which will recreate the aggregated spreadsheet as well as show you the difference between the original aggregated spreadsheet and the version with changes. If you *don't* change the spreadsheet, this should output a blank df." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9d42e01b-824b-4dfe-a8ad-4786c3714e6d", - "metadata": {}, - "outputs": [], - "source": [ - "# Group by relevant columns. We include plant_id_pudl here because many of the totals are plant-level totals\n", - "groups = full_df.groupby(['report_year', 'utility_id_ferc1', 'plant_id_pudl', 'plant_type'])\n", - "\n", - "# Test that the groups we've defined above all have the same values for the column avg_num_employees_agg\n", - "# This will spit out an error if that's not true\n", - "assert (groups.avg_num_employees_agg.nunique() > 1).any() == False, \"groups don't have the same avg_num_employees_agg\" \n", - "\n", - "# Group by plant and grab the first value in each avg_num_employees group because we know they are all the same\n", - "plant_groups_df = groups.agg('first').reset_index()\n", - "\n", - "# Now we'll aggregate up to the utility plant-type level which is what we want for the final version.\n", - "util_groups_df = (\n", - " plant_groups_df\n", - " .groupby(['report_year', 'utility_id_ferc1', 'plant_type'])\n", - " .agg('sum')\n", - " .assign(avg_num_employees=lambda x: x.avg_num_employees_agg.astype('Int64'))\n", - " .drop(columns=['plant_id_pudl'])\n", - " .reset_index())[['report_year', 'utility_id_ferc1', 'plant_type', 'avg_num_employees']]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5bdf845b-1354-4c6a-98f6-e888bdfe073b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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report_yearutility_id_ferc1plant_typeavg_num_employees
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [report_year, utility_id_ferc1, plant_type, avg_num_employees]\n", - "Index: []" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Show the differences between the original agg_df and your newly aggregated full_df\n", - "# If you don't change anything, this should be empty\n", - "agg_no_flag = agg_df.drop(columns=['avg_num_employees_flag'])\n", - "pd.concat([agg_no_flag, util_groups_df]).drop_duplicates(keep=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42a73048-fec1-4b09-8c80-d40a232129be", - "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": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 673ba75d2df047a3fab37032ffef6c2871485dfc Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 19 Jul 2022 12:08:14 -0600 Subject: [PATCH 20/80] Change unit_id_epa column in CEMS/Crosswalk to emissions_unit_id_epa --- src/pudl/extract/epacems.py | 2 +- src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py | 4 ++-- src/pudl/metadata/fields.py | 8 ++++---- src/pudl/metadata/resources/epacems.py | 8 ++++++-- src/pudl/metadata/resources/glue.py | 2 +- src/pudl/transform/epacems.py | 12 ++++++------ 6 files changed, 20 insertions(+), 16 deletions(-) diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index a6516d6e80..0a9fbabc35 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -18,7 +18,7 @@ "STATE": "state", # "FACILITY_NAME": "plant_name", # Not reading from CSV "ORISPL_CODE": "plant_id_epa", # Not quite the same as plant_id_eia - "UNITID": "unit_id_epa", # The smokestake unit + "UNITID": "emissions_unit_id_epa", # These op_date, op_hour, and op_time variables get converted to # operating_date, operating_datetime and operating_time_interval in # transform/epacems.py diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 25d121254e..5dab16f6ea 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -48,7 +48,7 @@ def transform( column_rename = { "camd_plant_id": "plant_id_epa", - "camd_unit_id": "unit_id_epa", + "camd_unit_id": "emissions_unit_id_epa", "camd_generator_id": "generator_id_epa", "eia_plant_id": "plant_id_eia", # "eia_boiler_id": "boiler_id", # Eventually change to boiler_id_eia @@ -87,7 +87,7 @@ def transform( # More indepth cleaning and droping rows with no plant_id_eia match. crosswalk_clean = ( crosswalk_clean.pipe(remove_leading_zeros_from_numeric_strings, "generator_id") - .pipe(remove_leading_zeros_from_numeric_strings, "unit_id_epa") + .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .dropna(subset="plant_id_eia") ) diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py index c06bb919dc..172d9d011a 100644 --- a/src/pudl/metadata/fields.py +++ b/src/pudl/metadata/fields.py @@ -419,6 +419,10 @@ "type": "number", "description": "FERC Account 102: Electric Plant Sold (Negative).", }, + "emissions_unit_id_epa": { + "type": "string", + "description": "Emissions (smokestack) unit monitored by EPA CEMS.", + }, "energy_charges": { "type": "number", "description": "Energy charges (USD).", @@ -1912,10 +1916,6 @@ "type": "string", "description": "EIA-assigned unit identification code.", }, - "unit_id_epa": { - "type": "string", - "description": "Emissions (smokestack) unit monitored by EPA CEMS.", - }, "unit_id_pudl": { "type": "integer", "description": "Dynamically assigned PUDL unit id. WARNING: This ID is not guaranteed to be static long term as the input data and algorithm may evolve over time.", diff --git a/src/pudl/metadata/resources/epacems.py b/src/pudl/metadata/resources/epacems.py index b637459410..a8e9a9859d 100644 --- a/src/pudl/metadata/resources/epacems.py +++ b/src/pudl/metadata/resources/epacems.py @@ -7,7 +7,7 @@ "schema": { "fields": [ "plant_id_eia", - "unit_id_epa", + "emissions_unit_id_epa", "operating_datetime_utc", "year", "state", @@ -24,7 +24,11 @@ "co2_mass_tons", "co2_mass_measurement_code", ], - "primary_key": ["plant_id_eia", "unit_id_epa", "operating_datetime_utc"], + "primary_key": [ + "plant_id_eia", + "emissions_unit_id_epa", + "operating_datetime_utc", + ], }, "sources": ["eia860", "epacems"], "field_namespace": "epacems", diff --git a/src/pudl/metadata/resources/glue.py b/src/pudl/metadata/resources/glue.py index 6e89652434..1ca1c4ae2d 100644 --- a/src/pudl/metadata/resources/glue.py +++ b/src/pudl/metadata/resources/glue.py @@ -6,7 +6,7 @@ "schema": { "fields": [ "plant_id_epa", - "unit_id_epa", + "emissions_unit_id_epa", "generator_id_epa", "plant_id_eia", "boiler_id", diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 564e2ed8c2..095a9156aa 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -48,11 +48,11 @@ def harmonize_eia_epa_orispl( """ # Already ran a test to make sure this works. When you group the crosswalk by - # plant_id_epa and unit_id_epa then calculate .nunique() for plant_id_eia, none of - # the values are greater than one meaning that this drop/merge is ok. Might want to - # make that an official test somwwhere. + # plant_id_epa and emissions_unit_id_epa then calculate .nunique() for plant_id_eia, + # none of the values are greater than one meaning that this drop/merge is ok. Might + # want to make that an official test somewhere. crosswalk_df = pd.read_sql("epacamd_eia_crosswalk", pudl_engine)[ - ["plant_id_eia", "plant_id_epa", "unit_id_epa"] + ["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"] ].drop_duplicates() # I wonder if there is a faster way to do this by checking if the id needs to be @@ -61,7 +61,7 @@ def harmonize_eia_epa_orispl( # Merge CEMS with Crosswalk to get correct EIA ORISPL code. Remove incorrect # plant_id_epa column to avoid confusion. df_merged = pd.merge( - df, crosswalk_df, on=["plant_id_epa", "unit_id_epa"], how="left" + df, crosswalk_df, on=["plant_id_epa", "emissions_unit_id_epa"], how="left" ).drop(columns=["plant_id_epa"]) return df_merged @@ -230,7 +230,7 @@ def transform(raw_df: pd.DataFrame, pudl_engine: sa.engine.Engine) -> pd.DataFra """ return ( raw_df.fillna({"gross_load_mw": 0.0, "heat_content_mmbtu": 0.0}) - .pipe(remove_leading_zeros_from_numeric_strings, "unit_id_epa") + .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .pipe(harmonize_eia_epa_orispl, pudl_engine) .pipe(fix_up_dates, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) .pipe(correct_gross_load_mw) From b8cd2cb11530008302466cbba95cb021e42e6614 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 20 Jul 2022 16:47:50 -0600 Subject: [PATCH 21/80] Add boiler id to crosswalk and address gaps in plant_id_eia Boiler id was commented out of the crosswalk metadata so I added it back in! Adding in boiler id created some issues with foreign keys for the testing environment that only tests one year of data. I had to implement the same hack as for generator_id: check to see if the settings indicate all years of data and if not filter the crosswalk based on the boiler_id values in the boilers_entity_eia table. I also had to feed the boiler_entity table to all the same functions that the generator_entity table is fed to for the same reason. Previously I had merged in the crosswalk plant_id_eia values and called that one of the crosswalk primary keys. Unfortunately the crosswalk still has some gaps and not all of the plant_id_epa values in cems have been mapped. That led to NA plant_id_eia values which isn't allowed for primary keys. Instead of deleting the plant_id_epa field in favor of the plant_id_eia field I kept both and called plant_id_epa the primary key. This caused some other issues with the fix_up_dates() function that was relying on the plant_id_eia column to map timezones. To satisfy this function I created a temporary field called plant_id_combined that filled the gaps of plant_id_eia with plant_id_epa for use in timezone calculation. Technically there is no difference in timezone between the epa and eia ids (I checked) but this process is more accurate (and maybe something will change in future years). I later remove the plant_id_combined column. Lastly, I had to update the tests because I added the plant_id_epa column back. Now there are 19 instead of 17 columns. I also added plant_id_epa back to the crosswalk schema. --- src/pudl/etl.py | 5 +- .../epacems_unitid_eia_plant_crosswalk.py | 36 +++++++++---- src/pudl/metadata/resources/epacems.py | 3 +- src/pudl/transform/epacems.py | 53 ++++++++++++++----- test/integration/epacems_test.py | 2 +- 5 files changed, 74 insertions(+), 25 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index baf4202b17..b367d529e9 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -406,7 +406,10 @@ def _etl_glue( ) glue_dfs.update( pudl.glue.epacems_unitid_eia_plant_crosswalk.crosswalk_et( - ds, sqlite_dfs["generators_entity_eia"], processing_all_eia_years + ds, + sqlite_dfs["generators_entity_eia"], + sqlite_dfs["boilers_entity_eia"], + processing_all_eia_years, ) ) diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 5dab16f6ea..5280427049 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -29,6 +29,7 @@ def extract(ds: Datastore) -> pd.DataFrame: def transform( epacamd_eia_crosswalk: pd.DataFrame, generators_entity_eia: pd.DataFrame, + boilers_entity_eia: pd.DataFrame, processing_all_eia_years: bool, ) -> dict[str, pd.DataFrame]: """Clean up the EPACAMD-EIA Crosswalk file. @@ -51,7 +52,7 @@ def transform( "camd_unit_id": "emissions_unit_id_epa", "camd_generator_id": "generator_id_epa", "eia_plant_id": "plant_id_eia", - # "eia_boiler_id": "boiler_id", # Eventually change to boiler_id_eia + "eia_boiler_id": "boiler_id", # Eventually change to boiler_id_eia "eia_generator_id": "generator_id", # Eventually change to generator_id_eia } @@ -60,7 +61,10 @@ def transform( epacamd_eia_crosswalk.pipe(simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) + .pipe(remove_leading_zeros_from_numeric_strings, "generator_id") + .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .pipe(apply_pudl_dtypes, "eia") + .dropna(subset=["plant_id_eia"]) ) # The crosswalk is a static file: there is no year field. The plant_id_eia and @@ -78,24 +82,31 @@ def transform( chosen subset of EIA years" ) crosswalk_clean = pd.merge( - crosswalk_clean.dropna(subset=["plant_id_eia"]), - generators_entity_eia[["plant_id_eia", "generator_id"]].drop_duplicates(), + crosswalk_clean, + generators_entity_eia[["plant_id_eia", "generator_id"]], on=["plant_id_eia", "generator_id"], how="inner", ) + crosswalk_clean = pd.merge( + crosswalk_clean, + boilers_entity_eia[["plant_id_eia", "boiler_id"]], + on=["plant_id_eia", "boiler_id"], + how="inner", + ) # More indepth cleaning and droping rows with no plant_id_eia match. - crosswalk_clean = ( - crosswalk_clean.pipe(remove_leading_zeros_from_numeric_strings, "generator_id") - .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") - .dropna(subset="plant_id_eia") - ) + # crosswalk_clean = crosswalk_clean.pipe( + # remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa" + # ).dropna(subset="plant_id_eia") return {"epacamd_eia_crosswalk": crosswalk_clean} def crosswalk_et( - ds: Datastore, generators_entity_eia: pd.DataFrame, processing_all_eia_years: bool + ds: Datastore, + generators_entity_eia: pd.DataFrame, + boilers_entiity_eia: pd.DataFrame, + processing_all_eia_years: bool, ) -> dict[str, pd.DataFrame]: """Clean raw crosswalk data. @@ -110,4 +121,9 @@ def crosswalk_et( Returns: A dictionary containing the cleaned EPACAMD-EIA crosswalk DataFrame. """ - return transform(extract(ds), generators_entity_eia, processing_all_eia_years) + return transform( + extract(ds), + generators_entity_eia, + boilers_entiity_eia, + processing_all_eia_years, + ) diff --git a/src/pudl/metadata/resources/epacems.py b/src/pudl/metadata/resources/epacems.py index a8e9a9859d..aeeda066c0 100644 --- a/src/pudl/metadata/resources/epacems.py +++ b/src/pudl/metadata/resources/epacems.py @@ -7,6 +7,7 @@ "schema": { "fields": [ "plant_id_eia", + "plant_id_epa", "emissions_unit_id_epa", "operating_datetime_utc", "year", @@ -25,7 +26,7 @@ "co2_mass_measurement_code", ], "primary_key": [ - "plant_id_eia", + "plant_id_epa", "emissions_unit_id_epa", "operating_datetime_utc", ], diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 095a9156aa..8773d14a2f 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -51,18 +51,34 @@ def harmonize_eia_epa_orispl( # plant_id_epa and emissions_unit_id_epa then calculate .nunique() for plant_id_eia, # none of the values are greater than one meaning that this drop/merge is ok. Might # want to make that an official test somewhere. - crosswalk_df = pd.read_sql("epacamd_eia_crosswalk", pudl_engine)[ - ["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"] - ].drop_duplicates() + + crosswalk_df = pd.read_sql( + "epacamd_eia_crosswalk", + con=pudl_engine, + columns=["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"], + ).drop_duplicates() # I wonder if there is a faster way to do this by checking if the id needs to be # fixed rather than just merging it all together (as done below). - # Merge CEMS with Crosswalk to get correct EIA ORISPL code. Remove incorrect - # plant_id_epa column to avoid confusion. + # Merge CEMS with Crosswalk to get correct EIA ORISPL code. df_merged = pd.merge( df, crosswalk_df, on=["plant_id_epa", "emissions_unit_id_epa"], how="left" - ).drop(columns=["plant_id_epa"]) + ) + + # Because the crosswalk isn't complete, there are some instances where the + # plant_id_eia value will be NA. This isn't great when it goes to grouping or + # merging data together. Specifically for the fix_up_dates() function below. + # This creates a column based on the plant_id_eia but backfills NA with + # plant_id_epa so it can be used to merge on. + df_merged["plant_id_combined"] = df_merged.plant_id_eia.fillna( + df_merged.plant_id_epa + ) + # assert ( + # ~df_merged.plant_id_combined.isna().any() + # ), "There shouldn't be any NA vales in the combined plant id column" + + assert len(df_merged) == len(df) return df_merged @@ -75,7 +91,7 @@ def fix_up_dates(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFra Args: df: A CEMS hourly dataframe for one year-state. - plant_utc_offset: A dataframe association plant_id_eia with timezones. + plant_utc_offset: A dataframe association plant_id_combined with timezones. Returns: The same data, with an op_datetime_utc column added and the op_date and op_hour @@ -91,12 +107,16 @@ def fix_up_dates(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFra x.op_date, format=r"%m-%d-%Y", exact=True, cache=True ) + pd.to_timedelta(x.op_hour, unit="h") # Add the hour - ).merge(plant_utc_offset, how="left", on="plant_id_eia") + ).merge( + plant_utc_offset.rename(columns={"plant_id_eia": "plant_id_combined"}), + how="left", + on="plant_id_combined", + ) # Some of the timezones in the plants_entity_eia table may be missing, # but none of the CEMS plants should be. if df["utc_offset"].isna().any(): - missing_plants = df.loc[df["utc_offset"].isna(), "plant_id_eia"].unique() + missing_plants = df.loc[df["utc_offset"].isna(), "plant_id_combined"].unique() raise ValueError( f"utc_offset should never be missing for CEMS plants, but was " f"missing for these: {str(list(missing_plants))}" @@ -107,7 +127,13 @@ def fix_up_dates(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFra # deprecated, but the PyArrow schema stores this data as UTC. See: # https://numpy.org/devdocs/reference/arrays.datetime.html#basic-datetimes df["operating_datetime_utc"] = df["op_datetime_naive"] - df["utc_offset"] - del df["op_date"], df["op_hour"], df["op_datetime_naive"], df["utc_offset"] + del ( + df["op_date"], + df["op_hour"], + df["op_datetime_naive"], + df["utc_offset"], + df["plant_id_combined"], + ) return df @@ -122,7 +148,7 @@ def _load_plant_utc_offset(pudl_engine): an existing PUDL DB. Returns: - pandas.DataFrame: With columns plant_id_eia and utc_offset. + pandas.DataFrame: With columns plant_id_combined and utc_offset. """ # Verify that we have a PUDL DB with plant attributes: @@ -217,7 +243,10 @@ def correct_gross_load_mw(df: pd.DataFrame) -> pd.DataFrame: return df -def transform(raw_df: pd.DataFrame, pudl_engine: sa.engine.Engine) -> pd.DataFrame: +def transform( + raw_df: pd.DataFrame, + pudl_engine: sa.engine.Engine, +) -> pd.DataFrame: """Transform EPA CEMS hourly data and ready it for export to Parquet. Args: diff --git a/test/integration/epacems_test.py b/test/integration/epacems_test.py index b33d562c4b..f61dad6a82 100644 --- a/test/integration/epacems_test.py +++ b/test/integration/epacems_test.py @@ -88,4 +88,4 @@ def test_epacems_parallel(pudl_settings_fixture, pudl_ds_kwargs, tmpdir_factory) engine="pyarrow", split_row_groups=True, ).compute() - assert df.shape == (96_360, 17) # nosec: B101 + assert df.shape == (96_360, 18) # nosec: B101 From 880fd4ee65c78bdc25e5efc78da277b750405ec8 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 20 Jul 2022 17:29:55 -0600 Subject: [PATCH 22/80] Remove glue wrapper function Previously I designed the glue epacems_unitid_to_eia_plant_crosswalk module with an extract function, a transform function, and a wrapper function that didn't do anything besides call the extract and transform. I removed that wrapper function and now call the extract and transform functions in the etl.py module. This is more in line with how the other BLAh_etl() functions work in that module anyways. --- src/pudl/etl.py | 14 +++++----- .../epacems_unitid_eia_plant_crosswalk.py | 27 ------------------- 2 files changed, 7 insertions(+), 34 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index b367d529e9..287130ad56 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -404,14 +404,14 @@ def _etl_glue( datasets["eia"].eia860.years == datasets["eia"].eia860.data_source.working_partitions["years"] ) - glue_dfs.update( - pudl.glue.epacems_unitid_eia_plant_crosswalk.crosswalk_et( - ds, - sqlite_dfs["generators_entity_eia"], - sqlite_dfs["boilers_entity_eia"], - processing_all_eia_years, - ) + glue_raw_dfs = pudl.glue.epacems_unitid_eia_plant_crosswalk.extract(ds) + glue_transformed_dfs = pudl.glue.epacems_unitid_eia_plant_crosswalk.transform( + glue_raw_dfs, + sqlite_dfs["generators_entity_eia"], + sqlite_dfs["boilers_entity_eia"], + processing_all_eia_years, ) + glue_dfs.update(glue_transformed_dfs) return glue_dfs diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py index 5280427049..569e678e97 100644 --- a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py +++ b/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py @@ -100,30 +100,3 @@ def transform( # ).dropna(subset="plant_id_eia") return {"epacamd_eia_crosswalk": crosswalk_clean} - - -def crosswalk_et( - ds: Datastore, - generators_entity_eia: pd.DataFrame, - boilers_entiity_eia: pd.DataFrame, - processing_all_eia_years: bool, -) -> dict[str, pd.DataFrame]: - """Clean raw crosswalk data. - - Args: - ds: Initialized datastore. - generators_entity_eia: The generators_entity_eia table. - processing_all_eia_years: A boolean indicating whether the years from the - Eia860Settings object match the EIA860 working partitions. This tell the - function whether to restrict the crosswalk data so the tests don't fail on - foreign key restraints. - - Returns: - A dictionary containing the cleaned EPACAMD-EIA crosswalk DataFrame. - """ - return transform( - extract(ds), - generators_entity_eia, - boilers_entiity_eia, - processing_all_eia_years, - ) From 7f1cc7052fce7b262887b634ceb0f69cda5d8e43 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 21 Jul 2022 11:00:16 -0600 Subject: [PATCH 23/80] Remove set gross_load_mw and heat_content_mmbtu with 0 if NA. I also fixed what I think is a little slice bug. --- src/pudl/metadata/fields.py | 10 ---------- src/pudl/transform/epacems.py | 5 ++--- 2 files changed, 2 insertions(+), 13 deletions(-) diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py index 172d9d011a..197815e520 100644 --- a/src/pudl/metadata/fields.py +++ b/src/pudl/metadata/fields.py @@ -2018,16 +2018,6 @@ FIELD_METADATA_BY_GROUP: dict[str, dict[str, Any]] = { "epacems": { "state": {"constraints": {"enum": EPACEMS_STATES}}, - "gross_load_mw": { - "constraints": { - "required": True, - } - }, - "heat_content_mmbtu": { - "constraints": { - "required": True, - } - }, "operating_datetime_utc": { "constraints": { "required": True, diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 8773d14a2f..01406e289d 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -239,7 +239,7 @@ def correct_gross_load_mw(df: pd.DataFrame) -> pd.DataFrame: # This is rare, so don't bother most of the time. bad = df["gross_load_mw"] > 2000 if bad.any(): - df.loc[bad, "gross_load_mw"] = df.loc[bad, "gross_load_mw"] / 1000 + df.loc[bad, "gross_load_mw"] = df.gross_load_mw / 1000 return df @@ -258,8 +258,7 @@ def transform( """ return ( - raw_df.fillna({"gross_load_mw": 0.0, "heat_content_mmbtu": 0.0}) - .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") + raw_df.pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .pipe(harmonize_eia_epa_orispl, pudl_engine) .pipe(fix_up_dates, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) .pipe(correct_gross_load_mw) From 74877b17e2bca6b9bd3994d45a6ab13cf7411f2d Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 21 Jul 2022 12:38:59 -0600 Subject: [PATCH 24/80] Make remove_leading_zeros_from_numeric_strings() helper function slightly better --- src/pudl/helpers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pudl/helpers.py b/src/pudl/helpers.py index d44d74a6fd..bc2c184a18 100644 --- a/src/pudl/helpers.py +++ b/src/pudl/helpers.py @@ -894,12 +894,12 @@ def remove_leading_zeros_from_numeric_strings( """ if col_name in df.columns: - leading_zeros = df[col_name].str.contains(r"^0+\d+$") + leading_zeros = df[col_name].str.contains(r"^0+\d+$").fillna(False) if leading_zeros.any(): logger.debug(f"Fixing leading zeros in {col_name} column") - df.loc[df[col_name].notna() & leading_zeros, col_name] = df[ - col_name - ].str.replace(r"^0+", "", regex=True) + df.loc[leading_zeros, col_name] = df[col_name].str.replace( + r"^0+", "", regex=True + ) else: logger.debug(f"Found no numeric leading zeros in {col_name}") return df From 4d2a1ce677aa68e5384c185adf26d854149c5662 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 21 Jul 2022 12:42:06 -0600 Subject: [PATCH 25/80] rename fix_up_dates() function in epacems transform to convert_to_utc --- src/pudl/transform/epacems.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 01406e289d..00fb76dc83 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -34,8 +34,8 @@ def harmonize_eia_epa_orispl( https://github.com/USEPA/camd-eia-crosswalk - Note that this transformation needs to be run *before* fix_up_dates, because - fix_up_dates uses the plant ID to look up timezones. + Note that this transformation needs to be run *before* convert_to_utc, because + convert_to_utc uses the plant ID to look up timezones. Args: pudl_engine: SQLAlchemy connection engine for connecting to an existing PUDL DB. @@ -68,7 +68,7 @@ def harmonize_eia_epa_orispl( # Because the crosswalk isn't complete, there are some instances where the # plant_id_eia value will be NA. This isn't great when it goes to grouping or - # merging data together. Specifically for the fix_up_dates() function below. + # merging data together. Specifically for the convert_to_utc() function below. # This creates a column based on the plant_id_eia but backfills NA with # plant_id_epa so it can be used to merge on. df_merged["plant_id_combined"] = df_merged.plant_id_eia.fillna( @@ -82,8 +82,8 @@ def harmonize_eia_epa_orispl( return df_merged -def fix_up_dates(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFrame: - """Fix the dates for the CEMS data. +def convert_to_utc(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataFrame: + """Convert CEMS datetime data to UTC timezones. Transformations include: @@ -260,7 +260,7 @@ def transform( return ( raw_df.pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .pipe(harmonize_eia_epa_orispl, pudl_engine) - .pipe(fix_up_dates, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) + .pipe(convert_to_utc, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) .pipe(correct_gross_load_mw) .pipe(apply_pudl_dtypes, group="epacems") ) From 72c2b382a3f2da091faec34188c422c9a0a2e161 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 21 Jul 2022 12:52:23 -0600 Subject: [PATCH 26/80] Update crosswalk module name Was epacems_unitid_eia_plant_crosswalk but now is epacamd_eia_crosswalk. We no longer use the unitid column name and there are also other fields besides unit and plant. The data also pertains to CAMD more broadly, not just CEMS. We still need to update the zenodo archiver and scraper and then update the old name in the pudl extractor. --- src/pudl/__init__.py | 2 +- src/pudl/etl.py | 4 ++-- ...id_eia_plant_crosswalk.py => epacamd_eia_crosswalk.py} | 0 src/pudl/metadata/sources.py | 8 ++++---- src/pudl/workspace/datastore.py | 4 ++-- 5 files changed, 9 insertions(+), 9 deletions(-) rename src/pudl/glue/{epacems_unitid_eia_plant_crosswalk.py => epacamd_eia_crosswalk.py} (100%) diff --git a/src/pudl/__init__.py b/src/pudl/__init__.py index 548769ee2d..808c1478b2 100644 --- a/src/pudl/__init__.py +++ b/src/pudl/__init__.py @@ -26,7 +26,7 @@ import pudl.extract.excel import pudl.extract.ferc1 import pudl.extract.ferc714 -import pudl.glue.epacems_unitid_eia_plant_crosswalk +import pudl.glue.epacamd_eia_crosswalk import pudl.glue.ferc1_eia import pudl.helpers import pudl.load diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 287130ad56..dd2fc0ef55 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -404,8 +404,8 @@ def _etl_glue( datasets["eia"].eia860.years == datasets["eia"].eia860.data_source.working_partitions["years"] ) - glue_raw_dfs = pudl.glue.epacems_unitid_eia_plant_crosswalk.extract(ds) - glue_transformed_dfs = pudl.glue.epacems_unitid_eia_plant_crosswalk.transform( + glue_raw_dfs = pudl.glue.epacamd_eia_crosswalk.extract(ds) + glue_transformed_dfs = pudl.glue.epacamd_eia_crosswalk.transform( glue_raw_dfs, sqlite_dfs["generators_entity_eia"], sqlite_dfs["boilers_entity_eia"], diff --git a/src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py b/src/pudl/glue/epacamd_eia_crosswalk.py similarity index 100% rename from src/pudl/glue/epacems_unitid_eia_plant_crosswalk.py rename to src/pudl/glue/epacamd_eia_crosswalk.py diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 00748a0ca0..42adf003f8 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -230,12 +230,12 @@ "license_raw": LICENSES["us-govt"], "license_pudl": LICENSES["cc-by-4.0"], }, - "epacems_unitid_eia_plant_crosswalk": { - "title": "EPA CEMS unitid to EIA Plant Crosswalk", + "epacamd_eia_crosswalk": { + "title": "EPA CAMD to EIA Data Crosswalk", "path": "https://github.com/USEPA/camd-eia-crosswalk", "description": ( - "A file created collaboratively by EPA and EIA that connects EPA CEMS " - "smokestacks (unitids) with cooresponding EIA plant part ids reported in " + "A file created collaboratively by EPA and EIA that connects EPA CAMD " + "smokestacks (units) with cooresponding EIA plant part ids reported in " "EIA Forms 860 and 923 (plant_id_eia, boiler_id, generator_id). This " "one-to-many connection is necessary because pollutants from various plant " "parts are collecitvely emitted and measured from one point-source." diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index 37f3d52393..992caa80c2 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -152,7 +152,7 @@ class ZenodoFetcher: "eia861": "10.5072/zenodo.687052", "eia923": "10.5072/zenodo.926301", "epacems": "10.5072/zenodo.672963", - "epacems_unitid_eia_plant_crosswalk": "10.5072/zenodo.1072001", + "epacems_unitid_eia_plant_crosswalk": "10.5072/zenodo.1072001", # Eventually change name "ferc1": "10.5072/zenodo.926302", "ferc714": "10.5072/zenodo.926660", }, @@ -163,7 +163,7 @@ class ZenodoFetcher: "eia861": "10.5281/zenodo.5602102", "eia923": "10.5281/zenodo.5596977", "epacems": "10.5281/zenodo.4660268", - "epacems_unitid_eia_plant_crosswalk": "10.5281/zenodo.6633770", + "epacems_unitid_eia_plant_crosswalk": "10.5281/zenodo.6633770", # Eventually change name "ferc1": "10.5281/zenodo.5534788", "ferc714": "10.5281/zenodo.5076672", }, From 67a53e8df421cf4767e1e1478bcec46eb11fd00a Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Fri, 22 Jul 2022 15:55:20 -0600 Subject: [PATCH 27/80] Remove NOX Rate columns from CEMS, udpate dictionary docs --- src/pudl/extract/epacems.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index 0a9fbabc35..1f8737856b 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -37,8 +37,8 @@ # "SO2_RATE": "so2_rate_lbs_mmbtu", # Not reading from CSV # "SO2_RATE_MEASURE_FLG": "so2_rate_measure_flg", # Not reading from CSV "NOX_RATE (lbs/mmBtu)": "nox_rate_lbs_mmbtu", - "NOX_RATE": "nox_rate_lbs_mmbtu", - "NOX_RATE_MEASURE_FLG": "nox_rate_measurement_code", + # "NOX_RATE": "nox_rate_lbs_mmbtu", # Not reading from CSV + # "NOX_RATE_MEASURE_FLG": "nox_rate_measurement_code", # Not reading from CSV "NOX_MASS (lbs)": "nox_mass_lbs", "NOX_MASS": "nox_mass_lbs", "NOX_MASS_MEASURE_FLG": "nox_mass_measurement_code", @@ -51,10 +51,11 @@ "HEAT_INPUT (mmBtu)": "heat_content_mmbtu", "HEAT_INPUT": "heat_content_mmbtu", # "FAC_ID": "facility_id", # IDK what this is, but it isn't helpful - # "UNIT_ID": "unit_id_epa", # IDK what this is, but it isn't helpful + # "UNIT_ID": "unit_id_what", # IDK what this is, but it isn't helpful } """dict: A dictionary containing EPA CEMS column names (keys) and replacement - names to use when reading those columns into PUDL (values). + names to use when reading those columns into PUDL (values). There are some + duplicate rename values because the column names change year to year. """ # Any column that exactly matches one of these won't be read @@ -66,6 +67,8 @@ "CO2_RATE (tons/mmBtu)", "CO2_RATE", "CO2_RATE_MEASURE_FLG", + "NOX_RATE_MEASURE_FLG", + "NOX_RATE", "FAC_ID", "UNIT_ID", } From 83f156a276f10226e8616d2211a4566912868420 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 26 Jul 2022 11:54:21 -0600 Subject: [PATCH 28/80] Update epacems data_source docs I reworked the EPACEMS data_source documentation page so that it more clearly reflects the importance of the EPA-EIA crosswalk. I also added an image from the EPA's crosswalk repo that depicts the different plant layouts that woudl lead to complex relationships between emissions units and generators. I also updated some of the links in the docs that were no longer live. For instance, the EPA used to house the CEMS data under the name AMPD but now it's CAMPD and the links changed. I made sure these links were updated in a few different places including the docs, metadata, notebooks, and the README. --- README.rst | 2 +- .../epacems/plant_configuration.png | Bin 0 -> 167189 bytes docs/dev/run_the_etl.rst | 2 + docs/templates/epacems_child.rst.jinja | 70 +++++++++++------- notebooks/work-in-progress/explore-CEMS.ipynb | 6 +- src/pudl/metadata/sources.py | 2 +- 6 files changed, 51 insertions(+), 31 deletions(-) create mode 100644 docs/data_sources/epacems/plant_configuration.png diff --git a/README.rst b/README.rst index 98e0ba4697..eef1c9f3ba 100644 --- a/README.rst +++ b/README.rst @@ -64,7 +64,7 @@ PUDL currently integrates data from: * `EIA Form 860m `__ (to 2021-12) * `EIA Form 861 `__ (2001-2020) * `EIA Form 923 `__ (2001-2020) -* `EPA Continuous Emissions Monitoring System (CEMS) `__ (1995-2020) +* `EPA Continuous Emissions Monitoring System (CEMS) `__ (1995-2020) * `FERC Form 1 `__ (1994-2020) * `FERC Form 714 `__ (2006-2020) * `US Census Demographic 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z!PUwNtP7=yK#JH*hfy9eF_lsZ>J^@F8y_Ff<96IFOCVn(9c&f05vtP>;0Fz(p*{p7 z56=E?b_#erus}F!vG&LD#614t3zj}N_=fx^f0qxY#vh87R@4JIfI>knXrPd^yOl7a zV>P6=#v=+1b`__{% endblock %} {% block background %} -As depicted by the EPA, `Continuous Emissions Monitoring Systems (CEMS) -`__ are the -“total equipment necessary for the determination of a gas or particulate matter -concentration or emission rate.” They are used to determine compliance with EPA -emissions standards and are therefore associated with a given “smokestack” and are -categorized in the raw data by a corresponding ``unitid``. Because point sources of -pollution are not alway correlated on a one-to-one basis with generation units, the -CEMS ``unitid`` serves as its own unique grouping. The EPA in collaboration with the -EIA has developed `a crosswalk table `__ -that maps the EPA’s ``unitid`` onto EIA’s ``boiler_id``, ``generator_id``, and -``plant_id_eia``. This file has been integrated into the SQL database. - -The EPA `Clean Air Markets Division (CAMD) `__ has -collected emissions data from CEMS units stretching back to 1995. Among the data -included in CEMS are hourly SO2, CO2, NOx emission and gross load. +`Continuous Emissions Monitoring Systems +`__ (CEMS) are used +to determine the rate of gas or particulate matter exiting a point source of emissions. +The EPA `Clean Air Markets Division (CAMD) `__ +has collected emissions data from CEMS units stretching back to 1995. Among the data +included in CEMS are hourly gross load, SO2, CO2, and NOx emissions. {% endblock %} {% block accessible %} @@ -53,19 +44,46 @@ A plain English explanation of the requirements of Part 75 is available in secti {% endblock %} {% block original_data %} -EPA CAMD publishes the CEMS data in an online `data portal `__ -. The files are available in a prepackaged format, accessible via a `user interface `__ -or `FTP site `__ with each downloadable zip file +EPA CAMD publishes the CEMS data in an online `data portal `__. +The files are available in a prepackaged format, accessible via a `user interface `__ +or `FTP site `__ with each downloadable zip file encompassing a year of data. {% endblock %} {% block notable_irregularities %} -CEMS is by far the largest dataset in PUDL at the moment with hourly records for -thousands of plants spanning decades. Note that the ETL process can easily take all -day for the full dataset. PUDL also provides a script that converts the raw EPA CEMS -data into Apache Parquet files that can be read and queried very efficiently with -Dask. Check out the `EPA CEMS example notebook `__ -in our -`pudl-examples repository `__ -on GitHub for pointers on how to access this big dataset efficiently using :mod:`dask`. + +CEMS is enourmous +----------------- +CEMS is by far the largest dataset in PUDL what with hourly records for +thousands of plants spanning decades. For this reason, we house CEMS data in `Apache +Parquet `__ files rather than the main PUDL database. +Still, running the ETL with all of the CEMS data can take a long time. Note that you can +:ref:`process CEMS Data seperately ` from the main ETL +script if you'd like. + +Check out the `EPA CEMS example notebook `__ +in our `pudl-examples repository `__ +on GitHub for pointers on how to access this dataset efficiently using :mod:`dask`. + +EPA units vs. EIA units +----------------------- +Another important thing to note is the difference between EPA "units" vs EIA "units". +Power plants are complex entities that have multiple subcomponents. In fossil powered +plants, emissions come from the combusion of fuel. This occurs in the boiler for coal +plants or the gas turbine for gas plants. When the EPA uses the term "unit" it is +refering to the emissions unit or smokestack where the CEMS equipment are (i.e., the +boiler or gas turbine). When the EIA refers to a "unit" it's usually refering to the +electricity generating unit (i.e. the generator). Some plants have a one-to-one +relationship between boilers and generators or gas turbines and generators, but many do +not. + +The EPA and EIA have addressed this discrepancy by creating a `crosswalk +`__ between the +various sub-plant groupings reported to them. The ``plant_id_eia`` values from the +crosswalk are integrated into the EPA CEMS Parquet files available in PUDL. + +Take a look at this helpful depiction of plant types from the EPA's crosswalk repo. + +.. image:: /data_sources/epacems/plant_configuration.png + {% endblock %} diff --git a/notebooks/work-in-progress/explore-CEMS.ipynb b/notebooks/work-in-progress/explore-CEMS.ipynb index 7d8641dd16..5413664c2f 100644 --- a/notebooks/work-in-progress/explore-CEMS.ipynb +++ b/notebooks/work-in-progress/explore-CEMS.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "CEMS or **Continuous Emissions Monitoring Systems** are used to track power plant's compliance with EPA emission standards. Included the data are hourly measurements of gross load, SO2, CO2, and NOx emissions associated with a given point source. The EPA's Clean Air Markets Division has collected CEMS data stretching back to 1995 and publicized it in their data portal. Combinging the CEMS data with geospatial, EIA and FERC data can enable greater and more specific analysis of utilities and their generation facilities. This notebook provides examples of working with the CEMS data in pudl." + "CEMS or **Continuous Emissions Monitoring Systems** are used to track power plant's compliance with EPA emission standards. Included the data are hourly measurements of gross load, SO2, CO2, and NOx emissions associated with a given point source. The EPA's Clean Air Markets Division has collected CEMS data stretching back to 1995 and publicized it in their data portal. Combinging the CEMS data with geospatial, EIA and FERC data can enable greater and more specific analysis of utilities and their generation facilities. This notebook provides examples of working with the CEMS data in pudl." ] }, { @@ -616,7 +616,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -757,7 +757,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 42adf003f8..ef749f3ea0 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -192,7 +192,7 @@ }, "epacems": { "title": "EPA Hourly Continuous Emission Monitoring System (CEMS)", - "path": "https://ampd.epa.gov/ampd", + "path": "https://campd.epa.gov/", "description": ( "US EPA hourly Continuous Emissions Monitoring System (CEMS) data." "Hourly CO2, SO2, NOx emissions and gross load." 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Data is currently available via the `PUDL Data Catalog `__. From 753be9d6cb4d20e8ec25194572f5fd4eb068149c Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 28 Jul 2022 15:44:26 -0600 Subject: [PATCH 30/80] Clean up CEMS transform module: Remove two functions that aren't used anymore: _all_na_or_values() and add_facility_id_unit_id_epa(). After talking to the EPA, I feel confident officially removing the FAC_ID and UNIT_ID values from the database. They are just unique identifiers used internally to EPA. I updated the comment next to the field name to explain that. I also made sure all the functions in the module had type hints. --- src/pudl/extract/epacems.py | 4 +-- src/pudl/transform/epacems.py | 59 ++--------------------------------- 2 files changed, 5 insertions(+), 58 deletions(-) diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index 1f8737856b..fb251a6c56 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -50,8 +50,8 @@ # "CO2_RATE_MEASURE_FLG": "co2_rate_measure_flg", # Not reading from CSV "HEAT_INPUT (mmBtu)": "heat_content_mmbtu", "HEAT_INPUT": "heat_content_mmbtu", - # "FAC_ID": "facility_id", # IDK what this is, but it isn't helpful - # "UNIT_ID": "unit_id_what", # IDK what this is, but it isn't helpful + # "FAC_ID": "facility_id", # unique facility id for internal EPA database management + # "UNIT_ID": "unit_id_what", # unique unit id for internal EPA database management } """dict: A dictionary containing EPA CEMS column names (keys) and replacement names to use when reading those columns into PUDL (values). There are some diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 00fb76dc83..8418f2f30d 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -3,7 +3,6 @@ import datetime import logging -import numpy as np import pandas as pd import pytz import sqlalchemy as sa @@ -137,18 +136,18 @@ def convert_to_utc(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataF return df -def _load_plant_utc_offset(pudl_engine): +def _load_plant_utc_offset(pudl_engine: sa.engine.Engine) -> pd.DataFrame: """Load the UTC offset each EIA plant. CEMS times don't change for DST, so we get get the UTC offset by using the offset for the plants' timezones in January. Args: - pudl_engine (sqlalchemy.engine.Engine): A database connection engine for + pudl_engine: A database connection engine for an existing PUDL DB. Returns: - pandas.DataFrame: With columns plant_id_combined and utc_offset. + Dataframe of applicable timezones taken from the plants_entity_eia table. """ # Verify that we have a PUDL DB with plant attributes: @@ -169,58 +168,6 @@ def _load_plant_utc_offset(pudl_engine): return timezones -def add_facility_id_unit_id_epa(df): - """Harmonize columns that are added later. - - The Parquet schema requires consistent column names across all partitions and - ``facility_id`` and ``unit_id_epa`` aren't present before August 2008, so this - function adds them in. - - Args: - df (pandas.DataFrame): A CEMS dataframe - - Returns: - pandas.Dataframe: The same DataFrame guaranteed to have int facility_id and - unit_id_epa cols. - - """ - if ("facility_id" not in df.columns) or ("unit_id_epa" not in df.columns): - # Can't just assign np.NaN and get an integer NaN, so make a new array - # with the right shape: - na_col = pd.array(np.full(df.shape[0], np.NaN), dtype="Int64") - if "facility_id" not in df.columns: - df["facility_id"] = na_col - if "unit_id_epa" not in df.columns: - df["unit_id_epa"] = na_col - return df - - -def _all_na_or_values(series, values): - """Test whether every element in the series is either missing or in values. - - This is fiddly because isin() changes behavior if the series is totally NaN (because - of type issues). - - Example: x = pd.DataFrame({'a': ['x', np.NaN], 'b': [np.NaN, np.NaN]}) - x.isin({'x', np.NaN}) - - Args: - series (pd.Series): A data column values (set): A set of values - - Returns: - bool: True or False, whether the elements are missing or in values - - """ - series_excl_na = series[series.notna()] - if not len(series_excl_na): - out = True - elif series_excl_na.isin(values).all(): - out = True - else: - out = False - return out - - def correct_gross_load_mw(df: pd.DataFrame) -> pd.DataFrame: """Fix values of gross load that are wrong by orders of magnitude. From 3513e9fb414b379edfdf3f881cc30fb43fd57039 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 28 Jul 2022 15:48:37 -0600 Subject: [PATCH 31/80] Remove all references to nox_rate fields from CEMS --- src/pudl/extract/epacems.py | 3 ++- src/pudl/metadata/fields.py | 10 ---------- src/pudl/metadata/resources/epacems.py | 2 -- 3 files changed, 2 insertions(+), 13 deletions(-) diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index fb251a6c56..3b6c9f501d 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -36,7 +36,7 @@ # "SO2_RATE (lbs/mmBtu)": "so2_rate_lbs_mmbtu", # Not reading from CSV # "SO2_RATE": "so2_rate_lbs_mmbtu", # Not reading from CSV # "SO2_RATE_MEASURE_FLG": "so2_rate_measure_flg", # Not reading from CSV - "NOX_RATE (lbs/mmBtu)": "nox_rate_lbs_mmbtu", + # "NOX_RATE (lbs/mmBtu)": "nox_rate_lbs_mmbtu", # "NOX_RATE": "nox_rate_lbs_mmbtu", # Not reading from CSV # "NOX_RATE_MEASURE_FLG": "nox_rate_measurement_code", # Not reading from CSV "NOX_MASS (lbs)": "nox_mass_lbs", @@ -69,6 +69,7 @@ "CO2_RATE_MEASURE_FLG", "NOX_RATE_MEASURE_FLG", "NOX_RATE", + "NOX_RATE (lbs/mmBtu)", "FAC_ID", "UNIT_ID", } diff --git a/src/pudl/metadata/fields.py b/src/pudl/metadata/fields.py index 197815e520..79adbe952d 100644 --- a/src/pudl/metadata/fields.py +++ b/src/pudl/metadata/fields.py @@ -1038,16 +1038,6 @@ "description": "Identifies whether the reported value of emissions was measured, calculated, or measured and substitute.", "constraints": {"enum": EPACEMS_MEASUREMENT_CODES}, }, - "nox_rate_lbs_mmbtu": { - "type": "number", - "description": "The average rate at which NOx was emitted during a given time period.", - "unit": "lb_per_MMBtu", - }, - "nox_rate_measurement_code": { - "type": "string", - "description": "Identifies whether the reported value of emissions was measured, calculated, or measured and substitute.", - "constraints": {"enum": EPACEMS_MEASUREMENT_CODES}, - }, "nuclear_acct320_land": { "type": "number", "description": "FERC Account 320: Nuclear Land and Land Rights.", diff --git a/src/pudl/metadata/resources/epacems.py b/src/pudl/metadata/resources/epacems.py index aeeda066c0..ff587661fc 100644 --- a/src/pudl/metadata/resources/epacems.py +++ b/src/pudl/metadata/resources/epacems.py @@ -18,8 +18,6 @@ "steam_load_1000_lbs", "so2_mass_lbs", "so2_mass_measurement_code", - "nox_rate_lbs_mmbtu", - "nox_rate_measurement_code", "nox_mass_lbs", "nox_mass_measurement_code", "co2_mass_tons", From 16c51ab39ecfdc66d74b3011086dab31907000bc Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Fri, 29 Jul 2022 15:11:46 -0600 Subject: [PATCH 32/80] Add epacamd_eia_crosswalk output table and validation test I created a pudltabl for the epacamd_eia_crosswalk and filled out the crosswalk function that was waiting empty in the output/epacems module. I also got rid of the comments there explaining that we needed to integrate the crosswalk. I deleted the old crosswalk unit test because it was based on the three tables statically imported from the glue analysis module rather than just the one that goes through the ETL. I added a new crosswalk validation test instead. I also updated epacems test to reflect the current number of columns in CEMS (I forgot to do that when I removed the NOX rate columns before). --- src/pudl/output/epacems.py | 24 +-- src/pudl/output/pudltabl.py | 24 +++ test/integration/epacems_test.py | 2 +- test/unit/analysis/epa_crosswalk_test.py | 220 -------------------- test/validate/epacamd_eia_crosswalk_test.py | 22 ++ 5 files changed, 55 insertions(+), 237 deletions(-) delete mode 100644 test/unit/analysis/epa_crosswalk_test.py create mode 100644 test/validate/epacamd_eia_crosswalk_test.py diff --git a/src/pudl/output/epacems.py b/src/pudl/output/epacems.py index 9d2b22d73e..4171b3551c 100644 --- a/src/pudl/output/epacems.py +++ b/src/pudl/output/epacems.py @@ -5,27 +5,19 @@ import dask.dataframe as dd import pandas as pd +import sqlalchemy as sa import pudl from pudl.settings import EpaCemsSettings -# TODO: hardcoded data version doesn't belong here, but will defer fixing it until -# the crosswalk is formally integrated into PUDL. See Issue #1123 -EPA_CROSSWALK_RELEASE = ( - "https://github.com/USEPA/camd-eia-crosswalk/releases/download/v0.2.1/" -) - -def epa_crosswalk() -> pd.DataFrame: - # TODO: formally integrate this into PUDL. See Issue #1123 - """Read EPA/EIA crosswalk from EPA github repo. - - See https://github.com/USEPA/camd-eia-crosswalk for details and data dictionary - - Returns: - pd.Dataframe: EPA/EIA crosswalk - """ - return pd.read_csv(EPA_CROSSWALK_RELEASE + "epa_eia_crosswalk.csv") +def epacamd_eia_crosswalk(pudl_engine: sa.engine.Engine) -> pd.DataFrame: + """Pull the EPACAMD-EIA Crosswalk table.""" + pt = pudl.output.pudltabl.get_table_meta(pudl_engine) + crosswalk_tbl = pt["utilities_entity_eia"] + crosswalk_select = sa.sql.select(crosswalk_tbl) + crosswalk_df = pd.read_sql(crosswalk_select, pudl_engine) + return crosswalk_df def year_state_filter( diff --git a/src/pudl/output/pudltabl.py b/src/pudl/output/pudltabl.py index ce81210ec2..f0e8e0ca60 100644 --- a/src/pudl/output/pudltabl.py +++ b/src/pudl/output/pudltabl.py @@ -1213,6 +1213,30 @@ def plant_parts_eia( return self._dfs["plant_parts_eia"] + ########################################################################### + # GLUE OUTPUTS + ########################################################################### + + def epacamd_eia_crosswalk( + self, + update: bool = False, + ) -> pd.DataFrame: + """Pull the EPACAMD-EIA Crosswalk Table. + + Args: + update: If true, re-calculate the output dataframe, even if + a cached version exists. + + Returns: + A denormalized table for interactive use. + + """ + if update or self._dfs["epacamd_eia_crosswalk"] is None: + self._dfs[ + "epacamd_eia_crosswalk" + ] = pudl.output.epacems.epacamd_eia_crosswalk(self.pudl_engine) + return self._dfs["epacamd_eia_crosswalk"] + def get_table_meta(pudl_engine): """Grab the pudl sqlitie database table metadata.""" diff --git a/test/integration/epacems_test.py b/test/integration/epacems_test.py index f61dad6a82..e76d1280e3 100644 --- a/test/integration/epacems_test.py +++ b/test/integration/epacems_test.py @@ -88,4 +88,4 @@ def test_epacems_parallel(pudl_settings_fixture, pudl_ds_kwargs, tmpdir_factory) engine="pyarrow", split_row_groups=True, ).compute() - assert df.shape == (96_360, 18) # nosec: B101 + assert df.shape == (96_360, 16) # nosec: B101 diff --git a/test/unit/analysis/epa_crosswalk_test.py b/test/unit/analysis/epa_crosswalk_test.py deleted file mode 100644 index 7f6107457e..0000000000 --- a/test/unit/analysis/epa_crosswalk_test.py +++ /dev/null @@ -1,220 +0,0 @@ -"""Unit tests for the :mod:`pudl.analysis.epa_crosswalk` module.""" -from collections.abc import Sequence - -import dask.dataframe as dd -import pandas as pd -import pytest -from pandas.testing import assert_frame_equal - -import pudl.analysis.epa_crosswalk as cw - - -def df_from_product(inputs: dict[str, Sequence], as_index=True) -> pd.DataFrame: - """Make a dataframe from cartesian product of input sequences. - - Args: - inputs (Dict[str, Sequence]): dataframe column names mapped - to their unique values. - as_index (bool): whether to set the product as the index - - Return: - df (pd.DataFrame): cartesian product dataframe - """ - names, iterables = zip(*inputs.items()) - df = pd.MultiIndex.from_product(iterables, names=names).to_frame(index=as_index) - - return df - - -@pytest.fixture() -def mock_crosswalk(): - """Minimal EPA Crosswalk. - - The crosswalk is basically a list of graph edges linking CAMD units (combustors) to EIA generators within plants. - - CAMD_PLANT_ID CAMD_UNIT_ID EIA_GENERATOR_ID MATCH_TYPE_GEN - 0 10 a 0 asdf - 1 12 a 0 asdf - 2 12 a 1 asdf - 3 11 a 0 asdf - 4 11 b 0 asdf - 5 10 b 1 asdf - 6 10 c 1 asdf - 7 10 c 2 asdf - """ - columns = ["CAMD_PLANT_ID", "CAMD_UNIT_ID", "EIA_GENERATOR_ID"] - one_to_one = pd.DataFrame( - dict( - zip( - columns, - [ - [ - 10, - ], - [ - "a", - ], - [ - 0, - ], - ], - ) - ) - ) - many_to_one = pd.DataFrame(dict(zip(columns, [[11, 11], ["a", "b"], [0, 0]]))) - one_to_many = pd.DataFrame(dict(zip(columns, [[12, 12], ["a", "a"], [0, 1]]))) - many_to_many = pd.DataFrame( - dict(zip(columns, [[10, 10, 10], ["b", "c", "c"], [1, 1, 2]])) - ) - crosswalk = pd.concat( - [one_to_one, one_to_many, many_to_one, many_to_many], axis=0, ignore_index=True - ) - crosswalk["MATCH_TYPE_GEN"] = "asdf" - - return crosswalk - - -@pytest.fixture() -def mock_cems_extended(): - """EPA CEMS with more units. Needed to cover all the cases in graph analysis. - - Timestamps are reduced to 2 for this test. - - NOTE: Only unique IDs are in the table below. The actual dataframe has 2 timestamps per row here. - unit_id_epa operating_datetime_utc plant_id_eia unitid gross_load_mw - 0 2019-12-31 22:00:00+00:00 10 a 0 - 1 2019-12-31 22:00:00+00:00 10 b 0 - 2 2019-12-31 22:00:00+00:00 10 c 0 - 3 2019-12-31 22:00:00+00:00 11 a 0 - 4 2019-12-31 22:00:00+00:00 11 b 0 - 5 2019-12-31 22:00:00+00:00 12 a 0 - """ - inputs = dict( - unit_id_epa=range(6), - operating_datetime_utc=pd.date_range( - start="2019-12-31 22:00", end="2019-12-31 23:00", freq="h", tz="UTC" - ), - ) - cems = df_from_product(inputs, as_index=False) - # add composite keys - # (duplicate each entry for other timestamp) - cems["plant_id_eia"] = [10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12] - cems["unitid"] = ["a", "a", "b", "b", "c", "c", "a", "a", "b", "b", "a", "a"] - cems["gross_load_mw"] = 0 # not needed for crosswalk testing - - cems = cems.set_index(["unit_id_epa", "operating_datetime_utc"], drop=False) - return cems - - -def test__get_unique_keys(mock_cems_extended): - """Test that dask and pandas dataframes give the same unique keys.""" - mock_cems_extended = mock_cems_extended.reset_index( - drop=True - ) # this func is used before index is set. Dask doesn't support MultiIndex - # sensititve to column order - expected = mock_cems_extended[::2][["plant_id_eia", "unitid", "unit_id_epa"]] - actual = cw._get_unique_keys(mock_cems_extended) - assert_frame_equal(actual, expected) - - dask_cems = dd.from_pandas(mock_cems_extended, npartitions=2) - actual = cw._get_unique_keys(dask_cems) - assert_frame_equal(actual, expected) - - -def test__convert_global_id_to_composite_id(mock_crosswalk, mock_cems_extended): - """Test conversion of global_subplant_id to a composite subplant_id. - - The global_subplant_id should be equivalent to the composite (CAMD_PLANT_ID, subplant_id). - - global_subplant_id subplant_id ... CAMD_PLANT_ID CAMD_UNIT_ID EIA_GENERATOR_ID ... - 0 0 0 ... 10 a 0 ... # one to one - 1 1 1 ... 10 b 1 ... # many to many - 2 1 1 ... 10 c 1 ... - 3 1 1 ... 10 c 2 ... - 4 2 0 ... 11 a 0 ... # one to many - 5 2 0 ... 11 b 0 ... - 6 3 0 ... 12 a 0 ... # many to one - 7 3 0 ... 12 a 1 ... - """ - cols = ["plant_id_eia", "unitid", "unit_id_epa"] - uniques = mock_cems_extended.loc[ - pd.IndexSlice[:, "2019-12-31 22:00:00+00:00"], cols - ].copy() - - # simulate join by duplicating rows as appropriate - one_to_many = uniques.query('plant_id_eia == 12 and unitid == "a"') - many_to_many = uniques.query('plant_id_eia == 10 and unitid == "c"') - expected = ( - pd.concat([uniques, one_to_many, many_to_many]) - .sort_index() - .reset_index(drop=True) - ) - - expected = expected.assign( - CAMD_PLANT_ID=expected["plant_id_eia"], - CAMD_UNIT_ID=expected["unitid"], - EIA_GENERATOR_ID=[0, 1, 1, 2, 0, 0, 0, 1], - MATCH_TYPE_GEN="asdf", - global_subplant_id=[0, 1, 1, 1, 2, 2, 3, 3], - subplant_id=[0, 1, 1, 1, 0, 0, 0, 0], - ) - # fix column order - expected = expected[ - cols - + ["CAMD_PLANT_ID", "CAMD_UNIT_ID", "EIA_GENERATOR_ID", "MATCH_TYPE_GEN"] - + ["global_subplant_id", "subplant_id"] - ] - - input_ = expected.drop(columns=["subplant_id"]) - actual = cw._convert_global_id_to_composite_id(input_) - assert_frame_equal(actual, expected) - - -def test_make_subplant_ids(mock_crosswalk, mock_cems_extended): - """Integration test for the subplant_id assignment process. - - The new subplant_id column is half of the compound key (CAMD_PLANT_ID, subplant_id) that - should identify disjoint subgraphs of units and generators. - - subplant_id ... CAMD_PLANT_ID CAMD_UNIT_ID EIA_GENERATOR_ID ... - 0 0 ... 10 a 0 ... # one to one - 1 1 ... 10 b 1 ... # many to many - 2 1 ... 10 c 1 ... - 3 1 ... 10 c 2 ... - 4 0 ... 11 a 0 ... # one to many - 5 0 ... 11 b 0 ... - 6 0 ... 12 a 0 ... # many to one - 7 0 ... 12 a 1 ... - """ - cols = ["plant_id_eia", "unitid", "unit_id_epa"] - uniques = mock_cems_extended.loc[ - pd.IndexSlice[:, "2019-12-31 22:00:00+00:00"], cols - ].copy() - - # simulate join by duplicating rows as appropriate - one_to_many = uniques.query('plant_id_eia == 12 and unitid == "a"') - many_to_many = uniques.query('plant_id_eia == 10 and unitid == "c"') - expected = ( - pd.concat([uniques, one_to_many, many_to_many]) - .sort_index() - .reset_index(drop=True) - ) - - expected = expected.assign( - CAMD_PLANT_ID=expected["plant_id_eia"], - CAMD_UNIT_ID=expected["unitid"], - EIA_GENERATOR_ID=[0, 1, 1, 2, 0, 0, 0, 1], - MATCH_TYPE_GEN="asdf", - subplant_id=[0, 1, 1, 1, 0, 0, 0, 0], - ) - # fix column order - expected = expected[ - ["subplant_id"] - + cols - + ["CAMD_PLANT_ID", "CAMD_UNIT_ID", "EIA_GENERATOR_ID", "MATCH_TYPE_GEN"] - ] - - # should be two separate tests but I ran out of time - actual = cw.filter_crosswalk(mock_crosswalk, mock_cems_extended) - actual = cw.make_subplant_ids(actual) - assert_frame_equal(actual, expected) diff --git a/test/validate/epacamd_eia_crosswalk_test.py b/test/validate/epacamd_eia_crosswalk_test.py new file mode 100644 index 0000000000..6609ff49b3 --- /dev/null +++ b/test/validate/epacamd_eia_crosswalk_test.py @@ -0,0 +1,22 @@ +"""Validate post-ETL EPACAMD-EIA Crosswalk data.""" +import logging + +import pytest + +from pudl.validate import check_unique_rows + +logger = logging.getLogger(__name__) + + +def test_unique_ids(pudl_out_eia, live_dbs): + """Test whether the EIA plants and EPA unit pairings are unique.""" + if not live_dbs: + pytest.skip("Data validation only works with a live PUDL DB.") + if pudl_out_eia.freq is not None: + pytest.skip("Test should only run on un-aggregated data.") + # Should I add these args to the pudl.validate module? + check_unique_rows( + pudl_out_eia.epacamd_eia_crosswalk, + ["plant_id_eia", "emissions_unit_id_epa"], + "epacamd_eia_crosswalk", + ) From ad76bc36d376e9f667bf796e644882091e832f49 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Sun, 21 Aug 2022 13:24:30 -0400 Subject: [PATCH 33/80] Fix two bugs: 1) add a dtype fix at the beginning and the end of the cems transform step. Annoying but needed for now. 2) Fix crosswalk output table so that it actually grabs the cems table not the utility entitiy table.... --- src/pudl/output/epacems.py | 2 +- src/pudl/transform/epacems.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/pudl/output/epacems.py b/src/pudl/output/epacems.py index 4171b3551c..cd9f1b5957 100644 --- a/src/pudl/output/epacems.py +++ b/src/pudl/output/epacems.py @@ -14,7 +14,7 @@ def epacamd_eia_crosswalk(pudl_engine: sa.engine.Engine) -> pd.DataFrame: """Pull the EPACAMD-EIA Crosswalk table.""" pt = pudl.output.pudltabl.get_table_meta(pudl_engine) - crosswalk_tbl = pt["utilities_entity_eia"] + crosswalk_tbl = pt["epacamd_eia_crosswalk"] crosswalk_select = sa.sql.select(crosswalk_tbl) crosswalk_df = pd.read_sql(crosswalk_select, pudl_engine) return crosswalk_df diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 8418f2f30d..569e3832f6 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -205,7 +205,8 @@ def transform( """ return ( - raw_df.pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") + raw_df.pipe(apply_pudl_dtypes, group="epacems") + .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") .pipe(harmonize_eia_epa_orispl, pudl_engine) .pipe(convert_to_utc, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) .pipe(correct_gross_load_mw) From 3552f0b1518a122d980cdd0b3d48da35ab9478ed Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Sun, 21 Aug 2022 13:25:29 -0400 Subject: [PATCH 34/80] Add two CEMS crosswalk notebooks to WIP folder --- .../work-in-progress/Combine_CEMS_EIA.ipynb | 798 ++ .../play_with_cems_crosswalk.ipynb | 7010 +++++++++++++++++ 2 files changed, 7808 insertions(+) create mode 100644 notebooks/work-in-progress/Combine_CEMS_EIA.ipynb create mode 100644 notebooks/work-in-progress/play_with_cems_crosswalk.ipynb diff --git a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb new file mode 100644 index 0000000000..7c63bc9caf --- /dev/null +++ b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb @@ -0,0 +1,798 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b4f6db74-0ad8-4418-bfc3-fdc12a07d750", + "metadata": {}, + "source": [ + "# CEMS Allocater" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eb7fcab6-4d89-4950-946f-4232be6341a8", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import pudl\n", + "import pandas as pd\n", + "import logging\n", + "import sys\n", + "import sqlalchemy as sa\n", + "import dask.dataframe as dd\n", + "\n", + "# basic setup for logging\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "handler = logging.StreamHandler(stream=sys.stdout)\n", + "formatter = logging.Formatter('%(message)s')\n", + "handler.setFormatter(formatter)\n", + "logger.handlers = [handler]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c76104c9-e83f-46ac-b5f3-ad5908a6af01", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "pudl_settings = pudl.workspace.setup.get_defaults()\n", + "pudl_engine = sa.create_engine(pudl_settings[\"pudl_db\"])\n", + "pudl_out = pudl.output.pudltabl.PudlTabl(pudl_engine,freq='AS')" + ] + }, + { + "cell_type": "markdown", + "id": "1e3c1eb4-bc59-40f7-96ad-088718e5d620", + "metadata": {}, + "source": [ + "#### CEMS" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e25d8b80-47b2-4e54-91e7-f2e674fc72bd", + "metadata": {}, + "outputs": [], + "source": [ + "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", + "cems_dd = dd.read_parquet(\n", + " epacems_path, \n", + " columns=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\", \"co2_mass_tons\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "132c57f5-90f7-4f2b-a07b-647e4dba9c36", + "metadata": {}, + "outputs": [], + "source": [ + "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"]).sum().compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "11f9f3e8-1df7-4483-8cd6-6dff9471e381", + "metadata": {}, + "outputs": [], + "source": [ + "cems_df = cems_df.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "250d5ad8-06d4-4fb3-9a83-2866fc151a3c", + "metadata": {}, + "outputs": [], + "source": [ + "cems_df[\"plant_id_eia\"] = cems_df.plant_id_eia.astype(\"Int64\")\n", + "cems_df[\"co2_mass_tons\"] = cems_df.co2_mass_tons.fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "dda4c669-4a7d-4024-b661-e618e9247903", + "metadata": {}, + "source": [ + "#### Crosswalk" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "59114182-0920-47fc-8cc9-6792b301fbef", + "metadata": {}, + "outputs": [], + "source": [ + "crosswalk_df = pudl_out.epacamd_eia_crosswalk()" + ] + }, + { + "cell_type": "markdown", + "id": "a520696a-5c4d-4353-85fc-194f8cc7386e", + "metadata": {}, + "source": [ + "#### EIA" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "adb143d7-14f8-4528-86e8-8e553ff6eda9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filling technology type\n", + "Filled technology_type coverage now at 98.1%\n" + ] + } + ], + "source": [ + "eia_gens_df = pudl_out.gens_eia860()" + ] + }, + { + "cell_type": "markdown", + "id": "e11438ec-759c-4726-bcc0-5e38dfa1cd6c", + "metadata": {}, + "source": [ + "#### Allocate" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "id": "fb5f633a-c11c-40a5-96d9-3889ba421107", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def allocate_cols(\n", + " to_allocate: pd.DataFrame, by: list, data_and_allocator_cols: dict\n", + ") -> pd.DataFrame:\n", + " \"\"\"\n", + " Allocate larger dataset records porportionally by EIA plant-part columns.\n", + " Args:\n", + " to_allocate: table of data that has been merged with the EIA plant-parts\n", + " records of the scale that you want the output to be in.\n", + " by: columns to group by.\n", + " data_and_allocator_cols: dict of data columns that you want to allocate (keys)\n", + " and ordered list of columns to allocate porportionally based on. Values\n", + " ordered based on priority: if non-null result from frist column, result\n", + " will include first column result, then second and so on.\n", + " Returns:\n", + " an augmented version of ``to_allocate`` with the data columns (keys in\n", + " ``data_and_allocator_cols``) allocated proportionally.\n", + " \"\"\"\n", + " # add a total column for all of the allocate cols.\n", + " all_allocator_cols = list(set(sum(data_and_allocator_cols.values(), [])))\n", + " to_allocate.loc[:, [f\"{c}_total\" for c in all_allocator_cols]] = (\n", + " to_allocate.groupby(by=by, dropna=False)[all_allocator_cols]\n", + " .transform(sum, min_count=1)\n", + " .add_suffix(\"_total\")\n", + " )\n", + " # for each of the columns we want to allocate the frc data by\n", + " # generate the % of the total group, so we can allocate the data_col\n", + " to_allocate = to_allocate.assign(\n", + " **{\n", + " f\"{col}_proportion\": to_allocate[col] / to_allocate[f\"{col}_total\"]\n", + " for col in all_allocator_cols\n", + " }\n", + " )\n", + " # do the allocation for each of the data columns\n", + " for data_col in data_and_allocator_cols:\n", + " output_col = f\"{data_col}_allocated\"\n", + " to_allocate.loc[:, output_col] = pd.NA\n", + " # choose the first non-null option. The order of the allocate_cols will\n", + " # determine which allocate_col will be used\n", + " for allocator_col in data_and_allocator_cols[data_col]:\n", + " to_allocate[output_col] = to_allocate[output_col].fillna(\n", + " to_allocate[data_col] * to_allocate[f\"{allocator_col}_proportion\"]\n", + " )\n", + " # drop and rename all the columns in the data_and_allocator_cols dict keys and\n", + " # return these columns in the dataframe\n", + " to_allocate = (\n", + " to_allocate.drop(columns=list(data_and_allocator_cols.keys()))\n", + " .rename(\n", + " columns={\n", + " f\"{data_col}_allocated\": data_col\n", + " for data_col in data_and_allocator_cols\n", + " }\n", + " )\n", + " .drop(\n", + " columns=list(to_allocate.filter(like=\"_proportion\").columns)\n", + " + [f\"{c}_total\" for c in all_allocator_cols]\n", + " )\n", + " )\n", + " return to_allocate" + ] + }, + { + "cell_type": "markdown", + "id": "6ed6ebfe-7a66-458a-81d9-31b797d2efb6", + "metadata": {}, + "source": [ + "#### TEST ALLOCATE" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "id": "4501bfc7-b25a-400c-9922-f5bcabddb499", + "metadata": {}, + "outputs": [], + "source": [ + "test = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "id": "7eca5275-da3f-4d1a-b32c-99f943685df0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "test_merge = test.merge(crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], how=\"left\", on=[\"plant_id_eia\", \"generator_id\"])\n", + "test_merge = test_merge[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\", \"capacity_mw\"]]\n", + "test_merge[\"year\"] = test_merge.report_date.dt.year\n", + "test_merge[\"year\"] = test_merge.year.astype(\"Int64\")\n", + "test_merge = test_merge.merge(cems_df, how=\"left\", on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", + "test_merge[\"co2_mass_tons\"] = test_merge.co2_mass_tons.fillna(0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "id": "090bd266-97d0-4407-82c3-cee1b36ffb92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n", + "13\n" + ] + } + ], + "source": [ + "print(len(test_merge))\n", + "print(len(test))" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "id": "c897e36d-827e-4d52-be24-0aba2b4019f0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# If you want to allocate by something other than generator (plant or prive mover),\n", + "# make sure the capacity value is for that level of aggregation.\n", + "\n", + "tt = allocate_cols(\n", + " to_allocate=test_merge,\n", + " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", + " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]} \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "id": "fee7731a-569f-47a8-9d2f-aab9d45df5fc", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "

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report_dateplant_id_eiagenerator_idcapacity_mwco2_mass_tons
02020-01-0131153.14.667000e+03
12020-01-0132153.11.697000e+03
22020-01-0133272.00.000000e+00
32020-01-0134403.71.640450e+05
42020-01-0135788.83.128656e+06
52020-01-013A1CT170.13.964052e+05
62020-01-013A1CT2170.14.309728e+05
72020-01-013A1ST390.49.494661e+05
82020-01-013A2C1170.14.108155e+05
92020-01-013A2C2170.14.137542e+05
102020-01-013A2ST390.49.462434e+05
112020-01-013A3C1464.00.000000e+00
122020-01-013A3ST310.00.000000e+00
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" + ], + "text/plain": [ + " report_date plant_id_eia generator_id capacity_mw co2_mass_tons\n", + "0 2020-01-01 3 1 153.1 4.667000e+03\n", + "1 2020-01-01 3 2 153.1 1.697000e+03\n", + "2 2020-01-01 3 3 272.0 0.000000e+00\n", + "3 2020-01-01 3 4 403.7 1.640450e+05\n", + "4 2020-01-01 3 5 788.8 3.128656e+06\n", + "5 2020-01-01 3 A1CT 170.1 3.964052e+05\n", + "6 2020-01-01 3 A1CT2 170.1 4.309728e+05\n", + "7 2020-01-01 3 A1ST 390.4 9.494661e+05\n", + "8 2020-01-01 3 A2C1 170.1 4.108155e+05\n", + "9 2020-01-01 3 A2C2 170.1 4.137542e+05\n", + "10 2020-01-01 3 A2ST 390.4 9.462434e+05\n", + "11 2020-01-01 3 A3C1 464.0 0.000000e+00\n", + "12 2020-01-01 3 A3ST 310.0 0.000000e+00" + ] + }, + "execution_count": 308, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now sum up to generator level \n", + "tt.groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"]).sum().reset_index().drop(columns=[\"year\"])" + ] + }, + { + "cell_type": "markdown", + "id": "7263bb02-a591-4c00-8d68-90bc420da840", + "metadata": {}, + "source": [ + "#### ALLOCATE WITH ALL GENS" + ] + }, + { + "cell_type": "code", + "execution_count": 356, + "id": "a9b4e5aa-5d28-49a0-8274-dd071a208232", + "metadata": {}, + "outputs": [], + "source": [ + "## Merge with whole CEMS!\n", + "#test = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]\n", + "cems_merge1 = eia_gens_df.merge(crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], how=\"left\", on=[\"plant_id_eia\", \"generator_id\"])\n", + "cems_merge1 = cems_merge1[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\", \"capacity_mw\", \"technology_description\"]]\n", + "cems_merge1[\"year\"] = cems_merge1.report_date.dt.year\n", + "cems_merge1[\"year\"] = cems_merge1.year.astype(\"Int64\")\n", + "cems_merge2 = cems_merge1.merge(cems_df, how=\"left\", on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", + "cems_merge2[\"co2_mass_tons\"] = cems_merge2.co2_mass_tons.fillna(0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "id": "6597f5d5-dd70-487d-9b3b-1182e60058d7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cems_gen_agg = allocate_cols(\n", + " to_allocate=cems_merge2,\n", + " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", + " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]}\n", + ").groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"]).sum(\n", + ").reset_index(\n", + ").drop(columns=[\"year\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "id": "100ca68f-3f4b-42d3-a32e-1f4ab19af3a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "491469\n" + ] + } + ], + "source": [ + "print(len(cems_gen_agg))" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "id": "057f5eb9-00b5-4732-97f2-3f2398666d39", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78.90833399461614\n", + "387810\n", + "491469\n", + "\n", + "68.23238873700286\n", + "219376\n", + "321513\n" + ] + } + ], + "source": [ + "bb = cems_merge1.drop_duplicates(subset=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", + "print(len(bb[bb[\"emissions_unit_id_epa\"].isna()]) / len(bb) * 100)\n", + "print(len(bb[bb[\"emissions_unit_id_epa\"].isna()]))\n", + "print(len(bb))\n", + "print(\"\")\n", + "\n", + "fossil = cems_merge1[cems_merge1[\"technology_description\"].isin(\n", + " [\"Conventional Steam Coal\",\n", + " \"Natural Gas Fired Combined Cycle\",\n", + " \"Natural Gas Fired Combustion Turbine\",\n", + " \"Natural Gas Steam Turbine\",\n", + " \"Petroleum Liquids\",\n", + " \"Natural Gas Internal Combustion Engine\",\n", + " \"Municipal Solid Waste\",\n", + " \"Wood/Wood Waste Biomass\",\n", + " \"Coal Integrated Gasification Combined Cycle\",\n", + " \"Petroleum Coke\",\n", + " \"Landfill Gas\",\n", + " \"Natural Gas with Compressed Air Storage\",\n", + " \"Other Gases\",\n", + " \"Other Waste Biomass\",\n", + " \"Other Natural Gas\"])]\n", + "\n", + "ff = fossil.drop_duplicates(subset=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", + "print(len(ff[ff[\"emissions_unit_id_epa\"].isna()]) / len(ff) * 100)\n", + "print(len(ff[ff[\"emissions_unit_id_epa\"].isna()]))\n", + "print(len(ff))" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "id": "b251e390-aa40-4a2d-a27b-3154cb9f258c", + "metadata": {}, + "outputs": [], + "source": [ + "non_agg.to_pickle(\"/Users/austensharpe/Desktop/non_agg.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 361, + "id": "7ef8817d-47d5-4577-8f23-efea140acb06", + "metadata": {}, + "outputs": [], + "source": [ + "agg.to_pickle(\"/Users/austensharpe/Desktop/agg.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "id": "e28101a6-661d-41e0-8bff-47efa3d869a5", + 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..................
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" + ], + "text/plain": [ + " report_date plant_id_eia generator_id capacity_mw co2_mass_tons\n", + "0 2001-01-01 2 1 45.0 0.0\n", + "1 2001-01-01 3 1 153.1 951508.0\n", + "2 2001-01-01 3 2 153.1 902068.0\n", + "3 2001-01-01 3 3 272.0 1969314.0\n", + "4 2001-01-01 3 4 403.7 2843765.0\n", + "... ... ... ... ... ...\n", + "491464 2021-01-01 65333 785 200.0 0.0\n", + "491465 2021-01-01 65334 PLTVW 81.0 0.0\n", + "491466 2021-01-01 65335 WAPPA 171.8 0.0\n", + "491467 2021-01-01 65337 MAYBK 5.0 0.0\n", + "491468 2021-01-01 65338 UNIS1 108.0 0.0\n", + "\n", + "[491469 rows x 5 columns]" + ] + }, + "execution_count": 362, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be47f82c-6273-47eb-9a87-d92d9beefd46", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb b/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb new file mode 100644 index 0000000000..d639a537a0 --- /dev/null +++ b/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb @@ -0,0 +1,7010 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "68e5e995-c646-4125-b742-82bd9e7459fc", + "metadata": {}, + "source": [ + "# CEMS Crosswalk Testing" + ] + }, + { + "cell_type": "markdown", + "id": "9e67606a-3696-400c-9f1a-daf3aa6bf13c", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "37965668-7e10-4022-853e-6f10dc386859", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5d204afa-7812-41a9-8d54-1573212a7538", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pudl\n", + "import pandas as pd\n", + "import logging\n", + "import sys\n", + "import sqlalchemy as sa\n", + "import dask.dataframe as dd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c3eea5de-2336-4974-90cc-4530375247b7", + "metadata": {}, + "outputs": [], + "source": [ + "# basic setup for logging\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "handler = logging.StreamHandler(stream=sys.stdout)\n", + "formatter = logging.Formatter('%(message)s')\n", + "handler.setFormatter(formatter)\n", + "logger.handlers = [handler]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "56cd672d-1464-44b6-9c64-4888e6d6df56", + "metadata": {}, + "outputs": [], + "source": [ + "pudl_settings = pudl.workspace.setup.get_defaults()\n", + "pudl_engine = sa.create_engine(pudl_settings[\"pudl_db\"])\n", + "start_date=None\n", + "end_date=None\n", + "freq='AS'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "092d330e-03ca-40e5-a037-9421ded88a5e", + "metadata": {}, + "outputs": [], + "source": [ + "pudl_out = pudl.output.pudltabl.PudlTabl(pudl_engine,freq='AS')" + ] + }, + { + "cell_type": "markdown", + "id": "b9e6577c-75b7-496e-a2a6-f0b02499a04a", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Get Transformed Crosswalk" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "75a7507d-b26f-4865-9077-f6a24a7dedc6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieving https://zenodo.org/api/deposit/depositions/6633770 from zenodo\n", + "Retrieving https://zenodo.org/api/files/4f9ac0dc-a9b4-4d2b-9e0c-c0e97a2fc7f6/datapackage.json from zenodo\n", + "Retrieving https://zenodo.org/api/files/4f9ac0dc-a9b4-4d2b-9e0c-c0e97a2fc7f6/epacems_unitid_eia_plant_crosswalk.zip from zenodo\n", + "Cleaning up the epacems-eia crosswalk\n" + ] + } + ], + "source": [ + "from pudl.workspace.datastore import Datastore\n", + "from pudl.glue.epacamd_eia_crosswalk import extract, transform\n", + "ds = Datastore()\n", + "gens_ent = pd.read_sql('generators_entity_eia', pudl_engine)\n", + "boiler_ent = pd.read_sql('boilers_entity_eia', pudl_engine)\n", + "\n", + "cems_crosswalk_dict = transform(extract(ds), gens_ent, boiler_ent, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "da08b2cc-e34d-46cf-82c5-6411159a1ac3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['epacamd_eia_crosswalk'])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cems_crosswalk_dict.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85fb5a54-f7ad-4c4f-a476-f52316799141", + "metadata": {}, + "outputs": [], + "source": [ + "cems_crosswalk = cems_crosswalk_dict[\"epacamd_eia_crosswalk\"]" + ] + }, + { + "cell_type": "markdown", + "id": "882bb0c6-a403-40fb-8df1-92b878aa407a", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Get CEMS from Parquet" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0d799db8-2bf5-4853-9258-c57d03ae8a90", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", + "\n", + "cems_dd = dd.read_parquet(\n", + " epacems_path, \n", + " columns=[\"year\", \"plant_id_eia\", \"unitid\", \"co2_mass_tons\"],\n", + ")\n", + "\n", + "# filters = pudl.output.epacems.year_state_filter(years=[2019], states=[\"ME\"])\n", + "\n", + "# cems_small_dd = dd.read_parquet(\n", + "# epacems_path,\n", + "# engine=\"pyarrow\",\n", + "# columns=[\"year\", \"state\", \"operating_datetime_utc\", \"operating_time_hours\", \"plant_id_eia\", \"facility_id\", \"unit_id_epa\", \"unitid\"],\n", + "# #filters=[[('year', '=', 2019)]],\n", + "# index=False\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "52382ebb-8984-4d95-a78b-9f80942ebcf0", + "metadata": {}, + "outputs": [], + "source": [ + "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"unitid\"]).sum().compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1271dd72-6316-49cd-9b57-ea2b79eefac8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#idaho_cems = cems_small_dd[(cems_small_dd[\"state\"]==\"ID\") & (cems_small_dd[\"year\"]==2019)].compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47dbc902-ec43-4d74-99b2-75d2f521ce13", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# This shows whether unitid and unit_id are 1:1 within a given year\n", + "test = cems_df_plant.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", + "ser = test.groupby([\"operating_datetime_utc\", \"operating_time_hours\", \"plant_id_eia\", \"unit_id_epa\"])[\"unitid\"].nunique() \n", + "print(ser[ser>1])\n", + "\n", + "# This shows whether plant_id_eia and facility_id are 1:1 within a given year\n", + "test = cems_df_plant.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", + "ser = test.groupby([\"operating_datetime_utc\", \"operating_time_hours\", \"facility_id\"])[\"plant_id_eia\"].nunique() \n", + "print(ser[ser>1])" + ] + }, + { + "cell_type": "markdown", + "id": "24f4a6c4-93ce-48b0-86dc-6db5d03fdd08", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Get Raw CEMS from Datastore" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "02dc5c76-5818-4ab0-a4b7-606d3e139b95", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from pudl.workspace.datastore import Datastore\n", + "from pathlib import Path\n", + "\n", + "# If you want to run the extractor with you LOCAL data, make sure you specify a path to your existing datastore with ds_kwargs\n", + "ds_kwargs = {\"local_cache_path\": Path(pudl_settings[\"pudl_in\"]) / \"data\"}\n", + "\n", + "# If you want to download the data from Zenodo, create the Datastore() instance without arguments.\n", + "ds = Datastore(**ds_kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a26ceadd-cee4-4568-b8aa-fcbe7d2e1906", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cems_datastore = pudl.extract.epacems.EpaCemsDatastore(ds)\n", + "cems_partition = pudl.extract.epacems.EpaCemsPartition(\"2019\", \"ID\")\n", + "\n", + "raw_idaho_cems = cems_datastore.get_data_frame(cems_partition)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2c522ded-2038-475f-9110-11739088f251", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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co2_mass_measurement_codeco2_mass_tonsemissions_unit_id_epagross_load_mwheat_content_mmbtunox_mass_lbsnox_mass_measurement_codeop_dateop_houroperating_time_hoursplant_id_epaso2_mass_lbsso2_mass_measurement_codestatesteam_load_1000_lbs
0NaNNaNNaNNaNNaNNaNNaN01-01-201900.07456NaNNaNIDNaN
1NaNNaNNaNNaNNaNNaNNaN01-01-201910.07456NaNNaNIDNaN
2NaNNaNNaNNaNNaNNaNNaN01-01-201920.07456NaNNaNIDNaN
3NaNNaNNaNNaNNaNNaNNaN01-01-201930.07456NaNNaNIDNaN
4NaNNaNNaNNaNNaNNaNNaN01-01-201940.07456NaNNaNIDNaN
................................................
70075Measured115.0CT1289.01935.613.549Calculated12-31-2019191.0570281.161MeasuredIDNaN
70076Measured110.8CT1277.01864.811.189Calculated12-31-2019201.0570281.119MeasuredIDNaN
70077Measured110.4CT1276.01857.911.147Calculated12-31-2019211.0570281.115MeasuredIDNaN
70078Measured106.0CT1264.01783.110.699Calculated12-31-2019221.0570281.070MeasuredIDNaN
70079Measured109.7CT1274.01845.511.073Calculated12-31-2019231.0570281.107MeasuredIDNaN
\n", + "

70080 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " co2_mass_measurement_code co2_mass_tons emissions_unit_id_epa gross_load_mw heat_content_mmbtu nox_mass_lbs nox_mass_measurement_code op_date op_hour operating_time_hours plant_id_epa so2_mass_lbs so2_mass_measurement_code state steam_load_1000_lbs\n", + "0 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 0 0.0 7456 NaN NaN ID NaN\n", + "1 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 1 0.0 7456 NaN NaN ID NaN\n", + "2 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 2 0.0 7456 NaN NaN ID NaN\n", + "3 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 3 0.0 7456 NaN NaN ID NaN\n", + "4 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 4 0.0 7456 NaN NaN ID NaN\n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "70075 Measured 115.0 CT1 289.0 1935.6 13.549 Calculated 12-31-2019 19 1.0 57028 1.161 Measured ID NaN\n", + "70076 Measured 110.8 CT1 277.0 1864.8 11.189 Calculated 12-31-2019 20 1.0 57028 1.119 Measured ID NaN\n", + "70077 Measured 110.4 CT1 276.0 1857.9 11.147 Calculated 12-31-2019 21 1.0 57028 1.115 Measured ID NaN\n", + "70078 Measured 106.0 CT1 264.0 1783.1 10.699 Calculated 12-31-2019 22 1.0 57028 1.070 Measured ID NaN\n", + "70079 Measured 109.7 CT1 274.0 1845.5 11.073 Calculated 12-31-2019 23 1.0 57028 1.107 Measured ID NaN\n", + "\n", + "[70080 rows x 15 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pudl.helpers.remove_leading_zeros_from_numeric_strings(raw_idaho_cems, \"emissions_unit_id_epa\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2a5dedeb-6f1e-4cd1-971a-b36719e99b6c", + "metadata": {}, + "outputs": [], + "source": [ + "raw_idaho_cems.loc[raw_idaho_cems[\"emissions_unit_id_epa\"]=='1', \"emissions_unit_id_epa\"] = np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9d4fb83e-52c8-45f2-a5af-08a72a97e6a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 NaN\n", + "2 NaN\n", + "3 NaN\n", + "4 NaN\n", + " ... \n", + "70075 True\n", + "70076 True\n", + "70077 True\n", + "70078 True\n", + "70079 True\n", + "Name: emissions_unit_id_epa, Length: 70080, dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_idaho_cems.emissions_unit_id_epa.str.contains(\"C\")" + ] + }, + { + "cell_type": "markdown", + "id": "005fab26-7da2-4a1b-8dde-b5e7db1d07d9", + "metadata": { + "tags": [] + }, + "source": [ + "## Compare Crosswalk and CEMS" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "7f301bda-d58d-4b58-a74e-ac93eb23a44d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "gens = pudl_out.gens_eia860()" + ] + }, + { + "cell_type": "markdown", + "id": "861a8a5b-fffc-4056-9181-ca5e7d957e96", + "metadata": { + "tags": [] + }, + "source": [ + "#### **Data missing from the crosswalk**:" + ] + }, + { + "cell_type": "markdown", + "id": "51b43242-d3eb-4feb-a318-aff300572761", + "metadata": {}, + "source": [ + "Plants that are in CEMS but not in the crosswalk" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "097ea6c6-c7e9-4bd4-8ec7-52c28ef8c53f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of plants NOT IN crosswalk: 281\n", + "Number of plants IN crosswalk: 1521\n" + ] + } + ], + "source": [ + "# Plants that are in CEMS but not in the crosswalk\n", + "cems_ids = cems_df.plant_id_eia.unique()\n", + "crosswalk_ids = cems_crosswalk.plant_id_epa.unique()\n", + "plant_id_not_in_crosswalk = [x for x in cems_ids if x not in crosswalk_ids]\n", + "plant_id_in_crosswalk = [x for x in cems_ids if x in crosswalk_ids]\n", + "print(\"Number of plants NOT IN crosswalk:\", len(plant_id_not_in_crosswalk))\n", + "print(\"Number of plants IN crosswalk:\", len(plant_id_in_crosswalk))" + ] + }, + { + "cell_type": "markdown", + "id": "c359890a-3f00-4c3e-bbe9-fcd9c0a24284", + "metadata": {}, + "source": [ + "Plants that are in the crosswalk but units are missing" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "672a071a-507e-49db-817e-da6757644ca6", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"['unit_id_epa'] not in index\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [105]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m cems_df_only_crosswalk \u001b[38;5;241m=\u001b[39m pudl\u001b[38;5;241m.\u001b[39mhelpers\u001b[38;5;241m.\u001b[39mremove_leading_zeros_from_numeric_strings(cems_df_only_crosswalk, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Merge the crosswalk with the cems df subset\u001b[39;00m\n\u001b[1;32m 12\u001b[0m merge_cems_with_crosswalk \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mmerge(\n\u001b[1;32m 13\u001b[0m cems_df_only_crosswalk, \n\u001b[0;32m---> 14\u001b[0m \u001b[43mcems_crosswalk\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mplant_id_epa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43munit_id_epa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mplant_id_eia\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdrop_duplicates()\u001b[38;5;241m.\u001b[39mdropna(), \n\u001b[1;32m 15\u001b[0m on\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplant_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 16\u001b[0m how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 17\u001b[0m )\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Print a list of the epa ids that have NA values for plant_id_eia matches. \u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# This is an indication of units that don't match cems. Usually this is because\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# the unit id does not exist in the crosswalk, but it could also be a misspelling or something.\u001b[39;00m\n\u001b[1;32m 22\u001b[0m merge_cems_with_crosswalk[merge_cems_with_crosswalk[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplant_id_eia\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mplant_id_epa\u001b[38;5;241m.\u001b[39munique()\n", + "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/frame.py:3511\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3510\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 3511\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3513\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", + "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 5779\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5780\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5782\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5784\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m 5785\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m 5786\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:5845\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 5842\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5844\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m-> 5845\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mKeyError\u001b[0m: \"['unit_id_epa'] not in index\"" + ] + } + ], + "source": [ + "# Get a subset of the cems df that is only the plant ids that do show up in the \n", + "# crosswalk. Theoretically this should show whether the unit ids are lining up.\n", + "plant_id_in_crosswalk = cems_crosswalk.dropna(subset=\"plant_id_eia\").plant_id_epa.unique()\n", + "cems_df_only_crosswalk = cems_df[~cems_df[\"plant_id_eia\"].isin(plant_id_not_in_crosswalk)]\n", + "cems_df_only_crosswalk = cems_df_only_crosswalk[cems_df_only_crosswalk[\"plant_id_eia\"].isin(plant_id_in_crosswalk)]\n", + "cems_df_only_crosswalk = cems_df_only_crosswalk.rename(columns={\"plant_id_eia\": \"plant_id_epa\", \"unitid\": \"unit_id_epa\"})\n", + "\n", + "# Clean cems subset\n", + "cems_df_only_crosswalk = pudl.helpers.remove_leading_zeros_from_numeric_strings(cems_df_only_crosswalk, \"unit_id_epa\")\n", + "\n", + "# Merge the crosswalk with the cems df subset\n", + "merge_cems_with_crosswalk = pd.merge(\n", + " cems_df_only_crosswalk, \n", + " cems_crosswalk[[\"plant_id_epa\", \"unit_id_epa\", \"plant_id_eia\"]].drop_duplicates().dropna(), \n", + " on=[\"plant_id_epa\", \"unit_id_epa\"],\n", + " how=\"left\"\n", + ")\n", + "\n", + "# Print a list of the epa ids that have NA values for plant_id_eia matches. \n", + "# This is an indication of units that don't match cems. Usually this is because\n", + "# the unit id does not exist in the crosswalk, but it could also be a misspelling or something.\n", + "merge_cems_with_crosswalk[merge_cems_with_crosswalk[\"plant_id_eia\"].isna()].plant_id_epa.unique()" + ] + }, + { + "cell_type": "markdown", + "id": "3b1ed507-38ee-40f5-a187-da16e63a4879", + "metadata": { + "tags": [] + }, + "source": [ + "#### **Note where EPA and EIA IDs don't match:**\n", + "This is usually a result of subcomponents being attributed to another plant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfde0c4e-85e6-4d47-8f0d-b7aacb5ff9dd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "miss_matched_ids = cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]!=cems_crosswalk[\"plant_id_epa\"]].plant_id_epa.unique()\n", + "print(\"Number of missmatched EPA and EIA ids:\", len(miss_matched_ids))\n", + "miss_matched_ids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ca8adc5-b502-4a96-8a48-2b13dcc25b1b", + "metadata": {}, + "outputs": [], + "source": [ + "miss_matched_ids_df = cems_crosswalk[cems_crosswalk[\"plant_id_epa\"].isin(miss_matched_ids)].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72d43364-aa3c-4e66-8158-e6525845417b", + "metadata": {}, + "outputs": [], + "source": [ + "# This shows that while there are different plant id eia values in a given plant id epa,\n", + "# they are as granular as the unit_id_epa which is good for integration in CEMS!!! \n", + "(miss_matched_ids_df.groupby([\"plant_id_epa\", \"unit_id_epa\"])[\"plant_id_eia\"].nunique() >1).any()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e51891-d525-45a1-8c76-14d2447e919f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cems_crosswalk[cems_crosswalk[\"plant_id_epa\"]==562]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98baa152-6747-42f5-80cd-03ac93343ce4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "gens[(gens[\"plant_id_eia\"]==562) & (gens[\"report_date\"].dt.year==2020)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "048ae153-8359-4e59-a024-7972bf78ed34", + "metadata": {}, + "outputs": [], + "source": [ + "plant_id_not_in_crosswalk[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10dfe6a3-7a71-42af-8391-060f8bd63fc0", + "metadata": {}, + "outputs": [], + "source": [ + "[5, 247, 312, 334, 375, 569, 596, 604, 646, 647]" + ] + }, + { + "cell_type": "markdown", + "id": "6ed0fc26-00a4-4661-a315-8c6605553d1f", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "#### **What are the primary keys?**" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "id": "91369996-f825-4b1d-85cf-6f84b357c6bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Shows that generator_id_epa does not distinguish between plant_id_eia values\n", + "(cems_crosswalk.groupby([\"plant_id_epa\", \"unit_id_epa\"])[\"plant_id_eia\"].nunique() > 1).any()" + ] + }, + { + "cell_type": "markdown", + "id": "fc54f8bc-22e4-45aa-bc4c-2d5baa0db268", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "#### **Explore**" + ] + }, + { + "cell_type": "markdown", + "id": "9f41ce3b-b772-45d9-be8c-fb9edbaa8961", + "metadata": {}, + "source": [ + "To Do: \n", + "- Check wheather `facility_id` and `unit_id_epa` mean anything\n", + "- rename columns in cems\n", + "- see if we can do any cems cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d059b45-13f1-4219-a756-2d96892efa96", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cems_df\n", + "test = cems_df.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", + "ser = test.groupby([\"plant_id_eia\", \"unitid\"])[\"unit_id_epa\"].nunique() \n", + "ser[ser>1]" + ] + }, + { + "cell_type": "markdown", + "id": "24f517e9-e13e-41ca-8025-b89843c99330", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Test Boiler ID" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "76abbe6c-7f62-4a61-8e91-7c238441a128", + "metadata": {}, + "outputs": [], + "source": [ + "# PUDL DB\n", + "#pudl_engine.table_names() # for a list of table names\n", + "boilers = pd.read_sql(\"boilers_entity_eia\", pudl_engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "dc33c4b2-e516-4dff-b35e-8088cbfb9b60", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "boiler_id_not_in_entity_df = cems_crosswalk[~cems_crosswalk[\"boiler_id\"].isin(boilers.boiler_id.unique())]\n", + "#boiler_id_not_in_entity_df = boiler_id_not_in_entity_df.dropna(subset=[\"boiler_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "8953ad10-dd0b-4115-a6bf-514b9bd76ee3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "boilers = pd.read_sql(\"boilers_entity_eia\", pudl_engine)\n", + "cw = cems_crosswalk[[\"plant_id_eia\", \"boiler_id\"]].drop_duplicates().dropna()\n", + "cw_tups = list(zip(cw.plant_id_eia, cw.boiler_id))\n", + "boiler_tups = list(zip(boilers.plant_id_eia, boilers.boiler_id))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "4d8fa425-f809-4415-8b3a-2939fd7cae2c", + "metadata": {}, + "outputs": [], + "source": [ + "# cw = cw.set_index(['plant_id_eia', 'boiler_id'])\n", + "# boilers = boilers.set_index(['plant_id_eia', 'boiler_id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "66d3074b-5c7d-4ad6-a54c-1b51700ec8b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex([], names=['plant_id_eia', 'boiler_id'])" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cw.index.difference(boilers.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "28db3736-29f3-4650-8015-010ebf000379", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[x for x in cw_tups if x not in boiler_tups]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "2be0a8f8-b60d-4390-b6eb-2c01a53e2d41", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "test1 = boilers[boilers[\"plant_id_eia\"]==302].boiler_id" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "48fff4aa-0214-43ec-9de9-466ca8a79bc5", + "metadata": {}, + "outputs": [], + "source": [ + "test2 = cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]==302].boiler_id" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "ab989c6d-8684-4d4a-a4f6-63daacac96eb", + "metadata": {}, + "outputs": [], + "source": [ + "bad_ids = [302,1552,2378,2535,2850,6031,6136,10333]" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "8d44bafc-ada2-4f44-a506-33a7cb64e2f8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2019-01-01 00:00:00')" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boil_years = pd.read_sql(\"boiler_generator_assn_eia860\", pudl_engine)\n", + "#pudl_engine.table_names()\n", + "boil_years[boil_years[\"plant_id_eia\"].isin(bad_ids)].report_date.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "4d1347ca-f9d5-4579-a3f8-9face6ed946c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[x for x in list(test2) if x not in list(test1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "cf69458f-1725-46c0-8b0a-d633c501438b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp('2021-01-01 00:00:00')" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gens[gens[\"plant_id_eia\"]==302].report_date.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "d0f183f6-fa6a-4b3c-8fd1-c46d39031e61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['5', '4', '3', '2', '1']\n", + "['1', '2', '3', '4', '5']\n" + ] + } + ], + "source": [ + "print(list(test1))\n", + "print(list(test2))" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "47458cd5-85b4-4758-a356-5f77539ada76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "161 1\n", + "163 2\n", + "164 3\n", + "165 4\n", + "166 5\n", + "Name: boiler_id, dtype: string" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test2" + ] + }, + { + "cell_type": "markdown", + "id": "7856d3dc-b4be-4620-893a-d79320107326", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Test Timezone" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "5e3c7e3d-9200-4ea9-b470-3f46353db50e", + "metadata": {}, + "outputs": [], + "source": [ + "# PUDL DB\n", + "#pudl_engine.table_names() # for a list of table names\n", + "plants_entity = pd.read_sql(\"plants_entity_eia\", pudl_engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "b112af07-26a3-4a84-bcc9-87a2c4ce5a04", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_44638/1566893324.py:1: SADeprecationWarning: The Engine.has_table() method is deprecated and will be removed in a future release. Please refer to Inspector.has_table(). (deprecated since: 1.4)\n", + " pudl_engine.has_table(\"assn_gen_eia_unit_epa\")\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pudl_engine.has_table(\"assn_gen_eia_unit_epa\")" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "a2b326e8-6d52-4142-a9c0-115070c8ca23", + "metadata": {}, + "outputs": [], + "source": [ + "timezones = plants_entity[[\"plant_id_eia\", \"timezone\"]].dropna().copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "48606241-d717-42af-844f-da9429e75104", + "metadata": {}, + "outputs": [], + "source": [ + "# import datetime\n", + "# import pytz\n", + "\n", + "# jan1 = datetime.datetime(2011, 1, 1) # year doesn't matter\n", + "# timezones[\"utc_offset\"] = timezones[\"timezone\"].apply(\n", + "# lambda tz: pytz.timezone(tz).localize(jan1).utcoffset()\n", + "# )\n", + "# del timezones[\"timezone\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "e060f4d1-d838-4ca8-8cd3-585ce52a8213", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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plant_id_eiatimezone
01America/Anchorage
12America/Chicago
23America/Chicago
34America/Chicago
45America/Chicago
.........
14929880100America/New_York
14930880101America/Chicago
14931880107America/New_York
14932880108America/Indiana/Vincennes
14933880109America/New_York
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14665 rows × 2 columns

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" + ], + "text/plain": [ + " plant_id_eia timezone\n", + "0 1 America/Anchorage\n", + "1 2 America/Chicago\n", + "2 3 America/Chicago\n", + "3 4 America/Chicago\n", + "4 5 America/Chicago\n", + "... ... ...\n", + "14929 880100 America/New_York\n", + "14930 880101 America/Chicago\n", + "14931 880107 America/New_York\n", + "14932 880108 America/Indiana/Vincennes\n", + "14933 880109 America/New_York\n", + "\n", + "[14665 rows x 2 columns]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timezones" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "3143daa2-76cc-4aa8-a53a-90c500fd3a99", + "metadata": {}, + "outputs": [], + "source": [ + "miss_matched_ids_df1 = miss_matched_ids_df.merge(timezones, on=[\"plant_id_eia\"], how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "f946cb99-4d8c-4b2f-9931-a3d1ab5dd2c1", + "metadata": {}, + "outputs": [], + "source": [ + "miss_matched_ids_df2 = miss_matched_ids_df1.merge(timezones, left_on=[\"plant_id_epa\"], right_on=[\"plant_id_eia\"], how=\"left\", suffixes=[\"_eia\", \"_epa\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "f6747fb0-5e04-45de-8ad2-c5d47ba32c8a", + "metadata": {}, + "outputs": [], + "source": [ + "miss_matched_ids_df2 = miss_matched_ids_df2.drop(columns=[\"plant_id_eia_epa\"]).rename(columns={\"plant_id_eia_eia\": \"plant_id_eia\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "2f299a36-82c9-419d-93fb-e0dfb6866676", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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plant_id_epaunit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_idtimezone_eiatimezone_epa
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [plant_id_epa, unit_id_epa, generator_id_epa, plant_id_eia, boiler_id, generator_id, timezone_eia, timezone_epa]\n", + "Index: []" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miss_matched_ids_df2[miss_matched_ids_df2[\"timezone_eia\"]!=miss_matched_ids_df2[\"timezone_epa\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "ac2f899b-30ae-4856-89b9-a0ff633c9216", + "metadata": {}, + "outputs": [], + "source": [ + "from sqlalchemy import inspect\n", + "insp = inspect(pudl_engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "id": "9ff4ebbb-9dbe-4574-8e1f-86591e5972fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.72 ms, sys: 1.09 ms, total: 2.81 ms\n", + "Wall time: 3.96 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "if not insp.has_table(\"assn_gen_eia_unit_epa\"):\n", + " print(\"bad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "id": "6b66264e-40dc-4be9-ab13-9f5ead906b9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 217 µs, sys: 160 µs, total: 377 µs\n", + "Wall time: 243 µs\n" + ] + } + ], + "source": [ + "%%time\n", + "if \"assn_gen_eia_unit_epa\" not in insp.get_table_names():\n", + " print(\"bad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "b025f8b9-4010-4b66-8569-ad17590af7b1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_44638/454621952.py:1: SADeprecationWarning: The Engine.table_names() method is deprecated and will be removed in a future release. Please refer to Inspector.get_table_names(). (deprecated since: 1.4)\n", + " pudl_engine.table_names()[:5]\n" + ] + }, + { + "data": { + "text/plain": [ + "['assn_gen_eia_unit_epa',\n", + " 'assn_plant_id_eia_epa',\n", + " 'boiler_fuel_eia923',\n", + " 'boiler_generator_assn_eia860',\n", + " 'boilers_entity_eia']" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pudl_engine.table_names()[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "id": "a5e9816a-085c-4c87-9d3d-750645a6d50d", + "metadata": {}, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "bad", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [212]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m6\u001b[39m:\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAssertionError\u001b[0m: bad" + ] + } + ], + "source": [ + "if \"plants_eia860\" not in inspector.get_table_names():" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "id": "d880b83f-0d61-4a52-9143-85991eec391f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(161, 7)" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miss_matched_ids_df1.shape" + ] + }, + { + "cell_type": "markdown", + "id": "64bd63c1-4ddd-4219-80f4-56b1dd2e03c3", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Test EPACEMS Output Table" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "1e0c0f53-c8ea-4fca-8ac0-3d637bd4c25f", + "metadata": {}, + "outputs": [], + "source": [ + "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", + "\n", + "test = pudl.output.epacems.epacems(\n", + " states = [\"ID\"],\n", + " years = [2019],\n", + " #columns: Sequence[str] | None = None,\n", + " epacems_path = epacems_path,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2f50ebbd-a77c-4849-b840-90a7b9b64fb8", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Next" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "48cfdca0-c54a-4f36-92ee-4824e0618e43", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
396612FMCT2AST16122AST1
397612FMCT2AST26122AST2
399612FMCT2BST16122BST1
400612FMCT2BST26122BST2
402612FMCT2CST16122CST1
403612FMCT2CST26122CST2
405612FMCT2DST16122DST1
406612FMCT2DST26122DST2
408612FMCT2EST16122EST1
409612FMCT2EST26122EST2
411612FMCT2FST16122FST1
412612FMCT2FST26122FST2
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" + ], + "text/plain": [ + " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", + "396 612 FMCT2A ST1 612 2A ST1\n", + "397 612 FMCT2A ST2 612 2A ST2\n", + "399 612 FMCT2B ST1 612 2B ST1\n", + "400 612 FMCT2B ST2 612 2B ST2\n", + "402 612 FMCT2C ST1 612 2C ST1\n", + "403 612 FMCT2C ST2 612 2C ST2\n", + "405 612 FMCT2D ST1 612 2D ST1\n", + "406 612 FMCT2D ST2 612 2D ST2\n", + "408 612 FMCT2E ST1 612 2E ST1\n", + "409 612 FMCT2E ST2 612 2E ST2\n", + "411 612 FMCT2F ST1 612 2F ST1\n", + "412 612 FMCT2F ST2 612 2F ST2" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = cems_crosswalk.dropna(subset=\"boiler_id\")\n", + "dups = test[test.duplicated(subset=[\"plant_id_eia\", \"generator_id\"], keep=False)]\n", + "tups = tuple(zip(dups.plant_id_eia, dups.boiler_id))\n", + "boil_dups = test[test.duplicated(subset=[\"plant_id_eia\", \"boiler_id\"], keep=False)]\n", + "boil_dup_tups = tuple(zip(boil_dups.plant_id_eia, boil_dups.boiler_id))\n", + "[x for x in tups if x in boil_dup_tups]\n", + "\n", + "test[test[\"plant_id_eia\"]==612]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ccf764c1-b76e-4261-b94b-49aa7d2e1ae4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filling technology type\n", + "Filled technology_type coverage now at 98.1%\n" + ] + } + ], + "source": [ + "gens = pudl_out.gens_eia860()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "13c2c5ba-f710-40ea-83c1-57e48047520c", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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601622020-01-01612205Fort Myers6452121Florida Power & Light Co2BFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852000-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601612020-01-01612205Fort Myers6452121Florida Power & Light Co2CFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852000-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601602020-01-01612205Fort Myers6452121Florida Power & Light Co2DFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-04-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601592020-01-01612205Fort Myers6452121Florida Power & Light Co2EFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601582020-01-01612205Fort Myers6452121Florida Power & Light Co2FFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601572020-01-01612205Fort Myers6452121Florida Power & Light Co3FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601562020-01-01612205Fort Myers6452121Florida Power & Light Co4FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601552020-01-01612205Fort Myers6452121Florida Power & Light Co5FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601542020-01-01612205Fort Myers6452121Florida Power & Light Co6FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601532020-01-01612205Fort Myers6452121Florida Power & Light Co7FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601522020-01-01612205Fort Myers6452121Florida Power & Light Co8FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601512020-01-01612205Fort Myers6452121Florida Power & Light Co9FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0False0.901974-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>61.5NaN33902
601502020-01-01612205Fort Myers6452121Florida Power & Light CoCT1FalseFPLFlorida Power & Light Company<NA>False188.2<NA>Ft. MyersTrueLeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852003-06-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>182.0NaN<NA>TrueFalseNatural Gas Fired Combustion Turbine1HAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>200.0NaN33902
601492020-01-01612205Fort Myers6452121Florida Power & Light CoCT2FalseFPLFlorida Power & Light Company<NA>False188.2<NA>Ft. MyersTrueLeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852003-06-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>182.0NaN<NA>TrueFalseNatural Gas Fired Combustion Turbine1HAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>200.0NaN33902
601482020-01-01612205Fort Myers6452121Florida Power & Light CoG10FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601472020-01-01612205Fort Myers6452121Florida Power & Light CoGT1FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0False0.901974-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>61.5NaN33902
601462020-01-01612205Fort Myers6452121Florida Power & Light CoGT2FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601452020-01-01612205Fort Myers6452121Florida Power & Light CoPFM3CFalseFPLFlorida Power & Light Company<NA>False229.5FalseFt. MyersFalseLeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852016-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>231.0NaN<NA>TrueFalseNatural Gas Fired Combustion Turbine1HAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>223.0NaN33902
601442020-01-01612205Fort Myers6452121Florida Power & Light CoPFM3DFalseFPLFlorida Power & Light Company<NA>False229.5FalseFt. MyersFalseLeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852016-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>231.0NaN<NA>TrueFalseNatural Gas Fired Combustion Turbine1HAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>223.0NaN33902
601432020-01-01612205Fort Myers6452121Florida Power & Light CoST1FalseFPLFlorida Power & Light Companyeia860_orgFalse156.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NG<NA><NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783127.8False0.851958-11-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CA<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>155.8NaN<NA><NA>FalseNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>141.0NaN33902
601422020-01-01612205Fort Myers6452121Florida Power & Light CoST2FalseFPLFlorida Power & Light Companyeia860_orgFalse436.1<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NG<NA><NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.7831146.6False0.891969-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CA<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>459.2NaN<NA><NA>FalseNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>404.0NaN33902
\n", + "
" + ], + "text/plain": [ + " report_date plant_id_eia plant_id_pudl plant_name_eia utility_id_eia utility_id_pudl utility_name_eia generator_id associated_combined_heat_power balancing_authority_code_eia balancing_authority_name_eia bga_source bypass_heat_recovery capacity_mw carbon_capture city cofire_fuels county current_planned_operating_date data_source deliver_power_transgrid distributed_generation duct_burners energy_source_1_transport_1 energy_source_1_transport_2 energy_source_1_transport_3 energy_source_2_transport_1 energy_source_2_transport_2 energy_source_2_transport_3 energy_source_code_1 energy_source_code_2 energy_source_code_3 energy_source_code_4 energy_source_code_5 energy_source_code_6 ferc_cogen_status ferc_exempt_wholesale_generator ferc_small_power_producer fluidized_bed_tech fuel_type_code_pudl fuel_type_count grid_voltage_2_kv grid_voltage_3_kv grid_voltage_kv iso_rto_code latitude longitude minimum_load_mw multiple_fuels \\\n", + "60165 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 11 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60164 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 12 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60163 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2A False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60162 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2B False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60161 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2C False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60160 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2D False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60159 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2E False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60158 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2F False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60157 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 3 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60156 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 4 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60155 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 5 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60154 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 6 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60153 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 7 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60152 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 8 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60151 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 9 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 False \n", + "60150 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co CT1 False FPL Florida Power & Light Company False 188.2 Ft. Myers True Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60149 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co CT2 False FPL Florida Power & Light Company False 188.2 Ft. Myers True Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60148 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co G10 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60147 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co GT1 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 False \n", + "60146 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co GT2 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", + "60145 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co PFM3C False FPL Florida Power & Light Company False 229.5 False Ft. Myers False Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60144 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co PFM3D False FPL Florida Power & Light Company False 229.5 False Ft. Myers False Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", + "60143 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co ST1 False FPL Florida Power & Light Company eia860_org False 156.2 Ft. Myers Lee NaT eia860 False NG False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 27.8 False \n", + "60142 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co ST2 False FPL Florida Power & Light Company eia860_org False 436.1 Ft. Myers Lee NaT eia860 False NG False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 146.6 False \n", + "\n", + " nameplate_power_factor operating_date operating_switch operational_status operational_status_code original_planned_operating_date other_combustion_tech other_modifications_date other_planned_modifications owned_by_non_utility ownership_code planned_derate_date planned_energy_source_code_1 planned_modifications planned_net_summer_capacity_derate_mw planned_net_summer_capacity_uprate_mw planned_net_winter_capacity_derate_mw planned_net_winter_capacity_uprate_mw planned_new_capacity_mw planned_new_prime_mover_code planned_repower_date planned_retirement_date planned_uprate_date previously_canceled primary_purpose_id_naics prime_mover_code pulverized_coal_tech reactive_power_output_mvar retirement_date rto_iso_lmp_node_id rto_iso_location_wholesale_reporting_id sector_id_eia sector_name_eia solid_fuel_gasification startup_source_code_1 startup_source_code_2 startup_source_code_3 startup_source_code_4 state stoker_tech street_address subcritical_tech \\\n", + "60165 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60164 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60163 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60162 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60161 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60160 0.85 2001-04-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60159 0.85 2001-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60158 0.85 2001-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60157 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60156 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60155 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60154 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60153 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60152 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60151 0.90 1974-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60150 0.85 2003-06-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60149 0.85 2003-06-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60148 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60147 0.90 1974-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60146 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", + "60145 0.85 2016-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60144 0.85 2016-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60143 0.85 1958-11-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CA NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "60142 0.89 1969-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CA NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", + "\n", + " summer_capacity_estimate summer_capacity_mw summer_estimated_capability_mw supercritical_tech switch_oil_gas syncronized_transmission_grid technology_description time_cold_shutdown_full_load_code timezone topping_bottoming_code turbines_inverters_hydrokinetics turbines_num ultrasupercritical_tech unit_id_pudl uprate_derate_completed_date uprate_derate_during_year winter_capacity_estimate winter_capacity_mw winter_estimated_capability_mw zip_code \n", + "60165 54.0 NaN False False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60164 54.0 NaN False False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60163 199.5 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60162 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60161 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60160 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60159 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60158 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", + "60157 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60156 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60155 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60154 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60153 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60152 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60151 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 61.5 NaN 33902 \n", + "60150 182.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 200.0 NaN 33902 \n", + "60149 182.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 200.0 NaN 33902 \n", + "60148 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60147 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 61.5 NaN 33902 \n", + "60146 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", + "60145 231.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 223.0 NaN 33902 \n", + "60144 231.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 223.0 NaN 33902 \n", + "60143 155.8 NaN False Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 141.0 NaN 33902 \n", + "60142 459.2 NaN False Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 404.0 NaN 33902 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gens[(gens[\"plant_id_eia\"]==612) & (gens[\"report_date\"].dt.year==2020)]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3530dfed-094e-466b-a68e-c868a7b3e94b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "491469" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(gens)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "9e8211ef-de62-4766-b5e7-81ee58432dcb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35646" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gen_pairs = gens[[\"plant_id_eia\", \"generator_id\", \"fuel_type_code_pudl\", \"capacity_mw\"]].drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"])\n", + "\n", + "len(gen_pairs)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "af2b4ab4-f41c-4321-96bf-4499f9fe3c34", + "metadata": {}, + "outputs": [], + "source": [ + "gen_cross = pd.merge(gen_pairs, cems_crosswalk, on=[\"plant_id_eia\", \"generator_id\"], how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "cbe469ef-139c-48c7-9353-27348cd6bd79", + "metadata": {}, + "outputs": [], + "source": [ + "no_dup_gen_cross = gen_cross.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "75e119fd-ea35-48f3-90d8-6163ade321f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "35646" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(no_dup_gen_cross)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "9466aeb2-6cc9-4424-b2c9-69195bd02491", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30349" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(no_dup_gen_cross[\n", + " no_dup_gen_cross[\"plant_id_epa\"].isna() \n", + " #& (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "4b2bf227-f25e-49a8-9c78-54ee2a5b0a69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5294" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(not_mapped := no_dup_gen_cross[\n", + " no_dup_gen_cross[\"plant_id_epa\"].isna() \n", + " & (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", + "])\n", + "\n", + "len(mapped := no_dup_gen_cross[\n", + " no_dup_gen_cross[\"plant_id_epa\"].notna() \n", + " & (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", + "])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "21d8e0a4-6cc7-4d46-ac03-f9d008ec2f0d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 17203\n", + "unique 6\n", + "top gas\n", + "freq 6828\n", + "Name: fuel_type_code_pudl, dtype: object" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "not_mapped.fuel_type_code_pudl.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "00a94fd7-4407-4e88-806e-2c57cf708d28", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(not_mapped.capacity_mw, bins=100, range=(0,100))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "7ad0a33b-e3f2-4b09-9508-40dd9ac6cd29", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(not_mapped.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,1000))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "ace35ecb-e085-4a6b-bd04-618a751344ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PADurag56IQ/s6E4bl9fhncCbgZ8taBvnPr8AmAfe100b3ZbkAsa4z1X1DeBvgUeBOeB/quoTjHGfF1hpHy/t9he3D6SFAB9oyX6rkjwH+Ajwpqr63rlO7dPW1OuQ5DXA2ao6Puglfdqa6jO9kejVwHuq6irgB/R+tV5K833u5n330Zsq+BXggiSvP9clfdqa6vMAlurjmvreQoCP7ZL9JE+nF94frKp7uuYz3a9VdNuzXfs4vA7XAq9NcoreVNgrknyA8e7zaeB0VT3QHd9NL9DHuc+vBL5WVfNV9WPgHuC3GO8+P2mlfTzd7S9uH0gLAT6WS/a7d5pvB05U1TsWPHQMONDtHwCOLmi/Jckzkjwf2EPvzY9mVNVbquqyqpqk9+/4yap6PePd528BX09yede0F/gSY9xnelMn1yR5dvdzvpfeezzj3OcnraiP3TTLE0mu6V6r319wzfI2+p3cAd/tfTW9uzS+Arx1o+sZUp9+m96vSp8HHuy+Xg08F5gBTnbb7QuueWv3GjzCCt6p3oxfwMv5xV0oY91n4Epgtvu3/mfg4i3Q578CHgYeAv6R3t0XY9Vn4A56c/w/pjeSvnU1fQSmutfpK8Df062QH+TLpfSS1KgWplAkSX0Y4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalR/weSMclItXuF2gAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(mapped.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,1000))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "57a3af05-ab40-449f-b912-8c1d67180fcc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eBzw07rGtlqo6WFXbqmoHg5/rP1fVO5nsnv8TeC7Jz7XSzcCTTHDPDKZ1bkzy6vZ7fjOD96wmuefz5uvxKLA3ySVJdgK7gEeXdISqmrgb8DYG/1nLvwMfWOvxrFKPv8zgn3dfBx5rt7cBP8ngXf+n2/0Vaz3WVer/JuCzbXmiewauB6bbz/ofgMs76PmPgW8ATwB/C1wyaT0D9zF4z+L7DM7kb79Qj8AHWqadBN661OP6NQyS1JFJnN6RJM3D0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kd+X96+BythUc9ewAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(no_dup_gen_cross.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,100))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "c74a09b2-3ef3-4bae-a130-288a43540f8a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
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.....................
6820609031360903<NA>3
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682370454MAG1MAG154538<NA>MAG1
682470454MAG2MAG254538<NA>MAG2
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6407 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", + "0 3 1 1 3 1 1\n", + "1 3 2 2 3 2 2\n", + "2 3 3 3 3 3 3\n", + "3 3 4 4 3 4 4\n", + "4 3 5 5 3 5 5\n", + "... ... ... ... ... ... ...\n", + "6820 60903 1 3 60903 3\n", + "6821 60903 2 2 60903 0002 2\n", + "6822 60903 2 4 60903 4\n", + "6823 70454 MAG1 MAG1 54538 MAG1\n", + "6824 70454 MAG2 MAG2 54538 MAG2\n", + "\n", + "[6407 rows x 6 columns]" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cems_crosswalk" + ] + }, + { + "cell_type": "markdown", + "id": "bfbadc61-8b5f-4612-97d2-d577e6bac96d", + "metadata": {}, + "source": [ + "## Investigate One-to-Many relationship" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e02c454-98fc-437b-a57c-5f196e54bf43", + "metadata": {}, + "outputs": [], + "source": [ + "crosswalk_essentials = cems_crosswalk[[\"plant_id_eia\", \"emissions_unit_id_epa\", \"generator_id\"]].drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "750856ca-ef7a-42ab-a84a-7fa22ef2e3e2", + "metadata": {}, + "outputs": [], + "source": [ + "# Add columns showing relationship between emissions unit and generator id columns\n", + "crosswalk_essentials[\"em_gen_1_m\"] = crosswalk_essentials.groupby([\"plant_id_eia\", \"emissions_unit_id_epa\"])[\"generator_id\"].transform(lambda x: x.count() > 1)\n", + "crosswalk_essentials[\"em_gen_m_1\"] = crosswalk_essentials.groupby([\"plant_id_eia\", \"generator_id\"])[\"emissions_unit_id_epa\"].transform(lambda x: x.count() > 1)\n", + "\n", + "crosswalk_essentials[crosswalk_essentials[\"em_gen_1_m\"] & crosswalk_essentials[\"em_gen_m_1\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "6bce1b10-70f8-45e7-830e-792fe819e508", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
0311311
1322322
2333333
3344344
4355355
536AA1CT3<NA>A1CT
636AA1ST36AA1ST
736BA1CT23<NA>A1CT2
836BA1ST36BA1ST
937AA2C13<NA>A2C1
1037AA2ST37AA2ST
1137BA2C23<NA>A2C2
1237BA2ST37BA2ST
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" + ], + "text/plain": [ + " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", + "0 3 1 1 3 1 1\n", + "1 3 2 2 3 2 2\n", + "2 3 3 3 3 3 3\n", + "3 3 4 4 3 4 4\n", + "4 3 5 5 3 5 5\n", + "5 3 6A A1CT 3 A1CT\n", + "6 3 6A A1ST 3 6A A1ST\n", + "7 3 6B A1CT2 3 A1CT2\n", + "8 3 6B A1ST 3 6B A1ST\n", + "9 3 7A A2C1 3 A2C1\n", + "10 3 7A A2ST 3 7A A2ST\n", + "11 3 7B A2C2 3 A2C2\n", + "12 3 7B A2ST 3 7B A2ST" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For all the em_gen_1_m = TRUE we will need to allocate by net generation or capacity\n", + "# This is an example: \n", + "cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]==3]" + ] + }, + { + "cell_type": "markdown", + "id": "479f79f5-fbb2-4882-8c50-7caecacab1a2", + "metadata": {}, + "source": [ + "## Missing Records" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "f851ccd8-038e-4c29-acdf-c9c48452a7ed", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "crosswalk_ids = (\n", + " cems_crosswalk[[\"plant_id_eia\", \"generator_id\"]]\n", + " .drop_duplicates()\n", + " .set_index([\"plant_id_eia\", \"generator_id\"])\n", + ")\n", + "gens_ids = (\n", + " gens[[\"plant_id_eia\", \"generator_id\"]]\n", + " .drop_duplicates()\n", + " .set_index([\"plant_id_eia\", \"generator_id\"])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "4cefe64f-284d-4ebf-baba-6c1188e751bc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex([( 1, '1'),\n", + " ( 1, '2'),\n", + " ( 1, '3'),\n", + " ( 1, '5'),\n", + " ( 1, 'WT1'),\n", + " ( 1, 'WT2'),\n", + " ( 2, '1'),\n", + " ( 3, 'A3C1'),\n", + " ( 3, 'A3ST'),\n", + " ( 4, '1'),\n", + " ...\n", + " (65328, '1'),\n", + " (65329, 'LAURL'),\n", + " (65330, 'BALB1'),\n", + " (65331, 'NORMA'),\n", + " (65332, 'MH1'),\n", + " (65333, '785'),\n", + " (65334, 'PLTVW'),\n", + " (65335, 'WAPPA'),\n", + " (65337, 'MAYBK'),\n", + " (65338, 'UNIS1')],\n", + " names=['plant_id_eia', 'generator_id'], length=30349)" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gens_ids.index.difference(crosswalk_ids.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "718fd76a-833d-4ad5-9d26-1b7c5e4f7172", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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report_dateplant_id_eiaplant_id_pudlplant_name_eiautility_id_eiautility_id_pudlutility_name_eiagenerator_idassociated_combined_heat_powerbalancing_authority_code_eiabalancing_authority_name_eiabga_sourcebypass_heat_recoverycapacity_mwcarbon_capturecitycofire_fuelscountycurrent_planned_operating_datedata_sourcedeliver_power_transgriddistributed_generationduct_burnersenergy_source_1_transport_1energy_source_1_transport_2energy_source_1_transport_3energy_source_2_transport_1energy_source_2_transport_2energy_source_2_transport_3energy_source_code_1energy_source_code_2energy_source_code_3energy_source_code_4energy_source_code_5energy_source_code_6ferc_cogen_statusferc_exempt_wholesale_generatorferc_small_power_producerfluidized_bed_techfuel_type_code_pudlfuel_type_countgrid_voltage_2_kvgrid_voltage_3_kvgrid_voltage_kviso_rto_codelatitudelongitudeminimum_load_mwmultiple_fuelsnameplate_power_factoroperating_dateoperating_switchoperational_statusoperational_status_codeoriginal_planned_operating_dateother_combustion_techother_modifications_dateother_planned_modificationsowned_by_non_utilityownership_codeplanned_derate_dateplanned_energy_source_code_1planned_modificationsplanned_net_summer_capacity_derate_mwplanned_net_summer_capacity_uprate_mwplanned_net_winter_capacity_derate_mwplanned_net_winter_capacity_uprate_mwplanned_new_capacity_mwplanned_new_prime_mover_codeplanned_repower_dateplanned_retirement_dateplanned_uprate_datepreviously_canceledprimary_purpose_id_naicsprime_mover_codepulverized_coal_techreactive_power_output_mvarretirement_daterto_iso_lmp_node_idrto_iso_location_wholesale_reporting_idsector_id_eiasector_name_eiasolid_fuel_gasificationstartup_source_code_1startup_source_code_2startup_source_code_3startup_source_code_4statestoker_techstreet_addresssubcritical_techsummer_capacity_estimatesummer_capacity_mwsummer_estimated_capability_mwsupercritical_techswitch_oil_gassyncronized_transmission_gridtechnology_descriptiontime_cold_shutdown_full_load_codetimezonetopping_bottoming_codeturbines_inverters_hydrokineticsturbines_numultrasupercritical_techunit_id_pudluprate_derate_completed_dateuprate_derate_during_yearwinter_capacity_estimatewinter_capacity_mwwinter_estimated_capability_mwzip_code
4914682001-01-012848Bankhead Dam19518Alabama Power Co1FalseSOCOSouthern Company Services, Inc. - Trans<NA>False45.0<NA>Northport<NA>TuscaloosaNaT<NA><NA>FalseFalse<NA><NA><NA><NA><NA><NA>WAT<NA><NA><NA><NA><NA>FalseFalseFalse<NA>hydro1NaNNaN115.0<NA>33.458665-87.35682NaN<NA>NaN1963-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22HY<NA>NaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>19001 Lock 17 Road<NA><NA>56.0NaN<NA><NA><NA>Conventional Hydroelectric<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>56.0NaN35476
4914672001-01-01332Barry19518Alabama Power Co1FalseSOCOSouthern Company Services, Inc. - Trans<NA>False153.1<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1954-02-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>138.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>138.0NaN36512
4914662001-01-01332Barry19518Alabama Power Co2FalseSOCOSouthern Company Services, Inc. - Trans<NA>False153.1<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1954-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>139.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>139.0NaN36512
4914652001-01-01332Barry19518Alabama Power Co3FalseSOCOSouthern Company Services, Inc. - Trans<NA>False272.0<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1959-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>251.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>251.0NaN36512
4914642001-01-01332Barry19518Alabama Power Co4FalseSOCOSouthern Company Services, Inc. - Trans<NA>False403.7<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1969-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>362.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>362.0NaN36512
................................................................................................................................................................................................................................................................................................................................................
42021-01-016533316132Shakes Solar610605634Cypress Creek Renewables785<NA>ERCO<NA><NA><NA>200.0<NA><NA><NA>Dimmit2024-11-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>28.442080-99.75582NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>TX<NA><NA><NA><NA>200.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>200.0NaN<NA>
32021-01-016533416161Platteview Solar LLC610125972AES Distributed EnergyPLTVW<NA>SWPP<NA><NA><NA>81.0<NA><NA><NA>Saunders2023-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>41.190999-96.37942NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>NE<NA><NA><NA><NA>81.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>81.0NaN<NA>
22021-01-016533516137Appaloosa Run Wind6465513725Appaloosa Run Wind, LLCWAPPA<NA>ERCO<NA><NA><NA>171.8<NA><NA><NA>Upton2022-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>WND<NA><NA><NA><NA><NA><NA><NA><NA><NA>wind1NaNNaNNaN<NA>31.157269-101.83160NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>WT<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>TX<NA><NA><NA><NA>NaNNaN<NA><NA><NA>Onshore Wind Turbine<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>NaNNaN<NA>
12021-01-016533716270Maybrook Solar, LLC569902626NJR Clean Energy Ventures CorporationMAYBK<NA>NYIS<NA><NA><NA>5.0<NA><NA><NA>Orange2022-09-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>41.463056-74.24778NaN<NA>NaNNaT<NA>proposedTNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>NY<NA><NA><NA><NA>5.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/New_York<NA><NA><NA><NA><NA>NaT<NA><NA>5.0NaN<NA>
02021-01-016533816151Union Ridge Solar501232261Bluarc Management Group LLCUNIS1<NA>PJM<NA><NA><NA>108.0<NA><NA><NA>Montgomery2023-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>39.984126-82.64130NaN<NA>NaNNaT<NA>proposedLNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>OH<NA><NA><NA><NA>108.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/New_York<NA><NA><NA><NA><NA>NaT<NA><NA>108.0NaN<NA>
\n", + "

491469 rows × 111 columns

\n", + "
" + ], + "text/plain": [ + " report_date plant_id_eia plant_id_pudl plant_name_eia utility_id_eia utility_id_pudl utility_name_eia generator_id associated_combined_heat_power balancing_authority_code_eia balancing_authority_name_eia bga_source bypass_heat_recovery capacity_mw carbon_capture city cofire_fuels county current_planned_operating_date data_source deliver_power_transgrid distributed_generation duct_burners energy_source_1_transport_1 energy_source_1_transport_2 energy_source_1_transport_3 energy_source_2_transport_1 energy_source_2_transport_2 energy_source_2_transport_3 energy_source_code_1 energy_source_code_2 energy_source_code_3 energy_source_code_4 energy_source_code_5 energy_source_code_6 ferc_cogen_status ferc_exempt_wholesale_generator ferc_small_power_producer fluidized_bed_tech fuel_type_code_pudl fuel_type_count grid_voltage_2_kv grid_voltage_3_kv grid_voltage_kv iso_rto_code latitude longitude \\\n", + "491468 2001-01-01 2 848 Bankhead Dam 195 18 Alabama Power Co 1 False SOCO Southern Company Services, Inc. - Trans False 45.0 Northport Tuscaloosa NaT False False WAT False False False hydro 1 NaN NaN 115.0 33.458665 -87.35682 \n", + "491467 2001-01-01 3 32 Barry 195 18 Alabama Power Co 1 False SOCO Southern Company Services, Inc. - Trans False 153.1 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", + "491466 2001-01-01 3 32 Barry 195 18 Alabama Power Co 2 False SOCO Southern Company Services, Inc. - Trans False 153.1 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", + "491465 2001-01-01 3 32 Barry 195 18 Alabama Power Co 3 False SOCO Southern Company Services, Inc. - Trans False 272.0 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", + "491464 2001-01-01 3 32 Barry 195 18 Alabama Power Co 4 False SOCO Southern Company Services, Inc. - Trans False 403.7 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "4 2021-01-01 65333 16132 Shakes Solar 61060 5634 Cypress Creek Renewables 785 ERCO 200.0 Dimmit 2024-11-01 eia860m SUN solar 1 NaN NaN NaN 28.442080 -99.75582 \n", + "3 2021-01-01 65334 16161 Platteview Solar LLC 61012 5972 AES Distributed Energy PLTVW SWPP 81.0 Saunders 2023-12-01 eia860m SUN solar 1 NaN NaN NaN 41.190999 -96.37942 \n", + "2 2021-01-01 65335 16137 Appaloosa Run Wind 64655 13725 Appaloosa Run Wind, LLC WAPPA ERCO 171.8 Upton 2022-12-01 eia860m WND wind 1 NaN NaN NaN 31.157269 -101.83160 \n", + "1 2021-01-01 65337 16270 Maybrook Solar, LLC 56990 2626 NJR Clean Energy Ventures Corporation MAYBK NYIS 5.0 Orange 2022-09-01 eia860m SUN solar 1 NaN NaN NaN 41.463056 -74.24778 \n", + "0 2021-01-01 65338 16151 Union Ridge Solar 50123 2261 Bluarc Management Group LLC UNIS1 PJM 108.0 Montgomery 2023-12-01 eia860m SUN solar 1 NaN NaN NaN 39.984126 -82.64130 \n", + "\n", + " minimum_load_mw multiple_fuels nameplate_power_factor operating_date operating_switch operational_status operational_status_code original_planned_operating_date other_combustion_tech other_modifications_date other_planned_modifications owned_by_non_utility ownership_code planned_derate_date planned_energy_source_code_1 planned_modifications planned_net_summer_capacity_derate_mw planned_net_summer_capacity_uprate_mw planned_net_winter_capacity_derate_mw planned_net_winter_capacity_uprate_mw planned_new_capacity_mw planned_new_prime_mover_code planned_repower_date planned_retirement_date planned_uprate_date previously_canceled primary_purpose_id_naics prime_mover_code pulverized_coal_tech reactive_power_output_mvar retirement_date rto_iso_lmp_node_id rto_iso_location_wholesale_reporting_id sector_id_eia sector_name_eia solid_fuel_gasification startup_source_code_1 startup_source_code_2 startup_source_code_3 startup_source_code_4 state stoker_tech \\\n", + "491468 NaN NaN 1963-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 HY NaN NaT 1 Electric Utility AL \n", + "491467 NaN NaN 1954-02-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", + "491466 NaN NaN 1954-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", + "491465 NaN NaN 1959-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", + "491464 NaN NaN 1969-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "4 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT TX \n", + "3 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT NE \n", + "2 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT WT NaN NaT TX \n", + "1 NaN NaN NaT proposed T NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT NY \n", + "0 NaN NaN NaT proposed L NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT OH \n", + "\n", + " street_address subcritical_tech summer_capacity_estimate summer_capacity_mw summer_estimated_capability_mw supercritical_tech switch_oil_gas syncronized_transmission_grid technology_description time_cold_shutdown_full_load_code timezone topping_bottoming_code turbines_inverters_hydrokinetics turbines_num ultrasupercritical_tech unit_id_pudl uprate_derate_completed_date uprate_derate_during_year winter_capacity_estimate winter_capacity_mw winter_estimated_capability_mw zip_code \n", + "491468 19001 Lock 17 Road 56.0 NaN Conventional Hydroelectric America/Chicago X NaT 56.0 NaN 35476 \n", + "491467 North Highway 43 True 138.0 NaN Conventional Steam Coal America/Chicago X NaT 138.0 NaN 36512 \n", + "491466 North Highway 43 True 139.0 NaN Conventional Steam Coal America/Chicago X NaT 139.0 NaN 36512 \n", + "491465 North Highway 43 True 251.0 NaN Conventional Steam Coal America/Chicago X NaT 251.0 NaN 36512 \n", + "491464 North Highway 43 True 362.0 NaN Conventional Steam Coal America/Chicago X NaT 362.0 NaN 36512 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "4 200.0 NaN Solar Photovoltaic America/Chicago NaT 200.0 NaN \n", + "3 81.0 NaN Solar Photovoltaic America/Chicago NaT 81.0 NaN \n", + "2 NaN NaN Onshore Wind Turbine America/Chicago NaT NaN NaN \n", + "1 5.0 NaN Solar Photovoltaic America/New_York NaT 5.0 NaN \n", + "0 108.0 NaN Solar Photovoltaic America/New_York NaT 108.0 NaN \n", + "\n", + "[491469 rows x 111 columns]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gens" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fb6afea-f9b2-4bc8-827b-b79d4ac9184c", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6db079e04bd5026aa3b4e7f8fd96f18686c34cfb Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 29 Aug 2022 16:08:09 -0600 Subject: [PATCH 35/80] Add raw CEMS columns we don't want to the list of columns to ignore rather than commenting them out of the rename dict --- src/pudl/extract/epacems.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/pudl/extract/epacems.py b/src/pudl/extract/epacems.py index 3b6c9f501d..4045a3b687 100644 --- a/src/pudl/extract/epacems.py +++ b/src/pudl/extract/epacems.py @@ -16,7 +16,7 @@ # EPA CEMS constants ##### RENAME_DICT = { "STATE": "state", - # "FACILITY_NAME": "plant_name", # Not reading from CSV + "FACILITY_NAME": "plant_name", # Not reading from CSV "ORISPL_CODE": "plant_id_epa", # Not quite the same as plant_id_eia "UNITID": "emissions_unit_id_epa", # These op_date, op_hour, and op_time variables get converted to @@ -33,25 +33,25 @@ "SO2_MASS (lbs)": "so2_mass_lbs", "SO2_MASS": "so2_mass_lbs", "SO2_MASS_MEASURE_FLG": "so2_mass_measurement_code", - # "SO2_RATE (lbs/mmBtu)": "so2_rate_lbs_mmbtu", # Not reading from CSV - # "SO2_RATE": "so2_rate_lbs_mmbtu", # Not reading from CSV - # "SO2_RATE_MEASURE_FLG": "so2_rate_measure_flg", # Not reading from CSV - # "NOX_RATE (lbs/mmBtu)": "nox_rate_lbs_mmbtu", - # "NOX_RATE": "nox_rate_lbs_mmbtu", # Not reading from CSV - # "NOX_RATE_MEASURE_FLG": "nox_rate_measurement_code", # Not reading from CSV + "SO2_RATE (lbs/mmBtu)": "so2_rate_lbs_mmbtu", # Not reading from CSV + "SO2_RATE": "so2_rate_lbs_mmbtu", # Not reading from CSV + "SO2_RATE_MEASURE_FLG": "so2_rate_measure_flg", # Not reading from CSV + "NOX_RATE (lbs/mmBtu)": "nox_rate_lbs_mmbtu", + "NOX_RATE": "nox_rate_lbs_mmbtu", # Not reading from CSV + "NOX_RATE_MEASURE_FLG": "nox_rate_measurement_code", # Not reading from CSV "NOX_MASS (lbs)": "nox_mass_lbs", "NOX_MASS": "nox_mass_lbs", "NOX_MASS_MEASURE_FLG": "nox_mass_measurement_code", "CO2_MASS (tons)": "co2_mass_tons", "CO2_MASS": "co2_mass_tons", "CO2_MASS_MEASURE_FLG": "co2_mass_measurement_code", - # "CO2_RATE (tons/mmBtu)": "co2_rate_tons_mmbtu", # Not reading from CSV - # "CO2_RATE": "co2_rate_tons_mmbtu", # Not reading from CSV - # "CO2_RATE_MEASURE_FLG": "co2_rate_measure_flg", # Not reading from CSV + "CO2_RATE (tons/mmBtu)": "co2_rate_tons_mmbtu", # Not reading from CSV + "CO2_RATE": "co2_rate_tons_mmbtu", # Not reading from CSV + "CO2_RATE_MEASURE_FLG": "co2_rate_measure_flg", # Not reading from CSV "HEAT_INPUT (mmBtu)": "heat_content_mmbtu", "HEAT_INPUT": "heat_content_mmbtu", - # "FAC_ID": "facility_id", # unique facility id for internal EPA database management - # "UNIT_ID": "unit_id_what", # unique unit id for internal EPA database management + "FAC_ID": "facility_id", # unique facility id for internal EPA database management + "UNIT_ID": "unit_id_what", # unique unit id for internal EPA database management } """dict: A dictionary containing EPA CEMS column names (keys) and replacement names to use when reading those columns into PUDL (values). There are some From 3ac574136b34d4a4c72797269ba5b03f0e94ce4c Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 29 Aug 2022 17:14:32 -0600 Subject: [PATCH 36/80] Change _etl_glue() function parameter from DatasetsSettings to EiaSettings --- src/pudl/etl.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 9f27f59195..08d19629bd 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -33,7 +33,6 @@ from pudl.metadata.dfs import FERC_ACCOUNTS, FERC_DEPRECIATION_LINES from pudl.metadata.fields import apply_pudl_dtypes from pudl.settings import ( - DatasetsSettings, EiaSettings, EpaCemsSettings, EtlSettings, @@ -336,7 +335,7 @@ def _etl_glue( glue_settings: GlueSettings, ds_kwargs: dict[str, Any], sqlite_dfs: dict[str, pd.DataFrame], - datasets: DatasetsSettings, + eia_settings: EiaSettings, ) -> dict[str, pd.DataFrame]: """Extract, transform and load CSVs for the Glue tables. @@ -352,9 +351,7 @@ def _etl_glue( ferc1_solo test (where no eia tables are in the sqlite_dfs dict). Passing the whole dict avoids this because the crosswalk will only load if there are eia tables in the dict, but the dict will always be there. - datasets: An immutable pydantic model to validate PUDL Dataset settings. This is - used to acess the eia settings years and working partitions used to restrict - the crosswalk in the case of ETL runs that aren't using all available years. + eia_settings: Validated ETL parameters required by this data source. Returns: A dictionary of DataFrames whose keys are the names of the corresponding @@ -374,8 +371,8 @@ def _etl_glue( # Check to see whether the settings file indicates the processing of all # available EIA years. processing_all_eia_years = ( - datasets["eia"].eia860.years - == datasets["eia"].eia860.data_source.working_partitions["years"] + eia_settings.eia860.years + == eia_settings.eia860.data_source.working_partitions["years"] ) glue_raw_dfs = pudl.glue.epacamd_eia_crosswalk.extract(ds) glue_transformed_dfs = pudl.glue.epacamd_eia_crosswalk.transform( @@ -487,7 +484,9 @@ def etl( # noqa: C901 sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) logger.info(sqlite_dfs.keys()) if datasets.get("glue", False): - sqlite_dfs.update(_etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, datasets)) + sqlite_dfs.update( + _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, datasets["eia"]) + ) # Load the ferc1 + eia data directly into the SQLite DB: pudl_engine = sa.create_engine(pudl_settings["pudl_db"]) From 474253222302bd081b4d2e560bb89d05d286b7f4 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 29 Aug 2022 17:17:46 -0600 Subject: [PATCH 37/80] remove logger statement used for testing in etl module --- src/pudl/etl.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 08d19629bd..0839dafd06 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -482,7 +482,6 @@ def etl( # noqa: C901 sqlite_dfs.update(_etl_ferc1(datasets["ferc1"], pudl_settings)) if datasets.get("eia", False): sqlite_dfs.update(_etl_eia(datasets["eia"], ds_kwargs)) - logger.info(sqlite_dfs.keys()) if datasets.get("glue", False): sqlite_dfs.update( _etl_glue(datasets["glue"], ds_kwargs, sqlite_dfs, datasets["eia"]) From 0ed292ea98775f8cb10ad1c860a03cd2c0de3ce0 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 29 Aug 2022 17:51:51 -0600 Subject: [PATCH 38/80] Add arg names to remove_leading_zeros_from_numeric_strings() helper function --- src/pudl/extract/eia860.py | 2 +- src/pudl/extract/eia860m.py | 2 +- src/pudl/extract/eia861.py | 2 +- src/pudl/extract/eia923.py | 2 +- src/pudl/glue/epacamd_eia_crosswalk.py | 6 ++++-- 5 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/pudl/extract/eia860.py b/src/pudl/extract/eia860.py index 399c0b0332..3f4d8448c2 100644 --- a/src/pudl/extract/eia860.py +++ b/src/pudl/extract/eia860.py @@ -41,7 +41,7 @@ def process_raw(self, df, page, **partition): if "report_year" not in df.columns: df["report_year"] = list(partition.values())[0] self.cols_added = ["report_year"] - df = remove_leading_zeros_from_numeric_strings(df, "generator_id") + df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") df = self.add_data_maturity(df, page, **partition) return df diff --git a/src/pudl/extract/eia860m.py b/src/pudl/extract/eia860m.py index edd7cdc36b..505d4c9752 100644 --- a/src/pudl/extract/eia860m.py +++ b/src/pudl/extract/eia860m.py @@ -47,7 +47,7 @@ def process_raw(self, df, page, **partition): ).year df = self.add_data_maturity(df, page, **partition) self.cols_added.append("report_year") - df = remove_leading_zeros_from_numeric_strings(df, "generator_id") + df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") return df def extract(self, settings: Eia860Settings = Eia860Settings()): diff --git a/src/pudl/extract/eia861.py b/src/pudl/extract/eia861.py index b6aada361b..b8fb2b7c5f 100644 --- a/src/pudl/extract/eia861.py +++ b/src/pudl/extract/eia861.py @@ -46,7 +46,7 @@ def process_raw(self, df, page, **partition): ) ) self.cols_added = [] - df = remove_leading_zeros_from_numeric_strings(df, "generator_id") + df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") return df def extract(self, settings: Eia861Settings = Eia861Settings()): diff --git a/src/pudl/extract/eia923.py b/src/pudl/extract/eia923.py index 9588a79f91..af986d4c4d 100644 --- a/src/pudl/extract/eia923.py +++ b/src/pudl/extract/eia923.py @@ -41,7 +41,7 @@ def process_raw(self, df, page, **partition): df.drop(to_drop, axis=1, inplace=True) df = df.rename(columns=self._metadata.get_column_map(page, **partition)) self.cols_added = [] - df = remove_leading_zeros_from_numeric_strings(df, "generator_id") + df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") # the 2021 early release data had some ding dang "."'s and nulls in the year column if "report_year" in df.columns: mask = (df.report_year == ".") | df.report_year.isnull() diff --git a/src/pudl/glue/epacamd_eia_crosswalk.py b/src/pudl/glue/epacamd_eia_crosswalk.py index 569e678e97..7b89c34bd7 100644 --- a/src/pudl/glue/epacamd_eia_crosswalk.py +++ b/src/pudl/glue/epacamd_eia_crosswalk.py @@ -61,8 +61,10 @@ def transform( epacamd_eia_crosswalk.pipe(simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) - .pipe(remove_leading_zeros_from_numeric_strings, "generator_id") - .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") + .pipe(remove_leading_zeros_from_numeric_strings, col_name="generator_id") + .pipe( + remove_leading_zeros_from_numeric_strings, col_name="emissions_unit_id_epa" + ) .pipe(apply_pudl_dtypes, "eia") .dropna(subset=["plant_id_eia"]) ) From 685ddaf2cf2ea42829a452d761b9d9cf6a938dab Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 29 Aug 2022 18:16:31 -0600 Subject: [PATCH 39/80] Replace assert statements with merge validation in epacems transform module --- src/pudl/transform/epacems.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 569e3832f6..8eaed35713 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -62,7 +62,11 @@ def harmonize_eia_epa_orispl( # Merge CEMS with Crosswalk to get correct EIA ORISPL code. df_merged = pd.merge( - df, crosswalk_df, on=["plant_id_epa", "emissions_unit_id_epa"], how="left" + df, + crosswalk_df, + on=["plant_id_epa", "emissions_unit_id_epa"], + how="left", + validate="one_to_one", ) # Because the crosswalk isn't complete, there are some instances where the @@ -73,11 +77,7 @@ def harmonize_eia_epa_orispl( df_merged["plant_id_combined"] = df_merged.plant_id_eia.fillna( df_merged.plant_id_epa ) - # assert ( - # ~df_merged.plant_id_combined.isna().any() - # ), "There shouldn't be any NA vales in the combined plant id column" - assert len(df_merged) == len(df) return df_merged From b87c450d13433c7b49ea7f22bcf526a3991bb25a Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 5 Sep 2022 12:41:54 -0600 Subject: [PATCH 40/80] Remove one_to_one validation in epacems transform module because plant_id and emissions_unit_id is repeated in hourly cems data and it's not one-to-one --- src/pudl/transform/epacems.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 8eaed35713..d42c0a271f 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -66,7 +66,6 @@ def harmonize_eia_epa_orispl( crosswalk_df, on=["plant_id_epa", "emissions_unit_id_epa"], how="left", - validate="one_to_one", ) # Because the crosswalk isn't complete, there are some instances where the From 164e358789c732f014016b096af5ef3cb87f8ffc Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 5 Sep 2022 13:44:29 -0600 Subject: [PATCH 41/80] Update the functions in the epacems transform module so that they don't take pudl_engine as an argument. This enables us to make unit tests for each of the functions independent of the rest of the data in the database. Add a unit test for the harmonize_epa_eia_orispl() function in the epacems transform module. --- src/pudl/transform/epacems.py | 20 +++++++------- test/unit/transform/epacems_test.py | 41 +++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 11 deletions(-) create mode 100644 test/unit/transform/epacems_test.py diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index d42c0a271f..530fb835bb 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -20,7 +20,7 @@ def harmonize_eia_epa_orispl( df: pd.DataFrame, - pudl_engine: sa.engine.Engine, + crosswalk_df: pd.DataFrame, ) -> pd.DataFrame: """Harmonize the ORISPL code to match the EIA data. @@ -37,10 +37,8 @@ def harmonize_eia_epa_orispl( convert_to_utc uses the plant ID to look up timezones. Args: - pudl_engine: SQLAlchemy connection engine for connecting to an existing PUDL DB. - This is used to access the crosswalk file for conversion. The crosswalk must - be processed prior to running this function or it won't work. df: A CEMS hourly dataframe for one year-month-state. + crosswalk_df: The epacamd_eia_crosswalk dataframe from the database. Returns: The same data, with the ORISPL plant codes corrected to match the EIA plant IDs. @@ -50,12 +48,9 @@ def harmonize_eia_epa_orispl( # plant_id_epa and emissions_unit_id_epa then calculate .nunique() for plant_id_eia, # none of the values are greater than one meaning that this drop/merge is ok. Might # want to make that an official test somewhere. - - crosswalk_df = pd.read_sql( - "epacamd_eia_crosswalk", - con=pudl_engine, - columns=["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"], - ).drop_duplicates() + crosswalk_df = crosswalk_df[ + ["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"] + ].drop_duplicates() # I wonder if there is a faster way to do this by checking if the id needs to be # fixed rather than just merging it all together (as done below). @@ -203,10 +198,13 @@ def transform( A single year-state of EPA CEMS data """ + # Create all the table inputs used for the subtransform functions below + crosswalk_df = pd.read_sql("epacamd_eia_crosswalk", con=pudl_engine) + return ( raw_df.pipe(apply_pudl_dtypes, group="epacems") .pipe(remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa") - .pipe(harmonize_eia_epa_orispl, pudl_engine) + .pipe(harmonize_eia_epa_orispl, crosswalk_df) .pipe(convert_to_utc, plant_utc_offset=_load_plant_utc_offset(pudl_engine)) .pipe(correct_gross_load_mw) .pipe(apply_pudl_dtypes, group="epacems") diff --git a/test/unit/transform/epacems_test.py b/test/unit/transform/epacems_test.py new file mode 100644 index 0000000000..6e117d81f6 --- /dev/null +++ b/test/unit/transform/epacems_test.py @@ -0,0 +1,41 @@ +"""Unit tests for the pudl.transform.epacems module.""" +import numpy as np +import pandas as pd + +import pudl.transform.epacems as epacems + + +def test_harmonize_eia_epa_orispl(): + """Make sure that incorrect EPA ORISPL codes are fixed.""" + # The test df includes a value for plant_id_epa whose value for plant_id_eia + # is different. Because the crosswalk gets trancated for the short tests based + # on available plant/gen and plant/boiler keys, not all of the plant ids are + # included. I had to find an example (2713-->58697) that would exist in the + # version of the crosswalk created by the fast etl (i.e. the last few years). + cems_test_df = pd.DataFrame( + { + "plant_id_epa": [2713, 3, 10, 1111], + "emissions_unit_id_epa": ["01A", "1", "2", "no-match"], + } + ) + crosswalk_test_df = pd.DataFrame( + { + "plant_id_epa": [2713, 3, 10], + "plant_id_eia": [58697, 3, 10], + "emissions_unit_id_epa": ["01A", "1", "2"], + } + ) + # The harmonize_eia_epa_orispl function should create a new column for the + # official plant_id_eia values and the combined id values for instances + # where there is no match for the the plant_id_epa/emissions_unit_id_epa + # combination. + expected_df = pd.DataFrame( + { + "plant_id_epa": [2713, 3, 10, 1111], + "emissions_unit_id_epa": ["01A", "1", "2", "no-match"], + "plant_id_eia": [58697, 3, 10, np.nan], + "plant_id_combined": [58697, 3, 10, 1111], + } + ) + actual_df = epacems.harmonize_eia_epa_orispl(cems_test_df, crosswalk_test_df) + pd.testing.assert_frame_equal(expected_df, actual_df, check_dtype=False) From 6db30a87e7e76ae45cb71b8016c0fd36d79bbade Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 5 Sep 2022 15:41:48 -0600 Subject: [PATCH 42/80] Fix plant_id_eia in CEMS Previously I merged the crosswalk with CEMS to create a more accurate plant_id_eia field. The crosswalk is not currently comprehensive, and that left lots of CEMS records without plant_id_eia fields. I made a temporary field called plant_id_combined for use in the function that fixed timezones, but then I dropped that field. Realistically, we want a comprehensive plant id field! So I got rid of plant_id_combined, and just updated the plant_id_eia field to contain the fixed values from the crosswalk as well as all the unmapped plant_id_epa fields from CEMS. I also updated the epacems transform unit tests to reflect this. --- src/pudl/transform/epacems.py | 29 +++++++++++------------------ test/unit/transform/epacems_test.py | 4 +--- 2 files changed, 12 insertions(+), 21 deletions(-) diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 530fb835bb..51e8c1b24f 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -52,25 +52,19 @@ def harmonize_eia_epa_orispl( ["plant_id_eia", "plant_id_epa", "emissions_unit_id_epa"] ].drop_duplicates() - # I wonder if there is a faster way to do this by checking if the id needs to be - # fixed rather than just merging it all together (as done below). - # Merge CEMS with Crosswalk to get correct EIA ORISPL code. + + # Because the crosswalk isn't complete, there are some instances where the + # plant_id_eia value will be NA. This isn't great when it goes to grouping or + # merging data together. Specifically for the convert_to_utc() function below. + # Until the crosswalk is comprehensive, we'll fill in all unmapped plant_id_eia + # values with whatever was in plant_id_epa.s df_merged = pd.merge( df, crosswalk_df, on=["plant_id_epa", "emissions_unit_id_epa"], how="left", - ) - - # Because the crosswalk isn't complete, there are some instances where the - # plant_id_eia value will be NA. This isn't great when it goes to grouping or - # merging data together. Specifically for the convert_to_utc() function below. - # This creates a column based on the plant_id_eia but backfills NA with - # plant_id_epa so it can be used to merge on. - df_merged["plant_id_combined"] = df_merged.plant_id_eia.fillna( - df_merged.plant_id_epa - ) + ).assign(plant_id_eia=lambda x: x.plant_id_eia.fillna(x.plant_id_epa)) return df_merged @@ -84,7 +78,7 @@ def convert_to_utc(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataF Args: df: A CEMS hourly dataframe for one year-state. - plant_utc_offset: A dataframe association plant_id_combined with timezones. + plant_utc_offset: A dataframe association with timezones. Returns: The same data, with an op_datetime_utc column added and the op_date and op_hour @@ -101,15 +95,15 @@ def convert_to_utc(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataF ) + pd.to_timedelta(x.op_hour, unit="h") # Add the hour ).merge( - plant_utc_offset.rename(columns={"plant_id_eia": "plant_id_combined"}), + plant_utc_offset, how="left", - on="plant_id_combined", + on="plant_id_eia", ) # Some of the timezones in the plants_entity_eia table may be missing, # but none of the CEMS plants should be. if df["utc_offset"].isna().any(): - missing_plants = df.loc[df["utc_offset"].isna(), "plant_id_combined"].unique() + missing_plants = df.loc[df["utc_offset"].isna(), "plant_id_eia"].unique() raise ValueError( f"utc_offset should never be missing for CEMS plants, but was " f"missing for these: {str(list(missing_plants))}" @@ -125,7 +119,6 @@ def convert_to_utc(df: pd.DataFrame, plant_utc_offset: pd.DataFrame) -> pd.DataF df["op_hour"], df["op_datetime_naive"], df["utc_offset"], - df["plant_id_combined"], ) return df diff --git a/test/unit/transform/epacems_test.py b/test/unit/transform/epacems_test.py index 6e117d81f6..f29a21e5c4 100644 --- a/test/unit/transform/epacems_test.py +++ b/test/unit/transform/epacems_test.py @@ -1,5 +1,4 @@ """Unit tests for the pudl.transform.epacems module.""" -import numpy as np import pandas as pd import pudl.transform.epacems as epacems @@ -33,8 +32,7 @@ def test_harmonize_eia_epa_orispl(): { "plant_id_epa": [2713, 3, 10, 1111], "emissions_unit_id_epa": ["01A", "1", "2", "no-match"], - "plant_id_eia": [58697, 3, 10, np.nan], - "plant_id_combined": [58697, 3, 10, 1111], + "plant_id_eia": [58697, 3, 10, 1111], } ) actual_df = epacems.harmonize_eia_epa_orispl(cems_test_df, crosswalk_test_df) From 690a86330661f7227ac80cc15a43c8ed830a4b92 Mon Sep 17 00:00:00 2001 From: cbz Date: Tue, 6 Sep 2022 16:28:19 -0400 Subject: [PATCH 43/80] first pass at filling in old BA Codes using bfill or most consistent --- src/pudl/output/eia860.py | 159 +++++++++++++++++++++++++++++++++++ src/pudl/transform/eia.py | 55 ++++++------ src/pudl/transform/eia861.py | 61 +++++++++----- 3 files changed, 226 insertions(+), 49 deletions(-) diff --git a/src/pudl/output/eia860.py b/src/pudl/output/eia860.py index b914585187..c5a0e5e933 100644 --- a/src/pudl/output/eia860.py +++ b/src/pudl/output/eia860.py @@ -7,6 +7,8 @@ import pudl from pudl.metadata.fields import apply_pudl_dtypes +from pudl.transform.eia import occurrence_consistency +from pudl.transform.eia861 import make_backfilled_ba_code_column logger = logging.getLogger(__name__) @@ -136,11 +138,168 @@ def plants_eia860(pudl_engine, start_date=None, end_date=None): out_df = ( pd.merge(out_df, utils_eia_df, how="left", on=["utility_id_eia"]) .dropna(subset=["report_date", "plant_id_eia"]) + .pipe(fill_in_missing_ba_codes) .pipe(apply_pudl_dtypes, group="eia") ) return out_df +def make_consistent_ba_code_column(plants: pd.DataFrame) -> pd.DataFrame: + """Make a columns of the most consistent balancing authority code. + + Employ the harvesting function :func:`occurrence_consistency` which determines how + consistent the values in a table are across all records within each plant. This + function grabs only the values determined to be at least 70% consitent and merges + them onto the plants table as a new column: ``balancing_authority_code_eia_consistent`` + """ + ba_code_consistent = occurrence_consistency( + entity_idx=["plant_id_eia"], + compiled_df=plants, + col="balancing_authority_code_eia", + cols_to_consit=["plant_id_eia"], + strictness=0.7, + ) + + static_plant_to_code_map = ba_code_consistent[ + ba_code_consistent.balancing_authority_code_eia_consistent + ][ + [ + "plant_id_eia", + "balancing_authority_code_eia", + "balancing_authority_code_eia_consistent_rate", + ] + ].drop_duplicates() + + plants = pd.merge( + plants, + static_plant_to_code_map, + how="left", + on=["plant_id_eia"], + suffixes=("", "_consistent"), + ) + logger.info( + f"{len(plants[plants.balancing_authority_code_eia_consistent.notnull()])/len(plants):.1%} of plant records have static BA Codes" + ) + return plants + + +def fill_in_missing_ba_codes(plants: pd.DataFrame) -> pd.DataFrame: + """Fill in missing ``balancing_authority_code_eia`` using either bfill or most consistent. + + Balancing authority codes did not begin being reported until 2013. This function + fills in the old years with BA codes using two main methods: + + * Backfilling from the oldest reported BA code (via :meth:`pd.bfill`) + * Using the most consistent reported value + + We add a column to represent each of these two methodologies via + :func:`make_backfilled_ba_code_column` and :func:`make_consistent_ba_code_column`. + + We know that the BA codes do change over time and are incorrectly reported at times. + Because of these two facts, we cann't simple :meth:`pd.fillna` with either the + backfilled or most consistent value. This function employs some specific filling in + methodologies based on an investigation of the data. Here is a description of each + of various stages of filling in which involves some bespoke cleanup: + + * if the backfilled code and the most consistent code are the same, use either! + * assign BA code ``PACW`` (``PacifiCorp - West``) when ``PACW`` is the most + consistent BA code and the ``state`` is within ``PACW``'s territory + * similarity, assign BA code ``PACE`` (``PacifiCorp - East``) when ``PACE`` is + the most consistent BA code and the ``state`` is within ``PACE``'s territory + * use the backfilled code for plants that have ``SWPP`` (``Southwest Power Pool``) + as their most consistent BA code because we know ``SWPP`` accumulated many smaller + balancing authorities + * use the backfilled code for plants that have a differnt backfilled and most + consistent code, but the most consistent code is has a consistency rate (how + frequent the most consistent BA code is reported / the total BA codes reported) + is less than 80%. This is meant to find plants where there are multiple years of + an older BA code before it switches - assuming a BA code being reported for + multiple years is not a reporting error. TODO: make this one more explicit. + + Args: + plants: table of annual plant attributes, including ``balancing_authority_code_eia`` + """ + + def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: + """Internal function to log progress on fillin in BA codes. + + Args: + plants: the current plants table to check + method_str: A description of the method employed. This will be inserted into + the log. + """ + currently_null_len = len(plants[plants.balancing_authority_code_eia.isnull()]) + logger.info( + f"Filled BA codes where {method_str}. " + f"Currently {currently_null_len/len(plants):.1%} of records ({currently_null_len}) with no BA codes" + ) + + plants = make_backfilled_ba_code_column(plants, by_cols=["plant_id_eia"]).pipe( + make_consistent_ba_code_column + ) + log_current_ba_code_nulls(plants, method_str="no treatment had been applied") + # when the backfilled code and the static code are the same, use either result + plants.loc[ + ( + plants.balancing_authority_code_eia_bfilled + == plants.balancing_authority_code_eia_consistent + ) + & (plants.balancing_authority_code_eia.isnull()), + "balancing_authority_code_eia", + ] = plants.balancing_authority_code_eia_consistent + + log_current_ba_code_nulls( + plants, method_str="backfilling and consistent value is the same" + ) + # we found a pattern of 2013 plants reporting BA codes of PACE when the + # consistent option was PACW and the states the plants were located in + # are PACW states + # use PACW for those records + plants.loc[ + (plants.balancing_authority_code_eia.isnull()) + & (plants.balancing_authority_code_eia_consistent == "PACW") + & (plants.state.isin(["OR", "CA"])), + "balancing_authority_code_eia", + ] = "PACW" + log_current_ba_code_nulls( + plants, method_str="consistent code is PACW and state is OR or CA" + ) + # found the opposite! Plants in UT labeled as PACW instead of PACE + plants.loc[ + (plants.balancing_authority_code_eia.isnull()) + & (plants.balancing_authority_code_eia_consistent == "PACE") + & (plants.state.isin(["UT"])), + "balancing_authority_code_eia", + ] = "PACE" + log_current_ba_code_nulls( + plants, method_str="consistent code is PACE and state is UT" + ) + # we know SWPP has done a ton of accumulation of smaller BA's + plants.loc[ + (plants.balancing_authority_code_eia.isnull()) + & (plants.balancing_authority_code_eia_consistent == "SWPP"), + "balancing_authority_code_eia", + ] = plants.balancing_authority_code_eia_bfilled + log_current_ba_code_nulls( + plants, method_str="SWPP is most consistent value. Filled w/ oldest BA code" + ) + + plants.loc[ + (plants.balancing_authority_code_eia.isnull()) + & ( + plants.balancing_authority_code_eia_bfilled + != plants.balancing_authority_code_eia_consistent + ) + & (plants.balancing_authority_code_eia_consistent_rate <= 0.8), + "balancing_authority_code_eia", + ] = plants.balancing_authority_code_eia_bfilled + log_current_ba_code_nulls( + plants, + method_str="most consistent BA code is less than 80% consistent. Used backfilled BA code", + ) + return plants + + def plants_utils_eia860(pudl_engine, start_date=None, end_date=None): """Create a dataframe of plant and utility IDs and names from EIA 860. diff --git a/src/pudl/transform/eia.py b/src/pudl/transform/eia.py index 35f34560ec..04f7ab7b32 100644 --- a/src/pudl/transform/eia.py +++ b/src/pudl/transform/eia.py @@ -161,10 +161,14 @@ def find_timezone(*, lng=None, lat=None, state=None, strict=True): return tz -def _occurrence_consistency( - entity_id, compiled_df, col, cols_to_consit, strictness=0.7 -): - """Find the occurence of plants & the consistency of records. +def occurrence_consistency( + entity_idx: list[str], + compiled_df: pd.DataFrame, + col: str, + cols_to_consit: list[str], + strictness: float = 0.7, +) -> pd.DataFrame: + """Find the occurence of entities & the consistency of records. We need to determine how consistent a reported value is in the records across all of the years or tables that the value is being reported, so we @@ -173,16 +177,16 @@ def _occurrence_consistency( information we can determine if the reported records are strict enough. Args: - entity_id (list): a list of the id(s) for the entity. Ex: for a plant - entity, the entity_id is ['plant_id_eia']. For a generator entity, - the entity_id is ['plant_id_eia', 'generator_id']. - compiled_df (pandas.DataFrame): a dataframe with every instance of the + entity_idx: a list of the id(s) for the entity. Ex: for a plant + entity, the entity_idx is ['plant_id_eia']. For a generator entity, + the entity_idx is ['plant_id_eia', 'generator_id']. + compiled_df: a dataframe with every instance of the column we are trying to harvest. - col (str): the column name of the column we are trying to harvest. - cols_to_consit (list): a list of the columns to determine consistency. + col: the column name of the column we are trying to harvest. + cols_to_consit: a list of the columns to determine consistency. This either the [entity_id] or the [entity_id, 'report_date'], depending on whether the entity is static or annual. - strictness (float): How consistent do you want the column records to + strictness: How consistent do you want the column records to be? The default setting is .7 (so 70% of the records need to be consistent in order to accept harvesting the record). @@ -194,7 +198,7 @@ def _occurrence_consistency( """ # select only the colums you want and drop the NaNs # we want to drop the NaNs because - col_df = compiled_df[entity_id + ["report_date", col, "table"]].copy() + col_df = compiled_df[entity_idx + ["report_date", col]].copy() if get_pudl_dtypes(group="eia")[col] == "string": nan_str_mask = (col_df[col] == "nan").fillna(False) col_df.loc[nan_str_mask, col] = pd.NA @@ -204,14 +208,13 @@ def _occurrence_consistency( col_df[f"{col}_consistent"] = pd.NA col_df[f"{col}_consistent_rate"] = pd.NA col_df["entity_occurences"] = pd.NA - col_df = col_df.drop(columns=["table"]) return col_df # determine how many times each entity occurs in col_df occur = ( - col_df.groupby(by=cols_to_consit, observed=True)[["table"]] + col_df.assign(entity_occurences=1) + .groupby(by=cols_to_consit, observed=True)[["entity_occurences"]] .count() .reset_index() - .rename(columns={"table": "entity_occurences"}) ) # add the occurances into the main dataframe @@ -219,17 +222,17 @@ def _occurrence_consistency( # determine how many instances of each of the records in col exist consist_df = ( - col_df.groupby(by=cols_to_consit + [col], observed=True)[["table"]] + col_df.assign(record_occurences=1) + .groupby(by=cols_to_consit + [col], observed=True)[["record_occurences"]] .count() .reset_index() - .rename(columns={"table": "record_occurences"}) ) # now in col_df we have # of times an entity occurred accross the tables # and we are going to merge in the # of times each value occured for each # entity record. When we merge the consistency in with the occurances, we # can determine if the records are more than 70% consistent across the # occurances of the entities. - col_df = col_df.merge(consist_df, how="outer").drop(columns=["table"]) + col_df = col_df.merge(consist_df, how="outer") # change all of the fully consistent records to True col_df[f"{col}_consistent_rate"] = ( col_df["record_occurences"] / col_df["entity_occurences"] @@ -240,7 +243,7 @@ def _occurrence_consistency( def _lat_long( - dirty_df, clean_df, entity_id_df, entity_id, col, cols_to_consit, round_to=2 + dirty_df, clean_df, entity_id_df, entity_idx, col, cols_to_consit, round_to=2 ): """Harvests more complete lat/long in special cases. @@ -256,9 +259,9 @@ def _lat_long( consistently reported lat/long. entity_id_df (pandas.DataFrame): a dataframe with a complete set of possible entity ids - entity_id (list): a list of the id(s) for the entity. Ex: for a plant - entity, the entity_id is ['plant_id_eia']. For a generator entity, - the entity_id is ['plant_id_eia', 'generator_id']. + entity_idx (list): a list of the id(s) for the entity. Ex: for a plant + entity, the entity_idx is ['plant_id_eia']. For a generator entity, + the entity_idx is ['plant_id_eia', 'generator_id']. col (string): the column name of the column we are trying to harvest. cols_to_consit (list): a list of the columns to determine consistency. This either the [entity_id] or the [entity_id, 'report_date'], @@ -277,11 +280,11 @@ def _lat_long( ll_df = dirty_df.round(decimals={col: round_to}) logger.debug(f"Dirty {col} records: {len(ll_df)}") ll_df["table"] = "special_case" - ll_df = _occurrence_consistency(entity_id, ll_df, col, cols_to_consit) + ll_df = occurrence_consistency(entity_idx, ll_df, col, cols_to_consit) # grab the clean plants ll_clean_df = clean_df.dropna() # find the new clean plant records by selecting the True consistent records - ll_df = ll_df[ll_df[f"{col}_consistent"]].drop_duplicates(subset=entity_id) + ll_df = ll_df[ll_df[f"{col}_consistent"]].drop_duplicates(subset=entity_idx) logger.debug(f"Clean {col} records: {len(ll_df)}") # add the newly cleaned records ll_clean_df = pd.concat([ll_clean_df, ll_df]) @@ -459,7 +462,7 @@ def harvesting( # noqa: C901 the outcome here to be perfect! We choose to pull the most consistent record as reported across all the EIA tables and years, but we also required a "strictness" level of 70% (this is currently a hard coded - argument for _occurrence_consistency). That means at least 70% of the + argument for :func:`occurrence_consistency`). That means at least 70% of the records must be the same for us to use that value. So if values for an entity haven't been reported 70% consistently, then it will show up as a null value. We built in the ability to add special cases for columns where @@ -535,7 +538,7 @@ def harvesting( # noqa: C901 cols_to_consit = id_cols strictness = _manage_strictness(col, eia860m) - col_df = _occurrence_consistency( + col_df = occurrence_consistency( id_cols, compiled_df, col, cols_to_consit, strictness=strictness ) diff --git a/src/pudl/transform/eia861.py b/src/pudl/transform/eia861.py index dcf95b3d6e..da6875f426 100644 --- a/src/pudl/transform/eia861.py +++ b/src/pudl/transform/eia861.py @@ -440,17 +440,14 @@ def _filter_non_class_cols(df, class_list): return df.filter(regex=regex) -def _ba_code_backfill(df): - """Backfill Balancing Authority Codes based on codes in later years. - - Note: - The BA Code to ID mapping can change from year to year. If a Balancing Authority - is bought by another entity, the code may change, but the old EIA BA ID will be - retained. +def make_backfilled_ba_code_column(df, by_cols: list[str]) -> pd.DataFrame: + """Make a backfilled Balancing Authority Codes based on codes in later years. Args: - ba_eia861 (pandas.DataFrame): The transformed EIA 861 Balancing Authority - dataframe (balancing_authority_eia861). + df: table with columns: ``balancing_authority_code_eia``, ``report_date`` and + all ``by_cols`` + by_cols: list of columns to use as ``by`` argument in :meth:`pd.groupby` + Returns: pandas.DataFrame: The balancing_authority_eia861 dataframe, but with many fewer @@ -464,30 +461,24 @@ def _ba_code_backfill(df): f"records ({start_nas/start_len:.2%})" ) ba_ids = ( - df[ - [ - "balancing_authority_id_eia", - "balancing_authority_code_eia", - "report_date", - ] - ] + df[by_cols + ["balancing_authority_code_eia", "report_date"]] .drop_duplicates() - .sort_values(["balancing_authority_id_eia", "report_date"]) + .sort_values(by_cols + ["report_date"]) ) - ba_ids["ba_code_filled"] = ba_ids.groupby("balancing_authority_id_eia")[ + ba_ids["balancing_authority_code_eia_bfilled"] = ba_ids.groupby(by_cols)[ "balancing_authority_code_eia" ].fillna(method="bfill") ba_eia861_filled = df.merge(ba_ids, how="left") - ba_eia861_filled = ba_eia861_filled.assign( - balancing_authority_code_eia=lambda x: x.ba_code_filled - ).drop("ba_code_filled", axis="columns") + end_len = len(ba_eia861_filled) if start_len != end_len: raise AssertionError( f"Number of rows in the dataframe changed {start_len}!={end_len}!" ) end_nas = len( - ba_eia861_filled.loc[ba_eia861_filled.balancing_authority_code_eia.isnull()] + ba_eia861_filled.loc[ + ba_eia861_filled.balancing_authority_code_eia_bfilled.isnull() + ] ) logger.info( f"Ended with {end_nas} missing BA Codes out of {end_len} " @@ -496,6 +487,30 @@ def _ba_code_backfill(df): return ba_eia861_filled +def backfill_ba_codes_by_ba_id(df: pd.DataFrame) -> pd.DataFrame: + """Fill in missing BA Codes by backfilling based on BA ID. + + Note: + The BA Code to ID mapping can change from year to year. If a Balancing Authority + is bought by another entity, the code may change, but the old EIA BA ID will be + retained. + + Args: + df: The transformed EIA 861 Balancing Authority dataframe + (balancing_authority_eia861). + """ + ba_eia861_filled = ( + make_backfilled_ba_code_column( + df, date_col="report_date", by_cols=["balancing_authority_id_eia"] + ) + .assign( + balancing_authority_code_eia=lambda x: x.balancing_authority_code_eia_bfilled + ) + .drop("balancing_authority_code_eia_bfilled", axis="columns") + ) + return ba_eia861_filled + + def _tidy_class_dfs(df, df_name, idx_cols, class_list, class_type, keep_totals=False): # Clean up values just enough to use primary key columns as a multi-index: logger.debug(f"Cleaning {df_name} table index columns so we can tidy data.") @@ -876,7 +891,7 @@ def balancing_authority(tfr_dfs): ) # Backfill BA Codes based on BA IDs: - df = df.reset_index().pipe(_ba_code_backfill) + df = df.reset_index().pipe(backfill_ba_codes_by_ba_id) # Typo: NEVP, BA ID is 13407, but in 2014-2015 in UT, entered as 13047 df.loc[ (df.balancing_authority_code_eia == "NEVP") From 76f789afe4aeae4c7a2b26e8964a4eb80be9a420 Mon Sep 17 00:00:00 2001 From: cbz Date: Tue, 6 Sep 2022 17:35:27 -0400 Subject: [PATCH 44/80] remove the now gone date_col arg --- src/pudl/transform/eia861.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/pudl/transform/eia861.py b/src/pudl/transform/eia861.py index da6875f426..22beaac0aa 100644 --- a/src/pudl/transform/eia861.py +++ b/src/pudl/transform/eia861.py @@ -500,9 +500,7 @@ def backfill_ba_codes_by_ba_id(df: pd.DataFrame) -> pd.DataFrame: (balancing_authority_eia861). """ ba_eia861_filled = ( - make_backfilled_ba_code_column( - df, date_col="report_date", by_cols=["balancing_authority_id_eia"] - ) + make_backfilled_ba_code_column(df, by_cols=["balancing_authority_id_eia"]) .assign( balancing_authority_code_eia=lambda x: x.balancing_authority_code_eia_bfilled ) From 00126ebb31081b6c39c3a63215bebcf8a7a604e8 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Wed, 7 Sep 2022 10:00:40 -0500 Subject: [PATCH 45/80] Use provision-micromamba and remove ferc1_solo ETL to speed up CI. --- .github/workflows/tox-pytest.yml | 23 +++++++++++----- .../package_data/settings/ferc1_solo_test.yml | 27 ------------------- test/test-environment.yml | 6 +++-- tox.ini | 12 --------- 4 files changed, 20 insertions(+), 48 deletions(-) delete mode 100644 src/pudl/package_data/settings/ferc1_solo_test.yml diff --git a/.github/workflows/tox-pytest.yml b/.github/workflows/tox-pytest.yml index 0ebdb227b7..28b474816f 100644 --- a/.github/workflows/tox-pytest.yml +++ b/.github/workflows/tox-pytest.yml @@ -14,15 +14,24 @@ jobs: with: fetch-depth: 2 - - name: Set up conda environment for testing - uses: conda-incubator/setup-miniconda@v2.1.1 + - name: Install Conda environment using mamba + uses: mamba-org/provision-with-micromamba@v13 with: - mamba-version: "*" - channels: conda-forge,defaults - channel-priority: true - python-version: "3.10" - activate-environment: pudl-test environment-file: test/test-environment.yml + cache-env: true + channels: conda-forge,defaults + channel-priority: strict + + # - name: Set up conda environment for testing + # uses: conda-incubator/setup-miniconda@v2.1.1 + # with: + # mamba-version: "*" + # channels: conda-forge,defaults + # channel-priority: true + # python-version: "3.10" + # activate-environment: pudl-test + # environment-file: test/test-environment.yml + - shell: bash -l {0} run: | conda info diff --git a/src/pudl/package_data/settings/ferc1_solo_test.yml b/src/pudl/package_data/settings/ferc1_solo_test.yml deleted file mode 100644 index 52ee0560fa..0000000000 --- a/src/pudl/package_data/settings/ferc1_solo_test.yml +++ /dev/null @@ -1,27 +0,0 @@ ---- -########################################################################### -# Settings for ferc1_to_sqlite script -########################################################################### -ferc1_to_sqlite_settings: - # What years of original FERC data should be cloned into the SQLite DB? - years: - [2020] - # A list of tables to be loaded into the local SQLite database. These are - # the table names as they appear in the 2015 FERC Form 1 database. - tables: - - f1_respondent_id - - f1_steam - - f1_fuel - -name: ferc1-solo -title: FERC Form 1 Solo ETL -description: > - A truly minimal FERC Form 1 ETL, just to demonstrate it can be loaded - independently of all other datasets. One year, fuel and steam tables. -version: 0.1.0 -datasets: - ferc1: - tables: - - fuel_ferc1 # requires plants_steam_ferc1 to load properly - - plants_steam_ferc1 - years: [2020] diff --git a/test/test-environment.yml b/test/test-environment.yml index 3e254b8968..07fddaa62b 100644 --- a/test/test-environment.yml +++ b/test/test-environment.yml @@ -2,13 +2,15 @@ name: pudl-test channels: - conda-forge + - defaults dependencies: - - geopandas>=0.9,<11 + - python>=3.10,<3.11 + - geopandas>=0.9,<0.12 - numba>=0.55.1,<0.56 - pip>=22,<23 - pygeos>=0.10,<0.13 - python-snappy>=0.6,<1 - - setuptools<63 + - setuptools<66 - sqlite>=3.36,<4 - tox>=3.24,<4 - google-cloud-sdk~=386.0.0 diff --git a/tox.ini b/tox.ini index bfcbb30050..9a996f1c86 100644 --- a/tox.ini +++ b/tox.ini @@ -111,15 +111,6 @@ commands = --doctest-modules {envsitepackagesdir}/pudl \ test/unit -[testenv:ferc1_solo] -description = Test whether FERC 1 can be loaded into the PUDL database alone. -extras = - test -commands = - pytest {posargs} {[testenv]covargs} \ - --etl-settings src/pudl/package_data/settings/ferc1_solo_test.yml \ - test/integration/etl_test.py::test_pudl_engine - [testenv:integration] description = Run all software integration tests and process a full year of data. extras = @@ -150,7 +141,6 @@ commands = {[testenv:linters]commands} {[testenv:docs]commands} {[testenv:unit]commands} - {[testenv:ferc1_solo]commands} {[testenv:integration]commands} {[testenv]covreport} @@ -187,7 +177,6 @@ commands = {[testenv:linters]commands} {[testenv:docs]commands} {[testenv:unit]commands} - {[testenv:ferc1_solo]commands} {[testenv:ferc1_schema]commands} {[testenv:full_integration]commands} {[testenv]covreport} @@ -205,7 +194,6 @@ commands = {[testenv:linters]commands} {[testenv:docs]commands} {[testenv:unit]commands} - {[testenv:ferc1_solo]commands} {[testenv:ferc1_schema]commands} {[testenv:integration]commands} bash -c 'rm -f tox-nuke.log' From d8fbc15e0dec7e52963ace378c2ed437c8f0320e Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Wed, 7 Sep 2022 10:12:33 -0500 Subject: [PATCH 46/80] Remove 'conda run' since provisioned env is automatically activated --- .github/workflows/tox-pytest.yml | 16 +++------------- 1 file changed, 3 insertions(+), 13 deletions(-) diff --git a/.github/workflows/tox-pytest.yml b/.github/workflows/tox-pytest.yml index 28b474816f..b9f6e90b76 100644 --- a/.github/workflows/tox-pytest.yml +++ b/.github/workflows/tox-pytest.yml @@ -22,16 +22,6 @@ jobs: channels: conda-forge,defaults channel-priority: strict - # - name: Set up conda environment for testing - # uses: conda-incubator/setup-miniconda@v2.1.1 - # with: - # mamba-version: "*" - # channels: conda-forge,defaults - # channel-priority: true - # python-version: "3.10" - # activate-environment: pudl-test - # environment-file: test/test-environment.yml - - shell: bash -l {0} run: | conda info @@ -61,8 +51,8 @@ jobs: - name: Log SQLite3 version run: | - conda run -n pudl-test which sqlite3 - conda run -n pudl-test sqlite3 --version + which sqlite3 + sqlite3 --version - name: Set default gcp credentials id: gcloud-auth @@ -74,7 +64,7 @@ jobs: env: API_KEY_EIA: ${{ secrets.API_KEY_EIA }} run: | - conda run -n pudl-test tox -- --gcs-cache-path gs://zenodo-cache.catalyst.coop + tox -- --gcs-cache-path gs://zenodo-cache.catalyst.coop - name: Log post-test Zenodo datastore contents run: find ~/pudl-work/data/ From 13922e401a13ab36790675c699a7c7ea146b97be Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Wed, 7 Sep 2022 10:20:15 -0500 Subject: [PATCH 47/80] Use bash -l {0} by default in ci-test job --- .github/workflows/tox-pytest.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/tox-pytest.yml b/.github/workflows/tox-pytest.yml index b9f6e90b76..25b486b99b 100644 --- a/.github/workflows/tox-pytest.yml +++ b/.github/workflows/tox-pytest.yml @@ -8,6 +8,9 @@ jobs: runs-on: ubuntu-latest strategy: fail-fast: false + defaults: + run: + shell: bash -l {0} steps: - uses: actions/checkout@v3 @@ -22,7 +25,7 @@ jobs: channels: conda-forge,defaults channel-priority: strict - - shell: bash -l {0} + - name: Log environment details run: | conda info conda list From c983633b5b298ea5cf3dc885616af1f6232d286e Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Wed, 7 Sep 2022 13:21:57 -0500 Subject: [PATCH 48/80] Aggregate data_maturity in gfn_eia923, update EIA ETL debugging Notebook Fix the aggregation of the `data_maturity` column in the transform step for the `generation_fuel_nuclear_eia923` table, so the aggregated records we create (to fix some missing primary key values) get the `data_maturity` value of their input records and we don't have NA values messing things up downstream. Also update the EIA ETL Debugging notebook to reflect the current state of the system on `dev`. Closes #1914 --- devtools/eia-etl-debug.ipynb | 56 +++++++++++++++++++++++------------- src/pudl/transform/eia923.py | 13 +++++++-- 2 files changed, 47 insertions(+), 22 deletions(-) diff --git a/devtools/eia-etl-debug.ipynb b/devtools/eia-etl-debug.ipynb index b8e1bc144d..9ca2d83f74 100644 --- a/devtools/eia-etl-debug.ipynb +++ b/devtools/eia-etl-debug.ipynb @@ -15,7 +15,7 @@ "outputs": [], "source": [ "%load_ext autoreload\n", - "%autoreload 2\n", + "%autoreload 3\n", "import pudl\n", "import logging\n", "import sys\n", @@ -64,15 +64,24 @@ "from pudl.metadata.classes import DataSource\n", "\n", "eia860_data_source = DataSource.from_id(\"eia860\")\n", - "eia860_years = eia860_data_source.working_partitions[\"years\"]\n", - "#eia860_years = [2020]\n", - "eia860_settings = Eia860Settings(years=eia860_years)\n", + "eia860_settings = Eia860Settings(\n", + "# Limit the years as needed if you're testing only a few of them. E.g.:\n", + " years=[2021],\n", + "# years=eia860_data_source.working_partitions[\"years\"]\n", + "# By default all of the tables will be processed.\n", + "# Select the relevant tables as needed if you're testing only a few of them. E.g.:\n", + "# tables=[\"generation_fuel_nuclear_eia923\", \"generation_fuel_eia923\"]\n", + ")\n", "\n", - "# Uncomment to use all available years:\n", "eia923_data_source = DataSource.from_id(\"eia923\")\n", - "eia923_years = eia923_data_source.working_partitions[\"years\"]\n", - "#eia923_years = [2020]\n", - "eia923_settings = Eia923Settings(years=eia923_years)\n", + "eia923_settings = Eia923Settings(\n", + "# Limit the years as needed if you're testing only a few of them. E.g.:\n", + " years = [2021]\n", + " # years = eia923_data_source.working_partitions[\"years\"]\n", + "# By default all of the tables will be processed.\n", + "# Select the relevant tables as needed if you're testing only a few of them. E.g.:\n", + "# tables=[\"generation_fuel_nuclear_eia923\", \"generation_fuel_eia923\"]\n", + ")\n", "\n", "eia_settings = EiaSettings(eia860=eia860_settings, eia923=eia923_settings)" ] @@ -116,10 +125,12 @@ "source": [ "%%time\n", "eia860_extractor = pudl.extract.eia860.Extractor(ds)\n", - "eia860_raw_dfs = eia860_extractor.extract(year=eia860_settings.years)\n", + "eia860_raw_dfs = eia860_extractor.extract(settings=eia860_settings)\n", + "\n", + "eia860m_extractor = pudl.extract.eia860m.Extractor(ds)\n", "if eia860_settings.eia860m:\n", - " eia860m_raw_dfs = pudl.extract.eia860m.Extractor(ds).extract(\n", - " year_month=eia860_settings.eia860m_date\n", + " eia860m_raw_dfs = eia860m_extractor.extract(\n", + " settings=eia860_settings\n", " )\n", " eia860_raw_dfs = pudl.extract.eia860m.append_eia860m(\n", " eia860_raw_dfs=eia860_raw_dfs,\n", @@ -143,7 +154,7 @@ "%%time\n", "eia860_transformed_dfs = pudl.transform.eia860.transform(\n", " eia860_raw_dfs,\n", - " eia860_tables=eia860_settings.tables,\n", + " eia860_settings=eia860_settings,\n", ")" ] }, @@ -169,7 +180,7 @@ "source": [ "%%time\n", "eia923_extractor = pudl.extract.eia923.Extractor(ds)\n", - "eia923_raw_dfs = eia923_extractor.extract(year=eia923_settings.years)" + "eia923_raw_dfs = eia923_extractor.extract(settings=eia_settings.eia923)" ] }, { @@ -188,7 +199,7 @@ "%%time\n", "eia923_transformed_dfs = pudl.transform.eia923.transform(\n", " eia923_raw_dfs,\n", - " eia923_tables=eia923_settings.tables,\n", + " eia923_settings=eia923_settings,\n", ")" ] }, @@ -224,14 +235,12 @@ " \n", "entities_dfs, eia_transformed_dfs = pudl.transform.eia.transform(\n", " eia_transformed_dfs,\n", - " eia860_years=eia860_settings.years,\n", - " eia923_years=eia923_settings.years,\n", - " eia860m=eia860_settings.eia860m,\n", + " eia_settings=eia_settings,\n", ")\n", "\n", "# Assign appropriate types to new entity tables:\n", "entities_dfs = {\n", - " name: pudl.helpers.apply_pudl_dtypes(df, group=\"eia\")\n", + " name: pudl.metadata.fields.apply_pudl_dtypes(df, group=\"eia\")\n", " for name, df in entities_dfs.items()\n", "}\n", "\n", @@ -242,10 +251,17 @@ " .encode(entities_dfs[table])\n", " )\n", "\n", - "out_dfs = pudl.etl._read_static_tables_eia()\n", + "out_dfs = pudl.etl._read_static_encoding_tables(etl_group=\"static_eia\")\n", "out_dfs.update(entities_dfs)\n", "out_dfs.update(eia_transformed_dfs)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -264,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/src/pudl/transform/eia923.py b/src/pudl/transform/eia923.py index 91c72259e8..3f02d787e2 100644 --- a/src/pudl/transform/eia923.py +++ b/src/pudl/transform/eia923.py @@ -307,6 +307,11 @@ def _aggregate_generation_fuel_duplicates( ) if not fuel_type_code_aer_is_unique: raise AssertionError("Duplicate fuels have different fuel_type_code_aer.") + data_maturity_is_unique = ( + duplicates.groupby(natural_key_fields).data_maturity.nunique().eq(1).all() + ) + if not data_maturity_is_unique: + raise AssertionError("Duplicate fuels have different data_maturity.") agg_fields = { "fuel_consumed_units": "sum", @@ -314,10 +319,14 @@ def _aggregate_generation_fuel_duplicates( "fuel_consumed_mmbtu": "sum", "fuel_consumed_for_electricity_mmbtu": "sum", "net_generation_mwh": "sum", - # We can safely select the first fuel_type_code_* because we know they - # are the same for each group of duplicates. + # We can safely select the first values here because we know they are unique + # within each group of duplicates. We check explicitly for fuel_type_code_aer + # and data_maturity above, and fuel_type_code_pudl maps to fuel_type_code_aer + # such that if fuel_type_code_aer is unique, fuel_type_code_pudl must also + # be unique. "fuel_type_code_aer": "first", "fuel_type_code_pudl": "first", + "data_maturity": "first", } resolved_dupes = ( From bc76270133e2380825ca9eb03337226146711f8d Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 7 Sep 2022 14:32:22 -0600 Subject: [PATCH 49/80] Update the CEMS documentation page to include more specific information about which units must report to EPA --- docs/templates/epacems_child.rst.jinja | 29 +++++++++++++++----------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/docs/templates/epacems_child.rst.jinja b/docs/templates/epacems_child.rst.jinja index 8da9c40a9f..a56c1e0e8c 100644 --- a/docs/templates/epacems_child.rst.jinja +++ b/docs/templates/epacems_child.rst.jinja @@ -12,8 +12,10 @@ EPACEMS Intake catalog. `__ (CEMS) are used to determine the rate of gas or particulate matter exiting a point source of emissions. The EPA `Clean Air Markets Division (CAMD) `__ -has collected emissions data from CEMS units stretching back to 1995. Among the data -included in CEMS are hourly gross load, SO2, CO2, and NOx emissions. +has collected data on power plant emissions from CEMS units stretching back to 1995. The +CEMS dataset includes hourly gross load, SO2, CO2, and NOx emissions associated with +a given point source, usually a boiler. Read more about this in "Notable +Irregularities"; it gets complicated. {% endblock %} {% block downloadable_pdfs %} @@ -35,15 +37,18 @@ Who is required to install CEMS and report to EPA? {% endblock %} {% block fill_out_form %} `Part 75 `__ -of the Federal Code of Regulations (FRC), the backbone of the Clean Air Act Title IV and -Acid Rain Program, requires coal and other solid-combusting units (see §72.2) to install -and use CEMS (see §75.2, §72.6). Certain low-sulfur fueled gas and oil units (see §72.2) -may seek exemption or alternative means of monitoring their emissions if desired (see -§§75.23, §§75.48, §§75.66). Once CEMS are installed, Part 75 requires hourly data -recording, including during startup, shutdown, and instances of malfunction as well as -quarterly data reporting to the EPA. The regulation further details the protocol for -missing data calculations and backup monitoring for instances of CEMS failure (see -§§75,31-37). +of the Code of Federal Regulations (CFR), the backbone of the Clean Air Act's Acid Rain +Program, requires fossil-combustion units to install and use CEMS. The qualifications +(§75.2(a), §72.6(a)) are closely followed by a myriad of exceptions (§75.2(b), §72.6(b), +§72.7, §72.8). Among the many extenuating circumstances depicted are exemptions for +retired units; old, simple conbustion turbine units; non-utility untis; units supplying +generators with 25MW or less in capacity; units that have never sold their electricity; +and units burning low-sulfer fuels. + +Once CEMS are installed, Part 75 requires hourly data recording, including during +startup, shutdown, and instances of malfunction as well as quarterly data reporting to +the EPA. The regulation further details the protocol for missing data calculations and +backup monitoring for instances of CEMS failure (see §§75.31-37). A plain English explanation of the requirements of Part 75 is available in section `2.0 Overview of Part 75 Monitoring Requirements `__ @@ -73,7 +78,7 @@ on GitHub for pointers on how to access this dataset efficiently using :mod:`das EPA units vs. EIA units ----------------------- -Another important thing to note is the difference between EPA "units" vs EIA "units". +Another important thing to note is the difference between EPA "units" and EIA "units". Power plants are complex entities that have multiple subcomponents. In fossil powered plants, emissions come from the combusion of fuel. This occurs in the boiler for coal plants or the gas turbine for gas plants. When the EPA uses the term "unit" it is From e3d3365d64a150bb4b2560dc21f4588f6ed9c107 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Wed, 7 Sep 2022 14:32:53 -0600 Subject: [PATCH 50/80] Remove old comments from crosswalk module --- src/pudl/glue/epacamd_eia_crosswalk.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/pudl/glue/epacamd_eia_crosswalk.py b/src/pudl/glue/epacamd_eia_crosswalk.py index 7b89c34bd7..86f7880e69 100644 --- a/src/pudl/glue/epacamd_eia_crosswalk.py +++ b/src/pudl/glue/epacamd_eia_crosswalk.py @@ -96,9 +96,4 @@ def transform( how="inner", ) - # More indepth cleaning and droping rows with no plant_id_eia match. - # crosswalk_clean = crosswalk_clean.pipe( - # remove_leading_zeros_from_numeric_strings, "emissions_unit_id_epa" - # ).dropna(subset="plant_id_eia") - return {"epacamd_eia_crosswalk": crosswalk_clean} From a9c8b13d4dfe5d62138d5459d881e12c569f845e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 8 Sep 2022 07:43:47 +0000 Subject: [PATCH 51/80] Update tox requirement from <3.26,>=3.20 to >=3.20,<3.27 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 2840db7373..3eb5645c0b 100644 --- a/setup.py +++ b/setup.py @@ -114,7 +114,7 @@ "pytest-cov>=2.10,<3.1", "responses>=0.14,<0.22", "rstcheck[sphinx]>=5.0,<6.2", - "tox>=3.20,<3.26", + "tox>=3.20,<3.27", ], "datasette": [ "datasette>=0.60,<0.63", From 4480234801469df0e414ff1d78285b5b650e455f Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 8 Sep 2022 10:26:09 -0600 Subject: [PATCH 52/80] Update Combine_CEMS_EIA notebook --- .../work-in-progress/Combine_CEMS_EIA.ipynb | 1057 +++++++++++------ 1 file changed, 675 insertions(+), 382 deletions(-) diff --git a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb index 7c63bc9caf..fc479996e5 100644 --- a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb +++ b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb @@ -3,22 +3,41 @@ { "cell_type": "markdown", "id": "b4f6db74-0ad8-4418-bfc3-fdc12a07d750", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "# CEMS Allocater" + "# CEMS-to-EIA Allocater" + ] + }, + { + "cell_type": "markdown", + "id": "55bad16e-25d6-499b-8d1e-75a4c436356a", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Setup" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 62, "id": "eb7fcab6-4d89-4950-946f-4232be6341a8", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -42,12 +61,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 63, "id": "c76104c9-e83f-46ac-b5f3-ad5908a6af01", "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], @@ -60,14 +76,16 @@ { "cell_type": "markdown", "id": "1e3c1eb4-bc59-40f7-96ad-088718e5d620", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "#### CEMS" + "#### Load CEMS" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 64, "id": "e25d8b80-47b2-4e54-91e7-f2e674fc72bd", "metadata": {}, "outputs": [], @@ -81,46 +99,38 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 66, "id": "132c57f5-90f7-4f2b-a07b-647e4dba9c36", "metadata": {}, "outputs": [], "source": [ - "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"]).sum().compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "11f9f3e8-1df7-4483-8cd6-6dff9471e381", - "metadata": {}, - "outputs": [], - "source": [ - "cems_df = cems_df.reset_index()" + "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"]).sum().compute().reset_index()" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 67, "id": "250d5ad8-06d4-4fb3-9a83-2866fc151a3c", "metadata": {}, "outputs": [], "source": [ "cems_df[\"plant_id_eia\"] = cems_df.plant_id_eia.astype(\"Int64\")\n", - "cems_df[\"co2_mass_tons\"] = cems_df.co2_mass_tons.fillna(0)" + "#cems_df[\"co2_mass_tons\"] = cems_df.co2_mass_tons.fillna(0)" ] }, { "cell_type": "markdown", "id": "dda4c669-4a7d-4024-b661-e618e9247903", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "#### Crosswalk" + "#### Load EPA-EIA Crosswalk" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 68, "id": "59114182-0920-47fc-8cc9-6792b301fbef", "metadata": {}, "outputs": [], @@ -131,14 +141,17 @@ { "cell_type": "markdown", "id": "a520696a-5c4d-4353-85fc-194f8cc7386e", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "#### EIA" + "#### Load EIA Generators" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 69, "id": "adb143d7-14f8-4528-86e8-8e553ff6eda9", "metadata": {}, "outputs": [ @@ -147,7 +160,7 @@ "output_type": "stream", "text": [ "Filling technology type\n", - "Filled technology_type coverage now at 98.1%\n" + "Filled technology_type coverage now at 98.3%\n" ] } ], @@ -157,17 +170,121 @@ }, { "cell_type": "markdown", - "id": "e11438ec-759c-4726-bcc0-5e38dfa1cd6c", + "id": "9f3a3006-88d9-4dff-a728-bd141b48eb54", + "metadata": { + "tags": [] + }, + "source": [ + "## Pre-Integration Stats\n", + "We don't expect all of the EIA plants to show up in CEMS because not all EIA plants are subject to the EPA's reporting requirements. The EIA plants we do expect to see in CEMS:\n", + "- Burn Fossil Fuels\n", + "- Have generators with more than 25MW of capacity\n", + "- Are utility-owned\n", + "- Are not retired\n", + "- Are not old, simple combustion turbine units" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "id": "9821e815-36ca-4aa1-aee2-34b7a8fe7f39", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PLANT STATS:\n", + "Total CEMS plants: 1831\n", + "CEMS plants NOT in crosswalk: 292 = 16 %\n", + "Total Crosswalk plants: 1542\n", + "\n", + "PLANT-GEN STATS:\n", + "Crosswalk plant gen pairs: 5297\n", + "EIA plant gen pairs: 36377\n", + "EIA plant gen pairs NOT in crosswalk: 31080 = 85 %\n" + ] + } + ], + "source": [ + "cems_plants = cems_df.plant_id_eia.unique().tolist()\n", + "crosswalk_plants = crosswalk_df.plant_id_eia.unique().tolist()\n", + "eia_plants = eia_gens_df.plant_id_eia.unique().tolist()\n", + "\n", + "print(\"PLANT STATS:\")\n", + "print(\"Total CEMS plants: \", len1:=len(cems_plants))\n", + "print(\"CEMS plants NOT in crosswalk: \", len2:=len([x for x in cems_plants if x not in crosswalk_plants]), \" = \", round(len2/len1*100), \"%\")\n", + "print(\"Total Crosswalk plants: \", len(crosswalk_plants))\n", + "print(\"\")\n", + "\n", + "\n", + "crosswalk_gen_plants = crosswalk_df.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"]).set_index([\"plant_id_eia\", \"generator_id\"])\n", + "eia_gen_plants = eia_gens_df.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"]).set_index([\"plant_id_eia\", \"generator_id\"])\n", + "\n", + "print(\"PLANT-GEN STATS:\")\n", + "print(\"Crosswalk plant gen pairs: \", len3:=len(crosswalk_gen_plants))\n", + "print(\"EIA plant gen pairs: \", len4:=len(eia_gen_plants))\n", + "print(\"EIA plant gen pairs NOT in crosswalk: \", len5:=len(eia_gen_plants.index.difference(crosswalk_gen_plants.index)), \" = \", round(len5/len4*100), \"%\")" + ] + }, + { + "cell_type": "markdown", + "id": "11e6c35f-bbf3-4d86-be30-d30a3fc25316", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Merge CEMS with EIA" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "id": "81b55d0b-1c16-42ae-b57c-914b2d46bb9b", "metadata": {}, + "outputs": [], + "source": [ + "eia_gens_cems_merge = (\n", + " eia_gens_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"capacity_mw\", \"technology_description\", \"operational_status\"]].merge(\n", + " crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], \n", + " how=\"left\", \n", + " on=[\"plant_id_eia\", \"generator_id\"])\n", + " .assign(year=lambda x: x.report_date.dt.year.astype(\"Int64\"))\n", + " .merge(\n", + " cems_df,\n", + " how=\"left\",\n", + " on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6517696a-199b-46f5-9702-3ea23e5e4e1b", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, "source": [ - "#### Allocate" + "## Allocate CEMS Emissions to EIA Generators" ] }, { "cell_type": "code", - "execution_count": 355, - "id": "fb5f633a-c11c-40a5-96d9-3889ba421107", + "execution_count": 316, + "id": "b8205019-1c1b-4730-a983-f239f5def642", "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -232,64 +349,89 @@ " return to_allocate" ] }, + { + "cell_type": "code", + "execution_count": 323, + "id": "0de47d7e-fe5e-4b06-94c4-99e9479fc3b6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "eia_gens_cems_agg = (\n", + " allocate_cols(\n", + " to_allocate=eia_gens_cems_merge,\n", + " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", + " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]})\n", + " .groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", + " .sum(min_count=1)\n", + " .reset_index()\n", + " .drop(columns=[\"year\"])\n", + " .merge(\n", + " eia_gens_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"technology_description\", \"operational_status\"]],\n", + " how=\"left\",\n", + " on=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", + ")" + ] + }, { "cell_type": "markdown", "id": "6ed6ebfe-7a66-458a-81d9-31b797d2efb6", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "#### TEST ALLOCATE" + "#### Test Allocation on 2020 Subset" ] }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 278, "id": "4501bfc7-b25a-400c-9922-f5bcabddb499", "metadata": {}, "outputs": [], "source": [ - "test = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]" + "test_df = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]" ] }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 279, "id": "7eca5275-da3f-4d1a-b32c-99f943685df0", "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "test_merge = test.merge(crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], how=\"left\", on=[\"plant_id_eia\", \"generator_id\"])\n", - "test_merge = test_merge[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\", \"capacity_mw\"]]\n", - "test_merge[\"year\"] = test_merge.report_date.dt.year\n", - "test_merge[\"year\"] = test_merge.year.astype(\"Int64\")\n", - "test_merge = test_merge.merge(cems_df, how=\"left\", on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", - "test_merge[\"co2_mass_tons\"] = test_merge.co2_mass_tons.fillna(0).astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "id": "090bd266-97d0-4407-82c3-cee1b36ffb92", - "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "15\n", - "13\n" + "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_7748/1557213781.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " test_merge = test_merge.append(fake_row, ignore_index=True)\n" ] } ], "source": [ - "print(len(test_merge))\n", - "print(len(test))" + "test_merge = (\n", + " test_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"capacity_mw\", \"technology_description\"]].merge(\n", + " crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], \n", + " how=\"left\", \n", + " on=[\"plant_id_eia\", \"generator_id\"])\n", + " .assign(year=lambda x: x.report_date.dt.year.astype(\"Int64\"))\n", + " .merge(\n", + " cems_df,\n", + " how=\"left\",\n", + " on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", + ")\n", + "\n", + "fake_row = test_merge.iloc[14].replace(np.nan, 0.1)\n", + "test_merge = test_merge.append(fake_row, ignore_index=True)" ] }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 280, "id": "c897e36d-827e-4d52-be24-0aba2b4019f0", "metadata": { "tags": [] @@ -299,279 +441,281 @@ "# If you want to allocate by something other than generator (plant or prive mover),\n", "# make sure the capacity value is for that level of aggregation.\n", "\n", - "tt = allocate_cols(\n", + "test_allocate = allocate_cols(\n", " to_allocate=test_merge,\n", " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]} \n", + ")\n", + "\n", + "# Now sum up to generator level \n", + "# It's very important to add min_count=1 to the groupby sum so that NA values\n", + "# Stay NA and aren't converted to 0.\n", + "\n", + "# NOTE THAT RIGHT NOW if a record has a NA and non-NA value that get grouped together,\n", + "# the NA is still treated like 0. Not ideal, but it depends.\n", + "\n", + "test_agg = (\n", + " test_allocate.groupby(\n", + " [\"report_date\", \"plant_id_eia\", \"generator_id\"]\n", + " ).sum(min_count=1).reset_index().drop(columns=[\"year\"])\n", ")" ] }, + { + "cell_type": "markdown", + "id": "5d183ccf-ab9f-4f22-b2dd-2a7ecca3bb66", + "metadata": {}, + "source": [ + "## Post Integration Stats" + ] + }, { "cell_type": "code", - "execution_count": 308, - "id": "fee7731a-569f-47a8-9d2f-aab9d45df5fc", + "execution_count": 324, + "id": "84e8742c-60bb-4bb4-8ee5-53e286dd43d6", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, + "outputs": [], + "source": [ + "no_cems_match = eia_gens_cems_agg[eia_gens_cems_agg[\"co2_mass_tons\"].isna()]\n", + "cems_match = eia_gens_cems_agg[eia_gens_cems_agg[\"co2_mass_tons\"].notna()]" + ] + }, + { + "cell_type": "markdown", + "id": "67f8cc74-37f2-4a36-9567-0ec63215b999", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### Technology Description" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "id": "1e1b1cfd-4fac-4c2b-b238-54533203e5d8", + "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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" - ], "text/plain": [ - " report_date plant_id_eia generator_id capacity_mw co2_mass_tons\n", - "0 2020-01-01 3 1 153.1 4.667000e+03\n", - "1 2020-01-01 3 2 153.1 1.697000e+03\n", - "2 2020-01-01 3 3 272.0 0.000000e+00\n", - "3 2020-01-01 3 4 403.7 1.640450e+05\n", - "4 2020-01-01 3 5 788.8 3.128656e+06\n", - "5 2020-01-01 3 A1CT 170.1 3.964052e+05\n", - "6 2020-01-01 3 A1CT2 170.1 4.309728e+05\n", - "7 2020-01-01 3 A1ST 390.4 9.494661e+05\n", - "8 2020-01-01 3 A2C1 170.1 4.108155e+05\n", - "9 2020-01-01 3 A2C2 170.1 4.137542e+05\n", - "10 2020-01-01 3 A2ST 390.4 9.462434e+05\n", - "11 2020-01-01 3 A3C1 464.0 0.000000e+00\n", - "12 2020-01-01 3 A3ST 310.0 0.000000e+00" + "" ] }, - "execution_count": 308, + "execution_count": 369, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "# Now sum up to generator level \n", - "tt.groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"]).sum().reset_index().drop(columns=[\"year\"])" + "no_cems_match.technology_description.value_counts(dropna=False).plot(kind=\"barh\", figsize=(10, 8))" ] }, { - "cell_type": "markdown", - "id": "7263bb02-a591-4c00-8d68-90bc420da840", + "cell_type": "code", + "execution_count": 370, + "id": "6b73856d-19b0-481f-b8a6-94096771bfcf", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 370, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cems_match.technology_description.value_counts(dropna=False).plot(kind=\"barh\", figsize=(10, 8))" + ] + }, + { + "cell_type": "markdown", + "id": "30111c06-6750-44c9-b9ce-4edd22a250c8", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "#### ALLOCATE WITH ALL GENS" + "### Capacity" ] }, { "cell_type": "code", - "execution_count": 356, - "id": "a9b4e5aa-5d28-49a0-8274-dd071a208232", + "execution_count": 371, + "id": "4fc4f440-10eb-4efd-b2b9-3d11933d4cde", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 371, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "## Merge with whole CEMS!\n", - "#test = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]\n", - "cems_merge1 = eia_gens_df.merge(crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], how=\"left\", on=[\"plant_id_eia\", \"generator_id\"])\n", - "cems_merge1 = cems_merge1[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\", \"capacity_mw\", \"technology_description\"]]\n", - "cems_merge1[\"year\"] = cems_merge1.report_date.dt.year\n", - "cems_merge1[\"year\"] = cems_merge1.year.astype(\"Int64\")\n", - "cems_merge2 = cems_merge1.merge(cems_df, how=\"left\", on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", - "cems_merge2[\"co2_mass_tons\"] = cems_merge2.co2_mass_tons.fillna(0).astype(int)" + "no_cems_match[no_cems_match[\"capacity_mw\"] < 1000].capacity_mw.hist(bins=50, alpha=0.5)" ] }, { "cell_type": "code", - "execution_count": 357, - "id": "6597f5d5-dd70-487d-9b3b-1182e60058d7", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 374, + "id": "6db31a26-dd72-4799-b794-8dada75564d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 433009.000000\n", + "mean 38.560670\n", + "std 115.317649\n", + "min 0.000000\n", + "25% 1.200000\n", + "50% 3.100000\n", + "75% 23.900000\n", + "max 7380.000000\n", + "Name: capacity_mw, dtype: float64" + ] + }, + "execution_count": 374, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "cems_gen_agg = allocate_cols(\n", - " to_allocate=cems_merge2,\n", - " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", - " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]}\n", - ").groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"]).sum(\n", - ").reset_index(\n", - ").drop(columns=[\"year\"])" + "no_cems_match.capacity_mw.describe()" ] }, { "cell_type": "code", - "execution_count": 343, - "id": "100ca68f-3f4b-42d3-a32e-1f4ab19af3a4", + "execution_count": 372, + "id": "59380fc8-065e-45ab-9817-e47323b99653", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "491469\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 372, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "print(len(cems_gen_agg))" + "cems_match[cems_match[\"capacity_mw\"] < 1000].capacity_mw.hist(bins=50, alpha=0.5)" ] }, { "cell_type": "code", - "execution_count": 346, - "id": "057f5eb9-00b5-4732-97f2-3f2398666d39", + "execution_count": 375, + "id": "8f9f9bde-8001-49cf-8fb4-f54cbcf2935d", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "78.90833399461614\n", - "387810\n", - "491469\n", - "\n", - "68.23238873700286\n", - "219376\n", - "321513\n" - ] + "data": { + "text/plain": [ + "count 89867.000000\n", + "mean 204.768463\n", + "std 252.371394\n", + "min 2.500000\n", + "25% 62.000000\n", + "50% 122.400000\n", + "75% 210.000000\n", + "max 7380.000000\n", + "Name: capacity_mw, dtype: float64" + ] + }, + "execution_count": 375, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "bb = cems_merge1.drop_duplicates(subset=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", - "print(len(bb[bb[\"emissions_unit_id_epa\"].isna()]) / len(bb) * 100)\n", - "print(len(bb[bb[\"emissions_unit_id_epa\"].isna()]))\n", - "print(len(bb))\n", - "print(\"\")\n", - "\n", - "fossil = cems_merge1[cems_merge1[\"technology_description\"].isin(\n", - " [\"Conventional Steam Coal\",\n", + "cems_match.capacity_mw.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "e9efc466-29e7-4bc7-92f9-45d6724178ea", + "metadata": {}, + "source": [ + "### Given what we know about CEMS Reporting..." + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "id": "6aefe58e-c044-4c42-be23-7174ad3e337c", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "fossil_fuels = [\n", + " \"Conventional Steam Coal\",\n", " \"Natural Gas Fired Combined Cycle\",\n", " \"Natural Gas Fired Combustion Turbine\",\n", " \"Natural Gas Steam Turbine\",\n", @@ -585,38 +729,62 @@ " \"Natural Gas with Compressed Air Storage\",\n", " \"Other Gases\",\n", " \"Other Waste Biomass\",\n", - " \"Other Natural Gas\"])]\n", - "\n", - "ff = fossil.drop_duplicates(subset=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", - "print(len(ff[ff[\"emissions_unit_id_epa\"].isna()]) / len(ff) * 100)\n", - "print(len(ff[ff[\"emissions_unit_id_epa\"].isna()]))\n", - "print(len(ff))" + " \"Other Natural Gas\"\n", + "]" ] }, { "cell_type": "code", - "execution_count": 360, - "id": "b251e390-aa40-4a2d-a27b-3154cb9f258c", + "execution_count": 391, + "id": "775774ff-37e3-4b13-8d7c-848cd6fe83db", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78 % of existing EIA fossil fuel generators > 25MW have a CEMS match\n" + ] + } + ], + "source": [ + "exclude_exemptions = eia_gens_cems_agg[\n", + " eia_gens_cems_agg[\"technology_description\"].isin(fossil_fuels)\n", + " & (eia_gens_cems_agg[\"capacity_mw\"] > 25)\n", + " & (eia_gens_cems_agg[\"operational_status\"]==\"existing\")\n", + "]\n", + "\n", + "print(100 - round(exclude_exemptions.co2_mass_tons.isna().sum() / len(exclude_exemptions) * 100), \"% of existing EIA fossil fuel generators > 25MW have a CEMS match\")" + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "id": "b399fa2d-8bc6-4bb8-8abb-6ab3837bdfc9", + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "non_agg.to_pickle(\"/Users/austensharpe/Desktop/non_agg.pkl\")" + "test = exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].isna()].technology_description.value_counts().reset_index()\n", + "test2 = test.merge(exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].notna()].technology_description.value_counts().reset_index(), how=\"outer\", on=[\"index\"], suffixes=[\"_isna\", \"_notna\"])\n", + "test3 = test2.merge(exclude_exemptions.technology_description.value_counts().reset_index(), how=\"outer\", on=[\"index\"])" ] }, { "cell_type": "code", - "execution_count": 361, - "id": "7ef8817d-47d5-4577-8f23-efea140acb06", + "execution_count": 433, + "id": "b3311eb2-a506-474d-9eb9-e6597f929793", "metadata": {}, "outputs": [], "source": [ - "agg.to_pickle(\"/Users/austensharpe/Desktop/agg.pkl\")" + "test3[\"pct_na\"] = round(test3.technology_description_isna / test3.technology_description * 100)" ] }, { "cell_type": "code", - "execution_count": 362, - "id": "e28101a6-661d-41e0-8bff-47efa3d869a5", + "execution_count": 436, + "id": "01ea75f4-b8a2-48e8-b09c-37ddb9f0db37", "metadata": {}, "outputs": [ { @@ -640,135 +808,260 @@ " \n", " \n", " \n", - " report_date\n", - " plant_id_eia\n", - " generator_id\n", - " capacity_mw\n", - " co2_mass_tons\n", + " index\n", + " technology_description_isna\n", + " technology_description_notna\n", + " technology_description\n", + " pct_na\n", " \n", " \n", " \n", " \n", - " 0\n", - " 2001-01-01\n", - " 2\n", - " 1\n", - " 45.0\n", - " 0.0\n", + " 11\n", + " Natural Gas with Compressed Air Storage\n", + " 22\n", + " <NA>\n", + " 22\n", + " 100.0\n", " \n", " \n", - " 1\n", - " 2001-01-01\n", - " 3\n", - " 1\n", - " 153.1\n", - " 951508.0\n", + " 6\n", + " Municipal Solid Waste\n", + " 918\n", + " 48\n", + " 966\n", + " 95.0\n", " \n", " \n", - " 2\n", - " 2001-01-01\n", - " 3\n", - " 2\n", - " 153.1\n", - " 902068.0\n", + " 9\n", + " Other Waste Biomass\n", + " 86\n", + " 12\n", + " 98\n", + " 88.0\n", " \n", " \n", " 3\n", - " 2001-01-01\n", - " 3\n", - " 3\n", - " 272.0\n", - " 1969314.0\n", + " Wood/Wood Waste Biomass\n", + " 2457\n", + " 556\n", + " 3013\n", + " 82.0\n", " \n", " \n", - " 4\n", - " 2001-01-01\n", - " 3\n", - " 4\n", - " 403.7\n", - " 2843765.0\n", + " 7\n", + " Other Gases\n", + " 556\n", + " 335\n", + " 891\n", + " 62.0\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 8\n", + " Petroleum Coke\n", + " 194\n", + " 131\n", + " 325\n", + " 60.0\n", " \n", " \n", - " 491464\n", - " 2021-01-01\n", - " 65333\n", - " 785\n", - " 200.0\n", - " 0.0\n", + " 10\n", + " Landfill Gas\n", + " 28\n", + " 44\n", + " 72\n", + " 39.0\n", " \n", " \n", - " 491465\n", - " 2021-01-01\n", - " 65334\n", - " PLTVW\n", - " 81.0\n", - " 0.0\n", + " 2\n", + " Petroleum Liquids\n", + " 2924\n", + " 5346\n", + " 8270\n", + " 35.0\n", " \n", " \n", - " 491466\n", - " 2021-01-01\n", - " 65335\n", - " WAPPA\n", - " 171.8\n", - " 0.0\n", + " 0\n", + " Natural Gas Fired Combustion Turbine\n", + " 7220\n", + " 26180\n", + " 33400\n", + " 22.0\n", " \n", " \n", - " 491467\n", - " 2021-01-01\n", - " 65337\n", - " MAYBK\n", - " 5.0\n", - " 0.0\n", + " 12\n", + " Coal Integrated Gasification Combined Cycle\n", + " 15\n", + " 55\n", + " 70\n", + " 21.0\n", " \n", " \n", - " 491468\n", - " 2021-01-01\n", - " 65338\n", - " UNIS1\n", - " 108.0\n", - " 0.0\n", + " 5\n", + " Natural Gas Steam Turbine\n", + " 1638\n", + " 6596\n", + " 8234\n", + " 20.0\n", + " \n", + " \n", + " 1\n", + " Natural Gas Fired Combined Cycle\n", + " 5712\n", + " 26345\n", + " 32057\n", + " 18.0\n", + " \n", + " \n", + " 4\n", + " Conventional Steam Coal\n", + " 2372\n", + " 17595\n", + " 19967\n", + " 12.0\n", " \n", " \n", "\n", - "

491469 rows × 5 columns

\n", "" ], "text/plain": [ - " report_date plant_id_eia generator_id capacity_mw co2_mass_tons\n", - "0 2001-01-01 2 1 45.0 0.0\n", - "1 2001-01-01 3 1 153.1 951508.0\n", - "2 2001-01-01 3 2 153.1 902068.0\n", - "3 2001-01-01 3 3 272.0 1969314.0\n", - "4 2001-01-01 3 4 403.7 2843765.0\n", - "... ... ... ... ... ...\n", - "491464 2021-01-01 65333 785 200.0 0.0\n", - "491465 2021-01-01 65334 PLTVW 81.0 0.0\n", - "491466 2021-01-01 65335 WAPPA 171.8 0.0\n", - "491467 2021-01-01 65337 MAYBK 5.0 0.0\n", - "491468 2021-01-01 65338 UNIS1 108.0 0.0\n", - "\n", - "[491469 rows x 5 columns]" + " index technology_description_isna technology_description_notna technology_description pct_na\n", + "11 Natural Gas with Compressed Air Storage 22 22 100.0\n", + "6 Municipal Solid Waste 918 48 966 95.0\n", + "9 Other Waste Biomass 86 12 98 88.0\n", + "3 Wood/Wood Waste Biomass 2457 556 3013 82.0\n", + "7 Other Gases 556 335 891 62.0\n", + "8 Petroleum Coke 194 131 325 60.0\n", + "10 Landfill Gas 28 44 72 39.0\n", + "2 Petroleum Liquids 2924 5346 8270 35.0\n", + "0 Natural Gas Fired Combustion Turbine 7220 26180 33400 22.0\n", + "12 Coal Integrated Gasification Combined Cycle 15 55 70 21.0\n", + "5 Natural Gas Steam Turbine 1638 6596 8234 20.0\n", + "1 Natural Gas Fired Combined Cycle 5712 26345 32057 18.0\n", + "4 Conventional Steam Coal 2372 17595 19967 12.0" ] }, - "execution_count": 362, + "execution_count": 436, "metadata": {}, "output_type": "execute_result" } ], - "source": [] + "source": [ + "test3.sort_values(\"pct_na\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 452, + "id": "cf0efadd-d301-427a-b1e9-469803b6364c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 452, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].isna()].report_date.value_counts().sort_index().plot.bar()" + ] }, { "cell_type": "code", "execution_count": null, - "id": "be47f82c-6273-47eb-9a87-d92d9beefd46", + "id": "4efb1b53-71f3-471a-9bd7-b4e339ef7654", "metadata": {}, "outputs": [], "source": [] From d8b8392d34b1f2dac6258beb2ea46f998e686fb9 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Thu, 8 Sep 2022 12:40:48 -0600 Subject: [PATCH 53/80] Change epacamd_eia_crosswalk to epacamd_eia --- .../work-in-progress/Combine_CEMS_EIA.ipynb | 16 ++++++++-------- src/pudl/__init__.py | 2 +- src/pudl/etl.py | 6 +++--- .../{epacamd_eia_crosswalk.py => epacamd_eia.py} | 0 src/pudl/metadata/resources/glue.py | 6 ++---- src/pudl/metadata/sources.py | 2 +- src/pudl/output/epacems.py | 4 ++-- src/pudl/output/pudltabl.py | 10 ++++------ src/pudl/transform/epacems.py | 4 ++-- test/validate/epacamd_eia_crosswalk_test.py | 4 ++-- 10 files changed, 25 insertions(+), 29 deletions(-) rename src/pudl/glue/{epacamd_eia_crosswalk.py => epacamd_eia.py} (100%) diff --git a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb index fc479996e5..b524b8ed16 100644 --- a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb +++ b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb @@ -135,7 +135,7 @@ "metadata": {}, "outputs": [], "source": [ - "crosswalk_df = pudl_out.epacamd_eia_crosswalk()" + "crosswalk_df = pudl_out.epacamd_eia()" ] }, { @@ -511,7 +511,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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9YyRJi2RBziEkWQ9cBjwOTFXVQeiGBnBB22wt8HLPsNlWW9uWj61LkhbRyN92muSdwJ8Av1lVPz7B4f9+K+oE9X6vtY3uoSWmpqbodDoDzxfgtL8+wtrX9w81dqWar+dO58ASzGZxzM3NDf1vZKWy58lwqnoeKRCSnEY3DD5fVV9s5VeSrKmqg+1w0KFWnwUu7Bm+DjjQ6uv61I9TVTuAHQDT09M1MzMz1Lx3fekrfPeMDUONXanWvr6/b8/Xz4zv1193Oh2G/TeyUtnzZDhVPY9ylVGAe4DnqupTPat2A1vb8lbgoZ76liSnJ9lA9+TxE+2w0uEkV7bnvKFnjCRpkYyyh/AB4B8DTyX5Vqv9LnA7sCvJjcBLwHUAVfVMkl3As3SvULq5qt5q424C7gXOBB5uN0nSIho6EKrqf9D/+D/AVfOM2Q5s71PfC1w67FwkSaPzk8qSJMBAkCQ1BoIkCViAzyFoZbtjzwt967dcPb6Xo0rqzz0ESRJgIEiSGgNBkgQYCJKkxpPKWjCeoJZWNvcQJEmAgSBJajxkpL48/CNNHgNBA5kvKCStfAaClp2l2jtZjntFy3FOGl8GgsbWocNH+v5CXY6/TN3z0nJgIGjFm++X6dpFnoe00hkI0iJyT0DLmYEgTQDPRehkGAhaMfzr+qfmey8uO22RJ6KxYiBIY2S+E+nSyfCTypIkwD0ELaGl+kt20Ncd5+Pvp7q3E73XC/UaK+ny4uXOQNApN2mHMMah30F78JfveDAQpCGNwy9+qZeBIGlkhuN4MBAkrQhL9Yn0STp8tmwCIckm4NPAKuAzVXX7Ek9J0hI41XsbS3ki/VS+7kJYFoGQZBXwH4GrgVngz5Lsrqpnl3ZmklaqhbqabJIsi0AArgD2VdVfACR5ANgMGAjSmPIX8Ntb7E+kL5dAWAu83PN4Fvj7x26UZBuwrT2cS/L8kK93PvD9IceuVPY8Gex5hfmt4YaN0vPfmm/FcgmE9KnVcYWqHcCOkV8s2VtV06M+z0piz5PBnifDqep5uXx1xSxwYc/jdcCBJZqLJE2k5RIIfwZsTLIhyTuALcDuJZ6TJE2UZXHIqKreTPIvga/Qvez0s1X1zCl8yZEPO61A9jwZ7HkynJKeU3XcoXpJ0gRaLoeMJElLzECQJAETFghJNiV5Psm+JLcu9XwWSpILk/xpkueSPJPkY61+XpI9SV5s9+f2jLmtvQ/PJ7lm6WY/miSrknwzyZfb47HuOcm7kjyY5Nvt5/3+Cej5lvbv+ukk9yc5Y9x6TvLZJIeSPN1TG7jHJJcneaqtuzNJv0v651dVE3Gje7L6O8B7gXcA/xO4eKnntUC9rQHe15Z/DngBuBj4d8CtrX4r8Mm2fHHr/3RgQ3tfVi11H0P2/lvAHwNfbo/HumdgJ/DP2/I7gHeNc890P7S6HzizPd4F/JNx6xn4ZeB9wNM9tYF7BJ4A3k/3s10PA786yDwmaQ/h/389RlX9BDj69RgrXlUdrKpvtOXDwHN0/0PaTPcXCO3+2ra8GXigqo5U1X5gH933Z0VJsg74NeAzPeWx7TnJOXR/cdwDUFU/qaofMcY9N6uBM5OsBs6i+xmlseq5qr4O/OCY8kA9JlkDnFNVj1Y3He7rGXNSJikQ+n09xqn+5txFl2Q9cBnwODBVVQehGxrABW2zcXkvfh/4beCve2rj3PN7ge8Bf9gOk30mydmMcc9V9V3g94CXgIPAq1X1Vca45x6D9ri2LR9bP2mTFAgn9fUYK1mSdwJ/AvxmVf34RJv2qa2o9yLJh4BDVfXkyQ7pU1tRPdP9S/l9wN1VdRnwGt1DCfNZ8T234+ab6R4aeQ9wdpKPnmhIn9qK6vkkzNfjyL1PUiCM9ddjJDmNbhh8vqq+2MqvtN1I2v2hVh+H9+IDwIeT/CXdw3//MMkfMd49zwKzVfV4e/wg3YAY555/BdhfVd+rqjeALwK/xHj3fNSgPc625WPrJ22SAmFsvx6jXUlwD/BcVX2qZ9VuYGtb3go81FPfkuT0JBuAjXRPRq0YVXVbVa2rqvV0f5Zfq6qPMt49/xXwcpJfbKWr6H5F/Nj2TPdQ0ZVJzmr/zq+ie45snHs+aqAe22Glw0mubO/VDT1jTs5Sn11f5DP5H6R7Bc53gI8v9XwWsK9/QHfX8M+Bb7XbB4F3A48AL7b783rGfLy9D88z4JUIy+0GzPDTq4zGumfg7wF728/6vwDnTkDP/xb4NvA08Dm6V9eMVc/A/XTPkbxB9y/9G4fpEZhu79N3gD+gfRvFyd786gpJEjBZh4wkSSdgIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSc3/A3TPuEffP1eaAAAAAElFTkSuQmCC", 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", 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\n", 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", 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\n", 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", 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" ] diff --git a/src/pudl/__init__.py b/src/pudl/__init__.py index 808c1478b2..e6afb1cba6 100644 --- a/src/pudl/__init__.py +++ b/src/pudl/__init__.py @@ -26,7 +26,7 @@ import pudl.extract.excel import pudl.extract.ferc1 import pudl.extract.ferc714 -import pudl.glue.epacamd_eia_crosswalk +import pudl.glue.epacamd_eia import pudl.glue.ferc1_eia import pudl.helpers import pudl.load diff --git a/src/pudl/etl.py b/src/pudl/etl.py index 0839dafd06..aa4df1f430 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -251,7 +251,7 @@ def etl_epacems( f"Trying to access PUDL DB: {pudl_engine}" ) # Verify that we have a PUDL DB with crosswalk data - if "epacamd_eia_crosswalk" not in inspector.get_table_names(): + if "epacamd_eia" not in inspector.get_table_names(): raise RuntimeError( "No EPA-EIA Crosswalk available in the PUDL DB! Have you run the ETL? " f"Trying to access PUDL DB: {pudl_engine}" @@ -374,8 +374,8 @@ def _etl_glue( eia_settings.eia860.years == eia_settings.eia860.data_source.working_partitions["years"] ) - glue_raw_dfs = pudl.glue.epacamd_eia_crosswalk.extract(ds) - glue_transformed_dfs = pudl.glue.epacamd_eia_crosswalk.transform( + glue_raw_dfs = pudl.glue.epacamd_eia.extract(ds) + glue_transformed_dfs = pudl.glue.epacamd_eia.transform( glue_raw_dfs, sqlite_dfs["generators_entity_eia"], sqlite_dfs["boilers_entity_eia"], diff --git a/src/pudl/glue/epacamd_eia_crosswalk.py b/src/pudl/glue/epacamd_eia.py similarity index 100% rename from src/pudl/glue/epacamd_eia_crosswalk.py rename to src/pudl/glue/epacamd_eia.py diff --git a/src/pudl/metadata/resources/glue.py b/src/pudl/metadata/resources/glue.py index 1ca1c4ae2d..c3b99c1ac9 100644 --- a/src/pudl/metadata/resources/glue.py +++ b/src/pudl/metadata/resources/glue.py @@ -2,7 +2,7 @@ from typing import Any RESOURCE_METADATA: dict[str, dict[str, Any]] = { - "epacamd_eia_crosswalk": { + "epacamd_eia": { "schema": { "fields": [ "plant_id_epa", @@ -15,9 +15,7 @@ }, "field_namespace": "glue", "etl_group": "glue", - "sources": [ - "epacamd_eia_crosswalk" - ], # eia_epa_crosswalk --> what is this anyways + "sources": ["epacamd_eia"], # --> what is this anyways }, } """ diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index c1d20d2585..9e0e3474b5 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -230,7 +230,7 @@ "license_raw": LICENSES["us-govt"], "license_pudl": LICENSES["cc-by-4.0"], }, - "epacamd_eia_crosswalk": { + "epacamd_eia": { "title": "EPA CAMD to EIA Data Crosswalk", "path": "https://github.com/USEPA/camd-eia-crosswalk", "description": ( diff --git a/src/pudl/output/epacems.py b/src/pudl/output/epacems.py index cd9f1b5957..1f6efcc36c 100644 --- a/src/pudl/output/epacems.py +++ b/src/pudl/output/epacems.py @@ -11,10 +11,10 @@ from pudl.settings import EpaCemsSettings -def epacamd_eia_crosswalk(pudl_engine: sa.engine.Engine) -> pd.DataFrame: +def epacamd_eia(pudl_engine: sa.engine.Engine) -> pd.DataFrame: """Pull the EPACAMD-EIA Crosswalk table.""" pt = pudl.output.pudltabl.get_table_meta(pudl_engine) - crosswalk_tbl = pt["epacamd_eia_crosswalk"] + crosswalk_tbl = pt["epacamd_eia"] crosswalk_select = sa.sql.select(crosswalk_tbl) crosswalk_df = pd.read_sql(crosswalk_select, pudl_engine) return crosswalk_df diff --git a/src/pudl/output/pudltabl.py b/src/pudl/output/pudltabl.py index f0e8e0ca60..aca802d182 100644 --- a/src/pudl/output/pudltabl.py +++ b/src/pudl/output/pudltabl.py @@ -1217,7 +1217,7 @@ def plant_parts_eia( # GLUE OUTPUTS ########################################################################### - def epacamd_eia_crosswalk( + def epacamd_eia( self, update: bool = False, ) -> pd.DataFrame: @@ -1231,11 +1231,9 @@ def epacamd_eia_crosswalk( A denormalized table for interactive use. """ - if update or self._dfs["epacamd_eia_crosswalk"] is None: - self._dfs[ - "epacamd_eia_crosswalk" - ] = pudl.output.epacems.epacamd_eia_crosswalk(self.pudl_engine) - return self._dfs["epacamd_eia_crosswalk"] + if update or self._dfs["epacamd_eia"] is None: + self._dfs["epacamd_eia"] = pudl.output.epacems.epacamd_eia(self.pudl_engine) + return self._dfs["epacamd_eia"] def get_table_meta(pudl_engine): diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 51e8c1b24f..6d0d178ac0 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -38,7 +38,7 @@ def harmonize_eia_epa_orispl( Args: df: A CEMS hourly dataframe for one year-month-state. - crosswalk_df: The epacamd_eia_crosswalk dataframe from the database. + crosswalk_df: The epacamd_eia dataframe from the database. Returns: The same data, with the ORISPL plant codes corrected to match the EIA plant IDs. @@ -192,7 +192,7 @@ def transform( """ # Create all the table inputs used for the subtransform functions below - crosswalk_df = pd.read_sql("epacamd_eia_crosswalk", con=pudl_engine) + crosswalk_df = pd.read_sql("epacamd_eia", con=pudl_engine) return ( raw_df.pipe(apply_pudl_dtypes, group="epacems") diff --git a/test/validate/epacamd_eia_crosswalk_test.py b/test/validate/epacamd_eia_crosswalk_test.py index 6609ff49b3..aebea3f90f 100644 --- a/test/validate/epacamd_eia_crosswalk_test.py +++ b/test/validate/epacamd_eia_crosswalk_test.py @@ -16,7 +16,7 @@ def test_unique_ids(pudl_out_eia, live_dbs): pytest.skip("Test should only run on un-aggregated data.") # Should I add these args to the pudl.validate module? check_unique_rows( - pudl_out_eia.epacamd_eia_crosswalk, + pudl_out_eia.epacamd_eia, ["plant_id_eia", "emissions_unit_id_epa"], - "epacamd_eia_crosswalk", + "epacamd_eia", ) From 4b37c11d3f1596803a9896991e50d6dd8968bee4 Mon Sep 17 00:00:00 2001 From: cbz Date: Thu, 8 Sep 2022 16:49:53 -0400 Subject: [PATCH 54/80] move ba code/state-based fixes into transform + DOCS --- src/pudl/output/eia860.py | 112 +++++++++++++++++------------------ src/pudl/transform/eia.py | 64 ++++++++++++++++++-- src/pudl/transform/eia861.py | 15 +++-- 3 files changed, 121 insertions(+), 70 deletions(-) diff --git a/src/pudl/output/eia860.py b/src/pudl/output/eia860.py index c5a0e5e933..3c6c18d3d2 100644 --- a/src/pudl/output/eia860.py +++ b/src/pudl/output/eia860.py @@ -8,7 +8,7 @@ import pudl from pudl.metadata.fields import apply_pudl_dtypes from pudl.transform.eia import occurrence_consistency -from pudl.transform.eia861 import make_backfilled_ba_code_column +from pudl.transform.eia861 import add_backfilled_ba_code_column logger = logging.getLogger(__name__) @@ -144,8 +144,8 @@ def plants_eia860(pudl_engine, start_date=None, end_date=None): return out_df -def make_consistent_ba_code_column(plants: pd.DataFrame) -> pd.DataFrame: - """Make a columns of the most consistent balancing authority code. +def add_consistent_ba_code_column(plants: pd.DataFrame) -> pd.DataFrame: + """Make a column containing each plant's most consistently reported BA code. Employ the harvesting function :func:`occurrence_consistency` which determines how consistent the values in a table are across all records within each plant. This @@ -159,9 +159,9 @@ def make_consistent_ba_code_column(plants: pd.DataFrame) -> pd.DataFrame: cols_to_consit=["plant_id_eia"], strictness=0.7, ) - - static_plant_to_code_map = ba_code_consistent[ - ba_code_consistent.balancing_authority_code_eia_consistent + # grab only the code that passed the consistency strictness test + ba_code_consistent = ba_code_consistent[ + ba_code_consistent.balancing_authority_code_eia_is_consistent ][ [ "plant_id_eia", @@ -172,13 +172,15 @@ def make_consistent_ba_code_column(plants: pd.DataFrame) -> pd.DataFrame: plants = pd.merge( plants, - static_plant_to_code_map, + ba_code_consistent, how="left", on=["plant_id_eia"], suffixes=("", "_consistent"), ) + plants_w_ba_codes = plants[plants.balancing_authority_code_eia_consistent.notnull()] logger.info( - f"{len(plants[plants.balancing_authority_code_eia_consistent.notnull()])/len(plants):.1%} of plant records have static BA Codes" + f"{len(plants_w_ba_codes)/len(plants):.1%} of plant records have consistently " + "reported BA Codes" ) return plants @@ -189,32 +191,29 @@ def fill_in_missing_ba_codes(plants: pd.DataFrame) -> pd.DataFrame: Balancing authority codes did not begin being reported until 2013. This function fills in the old years with BA codes using two main methods: - * Backfilling from the oldest reported BA code (via :meth:`pd.bfill`) - * Using the most consistent reported value + * Backfilling with the oldest reported BA code for each plant. + * Backfilling with the most frequently reported BA code for each plant. We add a column to represent each of these two methodologies via - :func:`make_backfilled_ba_code_column` and :func:`make_consistent_ba_code_column`. + :func:`add_backfilled_ba_code_column` and :func:`add_consistent_ba_code_column`. We know that the BA codes do change over time and are incorrectly reported at times. - Because of these two facts, we cann't simple :meth:`pd.fillna` with either the - backfilled or most consistent value. This function employs some specific filling in - methodologies based on an investigation of the data. Here is a description of each - of various stages of filling in which involves some bespoke cleanup: - - * if the backfilled code and the most consistent code are the same, use either! - * assign BA code ``PACW`` (``PacifiCorp - West``) when ``PACW`` is the most - consistent BA code and the ``state`` is within ``PACW``'s territory - * similarity, assign BA code ``PACE`` (``PacifiCorp - East``) when ``PACE`` is - the most consistent BA code and the ``state`` is within ``PACE``'s territory + This means we can't simply :meth:`pd.fillna` using either the oldest or most + consistently reported values. This function employs several filling methods based + on our investigation of the data: + + * if the backfilled code and the most consistent code are the same, use the + consistent value (either would work!) * use the backfilled code for plants that have ``SWPP`` (``Southwest Power Pool``) - as their most consistent BA code because we know ``SWPP`` accumulated many smaller - balancing authorities - * use the backfilled code for plants that have a differnt backfilled and most - consistent code, but the most consistent code is has a consistency rate (how - frequent the most consistent BA code is reported / the total BA codes reported) - is less than 80%. This is meant to find plants where there are multiple years of - an older BA code before it switches - assuming a BA code being reported for - multiple years is not a reporting error. TODO: make this one more explicit. + as their most consistent BA code because we know ``SWPP`` has acquired many + smaller balancing authorities in recent years. + * use the backfilled code for plants that have different backfilled and most + consistent codes, but the most consistent code has a consistency rate less than + 80% (the consistency rate is how frequently the most consistent BA code is + reported as a fraction of the total number of BA codes reported). This is meant + to find plants where there are multiple years of an older BA code before it + switches - assuming a BA code being reported for multiple years is not a + reporting error. TODO: make this one more explicit. Args: plants: table of annual plant attributes, including ``balancing_authority_code_eia`` @@ -230,14 +229,16 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: """ currently_null_len = len(plants[plants.balancing_authority_code_eia.isnull()]) logger.info( - f"Filled BA codes where {method_str}. " - f"Currently {currently_null_len/len(plants):.1%} of records ({currently_null_len}) with no BA codes" + f"{method_str}. {currently_null_len/len(plants):.1%} of records have no BA codes" ) - plants = make_backfilled_ba_code_column(plants, by_cols=["plant_id_eia"]).pipe( - make_consistent_ba_code_column + # add a column for each of our backfilling options + plants = add_backfilled_ba_code_column(plants, by_cols=["plant_id_eia"]).pipe( + add_consistent_ba_code_column + ) + log_current_ba_code_nulls( + plants=plants, method_str="Before any filling treatment has been applied" ) - log_current_ba_code_nulls(plants, method_str="no treatment had been applied") # when the backfilled code and the static code are the same, use either result plants.loc[ ( @@ -249,41 +250,29 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: ] = plants.balancing_authority_code_eia_consistent log_current_ba_code_nulls( - plants, method_str="backfilling and consistent value is the same" - ) - # we found a pattern of 2013 plants reporting BA codes of PACE when the - # consistent option was PACW and the states the plants were located in - # are PACW states - # use PACW for those records - plants.loc[ - (plants.balancing_authority_code_eia.isnull()) - & (plants.balancing_authority_code_eia_consistent == "PACW") - & (plants.state.isin(["OR", "CA"])), - "balancing_authority_code_eia", - ] = "PACW" - log_current_ba_code_nulls( - plants, method_str="consistent code is PACW and state is OR or CA" + plants=plants, method_str="backfilling and consistent value is the same" ) - # found the opposite! Plants in UT labeled as PACW instead of PACE + # we know SWPP has done a ton of accumulation of smaller BA's plants.loc[ (plants.balancing_authority_code_eia.isnull()) - & (plants.balancing_authority_code_eia_consistent == "PACE") - & (plants.state.isin(["UT"])), + & (plants.balancing_authority_code_eia_consistent == "SWPP"), "balancing_authority_code_eia", - ] = "PACE" + ] = plants.balancing_authority_code_eia_bfilled log_current_ba_code_nulls( - plants, method_str="consistent code is PACE and state is UT" + plants, method_str="SWPP is most consistent value. Filled w/ oldest BA code" ) - # we know SWPP has done a ton of accumulation of smaller BA's + # Several plants went from reporting a BA of WAUE (Western Area Power Administration + # - Upper Great Plains East) to reporting a BA of NWMT (NorthWestern Energy (NWMT)) + # we believe this is not a reporting error and should be bfilled plants.loc[ (plants.balancing_authority_code_eia.isnull()) - & (plants.balancing_authority_code_eia_consistent == "SWPP"), + & (plants.balancing_authority_code_eia_consistent == "NWMT") + & (plants.balancing_authority_code_eia_bfilled == "WAUE"), "balancing_authority_code_eia", ] = plants.balancing_authority_code_eia_bfilled log_current_ba_code_nulls( - plants, method_str="SWPP is most consistent value. Filled w/ oldest BA code" + plants, method_str="NWMT is most consistent value. Filled w/ oldest BA code" ) - plants.loc[ (plants.balancing_authority_code_eia.isnull()) & ( @@ -295,9 +284,14 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: ] = plants.balancing_authority_code_eia_bfilled log_current_ba_code_nulls( plants, - method_str="most consistent BA code is less than 80% consistent. Used backfilled BA code", + method_str="most consistent BA code is less than 70% consistent. Used backfilled BA code", + ) + return plants.drop( + columns=[ + "balancing_authority_code_eia_consistent", + "balancing_authority_code_eia_bfilled", + ] ) - return plants def plants_utils_eia860(pudl_engine, start_date=None, end_date=None): diff --git a/src/pudl/transform/eia.py b/src/pudl/transform/eia.py index 04f7ab7b32..f5b4059304 100644 --- a/src/pudl/transform/eia.py +++ b/src/pudl/transform/eia.py @@ -19,6 +19,7 @@ import importlib.resources import logging +from collections import namedtuple import networkx as nx import numpy as np @@ -205,7 +206,7 @@ def occurrence_consistency( col_df = col_df.dropna() if len(col_df) == 0: - col_df[f"{col}_consistent"] = pd.NA + col_df[f"{col}_is_consistent"] = pd.NA col_df[f"{col}_consistent_rate"] = pd.NA col_df["entity_occurences"] = pd.NA return col_df @@ -237,7 +238,7 @@ def occurrence_consistency( col_df[f"{col}_consistent_rate"] = ( col_df["record_occurences"] / col_df["entity_occurences"] ) - col_df[f"{col}_consistent"] = col_df[f"{col}_consistent_rate"] > strictness + col_df[f"{col}_is_consistent"] = col_df[f"{col}_consistent_rate"] > strictness col_df = col_df.sort_values(f"{col}_consistent_rate") return col_df @@ -284,7 +285,7 @@ def _lat_long( # grab the clean plants ll_clean_df = clean_df.dropna() # find the new clean plant records by selecting the True consistent records - ll_df = ll_df[ll_df[f"{col}_consistent"]].drop_duplicates(subset=entity_idx) + ll_df = ll_df[ll_df[f"{col}_is_consistent"]].drop_duplicates(subset=entity_idx) logger.debug(f"Clean {col} records: {len(ll_df)}") # add the newly cleaned records ll_clean_df = pd.concat([ll_clean_df, ll_df]) @@ -543,8 +544,8 @@ def harvesting( # noqa: C901 ) # pull the correct values out of the df and merge w/ the plant ids - col_correct_df = col_df[col_df[f"{col}_consistent"]].drop_duplicates( - subset=(cols_to_consit + [f"{col}_consistent"]) + col_correct_df = col_df[col_df[f"{col}_is_consistent"]].drop_duplicates( + subset=(cols_to_consit + [f"{col}_is_consistent"]) ) # we need this to be an empty df w/ columns bc we are going to use it @@ -592,7 +593,7 @@ def harvesting( # noqa: C901 if total > 0: ratio = ( len( - col_df[(col_df[f"{col}_consistent"])].drop_duplicates( + col_df[(col_df[f"{col}_is_consistent"])].drop_duplicates( subset=cols_to_consit ) ) @@ -1155,6 +1156,54 @@ def fillna_balancing_authority_codes_via_names(df: pd.DataFrame) -> pd.DataFrame return df +def fix_balancing_authority_codes_with_state( + plants: pd.DataFrame, plants_entity: pd.DataFrame +) -> pd.DataFrame: + """Fix selective balancing_authority_code_eia's based on states. + + There are some know errors in the ``balancing_authority_code_eia`` column that we + can identify and fix based on the state where the plant is located. + + This function should only be applied post-:func:`harvesting`. The ``state`` column + is a "static" entity column so the first step in this function is merging the static + and annually varying plants together. Then we fix known errors in the BA codes: + + * reported PACE, but state is OR or CA, code should be PACW + * reported PACW, but state is UT, code should be PACE + * reported ISNE, but state is NY, code should be NYIS + + Args: + plants: annually harvested plant table with columns: ``plant_id_eia``, + ``report_date`` and ``balancing_authority_code_eia``. + plants_entity: static harvested plant table with columns: ``plant_id_eia`` and + ``state``. + + Returns: + plants table that has the same set of columns and rows, with cleaned + ``balancing_authority_code_eia`` column. + + """ + plants = plants.merge( + plants_entity[["plant_id_eia", "state"]], # only merge in state, drop later + on=["plant_id_eia"], + how="left", + validate="m:1", + ) + BACodeFix = namedtuple("BACodeFix", ["ba_code_found", "ba_code_fix", "states"]) + fixes = [ + BACodeFix("PACE", "PACW", ["OR", "CA"]), + BACodeFix("PACW", "PACE", ["UT"]), + BACodeFix("ISNE", "NYIS", ["NY"]), + ] + for fix in fixes: + plants.loc[ + (plants.balancing_authority_code_eia == fix.ba_code_found) + & (plants.state.isin(fix.states)), + "balancing_authority_code_eia", + ] = fix.ba_code_fix + return plants.drop(columns=["state"]) + + def transform( eia_transformed_dfs, eia_settings: EiaSettings = EiaSettings(), debug=False ): @@ -1214,5 +1263,8 @@ def transform( eia_transformed_dfs["plants_eia860"] = fillna_balancing_authority_codes_via_names( df=eia_transformed_dfs["plants_eia860"] + ).pipe( + fix_balancing_authority_codes_with_state, + plants_entity=entities_dfs["plants_entity_eia"], ) return entities_dfs, eia_transformed_dfs diff --git a/src/pudl/transform/eia861.py b/src/pudl/transform/eia861.py index 22beaac0aa..d07525a0e3 100644 --- a/src/pudl/transform/eia861.py +++ b/src/pudl/transform/eia861.py @@ -440,8 +440,8 @@ def _filter_non_class_cols(df, class_list): return df.filter(regex=regex) -def make_backfilled_ba_code_column(df, by_cols: list[str]) -> pd.DataFrame: - """Make a backfilled Balancing Authority Codes based on codes in later years. +def add_backfilled_ba_code_column(df, by_cols: list[str]) -> pd.DataFrame: + """Make a backfilled Balancing Authority Code column based on codes in later years. Args: df: table with columns: ``balancing_authority_code_eia``, ``report_date`` and @@ -450,8 +450,8 @@ def make_backfilled_ba_code_column(df, by_cols: list[str]) -> pd.DataFrame: Returns: - pandas.DataFrame: The balancing_authority_eia861 dataframe, but with many fewer - NA values in the balancing_authority_code_eia column. + pandas.DataFrame: An altered version of ``df`` with an additional column + ``balancing_authority_code_eia_bfilled`` """ start_len = len(df) @@ -498,9 +498,14 @@ def backfill_ba_codes_by_ba_id(df: pd.DataFrame) -> pd.DataFrame: Args: df: The transformed EIA 861 Balancing Authority dataframe (balancing_authority_eia861). + + Returns: + pandas.DataFrame: The balancing_authority_eia861 dataframe, but with many fewer + NA values in the balancing_authority_code_eia column. + """ ba_eia861_filled = ( - make_backfilled_ba_code_column(df, by_cols=["balancing_authority_id_eia"]) + add_backfilled_ba_code_column(df, by_cols=["balancing_authority_id_eia"]) .assign( balancing_authority_code_eia=lambda x: x.balancing_authority_code_eia_bfilled ) From 5ff3fd333547a96a20a0424a072418f434095d7a Mon Sep 17 00:00:00 2001 From: cbz Date: Thu, 8 Sep 2022 17:38:57 -0400 Subject: [PATCH 55/80] make multi-year of old BA code bfill method more explicit --- src/pudl/output/eia860.py | 50 ++++++++++++++++++++++++++------------- 1 file changed, 33 insertions(+), 17 deletions(-) diff --git a/src/pudl/output/eia860.py b/src/pudl/output/eia860.py index 3c6c18d3d2..32bffb0150 100644 --- a/src/pudl/output/eia860.py +++ b/src/pudl/output/eia860.py @@ -202,18 +202,17 @@ def fill_in_missing_ba_codes(plants: pd.DataFrame) -> pd.DataFrame: consistently reported values. This function employs several filling methods based on our investigation of the data: - * if the backfilled code and the most consistent code are the same, use the + * if the oldest code and the most consistent code are the same, use the consistent value (either would work!) - * use the backfilled code for plants that have ``SWPP`` (``Southwest Power Pool``) + * use the oldest code for plants that have ``SWPP`` (``Southwest Power Pool``) as their most consistent BA code because we know ``SWPP`` has acquired many smaller balancing authorities in recent years. - * use the backfilled code for plants that have different backfilled and most - consistent codes, but the most consistent code has a consistency rate less than - 80% (the consistency rate is how frequently the most consistent BA code is - reported as a fraction of the total number of BA codes reported). This is meant - to find plants where there are multiple years of an older BA code before it - switches - assuming a BA code being reported for multiple years is not a - reporting error. TODO: make this one more explicit. + * use the oldest code for plants that have ``NWMT`` (``NorthWestern Energy``) + as their most consistent BA code and ``WAUE`` + (``Western Area Power Administration``) as their oldest BA code. + * use the oldest code for plants that have more than one year of older BA codes, + using the assumption that more than one year of consistent old BA codes is not a + reporting error. Args: plants: table of annual plant attributes, including ``balancing_authority_code_eia`` @@ -239,6 +238,10 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: log_current_ba_code_nulls( plants=plants, method_str="Before any filling treatment has been applied" ) + # determine oldest year of BA codes before any filling in + oldest_og_ba_code_date = min( + plants[plants.balancing_authority_code_eia.notnull()].report_date + ) # when the backfilled code and the static code are the same, use either result plants.loc[ ( @@ -250,7 +253,8 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: ] = plants.balancing_authority_code_eia_consistent log_current_ba_code_nulls( - plants=plants, method_str="backfilling and consistent value is the same" + plants=plants, + method_str="Backfilling and consistent value is the same. Filled w/ most consistent BA code", ) # we know SWPP has done a ton of accumulation of smaller BA's plants.loc[ @@ -273,23 +277,35 @@ def log_current_ba_code_nulls(plants: pd.DataFrame, method_str: str) -> None: log_current_ba_code_nulls( plants, method_str="NWMT is most consistent value. Filled w/ oldest BA code" ) - plants.loc[ - (plants.balancing_authority_code_eia.isnull()) - & ( - plants.balancing_authority_code_eia_bfilled - != plants.balancing_authority_code_eia_consistent + # bfill where there is more than one old BA code. + # add a one-year shifted ba code + plants.loc[:, "balancing_authority_code_eia_shifted"] = plants.groupby( + ["plant_id_eia"] + )[["balancing_authority_code_eia"]].shift(periods=1) + # all the plants where the oldest BA year has the same ba code as the oldest year +1 + two_year_old_ba_code_plant_ids = plants[ + ( + plants.balancing_authority_code_eia + == plants.balancing_authority_code_eia_shifted ) - & (plants.balancing_authority_code_eia_consistent_rate <= 0.8), + & (plants.report_date == oldest_og_ba_code_date) + ].plant_id_eia.unique() + + plants.loc[ + plants.balancing_authority_code_eia.isnull() + & plants.plant_id_eia.isin(two_year_old_ba_code_plant_ids), "balancing_authority_code_eia", ] = plants.balancing_authority_code_eia_bfilled + log_current_ba_code_nulls( plants, - method_str="most consistent BA code is less than 70% consistent. Used backfilled BA code", + method_str="Two or more years of oldest BA code. Filled w/ oldest BA code", ) return plants.drop( columns=[ "balancing_authority_code_eia_consistent", "balancing_authority_code_eia_bfilled", + "balancing_authority_code_eia_shifted", ] ) From 475c3ac931234ea2957e4ce4212acb2de1855bb4 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Thu, 8 Sep 2022 17:09:29 -0500 Subject: [PATCH 56/80] Update the name of the EPA CAMD to EIA crosswalk data source. --- src/pudl/metadata/sources.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 41e7c96c16..9e0e3474b5 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -192,7 +192,7 @@ }, "epacems": { "title": "EPA Hourly Continuous Emission Monitoring System (CEMS)", - "path": "https://ampd.epa.gov/ampd", + "path": "https://campd.epa.gov/", "description": ( "US EPA hourly Continuous Emissions Monitoring System (CEMS) data." "Hourly CO2, SO2, NOx emissions and gross load." @@ -230,12 +230,12 @@ "license_raw": LICENSES["us-govt"], "license_pudl": LICENSES["cc-by-4.0"], }, - "epacems_unitid_eia_plant_crosswalk": { - "title": "EPA CEMS unitid to EIA Plant Crosswalk", + "epacamd_eia": { + "title": "EPA CAMD to EIA Data Crosswalk", "path": "https://github.com/USEPA/camd-eia-crosswalk", "description": ( - "A file created collaboratively by EPA and EIA that connects EPA CEMS " - "smokestacks (unitids) with cooresponding EIA plant part ids reported in " + "A file created collaboratively by EPA and EIA that connects EPA CAMD " + "smokestacks (units) with cooresponding EIA plant part ids reported in " "EIA Forms 860 and 923 (plant_id_eia, boiler_id, generator_id). This " "one-to-many connection is necessary because pollutants from various plant " "parts are collecitvely emitted and measured from one point-source." From 3c74313d1af8e81bb4584f118d1f49752c3093d3 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Thu, 8 Sep 2022 17:26:50 -0500 Subject: [PATCH 57/80] Update key in glue dataframe dictionary to match name of resource: epacamd_eia --- src/pudl/glue/epacamd_eia.py | 8 ++++---- src/pudl/metadata/resources/glue.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/pudl/glue/epacamd_eia.py b/src/pudl/glue/epacamd_eia.py index 86f7880e69..24c7f8f8aa 100644 --- a/src/pudl/glue/epacamd_eia.py +++ b/src/pudl/glue/epacamd_eia.py @@ -27,7 +27,7 @@ def extract(ds: Datastore) -> pd.DataFrame: def transform( - epacamd_eia_crosswalk: pd.DataFrame, + epacamd_eia: pd.DataFrame, generators_entity_eia: pd.DataFrame, boilers_entity_eia: pd.DataFrame, processing_all_eia_years: bool, @@ -35,7 +35,7 @@ def transform( """Clean up the EPACAMD-EIA Crosswalk file. Args: - epacamd_eia_crosswalk: The result of running this module's extract() function. + epacamd_eia: The result of running this module's extract() function. generators_entity_eia: The generators_entity_eia table. processing_all_years: A boolean indicating whether the years from the Eia860Settings object match the EIA860 working partitions. This indicates @@ -58,7 +58,7 @@ def transform( # Basic column rename, selection, and dtype alignment. crosswalk_clean = ( - epacamd_eia_crosswalk.pipe(simplify_columns) + epacamd_eia.pipe(simplify_columns) .rename(columns=column_rename) .filter(list(column_rename.values())) .pipe(remove_leading_zeros_from_numeric_strings, col_name="generator_id") @@ -96,4 +96,4 @@ def transform( how="inner", ) - return {"epacamd_eia_crosswalk": crosswalk_clean} + return {"epacamd_eia": crosswalk_clean} diff --git a/src/pudl/metadata/resources/glue.py b/src/pudl/metadata/resources/glue.py index c3b99c1ac9..a29fe67982 100644 --- a/src/pudl/metadata/resources/glue.py +++ b/src/pudl/metadata/resources/glue.py @@ -15,7 +15,7 @@ }, "field_namespace": "glue", "etl_group": "glue", - "sources": ["epacamd_eia"], # --> what is this anyways + "sources": ["epacamd_eia"], }, } """ From 761f2a4ee848a9fef306bad6c24d0d0ce509c855 Mon Sep 17 00:00:00 2001 From: Alex Engel Date: Thu, 8 Sep 2022 16:54:13 -0700 Subject: [PATCH 58/80] updating 861 package_data for 2021 early release --- ...dvanced_metering_infrastructure_eia861.csv | 96 ++++---- .../balancing_authority_eia861.csv | 16 +- .../column_maps/delivery_companies_eia861.csv | 50 ++-- .../column_maps/demand_response_eia861.csv | 76 +++--- .../distribution_systems_eia861.csv | 16 +- .../column_maps/dynamic_pricing_eia861.csv | 64 +++--- .../column_maps/energy_efficiency_eia861.csv | 106 ++++----- .../eia861/column_maps/frame_eia861.csv | 44 ++-- .../eia861/column_maps/mergers_eia861.csv | 28 +-- .../column_maps/net_metering_eia861.csv | 216 +++++++++--------- .../column_maps/non_net_metering_eia861.csv | 142 ++++++------ .../column_maps/operational_data_eia861.csv | 72 +++--- .../eia861/column_maps/reliability_eia861.csv | 60 ++--- .../eia861/column_maps/sales_eia861.csv | 58 ++--- .../column_maps/service_territory_eia861.csv | 14 +- .../eia861/column_maps/short_form_eia861.csv | 30 +-- .../column_maps/utility_data_eia861.csv | 70 +++--- src/pudl/package_data/eia861/file_map.csv | 44 ++-- src/pudl/package_data/eia861/page_map.csv | 80 +++---- src/pudl/package_data/eia861/skipfooter.csv | 42 ++-- src/pudl/package_data/eia861/skiprows.csv | 42 ++-- 21 files changed, 683 insertions(+), 683 deletions(-) diff --git a/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv b/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv index 072188d09d..68643712dc 100644 --- a/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv @@ -1,48 +1,48 @@ -year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -entity_type,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3 -short_form,3,3,3,3,3,3,3,3,3,3,3,3,4,4 -state,4,4,4,4,4,4,4,4,4,4,4,4,5,5 -balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,5,5,5,5,6,6 -residential_automated_meter_reading,5,5,5,5,5,5,5,5,6,6,6,6,7,7 -commercial_automated_meter_reading,6,6,6,6,6,6,6,6,7,7,7,7,8,8 -industrial_automated_meter_reading,7,7,7,7,7,7,7,7,8,8,8,8,9,9 -transportation_automated_meter_reading,8,8,8,8,8,8,8,8,9,9,9,9,10,10 -total_automated_meter_reading,9,9,9,9,9,9,9,9,10,10,10,10,11,11 -residential_advanced_metering_infrastructure,10,10,10,10,10,10,10,10,11,11,11,11,12,12 -commercial_advanced_metering_infrastructure,11,11,11,11,11,11,11,11,12,12,12,12,13,13 -industrial_advanced_metering_infrastructure,12,12,12,12,12,12,12,12,13,13,13,13,14,14 -transportation_advanced_metering_infrastructure,13,13,13,13,13,13,13,13,14,14,14,14,15,15 -total_advanced_metering_infrastructure,14,14,14,14,14,14,14,14,15,15,15,15,16,16 -residential_home_area_network,-1,-1,-1,-1,-1,-1,15,15,16,16,16,16,17,17 -commercial_home_area_network,-1,-1,-1,-1,-1,-1,16,16,17,17,17,17,18,18 -industrial_home_area_network,-1,-1,-1,-1,-1,-1,17,17,18,18,18,18,19,19 -transportation_home_area_network,-1,-1,-1,-1,-1,-1,18,18,19,19,19,19,20,20 -total_home_area_network,-1,-1,-1,-1,-1,-1,19,19,20,20,20,20,21,21 -residential_non_amr_ami,-1,-1,-1,-1,-1,-1,20,20,21,21,21,21,22,22 -commercial_non_amr_ami,-1,-1,-1,-1,-1,-1,21,21,22,22,22,22,23,23 -industrial_non_amr_ami,-1,-1,-1,-1,-1,-1,22,22,23,23,23,23,24,24 -transportation_non_amr_ami,-1,-1,-1,-1,-1,-1,23,23,24,24,24,24,25,25 -total_non_amr_ami,-1,-1,-1,-1,-1,-1,24,24,25,25,25,25,26,26 -residential_total_meters,-1,-1,-1,-1,-1,-1,25,25,26,26,26,26,27,27 -commercial_total_meters,-1,-1,-1,-1,-1,-1,26,26,27,27,27,27,28,28 -industrial_total_meters,-1,-1,-1,-1,-1,-1,27,27,28,28,28,28,29,29 -transportation_total_meters,-1,-1,-1,-1,-1,-1,28,28,29,29,29,29,30,30 -total_total_meters,-1,-1,-1,-1,-1,-1,29,29,30,30,30,30,31,31 -residential_energy_served_ami_mwh,15,15,15,15,15,15,30,30,31,31,31,31,32,32 -commercial_energy_served_ami_mwh,16,16,16,16,16,16,31,31,32,32,32,32,33,33 -industrial_energy_served_ami_mwh,17,17,17,17,17,17,32,32,33,33,33,33,34,34 -transportation_energy_served_ami_mwh,18,18,18,18,18,18,33,33,34,34,34,34,35,35 -total_energy_served_ami_mwh,19,19,19,19,19,19,34,34,35,35,35,35,36,36 -residential_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,35,35,36,36,36,36,37,37 -commercial_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,36,36,37,37,37,37,38,38 -industrial_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,37,37,38,38,38,38,39,39 -transportation_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,38,38,39,39,39,39,40,40 -total_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,39,39,40,40,40,40,41,41 -residential_direct_load_control_customers,-1,-1,-1,-1,-1,-1,40,40,41,41,41,41,42,42 -commercial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,41,41,42,42,42,42,43,43 -industrial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,42,42,43,43,43,43,44,44 -transportation_direct_load_control_customers,-1,-1,-1,-1,-1,-1,43,43,44,44,44,44,45,45 -total_direct_load_control_customers,-1,-1,-1,-1,-1,-1,44,44,45,45,45,45,46,46 +year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +entity_type,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,4 +short_form,3,3,3,3,3,3,3,3,3,3,3,3,4,4,5 +state,4,4,4,4,4,4,4,4,4,4,4,4,5,5,6 +balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,5,5,5,5,6,6,7 +residential_automated_meter_reading,5,5,5,5,5,5,5,5,6,6,6,6,7,7,8 +commercial_automated_meter_reading,6,6,6,6,6,6,6,6,7,7,7,7,8,8,9 +industrial_automated_meter_reading,7,7,7,7,7,7,7,7,8,8,8,8,9,9,10 +transportation_automated_meter_reading,8,8,8,8,8,8,8,8,9,9,9,9,10,10,11 +total_automated_meter_reading,9,9,9,9,9,9,9,9,10,10,10,10,11,11,12 +residential_advanced_metering_infrastructure,10,10,10,10,10,10,10,10,11,11,11,11,12,12,13 +commercial_advanced_metering_infrastructure,11,11,11,11,11,11,11,11,12,12,12,12,13,13,14 +industrial_advanced_metering_infrastructure,12,12,12,12,12,12,12,12,13,13,13,13,14,14,15 +transportation_advanced_metering_infrastructure,13,13,13,13,13,13,13,13,14,14,14,14,15,15,16 +total_advanced_metering_infrastructure,14,14,14,14,14,14,14,14,15,15,15,15,16,16,17 +residential_home_area_network,-1,-1,-1,-1,-1,-1,15,15,16,16,16,16,17,17,18 +commercial_home_area_network,-1,-1,-1,-1,-1,-1,16,16,17,17,17,17,18,18,19 +industrial_home_area_network,-1,-1,-1,-1,-1,-1,17,17,18,18,18,18,19,19,20 +transportation_home_area_network,-1,-1,-1,-1,-1,-1,18,18,19,19,19,19,20,20,21 +total_home_area_network,-1,-1,-1,-1,-1,-1,19,19,20,20,20,20,21,21,22 +residential_non_amr_ami,-1,-1,-1,-1,-1,-1,20,20,21,21,21,21,22,22,23 +commercial_non_amr_ami,-1,-1,-1,-1,-1,-1,21,21,22,22,22,22,23,23,24 +industrial_non_amr_ami,-1,-1,-1,-1,-1,-1,22,22,23,23,23,23,24,24,25 +transportation_non_amr_ami,-1,-1,-1,-1,-1,-1,23,23,24,24,24,24,25,25,26 +total_non_amr_ami,-1,-1,-1,-1,-1,-1,24,24,25,25,25,25,26,26,27 +residential_total_meters,-1,-1,-1,-1,-1,-1,25,25,26,26,26,26,27,27,28 +commercial_total_meters,-1,-1,-1,-1,-1,-1,26,26,27,27,27,27,28,28,29 +industrial_total_meters,-1,-1,-1,-1,-1,-1,27,27,28,28,28,28,29,29,30 +transportation_total_meters,-1,-1,-1,-1,-1,-1,28,28,29,29,29,29,30,30,31 +total_total_meters,-1,-1,-1,-1,-1,-1,29,29,30,30,30,30,31,31,32 +residential_energy_served_ami_mwh,15,15,15,15,15,15,30,30,31,31,31,31,32,32,33 +commercial_energy_served_ami_mwh,16,16,16,16,16,16,31,31,32,32,32,32,33,33,34 +industrial_energy_served_ami_mwh,17,17,17,17,17,17,32,32,33,33,33,33,34,34,35 +transportation_energy_served_ami_mwh,18,18,18,18,18,18,33,33,34,34,34,34,35,35,36 +total_energy_served_ami_mwh,19,19,19,19,19,19,34,34,35,35,35,35,36,36,37 +residential_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,35,35,36,36,36,36,37,37,38 +commercial_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,36,36,37,37,37,37,38,38,39 +industrial_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,37,37,38,38,38,38,39,39,40 +transportation_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,38,38,39,39,39,39,40,40,41 +total_daily_digital_access_customers,-1,-1,-1,-1,-1,-1,39,39,40,40,40,40,41,41,42 +residential_direct_load_control_customers,-1,-1,-1,-1,-1,-1,40,40,41,41,41,41,42,42,43 +commercial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,41,41,42,42,42,42,43,43,44 +industrial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,42,42,43,43,43,43,44,44,45 +transportation_direct_load_control_customers,-1,-1,-1,-1,-1,-1,43,43,44,44,44,44,45,45,46 +total_direct_load_control_customers,-1,-1,-1,-1,-1,-1,44,44,45,45,45,45,46,46,47 diff --git a/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv b/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv index 06c4554ba3..d9e4a54368 100644 --- a/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv @@ -1,8 +1,8 @@ -year_index,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1 -utility_name_eia,-1,2,2,2,2,-1,-1,-1,-1,-1,-1,2,-1,-1,-1,-1,-1,-1,-1,-1 -balancing_authority_id_eia,2,3,3,3,3,2,2,2,2,2,2,3,1,1,1,1,1,1,1,1 -balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2 -state,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,3,3,3,3,3,3 -balancing_authority_name_eia,3,4,4,4,4,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4 +year_index,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +utility_name_eia,-1,2,2,2,2,-1,-1,-1,-1,-1,-1,2,-1,-1,-1,-1,-1,-1,-1,-1,-1 +balancing_authority_id_eia,2,3,3,3,3,2,2,2,2,2,2,3,1,1,1,1,1,1,1,1,2 +balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,3 +state,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,3,3,3,3,3,3,4 +balancing_authority_name_eia,3,4,4,4,4,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,5 diff --git a/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv b/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv index 0158569f4d..d8d1db3c8e 100644 --- a/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv @@ -1,25 +1,25 @@ -year_index,2020 -report_year,0 -utility_id_eia,1 -utility_name_eia,2 -business_model,3 -service_type,4 -data_observed,5 -state,6 -entity_type,7 -balancing_authority_code_eia,8 -residential_sales_revenue,9 -residential_sales_mwh,10 -residential_customers,11 -commercial_sales_revenue,12 -commercial_sales_mwh,13 -commercial_customers,14 -industrial_sales_revenue,15 -industrial_sales_mwh,16 -industrial_customers,17 -transportation_sales_revenue,18 -transportation_sales_mwh,19 -transportation_customers,20 -total_sales_revenue,21 -total_sales_mwh,22 -total_customers,23 +year_index,2020,2021 +report_year,0,1 +utility_id_eia,1,2 +utility_name_eia,2,3 +business_model,3,4 +service_type,4,5 +data_observed,5,6 +state,6,7 +entity_type,7,8 +balancing_authority_code_eia,8,9 +residential_sales_revenue,9,10 +residential_sales_mwh,10,11 +residential_customers,11,12 +commercial_sales_revenue,12,13 +commercial_sales_mwh,13,14 +commercial_customers,14,15 +industrial_sales_revenue,15,16 +industrial_sales_mwh,16,17 +industrial_customers,17,18 +transportation_sales_revenue,18,19 +transportation_sales_mwh,19,20 +transportation_customers,20,21 +total_sales_revenue,21,22 +total_sales_mwh,22,23 +total_customers,23,24 diff --git a/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv b/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv index 381df8ada1..47b49338a3 100644 --- a/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv @@ -1,38 +1,38 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,3,-1 -state,3,3,3,3,3,3,4,3 -balancing_authority_code_eia,-1,-1,-1,-1,4,4,5,4 -residential_customers,4,4,4,4,5,5,6,5 -commercial_customers,5,5,5,5,6,6,7,6 -industrial_customers,6,6,6,6,7,7,8,7 -transportation_customers,7,7,7,7,8,8,9,8 -total_customers,8,8,8,8,9,9,10,9 -residential_energy_savings_mwh,9,9,9,9,10,10,11,10 -commercial_energy_savings_mwh,10,10,10,10,11,11,12,11 -industrial_energy_savings_mwh,11,11,11,11,12,12,13,12 -transportation_energy_savings_mwh,12,12,12,12,13,13,14,13 -total_energy_savings_mwh,13,13,13,13,14,14,15,14 -residential_potential_peak_demand_savings_mw,14,14,14,14,15,15,16,15 -commercial_potential_peak_demand_savings_mw,15,15,15,15,16,16,17,16 -industrial_potential_peak_demand_savings_mw,16,16,16,16,17,17,18,17 -transportation_potential_peak_demand_savings_mw,17,17,17,17,18,18,19,18 -total_potential_peak_demand_savings_mw,18,18,18,18,19,19,20,19 -residential_actual_peak_demand_savings_mw,19,19,19,19,20,20,21,20 -commercial_actual_peak_demand_savings_mw,20,20,20,20,21,21,22,21 -industrial_actual_peak_demand_savings_mw,21,21,21,21,22,22,23,22 -transportation_actual_peak_demand_savings_mw,22,22,22,22,23,23,24,23 -total_actual_peak_demand_savings_mw,23,23,23,23,24,24,25,24 -residential_customer_incentives_cost,24,24,24,24,25,25,26,25 -commercial_customer_incentives_cost,25,25,25,25,26,26,27,26 -industrial_customer_incentives_cost,26,26,26,26,27,27,28,27 -transportation_customer_incentives_cost,27,27,27,27,28,28,29,28 -total_customer_incentives_cost,28,28,28,28,29,29,30,29 -residential_other_costs,29,29,29,29,30,30,31,30 -commercial_other_costs,30,30,30,30,31,31,32,31 -industrial_other_costs,31,31,31,31,32,32,33,32 -transportation_other_costs,32,32,32,32,33,33,34,33 -total_other_costs,33,33,33,33,34,34,35,34 -water_heater,34,34,34,34,35,35,36,35 +year_index,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,3,-1,-1 +state,3,3,3,3,3,3,4,3,4 +balancing_authority_code_eia,-1,-1,-1,-1,4,4,5,4,5 +residential_customers,4,4,4,4,5,5,6,5,6 +commercial_customers,5,5,5,5,6,6,7,6,7 +industrial_customers,6,6,6,6,7,7,8,7,8 +transportation_customers,7,7,7,7,8,8,9,8,9 +total_customers,8,8,8,8,9,9,10,9,10 +residential_energy_savings_mwh,9,9,9,9,10,10,11,10,11 +commercial_energy_savings_mwh,10,10,10,10,11,11,12,11,12 +industrial_energy_savings_mwh,11,11,11,11,12,12,13,12,13 +transportation_energy_savings_mwh,12,12,12,12,13,13,14,13,14 +total_energy_savings_mwh,13,13,13,13,14,14,15,14,15 +residential_potential_peak_demand_savings_mw,14,14,14,14,15,15,16,15,16 +commercial_potential_peak_demand_savings_mw,15,15,15,15,16,16,17,16,17 +industrial_potential_peak_demand_savings_mw,16,16,16,16,17,17,18,17,18 +transportation_potential_peak_demand_savings_mw,17,17,17,17,18,18,19,18,19 +total_potential_peak_demand_savings_mw,18,18,18,18,19,19,20,19,20 +residential_actual_peak_demand_savings_mw,19,19,19,19,20,20,21,20,21 +commercial_actual_peak_demand_savings_mw,20,20,20,20,21,21,22,21,22 +industrial_actual_peak_demand_savings_mw,21,21,21,21,22,22,23,22,23 +transportation_actual_peak_demand_savings_mw,22,22,22,22,23,23,24,23,24 +total_actual_peak_demand_savings_mw,23,23,23,23,24,24,25,24,25 +residential_customer_incentives_cost,24,24,24,24,25,25,26,25,26 +commercial_customer_incentives_cost,25,25,25,25,26,26,27,26,27 +industrial_customer_incentives_cost,26,26,26,26,27,27,28,27,28 +transportation_customer_incentives_cost,27,27,27,27,28,28,29,28,29 +total_customer_incentives_cost,28,28,28,28,29,29,30,29,30 +residential_other_costs,29,29,29,29,30,30,31,30,31 +commercial_other_costs,30,30,30,30,31,31,32,31,32 +industrial_other_costs,31,31,31,31,32,32,33,32,33 +transportation_other_costs,32,32,32,32,33,33,34,33,34 +total_other_costs,33,33,33,33,34,34,35,34,35 +water_heater,34,34,34,34,35,35,36,35,36 diff --git a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv index 7993f26d7a..a86bb7aa07 100644 --- a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv @@ -1,8 +1,8 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,3,-1 -state,3,3,3,3,3,3,4,3 -distribution_circuits,4,4,4,4,4,4,5,4 -circuits_with_voltage_optimization,5,5,5,5,5,5,6,5 +year_index,2013,2014,2015,2016,2017,2018,2019,2020, +report_year,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,3,-1,-1 +state,3,3,3,3,3,3,4,3,4 +distribution_circuits,4,4,4,4,4,4,5,4,5 +circuits_with_voltage_optimization,5,5,5,5,5,5,6,5,6 diff --git a/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv b/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv index 42b0d8e670..586944ed7d 100644 --- a/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv @@ -1,32 +1,32 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,3,3 -state,3,3,3,3,3,3,4,4 -balancing_authority_code_eia,-1,-1,-1,-1,4,4,5,5 -residential_customers,4,4,4,4,5,5,6,6 -commercial_customers,5,5,5,5,6,6,7,7 -industrial_customers,6,6,6,6,7,7,8,8 -transportation_customers,7,7,7,7,8,8,9,9 -total_customers,8,8,8,8,9,9,10,10 -residential_time_of_use_pricing,9,9,9,9,10,10,11,11 -commercial_time_of_use_pricing,10,10,10,10,11,11,12,12 -industrial_time_of_use_pricing,11,11,11,11,12,12,13,13 -transportation_time_of_use_pricing,12,12,12,12,13,13,14,14 -residential_real_time_pricing,13,13,13,13,14,14,15,15 -commercial_real_time_pricing,14,14,14,14,15,15,16,16 -industrial_real_time_pricing,15,15,15,15,16,16,17,17 -transportation_real_time_pricing,16,16,16,16,17,17,18,18 -residential_variable_peak_pricing,17,17,17,17,18,18,19,19 -commercial_variable_peak_pricing,18,18,18,18,19,19,20,20 -industrial_variable_peak_pricing,19,19,19,19,20,20,21,21 -transportation_variable_peak_pricing,20,20,20,20,21,21,22,22 -residential_critical_peak_pricing,21,21,21,21,22,22,23,23 -commercial_critical_peak_pricing,22,22,22,22,23,23,24,24 -industrial_critical_peak_pricing,23,23,23,23,24,24,25,25 -transportation_critical_peak_pricing,24,24,24,24,25,25,26,26 -residential_critical_peak_rebate,25,25,25,25,26,26,27,27 -commercial_critical_peak_rebate,26,26,26,26,27,27,28,28 -industrial_critical_peak_rebate,27,27,27,27,28,28,29,29 -transportation_critical_peak_rebate,28,28,28,28,29,29,30,30 +year_index,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,3,3,4 +state,3,3,3,3,3,3,4,4,5 +balancing_authority_code_eia,-1,-1,-1,-1,4,4,5,5,6 +residential_customers,4,4,4,4,5,5,6,6,7 +commercial_customers,5,5,5,5,6,6,7,7,8 +industrial_customers,6,6,6,6,7,7,8,8,9 +transportation_customers,7,7,7,7,8,8,9,9,10 +total_customers,8,8,8,8,9,9,10,10,11 +residential_time_of_use_pricing,9,9,9,9,10,10,11,11,12 +commercial_time_of_use_pricing,10,10,10,10,11,11,12,12,13 +industrial_time_of_use_pricing,11,11,11,11,12,12,13,13,14 +transportation_time_of_use_pricing,12,12,12,12,13,13,14,14,15 +residential_real_time_pricing,13,13,13,13,14,14,15,15,16 +commercial_real_time_pricing,14,14,14,14,15,15,16,16,17 +industrial_real_time_pricing,15,15,15,15,16,16,17,17,18 +transportation_real_time_pricing,16,16,16,16,17,17,18,18,19 +residential_variable_peak_pricing,17,17,17,17,18,18,19,19,20 +commercial_variable_peak_pricing,18,18,18,18,19,19,20,20,21 +industrial_variable_peak_pricing,19,19,19,19,20,20,21,21,22 +transportation_variable_peak_pricing,20,20,20,20,21,21,22,22,23 +residential_critical_peak_pricing,21,21,21,21,22,22,23,23,24 +commercial_critical_peak_pricing,22,22,22,22,23,23,24,24,25 +industrial_critical_peak_pricing,23,23,23,23,24,24,25,25,26 +transportation_critical_peak_pricing,24,24,24,24,25,25,26,26,27 +residential_critical_peak_rebate,25,25,25,25,26,26,27,27,28 +commercial_critical_peak_rebate,26,26,26,26,27,27,28,28,29 +industrial_critical_peak_rebate,27,27,27,27,28,28,29,29,30 +transportation_critical_peak_rebate,28,28,28,28,29,29,30,30,31 diff --git a/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv b/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv index 8406b14838..3238aec34e 100644 --- a/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv @@ -1,53 +1,53 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,3,-1 -state,3,3,3,3,3,3,4,3 -balancing_authority_code_eia,4,4,4,4,4,4,5,4 -residential_incremental_energy_savings_mwh,5,5,5,5,5,5,6,5 -commercial_incremental_energy_savings_mwh,6,6,6,6,6,6,7,6 -industrial_incremental_energy_savings_mwh,7,7,7,7,7,7,8,7 -transportation_incremental_energy_savings_mwh,8,8,8,8,8,8,9,8 -total_incremental_energy_savings_mwh,9,9,9,9,9,9,10,9 -residential_incremental_peak_reduction_mw,10,10,10,10,10,10,11,10 -commercial_incremental_peak_reduction_mw,11,11,11,11,11,11,12,11 -industrial_incremental_peak_reduction_mw,12,12,12,12,12,12,13,12 -other_incremental_peak_reduction_mw,-1,-1,-1,-1,-1,-1,-1,-1 -transportation_incremental_peak_reduction_mw,13,13,13,13,13,13,14,13 -total_incremental_peak_reduction_mw,14,14,14,14,14,14,15,14 -residential_incremental_life_cycle_energy_savings_mwh,15,15,15,15,15,15,16,15 -commercial_incremental_life_cycle_energy_savings_mwh,16,16,16,16,16,16,17,16 -industrial_incremental_life_cycle_energy_savings_mwh,17,17,17,17,17,17,18,17 -transportation_incremental_life_cycle_energy_savings_mwh,18,18,18,18,18,18,19,18 -total_incremental_life_cycle_energy_savings_mwh,19,19,19,19,19,19,20,19 -residential_incremental_life_cycle_peak_reduction_mwh,20,20,20,20,20,20,21,20 -commercial_incremental_life_cycle_peak_reduction_mwh,21,21,21,21,21,21,22,21 -industrial_incremental_life_cycle_peak_reduction_mwh,22,22,22,22,22,22,23,22 -transportation_incremental_life_cycle_peak_reduction_mwh,23,23,23,23,23,23,24,23 -total_incremental_life_cycle_peak_reduction_mwh,24,24,24,24,24,24,25,24 -residential_customer_incentives_incremental_cost,25,25,25,25,25,25,26,25 -commercial_customer_incentives_incremental_cost,26,26,26,26,26,26,27,26 -industrial_customer_incentives_incremental_cost,27,27,27,27,27,27,28,27 -transportation_customer_incentives_incremental_cost,28,28,28,28,28,28,29,28 -total_customer_incentives_incremental_cost,29,29,29,29,29,29,30,29 -residential_other_costs_incremental_cost,30,30,30,30,30,30,31,30 -commercial_other_costs_incremental_cost,31,31,31,31,31,31,32,31 -industrial_other_costs_incremental_cost,32,32,32,32,32,32,33,32 -transportation_other_costs_incremental_cost,33,33,33,33,33,33,34,33 -total_other_costs_incremental_cost,34,34,34,34,34,34,35,34 -residential_customer_incentives_incremental_life_cycle_cost,35,35,35,35,35,35,36,35 -commercial_customer_incentives_incremental_life_cycle_cost,36,36,36,36,36,36,37,36 -industrial_customer_incentives_incremental_life_cycle_cost,37,37,37,37,37,37,38,37 -transportation_customer_incentives_incremental_life_cycle_cost,38,38,38,38,38,38,39,38 -total_customer_incentives_incremental_life_cycle_cost,39,39,39,39,39,39,40,39 -residential_customer_other_costs_incremental_life_cycle_cost,40,40,40,40,40,40,41,40 -commercial_customer_other_costs_incremental_life_cycle_cost,41,41,41,41,41,41,42,41 -industrial_customer_other_costs_incremental_life_cycle_cost,42,42,42,42,42,42,43,42 -transportation_customer_other_costs_incremental_life_cycle_cost,43,43,43,43,43,43,44,43 -total_customer_other_costs_incremental_life_cycle_cost,44,44,44,44,44,44,45,44 -residential_weighted_average_life_years,45,45,45,45,45,45,46,45 -commercial_weighted_average_life_years,46,46,46,46,46,46,47,46 -industrial_weighted_average_life_years,47,47,47,47,47,47,48,47 -transportation_weighted_average_life_years,48,48,48,48,48,48,49,48 -website,49,49,49,49,49,49,50,49 +year_index,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,3,-1,-1 +state,3,3,3,3,3,3,4,3,4 +balancing_authority_code_eia,4,4,4,4,4,4,5,4,5 +residential_incremental_energy_savings_mwh,5,5,5,5,5,5,6,5,6 +commercial_incremental_energy_savings_mwh,6,6,6,6,6,6,7,6,7 +industrial_incremental_energy_savings_mwh,7,7,7,7,7,7,8,7,8 +transportation_incremental_energy_savings_mwh,8,8,8,8,8,8,9,8,9 +total_incremental_energy_savings_mwh,9,9,9,9,9,9,10,9,10 +residential_incremental_peak_reduction_mw,10,10,10,10,10,10,11,10,11 +commercial_incremental_peak_reduction_mw,11,11,11,11,11,11,12,11,12 +industrial_incremental_peak_reduction_mw,12,12,12,12,12,12,13,12,13 +other_incremental_peak_reduction_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1 +transportation_incremental_peak_reduction_mw,13,13,13,13,13,13,14,13,14 +total_incremental_peak_reduction_mw,14,14,14,14,14,14,15,14,15 +residential_incremental_life_cycle_energy_savings_mwh,15,15,15,15,15,15,16,15,16 +commercial_incremental_life_cycle_energy_savings_mwh,16,16,16,16,16,16,17,16,17 +industrial_incremental_life_cycle_energy_savings_mwh,17,17,17,17,17,17,18,17,18 +transportation_incremental_life_cycle_energy_savings_mwh,18,18,18,18,18,18,19,18,19 +total_incremental_life_cycle_energy_savings_mwh,19,19,19,19,19,19,20,19,20 +residential_incremental_life_cycle_peak_reduction_mwh,20,20,20,20,20,20,21,20,21 +commercial_incremental_life_cycle_peak_reduction_mwh,21,21,21,21,21,21,22,21,22 +industrial_incremental_life_cycle_peak_reduction_mwh,22,22,22,22,22,22,23,22,23 +transportation_incremental_life_cycle_peak_reduction_mwh,23,23,23,23,23,23,24,23,24 +total_incremental_life_cycle_peak_reduction_mwh,24,24,24,24,24,24,25,24,25 +residential_customer_incentives_incremental_cost,25,25,25,25,25,25,26,25,26 +commercial_customer_incentives_incremental_cost,26,26,26,26,26,26,27,26,27 +industrial_customer_incentives_incremental_cost,27,27,27,27,27,27,28,27,28 +transportation_customer_incentives_incremental_cost,28,28,28,28,28,28,29,28,29 +total_customer_incentives_incremental_cost,29,29,29,29,29,29,30,29,30 +residential_other_costs_incremental_cost,30,30,30,30,30,30,31,30,31 +commercial_other_costs_incremental_cost,31,31,31,31,31,31,32,31,32 +industrial_other_costs_incremental_cost,32,32,32,32,32,32,33,32,33 +transportation_other_costs_incremental_cost,33,33,33,33,33,33,34,33,34 +total_other_costs_incremental_cost,34,34,34,34,34,34,35,34,35 +residential_customer_incentives_incremental_life_cycle_cost,35,35,35,35,35,35,36,35,36 +commercial_customer_incentives_incremental_life_cycle_cost,36,36,36,36,36,36,37,36,37 +industrial_customer_incentives_incremental_life_cycle_cost,37,37,37,37,37,37,38,37,38 +transportation_customer_incentives_incremental_life_cycle_cost,38,38,38,38,38,38,39,38,39 +total_customer_incentives_incremental_life_cycle_cost,39,39,39,39,39,39,40,39,40 +residential_customer_other_costs_incremental_life_cycle_cost,40,40,40,40,40,40,41,40,41 +commercial_customer_other_costs_incremental_life_cycle_cost,41,41,41,41,41,41,42,41,42 +industrial_customer_other_costs_incremental_life_cycle_cost,42,42,42,42,42,42,43,42,43 +transportation_customer_other_costs_incremental_life_cycle_cost,43,43,43,43,43,43,44,43,44 +total_customer_other_costs_incremental_life_cycle_cost,44,44,44,44,44,44,45,44,45 +residential_weighted_average_life_years,45,45,45,45,45,45,46,45,46 +commercial_weighted_average_life_years,46,46,46,46,46,46,47,46,47 +industrial_weighted_average_life_years,47,47,47,47,47,47,48,47,48 +transportation_weighted_average_life_years,48,48,48,48,48,48,49,48,49 +website,49,49,49,49,49,49,50,49,50 diff --git a/src/pudl/package_data/eia861/column_maps/frame_eia861.csv b/src/pudl/package_data/eia861/column_maps/frame_eia861.csv index b1c0cc2498..dcb7bac13f 100644 --- a/src/pudl/package_data/eia861/column_maps/frame_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/frame_eia861.csv @@ -1,22 +1,22 @@ -year_index,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0 -utility_id_eia,1,1,1,1,1 -utility_name_eia,2,2,2,2,2 -ownership_code,3,3,3,4,4 -ownership,4,4,4,5,5 -monthly,-1,5,5,6,6 -short_form,5,6,6,3,3 -advanced_metering,6,7,7,7,7 -delivery_company,-1,-1,-1,-1,8 -demand_response,7,8,8,8,9 -distribution_systems,8,9,9,9,10 -dynamic_pricing,9,10,10,10,11 -energy_efficiency,10,11,11,11,12 -mergers,11,12,12,12,13 -net_metering,12,13,13,13,14 -non_net_metering_distributed,13,14,14,14,15 -operational_data,14,15,15,15,16 -reliability,15,16,16,16,17 -sales_to_ultimate_customers,16,17,17,17,18 -service_territory,17,18,18,18,19 -utility_data,18,19,19,19,20 +year_index,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,3 +ownership_code,3,3,3,4,4,5 +ownership,4,4,4,5,5,6 +monthly,-1,5,5,6,6,7 +short_form,5,6,6,3,3,4 +advanced_metering,6,7,7,7,7,8 +delivery_company,-1,-1,-1,-1,8,9 +demand_response,7,8,8,8,9,10 +distribution_systems,8,9,9,9,10,11 +dynamic_pricing,9,10,10,10,11,12 +energy_efficiency,10,11,11,11,12,13 +mergers,11,12,12,12,13,14 +net_metering,12,13,13,13,14,15 +non_net_metering_distributed,13,14,14,14,15,16 +operational_data,14,15,15,15,16,17 +reliability,15,16,16,16,17,18 +sales_to_ultimate_customers,16,17,17,17,18,19 +service_territory,17,18,18,18,19,20 +utility_data,18,19,19,19,20,21 diff --git a/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv b/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv index 5f6147f6d2..fdfa9cd76a 100644 --- a/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv @@ -1,14 +1,14 @@ -year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -state,3,3,3,3,3,3,-1,-1,-1,-1,-1,-1,-1,-1 -entity_type,4,4,4,4,4,-1,-1,-1,-1,-1,-1,-1,-1,-1 -merge_date,5,5,5,5,5,4,3,3,3,3,3,3,3,3 -merge_company,6,6,6,6,6,5,4,4,4,4,4,4,4,4 -new_parent,7,7,7,7,7,6,5,5,5,5,5,5,5,5 -merge_address,8,8,8,8,8,7,6,6,6,6,6,6,6,6 -merge_city,9,9,9,9,9,8,7,7,7,7,7,7,7,7 -merge_state,10,10,10,10,10,9,8,8,8,8,8,8,8,8 -zip_code,11,11,11,11,11,10,9,9,9,9,9,9,9,9 -zip_code_4,12,12,12,12,12,-1,-1,-1,-1,-1,-1,-1,-1,-1 +year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +state,3,3,3,3,3,3,-1,-1,-1,-1,-1,-1,-1,-1,-1 +entity_type,4,4,4,4,4,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +merge_date,5,5,5,5,5,4,3,3,3,3,3,3,3,3,4 +merge_company,6,6,6,6,6,5,4,4,4,4,4,4,4,4,5 +new_parent,7,7,7,7,7,6,5,5,5,5,5,5,5,5,6 +merge_address,8,8,8,8,8,7,6,6,6,6,6,6,6,6,7 +merge_city,9,9,9,9,9,8,7,7,7,7,7,7,7,7,8 +merge_state,10,10,10,10,10,9,8,8,8,8,8,8,8,8,9 +zip_code,11,11,11,11,11,10,9,9,9,9,9,9,9,9,10 +zip_code_4,12,12,12,12,12,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 diff --git a/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv b/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv index 905f03a6f3..401d67f769 100644 --- a/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv @@ -1,108 +1,108 @@ -year_index,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2 -utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3 -short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,-1 -state,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1 -residential_total_energy_displaced_mwh,-1,-1,-1,-1,-1,9,9,9,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -commercial_total_energy_displaced_mwh,-1,-1,-1,-1,-1,10,10,10,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -industrial_total_energy_displaced_mwh,-1,-1,-1,-1,-1,11,11,11,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -transportation_total_energy_displaced_mwh,-1,-1,-1,-1,-1,12,12,12,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_total_energy_displaced_mwh,-1,-1,-1,-1,-1,13,13,13,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -residential_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,4,4,14,14,14,14,35,36,36,37,36 -commercial_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,5,5,15,15,15,15,36,37,37,38,37 -industrial_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,6,6,16,16,16,16,37,38,38,39,38 -transportation_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,7,7,17,17,17,17,38,39,39,40,39 -total_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,8,8,18,18,18,18,39,40,40,41,40 -residential_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,9,9,4,4,4,4,5,6,6,7,6 -commercial_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,10,10,5,5,5,5,6,7,7,8,7 -industrial_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,11,11,6,6,6,6,7,8,8,9,8 -transportation_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,12,12,7,7,7,7,8,9,9,10,9 -total_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,13,13,8,8,8,8,9,10,10,11,10 -residential_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,14,14,9,9,9,9,10,11,11,12,11 -commercial_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,15,15,10,10,10,10,11,12,12,13,12 -industrial_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,16,16,11,11,11,11,12,13,13,14,13 -transportation_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,17,17,12,12,12,12,13,14,14,15,14 -total_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,18,18,13,13,13,13,14,15,15,16,15 -residential_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,19,19,29,29,29,29,50,51,51,52,51 -commercial_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,20,20,30,30,30,30,51,52,52,53,52 -industrial_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,21,21,31,31,31,31,52,53,53,54,53 -transportation_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,22,22,32,32,32,32,53,54,54,55,54 -total_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,23,23,33,33,33,33,54,55,55,56,55 -residential_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,24,24,19,19,19,19,40,41,41,42,41 -commercial_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,25,25,20,20,20,20,41,42,42,43,42 -industrial_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,26,26,21,21,21,21,42,43,43,44,43 -transportation_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,27,27,22,22,22,22,43,44,44,45,44 -total_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,28,28,23,23,23,23,44,45,45,46,45 -residential_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,29,29,24,24,24,24,45,46,46,47,46 -commercial_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,30,30,25,25,25,25,46,47,47,48,47 -industrial_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,31,31,26,26,26,26,47,48,48,49,48 -transportation_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,32,32,27,27,27,27,48,49,49,50,49 -total_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,33,33,28,28,28,28,49,50,50,51,50 -residential_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,34,34,-1,-1,-1,-1,-1,-1,-1,-1,-1 -commercial_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,35,35,-1,-1,-1,-1,-1,-1,-1,-1,-1 -industrial_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,36,36,-1,-1,-1,-1,-1,-1,-1,-1,-1 -transportation_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,37,37,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,38,38,-1,-1,-1,-1,-1,-1,-1,-1,-1 -residential_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,39,39,-1,-1,-1,-1,-1,-1,-1,-1,-1 -commercial_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,40,40,-1,-1,-1,-1,-1,-1,-1,-1,-1 -industrial_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,41,41,-1,-1,-1,-1,-1,-1,-1,-1,-1 -transportation_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,42,42,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,43,43,-1,-1,-1,-1,-1,-1,-1,-1,-1 -residential_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,44,44,-1,-1,-1,-1,-1,-1,-1,-1,-1 -commercial_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,45,45,-1,-1,-1,-1,-1,-1,-1,-1,-1 -industrial_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,46,46,-1,-1,-1,-1,-1,-1,-1,-1,-1 -transportation_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,47,47,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,48,48,-1,-1,-1,-1,-1,-1,-1,-1,-1 -residential_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,49,49,44,44,44,44,65,66,66,67,66 -commercial_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,50,50,45,45,45,45,66,67,67,68,67 -industrial_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,51,51,46,46,46,46,67,68,68,69,68 -transportation_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,52,52,47,47,47,47,68,69,69,70,69 -total_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,53,53,48,48,48,48,69,70,70,71,70 -residential_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,54,54,34,34,34,34,55,56,56,57,56 -commercial_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,55,55,35,35,35,35,56,57,57,58,57 -industrial_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,56,56,36,36,36,36,57,58,58,59,58 -transportation_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,57,57,37,37,37,37,58,59,59,60,59 -total_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,58,58,38,38,38,38,59,60,60,61,60 -residential_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,59,59,39,39,39,39,60,61,61,62,61 -commercial_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,60,60,40,40,40,40,61,62,62,63,62 -industrial_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,61,61,41,41,41,41,62,63,63,64,63 -transportation_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,62,62,42,42,42,42,63,64,64,65,64 -total_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,63,63,43,43,43,43,64,65,65,66,65 -residential_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,64,64,59,59,59,59,80,81,81,82,81 -commercial_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,65,65,60,60,60,60,81,82,82,83,82 -industrial_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,66,66,61,61,61,61,82,83,83,84,83 -transportation_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,67,67,62,62,62,62,83,84,84,85,84 -total_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,68,68,63,63,63,63,84,85,85,86,85 -residential_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,69,69,49,49,49,49,70,71,71,72,71 -commercial_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,70,70,50,50,50,50,71,72,72,73,72 -industrial_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,71,71,51,51,51,51,72,73,73,74,73 -transportation_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,72,72,52,52,52,52,73,74,74,75,74 -total_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,73,73,53,53,53,53,74,75,75,76,75 -residential_total_customers,4,4,4,4,4,4,4,4,74,74,54,54,54,54,75,76,76,77,76 -commercial_total_customers,5,5,5,5,5,5,5,5,75,75,55,55,55,55,76,77,77,78,77 -industrial_total_customers,6,6,6,6,6,6,6,6,76,76,56,56,56,56,77,78,78,79,78 -transportation_total_customers,7,7,7,7,7,7,7,7,77,77,57,57,57,57,78,79,79,80,79 -total_total_customers,8,8,8,8,8,8,8,8,78,78,58,58,58,58,79,80,80,81,80 -pv_current_flow_type,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,5,5,6,5 -balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,4,5,4 -residential_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,15,16,16,17,16 -commercial_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,16,17,17,18,17 -industrial_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,17,18,18,19,18 -transportation_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,18,19,19,20,19 -total_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,19,20,20,21,20 -residential_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,20,21,21,22,21 -commercial_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,21,22,22,23,22 -industrial_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,22,23,23,24,23 -transportation_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,23,24,24,25,24 -total_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,24,25,25,26,25 -residential_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,25,26,26,27,26 -commercial_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,26,27,27,28,27 -industrial_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,27,28,28,29,28 -transportation_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,28,29,29,30,29 -total_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,29,30,30,31,30 -residential_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,30,31,31,32,31 -commercial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,31,32,32,33,32 -industrial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,32,33,33,34,33 -transportation_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,33,34,34,35,34 -total_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,34,35,35,36,35 +year_index,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,3 +utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,4 +short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,-1,-1 +state,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,2 +residential_total_energy_displaced_mwh,-1,-1,-1,-1,-1,9,9,9,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +commercial_total_energy_displaced_mwh,-1,-1,-1,-1,-1,10,10,10,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +industrial_total_energy_displaced_mwh,-1,-1,-1,-1,-1,11,11,11,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +transportation_total_energy_displaced_mwh,-1,-1,-1,-1,-1,12,12,12,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_total_energy_displaced_mwh,-1,-1,-1,-1,-1,13,13,13,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +residential_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,4,4,14,14,14,14,35,36,36,37,36,37 +commercial_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,5,5,15,15,15,15,36,37,37,38,37,38 +industrial_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,6,6,16,16,16,16,37,38,38,39,38,39 +transportation_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,7,7,17,17,17,17,38,39,39,40,39,40 +total_pv_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,8,8,18,18,18,18,39,40,40,41,40,41 +residential_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,9,9,4,4,4,4,5,6,6,7,6,7 +commercial_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,10,10,5,5,5,5,6,7,7,8,7,8 +industrial_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,11,11,6,6,6,6,7,8,8,9,8,9 +transportation_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,12,12,7,7,7,7,8,9,9,10,9,10 +total_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,13,13,8,8,8,8,9,10,10,11,10,11 +residential_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,14,14,9,9,9,9,10,11,11,12,11,12 +commercial_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,15,15,10,10,10,10,11,12,12,13,12,13 +industrial_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,16,16,11,11,11,11,12,13,13,14,13,14 +transportation_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,17,17,12,12,12,12,13,14,14,15,14,15 +total_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,18,18,13,13,13,13,14,15,15,16,15,16 +residential_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,19,19,29,29,29,29,50,51,51,52,51,52 +commercial_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,20,20,30,30,30,30,51,52,52,53,52,53 +industrial_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,21,21,31,31,31,31,52,53,53,54,53,54 +transportation_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,22,22,32,32,32,32,53,54,54,55,54,55 +total_wind_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,23,23,33,33,33,33,54,55,55,56,55,56 +residential_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,24,24,19,19,19,19,40,41,41,42,41,42 +commercial_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,25,25,20,20,20,20,41,42,42,43,42,43 +industrial_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,26,26,21,21,21,21,42,43,43,44,43,44 +transportation_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,27,27,22,22,22,22,43,44,44,45,44,45 +total_wind_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,28,28,23,23,23,23,44,45,45,46,45,46 +residential_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,29,29,24,24,24,24,45,46,46,47,46,47 +commercial_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,30,30,25,25,25,25,46,47,47,48,47,48 +industrial_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,31,31,26,26,26,26,47,48,48,49,48,49 +transportation_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,32,32,27,27,27,27,48,49,49,50,49,50 +total_wind_customers,-1,-1,-1,-1,-1,-1,-1,-1,33,33,28,28,28,28,49,50,50,51,50,51 +residential_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,34,34,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +commercial_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,35,35,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +industrial_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,36,36,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +transportation_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,37,37,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_chp_cogen_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,38,38,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +residential_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,39,39,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +commercial_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,40,40,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +industrial_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,41,41,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +transportation_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,42,42,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_chp_cogen_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,43,43,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +residential_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,44,44,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +commercial_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,45,45,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +industrial_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,46,46,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +transportation_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,47,47,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_chp_cogen_customers,-1,-1,-1,-1,-1,-1,-1,-1,48,48,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +residential_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,49,49,44,44,44,44,65,66,66,67,66,67 +commercial_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,50,50,45,45,45,45,66,67,67,68,67,68 +industrial_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,51,51,46,46,46,46,67,68,68,69,68,69 +transportation_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,52,52,47,47,47,47,68,69,69,70,69,70 +total_other_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,53,53,48,48,48,48,69,70,70,71,70,71 +residential_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,54,54,34,34,34,34,55,56,56,57,56,57 +commercial_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,55,55,35,35,35,35,56,57,57,58,57,58 +industrial_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,56,56,36,36,36,36,57,58,58,59,58,59 +transportation_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,57,57,37,37,37,37,58,59,59,60,59,60 +total_other_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,58,58,38,38,38,38,59,60,60,61,60,61 +residential_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,59,59,39,39,39,39,60,61,61,62,61,62 +commercial_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,60,60,40,40,40,40,61,62,62,63,62,63 +industrial_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,61,61,41,41,41,41,62,63,63,64,63,64 +transportation_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,62,62,42,42,42,42,63,64,64,65,64,65 +total_other_customers,-1,-1,-1,-1,-1,-1,-1,-1,63,63,43,43,43,43,64,65,65,66,65,66 +residential_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,64,64,59,59,59,59,80,81,81,82,81,82 +commercial_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,65,65,60,60,60,60,81,82,82,83,82,83 +industrial_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,66,66,61,61,61,61,82,83,83,84,83,84 +transportation_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,67,67,62,62,62,62,83,84,84,85,84,85 +total_total_sold_to_utility_mwh,-1,-1,-1,-1,-1,-1,-1,-1,68,68,63,63,63,63,84,85,85,86,85,86 +residential_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,69,69,49,49,49,49,70,71,71,72,71,72 +commercial_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,70,70,50,50,50,50,71,72,72,73,72,73 +industrial_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,71,71,51,51,51,51,72,73,73,74,73,74 +transportation_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,72,72,52,52,52,52,73,74,74,75,74,75 +total_total_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,73,73,53,53,53,53,74,75,75,76,75,76 +residential_total_customers,4,4,4,4,4,4,4,4,74,74,54,54,54,54,75,76,76,77,76,77 +commercial_total_customers,5,5,5,5,5,5,5,5,75,75,55,55,55,55,76,77,77,78,77,78 +industrial_total_customers,6,6,6,6,6,6,6,6,76,76,56,56,56,56,77,78,78,79,78,79 +transportation_total_customers,7,7,7,7,7,7,7,7,77,77,57,57,57,57,78,79,79,80,79,80 +total_total_customers,8,8,8,8,8,8,8,8,78,78,58,58,58,58,79,80,80,81,80,81 +pv_current_flow_type,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,5,5,6,5,6 +balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,4,4,5,4,5 +residential_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,15,16,16,17,16,17 +commercial_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,16,17,17,18,17,18 +industrial_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,17,18,18,19,18,19 +transportation_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,18,19,19,20,19,20 +total_storage_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,19,20,20,21,20,21 +residential_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,20,21,21,22,21,22 +commercial_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,21,22,22,23,22,23 +industrial_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,22,23,23,24,23,24 +transportation_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,23,24,24,25,24,25 +total_storage_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,24,25,25,26,25,26 +residential_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,25,26,26,27,26,27 +commercial_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,26,27,27,28,27,28 +industrial_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,27,28,28,29,28,29 +transportation_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,28,29,29,30,29,30 +total_virtual_pv_capacity_mw,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,29,30,30,31,30,31 +residential_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,30,31,31,32,31,32 +commercial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,31,32,32,33,32,33 +industrial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,32,33,33,34,33,34 +transportation_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,33,34,34,35,34,35 +total_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,34,35,35,36,35,36 diff --git a/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv b/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv index 9812999a08..c9e131c03c 100644 --- a/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv @@ -1,71 +1,71 @@ -year_index,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0 -state,1,1,1,1,1 -utility_id_eia,2,2,2,2,2 -utility_name_eia,3,3,3,3,3 -balancing_authority_code_eia,4,4,4,4,4 -generators_number,5,5,5,5,5 -total_capacity_mw,6,6,6,6,6 -backup_capacity_mw,7,7,7,7,7 -utility_owned_capacity_mw,8,8,8,8,8 -pv_current_flow_type,9,9,9,9,9 -residential_pv_capacity_mw,10,10,10,10,10 -commercial_pv_capacity_mw,11,11,11,11,11 -industrial_pv_capacity_mw,12,12,12,12,12 -transportation_pv_capacity_mw,13,13,13,13,13 -direct_connection_pv_capacity_mw,14,14,14,14,14 -total_pv_capacity_mw,15,15,15,15,15 -residential_all_storage_capacity_mw,16,16,16,16,16 -commercial_all_storage_capacity_mw,17,17,17,17,17 -industrial_all_storage_capacity_mw,18,18,18,18,18 -transportation_all_storage_capacity_mw,19,19,19,19,19 -direct_connection_all_storage_capacity_mw,20,20,20,20,20 -total_all_storage_capacity_mw,21,21,21,21,21 -residential_wind_capacity_mw,22,22,22,22,22 -commercial_wind_capacity_mw,23,23,23,23,23 -industrial_wind_capacity_mw,24,24,24,24,24 -transportation_wind_capacity_mw,25,25,25,25,25 -direct_connection_wind_capacity_mw,26,26,26,26,26 -total_wind_capacity_mw,27,27,27,27,27 -residential_hydro_capacity_mw,28,28,28,28,28 -commercial_hydro_capacity_mw,29,29,29,29,29 -industrial_hydro_capacity_mw,30,30,30,30,30 -transportation_hydro_capacity_mw,31,31,31,31,31 -direct_connection_hydro_capacity_mw,32,32,32,32,32 -total_hydro_capacity_mw,33,33,33,33,33 -residential_fuel_cell_capacity_mw,34,34,34,34,34 -commercial_fuel_cell_capacity_mw,35,35,35,35,35 -industrial_fuel_cell_capacity_mw,36,36,36,36,36 -transportation_fuel_cell_capacity_mw,37,37,37,37,37 -direct_connection_fuel_cell_capacity_mw,38,38,38,38,38 -total_fuel_cell_capacity_mw,39,39,39,39,39 -residential_internal_combustion_capacity_mw,40,40,40,40,40 -commercial_internal_combustion_capacity_mw,41,41,41,41,41 -industrial_internal_combustion_capacity_mw,42,42,42,42,42 -transportation_internal_combustion_capacity_mw,43,43,43,43,43 -direct_connection_internal_combustion_capacity_mw,44,44,44,44,44 -total_internal_combustion_capacity_mw,45,45,45,45,45 -residential_combustion_turbine_capacity_mw,46,46,46,46,46 -commercial_combustion_turbine_capacity_mw,47,47,47,47,47 -industrial_combustion_turbine_capacity_mw,48,48,48,48,48 -transportation_combustion_turbine_capacity_mw,49,49,49,49,49 -direct_connection_combustion_turbine_capacity_mw,50,50,50,50,50 -total_combustion_turbine_capacity_mw,51,51,51,51,51 -residential_steam_capacity_mw,52,52,52,52,52 -commercial_steam_capacity_mw,53,53,53,53,53 -industrial_steam_capacity_mw,54,54,54,54,54 -transportation_steam_capacity_mw,55,55,55,55,55 -direct_connection_steam_capacity_mw,56,56,56,56,56 -total_steam_capacity_mw,57,57,57,57,57 -residential_other_capacity_mw,58,58,58,58,58 -commercial_other_capacity_mw,59,59,59,59,59 -industrial_other_capacity_mw,60,60,60,60,60 -transportation_other_capacity_mw,61,61,61,61,61 -direct_connection_other_capacity_mw,62,62,62,62,62 -total_other_capacity_mw,63,63,63,63,63 -residential_total_capacity_mw,64,64,64,64,64 -commercial_total_capacity_mw,65,65,65,65,65 -industrial_total_capacity_mw,66,66,66,66,66 -transportation_total_capacity_mw,67,67,67,67,67 -direct_connection_total_capacity_mw,68,68,68,68,68 -total_total_capacity_mw,69,69,69,69,69 +year_index,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,1 +state,1,1,1,1,1,2 +utility_id_eia,2,2,2,2,2,3 +utility_name_eia,3,3,3,3,3,4 +balancing_authority_code_eia,4,4,4,4,4,5 +generators_number,5,5,5,5,5,6 +total_capacity_mw,6,6,6,6,6,7 +backup_capacity_mw,7,7,7,7,7,8 +utility_owned_capacity_mw,8,8,8,8,8,9 +pv_current_flow_type,9,9,9,9,9,10 +residential_pv_capacity_mw,10,10,10,10,10,11 +commercial_pv_capacity_mw,11,11,11,11,11,12 +industrial_pv_capacity_mw,12,12,12,12,12,13 +transportation_pv_capacity_mw,13,13,13,13,13,14 +direct_connection_pv_capacity_mw,14,14,14,14,14,15 +total_pv_capacity_mw,15,15,15,15,15,16 +residential_all_storage_capacity_mw,16,16,16,16,16,17 +commercial_all_storage_capacity_mw,17,17,17,17,17,18 +industrial_all_storage_capacity_mw,18,18,18,18,18,19 +transportation_all_storage_capacity_mw,19,19,19,19,19,20 +direct_connection_all_storage_capacity_mw,20,20,20,20,20,21 +total_all_storage_capacity_mw,21,21,21,21,21,22 +residential_wind_capacity_mw,22,22,22,22,22,23 +commercial_wind_capacity_mw,23,23,23,23,23,24 +industrial_wind_capacity_mw,24,24,24,24,24,25 +transportation_wind_capacity_mw,25,25,25,25,25,26 +direct_connection_wind_capacity_mw,26,26,26,26,26,27 +total_wind_capacity_mw,27,27,27,27,27,28 +residential_hydro_capacity_mw,28,28,28,28,28,29 +commercial_hydro_capacity_mw,29,29,29,29,29,30 +industrial_hydro_capacity_mw,30,30,30,30,30,31 +transportation_hydro_capacity_mw,31,31,31,31,31,32 +direct_connection_hydro_capacity_mw,32,32,32,32,32,33 +total_hydro_capacity_mw,33,33,33,33,33,34 +residential_fuel_cell_capacity_mw,34,34,34,34,34,35 +commercial_fuel_cell_capacity_mw,35,35,35,35,35,36 +industrial_fuel_cell_capacity_mw,36,36,36,36,36,37 +transportation_fuel_cell_capacity_mw,37,37,37,37,37,38 +direct_connection_fuel_cell_capacity_mw,38,38,38,38,38,39 +total_fuel_cell_capacity_mw,39,39,39,39,39,40 +residential_internal_combustion_capacity_mw,40,40,40,40,40,41 +commercial_internal_combustion_capacity_mw,41,41,41,41,41,42 +industrial_internal_combustion_capacity_mw,42,42,42,42,42,43 +transportation_internal_combustion_capacity_mw,43,43,43,43,43,44 +direct_connection_internal_combustion_capacity_mw,44,44,44,44,44,45 +total_internal_combustion_capacity_mw,45,45,45,45,45,46 +residential_combustion_turbine_capacity_mw,46,46,46,46,46,47 +commercial_combustion_turbine_capacity_mw,47,47,47,47,47,48 +industrial_combustion_turbine_capacity_mw,48,48,48,48,48,49 +transportation_combustion_turbine_capacity_mw,49,49,49,49,49,50 +direct_connection_combustion_turbine_capacity_mw,50,50,50,50,50,51 +total_combustion_turbine_capacity_mw,51,51,51,51,51,52 +residential_steam_capacity_mw,52,52,52,52,52,53 +commercial_steam_capacity_mw,53,53,53,53,53,54 +industrial_steam_capacity_mw,54,54,54,54,54,55 +transportation_steam_capacity_mw,55,55,55,55,55,56 +direct_connection_steam_capacity_mw,56,56,56,56,56,57 +total_steam_capacity_mw,57,57,57,57,57,58 +residential_other_capacity_mw,58,58,58,58,58,59 +commercial_other_capacity_mw,59,59,59,59,59,60 +industrial_other_capacity_mw,60,60,60,60,60,61 +transportation_other_capacity_mw,61,61,61,61,61,62 +direct_connection_other_capacity_mw,62,62,62,62,62,63 +total_other_capacity_mw,63,63,63,63,63,64 +residential_total_capacity_mw,64,64,64,64,64,65 +commercial_total_capacity_mw,65,65,65,65,65,66 +industrial_total_capacity_mw,66,66,66,66,66,67 +transportation_total_capacity_mw,67,67,67,67,67,68 +direct_connection_total_capacity_mw,68,68,68,68,68,69 +total_total_capacity_mw,69,69,69,69,69,70 diff --git a/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv b/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv index ea5d09b696..e483fb4169 100644 --- a/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv @@ -1,36 +1,36 @@ -year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,-1 -state,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3 -entity_type,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,4 -nerc_region,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,5 -summer_peak_demand_mw,34,34,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,6 -winter_peak_demand_mw,35,35,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,7 -net_generation_mwh,40,38,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,9,8 -wholesale_power_purchases_mwh,-1,-1,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,9 -exchange_energy_received_mwh,44,40,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,10 -exchange_energy_delivered_mwh,45,41,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,11 -net_power_exchanged_mwh,46,42,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13,12 -wheeled_power_received_mwh,47,43,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,14,13 -wheeled_power_delivered_mwh,48,43,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,15,14 -net_wheeled_power_mwh,49,43,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,16,15 -transmission_by_other_losses_mwh,50,46,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,17,16 -total_sources_mwh,51,47,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,18,17 -retail_sales_mwh,52,48,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,19,18 -sales_for_resale_mwh,53,49,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,19 -furnished_without_charge_mwh,55,50,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,20 -consumed_by_respondent_without_charge_mwh,-1,-1,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,21 -consumed_by_facility_mwh,-1,-1,22,22,22,22,22,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_energy_losses_mwh,57,52,23,23,23,23,23,22,22,22,22,22,22,22,22,22,22,22,22,22,23,22 -total_disposition_mwh,58,53,24,24,24,24,24,23,23,23,23,23,23,23,23,23,23,23,23,23,24,23 -retail_sales_revenue,59,54,25,25,25,25,25,24,24,24,24,24,24,24,24,24,24,24,24,24,25,24 -unbundled_revenue,-1,-1,-1,-1,-1,-1,-1,-1,25,25,25,25,25,-1,-1,-1,-1,-1,-1,-1,-1,-1 -delivery_customers_revenue,-1,-1,26,26,26,26,26,25,-1,-1,-1,-1,-1,25,25,25,25,25,25,25,26,25 -sales_for_resale_revenue,60,55,27,27,27,27,27,26,26,26,26,26,26,26,26,26,26,26,26,26,27,26 -credits_or_adjustments_revenue,61,56,28,28,28,28,28,27,27,27,27,27,27,27,27,27,27,27,27,27,28,27 -transmission_revenue,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,28,28,28,28,28,28,28,28,28,29,28 -other_revenue,62,57,29,29,29,29,29,28,28,28,28,29,29,29,29,29,29,29,29,29,30,29 -total_revenue,63,58,30,30,30,30,30,29,29,29,29,30,30,30,30,30,30,30,30,30,31,30 -data_observed,-1,-1,31,31,31,31,31,30,30,30,30,31,31,31,31,31,31,31,31,31,32,31 +year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,-1,-1 +state,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3,4 +entity_type,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,4,5 +nerc_region,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,5,6 +summer_peak_demand_mw,34,34,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,6,7 +winter_peak_demand_mw,35,35,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,7,8 +net_generation_mwh,40,38,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,9,8,9 +wholesale_power_purchases_mwh,-1,-1,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,9,10 +exchange_energy_received_mwh,44,40,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,11,10,11 +exchange_energy_delivered_mwh,45,41,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,11,12 +net_power_exchanged_mwh,46,42,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,13,12,13 +wheeled_power_received_mwh,47,43,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,14,13,14 +wheeled_power_delivered_mwh,48,43,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,15,14,15 +net_wheeled_power_mwh,49,43,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,15,16,15,16 +transmission_by_other_losses_mwh,50,46,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,17,16,17 +total_sources_mwh,51,47,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,18,17,18 +retail_sales_mwh,52,48,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,19,18,19 +sales_for_resale_mwh,53,49,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,19,20 +furnished_without_charge_mwh,55,50,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,20,21 +consumed_by_respondent_without_charge_mwh,-1,-1,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,21,22 +consumed_by_facility_mwh,-1,-1,22,22,22,22,22,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_energy_losses_mwh,57,52,23,23,23,23,23,22,22,22,22,22,22,22,22,22,22,22,22,22,23,22,23 +total_disposition_mwh,58,53,24,24,24,24,24,23,23,23,23,23,23,23,23,23,23,23,23,23,24,23,24 +retail_sales_revenue,59,54,25,25,25,25,25,24,24,24,24,24,24,24,24,24,24,24,24,24,25,24,25 +unbundled_revenue,-1,-1,-1,-1,-1,-1,-1,-1,25,25,25,25,25,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +delivery_customers_revenue,-1,-1,26,26,26,26,26,25,-1,-1,-1,-1,-1,25,25,25,25,25,25,25,26,25,26 +sales_for_resale_revenue,60,55,27,27,27,27,27,26,26,26,26,26,26,26,26,26,26,26,26,26,27,26,27 +credits_or_adjustments_revenue,61,56,28,28,28,28,28,27,27,27,27,27,27,27,27,27,27,27,27,27,28,27,28 +transmission_revenue,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,28,28,28,28,28,28,28,28,28,29,28,29 +other_revenue,62,57,29,29,29,29,29,28,28,28,28,29,29,29,29,29,29,29,29,29,30,29,30 +total_revenue,63,58,30,30,30,30,30,29,29,29,29,30,30,30,30,30,30,30,30,30,31,30,31 +data_observed,-1,-1,31,31,31,31,31,30,30,30,30,31,31,31,31,31,31,31,31,31,32,31,32 diff --git a/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv b/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv index 53e7b40411..8f69bad164 100644 --- a/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv @@ -1,30 +1,30 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2 -state,3,3,3,3,3,3,3,3 -entity_type,4,4,4,4,4,4,4,4 -short_form,-1,-1,-1,-1,-1,-1,5,-1 -ieee_standard_saidi_w_major_event_days_minutes,5,5,5,5,5,5,6,5 -ieee_standard_saidi_wo_major_event_days_minutes,8,8,8,8,8,8,9,8 -ieee_standard_saidi_w_major_event_days_minus_loss_of_service_minutes,11,11,11,11,11,11,12,11 -ieee_standard_saifi_w_major_event_days_customers,6,6,6,6,6,6,7,6 -ieee_standard_caidi_w_major_event_days_minutes,7,7,7,7,7,7,8,7 -ieee_standard_saifi_wo_major_event_days_customers,9,9,9,9,9,9,10,9 -ieee_standard_caidi_wo_major_event_days_minutes,10,10,10,10,10,10,11,10 -ieee_standard_saifi_w_major_event_days_minus_loss_of_service_customers,12,12,12,12,12,12,13,12 -ieee_standard_caidi_w_major_event_days_minus_loss_of_service_minutes,13,13,13,13,13,13,14,13 -ieee_standard_customers,14,14,14,14,14,14,15,14 -ieee_standard_highest_distribution_voltage_kv,15,15,15,15,15,15,16,15 -ieee_standard_outages_recorded_automatically,16,16,16,16,16,16,17,16 -other_standard_saidi_w_major_event_days_minutes,17,17,17,17,17,17,18,17 -other_standard_saidi_wo_major_event_days_minutes,20,20,20,20,20,20,21,20 -other_standard_saifi_w_major_event_days_customers,18,18,18,18,18,18,19,18 -other_standard_caidi_w_major_event_days_minutes,19,19,19,19,19,19,20,19 -other_standard_saifi_wo_major_event_days_customers,21,21,21,21,21,21,22,21 -other_standard_caidi_wo_major_event_days_minutes,22,22,22,22,22,22,23,22 -other_standard_customers,23,23,23,23,23,23,24,23 -other_standard_inactive_accounts_included,24,24,24,24,24,24,25,24 -other_standard_momentary_interruption_definition,25,25,25,25,25,25,26,25 -other_standard_highest_distribution_voltage_kv,26,26,26,26,26,26,27,26 -other_standard_outages_recorded_automatically,27,27,27,27,27,27,28,27 +year_index,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,3 +state,3,3,3,3,3,3,3,3,4 +entity_type,4,4,4,4,4,4,4,4,5 +short_form,-1,-1,-1,-1,-1,-1,5,-1,-1 +ieee_standard_saidi_w_major_event_days_minutes,5,5,5,5,5,5,6,5,6 +ieee_standard_saidi_wo_major_event_days_minutes,8,8,8,8,8,8,9,8,9 +ieee_standard_saidi_w_major_event_days_minus_loss_of_service_minutes,11,11,11,11,11,11,12,11,12 +ieee_standard_saifi_w_major_event_days_customers,6,6,6,6,6,6,7,6,7 +ieee_standard_caidi_w_major_event_days_minutes,7,7,7,7,7,7,8,7,8 +ieee_standard_saifi_wo_major_event_days_customers,9,9,9,9,9,9,10,9,10 +ieee_standard_caidi_wo_major_event_days_minutes,10,10,10,10,10,10,11,10,11 +ieee_standard_saifi_w_major_event_days_minus_loss_of_service_customers,12,12,12,12,12,12,13,12,13 +ieee_standard_caidi_w_major_event_days_minus_loss_of_service_minutes,13,13,13,13,13,13,14,13,14 +ieee_standard_customers,14,14,14,14,14,14,15,14,15 +ieee_standard_highest_distribution_voltage_kv,15,15,15,15,15,15,16,15,16 +ieee_standard_outages_recorded_automatically,16,16,16,16,16,16,17,16,17 +other_standard_saidi_w_major_event_days_minutes,17,17,17,17,17,17,18,17,18 +other_standard_saidi_wo_major_event_days_minutes,20,20,20,20,20,20,21,20,21 +other_standard_saifi_w_major_event_days_customers,18,18,18,18,18,18,19,18,19 +other_standard_caidi_w_major_event_days_minutes,19,19,19,19,19,19,20,19,20 +other_standard_saifi_wo_major_event_days_customers,21,21,21,21,21,21,22,21,22 +other_standard_caidi_wo_major_event_days_minutes,22,22,22,22,22,22,23,22,23 +other_standard_customers,23,23,23,23,23,23,24,23,24 +other_standard_inactive_accounts_included,24,24,24,24,24,24,25,24,25 +other_standard_momentary_interruption_definition,25,25,25,25,25,25,26,25,26 +other_standard_highest_distribution_voltage_kv,26,26,26,26,26,26,27,26,27 +other_standard_outages_recorded_automatically,27,27,27,27,27,27,28,27,28 diff --git a/src/pudl/package_data/eia861/column_maps/sales_eia861.csv b/src/pudl/package_data/eia861/column_maps/sales_eia861.csv index 5fdf2bdfb6..3d42cea039 100644 --- a/src/pudl/package_data/eia861/column_maps/sales_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/sales_eia861.csv @@ -1,29 +1,29 @@ -year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -business_model,-1,-1,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3 -service_type,-1,-1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4 -data_observed,-1,-1,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 -state,2,2,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6 -entity_type,-1,-1,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7 -balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,8,8,8,8,8,8,8,8 -short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,9,-1 -residential_sales_revenue,3,3,8,8,8,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,10,9 -residential_sales_mwh,4,4,9,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,10,10,11,10 -residential_customers,5,5,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,12,11 -commercial_sales_revenue,6,6,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,13,12 -commercial_sales_mwh,7,7,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,14,13 -commercial_customers,8,8,13,13,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,14,15,14 -industrial_sales_revenue,9,9,14,14,14,14,14,14,14,14,14,14,14,14,15,15,15,15,15,15,16,15 -industrial_sales_mwh,10,10,15,15,15,15,15,15,15,15,15,15,15,15,16,16,16,16,16,16,17,16 -industrial_customers,11,11,16,16,16,16,16,16,16,16,16,16,16,16,17,17,17,17,17,17,18,17 -transportation_sales_revenue,12,12,17,17,17,17,17,17,17,17,17,17,17,17,18,18,18,18,18,18,19,18 -transportation_sales_mwh,13,13,18,18,18,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,20,19 -transportation_customers,14,14,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,21,20 -other_sales_revenue,15,15,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -other_sales_mwh,16,16,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -other_customers,17,17,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 -total_sales_revenue,18,18,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,22,21 -total_sales_mwh,19,19,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,23,22 -total_customers,20,20,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,24,23 +year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +business_model,-1,-1,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4 +service_type,-1,-1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5 +data_observed,-1,-1,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6 +state,2,2,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7 +entity_type,-1,-1,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8 +balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,8,8,8,8,8,8,8,8,9 +short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,9,-1,-1 +residential_sales_revenue,3,3,8,8,8,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,10,9,10 +residential_sales_mwh,4,4,9,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,10,10,11,10,11 +residential_customers,5,5,10,10,10,10,10,10,10,10,10,10,10,10,11,11,11,11,11,11,12,11,12 +commercial_sales_revenue,6,6,11,11,11,11,11,11,11,11,11,11,11,11,12,12,12,12,12,12,13,12,13 +commercial_sales_mwh,7,7,12,12,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,13,14,13,14 +commercial_customers,8,8,13,13,13,13,13,13,13,13,13,13,13,13,14,14,14,14,14,14,15,14,15 +industrial_sales_revenue,9,9,14,14,14,14,14,14,14,14,14,14,14,14,15,15,15,15,15,15,16,15,16 +industrial_sales_mwh,10,10,15,15,15,15,15,15,15,15,15,15,15,15,16,16,16,16,16,16,17,16,17 +industrial_customers,11,11,16,16,16,16,16,16,16,16,16,16,16,16,17,17,17,17,17,17,18,17,18 +transportation_sales_revenue,12,12,17,17,17,17,17,17,17,17,17,17,17,17,18,18,18,18,18,18,19,18,19 +transportation_sales_mwh,13,13,18,18,18,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,20,19,20 +transportation_customers,14,14,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,21,20,21 +other_sales_revenue,15,15,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +other_sales_mwh,16,16,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +other_customers,17,17,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +total_sales_revenue,18,18,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,22,21,22 +total_sales_mwh,19,19,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,23,22,23 +total_customers,20,20,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,24,23,24 diff --git a/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv b/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv index 18e238b850..21287d9d1e 100644 --- a/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv @@ -1,7 +1,7 @@ -year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3 -state,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4 -county,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5 +year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,4 +state,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,5 +county,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,6 diff --git a/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv b/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv index a5a3c45bcc..a2300d686c 100644 --- a/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv @@ -1,15 +1,15 @@ -year_index,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,-1,0 -utility_id_eia,1,1,1,1,1,1,1,-1,1 -utility_name_eia,2,2,2,2,2,2,2,-1,2 -entity_type,-1,-1,-1,3,3,3,3,-1,3 -state,3,3,3,4,4,4,4,-1,4 -ba_code,-1,4,4,5,5,5,5,-1,5 -total_revenue,4,5,5,6,6,6,6,-1,6 -total_sales,5,6,6,7,7,7,7,-1,7 -total_customers,6,7,7,8,8,8,8,-1,8 -water_heater,-1,8,8,9,9,9,9,-1,9 -net_metering,8,9,9,10,10,10,10,-1,10 -demand_side_management,9,10,10,11,11,11,11,-1,11 -time_based_programs,10,11,11,12,12,12,12,-1,12 -green_pricing,7,-1,-1,-1,-1,-1,-1,-1,-1 +year_index,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,-1,0,1 +utility_id_eia,1,1,1,1,1,1,1,-1,1,2 +utility_name_eia,2,2,2,2,2,2,2,-1,2,3 +entity_type,-1,-1,-1,3,3,3,3,-1,3,4 +state,3,3,3,4,4,4,4,-1,4,5 +ba_code,-1,4,4,5,5,5,5,-1,5,6 +total_revenue,4,5,5,6,6,6,6,-1,6,7 +total_sales,5,6,6,7,7,7,7,-1,7,8 +total_customers,6,7,7,8,8,8,8,-1,8,9 +water_heater,-1,8,8,9,9,9,9,-1,9,10 +net_metering,8,9,9,10,10,10,10,-1,10,11 +demand_side_management,9,10,10,11,11,11,11,-1,11,12 +time_based_programs,10,11,11,12,12,12,12,-1,12,13 +green_pricing,7,-1,-1,-1,-1,-1,-1,-1,-1,-1 diff --git a/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv b/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv index 6cfa0563b8..dab1acf021 100644 --- a/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv @@ -1,35 +1,35 @@ -year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1 -utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2 -short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,-1 -state,3,3,3,3,3,3,3,3,3,3,3,3,4,3 -entity_type,4,4,4,4,4,4,4,4,4,4,4,4,5,4 -nerc_region,5,5,5,5,5,5,5,5,5,5,5,5,6,5 -tre_nerc_region_operation,6,6,6,6,6,6,6,6,6,6,6,6,7,6 -frcc_nerc_region_operation,7,7,7,7,7,7,7,7,7,7,7,7,8,7 -mro_nerc_region_operation,8,8,8,8,8,8,8,8,8,8,8,8,9,8 -npcc_nerc_region_operation,9,9,9,9,9,9,9,9,9,9,9,9,10,9 -rfc_nerc_region_operation,10,10,10,10,10,10,10,10,10,10,10,10,11,10 -serc_nerc_region_operation,11,11,11,11,11,11,11,11,11,11,11,11,12,11 -spp_nerc_region_operation,12,12,12,12,12,12,12,12,12,12,12,12,13,12 -wecc_nerc_region_operation,13,13,13,13,13,13,13,13,13,13,13,13,14,13 -operates_generating_plant,14,14,14,22,22,-1,-1,-1,-1,-1,-1,-1,-1,-1 -generation_activity,15,15,15,23,23,22,22,22,22,22,22,22,23,22 -transmission_activity,16,16,16,24,24,23,23,23,23,23,23,23,24,23 -buying_transmission_activity,17,17,17,25,25,24,24,24,24,24,24,24,25,24 -distribution_activity,18,18,18,26,26,25,25,25,25,25,25,25,26,25 -buying_distribution_activity,19,19,19,27,27,26,26,26,26,26,26,26,27,26 -wholesale_marketing_activity,20,20,20,28,28,27,27,27,27,27,27,27,28,27 -retail_marketing_activity,21,21,21,29,29,28,28,28,28,28,28,28,29,28 -bundled_activity,22,22,22,30,30,29,29,29,29,29,29,29,30,29 -alternative_fuel_vehicle_activity,23,23,23,31,31,30,30,30,30,30,30,30,31,30 -alternative_fuel_vehicle_2_activity,24,24,24,32,32,31,31,31,31,31,31,31,32,31 -caiso_rto_operation,-1,-1,-1,14,14,14,14,14,14,14,14,14,15,14 -ercot_rto_operation,-1,-1,-1,15,15,15,15,15,15,15,15,15,16,15 -pjm_rto_operation,-1,-1,-1,16,16,16,16,16,16,16,16,16,17,16 -nyiso_rto_operation,-1,-1,-1,17,17,17,17,17,17,17,17,17,18,17 -spp_rto_operation,-1,-1,-1,18,18,18,18,18,18,18,18,18,19,18 -miso_rto_operation,-1,-1,-1,19,19,19,19,19,19,19,19,19,20,19 -isone_rto_operation,-1,-1,-1,20,20,20,20,20,20,20,20,20,21,20 -other_rto_operation,-1,-1,-1,21,21,21,21,21,21,21,21,21,22,21 +year_index,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +report_year,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +utility_id_eia,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2 +utility_name_eia,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,-1,-1 +state,3,3,3,3,3,3,3,3,3,3,3,3,4,3,4 +entity_type,4,4,4,4,4,4,4,4,4,4,4,4,5,4,5 +nerc_region,5,5,5,5,5,5,5,5,5,5,5,5,6,5,6 +tre_nerc_region_operation,6,6,6,6,6,6,6,6,6,6,6,6,7,6,7 +frcc_nerc_region_operation,7,7,7,7,7,7,7,7,7,7,7,7,8,7,8 +mro_nerc_region_operation,8,8,8,8,8,8,8,8,8,8,8,8,9,8,9 +npcc_nerc_region_operation,9,9,9,9,9,9,9,9,9,9,9,9,10,9,10 +rfc_nerc_region_operation,10,10,10,10,10,10,10,10,10,10,10,10,11,10,11 +serc_nerc_region_operation,11,11,11,11,11,11,11,11,11,11,11,11,12,11,12 +spp_nerc_region_operation,12,12,12,12,12,12,12,12,12,12,12,12,13,12,13 +wecc_nerc_region_operation,13,13,13,13,13,13,13,13,13,13,13,13,14,13,14 +operates_generating_plant,14,14,14,22,22,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +generation_activity,15,15,15,23,23,22,22,22,22,22,22,22,23,22,23 +transmission_activity,16,16,16,24,24,23,23,23,23,23,23,23,24,23,24 +buying_transmission_activity,17,17,17,25,25,24,24,24,24,24,24,24,25,24,25 +distribution_activity,18,18,18,26,26,25,25,25,25,25,25,25,26,25,26 +buying_distribution_activity,19,19,19,27,27,26,26,26,26,26,26,26,27,26,27 +wholesale_marketing_activity,20,20,20,28,28,27,27,27,27,27,27,27,28,27,28 +retail_marketing_activity,21,21,21,29,29,28,28,28,28,28,28,28,29,28,29 +bundled_activity,22,22,22,30,30,29,29,29,29,29,29,29,30,29,30 +alternative_fuel_vehicle_activity,23,23,23,31,31,30,30,30,30,30,30,30,31,30,31 +alternative_fuel_vehicle_2_activity,24,24,24,32,32,31,31,31,31,31,31,31,32,31,32 +caiso_rto_operation,-1,-1,-1,14,14,14,14,14,14,14,14,14,15,14,15 +ercot_rto_operation,-1,-1,-1,15,15,15,15,15,15,15,15,15,16,15,16 +pjm_rto_operation,-1,-1,-1,16,16,16,16,16,16,16,16,16,17,16,17 +nyiso_rto_operation,-1,-1,-1,17,17,17,17,17,17,17,17,17,18,17,18 +spp_rto_operation,-1,-1,-1,18,18,18,18,18,18,18,18,18,19,18,19 +miso_rto_operation,-1,-1,-1,19,19,19,19,19,19,19,19,19,20,19,20 +isone_rto_operation,-1,-1,-1,20,20,20,20,20,20,20,20,20,21,20,21 +other_rto_operation,-1,-1,-1,21,21,21,21,21,21,21,21,21,22,21,22 diff --git a/src/pudl/package_data/eia861/file_map.csv b/src/pudl/package_data/eia861/file_map.csv index 834e9e4ee6..0ccff75cbe 100644 --- a/src/pudl/package_data/eia861/file_map.csv +++ b/src/pudl/package_data/eia861/file_map.csv @@ -1,22 +1,22 @@ -page,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -advanced_metering_infrastructure_eia861,-1,-1,-1,-1,-1,-1,-1,-1,2007/file8_2007.xls,2008/file8_2008.xls,2009/file8_2009.xls,file8_2010.xls,file8_2011.xls,advanced_meters_2012.xls,Advanced_Meters_2013.xls,Advanced_Meters_2014.xls,Advanced_Meters_2015.xlsx,Advanced_Meters_2016.xlsx,Advanced_Meters_2017.xlsx,Advanced_Meters_2018.xlsx,Advanced_Meters_2019.xlsx,Advanced_Meters_2020.xlsx -balancing_authority_eia861,-1,-1,2001/file1_cao.xls,2002/file1_cao.xls,2003/file1_cao.xls,2004/file1_cao.xls,2005/file1_cao.xls,file1_cao.xls,2007/file1_cao_2007.xls,2008/file1_cao_2008.xls,2009/file1_cao_2009.xls,file1_cao_2010.xls,file1_cao_2011.xls,balancing_authority_2012.xls,Balancing_Authority_2013.xls,Balancing_Authority_2014.xls,Balancing_Authority_2015.xlsx,Balancing_Authority_2016.xlsx,Balancing_Authority_2017.xlsx,Balancing_Authority_2018.xlsx,Balancing_Authority_2019.xlsx,Balancing_Authority_2020.xlsx -delivery_companies_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Delivery_Companies_2020.xlsx -demand_response_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Demand_Response_2013.xls,Demand_Response_2014.xls,Demand_Response_2015.xlsx,Demand_Response_2016.xlsx,Demand_Response_2017.xlsx,Demand_Response_2018.xlsx,Demand_Response_2019.xlsx,Demand_Response_2020.xlsx -demand_side_management_eia861,FILE4.xls,FILE4.xls,2001/file3.xls,2002/file3.xls,2003/file3.xls,2004/file3.xls,2005/file3.xls,file3.xls,2007/file3_2007.xls,2008/file3_2008.xls,2009/file3_2009.xls,file3_2010.xls,file3_2011.xls,dsm_2012.xls,-1,-1,-1,-1,-1,-1,-1,-1 -distributed_generation_eia861,-1,-1,-1,-1,-1,2004/file6.xls,2005/file6.xls,file6.xls,2007/file6_2007.xls,2008/file6_2008.xls,2009/file6_2009.xls,file6_2010.xls,file6_2011.xls,distributed_generation_2012.xls,Distributed_Generation_2013.xls,Distributed_Generation_2014.xls,Distributed_Generation_2015.xlsx,-1,-1,-1,-1,-1 -distribution_systems_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Distribution_Systems_2016.xlsx,Distribution_Systems_2017.xlsx,Distribution_Systems_2018.xlsx,Distribution_Systems_2019.xlsx,Distribution_Systems_2020.xlsx -dynamic_pricing_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Dynamic_Pricing2013.xls,Dynamic_Pricing2014.xls,Dynamic_Pricing2015.xlsx,Dynamic_Pricing2016.xlsx,Dynamic_Pricing_2017.xlsx,Dynamic_Pricing_2018.xlsx,Dynamic_Pricing_2019.xlsx,Dynamic_Pricing_2020.xlsx -energy_efficiency_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Energy_Efficiency_2013.xlsx,Energy_Efficiency_2014.xlsx,Energy_Efficiency_2015.xlsx,Energy_Efficiency_2016.xlsx,Energy_Efficiency_2017.xlsx,Energy_Efficiency_2018.xlsx,Energy_Efficiency_2019.xlsx,Energy_Efficiency_2020.xlsx -frame_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Frame_2016.xlsx,Frame_2017.xlsx,Frame_2018.xlsx,Frame_2019.xlsx,Frame_2020.xlsx -green_pricing_eia861,-1,-1,-1,2002/file5.xls,2003/file5.xls,2004/file5.xls,2005/file5.xls,file5.xls,2007/file5_2007.xls,2008/file5_2008.xls,2009/file5_2009.xls,file5_2010.xls,file5_2011.xls,green_pricing_2012.xls,-1,-1,-1,-1,-1,-1,-1,-1 -mergers_eia861,-1,-1,-1,-1,-1,-1,-1,-1,2007/file7_2007.xls,2008/file7_2008.xls,2009/file7_2009.xls,file7_2010.xls,file7_2011.xls,mergers_2012.xls,Mergers_2013.xls,Mergers_2014.xls,Mergers_2015.xlsx,Mergers_2016.xlsx,Mergers_2017.xlsx,Mergers_2018.xlsx,Mergers_2019.xlsx,Mergers_2020.xlsx -net_metering_eia861,-1,-1,-1,2002/file5.xls,2003/file5.xls,2004/file5.xls,2005/file5.xls,file5.xls,2007/file5_2007.xls,2008/file5_2008.xls,2009/file5_2009.xls,file5_2010.xls,file5_2011.xls,net_metering_2012.xls,Net_Metering_2013.xls,Net_Metering_2014.xls,Net_Metering_2015.xlsx,Net_Metering_2016.xlsx,Net_Metering_2017.xlsx,Net_Metering_2018.xlsx,Net_Metering_2019.xlsx,Net_Metering_2020.xlsx -non_net_metering_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Non_Net_Metering_Distributed_2016.xlsx,Non_Net_Metering_Distributed_2017.xlsx,Non_Net_Metering_Distributed_2018.xlsx,Non_Net_Metering_Distributed_2019.xlsx,Non_Net_Metering_Distributed_2020.xlsx -operational_data_eia861,File1.xls,FILE1.xls,2001/file1.xls,2002/file1.xls,2003/file1.xls,2004/file1.xls,2005/file1.xls,file1.xls,2007/file1_2007.xls,2008/file1_2008.xls,2009/file1_2009.xls,file1_2010.xls,file1_2011.xls,operational_data_2012.xls,Operational_Data_2013.xlsx,Operational_Data_2014.xlsx,Operational_Data_2015.xlsx,Operational_Data_2016.xlsx,Operational_Data_2017.xlsx,Operational_Data_2018.xlsx,Operational_Data_2019.xlsx,Operational_Data_2020.xlsx -reliability_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Reliability_2013.xlsx,Reliability_2014.xlsx,Reliability_2015.xlsx,Reliability_2016.xlsx,Reliability_2017.xlsx,Reliability_2018.xlsx,Reliability_2019.xlsx,Reliability_2020.xlsx 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+energy_efficiency_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0 +energy_efficiency_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1 +frame_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0 +green_pricing_eia861,-1,-1,-1,0,0,0,0,0,0,1,1,0,0,0,-1,-1,-1,-1,-1,-1,-1,-1,-1 +green_pricing_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +mergers_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +net_metering_eia861,-1,-1,-1,1,1,1,1,1,1,0,0,2,2,0,0,0,0,0,0,0,0,0,0 +net_metering_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,1,1,1,1,1,1,1,1,1,1 +net_metering_tpos_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,2 +non_net_metering_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0 +non_net_metering_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1 +operational_data_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +operational_data_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1 +reliability_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0 +reliability_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1 +sales_decoupled_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2 +sales_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +sales_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 +service_territory_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +service_territory_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1 +short_form_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,-1,0,0 +utility_data_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +utility_data_territories_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1 diff --git a/src/pudl/package_data/eia861/skipfooter.csv b/src/pudl/package_data/eia861/skipfooter.csv index 214ac77305..f03c608166 100644 --- a/src/pudl/package_data/eia861/skipfooter.csv +++ b/src/pudl/package_data/eia861/skipfooter.csv @@ -1,21 +1,21 @@ -year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -advanced_metering_infrastructure_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1 -balancing_authority_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -delivery_companies_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 -demand_response_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -demand_side_management_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -distributed_generation_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -distribution_systems_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -dynamic_pricing_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -energy_efficiency_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1 -frame_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -green_pricing_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -mergers_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -net_metering_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1 -non_net_metering_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1 -operational_data_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -reliability_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1 -sales_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1 -service_territory_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -short_form_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1 -utility_data_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +advanced_metering_infrastructure_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1 +balancing_authority_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +delivery_companies_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1 +demand_response_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +demand_side_management_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +distributed_generation_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +distribution_systems_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +dynamic_pricing_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +energy_efficiency_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +frame_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +green_pricing_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +mergers_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +net_metering_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1 +non_net_metering_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1 +operational_data_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +reliability_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +sales_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +service_territory_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +short_form_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1 +utility_data_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/src/pudl/package_data/eia861/skiprows.csv b/src/pudl/package_data/eia861/skiprows.csv index efc7f23dc8..65197ee0b7 100644 --- a/src/pudl/package_data/eia861/skiprows.csv +++ b/src/pudl/package_data/eia861/skiprows.csv @@ -1,21 +1,21 @@ -year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 -advanced_metering_infrastructure_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,10,13,1,1,1,1,1,1,1,1,1 -balancing_authority_eia861,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -delivery_companies_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2 -demand_response_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2 -demand_side_management_eia861,0,0,0,0,0,0,0,0,0,0,0,11,11,2,-1,-1,-1,-1,-1,-1,-1,-1 -distributed_generation_eia861,-1,-1,-1,-1,-1,0,0,0,0,0,0,9,10,1,1,1,1,-1,-1,-1,-1,-1 -distribution_systems_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0 -dynamic_pricing_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2 -energy_efficiency_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2 -frame_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0 -green_pricing_eia861,-1,-1,-1,0,0,0,0,0,0,0,0,10,8,1,-1,-1,-1,-1,-1,-1,-1,-1 -mergers_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -net_metering_eia861,-1,-1,-1,0,0,0,0,0,0,0,0,8,8,2,2,2,2,3,2,2,2,2 -non_net_metering_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,1,1,1,1 -operational_data_eia861,0,0,0,0,0,0,0,0,0,0,0,7,8,2,2,2,2,2,2,2,2,2 -reliability_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1 -sales_eia861,0,0,0,0,0,0,0,0,0,2,2,2,2,2,2,2,2,2,2,2,2,2 -service_territory_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -short_form_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,-1,0 -utility_data_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,1,1,1,1,1,1,1,1,1 +year_index,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 +advanced_metering_infrastructure_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,10,13,1,1,1,1,1,1,1,1,1,2 +balancing_authority_eia861,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +delivery_companies_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,3 +demand_response_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,3 +demand_side_management_eia861,0,0,0,0,0,0,0,0,0,0,0,11,11,2,-1,-1,-1,-1,-1,-1,-1,-1,-1 +distributed_generation_eia861,-1,-1,-1,-1,-1,0,0,0,0,0,0,9,10,1,1,1,1,-1,-1,-1,-1,-1,-1 +distribution_systems_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,1 +dynamic_pricing_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,3 +energy_efficiency_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,3 +frame_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,1 +green_pricing_eia861,-1,-1,-1,0,0,0,0,0,0,0,0,10,8,1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +mergers_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +net_metering_eia861,-1,-1,-1,0,0,0,0,0,0,0,0,8,8,2,2,2,2,3,2,2,2,2,3 +non_net_metering_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,1,1,1,1,2 +operational_data_eia861,0,0,0,0,0,0,0,0,0,0,0,7,8,2,2,2,2,2,2,2,2,2,3 +reliability_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,2 +sales_eia861,0,0,0,0,0,0,0,0,0,2,2,2,2,2,2,2,2,2,2,2,2,2,3 +service_territory_eia861,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +short_form_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,-1,0,1 +utility_data_eia861,-1,-1,-1,-1,-1,-1,-1,-1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,2 From f04832e3afe1c1dcb4a01704f360977231aaa8a9 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Thu, 8 Sep 2022 19:00:51 -0500 Subject: [PATCH 59/80] Add DOIs for the new EPA CAMD to EIA Crosswalk archives. --- src/pudl/workspace/datastore.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index 36f5ea6444..0f9f03aea6 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -152,6 +152,7 @@ class ZenodoFetcher: "eia860m": "10.5072/zenodo.926659", "eia861": "10.5072/zenodo.687052", "eia923": "10.5072/zenodo.1090056", + "epacamd_eia": "10.5072/zenodo.1103224", "epacems": "10.5072/zenodo.672963", "ferc1": "10.5072/zenodo.926302", "ferc714": "10.5072/zenodo.926660", @@ -162,6 +163,7 @@ class ZenodoFetcher: "eia860m": "10.5281/zenodo.6929086", "eia861": "10.5281/zenodo.5602102", "eia923": "10.5281/zenodo.7003886", + "epacamd_eia": "10.5281/zenodo.7063255", "epacems": "10.5281/zenodo.6910058", "ferc1": "10.5281/zenodo.5534788", "ferc714": "10.5281/zenodo.5076672", From b92da9dbda02d07e80e8257c8c1f0332d7fe4f80 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Thu, 8 Sep 2022 20:00:45 -0500 Subject: [PATCH 60/80] Update the last old EPA CAMD EIA crosswalk names. --- src/pudl/glue/epacamd_eia.py | 7 +++---- src/pudl/metadata/sources.py | 2 +- src/pudl/workspace/datastore.py | 2 -- 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/src/pudl/glue/epacamd_eia.py b/src/pudl/glue/epacamd_eia.py index 24c7f8f8aa..4fd8aaeeb6 100644 --- a/src/pudl/glue/epacamd_eia.py +++ b/src/pudl/glue/epacamd_eia.py @@ -19,10 +19,9 @@ def extract(ds: Datastore) -> pd.DataFrame: """Extract the EPACAMD-EIA Crosswalk from the Datastore.""" - with ds.get_zipfile_resource( - "epacems_unitid_eia_plant_crosswalk", # eventually change these names? - name="epacems_unitid_eia_plant_crosswalk.zip", # eventually change these names? - ).open("camd-eia-crosswalk-master/epa_eia_crosswalk.csv") as f: + with ds.get_zipfile_resource("epacamd_eia", name="epacamd_eia.zip").open( + "camd-eia-crosswalk-master/epa_eia_crosswalk.csv" + ) as f: return pd.read_csv(f) diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 9e0e3474b5..b8acf7bc12 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -242,7 +242,7 @@ ), "source_file_dict": { "records_liberated": "~7000", - "source_format": "Microsoft Excel (.xlsx)", + "source_format": "Comma Separated Value (.csv)", }, "field_namespace": "glue", "working_partitions": {}, diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index b6715fad4e..0f9f03aea6 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -154,7 +154,6 @@ class ZenodoFetcher: "eia923": "10.5072/zenodo.1090056", "epacamd_eia": "10.5072/zenodo.1103224", "epacems": "10.5072/zenodo.672963", - "epacems_unitid_eia_plant_crosswalk": "10.5072/zenodo.1072001", # Eventually change name "ferc1": "10.5072/zenodo.926302", "ferc714": "10.5072/zenodo.926660", }, @@ -166,7 +165,6 @@ class ZenodoFetcher: "eia923": "10.5281/zenodo.7003886", "epacamd_eia": "10.5281/zenodo.7063255", "epacems": "10.5281/zenodo.6910058", - "epacems_unitid_eia_plant_crosswalk": "10.5281/zenodo.6633770", # Eventually change name "ferc1": "10.5281/zenodo.5534788", "ferc714": "10.5281/zenodo.5076672", }, From c74003c5557300e0b624a443f71899b341808725 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Thu, 8 Sep 2022 22:12:24 -0500 Subject: [PATCH 61/80] Add EIA-861 2021ER data; fix typo in column map. --- src/pudl/metadata/sources.py | 2 +- .../eia861/column_maps/distribution_systems_eia861.csv | 2 +- src/pudl/workspace/datastore.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 9e0e3474b5..ff30bd76bb 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -106,7 +106,7 @@ ), "field_namespace": "eia", "working_partitions": { - "years": sorted(set(range(2001, 2021))), + "years": sorted(set(range(2001, 2022))), }, "contributors": [], "keywords": sorted( diff --git a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv index a86bb7aa07..5568b489c9 100644 --- a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv @@ -1,4 +1,4 @@ -year_index,2013,2014,2015,2016,2017,2018,2019,2020, +year_index,2013,2014,2015,2016,2017,2018,2019,2020,2021 report_year,0,0,0,0,0,0,0,0,1 utility_id_eia,1,1,1,1,1,1,1,1,2 utility_name_eia,2,2,2,2,2,2,2,2,3 diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index 0f9f03aea6..552c1ccf26 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -150,7 +150,7 @@ class ZenodoFetcher: "censusdp1tract": "10.5072/zenodo.674992", "eia860": "10.5072/zenodo.926292", "eia860m": "10.5072/zenodo.926659", - "eia861": "10.5072/zenodo.687052", + "eia861": "10.5072/zenodo.1103262", "eia923": "10.5072/zenodo.1090056", "epacamd_eia": "10.5072/zenodo.1103224", "epacems": "10.5072/zenodo.672963", @@ -161,7 +161,7 @@ class ZenodoFetcher: "censusdp1tract": "10.5281/zenodo.4127049", "eia860": "10.5281/zenodo.6954131", "eia860m": "10.5281/zenodo.6929086", - "eia861": "10.5281/zenodo.5602102", + "eia861": "10.5281/zenodo.7063401", "eia923": "10.5281/zenodo.7003886", "epacamd_eia": "10.5281/zenodo.7063255", "epacems": "10.5281/zenodo.6910058", From 801d7b6b748d9427dd91e17cbb13a2efb8497d8d Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Fri, 9 Sep 2022 10:28:56 -0500 Subject: [PATCH 62/80] Add early_release column maps & missing dynamic_pricing filename. --- .../column_maps/advanced_metering_infrastructure_eia861.csv | 1 + .../eia861/column_maps/balancing_authority_eia861.csv | 1 + .../eia861/column_maps/delivery_companies_eia861.csv | 1 + .../package_data/eia861/column_maps/demand_response_eia861.csv | 1 + .../eia861/column_maps/distribution_systems_eia861.csv | 1 + .../package_data/eia861/column_maps/dynamic_pricing_eia861.csv | 1 + .../eia861/column_maps/energy_efficiency_eia861.csv | 1 + src/pudl/package_data/eia861/column_maps/frame_eia861.csv | 1 + src/pudl/package_data/eia861/column_maps/mergers_eia861.csv | 1 + .../package_data/eia861/column_maps/net_metering_eia861.csv | 1 + .../package_data/eia861/column_maps/non_net_metering_eia861.csv | 1 + .../package_data/eia861/column_maps/operational_data_eia861.csv | 1 + src/pudl/package_data/eia861/column_maps/reliability_eia861.csv | 1 + src/pudl/package_data/eia861/column_maps/sales_eia861.csv | 1 + .../eia861/column_maps/service_territory_eia861.csv | 1 + src/pudl/package_data/eia861/column_maps/short_form_eia861.csv | 1 + .../package_data/eia861/column_maps/utility_data_eia861.csv | 1 + src/pudl/package_data/eia861/file_map.csv | 2 +- 18 files changed, 18 insertions(+), 1 deletion(-) diff --git a/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv b/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv index 68643712dc..1a51020666 100644 --- a/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/advanced_metering_infrastructure_eia861.csv @@ -46,3 +46,4 @@ commercial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,41,41,42,42,42,42,43, industrial_direct_load_control_customers,-1,-1,-1,-1,-1,-1,42,42,43,43,43,43,44,44,45 transportation_direct_load_control_customers,-1,-1,-1,-1,-1,-1,43,43,44,44,44,44,45,45,46 total_direct_load_control_customers,-1,-1,-1,-1,-1,-1,44,44,45,45,45,45,46,46,47 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv b/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv index d9e4a54368..62fe36c8c0 100644 --- a/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/balancing_authority_eia861.csv @@ -6,3 +6,4 @@ balancing_authority_id_eia,2,3,3,3,3,2,2,2,2,2,2,3,1,1,1,1,1,1,1,1,2 balancing_authority_code_eia,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,2,2,2,2,2,2,2,3 state,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,3,3,3,3,3,3,4 balancing_authority_name_eia,3,4,4,4,4,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,5 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv b/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv index d8d1db3c8e..93e0e131e2 100644 --- a/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/delivery_companies_eia861.csv @@ -23,3 +23,4 @@ transportation_customers,20,21 total_sales_revenue,21,22 total_sales_mwh,22,23 total_customers,23,24 +early_release,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv b/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv index 47b49338a3..7174b36e2d 100644 --- a/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/demand_response_eia861.csv @@ -36,3 +36,4 @@ industrial_other_costs,31,31,31,31,32,32,33,32,33 transportation_other_costs,32,32,32,32,33,33,34,33,34 total_other_costs,33,33,33,33,34,34,35,34,35 water_heater,34,34,34,34,35,35,36,35,36 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv index 5568b489c9..2e98a4197f 100644 --- a/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/distribution_systems_eia861.csv @@ -6,3 +6,4 @@ short_form,-1,-1,-1,-1,-1,-1,3,-1,-1 state,3,3,3,3,3,3,4,3,4 distribution_circuits,4,4,4,4,4,4,5,4,5 circuits_with_voltage_optimization,5,5,5,5,5,5,6,5,6 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv b/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv index 586944ed7d..3e79a8dfe5 100644 --- a/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/dynamic_pricing_eia861.csv @@ -30,3 +30,4 @@ residential_critical_peak_rebate,25,25,25,25,26,26,27,27,28 commercial_critical_peak_rebate,26,26,26,26,27,27,28,28,29 industrial_critical_peak_rebate,27,27,27,27,28,28,29,29,30 transportation_critical_peak_rebate,28,28,28,28,29,29,30,30,31 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv b/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv index 3238aec34e..00666db7a1 100644 --- a/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/energy_efficiency_eia861.csv @@ -51,3 +51,4 @@ commercial_weighted_average_life_years,46,46,46,46,46,46,47,46,47 industrial_weighted_average_life_years,47,47,47,47,47,47,48,47,48 transportation_weighted_average_life_years,48,48,48,48,48,48,49,48,49 website,49,49,49,49,49,49,50,49,50 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/frame_eia861.csv b/src/pudl/package_data/eia861/column_maps/frame_eia861.csv index dcb7bac13f..49e0195c7f 100644 --- a/src/pudl/package_data/eia861/column_maps/frame_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/frame_eia861.csv @@ -20,3 +20,4 @@ reliability,15,16,16,16,17,18 sales_to_ultimate_customers,16,17,17,17,18,19 service_territory,17,18,18,18,19,20 utility_data,18,19,19,19,20,21 +early_release,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv b/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv index fdfa9cd76a..6109814a01 100644 --- a/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/mergers_eia861.csv @@ -12,3 +12,4 @@ merge_city,9,9,9,9,9,8,7,7,7,7,7,7,7,7,8 merge_state,10,10,10,10,10,9,8,8,8,8,8,8,8,8,9 zip_code,11,11,11,11,11,10,9,9,9,9,9,9,9,9,10 zip_code_4,12,12,12,12,12,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv b/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv index 401d67f769..b7127f2687 100644 --- a/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/net_metering_eia861.csv @@ -106,3 +106,4 @@ commercial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,31,32, industrial_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,32,33,33,34,33,34 transportation_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,33,34,34,35,34,35 total_virtual_pv_customers,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,34,35,35,36,35,36 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv b/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv index c9e131c03c..4b0b90ac64 100644 --- a/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/non_net_metering_eia861.csv @@ -69,3 +69,4 @@ industrial_total_capacity_mw,66,66,66,66,66,67 transportation_total_capacity_mw,67,67,67,67,67,68 direct_connection_total_capacity_mw,68,68,68,68,68,69 total_total_capacity_mw,69,69,69,69,69,70 +early_release,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv b/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv index e483fb4169..ea383416d0 100644 --- a/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/operational_data_eia861.csv @@ -34,3 +34,4 @@ transmission_revenue,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,28,28,28,28,28,28,28,28,28 other_revenue,62,57,29,29,29,29,29,28,28,28,28,29,29,29,29,29,29,29,29,29,30,29,30 total_revenue,63,58,30,30,30,30,30,29,29,29,29,30,30,30,30,30,30,30,30,30,31,30,31 data_observed,-1,-1,31,31,31,31,31,30,30,30,30,31,31,31,31,31,31,31,31,31,32,31,32 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv b/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv index 8f69bad164..cfc67d3e7f 100644 --- a/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/reliability_eia861.csv @@ -28,3 +28,4 @@ other_standard_inactive_accounts_included,24,24,24,24,24,24,25,24,25 other_standard_momentary_interruption_definition,25,25,25,25,25,25,26,25,26 other_standard_highest_distribution_voltage_kv,26,26,26,26,26,26,27,26,27 other_standard_outages_recorded_automatically,27,27,27,27,27,27,28,27,28 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/sales_eia861.csv b/src/pudl/package_data/eia861/column_maps/sales_eia861.csv index 3d42cea039..7c2e196764 100644 --- a/src/pudl/package_data/eia861/column_maps/sales_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/sales_eia861.csv @@ -27,3 +27,4 @@ other_customers,17,17,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,- total_sales_revenue,18,18,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,22,21,22 total_sales_mwh,19,19,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,23,22,23 total_customers,20,20,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,24,23,24 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv b/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv index 21287d9d1e..076f6d1726 100644 --- a/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/service_territory_eia861.csv @@ -5,3 +5,4 @@ utility_name_eia,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3 short_form,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,3,3,4 state,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,5 county,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,6 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv b/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv index a2300d686c..cd5b44d917 100644 --- a/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/short_form_eia861.csv @@ -13,3 +13,4 @@ net_metering,8,9,9,10,10,10,10,-1,10,11 demand_side_management,9,10,10,11,11,11,11,-1,11,12 time_based_programs,10,11,11,12,12,12,12,-1,12,13 green_pricing,7,-1,-1,-1,-1,-1,-1,-1,-1,-1 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv b/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv index dab1acf021..e567193104 100644 --- a/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv +++ b/src/pudl/package_data/eia861/column_maps/utility_data_eia861.csv @@ -33,3 +33,4 @@ spp_rto_operation,-1,-1,-1,18,18,18,18,18,18,18,18,18,19,18,19 miso_rto_operation,-1,-1,-1,19,19,19,19,19,19,19,19,19,20,19,20 isone_rto_operation,-1,-1,-1,20,20,20,20,20,20,20,20,20,21,20,21 other_rto_operation,-1,-1,-1,21,21,21,21,21,21,21,21,21,22,21,22 +early_release,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0 diff --git a/src/pudl/package_data/eia861/file_map.csv b/src/pudl/package_data/eia861/file_map.csv index 0ccff75cbe..9fbaea16af 100644 --- a/src/pudl/package_data/eia861/file_map.csv +++ b/src/pudl/package_data/eia861/file_map.csv @@ -6,7 +6,7 @@ demand_response_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Demand_Response demand_side_management_eia861,FILE4.xls,FILE4.xls,2001/file3.xls,2002/file3.xls,2003/file3.xls,2004/file3.xls,2005/file3.xls,file3.xls,2007/file3_2007.xls,2008/file3_2008.xls,2009/file3_2009.xls,file3_2010.xls,file3_2011.xls,dsm_2012.xls,-1,-1,-1,-1,-1,-1,-1,-1,-1 distributed_generation_eia861,-1,-1,-1,-1,-1,2004/file6.xls,2005/file6.xls,file6.xls,2007/file6_2007.xls,2008/file6_2008.xls,2009/file6_2009.xls,file6_2010.xls,file6_2011.xls,distributed_generation_2012.xls,Distributed_Generation_2013.xls,Distributed_Generation_2014.xls,Distributed_Generation_2015.xlsx,-1,-1,-1,-1,-1,-1 distribution_systems_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Distribution_Systems_2016.xlsx,Distribution_Systems_2017.xlsx,Distribution_Systems_2018.xlsx,Distribution_Systems_2019.xlsx,Distribution_Systems_2020.xlsx,Distribution_Systems_2021_Data_Early_Release.xlsx -dynamic_pricing_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Dynamic_Pricing2013.xls,Dynamic_Pricing2014.xls,Dynamic_Pricing2015.xlsx,Dynamic_Pricing2016.xlsx,Dynamic_Pricing_2017.xlsx,Dynamic_Pricing_2018.xlsx,Dynamic_Pricing_2019.xlsx,Dynamic_Pricing_2020.xlsx, +dynamic_pricing_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Dynamic_Pricing2013.xls,Dynamic_Pricing2014.xls,Dynamic_Pricing2015.xlsx,Dynamic_Pricing2016.xlsx,Dynamic_Pricing_2017.xlsx,Dynamic_Pricing_2018.xlsx,Dynamic_Pricing_2019.xlsx,Dynamic_Pricing_2020.xlsx,Dynamic_Pricing_2021_Data_Early_Release.xlsx energy_efficiency_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Energy_Efficiency_2013.xlsx,Energy_Efficiency_2014.xlsx,Energy_Efficiency_2015.xlsx,Energy_Efficiency_2016.xlsx,Energy_Efficiency_2017.xlsx,Energy_Efficiency_2018.xlsx,Energy_Efficiency_2019.xlsx,Energy_Efficiency_2020.xlsx,Energy_Efficiency_2021_Data_Early_Release.xlsx frame_eia861,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,Frame_2016.xlsx,Frame_2017.xlsx,Frame_2018.xlsx,Frame_2019.xlsx,Frame_2020.xlsx,Frame_2021_Data_Early_Release.xlsx green_pricing_eia861,-1,-1,-1,2002/file5.xls,2003/file5.xls,2004/file5.xls,2005/file5.xls,file5.xls,2007/file5_2007.xls,2008/file5_2008.xls,2009/file5_2009.xls,file5_2010.xls,file5_2011.xls,green_pricing_2012.xls,-1,-1,-1,-1,-1,-1,-1,-1,-1 From 089624c0c2f83ddea296a165d0b4ab5bee531069 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Fri, 9 Sep 2022 10:36:30 -0500 Subject: [PATCH 63/80] Add data_maturity label to EIA-861 tables. --- src/pudl/extract/eia861.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/pudl/extract/eia861.py b/src/pudl/extract/eia861.py index 7b1d3377b0..128a6a6755 100644 --- a/src/pudl/extract/eia861.py +++ b/src/pudl/extract/eia861.py @@ -47,6 +47,7 @@ def process_raw(self, df, page, **partition): ) self.cols_added = [] df = fix_leading_zero_gen_ids(df) + df = self.add_data_maturity(df, page, **partition) return df def extract(self, settings: Eia861Settings = Eia861Settings()): From f94ce2ffc788e4dc4a6525041fc36a91c0abbb01 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Fri, 9 Sep 2022 12:33:27 -0500 Subject: [PATCH 64/80] Update README and release notes to reflect EIA-861 2021ER data. --- README.rst | 3 ++- docs/release_notes.rst | 17 ++++++++++++----- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 848b717736..bcbed4b764 100644 --- a/README.rst +++ b/README.rst @@ -63,7 +63,8 @@ PUDL currently integrates data from: * `EIA Form 860 `__: 2001-2021 (2021 is early release - use with caution) * `EIA Form 860m `__: 2022-06 -* `EIA Form 861 `__: 2001-2020 +* `EIA Form 861 `__: 2001-2021 (2021 is + early release - use with caution) * `EIA Form 923 `__: 2001-2021 (2021 is early release - use with caution) * `EPA Continuous Emissions Monitoring System (CEMS) `__: 1995-2021 diff --git a/docs/release_notes.rst b/docs/release_notes.rst index d6305c6470..2d80826f75 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -11,11 +11,12 @@ PUDL Release Notes Data Coverage ^^^^^^^^^^^^^ * Incorporated 2021 data from the :doc:`data_sources/epacems` dataset. See :pr:`1778` -* Incorporated 2021 data from the :doc:`data_sources/eia860` and - :doc:`data_sources/eia923`. Early Release. Early release data is EIA's preliminary - annual release and should be used with caution. We also integrated a ``data_maturity`` - column and related ``data_maturities`` table into most of the EIA data tables in - order to alter users to the level of finality of the data. :pr:`1834` :pr:`1855` +* Incorporated Early Release 2021 data from the :doc:`data_sources/eia860`, + :ref:`data-eia861`, and :doc:`data_sources/eia923`. Early release data is EIA's + preliminary annual release and should be used with caution. We also integrated a + ``data_maturity`` column and related ``data_maturities`` table into most of the EIA + data tables in order to alter users to the level of finality of the data. See + :pr:`1834,1855,1915,1921` * Incorporated 2022 data from the :doc:`data_sources/eia860` monthly update from June 2022. See :pr:`1834`. This included adding new ``energy_storage_capacity_mwh`` (for batteries) and ``net_capacity_mwdc`` (for behind-the-meter solar PV) attributes to the @@ -77,6 +78,12 @@ Database Schema Changes * Renamed ``grid_voltage_kv`` to ``grid_voltage_1_kv`` in the :ref:`plants_eia860` table, to follow the pattern of many other multiply reported values. +* Added a :ref:`balancing_authorities_eia` coding table mapping BA codes found in the + :doc:`data_sources/eia860` and :doc:`data_sources/eia923` to their names, cleaning up + non-standard codes, and fixing some reporting errors for ``PACW`` vs. ``PACE`` + (PacifiCorp West vs. East) based on the state associated with the plant reporting the + code. Also added backfilling for codes in years before 2013 when BA Codes first + started being reported), but only in the output tables. See: :pr:`1906,1911` Date Merge Helper Function ^^^^^^^^^^^^^^^^^^^^^^^^^^ From fb7cc64373818733a97136d8ca8ef1446290ef4c Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Fri, 9 Sep 2022 17:20:23 -0600 Subject: [PATCH 65/80] Add to harmonize_eia_epa_orispl_code transform function doc string --- src/pudl/transform/epacems.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/pudl/transform/epacems.py b/src/pudl/transform/epacems.py index 6d0d178ac0..7dfc4f0220 100644 --- a/src/pudl/transform/epacems.py +++ b/src/pudl/transform/epacems.py @@ -28,6 +28,12 @@ def harmonize_eia_epa_orispl( compiled a crosswalk that maps one set of IDs to the other. The crosswalk is integrated into the PUDL db. + This function merges the crosswalk with the cems data thus adding the official + plant_id_eia column to CEMS. In cases where there is no plant_id_eia value for a + given plant_id_epa (i.e., this plant isn't in the crosswalk yet), we use + fillna() to add the plant_id_epa value to the plant_id_eia column. Because the + plant_id_epa is almost always correct this is reasonable. + EIA IDs are more correct so use the crosswalk to fix any erronious EPA IDs and get rid of that column to avoid confusion. From 676b3dbee177bbf665e0e6a0adc7719b6aaa4046 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Sat, 10 Sep 2022 11:23:08 -0500 Subject: [PATCH 66/80] Draft metadata for EIA Bulk Electricity data source. --- devtools/environment.yml | 4 ++-- pyproject.toml | 2 +- src/pudl/metadata/sources.py | 35 ++++++++++++++++++++++++++++++++++- 3 files changed, 37 insertions(+), 4 deletions(-) diff --git a/devtools/environment.yml b/devtools/environment.yml index 081b05ce3f..353c611c23 100644 --- a/devtools/environment.yml +++ b/devtools/environment.yml @@ -7,11 +7,11 @@ dependencies: # Used to set up the environment - pip>=21,<23 - python>=3.10,<3.11 - - setuptools<64 + - setuptools<66 # These packages are also specified in setup.py # However, they depend on or benefit from binary libraries # which conda can install. - - geopandas>=0.9,<0.11 + - geopandas>=0.9,<0.12 - pygeos>=0.10,<0.13 # Python wrappers for the GEOS spatial libraries - python-snappy>=0.6,<1 - sqlite>=3.36,<4 diff --git a/pyproject.toml b/pyproject.toml index cf1135a238..8a87236295 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] requires = [ - "setuptools<64", + "setuptools<66", "setuptools_scm[toml]>=3.5.0", ] build-backend = "setuptools.build_meta" diff --git a/src/pudl/metadata/sources.py b/src/pudl/metadata/sources.py index 9e0e3474b5..b6193b043e 100644 --- a/src/pudl/metadata/sources.py +++ b/src/pudl/metadata/sources.py @@ -180,6 +180,39 @@ "license_raw": LICENSES["us-govt"], "license_pudl": LICENSES["cc-by-4.0"], }, + "eia_bulk_elec": { + "title": "EIA Bulk Electricity API Data", + "path": "https://www.eia.gov/opendata/bulkfiles.php", + "description": ( + "Aggregate national, state, and plant-level electricity generation " + "statistics, including fuel quality and consumption, for grid-connected " + "plants with nameplate capacity of 1 megawatt or greater" + ), + "source_file_dict": { + "respondents": ( + "Electric, CHP plants, and sometimes fuel transfer termianls with " + "either 1MW+ or the ability to receive and deliver power to the grid." + ), + "source_format": "JSON", + }, + "field_namespace": "eia", + "working_partitions": {}, + "contributors": [ + CONTRIBUTORS["catalyst-cooperative"], + CONTRIBUTORS["zane-selvans"], + CONTRIBUTORS["trenton-bush"], + ], + "keywords": sorted( + set( + KEYWORDS["eia"] + + KEYWORDS["us_govt"] + + KEYWORDS["electricity"] + + KEYWORDS["environment"] + ) + ), + "license_raw": LICENSES["us-govt"], + "license_pudl": LICENSES["cc-by-4.0"], + }, "eiawater": { "title": "EIA Thermoelectric Cooling Water", "path": "https://www.eia.gov/electricity/data/water", @@ -198,7 +231,7 @@ "Hourly CO2, SO2, NOx emissions and gross load." ), "source_file_dict": { - "respondents": "Coal and high-sulfur fueled plants", + "respondents": "Coal and high-sulfur fueled plants over 25MW", "records_liberated": "~800 million", "source_format": "Comma Separated Value (.csv)", }, From b3d5fda1a7150152762c94d084ea1c190783b893 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Sat, 10 Sep 2022 14:35:39 -0500 Subject: [PATCH 67/80] Add new DOIs for EIA Bulk Electricity data archives. --- src/pudl/workspace/datastore.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/pudl/workspace/datastore.py b/src/pudl/workspace/datastore.py index 0f9f03aea6..3bde88d1c7 100644 --- a/src/pudl/workspace/datastore.py +++ b/src/pudl/workspace/datastore.py @@ -152,6 +152,7 @@ class ZenodoFetcher: "eia860m": "10.5072/zenodo.926659", "eia861": "10.5072/zenodo.687052", "eia923": "10.5072/zenodo.1090056", + "eia_bulk_elec": "10.5072/zenodo.1103572", "epacamd_eia": "10.5072/zenodo.1103224", "epacems": "10.5072/zenodo.672963", "ferc1": "10.5072/zenodo.926302", @@ -163,6 +164,7 @@ class ZenodoFetcher: "eia860m": "10.5281/zenodo.6929086", "eia861": "10.5281/zenodo.5602102", "eia923": "10.5281/zenodo.7003886", + "eia_bulk_elec": "10.5281/zenodo.7067367", "epacamd_eia": "10.5281/zenodo.7063255", "epacems": "10.5281/zenodo.6910058", "ferc1": "10.5281/zenodo.5534788", From aa83e18c6d6df18e8969c5f32578444c5f3969d3 Mon Sep 17 00:00:00 2001 From: Zane Selvans Date: Sat, 10 Sep 2022 15:45:31 -0500 Subject: [PATCH 68/80] Add release notes on addition of EIA Bulk Electricity data. --- docs/release_notes.rst | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/docs/release_notes.rst b/docs/release_notes.rst index d6305c6470..0b8c8ba9f0 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -2,14 +2,23 @@ PUDL Release Notes ======================================================================================= -.. _release-v2022.08.XX: +.. _release-v2022.09.XX: --------------------------------------------------------------------------------------- -2022.08.XX +2022.09.XX --------------------------------------------------------------------------------------- Data Coverage ^^^^^^^^^^^^^ +* Added archives of the bulk EIA electricity API data to our datastore, since the API + itself is too unreliable for production use. This is part of :issue:`1763`. The code + for this new data is ``eia_bulk_elec`` and the data comes as a single 200MB zipped + JSON file. :pr:`1922` updates the datastore to include + `this archive on Zenodo `__ but most of the work + happened in the + `pudl-scrapers `__ and + `pudl-zenodo-storage `__ + repositories. See issue :issue:`catalyst-cooperative/pudl-zenodo-storage#29`. * Incorporated 2021 data from the :doc:`data_sources/epacems` dataset. See :pr:`1778` * Incorporated 2021 data from the :doc:`data_sources/eia860` and :doc:`data_sources/eia923`. Early Release. Early release data is EIA's preliminary From 016010f8c93e1c78b6eb143f1f2fc9764ef08ff6 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Mon, 12 Sep 2022 11:01:22 -0600 Subject: [PATCH 69/80] Convert last references to old crosswalk name Mostly convert docs or logging references to EPACEMS-EIA crosswalk to EPACAMD-EIA Crosswalk. The larger changes include renaming the validation test module and deleting the old epa_crosswalk analysis module that we don't use anymore now that the crosswalk is in the db. --- .../play_with_cems_crosswalk.ipynb | 11 +- src/pudl/analysis/epa_crosswalk.py | 273 ------------------ src/pudl/etl.py | 4 +- src/pudl/glue/epacamd_eia.py | 7 +- ..._crosswalk_test.py => epacamd_eia_test.py} | 2 - 5 files changed, 11 insertions(+), 286 deletions(-) delete mode 100644 src/pudl/analysis/epa_crosswalk.py rename test/validate/{epacamd_eia_crosswalk_test.py => epacamd_eia_test.py} (84%) diff --git a/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb b/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb index d639a537a0..92c3b5f614 100644 --- a/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb +++ b/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb @@ -90,7 +90,6 @@ "cell_type": "markdown", "id": "b9e6577c-75b7-496e-a2a6-f0b02499a04a", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -99,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "75a7507d-b26f-4865-9077-f6a24a7dedc6", "metadata": { "tags": [] @@ -109,16 +108,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Retrieving https://zenodo.org/api/deposit/depositions/6633770 from zenodo\n", - "Retrieving https://zenodo.org/api/files/4f9ac0dc-a9b4-4d2b-9e0c-c0e97a2fc7f6/datapackage.json from zenodo\n", - "Retrieving https://zenodo.org/api/files/4f9ac0dc-a9b4-4d2b-9e0c-c0e97a2fc7f6/epacems_unitid_eia_plant_crosswalk.zip from zenodo\n", + "Retrieving https://zenodo.org/api/deposit/depositions/7063255 from zenodo\n", + "Retrieving https://zenodo.org/api/files/afca4bd7-d94f-4af3-b1f3-abaeaec6fa68/datapackage.json from zenodo\n", + "Retrieving https://zenodo.org/api/files/afca4bd7-d94f-4af3-b1f3-abaeaec6fa68/epacamd_eia.zip from zenodo\n", "Cleaning up the epacems-eia crosswalk\n" ] } ], "source": [ "from pudl.workspace.datastore import Datastore\n", - "from pudl.glue.epacamd_eia_crosswalk import extract, transform\n", + "from pudl.glue.epacamd_eia import extract, transform\n", "ds = Datastore()\n", "gens_ent = pd.read_sql('generators_entity_eia', pudl_engine)\n", "boiler_ent = pd.read_sql('boilers_entity_eia', pudl_engine)\n", diff --git a/src/pudl/analysis/epa_crosswalk.py b/src/pudl/analysis/epa_crosswalk.py deleted file mode 100644 index dbb89a59be..0000000000 --- a/src/pudl/analysis/epa_crosswalk.py +++ /dev/null @@ -1,273 +0,0 @@ -"""Use the EPA crosswalk to connect EPA units to EIA generators and other data. - -A major use case for this dataset is to identify subplants within plant_ids, which are -the smallest coherent units for aggregation. Despite the name, plant_id refers to a -legal entity that often contains multiple distinct power plants, even of different -technology or fuel types. - -EPA CEMS data combines information from several parts of a power plant: - -* emissions from smokestacks -* fuel use from combustors -* electricty production from generators - -But smokestacks, combustors, and generators can be connected in complex, many-to-many -relationships. This complexity makes attribution difficult for, as an example, -allocating pollution to energy producers. Furthermore, heterogeneity within plant_ids -make aggregation to the parent entity difficult or inappropriate. - -But by analyzing the relationships between combustors and generators, as provided in the -EPA/EIA crosswalk, we can identify distinct power plants. These are the smallest -coherent units of aggregation. - -In graph analysis terminology, the crosswalk is a list of edges between nodes -(combustors and generators) in a bipartite graph. The networkx python package provides -functions to analyze this edge list and extract disjoint subgraphs (groups of combustors -and generators that are connected to each other). These are the distinct power plants. -To avoid a name collision with plant_id, we term these collections 'subplants', and -identify them with a subplant_id that is unique within each plant_id. Subplants are thus -identified with the composite key (plant_id, subplant_id). - -Through this analysis, we found that 56% of plant_ids contain multiple distinct -subplants, and 11% contain subplants with different technology types, such as a gas -boiler and gas turbine (not in a combined cycle). - -Usage Example: - -epacems = pudl.output.epacems.epacems(states=['ID']) # small subset for quick test -epa_crosswalk_df = pudl.output.epacems.epa_crosswalk() -filtered_crosswalk = filter_crosswalk(epa_crosswalk_df, epacems) -crosswalk_with_subplant_ids = make_subplant_ids(filtered_crosswalk) -""" -import dask.dataframe as dd -import networkx as nx -import pandas as pd - - -def _get_unique_keys(epacems: pd.DataFrame | dd.DataFrame) -> pd.DataFrame: - """Get unique unit IDs from CEMS data. - - Args: - epacems: dataset from :func:`pudl.output.epacems.epacems` - - Returns: - Unique keys from the epacems dataset. - - """ - # The purpose of this function is mostly to resolve the - # ambiguity between dask and pandas dataframes - ids = epacems[["plant_id_eia", "unitid", "unit_id_epa"]].drop_duplicates() - if isinstance(epacems, dd.DataFrame): - ids = ids.compute() - return ids - - -def filter_crosswalk_by_epacems( - crosswalk: pd.DataFrame, epacems: pd.DataFrame | dd.DataFrame -) -> pd.DataFrame: - """Inner join unique CEMS units with the EPA crosswalk. - - This is essentially an empirical filter on EPA units. Instead of filtering by - construction/retirement dates in the crosswalk (thus assuming they are accurate), - use the presence/absence of CEMS data to filter the units. - - Args: - crosswalk: the EPA crosswalk, as from pudl.output.epacems.epa_crosswalk() - unique_epacems_ids: unique ids from _get_unique_keys - - Returns: - The inner join of the EPA crosswalk and unique epacems units. Adds the global ID - column unit_id_epa. - - """ - unique_epacems_ids = _get_unique_keys(epacems) - key_map = unique_epacems_ids.merge( - crosswalk, - left_on=["plant_id_eia", "unitid"], - right_on=["CAMD_PLANT_ID", "CAMD_UNIT_ID"], - how="inner", - ) - return key_map - - -def filter_out_unmatched(crosswalk: pd.DataFrame) -> pd.DataFrame: - """Remove unmatched or excluded (non-exporting) units. - - Unmatched rows are limitations of the completeness of the EPA crosswalk itself, not - of PUDL. - - Args: - crosswalk: the EPA crosswalk, as from :func:`pudl.output.epacems.epa_crosswalk` - - Returns: - The EPA crosswalk with unmatched units removed. - """ - bad = crosswalk["MATCH_TYPE_GEN"].isin({"CAMD Unmatched", "Manual CAMD Excluded"}) - return crosswalk.loc[~bad].copy() - - -def filter_out_boiler_rows(crosswalk: pd.DataFrame) -> pd.DataFrame: - """Remove rows that represent graph edges between generators and boilers. - - Args: - crosswalk: the EPA crosswalk, as from :func:`pudl.output.epacems.epa_crosswalk` - - Returns: - The EPA crosswalk with boiler rows (many/one-to-many) removed - """ - crosswalk = crosswalk.drop_duplicates( - subset=["CAMD_PLANT_ID", "CAMD_UNIT_ID", "EIA_GENERATOR_ID"] - ) - return crosswalk - - -def _prep_for_networkx(crosswalk: pd.DataFrame) -> pd.DataFrame: - """Make surrogate keys for combustors and generators. - - Args: - crosswalk: EPA crosswalk, as from :func:`pudl.output.epacems.epa_crosswalk` - - Returns: - A copy of EPA crosswalk with new surrogate ID columns 'combustor_id' and - 'generator_id' - """ - prepped = crosswalk.copy() - # networkx can't handle composite keys, so make surrogates - prepped["combustor_id"] = prepped.groupby( - by=["CAMD_PLANT_ID", "CAMD_UNIT_ID"] - ).ngroup() - # node IDs can't overlap so add (max + 1) - prepped["generator_id"] = ( - prepped.groupby(by=["CAMD_PLANT_ID", "EIA_GENERATOR_ID"]).ngroup() - + prepped["combustor_id"].max() - + 1 - ) - return prepped - - -def _subplant_ids_from_prepped_crosswalk(prepped: pd.DataFrame) -> pd.DataFrame: - """Use graph analysis to create global subplant IDs from a crosswalk edge list. - - Args: - prepped: an EPA crosswalk that has passed through :func:`_prep_for_networkx` - - Returns: - A copy of EPA crosswalk plus new column 'global_subplant_id' - """ - graph = nx.from_pandas_edgelist( - prepped, - source="combustor_id", - target="generator_id", - edge_attr=True, - ) - for i, node_set in enumerate(nx.connected_components(graph)): - subgraph = graph.subgraph(node_set) - assert nx.algorithms.bipartite.is_bipartite( - subgraph - ), f"non-bipartite: i={i}, node_set={node_set}" - nx.set_edge_attributes(subgraph, name="global_subplant_id", values=i) - return nx.to_pandas_edgelist(graph) - - -def _convert_global_id_to_composite_id( - crosswalk_with_ids: pd.DataFrame, -) -> pd.DataFrame: - """Convert global_subplant_id to a composite key (CAMD_PLANT_ID, subplant_id). - - The composite key will be much more stable (though not fully stable!) in time. - The global ID changes if ANY unit or generator changes, whereas the - compound key only changes if units/generators change within that specific plant. - - A global ID could also tempt users into using it as a crutch, even though it isn't - stable. A compound key should discourage that behavior. - - Args: - crosswalk_with_ids: crosswalk with global_subplant_id, as from - :func:`_subplant_ids_from_prepped_crosswalk` - - Raises: - ValueError: if crosswalk_with_ids has a MultiIndex - - Returns: - A copy of crosswalk_with_ids with an added column: 'subplant_id' - """ - if isinstance(crosswalk_with_ids.index, pd.MultiIndex): - raise ValueError( - "Input crosswalk must have single level index. " - f"Given levels: {crosswalk_with_ids.index.names}" - ) - - reindexed = crosswalk_with_ids.reset_index() # copy - idx_name = crosswalk_with_ids.index.name - if idx_name is None: - # Indices with no name (None) are set to a pandas default name ('index'), which - # could (though probably won't) change. - idx_col = reindexed.columns.symmetric_difference(crosswalk_with_ids.columns)[ - 0 - ] # get index name - else: - idx_col = idx_name - - composite_key: pd.Series = reindexed.groupby("CAMD_PLANT_ID", as_index=False).apply( - lambda x: x.groupby("global_subplant_id").ngroup() - ) - - # Recombine. Could use index join but I chose to reindex, sort and assign. - # Errors like mismatched length will raise exceptions, which is good. - - # drop the outer group, leave the reindexed row index - composite_key.reset_index(level=0, drop=True, inplace=True) - composite_key.sort_index(inplace=True) # put back in same order as reindexed - reindexed["subplant_id"] = composite_key - # restore original index - reindexed.set_index(idx_col, inplace=True) # restore values - reindexed.index.rename(idx_name, inplace=True) # restore original name - return reindexed - - -def filter_crosswalk( - crosswalk: pd.DataFrame, epacems: pd.DataFrame | dd.DataFrame -) -> pd.DataFrame: - """Remove irrelevant or duplicated rows from the crosswalk. - - Remove crosswalk rows that do not correspond to an EIA facility or are duplicated - due to many-to-many boiler relationships. - - Args: - crosswalk: The EPA/EIA crosswalk from :func:`pudl.output.epacems.epa_crosswalk` - epacems: Emissions data. Must contain columns named - ["plant_id_eia", "unitid", "unit_id_epa"] - - Returns: - A filtered copy of EPA crosswalk. - """ - filtered_crosswalk = filter_out_unmatched(crosswalk) - filtered_crosswalk = filter_out_boiler_rows(filtered_crosswalk) - key_map = filter_crosswalk_by_epacems(filtered_crosswalk, epacems) - return key_map - - -def make_subplant_ids(crosswalk: pd.DataFrame) -> pd.DataFrame: - """Identify sub-plants in the EPA/EIA crosswalk graph. - - Any row filtering should be done before this step. - - Usage Example: - - epacems = pudl.output.epacems.epacems(states=['ID']) # small subset for quick test - epa_crosswalk_df = pudl.output.epacems.epa_crosswalk() - filtered_crosswalk = filter_crosswalk(epa_crosswalk_df, epacems) - crosswalk_with_subplant_ids = make_subplant_ids(filtered_crosswalk) - - Args: - crosswalk: The EPA/EIA crosswalk, from :func:`pudl.output.epacems.epa_crosswalk` - - Returns: - An edge list connecting EPA units to EIA generators, with connected pieces - issued a subplant_id - """ - edge_list = _prep_for_networkx(crosswalk) - edge_list = _subplant_ids_from_prepped_crosswalk(edge_list) - edge_list = _convert_global_id_to_composite_id(edge_list) - column_order = ["subplant_id"] + list(crosswalk.columns) - return edge_list[column_order] # reorder and drop global_subplant_id diff --git a/src/pudl/etl.py b/src/pudl/etl.py index aa4df1f430..e778836dce 100644 --- a/src/pudl/etl.py +++ b/src/pudl/etl.py @@ -253,7 +253,7 @@ def etl_epacems( # Verify that we have a PUDL DB with crosswalk data if "epacamd_eia" not in inspector.get_table_names(): raise RuntimeError( - "No EPA-EIA Crosswalk available in the PUDL DB! Have you run the ETL? " + "No EPACAMD-EIA Crosswalk available in the PUDL DB! Have you run the ETL? " f"Trying to access PUDL DB: {pudl_engine}" ) @@ -344,7 +344,7 @@ def _etl_glue( ds_kwargs: Keyword arguments for instantiating a PUDL datastore, so that the ETL can access the raw input data. sqlite_dfs: The dictionary of dataframes to be loaded into the pudl database. - We pass the dictionary though because the EPACEMS-EIA crosswalk needs to + We pass the dictionary though because the EPACAMD-EIA crosswalk needs to know which EIA plants and generators are being loaded into the database (based on whether we run the full or fast etl). The tests will break if we pass the generators_entity_eia table as an argument because of the diff --git a/src/pudl/glue/epacamd_eia.py b/src/pudl/glue/epacamd_eia.py index 4fd8aaeeb6..969a73fb31 100644 --- a/src/pudl/glue/epacamd_eia.py +++ b/src/pudl/glue/epacamd_eia.py @@ -19,6 +19,7 @@ def extract(ds: Datastore) -> pd.DataFrame: """Extract the EPACAMD-EIA Crosswalk from the Datastore.""" + logger.info("Extracting the EPACAMD-EIA crosswalk from Zenodo") with ds.get_zipfile_resource("epacamd_eia", name="epacamd_eia.zip").open( "camd-eia-crosswalk-master/epa_eia_crosswalk.csv" ) as f: @@ -44,7 +45,7 @@ def transform( Returns: A dictionary containing the cleaned EPACAMD-EIA crosswalk DataFrame. """ - logger.info("Cleaning up the epacems-eia crosswalk") + logger.info("Transforming the EPACAMD-EIA crosswalk") column_rename = { "camd_plant_id": "plant_id_epa", @@ -79,8 +80,8 @@ def transform( # discrepancies. if not processing_all_eia_years: logger.info( - "Selected subset of avilable EIA years--restricting EIA-EPA Crosswalk to \ - chosen subset of EIA years" + "Selected subset of avilable EIA years--restricting EPACAMD-EIA Crosswalk \ + to chosen subset of EIA years" ) crosswalk_clean = pd.merge( crosswalk_clean, diff --git a/test/validate/epacamd_eia_crosswalk_test.py b/test/validate/epacamd_eia_test.py similarity index 84% rename from test/validate/epacamd_eia_crosswalk_test.py rename to test/validate/epacamd_eia_test.py index aebea3f90f..32187a8062 100644 --- a/test/validate/epacamd_eia_crosswalk_test.py +++ b/test/validate/epacamd_eia_test.py @@ -12,8 +12,6 @@ def test_unique_ids(pudl_out_eia, live_dbs): """Test whether the EIA plants and EPA unit pairings are unique.""" if not live_dbs: pytest.skip("Data validation only works with a live PUDL DB.") - if pudl_out_eia.freq is not None: - pytest.skip("Test should only run on un-aggregated data.") # Should I add these args to the pudl.validate module? check_unique_rows( pudl_out_eia.epacamd_eia, From 474b2ed862149c190a01524820ff661e28c5b13e Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 12:09:54 -0600 Subject: [PATCH 70/80] Add boilers_entity_eia to epacamd_eia transform docstring --- src/pudl/glue/epacamd_eia.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/pudl/glue/epacamd_eia.py b/src/pudl/glue/epacamd_eia.py index 969a73fb31..1bb3829213 100644 --- a/src/pudl/glue/epacamd_eia.py +++ b/src/pudl/glue/epacamd_eia.py @@ -37,6 +37,7 @@ def transform( Args: epacamd_eia: The result of running this module's extract() function. generators_entity_eia: The generators_entity_eia table. + boilers_entity_eia: The boilers_entitiy_eia table. processing_all_years: A boolean indicating whether the years from the Eia860Settings object match the EIA860 working partitions. This indicates whether or not to restrict the crosswalk data so the tests don't fail on From a23beb7a6556d0377d2806bf1bb0ec5439e36c00 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 12:13:08 -0600 Subject: [PATCH 71/80] Describe need for epacamd_eia crosswalk restriction for tests in transform docstrings instead of in comments --- src/pudl/glue/epacamd_eia.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/src/pudl/glue/epacamd_eia.py b/src/pudl/glue/epacamd_eia.py index 1bb3829213..694edd0d6d 100644 --- a/src/pudl/glue/epacamd_eia.py +++ b/src/pudl/glue/epacamd_eia.py @@ -34,6 +34,16 @@ def transform( ) -> dict[str, pd.DataFrame]: """Clean up the EPACAMD-EIA Crosswalk file. + The crosswalk is a static file: there is no year field. The plant_id_eia and + generator_id fields, however, are foreign keys from an annualized table. If the + fast ETL is run (on one year of data) the test will break because the crosswalk + tables with plant_id_eia and generator_id contain values from various years. To + keep the crosswalk in alignment with the available eia data, we'll restrict it + based on the generator entity table which has plant_id_eia and generator_id so long + as it's not using the full suite of avilable years. If it is, we don't want to + restrict the crosswalk so we can get warnings and errors from any foreign key + discrepancies. This isn't an ideal solution, but it works for now. + Args: epacamd_eia: The result of running this module's extract() function. generators_entity_eia: The generators_entity_eia table. @@ -70,15 +80,7 @@ def transform( .dropna(subset=["plant_id_eia"]) ) - # The crosswalk is a static file: there is no year field. The plant_id_eia and - # generator_id fields, however, are foreign keys from an annualized table. If the - # fast ETL is run (on one year of data) the test will break because the crosswalk - # tables with plant_id_eia and generator_id contain values from various years. To - # keep the crosswalk in alignment with the available eia data, we'll restrict it - # based on the generator entity table which has plant id and generator id so long - # as it's not using the full suite of avilable years. If it is, we don't want to - # restrict the crosswalk so we can get warnings and errors from any foreign key - # discrepancies. + # Restrict crosswalk for tests if running fast etl if not processing_all_eia_years: logger.info( "Selected subset of avilable EIA years--restricting EPACAMD-EIA Crosswalk \ From e4cece8b0c8dd676e95e3293147ec5c1ad0d47c0 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 12:52:48 -0600 Subject: [PATCH 72/80] Remove if statement in remove_leading_zeros_from_numeric_strings helper function so it fails if the column it's supposed to fix isn't there --- src/pudl/helpers.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/src/pudl/helpers.py b/src/pudl/helpers.py index bc2c184a18..8b74e40fb1 100644 --- a/src/pudl/helpers.py +++ b/src/pudl/helpers.py @@ -893,15 +893,14 @@ def remove_leading_zeros_from_numeric_strings( column. """ - if col_name in df.columns: - leading_zeros = df[col_name].str.contains(r"^0+\d+$").fillna(False) - if leading_zeros.any(): - logger.debug(f"Fixing leading zeros in {col_name} column") - df.loc[leading_zeros, col_name] = df[col_name].str.replace( - r"^0+", "", regex=True - ) - else: - logger.debug(f"Found no numeric leading zeros in {col_name}") + leading_zeros = df[col_name].str.contains(r"^0+\d+$").fillna(False) + if leading_zeros.any(): + logger.debug(f"Fixing leading zeros in {col_name} column") + df.loc[leading_zeros, col_name] = df[col_name].str.replace( + r"^0+", "", regex=True + ) + else: + logger.debug(f"Found no numeric leading zeros in {col_name}") return df From e3312b425a901f1ad74dce7858a5292c411e87f0 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 13:06:35 -0600 Subject: [PATCH 73/80] Add release notes for this PR --- docs/release_notes.rst | 38 +++++++++++++++++++++++++++++++++----- 1 file changed, 33 insertions(+), 5 deletions(-) diff --git a/docs/release_notes.rst b/docs/release_notes.rst index 817ed706d6..dba98d9c70 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -31,8 +31,16 @@ Data Coverage batteries) and ``net_capacity_mwdc`` (for behind-the-meter solar PV) attributes to the :ref:`generators_eia860` table, as they appear in the :doc:`data_sources/eia860` monthly updates for 2022. -* We've integrated several new columns into the EIA 860 and EIA 923 including several +* Integrated several new columns into the EIA 860 and EIA 923 including several codes with coding tables (See :doc:`data_dictionaries/codes_and_labels`). :pr:`1836` +* Added the `EPACAMD-EIA Crosswalk `__` to + the database. Previously, the crosswalk was a csv stored in ``package_data/glue``, + but now it has its own scraper + :pr:`https://github.com/catalyst-cooperative/pudl-scrapers/pull/20`, archiver, + :pr:`https://github.com/catalyst-cooperative/pudl-zenodo-storage/pull/20` + and place in the PUDL db. For now there's a ``epacamd_eia`` output table you can use + to merge CEMS and EIA data yourself :pr:`1692`. Eventually we'll work these crosswalk + values into an output table combining CEMS and EIA. Nightly Data Builds ^^^^^^^^^^^^^^^^^^^ @@ -92,10 +100,18 @@ Database Schema Changes non-standard codes, and fixing some reporting errors for ``PACW`` vs. ``PACE`` (PacifiCorp West vs. East) based on the state associated with the plant reporting the code. Also added backfilling for codes in years before 2013 when BA Codes first - started being reported), but only in the output tables. See: :pr:`1906,1911` - -Date Merge Helper Function -^^^^^^^^^^^^^^^^^^^^^^^^^^ + started being reported, but only in the output tables. See: :pr:`1906,1911` +* Changed and removed some columns in the :doc:`data_sources/epacems` dataset. + ``unitid`` was changed to ``emissions_unit_id_epa`` to clarify the type of unit it + represents. ``plant_id_eia`` was supplemented with values from the newly integrated + ``epacamd_eia`` crosswalk as not all EPA's ORISPL codes are correct. ``unit_id_epa`` + was removed because it is a unique identifyer for ``emissions_unit_id_epa`` and not + otherwise useful or transferable to other datasets. ``facility_id`` was removed + because it is specific to EPA's internal database and does not aid in connection with + other data. :pr:`1692` + +Helper Function Updates +^^^^^^^^^^^^^^^^^^^^^^^ * Replaced the PUDL helper function ``clean_merge_asof`` that merged two dataframes reported on different temporal granularities, for example monthly vs yearly data. The reworked function, :mod:`pudl.helpers.date_merge`, is more encapsulating and @@ -110,6 +126,10 @@ Date Merge Helper Function makes this function optionally used to generate the MCOE table that includes a full monthly timeseries even in years when annually reported generators don't have matching monthly data. See :pr:`1550` +* Updated the ``fix_leading_zero_gen_ids`` fuction by changing the name to + ``remove_leading_zeros_from_numeric_strings`` because it's used to fix more than just + the ``generator_id`` column. Included a new argument to specify which column you'd + like to fix. Plant Parts List Module Changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -138,6 +158,14 @@ Metadata * Used the data source metadata class added in release 0.6.0 to dynamically generate the data source documentation (See :doc:`data_sources/index`). :pr:`1532` +Documentation +^^^^^^^^^^^^^ +* Fixed broken links in the documentation since the Air Markets Program Data (AMPD) + changed to Clean Air Markets Data (CAMD). +* Added graphics and clearer descriptions of EPA data and reporting requirements to the + :doc:`data_sources/epacems` page. Also included information about the ``epacamd_eia`` + crosswalk. + Bug Fixes ^^^^^^^^^ * `Dask v2022.4.2 `__ From 27405f453331e281f45ff601620c59f5e9621001 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 13:45:11 -0600 Subject: [PATCH 74/80] Update the docstring in to differentiate it from the module --- src/pudl/metadata/resources/pudl.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/pudl/metadata/resources/pudl.py b/src/pudl/metadata/resources/pudl.py index 17ea412764..fe5ac8b5bb 100644 --- a/src/pudl/metadata/resources/pudl.py +++ b/src/pudl/metadata/resources/pudl.py @@ -1,4 +1,8 @@ -"""Definitions for the glue/crosswalk tables that connect data groups.""" +"""Definitions for the connection between PUDL-specific IDs and other datasets. + +Most of this is compiled from handmapping records. + +""" from typing import Any RESOURCE_METADATA: dict[str, dict[str, Any]] = { From 3903e253b90834fb7732f23ad3c1a516f52de809 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 14:04:41 -0600 Subject: [PATCH 75/80] Add release notes explaining removal of fillna(0) for cems fields --- docs/release_notes.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/release_notes.rst b/docs/release_notes.rst index dba98d9c70..7947e3436e 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -110,6 +110,12 @@ Database Schema Changes because it is specific to EPA's internal database and does not aid in connection with other data. :pr:`1692` +Data Accuracy +^^^^^^^^^^^^^ +* Retain NA values for :doc:`data_sources/epacems` fields ``gross_load_mw`` and + ``heat_content_mmbtu``. Previously, these fields converted NA to 0, but this is not + accurate, so we removed this step. + Helper Function Updates ^^^^^^^^^^^^^^^^^^^^^^^ * Replaced the PUDL helper function ``clean_merge_asof`` that merged two dataframes From acb5548e999aa85fda53a84684ce6e0ac06adbac Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 14:11:59 -0600 Subject: [PATCH 76/80] Move release note about changing the contents of the plant_id_Eia field in CEMS to the data accuracy section --- docs/release_notes.rst | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/release_notes.rst b/docs/release_notes.rst index 7947e3436e..1be1ca53eb 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -101,20 +101,21 @@ Database Schema Changes (PacifiCorp West vs. East) based on the state associated with the plant reporting the code. Also added backfilling for codes in years before 2013 when BA Codes first started being reported, but only in the output tables. See: :pr:`1906,1911` -* Changed and removed some columns in the :doc:`data_sources/epacems` dataset. +* Renamed and removed some columns in the :doc:`data_sources/epacems` dataset. ``unitid`` was changed to ``emissions_unit_id_epa`` to clarify the type of unit it - represents. ``plant_id_eia`` was supplemented with values from the newly integrated - ``epacamd_eia`` crosswalk as not all EPA's ORISPL codes are correct. ``unit_id_epa`` - was removed because it is a unique identifyer for ``emissions_unit_id_epa`` and not - otherwise useful or transferable to other datasets. ``facility_id`` was removed - because it is specific to EPA's internal database and does not aid in connection with - other data. :pr:`1692` + represents. ``unit_id_epa`` was removed because it is a unique identifyer for + ``emissions_unit_id_epa`` and not otherwise useful or transferable to other datasets. + ``facility_id`` was removed because it is specific to EPA's internal database and does + not aid in connection with other data. :pr:`1692` Data Accuracy ^^^^^^^^^^^^^ * Retain NA values for :doc:`data_sources/epacems` fields ``gross_load_mw`` and ``heat_content_mmbtu``. Previously, these fields converted NA to 0, but this is not accurate, so we removed this step. +* Update the ``plant_id_eia`` field from :doc:`data_sources/epacems` with values from + the newly integrated ``epacamd_eia`` crosswalk as not all EPA's ORISPL codes are + correct. Helper Function Updates ^^^^^^^^^^^^^^^^^^^^^^^ From 9451f533a385317fd52235fd69964048e3fd9ebe Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 15:54:51 -0600 Subject: [PATCH 77/80] Add if generator_id in df.columns statement to each of the extract modules for eia860, eia860m, eia861, and eia923 that use the function so that it will still work. Kept this if outside the function so that it would catch columns that were miss-entered when used in other contexts (like for epacems) --- src/pudl/extract/eia860.py | 6 +++++- src/pudl/extract/eia860m.py | 6 +++++- src/pudl/extract/eia861.py | 6 +++++- src/pudl/extract/eia923.py | 6 +++++- 4 files changed, 20 insertions(+), 4 deletions(-) diff --git a/src/pudl/extract/eia860.py b/src/pudl/extract/eia860.py index 3f4d8448c2..6ea554a733 100644 --- a/src/pudl/extract/eia860.py +++ b/src/pudl/extract/eia860.py @@ -41,7 +41,11 @@ def process_raw(self, df, page, **partition): if "report_year" not in df.columns: df["report_year"] = list(partition.values())[0] self.cols_added = ["report_year"] - df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") + # Eventually we should probably make this a transform + if "generator_id" in df.columns: + df = remove_leading_zeros_from_numeric_strings( + df=df, col_name="generator_id" + ) df = self.add_data_maturity(df, page, **partition) return df diff --git a/src/pudl/extract/eia860m.py b/src/pudl/extract/eia860m.py index 505d4c9752..6c836fd3e0 100644 --- a/src/pudl/extract/eia860m.py +++ b/src/pudl/extract/eia860m.py @@ -47,7 +47,11 @@ def process_raw(self, df, page, **partition): ).year df = self.add_data_maturity(df, page, **partition) self.cols_added.append("report_year") - df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") + # Eventually we should probably make this a transform + if "generator_id" in df.columns: + df = remove_leading_zeros_from_numeric_strings( + df=df, col_name="generator_id" + ) return df def extract(self, settings: Eia860Settings = Eia860Settings()): diff --git a/src/pudl/extract/eia861.py b/src/pudl/extract/eia861.py index 584d4f5c83..6715a8644d 100644 --- a/src/pudl/extract/eia861.py +++ b/src/pudl/extract/eia861.py @@ -46,7 +46,11 @@ def process_raw(self, df, page, **partition): ) ) self.cols_added = [] - df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") + # Eventually we should probably make this a transform + if "generator_id" in df.columns: + df = remove_leading_zeros_from_numeric_strings( + df=df, col_name="generator_id" + ) df = self.add_data_maturity(df, page, **partition) return df diff --git a/src/pudl/extract/eia923.py b/src/pudl/extract/eia923.py index af986d4c4d..b4f943b68c 100644 --- a/src/pudl/extract/eia923.py +++ b/src/pudl/extract/eia923.py @@ -41,7 +41,11 @@ def process_raw(self, df, page, **partition): df.drop(to_drop, axis=1, inplace=True) df = df.rename(columns=self._metadata.get_column_map(page, **partition)) self.cols_added = [] - df = remove_leading_zeros_from_numeric_strings(df=df, col_name="generator_id") + # Eventually we should probably make this a transform + if "generator_id" in df.columns: + df = remove_leading_zeros_from_numeric_strings( + df=df, col_name="generator_id" + ) # the 2021 early release data had some ding dang "."'s and nulls in the year column if "report_year" in df.columns: mask = (df.report_year == ".") | df.report_year.isnull() From 481738b2e1c1fea55f67c715cf101d613deb1912 Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 15:58:26 -0600 Subject: [PATCH 78/80] Remove notebooks used to better understand crosswalk --- .../work-in-progress/Combine_CEMS_EIA.ipynb | 1091 --- .../play_with_cems_crosswalk.ipynb | 7009 ----------------- 2 files changed, 8100 deletions(-) delete mode 100644 notebooks/work-in-progress/Combine_CEMS_EIA.ipynb delete mode 100644 notebooks/work-in-progress/play_with_cems_crosswalk.ipynb diff --git a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb b/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb deleted file mode 100644 index b524b8ed16..0000000000 --- a/notebooks/work-in-progress/Combine_CEMS_EIA.ipynb +++ /dev/null @@ -1,1091 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b4f6db74-0ad8-4418-bfc3-fdc12a07d750", - "metadata": { - "tags": [] - }, - "source": [ - "# CEMS-to-EIA Allocater" - ] - }, - { - "cell_type": "markdown", - "id": "55bad16e-25d6-499b-8d1e-75a4c436356a", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "eb7fcab6-4d89-4950-946f-4232be6341a8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import pudl\n", - "import pandas as pd\n", - "import logging\n", - "import sys\n", - "import sqlalchemy as sa\n", - "import dask.dataframe as dd\n", - "\n", - "# basic setup for logging\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.INFO)\n", - "handler = logging.StreamHandler(stream=sys.stdout)\n", - "formatter = logging.Formatter('%(message)s')\n", - "handler.setFormatter(formatter)\n", - "logger.handlers = [handler]" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "c76104c9-e83f-46ac-b5f3-ad5908a6af01", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pudl_settings = pudl.workspace.setup.get_defaults()\n", - "pudl_engine = sa.create_engine(pudl_settings[\"pudl_db\"])\n", - "pudl_out = pudl.output.pudltabl.PudlTabl(pudl_engine,freq='AS')" - ] - }, - { - "cell_type": "markdown", - "id": "1e3c1eb4-bc59-40f7-96ad-088718e5d620", - "metadata": { - "tags": [] - }, - "source": [ - "#### Load CEMS" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "e25d8b80-47b2-4e54-91e7-f2e674fc72bd", - "metadata": {}, - "outputs": [], - "source": [ - "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", - "cems_dd = dd.read_parquet(\n", - " epacems_path, \n", - " columns=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\", \"co2_mass_tons\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "132c57f5-90f7-4f2b-a07b-647e4dba9c36", - "metadata": {}, - "outputs": [], - "source": [ - "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"]).sum().compute().reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "250d5ad8-06d4-4fb3-9a83-2866fc151a3c", - "metadata": {}, - "outputs": [], - "source": [ - "cems_df[\"plant_id_eia\"] = cems_df.plant_id_eia.astype(\"Int64\")\n", - "#cems_df[\"co2_mass_tons\"] = cems_df.co2_mass_tons.fillna(0)" - ] - }, - { - "cell_type": "markdown", - "id": "dda4c669-4a7d-4024-b661-e618e9247903", - "metadata": { - "tags": [] - }, - "source": [ - "#### Load EPA-EIA Crosswalk" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "59114182-0920-47fc-8cc9-6792b301fbef", - "metadata": {}, - "outputs": [], - "source": [ - "crosswalk_df = pudl_out.epacamd_eia()" - ] - }, - { - "cell_type": "markdown", - "id": "a520696a-5c4d-4353-85fc-194f8cc7386e", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "#### Load EIA Generators" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "adb143d7-14f8-4528-86e8-8e553ff6eda9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling technology type\n", - "Filled technology_type coverage now at 98.3%\n" - ] - } - ], - "source": [ - "eia_gens_df = pudl_out.gens_eia860()" - ] - }, - { - "cell_type": "markdown", - "id": "9f3a3006-88d9-4dff-a728-bd141b48eb54", - "metadata": { - "tags": [] - }, - "source": [ - "## Pre-Integration Stats\n", - "We don't expect all of the EIA plants to show up in CEMS because not all EIA plants are subject to the EPA's reporting requirements. The EIA plants we do expect to see in CEMS:\n", - "- Burn Fossil Fuels\n", - "- Have generators with more than 25MW of capacity\n", - "- Are utility-owned\n", - "- Are not retired\n", - "- Are not old, simple combustion turbine units" - ] - }, - { - "cell_type": "code", - "execution_count": 266, - "id": "9821e815-36ca-4aa1-aee2-34b7a8fe7f39", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PLANT STATS:\n", - "Total CEMS plants: 1831\n", - "CEMS plants NOT in crosswalk: 292 = 16 %\n", - "Total Crosswalk plants: 1542\n", - "\n", - "PLANT-GEN STATS:\n", - "Crosswalk plant gen pairs: 5297\n", - "EIA plant gen pairs: 36377\n", - "EIA plant gen pairs NOT in crosswalk: 31080 = 85 %\n" - ] - } - ], - "source": [ - "cems_plants = cems_df.plant_id_eia.unique().tolist()\n", - "crosswalk_plants = crosswalk_df.plant_id_eia.unique().tolist()\n", - "eia_plants = eia_gens_df.plant_id_eia.unique().tolist()\n", - "\n", - "print(\"PLANT STATS:\")\n", - "print(\"Total CEMS plants: \", len1:=len(cems_plants))\n", - "print(\"CEMS plants NOT in crosswalk: \", len2:=len([x for x in cems_plants if x not in crosswalk_plants]), \" = \", round(len2/len1*100), \"%\")\n", - "print(\"Total Crosswalk plants: \", len(crosswalk_plants))\n", - "print(\"\")\n", - "\n", - "\n", - "crosswalk_gen_plants = crosswalk_df.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"]).set_index([\"plant_id_eia\", \"generator_id\"])\n", - "eia_gen_plants = eia_gens_df.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"]).set_index([\"plant_id_eia\", \"generator_id\"])\n", - "\n", - "print(\"PLANT-GEN STATS:\")\n", - "print(\"Crosswalk plant gen pairs: \", len3:=len(crosswalk_gen_plants))\n", - "print(\"EIA plant gen pairs: \", len4:=len(eia_gen_plants))\n", - "print(\"EIA plant gen pairs NOT in crosswalk: \", len5:=len(eia_gen_plants.index.difference(crosswalk_gen_plants.index)), \" = \", round(len5/len4*100), \"%\")" - ] - }, - { - "cell_type": "markdown", - "id": "11e6c35f-bbf3-4d86-be30-d30a3fc25316", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Merge CEMS with EIA" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "id": "81b55d0b-1c16-42ae-b57c-914b2d46bb9b", - "metadata": {}, - "outputs": [], - "source": [ - "eia_gens_cems_merge = (\n", - " eia_gens_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"capacity_mw\", \"technology_description\", \"operational_status\"]].merge(\n", - " crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], \n", - " how=\"left\", \n", - " on=[\"plant_id_eia\", \"generator_id\"])\n", - " .assign(year=lambda x: x.report_date.dt.year.astype(\"Int64\"))\n", - " .merge(\n", - " cems_df,\n", - " how=\"left\",\n", - " on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6517696a-199b-46f5-9702-3ea23e5e4e1b", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "source": [ - "## Allocate CEMS Emissions to EIA Generators" - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "id": "b8205019-1c1b-4730-a983-f239f5def642", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def allocate_cols(\n", - " to_allocate: pd.DataFrame, by: list, data_and_allocator_cols: dict\n", - ") -> pd.DataFrame:\n", - " \"\"\"\n", - " Allocate larger dataset records porportionally by EIA plant-part columns.\n", - " Args:\n", - " to_allocate: table of data that has been merged with the EIA plant-parts\n", - " records of the scale that you want the output to be in.\n", - " by: columns to group by.\n", - " data_and_allocator_cols: dict of data columns that you want to allocate (keys)\n", - " and ordered list of columns to allocate porportionally based on. Values\n", - " ordered based on priority: if non-null result from frist column, result\n", - " will include first column result, then second and so on.\n", - " Returns:\n", - " an augmented version of ``to_allocate`` with the data columns (keys in\n", - " ``data_and_allocator_cols``) allocated proportionally.\n", - " \"\"\"\n", - " # add a total column for all of the allocate cols.\n", - " all_allocator_cols = list(set(sum(data_and_allocator_cols.values(), [])))\n", - " to_allocate.loc[:, [f\"{c}_total\" for c in all_allocator_cols]] = (\n", - " to_allocate.groupby(by=by, dropna=False)[all_allocator_cols]\n", - " .transform(sum, min_count=1)\n", - " .add_suffix(\"_total\")\n", - " )\n", - " # for each of the columns we want to allocate the frc data by\n", - " # generate the % of the total group, so we can allocate the data_col\n", - " to_allocate = to_allocate.assign(\n", - " **{\n", - " f\"{col}_proportion\": to_allocate[col] / to_allocate[f\"{col}_total\"]\n", - " for col in all_allocator_cols\n", - " }\n", - " )\n", - " # do the allocation for each of the data columns\n", - " for data_col in data_and_allocator_cols:\n", - " output_col = f\"{data_col}_allocated\"\n", - " to_allocate.loc[:, output_col] = pd.NA\n", - " # choose the first non-null option. The order of the allocate_cols will\n", - " # determine which allocate_col will be used\n", - " for allocator_col in data_and_allocator_cols[data_col]:\n", - " to_allocate[output_col] = to_allocate[output_col].fillna(\n", - " to_allocate[data_col] * to_allocate[f\"{allocator_col}_proportion\"]\n", - " )\n", - " # drop and rename all the columns in the data_and_allocator_cols dict keys and\n", - " # return these columns in the dataframe\n", - " to_allocate = (\n", - " to_allocate.drop(columns=list(data_and_allocator_cols.keys()))\n", - " .rename(\n", - " columns={\n", - " f\"{data_col}_allocated\": data_col\n", - " for data_col in data_and_allocator_cols\n", - " }\n", - " )\n", - " .drop(\n", - " columns=list(to_allocate.filter(like=\"_proportion\").columns)\n", - " + [f\"{c}_total\" for c in all_allocator_cols]\n", - " )\n", - " )\n", - " return to_allocate" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "id": "0de47d7e-fe5e-4b06-94c4-99e9479fc3b6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "eia_gens_cems_agg = (\n", - " allocate_cols(\n", - " to_allocate=eia_gens_cems_merge,\n", - " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", - " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]})\n", - " .groupby([\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", - " .sum(min_count=1)\n", - " .reset_index()\n", - " .drop(columns=[\"year\"])\n", - " .merge(\n", - " eia_gens_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"technology_description\", \"operational_status\"]],\n", - " how=\"left\",\n", - " on=[\"report_date\", \"plant_id_eia\", \"generator_id\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6ed6ebfe-7a66-458a-81d9-31b797d2efb6", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "#### Test Allocation on 2020 Subset" - ] - }, - { - "cell_type": "code", - "execution_count": 278, - "id": "4501bfc7-b25a-400c-9922-f5bcabddb499", - "metadata": {}, - "outputs": [], - "source": [ - "test_df = eia_gens_df[(eia_gens_df[\"plant_id_eia\"]==3) & (eia_gens_df[\"report_date\"].dt.year==2020)]" - ] - }, - { - "cell_type": "code", - "execution_count": 279, - "id": "7eca5275-da3f-4d1a-b32c-99f943685df0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_7748/1557213781.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - " test_merge = test_merge.append(fake_row, ignore_index=True)\n" - ] - } - ], - "source": [ - "test_merge = (\n", - " test_df[[\"report_date\", \"plant_id_eia\", \"generator_id\", \"capacity_mw\", \"technology_description\"]].merge(\n", - " crosswalk_df[[\"plant_id_eia\", \"generator_id\", \"emissions_unit_id_epa\"]], \n", - " how=\"left\", \n", - " on=[\"plant_id_eia\", \"generator_id\"])\n", - " .assign(year=lambda x: x.report_date.dt.year.astype(\"Int64\"))\n", - " .merge(\n", - " cems_df,\n", - " how=\"left\",\n", - " on=[\"year\", \"plant_id_eia\", \"emissions_unit_id_epa\"])\n", - ")\n", - "\n", - "fake_row = test_merge.iloc[14].replace(np.nan, 0.1)\n", - "test_merge = test_merge.append(fake_row, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 280, - "id": "c897e36d-827e-4d52-be24-0aba2b4019f0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# If you want to allocate by something other than generator (plant or prive mover),\n", - "# make sure the capacity value is for that level of aggregation.\n", - "\n", - "test_allocate = allocate_cols(\n", - " to_allocate=test_merge,\n", - " by=[\"report_date\", \"plant_id_eia\", \"emissions_unit_id_epa\"],\n", - " data_and_allocator_cols={\"co2_mass_tons\": [\"capacity_mw\"]} \n", - ")\n", - "\n", - "# Now sum up to generator level \n", - "# It's very important to add min_count=1 to the groupby sum so that NA values\n", - "# Stay NA and aren't converted to 0.\n", - "\n", - "# NOTE THAT RIGHT NOW if a record has a NA and non-NA value that get grouped together,\n", - "# the NA is still treated like 0. Not ideal, but it depends.\n", - "\n", - "test_agg = (\n", - " test_allocate.groupby(\n", - " [\"report_date\", \"plant_id_eia\", \"generator_id\"]\n", - " ).sum(min_count=1).reset_index().drop(columns=[\"year\"])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5d183ccf-ab9f-4f22-b2dd-2a7ecca3bb66", - "metadata": {}, - "source": [ - "## Post Integration Stats" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "id": "84e8742c-60bb-4bb4-8ee5-53e286dd43d6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "no_cems_match = eia_gens_cems_agg[eia_gens_cems_agg[\"co2_mass_tons\"].isna()]\n", - "cems_match = eia_gens_cems_agg[eia_gens_cems_agg[\"co2_mass_tons\"].notna()]" - ] - }, - { - "cell_type": "markdown", - "id": "67f8cc74-37f2-4a36-9567-0ec63215b999", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Technology Description" - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "id": "1e1b1cfd-4fac-4c2b-b238-54533203e5d8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 369, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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"output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "cems_match[cems_match[\"capacity_mw\"] < 1000].capacity_mw.hist(bins=50, alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "id": "8f9f9bde-8001-49cf-8fb4-f54cbcf2935d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 89867.000000\n", - "mean 204.768463\n", - "std 252.371394\n", - "min 2.500000\n", - "25% 62.000000\n", - "50% 122.400000\n", - "75% 210.000000\n", - "max 7380.000000\n", - "Name: capacity_mw, dtype: float64" - ] - }, - "execution_count": 375, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cems_match.capacity_mw.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "e9efc466-29e7-4bc7-92f9-45d6724178ea", - "metadata": {}, - "source": [ - "### Given what we know about CEMS Reporting..." - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "id": "6aefe58e-c044-4c42-be23-7174ad3e337c", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [] - }, - "outputs": [], - "source": [ - "fossil_fuels = [\n", - " \"Conventional Steam Coal\",\n", - " \"Natural Gas Fired Combined Cycle\",\n", - " \"Natural Gas Fired Combustion Turbine\",\n", - " \"Natural Gas Steam Turbine\",\n", - " \"Petroleum Liquids\",\n", - " \"Natural Gas Internal Combustion Engine\",\n", - " \"Municipal Solid Waste\",\n", - " \"Wood/Wood Waste Biomass\",\n", - " \"Coal Integrated Gasification Combined Cycle\",\n", - " \"Petroleum Coke\",\n", - " \"Landfill Gas\",\n", - " \"Natural Gas with Compressed Air Storage\",\n", - " \"Other Gases\",\n", - " \"Other Waste Biomass\",\n", - " \"Other Natural Gas\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 391, - "id": "775774ff-37e3-4b13-8d7c-848cd6fe83db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "78 % of existing EIA fossil fuel generators > 25MW have a CEMS match\n" - ] - } - ], - "source": [ - "exclude_exemptions = eia_gens_cems_agg[\n", - " eia_gens_cems_agg[\"technology_description\"].isin(fossil_fuels)\n", - " & (eia_gens_cems_agg[\"capacity_mw\"] > 25)\n", - " & (eia_gens_cems_agg[\"operational_status\"]==\"existing\")\n", - "]\n", - "\n", - "print(100 - round(exclude_exemptions.co2_mass_tons.isna().sum() / len(exclude_exemptions) * 100), \"% of existing EIA fossil fuel generators > 25MW have a CEMS match\")" - ] - }, - { - "cell_type": "code", - "execution_count": 429, - "id": "b399fa2d-8bc6-4bb8-8abb-6ab3837bdfc9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test = exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].isna()].technology_description.value_counts().reset_index()\n", - "test2 = test.merge(exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].notna()].technology_description.value_counts().reset_index(), how=\"outer\", on=[\"index\"], suffixes=[\"_isna\", \"_notna\"])\n", - "test3 = test2.merge(exclude_exemptions.technology_description.value_counts().reset_index(), how=\"outer\", on=[\"index\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 433, - "id": "b3311eb2-a506-474d-9eb9-e6597f929793", - "metadata": {}, - "outputs": [], - "source": [ - "test3[\"pct_na\"] = round(test3.technology_description_isna / test3.technology_description * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": 436, - "id": "01ea75f4-b8a2-48e8-b09c-37ddb9f0db37", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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indextechnology_description_isnatechnology_description_notnatechnology_descriptionpct_na
11Natural Gas with Compressed Air Storage22<NA>22100.0
6Municipal Solid Waste9184896695.0
9Other Waste Biomass86129888.0
3Wood/Wood Waste Biomass2457556301382.0
7Other Gases55633589162.0
8Petroleum Coke19413132560.0
10Landfill Gas28447239.0
2Petroleum Liquids29245346827035.0
0Natural Gas Fired Combustion Turbine7220261803340022.0
12Coal Integrated Gasification Combined Cycle15557021.0
5Natural Gas Steam Turbine16386596823420.0
1Natural Gas Fired Combined Cycle5712263453205718.0
4Conventional Steam Coal2372175951996712.0
\n", - "
" - ], - "text/plain": [ - " index technology_description_isna technology_description_notna technology_description pct_na\n", - "11 Natural Gas with Compressed Air Storage 22 22 100.0\n", - "6 Municipal Solid Waste 918 48 966 95.0\n", - "9 Other Waste Biomass 86 12 98 88.0\n", - "3 Wood/Wood Waste Biomass 2457 556 3013 82.0\n", - "7 Other Gases 556 335 891 62.0\n", - "8 Petroleum Coke 194 131 325 60.0\n", - "10 Landfill Gas 28 44 72 39.0\n", - "2 Petroleum Liquids 2924 5346 8270 35.0\n", - "0 Natural Gas Fired Combustion Turbine 7220 26180 33400 22.0\n", - "12 Coal Integrated Gasification Combined Cycle 15 55 70 21.0\n", - "5 Natural Gas Steam Turbine 1638 6596 8234 20.0\n", - "1 Natural Gas Fired Combined Cycle 5712 26345 32057 18.0\n", - "4 Conventional Steam Coal 2372 17595 19967 12.0" - ] - }, - "execution_count": 436, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test3.sort_values(\"pct_na\", ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 452, - "id": "cf0efadd-d301-427a-b1e9-469803b6364c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 452, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "still_na = exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].isna() & (exclude_exemptions[\"capacity_mw\"]<1000)]\n", - "still_na.capacity_mw.hist(bins=30)" - ] - }, - { - "cell_type": "code", - "execution_count": 456, - "id": "2c53588e-5ff2-4015-9cb0-3a4501bcf438", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 456, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "exclude_exemptions[exclude_exemptions[\"co2_mass_tons\"].isna()].report_date.value_counts().sort_index().plot.bar()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4efb1b53-71f3-471a-9bd7-b4e339ef7654", - "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": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb b/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb deleted file mode 100644 index 92c3b5f614..0000000000 --- a/notebooks/work-in-progress/play_with_cems_crosswalk.ipynb +++ /dev/null @@ -1,7009 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "68e5e995-c646-4125-b742-82bd9e7459fc", - "metadata": {}, - "source": [ - "# CEMS Crosswalk Testing" - ] - }, - { - "cell_type": "markdown", - "id": "9e67606a-3696-400c-9f1a-daf3aa6bf13c", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "37965668-7e10-4022-853e-6f10dc386859", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d204afa-7812-41a9-8d54-1573212a7538", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pudl\n", - "import pandas as pd\n", - "import logging\n", - "import sys\n", - "import sqlalchemy as sa\n", - "import dask.dataframe as dd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c3eea5de-2336-4974-90cc-4530375247b7", - "metadata": {}, - "outputs": [], - "source": [ - "# basic setup for logging\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.INFO)\n", - "handler = logging.StreamHandler(stream=sys.stdout)\n", - "formatter = logging.Formatter('%(message)s')\n", - "handler.setFormatter(formatter)\n", - "logger.handlers = [handler]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "56cd672d-1464-44b6-9c64-4888e6d6df56", - "metadata": {}, - "outputs": [], - "source": [ - "pudl_settings = pudl.workspace.setup.get_defaults()\n", - "pudl_engine = sa.create_engine(pudl_settings[\"pudl_db\"])\n", - "start_date=None\n", - "end_date=None\n", - "freq='AS'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "092d330e-03ca-40e5-a037-9421ded88a5e", - "metadata": {}, - "outputs": [], - "source": [ - "pudl_out = pudl.output.pudltabl.PudlTabl(pudl_engine,freq='AS')" - ] - }, - { - "cell_type": "markdown", - "id": "b9e6577c-75b7-496e-a2a6-f0b02499a04a", - "metadata": { - "tags": [] - }, - "source": [ - "## Get Transformed Crosswalk" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "75a7507d-b26f-4865-9077-f6a24a7dedc6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Retrieving https://zenodo.org/api/deposit/depositions/7063255 from zenodo\n", - "Retrieving https://zenodo.org/api/files/afca4bd7-d94f-4af3-b1f3-abaeaec6fa68/datapackage.json from zenodo\n", - "Retrieving https://zenodo.org/api/files/afca4bd7-d94f-4af3-b1f3-abaeaec6fa68/epacamd_eia.zip from zenodo\n", - "Cleaning up the epacems-eia crosswalk\n" - ] - } - ], - "source": [ - "from pudl.workspace.datastore import Datastore\n", - "from pudl.glue.epacamd_eia import extract, transform\n", - "ds = Datastore()\n", - "gens_ent = pd.read_sql('generators_entity_eia', pudl_engine)\n", - "boiler_ent = pd.read_sql('boilers_entity_eia', pudl_engine)\n", - "\n", - "cems_crosswalk_dict = transform(extract(ds), gens_ent, boiler_ent, True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "da08b2cc-e34d-46cf-82c5-6411159a1ac3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['epacamd_eia_crosswalk'])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cems_crosswalk_dict.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "85fb5a54-f7ad-4c4f-a476-f52316799141", - "metadata": {}, - "outputs": [], - "source": [ - "cems_crosswalk = cems_crosswalk_dict[\"epacamd_eia_crosswalk\"]" - ] - }, - { - "cell_type": "markdown", - "id": "882bb0c6-a403-40fb-8df1-92b878aa407a", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Get CEMS from Parquet" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0d799db8-2bf5-4853-9258-c57d03ae8a90", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", - "\n", - "cems_dd = dd.read_parquet(\n", - " epacems_path, \n", - " columns=[\"year\", \"plant_id_eia\", \"unitid\", \"co2_mass_tons\"],\n", - ")\n", - "\n", - "# filters = pudl.output.epacems.year_state_filter(years=[2019], states=[\"ME\"])\n", - "\n", - "# cems_small_dd = dd.read_parquet(\n", - "# epacems_path,\n", - "# engine=\"pyarrow\",\n", - "# columns=[\"year\", \"state\", \"operating_datetime_utc\", \"operating_time_hours\", \"plant_id_eia\", \"facility_id\", \"unit_id_epa\", \"unitid\"],\n", - "# #filters=[[('year', '=', 2019)]],\n", - "# index=False\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "52382ebb-8984-4d95-a78b-9f80942ebcf0", - "metadata": {}, - "outputs": [], - "source": [ - "cems_df = cems_dd.groupby([\"year\", \"plant_id_eia\", \"unitid\"]).sum().compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1271dd72-6316-49cd-9b57-ea2b79eefac8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#idaho_cems = cems_small_dd[(cems_small_dd[\"state\"]==\"ID\") & (cems_small_dd[\"year\"]==2019)].compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47dbc902-ec43-4d74-99b2-75d2f521ce13", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# This shows whether unitid and unit_id are 1:1 within a given year\n", - "test = cems_df_plant.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", - "ser = test.groupby([\"operating_datetime_utc\", \"operating_time_hours\", \"plant_id_eia\", \"unit_id_epa\"])[\"unitid\"].nunique() \n", - "print(ser[ser>1])\n", - "\n", - "# This shows whether plant_id_eia and facility_id are 1:1 within a given year\n", - "test = cems_df_plant.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", - "ser = test.groupby([\"operating_datetime_utc\", \"operating_time_hours\", \"facility_id\"])[\"plant_id_eia\"].nunique() \n", - "print(ser[ser>1])" - ] - }, - { - "cell_type": "markdown", - "id": "24f4a6c4-93ce-48b0-86dc-6db5d03fdd08", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Get Raw CEMS from Datastore" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "02dc5c76-5818-4ab0-a4b7-606d3e139b95", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from pudl.workspace.datastore import Datastore\n", - "from pathlib import Path\n", - "\n", - "# If you want to run the extractor with you LOCAL data, make sure you specify a path to your existing datastore with ds_kwargs\n", - "ds_kwargs = {\"local_cache_path\": Path(pudl_settings[\"pudl_in\"]) / \"data\"}\n", - "\n", - "# If you want to download the data from Zenodo, create the Datastore() instance without arguments.\n", - "ds = Datastore(**ds_kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "a26ceadd-cee4-4568-b8aa-fcbe7d2e1906", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "cems_datastore = pudl.extract.epacems.EpaCemsDatastore(ds)\n", - "cems_partition = pudl.extract.epacems.EpaCemsPartition(\"2019\", \"ID\")\n", - "\n", - "raw_idaho_cems = cems_datastore.get_data_frame(cems_partition)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2c522ded-2038-475f-9110-11739088f251", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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co2_mass_measurement_codeco2_mass_tonsemissions_unit_id_epagross_load_mwheat_content_mmbtunox_mass_lbsnox_mass_measurement_codeop_dateop_houroperating_time_hoursplant_id_epaso2_mass_lbsso2_mass_measurement_codestatesteam_load_1000_lbs
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" - ], - "text/plain": [ - " co2_mass_measurement_code co2_mass_tons emissions_unit_id_epa gross_load_mw heat_content_mmbtu nox_mass_lbs nox_mass_measurement_code op_date op_hour operating_time_hours plant_id_epa so2_mass_lbs so2_mass_measurement_code state steam_load_1000_lbs\n", - "0 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 0 0.0 7456 NaN NaN ID NaN\n", - "1 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 1 0.0 7456 NaN NaN ID NaN\n", - "2 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 2 0.0 7456 NaN NaN ID NaN\n", - "3 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 3 0.0 7456 NaN NaN ID NaN\n", - "4 NaN NaN NaN NaN NaN NaN NaN 01-01-2019 4 0.0 7456 NaN NaN ID NaN\n", - "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", - "70075 Measured 115.0 CT1 289.0 1935.6 13.549 Calculated 12-31-2019 19 1.0 57028 1.161 Measured ID NaN\n", - "70076 Measured 110.8 CT1 277.0 1864.8 11.189 Calculated 12-31-2019 20 1.0 57028 1.119 Measured ID NaN\n", - "70077 Measured 110.4 CT1 276.0 1857.9 11.147 Calculated 12-31-2019 21 1.0 57028 1.115 Measured ID NaN\n", - "70078 Measured 106.0 CT1 264.0 1783.1 10.699 Calculated 12-31-2019 22 1.0 57028 1.070 Measured ID NaN\n", - "70079 Measured 109.7 CT1 274.0 1845.5 11.073 Calculated 12-31-2019 23 1.0 57028 1.107 Measured ID NaN\n", - "\n", - "[70080 rows x 15 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pudl.helpers.remove_leading_zeros_from_numeric_strings(raw_idaho_cems, \"emissions_unit_id_epa\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "2a5dedeb-6f1e-4cd1-971a-b36719e99b6c", - "metadata": {}, - "outputs": [], - "source": [ - "raw_idaho_cems.loc[raw_idaho_cems[\"emissions_unit_id_epa\"]=='1', \"emissions_unit_id_epa\"] = np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "9d4fb83e-52c8-45f2-a5af-08a72a97e6a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 NaN\n", - "1 NaN\n", - "2 NaN\n", - "3 NaN\n", - "4 NaN\n", - " ... \n", - "70075 True\n", - "70076 True\n", - "70077 True\n", - "70078 True\n", - "70079 True\n", - "Name: emissions_unit_id_epa, Length: 70080, dtype: object" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_idaho_cems.emissions_unit_id_epa.str.contains(\"C\")" - ] - }, - { - "cell_type": "markdown", - "id": "005fab26-7da2-4a1b-8dde-b5e7db1d07d9", - "metadata": { - "tags": [] - }, - "source": [ - "## Compare Crosswalk and CEMS" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "7f301bda-d58d-4b58-a74e-ac93eb23a44d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "gens = pudl_out.gens_eia860()" - ] - }, - { - "cell_type": "markdown", - "id": "861a8a5b-fffc-4056-9181-ca5e7d957e96", - "metadata": { - "tags": [] - }, - "source": [ - "#### **Data missing from the crosswalk**:" - ] - }, - { - "cell_type": "markdown", - "id": "51b43242-d3eb-4feb-a318-aff300572761", - "metadata": {}, - "source": [ - "Plants that are in CEMS but not in the crosswalk" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "097ea6c6-c7e9-4bd4-8ec7-52c28ef8c53f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of plants NOT IN crosswalk: 281\n", - "Number of plants IN crosswalk: 1521\n" - ] - } - ], - "source": [ - "# Plants that are in CEMS but not in the crosswalk\n", - "cems_ids = cems_df.plant_id_eia.unique()\n", - "crosswalk_ids = cems_crosswalk.plant_id_epa.unique()\n", - "plant_id_not_in_crosswalk = [x for x in cems_ids if x not in crosswalk_ids]\n", - "plant_id_in_crosswalk = [x for x in cems_ids if x in crosswalk_ids]\n", - "print(\"Number of plants NOT IN crosswalk:\", len(plant_id_not_in_crosswalk))\n", - "print(\"Number of plants IN crosswalk:\", len(plant_id_in_crosswalk))" - ] - }, - { - "cell_type": "markdown", - "id": "c359890a-3f00-4c3e-bbe9-fcd9c0a24284", - "metadata": {}, - "source": [ - "Plants that are in the crosswalk but units are missing" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "672a071a-507e-49db-817e-da6757644ca6", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"['unit_id_epa'] not in index\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [105]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m cems_df_only_crosswalk \u001b[38;5;241m=\u001b[39m pudl\u001b[38;5;241m.\u001b[39mhelpers\u001b[38;5;241m.\u001b[39mremove_leading_zeros_from_numeric_strings(cems_df_only_crosswalk, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Merge the crosswalk with the cems df subset\u001b[39;00m\n\u001b[1;32m 12\u001b[0m merge_cems_with_crosswalk \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mmerge(\n\u001b[1;32m 13\u001b[0m cems_df_only_crosswalk, \n\u001b[0;32m---> 14\u001b[0m \u001b[43mcems_crosswalk\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mplant_id_epa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43munit_id_epa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mplant_id_eia\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdrop_duplicates()\u001b[38;5;241m.\u001b[39mdropna(), \n\u001b[1;32m 15\u001b[0m on\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplant_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit_id_epa\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 16\u001b[0m how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 17\u001b[0m )\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Print a list of the epa ids that have NA values for plant_id_eia matches. \u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# This is an indication of units that don't match cems. Usually this is because\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# the unit id does not exist in the crosswalk, but it could also be a misspelling or something.\u001b[39;00m\n\u001b[1;32m 22\u001b[0m merge_cems_with_crosswalk[merge_cems_with_crosswalk[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplant_id_eia\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mplant_id_epa\u001b[38;5;241m.\u001b[39munique()\n", - "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/frame.py:3511\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3510\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 3511\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3513\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", - "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 5779\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5780\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5782\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5784\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m 5785\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m 5786\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/pudl-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:5845\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 5842\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5844\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m-> 5845\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"['unit_id_epa'] not in index\"" - ] - } - ], - "source": [ - "# Get a subset of the cems df that is only the plant ids that do show up in the \n", - "# crosswalk. Theoretically this should show whether the unit ids are lining up.\n", - "plant_id_in_crosswalk = cems_crosswalk.dropna(subset=\"plant_id_eia\").plant_id_epa.unique()\n", - "cems_df_only_crosswalk = cems_df[~cems_df[\"plant_id_eia\"].isin(plant_id_not_in_crosswalk)]\n", - "cems_df_only_crosswalk = cems_df_only_crosswalk[cems_df_only_crosswalk[\"plant_id_eia\"].isin(plant_id_in_crosswalk)]\n", - "cems_df_only_crosswalk = cems_df_only_crosswalk.rename(columns={\"plant_id_eia\": \"plant_id_epa\", \"unitid\": \"unit_id_epa\"})\n", - "\n", - "# Clean cems subset\n", - "cems_df_only_crosswalk = pudl.helpers.remove_leading_zeros_from_numeric_strings(cems_df_only_crosswalk, \"unit_id_epa\")\n", - "\n", - "# Merge the crosswalk with the cems df subset\n", - "merge_cems_with_crosswalk = pd.merge(\n", - " cems_df_only_crosswalk, \n", - " cems_crosswalk[[\"plant_id_epa\", \"unit_id_epa\", \"plant_id_eia\"]].drop_duplicates().dropna(), \n", - " on=[\"plant_id_epa\", \"unit_id_epa\"],\n", - " how=\"left\"\n", - ")\n", - "\n", - "# Print a list of the epa ids that have NA values for plant_id_eia matches. \n", - "# This is an indication of units that don't match cems. Usually this is because\n", - "# the unit id does not exist in the crosswalk, but it could also be a misspelling or something.\n", - "merge_cems_with_crosswalk[merge_cems_with_crosswalk[\"plant_id_eia\"].isna()].plant_id_epa.unique()" - ] - }, - { - "cell_type": "markdown", - "id": "3b1ed507-38ee-40f5-a187-da16e63a4879", - "metadata": { - "tags": [] - }, - "source": [ - "#### **Note where EPA and EIA IDs don't match:**\n", - "This is usually a result of subcomponents being attributed to another plant" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfde0c4e-85e6-4d47-8f0d-b7aacb5ff9dd", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "miss_matched_ids = cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]!=cems_crosswalk[\"plant_id_epa\"]].plant_id_epa.unique()\n", - "print(\"Number of missmatched EPA and EIA ids:\", len(miss_matched_ids))\n", - "miss_matched_ids" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ca8adc5-b502-4a96-8a48-2b13dcc25b1b", - "metadata": {}, - "outputs": [], - "source": [ - "miss_matched_ids_df = cems_crosswalk[cems_crosswalk[\"plant_id_epa\"].isin(miss_matched_ids)].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72d43364-aa3c-4e66-8158-e6525845417b", - "metadata": {}, - "outputs": [], - "source": [ - "# This shows that while there are different plant id eia values in a given plant id epa,\n", - "# they are as granular as the unit_id_epa which is good for integration in CEMS!!! \n", - "(miss_matched_ids_df.groupby([\"plant_id_epa\", \"unit_id_epa\"])[\"plant_id_eia\"].nunique() >1).any()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5e51891-d525-45a1-8c76-14d2447e919f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "cems_crosswalk[cems_crosswalk[\"plant_id_epa\"]==562]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98baa152-6747-42f5-80cd-03ac93343ce4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "gens[(gens[\"plant_id_eia\"]==562) & (gens[\"report_date\"].dt.year==2020)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "048ae153-8359-4e59-a024-7972bf78ed34", - "metadata": {}, - "outputs": [], - "source": [ - "plant_id_not_in_crosswalk[0:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10dfe6a3-7a71-42af-8391-060f8bd63fc0", - "metadata": {}, - "outputs": [], - "source": [ - "[5, 247, 312, 334, 375, 569, 596, 604, 646, 647]" - ] - }, - { - "cell_type": "markdown", - "id": "6ed0fc26-00a4-4661-a315-8c6605553d1f", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "#### **What are the primary keys?**" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "id": "91369996-f825-4b1d-85cf-6f84b357c6bf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 234, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Shows that generator_id_epa does not distinguish between plant_id_eia values\n", - "(cems_crosswalk.groupby([\"plant_id_epa\", \"unit_id_epa\"])[\"plant_id_eia\"].nunique() > 1).any()" - ] - }, - { - "cell_type": "markdown", - "id": "fc54f8bc-22e4-45aa-bc4c-2d5baa0db268", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "#### **Explore**" - ] - }, - { - "cell_type": "markdown", - "id": "9f41ce3b-b772-45d9-be8c-fb9edbaa8961", - "metadata": {}, - "source": [ - "To Do: \n", - "- Check wheather `facility_id` and `unit_id_epa` mean anything\n", - "- rename columns in cems\n", - "- see if we can do any cems cleaning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d059b45-13f1-4219-a756-2d96892efa96", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "cems_df\n", - "test = cems_df.dropna(subset=\"unit_id_epa\").pipe(pudl.helpers.remove_leading_zeros_from_numeric_strings, \"unitid\")\n", - "ser = test.groupby([\"plant_id_eia\", \"unitid\"])[\"unit_id_epa\"].nunique() \n", - "ser[ser>1]" - ] - }, - { - "cell_type": "markdown", - "id": "24f517e9-e13e-41ca-8025-b89843c99330", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Test Boiler ID" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "76abbe6c-7f62-4a61-8e91-7c238441a128", - "metadata": {}, - "outputs": [], - "source": [ - "# PUDL DB\n", - "#pudl_engine.table_names() # for a list of table names\n", - "boilers = pd.read_sql(\"boilers_entity_eia\", pudl_engine)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "dc33c4b2-e516-4dff-b35e-8088cbfb9b60", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "boiler_id_not_in_entity_df = cems_crosswalk[~cems_crosswalk[\"boiler_id\"].isin(boilers.boiler_id.unique())]\n", - "#boiler_id_not_in_entity_df = boiler_id_not_in_entity_df.dropna(subset=[\"boiler_id\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "8953ad10-dd0b-4115-a6bf-514b9bd76ee3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "boilers = pd.read_sql(\"boilers_entity_eia\", pudl_engine)\n", - "cw = cems_crosswalk[[\"plant_id_eia\", \"boiler_id\"]].drop_duplicates().dropna()\n", - "cw_tups = list(zip(cw.plant_id_eia, cw.boiler_id))\n", - "boiler_tups = list(zip(boilers.plant_id_eia, boilers.boiler_id))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "4d8fa425-f809-4415-8b3a-2939fd7cae2c", - "metadata": {}, - "outputs": [], - "source": [ - "# cw = cw.set_index(['plant_id_eia', 'boiler_id'])\n", - "# boilers = boilers.set_index(['plant_id_eia', 'boiler_id'])" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "66d3074b-5c7d-4ad6-a54c-1b51700ec8b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiIndex([], names=['plant_id_eia', 'boiler_id'])" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cw.index.difference(boilers.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "28db3736-29f3-4650-8015-010ebf000379", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[x for x in cw_tups if x not in boiler_tups]" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "2be0a8f8-b60d-4390-b6eb-2c01a53e2d41", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test1 = boilers[boilers[\"plant_id_eia\"]==302].boiler_id" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "48fff4aa-0214-43ec-9de9-466ca8a79bc5", - "metadata": {}, - "outputs": [], - "source": [ - "test2 = cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]==302].boiler_id" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "ab989c6d-8684-4d4a-a4f6-63daacac96eb", - "metadata": {}, - "outputs": [], - "source": [ - "bad_ids = [302,1552,2378,2535,2850,6031,6136,10333]" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "8d44bafc-ada2-4f44-a506-33a7cb64e2f8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2019-01-01 00:00:00')" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "boil_years = pd.read_sql(\"boiler_generator_assn_eia860\", pudl_engine)\n", - "#pudl_engine.table_names()\n", - "boil_years[boil_years[\"plant_id_eia\"].isin(bad_ids)].report_date.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "4d1347ca-f9d5-4579-a3f8-9face6ed946c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[x for x in list(test2) if x not in list(test1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "cf69458f-1725-46c0-8b0a-d633c501438b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp('2021-01-01 00:00:00')" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gens[gens[\"plant_id_eia\"]==302].report_date.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "d0f183f6-fa6a-4b3c-8fd1-c46d39031e61", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['5', '4', '3', '2', '1']\n", - "['1', '2', '3', '4', '5']\n" - ] - } - ], - "source": [ - "print(list(test1))\n", - "print(list(test2))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "47458cd5-85b4-4758-a356-5f77539ada76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "161 1\n", - "163 2\n", - "164 3\n", - "165 4\n", - "166 5\n", - "Name: boiler_id, dtype: string" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test2" - ] - }, - { - "cell_type": "markdown", - "id": "7856d3dc-b4be-4620-893a-d79320107326", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Test Timezone" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "5e3c7e3d-9200-4ea9-b470-3f46353db50e", - "metadata": {}, - "outputs": [], - "source": [ - "# PUDL DB\n", - "#pudl_engine.table_names() # for a list of table names\n", - "plants_entity = pd.read_sql(\"plants_entity_eia\", pudl_engine)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "id": "b112af07-26a3-4a84-bcc9-87a2c4ce5a04", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_44638/1566893324.py:1: SADeprecationWarning: The Engine.has_table() method is deprecated and will be removed in a future release. Please refer to Inspector.has_table(). (deprecated since: 1.4)\n", - " pudl_engine.has_table(\"assn_gen_eia_unit_epa\")\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pudl_engine.has_table(\"assn_gen_eia_unit_epa\")" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "a2b326e8-6d52-4142-a9c0-115070c8ca23", - "metadata": {}, - "outputs": [], - "source": [ - "timezones = plants_entity[[\"plant_id_eia\", \"timezone\"]].dropna().copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "48606241-d717-42af-844f-da9429e75104", - "metadata": {}, - "outputs": [], - "source": [ - "# import datetime\n", - "# import pytz\n", - "\n", - "# jan1 = datetime.datetime(2011, 1, 1) # year doesn't matter\n", - "# timezones[\"utc_offset\"] = timezones[\"timezone\"].apply(\n", - "# lambda tz: pytz.timezone(tz).localize(jan1).utcoffset()\n", - "# )\n", - "# del timezones[\"timezone\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "e060f4d1-d838-4ca8-8cd3-585ce52a8213", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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plant_id_eiatimezone
01America/Anchorage
12America/Chicago
23America/Chicago
34America/Chicago
45America/Chicago
.........
14929880100America/New_York
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14931880107America/New_York
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14665 rows × 2 columns

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" - ], - "text/plain": [ - " plant_id_eia timezone\n", - "0 1 America/Anchorage\n", - "1 2 America/Chicago\n", - "2 3 America/Chicago\n", - "3 4 America/Chicago\n", - "4 5 America/Chicago\n", - "... ... ...\n", - "14929 880100 America/New_York\n", - "14930 880101 America/Chicago\n", - "14931 880107 America/New_York\n", - "14932 880108 America/Indiana/Vincennes\n", - "14933 880109 America/New_York\n", - "\n", - "[14665 rows x 2 columns]" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timezones" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "3143daa2-76cc-4aa8-a53a-90c500fd3a99", - "metadata": {}, - "outputs": [], - "source": [ - "miss_matched_ids_df1 = miss_matched_ids_df.merge(timezones, on=[\"plant_id_eia\"], how=\"left\")" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "f946cb99-4d8c-4b2f-9931-a3d1ab5dd2c1", - "metadata": {}, - "outputs": [], - "source": [ - "miss_matched_ids_df2 = miss_matched_ids_df1.merge(timezones, left_on=[\"plant_id_epa\"], right_on=[\"plant_id_eia\"], how=\"left\", suffixes=[\"_eia\", \"_epa\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "f6747fb0-5e04-45de-8ad2-c5d47ba32c8a", - "metadata": {}, - "outputs": [], - "source": [ - "miss_matched_ids_df2 = miss_matched_ids_df2.drop(columns=[\"plant_id_eia_epa\"]).rename(columns={\"plant_id_eia_eia\": \"plant_id_eia\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "2f299a36-82c9-419d-93fb-e0dfb6866676", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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plant_id_epaunit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_idtimezone_eiatimezone_epa
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [plant_id_epa, unit_id_epa, generator_id_epa, plant_id_eia, boiler_id, generator_id, timezone_eia, timezone_epa]\n", - "Index: []" - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "miss_matched_ids_df2[miss_matched_ids_df2[\"timezone_eia\"]!=miss_matched_ids_df2[\"timezone_epa\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "id": "ac2f899b-30ae-4856-89b9-a0ff633c9216", - "metadata": {}, - "outputs": [], - "source": [ - "from sqlalchemy import inspect\n", - "insp = inspect(pudl_engine)" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "9ff4ebbb-9dbe-4574-8e1f-86591e5972fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.72 ms, sys: 1.09 ms, total: 2.81 ms\n", - "Wall time: 3.96 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "if not insp.has_table(\"assn_gen_eia_unit_epa\"):\n", - " print(\"bad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "6b66264e-40dc-4be9-ab13-9f5ead906b9b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 217 µs, sys: 160 µs, total: 377 µs\n", - "Wall time: 243 µs\n" - ] - } - ], - "source": [ - "%%time\n", - "if \"assn_gen_eia_unit_epa\" not in insp.get_table_names():\n", - " print(\"bad\")" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "id": "b025f8b9-4010-4b66-8569-ad17590af7b1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/cd/6w7fpp711lsglpq_fxb57l3m0000gn/T/ipykernel_44638/454621952.py:1: SADeprecationWarning: The Engine.table_names() method is deprecated and will be removed in a future release. Please refer to Inspector.get_table_names(). (deprecated since: 1.4)\n", - " pudl_engine.table_names()[:5]\n" - ] - }, - { - "data": { - "text/plain": [ - "['assn_gen_eia_unit_epa',\n", - " 'assn_plant_id_eia_epa',\n", - " 'boiler_fuel_eia923',\n", - " 'boiler_generator_assn_eia860',\n", - " 'boilers_entity_eia']" - ] - }, - "execution_count": 208, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pudl_engine.table_names()[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "id": "a5e9816a-085c-4c87-9d3d-750645a6d50d", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "bad", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [212]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m6\u001b[39m:\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbad\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mAssertionError\u001b[0m: bad" - ] - } - ], - "source": [ - "if \"plants_eia860\" not in inspector.get_table_names():" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "id": "d880b83f-0d61-4a52-9143-85991eec391f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(161, 7)" - ] - }, - "execution_count": 224, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "miss_matched_ids_df1.shape" - ] - }, - { - "cell_type": "markdown", - "id": "64bd63c1-4ddd-4219-80f4-56b1dd2e03c3", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Test EPACEMS Output Table" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "1e0c0f53-c8ea-4fca-8ac0-3d637bd4c25f", - "metadata": {}, - "outputs": [], - "source": [ - "epacems_path = (pudl_settings['parquet_dir'] + f'/epacems/hourly_emissions_epacems.parquet')\n", - "\n", - "test = pudl.output.epacems.epacems(\n", - " states = [\"ID\"],\n", - " years = [2019],\n", - " #columns: Sequence[str] | None = None,\n", - " epacems_path = epacems_path,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2f50ebbd-a77c-4849-b840-90a7b9b64fb8", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Next" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "48cfdca0-c54a-4f36-92ee-4824e0618e43", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
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" - ], - "text/plain": [ - " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", - "396 612 FMCT2A ST1 612 2A ST1\n", - "397 612 FMCT2A ST2 612 2A ST2\n", - "399 612 FMCT2B ST1 612 2B ST1\n", - "400 612 FMCT2B ST2 612 2B ST2\n", - "402 612 FMCT2C ST1 612 2C ST1\n", - "403 612 FMCT2C ST2 612 2C ST2\n", - "405 612 FMCT2D ST1 612 2D ST1\n", - "406 612 FMCT2D ST2 612 2D ST2\n", - "408 612 FMCT2E ST1 612 2E ST1\n", - "409 612 FMCT2E ST2 612 2E ST2\n", - "411 612 FMCT2F ST1 612 2F ST1\n", - "412 612 FMCT2F ST2 612 2F ST2" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test = cems_crosswalk.dropna(subset=\"boiler_id\")\n", - "dups = test[test.duplicated(subset=[\"plant_id_eia\", \"generator_id\"], keep=False)]\n", - "tups = tuple(zip(dups.plant_id_eia, dups.boiler_id))\n", - "boil_dups = test[test.duplicated(subset=[\"plant_id_eia\", \"boiler_id\"], keep=False)]\n", - "boil_dup_tups = tuple(zip(boil_dups.plant_id_eia, boil_dups.boiler_id))\n", - "[x for x in tups if x in boil_dup_tups]\n", - "\n", - "test[test[\"plant_id_eia\"]==612]" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "ccf764c1-b76e-4261-b94b-49aa7d2e1ae4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filling technology type\n", - "Filled technology_type coverage now at 98.1%\n" - ] - } - ], - "source": [ - "gens = pudl_out.gens_eia860()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "13c2c5ba-f710-40ea-83c1-57e48047520c", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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601642020-01-01612205Fort Myers6452121Florida Power & Light Co12FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA>FalseFalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601632020-01-01612205Fort Myers6452121Florida Power & Light Co2AFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852000-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>199.5NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601622020-01-01612205Fort Myers6452121Florida Power & Light Co2BFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852000-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601612020-01-01612205Fort Myers6452121Florida Power & Light Co2CFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852000-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601602020-01-01612205Fort Myers6452121Florida Power & Light Co2DFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-04-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601592020-01-01612205Fort Myers6452121Florida Power & Light Co2EFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601582020-01-01612205Fort Myers6452121Florida Power & Light Co2FFalseFPLFlorida Power & Light Companyunit_connectionTrue188.2<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852001-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22CT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>197.4NaN<NA>FalseTrueNatural Gas Fired Combined Cycle1HAmerica/New_YorkX<NA><NA><NA>1NaTFalse<NA>207.0NaN33902
601572020-01-01612205Fort Myers6452121Florida Power & Light Co3FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601562020-01-01612205Fort Myers6452121Florida Power & Light Co4FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601552020-01-01612205Fort Myers6452121Florida Power & Light Co5FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601542020-01-01612205Fort Myers6452121Florida Power & Light Co6FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601532020-01-01612205Fort Myers6452121Florida Power & Light Co7FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601522020-01-01612205Fort Myers6452121Florida Power & Light Co8FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
601512020-01-01612205Fort Myers6452121Florida Power & Light Co9FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0False0.901974-05-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>61.5NaN33902
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601492020-01-01612205Fort Myers6452121Florida Power & Light CoCT2FalseFPLFlorida Power & Light Company<NA>False188.2<NA>Ft. MyersTrueLeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>NGDFO<NA><NA><NA><NA>FalseFalseFalse<NA>gas2NaNNaN230.0<NA>26.6967-81.783190.0True0.852003-06-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaNNaT<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>182.0NaN<NA>TrueFalseNatural Gas Fired Combustion Turbine1HAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>200.0NaN33902
601482020-01-01612205Fort Myers6452121Florida Power & Light CoG10FalseFPLFlorida Power & Light Company<NA>False62.0<NA>Ft. Myers<NA>LeeNaTeia860<NA><NA>False<NA><NA><NA><NA><NA><NA>DFO<NA><NA><NA><NA><NA>FalseFalseFalse<NA>oil2NaNNaN230.0<NA>26.6967-81.78315.0<NA>0.901974-05-01<NA>retiredRENaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22GT<NA>NaN2016-12-01<NA><NA>1Electric UtilityFalse<NA><NA><NA><NA>FL<NA>10650 State Rd 80<NA><NA>54.0NaN<NA><NA>FalsePetroleum Liquids10MAmerica/New_YorkX<NA><NA><NA><NA>NaTFalse<NA>60.0NaN33902
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\n", - "
" - ], - "text/plain": [ - " report_date plant_id_eia plant_id_pudl plant_name_eia utility_id_eia utility_id_pudl utility_name_eia generator_id associated_combined_heat_power balancing_authority_code_eia balancing_authority_name_eia bga_source bypass_heat_recovery capacity_mw carbon_capture city cofire_fuels county current_planned_operating_date data_source deliver_power_transgrid distributed_generation duct_burners energy_source_1_transport_1 energy_source_1_transport_2 energy_source_1_transport_3 energy_source_2_transport_1 energy_source_2_transport_2 energy_source_2_transport_3 energy_source_code_1 energy_source_code_2 energy_source_code_3 energy_source_code_4 energy_source_code_5 energy_source_code_6 ferc_cogen_status ferc_exempt_wholesale_generator ferc_small_power_producer fluidized_bed_tech fuel_type_code_pudl fuel_type_count grid_voltage_2_kv grid_voltage_3_kv grid_voltage_kv iso_rto_code latitude longitude minimum_load_mw multiple_fuels \\\n", - "60165 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 11 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60164 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 12 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60163 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2A False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60162 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2B False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60161 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2C False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60160 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2D False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60159 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2E False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60158 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 2F False FPL Florida Power & Light Company unit_connection True 188.2 Ft. Myers Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60157 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 3 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60156 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 4 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60155 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 5 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60154 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 6 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60153 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 7 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60152 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 8 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60151 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co 9 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 False \n", - "60150 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co CT1 False FPL Florida Power & Light Company False 188.2 Ft. Myers True Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60149 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co CT2 False FPL Florida Power & Light Company False 188.2 Ft. Myers True Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60148 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co G10 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60147 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co GT1 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 False \n", - "60146 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co GT2 False FPL Florida Power & Light Company False 62.0 Ft. Myers Lee NaT eia860 False DFO False False False oil 2 NaN NaN 230.0 26.6967 -81.7831 5.0 \n", - "60145 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co PFM3C False FPL Florida Power & Light Company False 229.5 False Ft. Myers False Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60144 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co PFM3D False FPL Florida Power & Light Company False 229.5 False Ft. Myers False Lee NaT eia860 False NG DFO False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 90.0 True \n", - "60143 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co ST1 False FPL Florida Power & Light Company eia860_org False 156.2 Ft. Myers Lee NaT eia860 False NG False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 27.8 False \n", - "60142 2020-01-01 612 205 Fort Myers 6452 121 Florida Power & Light Co ST2 False FPL Florida Power & Light Company eia860_org False 436.1 Ft. Myers Lee NaT eia860 False NG False False False gas 2 NaN NaN 230.0 26.6967 -81.7831 146.6 False \n", - "\n", - " nameplate_power_factor operating_date operating_switch operational_status operational_status_code original_planned_operating_date other_combustion_tech other_modifications_date other_planned_modifications owned_by_non_utility ownership_code planned_derate_date planned_energy_source_code_1 planned_modifications planned_net_summer_capacity_derate_mw planned_net_summer_capacity_uprate_mw planned_net_winter_capacity_derate_mw planned_net_winter_capacity_uprate_mw planned_new_capacity_mw planned_new_prime_mover_code planned_repower_date planned_retirement_date planned_uprate_date previously_canceled primary_purpose_id_naics prime_mover_code pulverized_coal_tech reactive_power_output_mvar retirement_date rto_iso_lmp_node_id rto_iso_location_wholesale_reporting_id sector_id_eia sector_name_eia solid_fuel_gasification startup_source_code_1 startup_source_code_2 startup_source_code_3 startup_source_code_4 state stoker_tech street_address subcritical_tech \\\n", - "60165 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60164 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60163 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60162 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60161 0.85 2000-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60160 0.85 2001-04-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60159 0.85 2001-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60158 0.85 2001-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60157 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60156 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60155 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60154 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60153 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60152 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60151 0.90 1974-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60150 0.85 2003-06-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60149 0.85 2003-06-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60148 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60147 0.90 1974-05-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60146 0.90 1974-05-01 retired RE NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN 2016-12-01 1 Electric Utility False FL 10650 State Rd 80 \n", - "60145 0.85 2016-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60144 0.85 2016-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 GT NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60143 0.85 1958-11-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CA NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "60142 0.89 1969-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 CA NaN NaT 1 Electric Utility False FL 10650 State Rd 80 \n", - "\n", - " summer_capacity_estimate summer_capacity_mw summer_estimated_capability_mw supercritical_tech switch_oil_gas syncronized_transmission_grid technology_description time_cold_shutdown_full_load_code timezone topping_bottoming_code turbines_inverters_hydrokinetics turbines_num ultrasupercritical_tech unit_id_pudl uprate_derate_completed_date uprate_derate_during_year winter_capacity_estimate winter_capacity_mw winter_estimated_capability_mw zip_code \n", - "60165 54.0 NaN False False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60164 54.0 NaN False False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60163 199.5 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60162 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60161 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60160 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60159 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60158 197.4 NaN False True Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 207.0 NaN 33902 \n", - "60157 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60156 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60155 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60154 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60153 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60152 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60151 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 61.5 NaN 33902 \n", - "60150 182.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 200.0 NaN 33902 \n", - "60149 182.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 200.0 NaN 33902 \n", - "60148 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60147 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 61.5 NaN 33902 \n", - "60146 54.0 NaN False Petroleum Liquids 10M America/New_York X NaT False 60.0 NaN 33902 \n", - "60145 231.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 223.0 NaN 33902 \n", - "60144 231.0 NaN True False Natural Gas Fired Combustion Turbine 1H America/New_York X NaT False 223.0 NaN 33902 \n", - "60143 155.8 NaN False Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 141.0 NaN 33902 \n", - "60142 459.2 NaN False Natural Gas Fired Combined Cycle 1H America/New_York X 1 NaT False 404.0 NaN 33902 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gens[(gens[\"plant_id_eia\"]==612) & (gens[\"report_date\"].dt.year==2020)]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3530dfed-094e-466b-a68e-c868a7b3e94b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "491469" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(gens)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "9e8211ef-de62-4766-b5e7-81ee58432dcb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35646" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gen_pairs = gens[[\"plant_id_eia\", \"generator_id\", \"fuel_type_code_pudl\", \"capacity_mw\"]].drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"])\n", - "\n", - "len(gen_pairs)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "af2b4ab4-f41c-4321-96bf-4499f9fe3c34", - "metadata": {}, - "outputs": [], - "source": [ - "gen_cross = pd.merge(gen_pairs, cems_crosswalk, on=[\"plant_id_eia\", \"generator_id\"], how=\"left\")" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "cbe469ef-139c-48c7-9353-27348cd6bd79", - "metadata": {}, - "outputs": [], - "source": [ - "no_dup_gen_cross = gen_cross.drop_duplicates(subset=[\"plant_id_eia\", \"generator_id\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "75e119fd-ea35-48f3-90d8-6163ade321f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "35646" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(no_dup_gen_cross)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "9466aeb2-6cc9-4424-b2c9-69195bd02491", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30349" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(no_dup_gen_cross[\n", - " no_dup_gen_cross[\"plant_id_epa\"].isna() \n", - " #& (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "4b2bf227-f25e-49a8-9c78-54ee2a5b0a69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5294" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(not_mapped := no_dup_gen_cross[\n", - " no_dup_gen_cross[\"plant_id_epa\"].isna() \n", - " & (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", - "])\n", - "\n", - "len(mapped := no_dup_gen_cross[\n", - " no_dup_gen_cross[\"plant_id_epa\"].notna() \n", - " & (~no_dup_gen_cross[\"fuel_type_code_pudl\"].isin([\"solar\", \"wind\", \"hydro\"]))\n", - "])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "21d8e0a4-6cc7-4d46-ac03-f9d008ec2f0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 17203\n", - "unique 6\n", - "top gas\n", - "freq 6828\n", - "Name: fuel_type_code_pudl, dtype: object" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "not_mapped.fuel_type_code_pudl.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "00a94fd7-4407-4e88-806e-2c57cf708d28", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.hist(not_mapped.capacity_mw, bins=100, range=(0,100))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "7ad0a33b-e3f2-4b09-9508-40dd9ac6cd29", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.hist(not_mapped.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,1000))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "ace35ecb-e085-4a6b-bd04-618a751344ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(mapped.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,1000))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "57a3af05-ab40-449f-b912-8c1d67180fcc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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eBzw07rGtlqo6WFXbqmoHg5/rP1fVO5nsnv8TeC7Jz7XSzcCTTHDPDKZ1bkzy6vZ7fjOD96wmuefz5uvxKLA3ySVJdgK7gEeXdISqmrgb8DYG/1nLvwMfWOvxrFKPv8zgn3dfBx5rt7cBP8ngXf+n2/0Vaz3WVer/JuCzbXmiewauB6bbz/ofgMs76PmPgW8ATwB/C1wyaT0D9zF4z+L7DM7kb79Qj8AHWqadBN661OP6NQyS1JFJnN6RJM3D0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kd+X96+BythUc9ewAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(no_dup_gen_cross.groupby(\"plant_id_eia\").capacity_mw.sum(), bins=100, range=(0,100))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "c74a09b2-3ef3-4bae-a130-288a43540f8a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
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.....................
6820609031360903<NA>3
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682370454MAG1MAG154538<NA>MAG1
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6407 rows × 6 columns

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" - ], - "text/plain": [ - " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", - "0 3 1 1 3 1 1\n", - "1 3 2 2 3 2 2\n", - "2 3 3 3 3 3 3\n", - "3 3 4 4 3 4 4\n", - "4 3 5 5 3 5 5\n", - "... ... ... ... ... ... ...\n", - "6820 60903 1 3 60903 3\n", - "6821 60903 2 2 60903 0002 2\n", - "6822 60903 2 4 60903 4\n", - "6823 70454 MAG1 MAG1 54538 MAG1\n", - "6824 70454 MAG2 MAG2 54538 MAG2\n", - "\n", - "[6407 rows x 6 columns]" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cems_crosswalk" - ] - }, - { - "cell_type": "markdown", - "id": "bfbadc61-8b5f-4612-97d2-d577e6bac96d", - "metadata": {}, - "source": [ - "## Investigate One-to-Many relationship" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e02c454-98fc-437b-a57c-5f196e54bf43", - "metadata": {}, - "outputs": [], - "source": [ - "crosswalk_essentials = cems_crosswalk[[\"plant_id_eia\", \"emissions_unit_id_epa\", \"generator_id\"]].drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "750856ca-ef7a-42ab-a84a-7fa22ef2e3e2", - "metadata": {}, - "outputs": [], - "source": [ - "# Add columns showing relationship between emissions unit and generator id columns\n", - "crosswalk_essentials[\"em_gen_1_m\"] = crosswalk_essentials.groupby([\"plant_id_eia\", \"emissions_unit_id_epa\"])[\"generator_id\"].transform(lambda x: x.count() > 1)\n", - "crosswalk_essentials[\"em_gen_m_1\"] = crosswalk_essentials.groupby([\"plant_id_eia\", \"generator_id\"])[\"emissions_unit_id_epa\"].transform(lambda x: x.count() > 1)\n", - "\n", - "crosswalk_essentials[crosswalk_essentials[\"em_gen_1_m\"] & crosswalk_essentials[\"em_gen_m_1\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "6bce1b10-70f8-45e7-830e-792fe819e508", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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plant_id_epaemissions_unit_id_epagenerator_id_epaplant_id_eiaboiler_idgenerator_id
0311311
1322322
2333333
3344344
4355355
536AA1CT3<NA>A1CT
636AA1ST36AA1ST
736BA1CT23<NA>A1CT2
836BA1ST36BA1ST
937AA2C13<NA>A2C1
1037AA2ST37AA2ST
1137BA2C23<NA>A2C2
1237BA2ST37BA2ST
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" - ], - "text/plain": [ - " plant_id_epa emissions_unit_id_epa generator_id_epa plant_id_eia boiler_id generator_id\n", - "0 3 1 1 3 1 1\n", - "1 3 2 2 3 2 2\n", - "2 3 3 3 3 3 3\n", - "3 3 4 4 3 4 4\n", - "4 3 5 5 3 5 5\n", - "5 3 6A A1CT 3 A1CT\n", - "6 3 6A A1ST 3 6A A1ST\n", - "7 3 6B A1CT2 3 A1CT2\n", - "8 3 6B A1ST 3 6B A1ST\n", - "9 3 7A A2C1 3 A2C1\n", - "10 3 7A A2ST 3 7A A2ST\n", - "11 3 7B A2C2 3 A2C2\n", - "12 3 7B A2ST 3 7B A2ST" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For all the em_gen_1_m = TRUE we will need to allocate by net generation or capacity\n", - "# This is an example: \n", - "cems_crosswalk[cems_crosswalk[\"plant_id_eia\"]==3]" - ] - }, - { - "cell_type": "markdown", - "id": "479f79f5-fbb2-4882-8c50-7caecacab1a2", - "metadata": {}, - "source": [ - "## Missing Records" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "f851ccd8-038e-4c29-acdf-c9c48452a7ed", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "crosswalk_ids = (\n", - " cems_crosswalk[[\"plant_id_eia\", \"generator_id\"]]\n", - " .drop_duplicates()\n", - " .set_index([\"plant_id_eia\", \"generator_id\"])\n", - ")\n", - "gens_ids = (\n", - " gens[[\"plant_id_eia\", \"generator_id\"]]\n", - " .drop_duplicates()\n", - " .set_index([\"plant_id_eia\", \"generator_id\"])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "4cefe64f-284d-4ebf-baba-6c1188e751bc", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiIndex([( 1, '1'),\n", - " ( 1, '2'),\n", - " ( 1, '3'),\n", - " ( 1, '5'),\n", - " ( 1, 'WT1'),\n", - " ( 1, 'WT2'),\n", - " ( 2, '1'),\n", - " ( 3, 'A3C1'),\n", - " ( 3, 'A3ST'),\n", - " ( 4, '1'),\n", - " ...\n", - " (65328, '1'),\n", - " (65329, 'LAURL'),\n", - " (65330, 'BALB1'),\n", - " (65331, 'NORMA'),\n", - " (65332, 'MH1'),\n", - " (65333, '785'),\n", - " (65334, 'PLTVW'),\n", - " (65335, 'WAPPA'),\n", - " (65337, 'MAYBK'),\n", - " (65338, 'UNIS1')],\n", - " names=['plant_id_eia', 'generator_id'], length=30349)" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gens_ids.index.difference(crosswalk_ids.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "718fd76a-833d-4ad5-9d26-1b7c5e4f7172", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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report_dateplant_id_eiaplant_id_pudlplant_name_eiautility_id_eiautility_id_pudlutility_name_eiagenerator_idassociated_combined_heat_powerbalancing_authority_code_eiabalancing_authority_name_eiabga_sourcebypass_heat_recoverycapacity_mwcarbon_capturecitycofire_fuelscountycurrent_planned_operating_datedata_sourcedeliver_power_transgriddistributed_generationduct_burnersenergy_source_1_transport_1energy_source_1_transport_2energy_source_1_transport_3energy_source_2_transport_1energy_source_2_transport_2energy_source_2_transport_3energy_source_code_1energy_source_code_2energy_source_code_3energy_source_code_4energy_source_code_5energy_source_code_6ferc_cogen_statusferc_exempt_wholesale_generatorferc_small_power_producerfluidized_bed_techfuel_type_code_pudlfuel_type_countgrid_voltage_2_kvgrid_voltage_3_kvgrid_voltage_kviso_rto_codelatitudelongitudeminimum_load_mwmultiple_fuelsnameplate_power_factoroperating_dateoperating_switchoperational_statusoperational_status_codeoriginal_planned_operating_dateother_combustion_techother_modifications_dateother_planned_modificationsowned_by_non_utilityownership_codeplanned_derate_dateplanned_energy_source_code_1planned_modificationsplanned_net_summer_capacity_derate_mwplanned_net_summer_capacity_uprate_mwplanned_net_winter_capacity_derate_mwplanned_net_winter_capacity_uprate_mwplanned_new_capacity_mwplanned_new_prime_mover_codeplanned_repower_dateplanned_retirement_dateplanned_uprate_datepreviously_canceledprimary_purpose_id_naicsprime_mover_codepulverized_coal_techreactive_power_output_mvarretirement_daterto_iso_lmp_node_idrto_iso_location_wholesale_reporting_idsector_id_eiasector_name_eiasolid_fuel_gasificationstartup_source_code_1startup_source_code_2startup_source_code_3startup_source_code_4statestoker_techstreet_addresssubcritical_techsummer_capacity_estimatesummer_capacity_mwsummer_estimated_capability_mwsupercritical_techswitch_oil_gassyncronized_transmission_gridtechnology_descriptiontime_cold_shutdown_full_load_codetimezonetopping_bottoming_codeturbines_inverters_hydrokineticsturbines_numultrasupercritical_techunit_id_pudluprate_derate_completed_dateuprate_derate_during_yearwinter_capacity_estimatewinter_capacity_mwwinter_estimated_capability_mwzip_code
4914682001-01-012848Bankhead Dam19518Alabama Power Co1FalseSOCOSouthern Company Services, Inc. - Trans<NA>False45.0<NA>Northport<NA>TuscaloosaNaT<NA><NA>FalseFalse<NA><NA><NA><NA><NA><NA>WAT<NA><NA><NA><NA><NA>FalseFalseFalse<NA>hydro1NaNNaN115.0<NA>33.458665-87.35682NaN<NA>NaN1963-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22HY<NA>NaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>19001 Lock 17 Road<NA><NA>56.0NaN<NA><NA><NA>Conventional Hydroelectric<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>56.0NaN35476
4914672001-01-01332Barry19518Alabama Power Co1FalseSOCOSouthern Company Services, Inc. - Trans<NA>False153.1<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1954-02-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>138.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>138.0NaN36512
4914662001-01-01332Barry19518Alabama Power Co2FalseSOCOSouthern Company Services, Inc. - Trans<NA>False153.1<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1954-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>139.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>139.0NaN36512
4914652001-01-01332Barry19518Alabama Power Co3FalseSOCOSouthern Company Services, Inc. - Trans<NA>False272.0<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1959-07-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>251.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>251.0NaN36512
4914642001-01-01332Barry19518Alabama Power Co4FalseSOCOSouthern Company Services, Inc. - Trans<NA>False403.7<NA>Bucks<NA>MobileNaT<NA><NA>FalseFalseWT<NA><NA>PL<NA><NA>BITNG<NA><NA><NA><NA>FalseFalseFalse<NA>coal3NaNNaN230.0<NA>31.006900-88.01030NaN<NA>NaN1969-12-01<NA>existingOPNaT<NA>NaT<NA><NA>SNaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA>22STTrueNaNNaT<NA><NA>1Electric Utility<NA><NA><NA><NA><NA>AL<NA>North Highway 43True<NA>362.0NaN<NA><NA><NA>Conventional Steam Coal<NA>America/ChicagoX<NA><NA><NA><NA>NaT<NA><NA>362.0NaN36512
................................................................................................................................................................................................................................................................................................................................................
42021-01-016533316132Shakes Solar610605634Cypress Creek Renewables785<NA>ERCO<NA><NA><NA>200.0<NA><NA><NA>Dimmit2024-11-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>28.442080-99.75582NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>TX<NA><NA><NA><NA>200.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>200.0NaN<NA>
32021-01-016533416161Platteview Solar LLC610125972AES Distributed EnergyPLTVW<NA>SWPP<NA><NA><NA>81.0<NA><NA><NA>Saunders2023-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>41.190999-96.37942NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>NE<NA><NA><NA><NA>81.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>81.0NaN<NA>
22021-01-016533516137Appaloosa Run Wind6465513725Appaloosa Run Wind, LLCWAPPA<NA>ERCO<NA><NA><NA>171.8<NA><NA><NA>Upton2022-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>WND<NA><NA><NA><NA><NA><NA><NA><NA><NA>wind1NaNNaNNaN<NA>31.157269-101.83160NaN<NA>NaNNaT<NA>proposedUNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>WT<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>TX<NA><NA><NA><NA>NaNNaN<NA><NA><NA>Onshore Wind Turbine<NA>America/Chicago<NA><NA><NA><NA><NA>NaT<NA><NA>NaNNaN<NA>
12021-01-016533716270Maybrook Solar, LLC569902626NJR Clean Energy Ventures CorporationMAYBK<NA>NYIS<NA><NA><NA>5.0<NA><NA><NA>Orange2022-09-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>41.463056-74.24778NaN<NA>NaNNaT<NA>proposedTNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>NY<NA><NA><NA><NA>5.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/New_York<NA><NA><NA><NA><NA>NaT<NA><NA>5.0NaN<NA>
02021-01-016533816151Union Ridge Solar501232261Bluarc Management Group LLCUNIS1<NA>PJM<NA><NA><NA>108.0<NA><NA><NA>Montgomery2023-12-01eia860m<NA><NA><NA><NA><NA><NA><NA><NA><NA>SUN<NA><NA><NA><NA><NA><NA><NA><NA><NA>solar1NaNNaNNaN<NA>39.984126-82.64130NaN<NA>NaNNaT<NA>proposedLNaT<NA>NaT<NA><NA><NA>NaT<NA><NA>NaNNaNNaNNaNNaN<NA>NaTNaTNaT<NA><NA>PV<NA>NaNNaT<NA><NA><NA><NA><NA><NA><NA><NA><NA>OH<NA><NA><NA><NA>108.0NaN<NA><NA><NA>Solar Photovoltaic<NA>America/New_York<NA><NA><NA><NA><NA>NaT<NA><NA>108.0NaN<NA>
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491469 rows × 111 columns

\n", - "
" - ], - "text/plain": [ - " report_date plant_id_eia plant_id_pudl plant_name_eia utility_id_eia utility_id_pudl utility_name_eia generator_id associated_combined_heat_power balancing_authority_code_eia balancing_authority_name_eia bga_source bypass_heat_recovery capacity_mw carbon_capture city cofire_fuels county current_planned_operating_date data_source deliver_power_transgrid distributed_generation duct_burners energy_source_1_transport_1 energy_source_1_transport_2 energy_source_1_transport_3 energy_source_2_transport_1 energy_source_2_transport_2 energy_source_2_transport_3 energy_source_code_1 energy_source_code_2 energy_source_code_3 energy_source_code_4 energy_source_code_5 energy_source_code_6 ferc_cogen_status ferc_exempt_wholesale_generator ferc_small_power_producer fluidized_bed_tech fuel_type_code_pudl fuel_type_count grid_voltage_2_kv grid_voltage_3_kv grid_voltage_kv iso_rto_code latitude longitude \\\n", - "491468 2001-01-01 2 848 Bankhead Dam 195 18 Alabama Power Co 1 False SOCO Southern Company Services, Inc. - Trans False 45.0 Northport Tuscaloosa NaT False False WAT False False False hydro 1 NaN NaN 115.0 33.458665 -87.35682 \n", - "491467 2001-01-01 3 32 Barry 195 18 Alabama Power Co 1 False SOCO Southern Company Services, Inc. - Trans False 153.1 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", - "491466 2001-01-01 3 32 Barry 195 18 Alabama Power Co 2 False SOCO Southern Company Services, Inc. - Trans False 153.1 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", - "491465 2001-01-01 3 32 Barry 195 18 Alabama Power Co 3 False SOCO Southern Company Services, Inc. - Trans False 272.0 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", - "491464 2001-01-01 3 32 Barry 195 18 Alabama Power Co 4 False SOCO Southern Company Services, Inc. - Trans False 403.7 Bucks Mobile NaT False False WT PL BIT NG False False False coal 3 NaN NaN 230.0 31.006900 -88.01030 \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "4 2021-01-01 65333 16132 Shakes Solar 61060 5634 Cypress Creek Renewables 785 ERCO 200.0 Dimmit 2024-11-01 eia860m SUN solar 1 NaN NaN NaN 28.442080 -99.75582 \n", - "3 2021-01-01 65334 16161 Platteview Solar LLC 61012 5972 AES Distributed Energy PLTVW SWPP 81.0 Saunders 2023-12-01 eia860m SUN solar 1 NaN NaN NaN 41.190999 -96.37942 \n", - "2 2021-01-01 65335 16137 Appaloosa Run Wind 64655 13725 Appaloosa Run Wind, LLC WAPPA ERCO 171.8 Upton 2022-12-01 eia860m WND wind 1 NaN NaN NaN 31.157269 -101.83160 \n", - "1 2021-01-01 65337 16270 Maybrook Solar, LLC 56990 2626 NJR Clean Energy Ventures Corporation MAYBK NYIS 5.0 Orange 2022-09-01 eia860m SUN solar 1 NaN NaN NaN 41.463056 -74.24778 \n", - "0 2021-01-01 65338 16151 Union Ridge Solar 50123 2261 Bluarc Management Group LLC UNIS1 PJM 108.0 Montgomery 2023-12-01 eia860m SUN solar 1 NaN NaN NaN 39.984126 -82.64130 \n", - "\n", - " minimum_load_mw multiple_fuels nameplate_power_factor operating_date operating_switch operational_status operational_status_code original_planned_operating_date other_combustion_tech other_modifications_date other_planned_modifications owned_by_non_utility ownership_code planned_derate_date planned_energy_source_code_1 planned_modifications planned_net_summer_capacity_derate_mw planned_net_summer_capacity_uprate_mw planned_net_winter_capacity_derate_mw planned_net_winter_capacity_uprate_mw planned_new_capacity_mw planned_new_prime_mover_code planned_repower_date planned_retirement_date planned_uprate_date previously_canceled primary_purpose_id_naics prime_mover_code pulverized_coal_tech reactive_power_output_mvar retirement_date rto_iso_lmp_node_id rto_iso_location_wholesale_reporting_id sector_id_eia sector_name_eia solid_fuel_gasification startup_source_code_1 startup_source_code_2 startup_source_code_3 startup_source_code_4 state stoker_tech \\\n", - "491468 NaN NaN 1963-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 HY NaN NaT 1 Electric Utility AL \n", - "491467 NaN NaN 1954-02-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", - "491466 NaN NaN 1954-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", - "491465 NaN NaN 1959-07-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", - "491464 NaN NaN 1969-12-01 existing OP NaT NaT S NaT NaN NaN NaN NaN NaN NaT NaT NaT 22 ST True NaN NaT 1 Electric Utility AL \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "4 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT TX \n", - "3 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT NE \n", - "2 NaN NaN NaT proposed U NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT WT NaN NaT TX \n", - "1 NaN NaN NaT proposed T NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT NY \n", - "0 NaN NaN NaT proposed L NaT NaT NaT NaN NaN NaN NaN NaN NaT NaT NaT PV NaN NaT OH \n", - "\n", - " street_address subcritical_tech summer_capacity_estimate summer_capacity_mw summer_estimated_capability_mw supercritical_tech switch_oil_gas syncronized_transmission_grid technology_description time_cold_shutdown_full_load_code timezone topping_bottoming_code turbines_inverters_hydrokinetics turbines_num ultrasupercritical_tech unit_id_pudl uprate_derate_completed_date uprate_derate_during_year winter_capacity_estimate winter_capacity_mw winter_estimated_capability_mw zip_code \n", - "491468 19001 Lock 17 Road 56.0 NaN Conventional Hydroelectric America/Chicago X NaT 56.0 NaN 35476 \n", - "491467 North Highway 43 True 138.0 NaN Conventional Steam Coal America/Chicago X NaT 138.0 NaN 36512 \n", - "491466 North Highway 43 True 139.0 NaN Conventional Steam Coal America/Chicago X NaT 139.0 NaN 36512 \n", - "491465 North Highway 43 True 251.0 NaN Conventional Steam Coal America/Chicago X NaT 251.0 NaN 36512 \n", - "491464 North Highway 43 True 362.0 NaN Conventional Steam Coal America/Chicago X NaT 362.0 NaN 36512 \n", - "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", - "4 200.0 NaN Solar Photovoltaic America/Chicago NaT 200.0 NaN \n", - "3 81.0 NaN Solar Photovoltaic America/Chicago NaT 81.0 NaN \n", - "2 NaN NaN Onshore Wind Turbine America/Chicago NaT NaN NaN \n", - "1 5.0 NaN Solar Photovoltaic America/New_York NaT 5.0 NaN \n", - "0 108.0 NaN Solar Photovoltaic America/New_York NaT 108.0 NaN \n", - "\n", - "[491469 rows x 111 columns]" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gens" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fb6afea-f9b2-4bc8-827b-b79d4ac9184c", - "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": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 57ebb43cdfcc7bf9b3deed8dfae490cf4b311eef Mon Sep 17 00:00:00 2001 From: Austen Sharpe Date: Tue, 13 Sep 2022 16:03:12 -0600 Subject: [PATCH 79/80] Fix link to crosswalk in release notes --- docs/release_notes.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/release_notes.rst b/docs/release_notes.rst index 1be1ca53eb..7dbf05ac42 100644 --- a/docs/release_notes.rst +++ b/docs/release_notes.rst @@ -33,7 +33,7 @@ Data Coverage monthly updates for 2022. * Integrated several new columns into the EIA 860 and EIA 923 including several codes with coding tables (See :doc:`data_dictionaries/codes_and_labels`). :pr:`1836` -* Added the `EPACAMD-EIA Crosswalk `__` to +* Added the `EPACAMD-EIA Crosswalk `__ to the database. Previously, the crosswalk was a csv stored in ``package_data/glue``, but now it has its own scraper :pr:`https://github.com/catalyst-cooperative/pudl-scrapers/pull/20`, archiver, From 6413c0fab4991f95abfaa922794899de7c9c6449 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 14 Sep 2022 07:44:43 +0000 Subject: [PATCH 80/80] Bump slackapi/slack-github-action from 1.21.0 to 1.22.0 --- .github/workflows/build-deploy-pudl.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-deploy-pudl.yml b/.github/workflows/build-deploy-pudl.yml index 7ba634547e..3300a07a2a 100644 --- a/.github/workflows/build-deploy-pudl.yml +++ b/.github/workflows/build-deploy-pudl.yml @@ -107,7 +107,7 @@ jobs: - name: Post to a pudl-deployments channel id: slack - uses: slackapi/slack-github-action@v1.21.0 + uses: slackapi/slack-github-action@v1.22.0 with: channel-id: "C03FHB9N0PQ" slack-message: "build-deploy-pudl status: ${{ job.status }}\n${{ env.ACTION_SHA }}-${{ env.GITHUB_REF }}"