diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 2f6f9c3cc..eab00a657 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
[bumpversion]
-current_version = 0.3.37
+current_version = 0.3.38
commit = True
message = chore: bump covidcast-indicators to {new_version}
tag = False
diff --git a/_delphi_utils_python/.bumpversion.cfg b/_delphi_utils_python/.bumpversion.cfg
index f0b715186..9fe154574 100644
--- a/_delphi_utils_python/.bumpversion.cfg
+++ b/_delphi_utils_python/.bumpversion.cfg
@@ -1,5 +1,5 @@
[bumpversion]
-current_version = 0.3.14
+current_version = 0.3.15
commit = True
message = chore: bump delphi_utils to {new_version}
tag = False
diff --git a/_delphi_utils_python/data_proc/geomap/README.md b/_delphi_utils_python/data_proc/geomap/README.md
index 84fdbefb2..08075fff9 100644
--- a/_delphi_utils_python/data_proc/geomap/README.md
+++ b/_delphi_utils_python/data_proc/geomap/README.md
@@ -24,7 +24,7 @@ We support the following geocodes.
- We are reserving 10001-10099 for states codes of the form 100XX where XX is the FIPS code for the state (the current smallest CBSA is 10100). In the case that the CBSA codes change then it should be verified that these are not used.
- State codes are a series of equivalent identifiers for US state. They include the state name, the state number (state_id), and the state two-letter abbreviation (state_code). The state number is the state FIPS code. See [here](https://en.wikipedia.org/wiki/List_of_U.S._state_and_territory_abbreviations) for more.
- The Hospital Referral Region (HRR) and the Hospital Service Area (HSA). More information [here](https://www.dartmouthatlas.org/covid-19/hrr-mapping/).
-- The JHU signal contains its own geographic identifier, labeled the UID. Documentation is provided at [their repo](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data#uid-lookup-table-logic). Its FIPS codes depart in some special cases, so we produce manual changes listed below.
+FIPS codes depart in some special cases, so we produce manual changes listed below.
## Source files
@@ -34,20 +34,13 @@ The source files are requested from a government URL when `geo_data_proc.py` is
- ZIP -> HRR -> HSA crosswalk file comes from the 2018 version at the [Dartmouth Atlas Project](https://atlasdata.dartmouth.edu/static/supp_research_data).
- FIPS -> MSA crosswalk file comes from the September 2018 version of the delineation files at the [US Census Bureau](https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html).
- State Code -> State ID -> State Name comes from the ANSI standard at the [US Census](https://www.census.gov/library/reference/code-lists/ansi.html#par_textimage_3). The first two digits of a FIPS codes should match the state code here.
-- JHU UID -> FIPS comes from [the JHU documentation](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data#uid-lookup-table-logic). We have to do some hand modifications to the JHU UID because the mapping to FIPS isn't always consistent.
+
## Derived files
The rest of the crosswalk tables are derived from the mappings above. We provide crosswalk functions from granular to coarser codes, but not the other way around. This is because there is no information gained when crosswalking from coarse to granular.
-## JHU UID mapping changes
-- Dukes and Nantucket counties in Massachusets are aggregated, so we split them with population-proportional weights (approximately 2/3 Dukes and 1/3 Nantucket).
-- The same procedure is followed by Kansas City and four of its counties.
-- Kusilvak, Alaska is mapped to the FIPS code 02270.
-- Ogalala Lakota, South Dakota is mapped to the FIPS code 46113.
-- Utah reports at a territory level, so we only report it at in a state level megaFIPS 49000.
-- JHU places cases and deaths that cannot be localized to a single county into "Out of State" and "Unassigned" categories. We map these to the "megaFIPS" code XX000, where XX is the state FIPS code. This way, the data is recovered when aggregating up to the state level, but does not interfere with other counties.
## Deprecated source files
@@ -55,7 +48,6 @@ The rest of the crosswalk tables are derived from the mappings above. We provide
- The `02_20_uszips.csv` file is based on the newest consensus data including 5-digit zipcode, fips code, county name, state, population, HRR, HSA (I downloaded the original file from [here](https://simplemaps.com/data/us-zips). This file matches best to the most recent (2020) situation in terms of the population. But there still exist some matching problems. I manually checked and corrected those lines (~20) with [zip-codes](https://www.zip-codes.com/zip-code/58439/zip-code-58439.asp). The mapping from 5-digit zipcode to HRR is based on the file in 2017 version downloaded from [here](https://atlasdata.dartmouth.edu/static/supp_research_data).
- ZIP -> FIPS is provided by [huduser.gov](https://www.huduser.gov/portal/datasets/usps_crosswalk.html) for zip -> fips?
- FIPS county population data from [US Census Bureau](http://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html). Details of Bedford, Virginia counting [here](https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes.html).
-- JHU UID crosswalk table [here](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data#uid-lookup-table-logic)
- CBSA -> FIPS crosswalk from [here](https://data.nber.org/data/cbsa-fips-county-crosswalk.html) (the file is `cbsatocountycrosswalk.csv`).
- MSA tables from March 2020 [here](https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html). This file seems to differ in a few fips codes from the source for the 02_20_uszip file which Jingjing constructed. There are at least 10 additional fips in 03_20_msa that are not in the uszip file, and one of the msa codes seems to be incorrect: 49020 (a google search confirms that it is incorrect in uszip and correct in the census data).
- MSA tables from 2019 [here](https://apps.bea.gov/regional/docs/msalist.cfm)
diff --git a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
index d51f9b551..287667812 100755
--- a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
+++ b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
@@ -27,7 +27,6 @@
ZIP_HSA_HRR_URL = "https://atlasdata.dartmouth.edu/downloads/geography/ZipHsaHrr18.csv.zip"
ZIP_HSA_HRR_FILENAME = "ZipHsaHrr18.csv"
FIPS_MSA_URL = "https://www2.census.gov/programs-surveys/metro-micro/geographies/reference-files/2018/delineation-files/list1_Sep_2018.xls"
-JHU_FIPS_URL = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv"
STATE_CODES_URL = "http://www2.census.gov/geo/docs/reference/state.txt?#"
FIPS_POPULATION_URL = f"https://www2.census.gov/programs-surveys/popest/datasets/2010-{YEAR}/counties/totals/co-est{YEAR}-alldata.csv"
FIPS_PUERTO_RICO_POPULATION_URL = "https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txt?"
@@ -57,7 +56,6 @@
STATE_POPULATION_OUT_FILENAME = "state_pop.csv"
HHS_POPULATION_OUT_FILENAME = "hhs_pop.csv"
NATION_POPULATION_OUT_FILENAME = "nation_pop.csv"
-JHU_FIPS_OUT_FILENAME = "jhu_uid_fips_table.csv"
def create_fips_zip_crosswalk():
@@ -111,101 +109,6 @@ def create_fips_msa_crosswalk():
msa_df.sort_values(["fips", "msa"]).to_csv(join(OUTPUT_DIR, FIPS_MSA_OUT_FILENAME), columns=["fips", "msa"], index=False)
-def create_jhu_uid_fips_crosswalk():
- """Build a crosswalk table from JHU UID to FIPS."""
- # These are hand modifications that need to be made to the translation
- # between JHU UID and FIPS. See below for the special cases information
- # https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html#geographical-exceptions
- hand_additions = pd.DataFrame(
- [
- {
- "jhu_uid": "84070002",
- "fips": "25007", # Split aggregation of Dukes and Nantucket, Massachusetts
- "weight": 16535 / (16535 + 10172), # Population: 16535
- },
- {
- "jhu_uid": "84070002",
- "fips": "25019",
- "weight": 10172 / (16535 + 10172), # Population: 10172
- },
- {
- "jhu_uid": "84070003",
- "fips": "29095", # Kansas City, Missouri
- "weight": 674158 / 1084897, # Population: 674158
- },
- {
- "jhu_uid": "84070003",
- "fips": "29165",
- "weight": 89322 / 1084897, # Population: 89322
- },
- {
- "jhu_uid": "84070003",
- "fips": "29037",
- "weight": 99478 / 1084897, # Population: 99478
- },
- {
- "jhu_uid": "84070003",
- "fips": "29047",
- "weight": 221939 / 1084897, # Population: 221939
- },
- # Kusilvak, Alaska
- {"jhu_uid": "84002158", "fips": "02270", "weight": 1.0},
- # Oglala Lakota
- {"jhu_uid": "84046102", "fips": "46113", "weight": 1.0},
- # Aggregate Utah territories into a "State FIPS"
- {"jhu_uid": "84070015", "fips": "49000", "weight": 1.0},
- {"jhu_uid": "84070016", "fips": "49000", "weight": 1.0},
- {"jhu_uid": "84070017", "fips": "49000", "weight": 1.0},
- {"jhu_uid": "84070018", "fips": "49000", "weight": 1.0},
- {"jhu_uid": "84070019", "fips": "49000", "weight": 1.0},
- {"jhu_uid": "84070020", "fips": "49000", "weight": 1.0},
- ]
- )
- # Map the Unassigned category to a custom megaFIPS XX000
- unassigned_states = pd.DataFrame(
- {"jhu_uid": str(x), "fips": str(x)[-2:].ljust(5, "0"), "weight": 1.0}
- for x in range(84090001, 84090057)
- )
- # Map the Out of State category to a custom megaFIPS XX000
- out_of_state = pd.DataFrame(
- {"jhu_uid": str(x), "fips": str(x)[-2:].ljust(5, "0"), "weight": 1.0}
- for x in range(84080001, 84080057)
- )
- # Map the Unassigned and Out of State categories to the cusom megaFIPS 72000
- puerto_rico_unassigned = pd.DataFrame(
- [
- {"jhu_uid": "63072888", "fips": "72000", "weight": 1.0},
- {"jhu_uid": "63072999", "fips": "72000", "weight": 1.0},
- ]
- )
- cruise_ships = pd.DataFrame(
- [
- {"jhu_uid": "84088888", "fips": "88888", "weight": 1.0},
- {"jhu_uid": "84099999", "fips": "99999", "weight": 1.0},
- ]
- )
-
-
- jhu_df = pd.read_csv(JHU_FIPS_URL, dtype={"UID": str, "FIPS": str}).query("Country_Region == 'US'")
- jhu_df = jhu_df.rename(columns={"UID": "jhu_uid", "FIPS": "fips"}).dropna(subset=["fips"])
-
- # FIPS Codes that are just two digits long should be zero filled on the right.
- # These are US state codes (XX) and the territories Guam (66), Northern Mariana Islands (69),
- # Virgin Islands (78), and Puerto Rico (72).
- fips_territories = jhu_df["fips"].str.len() <= 2
- jhu_df.loc[fips_territories, "fips"] = jhu_df.loc[fips_territories, "fips"].str.ljust(5, "0")
-
- # Drop the JHU UIDs that were hand-modified
- manual_correction_ids = pd.concat([hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned, cruise_ships])["jhu_uid"]
- jhu_df.drop(jhu_df.index[jhu_df["jhu_uid"].isin(manual_correction_ids)], inplace=True)
-
- # Add weights of 1.0 to everything not in hand additions, then merge in hand-additions
- # Finally, zero fill FIPS
- jhu_df["weight"] = 1.0
- jhu_df = pd.concat([jhu_df, hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned])
- jhu_df["fips"] = jhu_df["fips"].astype(int).astype(str).str.zfill(5)
- jhu_df.sort_values(["jhu_uid", "fips"]).to_csv(join(OUTPUT_DIR, JHU_FIPS_OUT_FILENAME), columns=["jhu_uid", "fips", "weight"], index=False)
-
def create_state_codes_crosswalk():
"""Build a State ID -> State Name -> State code crosswalk file."""
@@ -659,7 +562,6 @@ def clear_dir(dir_path: str):
create_fips_zip_crosswalk()
create_zip_hsa_hrr_crosswalk()
create_fips_msa_crosswalk()
- create_jhu_uid_fips_crosswalk()
create_state_codes_crosswalk()
create_state_hhs_crosswalk()
create_fips_population_table()
diff --git a/_delphi_utils_python/delphi_utils/__init__.py b/_delphi_utils_python/delphi_utils/__init__.py
index 3a9667a2f..c1bbfca0a 100644
--- a/_delphi_utils_python/delphi_utils/__init__.py
+++ b/_delphi_utils_python/delphi_utils/__init__.py
@@ -15,4 +15,4 @@
from .nancodes import Nans
from .weekday import Weekday
-__version__ = "0.3.14"
+__version__ = "0.3.15"
diff --git a/_delphi_utils_python/delphi_utils/data/2019/jhu_uid_fips_table.csv b/_delphi_utils_python/delphi_utils/data/2019/jhu_uid_fips_table.csv
deleted file mode 100644
index 4260c1f6b..000000000
--- a/_delphi_utils_python/delphi_utils/data/2019/jhu_uid_fips_table.csv
+++ /dev/null
@@ -1,3405 +0,0 @@
-jhu_uid,fips,weight
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diff --git a/_delphi_utils_python/delphi_utils/data/2020/jhu_uid_fips_table.csv b/_delphi_utils_python/delphi_utils/data/2020/jhu_uid_fips_table.csv
deleted file mode 100644
index 4260c1f6b..000000000
--- a/_delphi_utils_python/delphi_utils/data/2020/jhu_uid_fips_table.csv
+++ /dev/null
@@ -1,3405 +0,0 @@
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diff --git a/_delphi_utils_python/delphi_utils/geomap.py b/_delphi_utils_python/delphi_utils/geomap.py
index 4782798a0..f43b80504 100644
--- a/_delphi_utils_python/delphi_utils/geomap.py
+++ b/_delphi_utils_python/delphi_utils/geomap.py
@@ -102,7 +102,6 @@ class GeoMapper: # pylint: disable=too-many-public-methods
"state_name": {
"pop": "state_pop.csv"
},
- "jhu_uid": {"fips": "jhu_uid_fips_table.csv"},
"hhs": {"pop": "hhs_pop.csv"},
"nation": {"pop": "nation_pop.csv"},
}
@@ -125,7 +124,7 @@ def __init__(self, census_year=2020):
for subkey in self.CROSSWALK_FILENAMES[mainkey]
}.union(
set(self.CROSSWALK_FILENAMES.keys())
- ) - set(["state", "pop", "jhu_uid"])
+ ) - set(["state", "pop"])
for from_code, to_codes in self.CROSSWALK_FILENAMES.items():
for to_code, file_path in to_codes.items():
@@ -159,7 +158,7 @@ def _load_geo_values(self, geo_type):
to_code = from_code = "state"
elif geo_type == "fips":
from_code = "fips"
- to_code = "pop"
+ to_code = "state"
else:
from_code = "fips"
to_code = geo_type
@@ -237,7 +236,6 @@ def add_geocode(
- fips -> state_code, state_id, state_name, zip, msa, hrr, nation, hhs, chng-fips
- chng-fips -> state_code, state_id, state_name
- zip -> state_code, state_id, state_name, fips, msa, hrr, nation, hhs
- - jhu_uid -> fips
- state_x -> state_y (where x and y are in {code, id, name}), nation
- state_code -> hhs, nation
@@ -245,7 +243,7 @@ def add_geocode(
---------
df: pd.DataFrame
Input dataframe.
- from_code: {'fips', 'chng-fips', 'zip', 'jhu_uid', 'state_code',
+ from_code: {'fips', 'chng-fips', 'zip', 'state_code',
'state_id', 'state_name'}
Specifies the geocode type of the data in from_col.
new_code: {'fips', 'chng-fips', 'zip', 'state_code', 'state_id',
@@ -351,7 +349,6 @@ def replace_geocode(
- fips -> chng-fips, state_code, state_id, state_name, zip, msa, hrr, nation
- chng-fips -> state_code, state_id, state_name
- zip -> state_code, state_id, state_name, fips, msa, hrr, nation
- - jhu_uid -> fips
- state_x -> state_y (where x and y are in {code, id, name}), nation
- state_code -> hhs, nation
@@ -361,7 +358,7 @@ def replace_geocode(
Input dataframe.
from_col: str
Name of the column in data to match and remove.
- from_code: {'fips', 'zip', 'jhu_uid', 'state_code', 'state_id', 'state_name'}
+ from_code: {'fips', 'zip', 'state_code', 'state_id', 'state_name'}
Specifies the geocode type of the data in from_col.
new_col: str
Name of the new column to add to data.
diff --git a/_delphi_utils_python/delphi_utils/notebooks/geo_utility_demonstration.ipynb b/_delphi_utils_python/delphi_utils/notebooks/geo_utility_demonstration.ipynb
new file mode 100644
index 000000000..ea6fb499d
--- /dev/null
+++ b/_delphi_utils_python/delphi_utils/notebooks/geo_utility_demonstration.ipynb
@@ -0,0 +1,1158 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Geocoding Utility Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "from delphi_utils import GeoMapper\n",
+ "\n",
+ "os.chdir(\"_delphi_utils_python/delphi_utils/data/2020/\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Basic Utility Usage\n",
+ "Two functions: `add_geocode` and `replace_geocode`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fips date count total zip weight\n",
+ "0 01123 2018-01-01 2.0 4.0 35010 0.461001\n",
+ "1 01123 2018-01-01 2.0 4.0 35072 0.013264\n",
+ "2 01123 2018-01-01 2.0 4.0 35089 0.017661\n",
+ "3 01123 2018-01-01 2.0 4.0 36078 0.113826\n",
+ "4 01123 2018-01-01 2.0 4.0 36255 0.000433\n",
+ " date zip count total\n",
+ "0 2018-01-01 00602 0.000000 0.000000\n",
+ "1 2018-01-01 00610 0.000000 0.000000\n",
+ "2 2018-01-01 00676 0.000000 0.000000\n",
+ "3 2018-01-01 00677 0.000000 0.000000\n",
+ "4 2018-01-01 35010 0.922001 1.844002\n"
+ ]
+ }
+ ],
+ "source": [
+ "fips_data = pd.DataFrame({\n",
+ " \"fips\":[1123,48253,72003,18181],\n",
+ " \"date\":[pd.Timestamp('2018-01-01')]*4,\n",
+ " \"count\": [2,1,np.nan,10021],\n",
+ " \"total\": [4,1,np.nan,100001]\n",
+ " })\n",
+ "\n",
+ "# Add a new column with the new code\n",
+ "gmpr = GeoMapper()\n",
+ "df = gmpr.add_geocode(fips_data, \"fips\", \"zip\")\n",
+ "print(df.head())\n",
+ "\n",
+ "# Convert a column with the new code\n",
+ "gmpr = GeoMapper()\n",
+ "df = gmpr.replace_geocode(fips_data, \"fips\", \"zip\")\n",
+ "print(df.head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " date \n",
+ " hrr \n",
+ " count \n",
+ " total \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2018-01-01 \n",
+ " 1 \n",
+ " 1.772347 \n",
+ " 3.544694 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2018-01-01 \n",
+ " 183 \n",
+ " 7157.392404 \n",
+ " 71424.648014 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2018-01-01 \n",
+ " 184 \n",
+ " 2863.607596 \n",
+ " 28576.351986 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 2018-01-01 \n",
+ " 382 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2018-01-01 \n",
+ " 7 \n",
+ " 0.227653 \n",
+ " 0.455306 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " date hrr count total\n",
+ "0 2018-01-01 1 1.772347 3.544694\n",
+ "1 2018-01-01 183 7157.392404 71424.648014\n",
+ "2 2018-01-01 184 2863.607596 28576.351986\n",
+ "3 2018-01-01 382 1.000000 1.000000\n",
+ "4 2018-01-01 7 0.227653 0.455306"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gmpr = GeoMapper()\n",
+ "df = gmpr.replace_geocode(fips_data, \"fips\", \"hrr\")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = gmpr.replace_geocode(fips_data, \"fips\", \"hrr\")\n",
+ "df2 = gmpr.replace_geocode(fips_data, \"fips\", \"zip\")\n",
+ "df2 = gmpr.replace_geocode(df2, \"zip\", \"hrr\")\n",
+ "np.allclose(df[['count', 'total']].values, df2[['count', 'total']].values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Utility Inner Workings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deriving a crosswalk\n",
+ "Given two crosswalks, we create a derived crosswalk by merging on the common code. This is the method used in `geo_data_proc.py`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " zip \n",
+ " weight \n",
+ " state_code \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
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+ " 00601 \n",
+ " 0.994346 \n",
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+ " \n",
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+ " 44409 \n",
+ " 99929 \n",
+ " 1.000000 \n",
+ " 02 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
44410 rows × 3 columns
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+ "
"
+ ],
+ "text/plain": [
+ " zip weight state_code\n",
+ "0 00601 0.994346 72\n",
+ "1 00601 0.005654 72\n",
+ "2 00602 1.000000 72\n",
+ "3 00603 1.000000 72\n",
+ "4 00606 0.948753 72\n",
+ "... ... ... ...\n",
+ "44405 99923 1.000000 02\n",
+ "44406 99925 1.000000 02\n",
+ "44407 99926 1.000000 02\n",
+ "44408 99927 1.000000 02\n",
+ "44409 99929 1.000000 02\n",
+ "\n",
+ "[44410 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "state_df = pd.read_csv(\"state_codes_table.csv\", dtype={\"state_code\": str, \"state_id\": str, \"state_name\": str})\n",
+ "zip_fips_df = pd.read_csv(\"zip_fips_table.csv\", dtype={\"zip\": str, \"fips\": str})\n",
+ "zip_fips_df[\"state_code\"] = zip_fips_df[\"fips\"].str[:2]\n",
+ "zip_state_code_df = zip_fips_df.merge(state_df, on=\"state_code\", how=\"left\").drop(columns=[\"fips\", \"state_id\", \"state_name\"])\n",
+ "assert 52 == len(zip_state_code_df.state_code.unique())\n",
+ "zip_state_code_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A weighted crosswalk requires a summation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
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+ " hrr \n",
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+ " \n",
+ " \n",
+ "
\n",
+ "
5183 rows × 3 columns
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+ "
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+ ],
+ "text/plain": [
+ " fips hrr weight\n",
+ "0 01001 1 0.039105\n",
+ "1 01001 7 0.960895\n",
+ "2 01003 134 0.031998\n",
+ "3 01003 6 0.968002\n",
+ "4 01005 2 0.974360\n",
+ "... ... ... ...\n",
+ "5178 56039 274 0.003804\n",
+ "5179 56039 423 0.996196\n",
+ "5180 56041 423 1.000000\n",
+ "5181 56043 274 1.000000\n",
+ "5182 56045 457 1.000000\n",
+ "\n",
+ "[5183 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "FIPS_ZIP_OUT_FILENAME = \"fips_zip_table.csv\"\n",
+ "ZIP_HRR_OUT_FILENAME = \"zip_hrr_table.csv\"\n",
+ "from os.path import join, isfile\n",
+ "\n",
+ "fz_df = pd.read_csv(\n",
+ " FIPS_ZIP_OUT_FILENAME,\n",
+ " dtype={\"fips\": str, \"zip\": str, \"weight\": float},\n",
+ ")\n",
+ "zh_df = pd.read_csv(\n",
+ " ZIP_HRR_OUT_FILENAME,\n",
+ " dtype={\"zip\": str, \"hrr\": str},\n",
+ ")\n",
+ "\n",
+ "df = (fz_df.merge(zh_df, on=\"zip\", how=\"left\")\n",
+ " .drop(columns=\"zip\")\n",
+ " .groupby([\"fips\", \"hrr\"])\n",
+ " .sum()\n",
+ " .reset_index())\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adding a geocode column\n",
+ "Adding a new geocode column is a merge using a matching geocode (left or inner joins, depending on whether we wish to keep NAs or not). Here we translate from zip to fips on some faux data. Since this a merge on the left, invalid ZIP values present in the data, but not present in the crosswalk simply get NAN entries in their columns. If the crosswalk is weighted, a \"weights\" column is added also."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " zip \n",
+ " date \n",
+ " count \n",
+ " total \n",
+ " fips \n",
+ " weight \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 45140 \n",
+ " 2018-01-01 \n",
+ " 2.0 \n",
+ " 2.0 \n",
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+ " \n",
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+ " 2018-01-01 \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 95616 \n",
+ " 2018-01-04 \n",
+ " 100.0 \n",
+ " NaN \n",
+ " 06113 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 95618 \n",
+ " 2018-01-05 \n",
+ " 21.0 \n",
+ " 20.0 \n",
+ " 06095 \n",
+ " 0.003372 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 95618 \n",
+ " 2018-01-05 \n",
+ " 21.0 \n",
+ " 20.0 \n",
+ " 06113 \n",
+ " 0.996628 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " zip date count total fips weight\n",
+ "0 45140 2018-01-01 2.0 2.0 39025 0.523570\n",
+ "1 45140 2018-01-01 2.0 2.0 39061 0.288115\n",
+ "2 45140 2018-01-01 2.0 2.0 39165 0.188315\n",
+ "3 45147 2018-01-02 NaN 20.0 39025 0.938776\n",
+ "4 45147 2018-01-02 NaN 20.0 39061 0.061224\n",
+ "5 00500 2018-01-03 20.0 40.0 NaN NaN\n",
+ "6 95616 2018-01-04 100.0 NaN 06113 1.000000\n",
+ "7 95618 2018-01-05 21.0 20.0 06095 0.003372\n",
+ "8 95618 2018-01-05 21.0 20.0 06113 0.996628"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "zip_data = pd.DataFrame(\n",
+ " {\n",
+ " \"zip\": [\"45140\", \"45147\", \"00500\", \"95616\", \"95618\"],\n",
+ " \"date\": pd.date_range(\"2018-01-01\", periods=5),\n",
+ " \"count\": [2, np.nan, 20, 100, 21],\n",
+ " \"total\": [2, 20, 40, np.nan, 20]\n",
+ " }\n",
+ " )\n",
+ "zip_fips_df = pd.read_csv(\"zip_fips_table.csv\", dtype={\"zip\": str, \"fips\": str})\n",
+ "\n",
+ "data_df = zip_data.merge(zip_fips_df, left_on=\"zip\", right_on=\"zip\", how=\"left\")\n",
+ "data_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Replacing a column\n",
+ "If there are no weights, we just drop the old column and we're done. If there are weights, we multiply the data by the weights and sum over the old codes. A helpful way to think of the operation is a multiplication of the data matrix (row vectors are columns of the dataframe) $D$ by the weights matrix $W$, resulting in $D*W$. The weights matrix is row-stochastic (i.e. rows sum to 1). \n",
+ "\n",
+ "Note that the aggregation step (i.e. linear combination of source code values) requires a decision for how to handle NA values. We choose to zero-fill them to avoid propagating NAs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " date \n",
+ " fips \n",
+ " count \n",
+ " total \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 2018-01-01 \n",
+ " 39025 \n",
+ " 1.047140 \n",
+ " 1.047140 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2018-01-01 \n",
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+ " \n",
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+ " 2018-01-01 \n",
+ " 39165 \n",
+ " 0.376631 \n",
+ " 0.376631 \n",
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+ " 39025 \n",
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+ " \n",
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+ " 2018-01-02 \n",
+ " 39061 \n",
+ " 0.000000 \n",
+ " 1.224490 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 2018-01-04 \n",
+ " 06113 \n",
+ " 100.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 2018-01-05 \n",
+ " 06095 \n",
+ " 0.070819 \n",
+ " 0.067446 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 2018-01-05 \n",
+ " 06113 \n",
+ " 20.929181 \n",
+ " 19.932554 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " date fips count total\n",
+ "0 2018-01-01 39025 1.047140 1.047140\n",
+ "1 2018-01-01 39061 0.576229 0.576229\n",
+ "2 2018-01-01 39165 0.376631 0.376631\n",
+ "3 2018-01-02 39025 0.000000 18.775510\n",
+ "4 2018-01-02 39061 0.000000 1.224490\n",
+ "5 2018-01-04 06113 100.000000 0.000000\n",
+ "6 2018-01-05 06095 0.070819 0.067446\n",
+ "7 2018-01-05 06113 20.929181 19.932554"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_df = data_df.drop(columns=\"zip\")\n",
+ "\n",
+ "# Multiply and aggregate\n",
+ "data_df[[\"count\", \"total\"]] = data_df[[\"count\", \"total\"]].multiply(data_df[\"weight\"], axis=0)\n",
+ "data_df = (data_df.drop(\"weight\", axis=1)\n",
+ " .groupby([\"date\", \"fips\"])\n",
+ " .sum()\n",
+ " .reset_index())\n",
+ "data_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building population weights for FIPS <-> ZIP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " pop \n",
+ " \n",
+ " \n",
+ " fips \n",
+ " zip \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 72001 \n",
+ " 00601 \n",
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+ " \n",
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+ " 105 \n",
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+ " \n",
+ " \n",
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+ " ... \n",
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+ " \n",
+ " 02198 \n",
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+ " 87 \n",
+ " \n",
+ " \n",
+ " 99925 \n",
+ " 819 \n",
+ " \n",
+ " \n",
+ " 99926 \n",
+ " 1460 \n",
+ " \n",
+ " \n",
+ " 99927 \n",
+ " 94 \n",
+ " \n",
+ " \n",
+ " 02275 \n",
+ " 99929 \n",
+ " 2338 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
44410 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " pop\n",
+ "fips zip \n",
+ "72001 00601 18465\n",
+ "72141 00601 105\n",
+ "72003 00602 41520\n",
+ "72005 00603 54689\n",
+ "72093 00606 6276\n",
+ "... ...\n",
+ "02198 99923 87\n",
+ " 99925 819\n",
+ " 99926 1460\n",
+ " 99927 94\n",
+ "02275 99929 2338\n",
+ "\n",
+ "[44410 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "FIPS_BY_ZIP_POP_URL = (\n",
+ " \"https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txt?#\"\n",
+ ")\n",
+ "pop_df = pd.read_csv(FIPS_BY_ZIP_POP_URL)\n",
+ "\n",
+ "# Create the FIPS column by combining the state and county codes\n",
+ "pop_df[\"fips\"] = pop_df[\"STATE\"].astype(str).str.zfill(2) + pop_df[\"COUNTY\"].astype(\n",
+ " str\n",
+ ").str.zfill(3)\n",
+ "\n",
+ "# Create the ZIP column by adding leading zeros to the ZIP\n",
+ "pop_df[\"zip\"] = pop_df[\"ZCTA5\"].astype(str).str.zfill(5)\n",
+ "\n",
+ "# Pare down the dataframe to just the relevant columns: zip, fips, and population\n",
+ "pop_df = pop_df[[\"zip\", \"fips\", \"POPPT\"]].rename(columns={\"POPPT\": \"pop\"})\n",
+ "\n",
+ "pop_df.set_index(\n",
+ " [\"fips\", \"zip\"], inplace=True\n",
+ ") # can we do without this and resetting index below?\n",
+ "pop_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "312462997"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2010 Census, corresponds to 308 million population figure\n",
+ "pop_df[\"pop\"].sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## US Census FIPS <-> ZIP crosswalk versus simplemaps.com\n",
+ "We're switching to the US Census table for safety. The FIPS to ZIP weights in the two are essentially the same."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "326256148\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_census = GeoMapper().load_crosswalk(\"zip\", \"fips\")\n",
+ "df_simplemaps = pd.read_csv(\"../../data_proc/geomap/uszips.csv\")\n",
+ "print(df_simplemaps[\"population\"].sum())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df_simplemaps[\"county_weights\"] = df_simplemaps[\"county_weights\"].transform(lambda x: list(eval(x).items()))\n",
+ "df_simplemaps = df_simplemaps.explode(\"county_weights\")\n",
+ "df_simplemaps[\"county_fips\"] = df_simplemaps[\"county_weights\"].apply(lambda x: x[0])\n",
+ "df_simplemaps[\"county_weights\"] = df_simplemaps[\"county_weights\"].apply(lambda x: x[1]/100)\n",
+ "df_simplemaps = df_simplemaps.rename(columns={\"county_fips\": \"fips\"})\n",
+ "df_simplemaps[\"zip\"] = df_simplemaps[\"zip\"].astype(str).str.zfill(5)\n",
+ "df_simplemaps[\"fips\"] = df_simplemaps[\"fips\"].astype(str).str.zfill(5)\n",
+ "df = df_census.merge(df_simplemaps, on=[\"zip\", \"fips\"], how=\"left\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.1494991956541422e-05"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df[\"weight\"].sub(df[\"county_weights\"]).abs().mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.307895680646709e-09"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "1 - df[\"weight\"].corr(df[\"county_weights\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "113.4559999704361 147.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = df.dropna(subset=[\"population\"])\n",
+ "print(df.groupby(\"zip\")[\"population\"].unique().sum()[0] - df[\"population\"].multiply(df[\"county_weights\"]).sum(),\n",
+ " df.groupby(\"zip\")[\"population\"].unique().sum()[0] - df[\"population\"].multiply(df[\"weight\"]).sum())"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## We have updated the FIPS to HRR tables since the last version (James' version)\n",
+ "And they haven't changed by very much. \n",
+ "Note: Since JHU is now deactivated, this code may not work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_new = GeoMapper().load_crosswalk(\"fips\", \"hrr\")\n",
+ "df_old = pd.read_csv(\"https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/jhu_fix_0824/_delphi_utils_python/delphi_utils/data/fips_hrr_cross.csv?token=AANZ76Q7CUS7REWHRIGNKV27KHH6U\", dtype={\"fips\": str, \"hrr\": str, \"weight\": float})\n",
+ "df_old[\"fips\"] = df_old[\"fips\"].str.zfill(5)\n",
+ "df = df_new.groupby([\"hrr\", \"fips\"]).sum().reset_index().merge(df_old, on=[\"fips\", \"hrr\"], how=\"left\")\n",
+ "df.weight_x.sub(df.weight_y).abs().mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adding HHS codes\n",
+ "These are the department of health and human services region codes. They aggregate states into larger regions. I couldn't find a crosswalk file on the web, so I built one manually below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(\"../../data_proc/geomap/hhs.txt\") as f:\n",
+ " s = f.readlines()\n",
+ "\n",
+ "# Process text from https://www.hhs.gov/about/agencies/iea/regional-offices/index.html\n",
+ "s = [int(st[7:9]) if \"Region\" in st else st for st in s]\n",
+ "s = [st.strip().split(\", \") if type(st) == str else st for st in s]\n",
+ "d = {s[i]:s[i+1] for i in range(0, len(s), 2)}\n",
+ "d = {key:[s.lstrip(' and') for s in d[key]] for key in d}\n",
+ "\n",
+ "# Flatten\n",
+ "d = [[(key,x) for x in d[key]] for key in d]\n",
+ "d = [x for y in d for x in y]\n",
+ "\n",
+ "# Make naming adjustments\n",
+ "d.remove((2, \"the Virgin Islands\"))\n",
+ "d.append((2, \"U.S. Virgin Islands\"))\n",
+ "d.remove((9, \"Commonwealth of the Northern Mariana Islands\"))\n",
+ "d.append((9, \"Northern Mariana Islands\"))\n",
+ "\n",
+ "# Make dataframe\n",
+ "hhs = pd.DataFrame(d, columns=[\"hhs\", \"state_name\"])\n",
+ "hhs['hhs'] = hhs['hhs'].astype(str)\n",
+ "\n",
+ "ss_df = pd.read_csv(\"state_codes_table.csv\",\n",
+ " dtype={\"state_code\": str, \"state_name\": str, \"state_id\": str},\n",
+ ")\n",
+ "\n",
+ "ss_df = ss_df.merge(hhs, on=\"state_name\", how=\"left\").dropna()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.11.1 (main, Dec 23 2022, 09:28:24) [Clang 14.0.0 (clang-1400.0.29.202)]"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_delphi_utils_python/delphi_utils/validator/PLANS.md b/_delphi_utils_python/delphi_utils/validator/PLANS.md
index d7ca0c263..6f490aeec 100644
--- a/_delphi_utils_python/delphi_utils/validator/PLANS.md
+++ b/_delphi_utils_python/delphi_utils/validator/PLANS.md
@@ -41,7 +41,7 @@
### Starter/small issues
-* Backfill problems, especially with JHU and USA Facts, where a change to old data results in a datapoint that doesn’t agree with surrounding data ([JHU examples](https://delphi-org.slack.com/archives/CF9G83ZJ9/p1600729151013900)) or is very different from the value it replaced. If date is already in the API, have any values changed significantly within the "backfill" window (use span_length setting). See [this](https://github.com/cmu-delphi/covidcast-indicators/pull/155#discussion_r504195207) for context.
+* Backfill problems, especially with JHU and USA Facts (Both are now deactivated), where a change to old data results in a datapoint that doesn’t agree with surrounding data ([JHU examples](https://delphi-org.slack.com/archives/CF9G83ZJ9/p1600729151013900)) or is very different from the value it replaced. If date is already in the API, have any values changed significantly within the "backfill" window (use span_length setting). See [this](https://github.com/cmu-delphi/covidcast-indicators/pull/155#discussion_r504195207) for context.
* Run check_missing_date_files (or similar) on every geo type-signal type separately in comparative checks loop.
### Larger issues
diff --git a/_delphi_utils_python/delphi_utils/validator/static.py b/_delphi_utils_python/delphi_utils/validator/static.py
index d58096d97..d4449b27b 100644
--- a/_delphi_utils_python/delphi_utils/validator/static.py
+++ b/_delphi_utils_python/delphi_utils/validator/static.py
@@ -166,8 +166,6 @@ def _get_valid_geo_values(self, geo_type):
gmpr = GeoMapper()
valid_geos = gmpr.get_geo_values(geomap_type)
valid_geos |= set(self.params.additional_valid_geo_values.get(geo_type, []))
- if geo_type == "county":
- valid_geos |= set(x + "000" for x in gmpr.get_geo_values("state_code"))
return valid_geos
def check_bad_geo_id_value(self, df_to_test, filename, geo_type, report):
diff --git a/_delphi_utils_python/setup.py b/_delphi_utils_python/setup.py
index a1dc976e3..da9802263 100644
--- a/_delphi_utils_python/setup.py
+++ b/_delphi_utils_python/setup.py
@@ -26,7 +26,7 @@
setup(
name="delphi_utils",
- version="0.3.14",
+ version="0.3.15",
description="Shared Utility Functions for Indicators",
long_description=long_description,
long_description_content_type="text/markdown",
diff --git a/_delphi_utils_python/tests/test_geomap.py b/_delphi_utils_python/tests/test_geomap.py
index 78fccca77..ab86c143d 100644
--- a/_delphi_utils_python/tests/test_geomap.py
+++ b/_delphi_utils_python/tests/test_geomap.py
@@ -112,24 +112,6 @@ class TestGeoMapper:
"count": [2, 1, 5, 7, 3, 10021],
}
)
- jhu_uid_data = pd.DataFrame(
- {
- "jhu_uid": [
- 84048315,
- 84048137,
- 84013299,
- 84013299,
- 84070002,
- 84000013,
- 84090002,
- ],
- "timestamp": [pd.Timestamp("2018-01-01")] * 3
- + [pd.Timestamp("2018-01-03")] * 3
- + [pd.Timestamp("2018-01-01")],
- "count": [1, 2, 3, 4, 8, 5, 20],
- "total": [2, 4, 7, 11, 100, 10, 40],
- }
- )
state_data = pd.DataFrame(
{
"state_code": ["01", "02", "04"],
@@ -148,7 +130,6 @@ class TestGeoMapper:
"count": [7],
}
)
- # jhu_big_data = pd.read_csv("test_dir/small_deaths.csv")
# Loading tests updated 8/26
def test_crosswalks(self, geomapper):
@@ -161,8 +142,6 @@ def test_crosswalks(self, geomapper):
) # some weight discrepancy is fine for HRR
cw = geomapper.get_crosswalk(from_code="fips", to_code="zip")
assert cw.groupby("fips")["weight"].sum().round(5).eq(1.0).all()
- cw = geomapper.get_crosswalk(from_code="jhu_uid", to_code="fips")
- assert cw.groupby("jhu_uid")["weight"].sum().round(5).eq(1.0).all()
cw = geomapper.get_crosswalk(from_code="zip", to_code="fips")
assert cw.groupby("zip")["weight"].sum().round(5).eq(1.0).all()
# weight discrepancy is fine for MSA, for the same reasons as HRR
@@ -194,10 +173,6 @@ def test_load_fips_chngfips_table(self, geomapper):
chngfips_data = geomapper.get_crosswalk(from_code="fips", to_code="chng-fips")
assert tuple(chngfips_data.columns) == ("fips", "chng-fips")
- def test_load_jhu_uid_fips_table(self, geomapper):
- jhu_data = geomapper.get_crosswalk(from_code="jhu_uid", to_code="fips")
- assert np.allclose(jhu_data.groupby("jhu_uid").sum(numeric_only=True), 1.0)
-
def test_load_zip_hrr_table(self, geomapper):
zip_data = geomapper.get_crosswalk(from_code="zip", to_code="hrr")
assert pd.api.types.is_string_dtype(zip_data["zip"])
@@ -398,13 +373,13 @@ def test_add_geocode(self, geomapper):
def test_get_geos(self, geomapper):
assert geomapper.get_geo_values("nation") == {"us"}
assert geomapper.get_geo_values("hhs") == set(str(i) for i in range(1, 11))
- assert len(geomapper.get_geo_values("fips")) == 3236
+ assert len(geomapper.get_geo_values("fips")) == 3293
assert len(geomapper.get_geo_values("chng-fips")) == 2711
assert len(geomapper.get_geo_values("state_id")) == 60
assert len(geomapper.get_geo_values("zip")) == 32976
def test_get_geos_2019(self, geomapper_2019):
- assert len(geomapper_2019.get_geo_values("fips")) == 3235
+ assert len(geomapper_2019.get_geo_values("fips")) == 3292
assert len(geomapper_2019.get_geo_values("chng-fips")) == 2710
def test_get_geos_within(self, geomapper):
diff --git a/changehc/version.cfg b/changehc/version.cfg
index b5059a674..f48115d3f 100644
--- a/changehc/version.cfg
+++ b/changehc/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/claims_hosp/version.cfg b/claims_hosp/version.cfg
index b5059a674..f48115d3f 100644
--- a/claims_hosp/version.cfg
+++ b/claims_hosp/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/doctor_visits/version.cfg b/doctor_visits/version.cfg
index b5059a674..f48115d3f 100644
--- a/doctor_visits/version.cfg
+++ b/doctor_visits/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/dsew_community_profile/version.cfg b/dsew_community_profile/version.cfg
index b5059a674..f48115d3f 100644
--- a/dsew_community_profile/version.cfg
+++ b/dsew_community_profile/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/google_symptoms/version.cfg b/google_symptoms/version.cfg
index b5059a674..f48115d3f 100644
--- a/google_symptoms/version.cfg
+++ b/google_symptoms/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/hhs_hosp/version.cfg b/hhs_hosp/version.cfg
index b5059a674..f48115d3f 100644
--- a/hhs_hosp/version.cfg
+++ b/hhs_hosp/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/nchs_mortality/version.cfg b/nchs_mortality/version.cfg
index b5059a674..f48115d3f 100644
--- a/nchs_mortality/version.cfg
+++ b/nchs_mortality/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/nowcast/version.cfg b/nowcast/version.cfg
index b5059a674..f48115d3f 100644
--- a/nowcast/version.cfg
+++ b/nowcast/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/quidel_covidtest/version.cfg b/quidel_covidtest/version.cfg
index b5059a674..f48115d3f 100644
--- a/quidel_covidtest/version.cfg
+++ b/quidel_covidtest/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/sir_complainsalot/version.cfg b/sir_complainsalot/version.cfg
index b5059a674..f48115d3f 100644
--- a/sir_complainsalot/version.cfg
+++ b/sir_complainsalot/version.cfg
@@ -1 +1 @@
-current_version = 0.3.37
+current_version = 0.3.38
diff --git a/testing_utils/geo_utility_demonstration.ipynb b/testing_utils/geo_utility_demonstration.ipynb
deleted file mode 100644
index 1aaf2c2fd..000000000
--- a/testing_utils/geo_utility_demonstration.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
- {
- "source": [
- "# Geocoding Utility Demo"
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "from delphi_utils import GeoMapper\n",
- "\n",
- "os.chdir(\"_delphi_utils_python/delphi_utils/data/\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Basic Utility Usage\n",
- "Two functions: `add_geocode` and `replace_geocode`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "fips date count total zip weight\n0 01123 2018-01-01 2.0 4.0 35010 0.461001\n1 01123 2018-01-01 2.0 4.0 35072 0.013264\n2 01123 2018-01-01 2.0 4.0 35089 0.017661\n3 01123 2018-01-01 2.0 4.0 36078 0.113826\n4 01123 2018-01-01 2.0 4.0 36255 0.000433\n date zip count total\n0 2018-01-01 00602 0.000000 0.000000\n1 2018-01-01 00610 0.000000 0.000000\n2 2018-01-01 00676 0.000000 0.000000\n3 2018-01-01 00677 0.000000 0.000000\n4 2018-01-01 35010 0.922001 1.844002\n"
- }
- ],
- "source": [
- "fips_data = pd.DataFrame({\n",
- " \"fips\":[1123,48253,72003,18181],\n",
- " \"date\":[pd.Timestamp('2018-01-01')]*4,\n",
- " \"count\": [2,1,np.nan,10021],\n",
- " \"total\": [4,1,np.nan,100001]\n",
- " })\n",
- "\n",
- "# Add a new column with the new code\n",
- "gmpr = GeoMapper()\n",
- "df = gmpr.add_geocode(fips_data, \"fips\", \"zip\")\n",
- "print(df.head())\n",
- "\n",
- "# Convert a column with the new code\n",
- "gmpr = GeoMapper()\n",
- "df = gmpr.replace_geocode(fips_data, \"fips\", \"zip\")\n",
- "print(df.head())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " date hrr count total\n0 2018-01-01 1 1.772347 3.544694\n1 2018-01-01 183 7157.392404 71424.648014\n2 2018-01-01 184 2863.607596 28576.351986\n3 2018-01-01 382 1.000000 1.000000\n4 2018-01-01 7 0.227653 0.455306",
- "text/html": "\n\n
\n \n \n \n date \n hrr \n count \n total \n \n \n \n \n 0 \n 2018-01-01 \n 1 \n 1.772347 \n 3.544694 \n \n \n 1 \n 2018-01-01 \n 183 \n 7157.392404 \n 71424.648014 \n \n \n 2 \n 2018-01-01 \n 184 \n 2863.607596 \n 28576.351986 \n \n \n 3 \n 2018-01-01 \n 382 \n 1.000000 \n 1.000000 \n \n \n 4 \n 2018-01-01 \n 7 \n 0.227653 \n 0.455306 \n \n \n
\n
"
- },
- "metadata": {},
- "execution_count": 16
- }
- ],
- "source": [
- "gmpr = GeoMapper()\n",
- "df = gmpr.replace_geocode(fips_data, \"fips\", \"hrr\")\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "True"
- },
- "metadata": {},
- "execution_count": 19
- }
- ],
- "source": [
- "df = gmpr.replace_geocode(fips_data, \"fips\", \"hrr\")\n",
- "df2 = gmpr.replace_geocode(fips_data, \"fips\", \"zip\")\n",
- "df2 = gmpr.replace_geocode(df2, \"zip\", \"hrr\")\n",
- "np.allclose(df[['count', 'total']].values, df2[['count', 'total']].values)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Utility Inner Workings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Deriving a crosswalk\n",
- "Given two crosswalks, we create a derived crosswalk by merging on the common code. This is the method used in `geo_data_proc.py`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " zip weight state_code\n0 00601 0.994346 72\n1 00601 0.005654 72\n2 00602 1.000000 72\n3 00603 1.000000 72\n4 00606 0.948753 72\n... ... ... ...\n44405 99923 1.000000 02\n44406 99925 1.000000 02\n44407 99926 1.000000 02\n44408 99927 1.000000 02\n44409 99929 1.000000 02\n\n[44410 rows x 3 columns]",
- "text/html": "\n\n
\n \n \n \n zip \n weight \n state_code \n \n \n \n \n 0 \n 00601 \n 0.994346 \n 72 \n \n \n 1 \n 00601 \n 0.005654 \n 72 \n \n \n 2 \n 00602 \n 1.000000 \n 72 \n \n \n 3 \n 00603 \n 1.000000 \n 72 \n \n \n 4 \n 00606 \n 0.948753 \n 72 \n \n \n ... \n ... \n ... \n ... \n \n \n 44405 \n 99923 \n 1.000000 \n 02 \n \n \n 44406 \n 99925 \n 1.000000 \n 02 \n \n \n 44407 \n 99926 \n 1.000000 \n 02 \n \n \n 44408 \n 99927 \n 1.000000 \n 02 \n \n \n 44409 \n 99929 \n 1.000000 \n 02 \n \n \n
\n
44410 rows × 3 columns
\n
"
- },
- "metadata": {},
- "execution_count": 21
- }
- ],
- "source": [
- "state_df = pd.read_csv(\"state_codes_table.csv\", dtype={\"state_code\": str, \"state_id\": str, \"state_name\": str})\n",
- "zip_fips_df = pd.read_csv(\"zip_fips_table.csv\", dtype={\"zip\": str, \"fips\": str})\n",
- "zip_fips_df[\"state_code\"] = zip_fips_df[\"fips\"].str[:2]\n",
- "zip_state_code_df = zip_fips_df.merge(state_df, on=\"state_code\", how=\"left\").drop(columns=[\"fips\", \"state_id\", \"state_name\"])\n",
- "assert 52 == len(zip_state_code_df.state_code.unique())\n",
- "zip_state_code_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A weighted crosswalk requires a summation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " fips hrr weight\n0 01001 1 0.039105\n1 01001 7 0.960895\n2 01003 134 0.031998\n3 01003 6 0.968002\n4 01005 2 0.974360\n... ... ... ...\n5178 56039 274 0.003804\n5179 56039 423 0.996196\n5180 56041 423 1.000000\n5181 56043 274 1.000000\n5182 56045 457 1.000000\n\n[5183 rows x 3 columns]",
- "text/html": "\n\n
\n \n \n \n fips \n hrr \n weight \n \n \n \n \n 0 \n 01001 \n 1 \n 0.039105 \n \n \n 1 \n 01001 \n 7 \n 0.960895 \n \n \n 2 \n 01003 \n 134 \n 0.031998 \n \n \n 3 \n 01003 \n 6 \n 0.968002 \n \n \n 4 \n 01005 \n 2 \n 0.974360 \n \n \n ... \n ... \n ... \n ... \n \n \n 5178 \n 56039 \n 274 \n 0.003804 \n \n \n 5179 \n 56039 \n 423 \n 0.996196 \n \n \n 5180 \n 56041 \n 423 \n 1.000000 \n \n \n 5181 \n 56043 \n 274 \n 1.000000 \n \n \n 5182 \n 56045 \n 457 \n 1.000000 \n \n \n
\n
5183 rows × 3 columns
\n
"
- },
- "metadata": {},
- "execution_count": 25
- }
- ],
- "source": [
- "FIPS_ZIP_OUT_FILENAME = \"fips_zip_table.csv\"\n",
- "ZIP_HRR_OUT_FILENAME = \"zip_hrr_table.csv\"\n",
- "OUTPUT_DIR = \"../../delphi_utils/data\"\n",
- "from os.path import join, isfile\n",
- "\n",
- "fz_df = pd.read_csv(\n",
- " join(OUTPUT_DIR, FIPS_ZIP_OUT_FILENAME),\n",
- " dtype={\"fips\": str, \"zip\": str, \"weight\": float},\n",
- ")\n",
- "zh_df = pd.read_csv(\n",
- " join(OUTPUT_DIR, ZIP_HRR_OUT_FILENAME),\n",
- " dtype={\"zip\": str, \"hrr\": str},\n",
- ")\n",
- "\n",
- "df = (fz_df.merge(zh_df, on=\"zip\", how=\"left\")\n",
- " .drop(columns=\"zip\")\n",
- " .groupby([\"fips\", \"hrr\"])\n",
- " .sum()\n",
- " .reset_index())\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Adding a geocode column\n",
- "Adding a new geocode column is a merge using a matching geocode (left or inner joins, depending on whether we wish to keep NAs or not). Here we translate from zip to fips on some faux data. Since this a merge on the left, invalid ZIP values present in the data, but not present in the crosswalk simply get NAN entries in their columns. If the crosswalk is weighted, a \"weights\" column is added also."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " zip date count total fips weight\n0 45140 2018-01-01 2.0 2.0 39025 0.523570\n1 45140 2018-01-01 2.0 2.0 39061 0.288115\n2 45140 2018-01-01 2.0 2.0 39165 0.188315\n3 45147 2018-01-02 NaN 20.0 39025 0.938776\n4 45147 2018-01-02 NaN 20.0 39061 0.061224\n5 00500 2018-01-03 20.0 40.0 NaN NaN\n6 95616 2018-01-04 100.0 NaN 06113 1.000000\n7 95618 2018-01-05 21.0 20.0 06095 0.003372\n8 95618 2018-01-05 21.0 20.0 06113 0.996628",
- "text/html": "\n\n
\n \n \n \n zip \n date \n count \n total \n fips \n weight \n \n \n \n \n 0 \n 45140 \n 2018-01-01 \n 2.0 \n 2.0 \n 39025 \n 0.523570 \n \n \n 1 \n 45140 \n 2018-01-01 \n 2.0 \n 2.0 \n 39061 \n 0.288115 \n \n \n 2 \n 45140 \n 2018-01-01 \n 2.0 \n 2.0 \n 39165 \n 0.188315 \n \n \n 3 \n 45147 \n 2018-01-02 \n NaN \n 20.0 \n 39025 \n 0.938776 \n \n \n 4 \n 45147 \n 2018-01-02 \n NaN \n 20.0 \n 39061 \n 0.061224 \n \n \n 5 \n 00500 \n 2018-01-03 \n 20.0 \n 40.0 \n NaN \n NaN \n \n \n 6 \n 95616 \n 2018-01-04 \n 100.0 \n NaN \n 06113 \n 1.000000 \n \n \n 7 \n 95618 \n 2018-01-05 \n 21.0 \n 20.0 \n 06095 \n 0.003372 \n \n \n 8 \n 95618 \n 2018-01-05 \n 21.0 \n 20.0 \n 06113 \n 0.996628 \n \n \n
\n
"
- },
- "metadata": {},
- "execution_count": 27
- }
- ],
- "source": [
- "zip_data = pd.DataFrame(\n",
- " {\n",
- " \"zip\": [\"45140\", \"45147\", \"00500\", \"95616\", \"95618\"],\n",
- " \"date\": pd.date_range(\"2018-01-01\", periods=5),\n",
- " \"count\": [2, np.nan, 20, 100, 21],\n",
- " \"total\": [2, 20, 40, np.nan, 20]\n",
- " }\n",
- " )\n",
- "zip_fips_df = pd.read_csv(\"zip_fips_table.csv\", dtype={\"zip\": str, \"fips\": str})\n",
- "\n",
- "data_df = zip_data.merge(zip_fips_df, left_on=\"zip\", right_on=\"zip\", how=\"left\")\n",
- "data_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Replacing a column\n",
- "If there are no weights, we just drop the old column and we're done. If there are weights, we multiply the data by the weights and sum over the old codes. A helpful way to think of the operation is a multiplication of the data matrix (row vectors are columns of the dataframe) $D$ by the weights matrix $W$, resulting in $D*W$. The weights matrix is row-stochastic (i.e. rows sum to 1). \n",
- "\n",
- "Note that the aggregation step (i.e. linear combination of source code values) requires a decision for how to handle NA values. We choose to zero-fill them to avoid propagating NAs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " date fips count total\n0 2018-01-01 39025 1.047140 1.047140\n1 2018-01-01 39061 0.576229 0.576229\n2 2018-01-01 39165 0.376631 0.376631\n3 2018-01-02 39025 0.000000 18.775510\n4 2018-01-02 39061 0.000000 1.224490\n5 2018-01-04 06113 100.000000 0.000000\n6 2018-01-05 06095 0.070819 0.067446\n7 2018-01-05 06113 20.929181 19.932554",
- "text/html": "\n\n
\n \n \n \n date \n fips \n count \n total \n \n \n \n \n 0 \n 2018-01-01 \n 39025 \n 1.047140 \n 1.047140 \n \n \n 1 \n 2018-01-01 \n 39061 \n 0.576229 \n 0.576229 \n \n \n 2 \n 2018-01-01 \n 39165 \n 0.376631 \n 0.376631 \n \n \n 3 \n 2018-01-02 \n 39025 \n 0.000000 \n 18.775510 \n \n \n 4 \n 2018-01-02 \n 39061 \n 0.000000 \n 1.224490 \n \n \n 5 \n 2018-01-04 \n 06113 \n 100.000000 \n 0.000000 \n \n \n 6 \n 2018-01-05 \n 06095 \n 0.070819 \n 0.067446 \n \n \n 7 \n 2018-01-05 \n 06113 \n 20.929181 \n 19.932554 \n \n \n
\n
"
- },
- "metadata": {},
- "execution_count": 28
- }
- ],
- "source": [
- "data_df = data_df.drop(columns=\"zip\")\n",
- "\n",
- "# Multiply and aggregate\n",
- "data_df[[\"count\", \"total\"]] = data_df[[\"count\", \"total\"]].multiply(data_df[\"weight\"], axis=0)\n",
- "data_df = (data_df.drop(\"weight\", axis=1)\n",
- " .groupby([\"date\", \"fips\"])\n",
- " .sum()\n",
- " .reset_index())\n",
- "data_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building population weights for FIPS <-> ZIP"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": " pop\nfips zip \n72001 00601 18465\n72141 00601 105\n72003 00602 41520\n72005 00603 54689\n72093 00606 6276\n... ...\n02198 99923 87\n 99925 819\n 99926 1460\n 99927 94\n02275 99929 2338\n\n[44410 rows x 1 columns]",
- "text/html": "\n\n
\n \n \n \n \n pop \n \n \n fips \n zip \n \n \n \n \n \n 72001 \n 00601 \n 18465 \n \n \n 72141 \n 00601 \n 105 \n \n \n 72003 \n 00602 \n 41520 \n \n \n 72005 \n 00603 \n 54689 \n \n \n 72093 \n 00606 \n 6276 \n \n \n ... \n ... \n ... \n \n \n 02198 \n 99923 \n 87 \n \n \n 99925 \n 819 \n \n \n 99926 \n 1460 \n \n \n 99927 \n 94 \n \n \n 02275 \n 99929 \n 2338 \n \n \n
\n
44410 rows × 1 columns
\n
"
- },
- "metadata": {},
- "execution_count": 29
- }
- ],
- "source": [
- "FIPS_BY_ZIP_POP_URL = (\n",
- " \"https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txt?#\"\n",
- ")\n",
- "pop_df = pd.read_csv(FIPS_BY_ZIP_POP_URL)\n",
- "\n",
- "# Create the FIPS column by combining the state and county codes\n",
- "pop_df[\"fips\"] = pop_df[\"STATE\"].astype(str).str.zfill(2) + pop_df[\"COUNTY\"].astype(\n",
- " str\n",
- ").str.zfill(3)\n",
- "\n",
- "# Create the ZIP column by adding leading zeros to the ZIP\n",
- "pop_df[\"zip\"] = pop_df[\"ZCTA5\"].astype(str).str.zfill(5)\n",
- "\n",
- "# Pare down the dataframe to just the relevant columns: zip, fips, and population\n",
- "pop_df = pop_df[[\"zip\", \"fips\", \"POPPT\"]].rename(columns={\"POPPT\": \"pop\"})\n",
- "\n",
- "pop_df.set_index(\n",
- " [\"fips\", \"zip\"], inplace=True\n",
- ") # can we do without this and resetting index below?\n",
- "pop_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "312462997"
- },
- "metadata": {},
- "execution_count": 31
- }
- ],
- "source": [
- "# 2010 Census, corresponds to 308 million population figure\n",
- "pop_df[\"pop\"].sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## US Census FIPS <-> ZIP crosswalk versus simplemaps.com\n",
- "We're switching to the US Census table for safety. The FIPS to ZIP weights in the two are essentially the same."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 110,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "326256148\n"
- }
- ],
- "source": [
- "df_census = GeoMapper().load_crosswalk(\"zip\", \"fips\")\n",
- "df_simplemaps = pd.read_csv(\"../../data_proc/geomap/uszips.csv\")\n",
- "print(df_simplemaps[\"population\"].sum())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 111,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "df_simplemaps[\"county_weights\"] = df_simplemaps[\"county_weights\"].transform(lambda x: list(eval(x).items()))\n",
- "df_simplemaps = df_simplemaps.explode(\"county_weights\")\n",
- "df_simplemaps[\"county_fips\"] = df_simplemaps[\"county_weights\"].apply(lambda x: x[0])\n",
- "df_simplemaps[\"county_weights\"] = df_simplemaps[\"county_weights\"].apply(lambda x: x[1]/100)\n",
- "df_simplemaps = df_simplemaps.rename(columns={\"county_fips\": \"fips\"})\n",
- "df_simplemaps[\"zip\"] = df_simplemaps[\"zip\"].astype(str).str.zfill(5)\n",
- "df_simplemaps[\"fips\"] = df_simplemaps[\"fips\"].astype(str).str.zfill(5)\n",
- "df = df_census.merge(df_simplemaps, on=[\"zip\", \"fips\"], how=\"left\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "1.1494991956541422e-05"
- },
- "metadata": {},
- "execution_count": 62
- }
- ],
- "source": [
- "df[\"weight\"].sub(df[\"county_weights\"]).abs().mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "1.307895680646709e-09"
- },
- "metadata": {},
- "execution_count": 68
- }
- ],
- "source": [
- "1 - df[\"weight\"].corr(df[\"county_weights\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 120,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": "113.4559999704361 147.0\n"
- }
- ],
- "source": [
- "df = df.dropna(subset=[\"population\"])\n",
- "print(df.groupby(\"zip\")[\"population\"].unique().sum()[0] - df[\"population\"].multiply(df[\"county_weights\"]).sum(),\n",
- " df.groupby(\"zip\")[\"population\"].unique().sum()[0] - df[\"population\"].multiply(df[\"weight\"]).sum())"
- ]
- },
- {
- "source": [
- "## We have updated the FIPS to HRR tables since the last version (James' version)\n",
- "And they haven't changed by very much."
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_new = GeoMapper().load_crosswalk(\"fips\", \"hrr\")\n",
- "df_old = pd.read_csv(\"https://raw.githubusercontent.com/cmu-delphi/covidcast-indicators/jhu_fix_0824/_delphi_utils_python/delphi_utils/data/fips_hrr_cross.csv?token=AANZ76Q7CUS7REWHRIGNKV27KHH6U\", dtype={\"fips\": str, \"hrr\": str, \"weight\": float})\n",
- "df_old[\"fips\"] = df_old[\"fips\"].str.zfill(5)\n",
- "df = df_new.groupby([\"hrr\", \"fips\"]).sum().reset_index().merge(df_old, on=[\"fips\", \"hrr\"], how=\"left\")\n",
- "df.weight_x.sub(df.weight_y).abs().mean()"
- ]
- },
- {
- "source": [
- "## Adding HHS codes\n",
- "These are the department of health and human services region codes. They aggregate states into larger regions. I couldn't find a crosswalk file on the web, so I built one manually below."
- ],
- "cell_type": "markdown",
- "metadata": {}
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with open(\"../../data_proc/geomap/hhs.txt\") as f:\n",
- " s = f.readlines()\n",
- "\n",
- "# Process text from https://www.hhs.gov/about/agencies/iea/regional-offices/index.html\n",
- "s = [int(st[7:9]) if \"Region\" in st else st for st in s]\n",
- "s = [st.strip().split(\", \") if type(st) == str else st for st in s]\n",
- "d = {s[i]:s[i+1] for i in range(0, len(s), 2)}\n",
- "d = {key:[s.lstrip(' and') for s in d[key]] for key in d}\n",
- "\n",
- "# Flatten\n",
- "d = [[(key,x) for x in d[key]] for key in d]\n",
- "d = [x for y in d for x in y]\n",
- "\n",
- "# Make naming adjustments\n",
- "d.remove((2, \"the Virgin Islands\"))\n",
- "d.append((2, \"U.S. Virgin Islands\"))\n",
- "d.remove((9, \"Commonwealth of the Northern Mariana Islands\"))\n",
- "d.append((9, \"Northern Mariana Islands\"))\n",
- "\n",
- "# Make dataframe\n",
- "hhs = pd.DataFrame(d, columns=[\"hhs\", \"state_name\"])\n",
- "hhs['hhs'] = hhs['hhs'].astype(str)\n",
- "\n",
- "ss_df = pd.read_csv(\"state_codes_table.csv\",\n",
- " dtype={\"state_code\": str, \"state_name\": str, \"state_id\": str},\n",
- ")\n",
- "\n",
- "ss_df = ss_df.merge(hhs, on=\"state_name\", how=\"left\").dropna()\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.8.5 64-bit ('delphi': venv)",
- "language": "python",
- "name": "python_defaultSpec_1599600467099"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5-final"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/testing_utils/indicator_validation.template.ipynb b/testing_utils/indicator_validation.template.ipynb
deleted file mode 100644
index 8656a82f9..000000000
--- a/testing_utils/indicator_validation.template.ipynb
+++ /dev/null
@@ -1,247 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Indicator Validation\n",
- "This notebook is aimed at assisting developers with tracking large scale indicator changes beyond what can be picked up by the unit tests. While unit tests perform local sanity checks on the operations, the tests here will be more qualitative in nature, comparing the live version of an indicator with the propagating changes.\n",
- "\n",
- "## Usage\n",
- "Since each indicator will have different points of interest, this notebook will only provide a framework to get started. The goal is to support the comparison of the dataframes resulting from the data cleaning and shaping that our indicator code provides."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set your base directory (assuming this notebook is run from \"covidcast_indicators/testing_utils/\")\n",
- "os.chdir(\"../\")\n",
- "\n",
- "%run testing_utils/indicator_validation.py"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The above will likely cause an error due to missing dependencies, run this to fix\n",
- "%%capture\n",
- "!pip install pandas matplotlib joblib covidcast"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Installation\n",
- "Install the utilities and the indicator you plan to test."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "os.chdir(join(ROOT_DIR, \"_delphi_utils_python\"))\n",
- "!pip install -e .\n",
- "os.chdir(join(ROOT_DIR, \"jhu\"))\n",
- "!pip install -e ."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run the indicator\n",
- "If you are planning on testing your local receiving directory, you will need to generate those files. You can do that by running the cell below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "os.chdir(join(ROOT_DIR, \"jhu\"))\n",
- "!python -m delphi_jhu"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Qualitative Comparisons\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Loading Data\n",
- "To load a local indicator and a remote `covidcast` indicator as dataframes for comparison use the function `load_signal_data(local_signal_dir, remote_signal_name, signal_type, start_day, end_day, geo_type)`. \n",
- "\n",
- "Separate functions for loading just the local and remote data exist as well. **Note that the local and remote values are cached to disk** to speed up computation and reduce API calls. See function docstring for instructions on clearing the cache."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "local_signal_dir = \"jhu\"\n",
- "remote_signal_name = \"usa-facts\" #warning: This indicator has been deprecated\n",
- "signal_type = \"confirmed_incidence_prop\"\n",
- "start_day = date(2020, 8, 1)\n",
- "end_day = date.today()\n",
- "geo_type = \"state\"\n",
- "\n",
- "# load_local_signal_data.clear()\n",
- "local_data, remote_data = load_signal_data(local_signal_dir, remote_signal_name, signal_type, start_day, end_day, geo_type)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Comparing Geocode Signals\n",
- "A simple plotting demo."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Total difference: -0.18981468089412878\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-10-18T15:40:09.231808 \n image/svg+xml \n \n \n Matplotlib v3.3.2, https://matplotlib.org/ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "geo_code = \"ca\"\n",
- "rd = remote_data.loc[geo_code, :][\"value\"]\n",
- "ld = local_data.loc[geo_code, :][\"val\"]\n",
- "plt.figure(figsize=(16, 6))\n",
- "plt.plot(rd, label=\"remote\")\n",
- "plt.plot(ld, label=\"local\")\n",
- "plt.title(geo_code)\n",
- "plt.legend()\n",
- "print(\"Total difference: \", rd.sub(ld).sum())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Automatically Detecting Outliers\n",
- "Defining your own comparison statistics, we can automatically plot outlier signals."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-10-18T15:53:15.961372 \n image/svg+xml \n \n \n Matplotlib v3.3.2, https://matplotlib.org/ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "def simple_outlier_test(ld, rd):\n",
- " \"\"\"\n",
- " Returns True if the rd and ld time series are similar, False otherwise.\n",
- " \"\"\"\n",
- " max_percent_diff = rd.sub(ld).abs().max() / rd.abs().max()\n",
- " sum_percent_diff = rd.sub(ld).abs().sum() / rd.abs().sum()\n",
- " return False if max_percent_diff > 1.1 or sum_percent_diff > 1.1 else True\n",
- "\n",
- "def plot_outliers(ld, rd, outlier_check):\n",
- " \"\"\"\n",
- " Provides time series plots of geocodes that are sufficiently different.\n",
- " \"\"\"\n",
- " local_geo_codes = set(local_data.reset_index()[\"geo_id\"].unique())\n",
- " remote_geo_codes = set(remote_data.reset_index()[\"geo_value\"].unique())\n",
- " geo_codes = local_geo_codes.intersection(remote_geo_codes)\n",
- " for geo_code in geo_codes:\n",
- " rd = remote_data.loc[geo_code, :][\"value\"]\n",
- " ld = local_data.loc[geo_code, :][\"val\"]\n",
- " if outlier_check(ld, rd) == False:\n",
- " plt.figure(figsize=(16, 6))\n",
- " plt.plot(rd, label=\"remote\")\n",
- " plt.plot(ld, label=\"local\")\n",
- " plt.title(geo_code)\n",
- " plt.legend()\n",
- "\n",
- "plot_outliers(ld, rd, simple_outlier_test)"
- ]
- }
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