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Propagate JHU linting fixes into main #406

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Merged
merged 4 commits into from
Oct 29, 2020
Merged

Propagate JHU linting fixes into main #406

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84049be
Remove cruise ships FIPS codes, 88888 and 99999
dshemetov Oct 20, 2020
7d50e4e
Merge pull request #335 from cmu-delphi/jhu_cruise_ships_fix
krivard Oct 29, 2020
beb5d87
JHU: Appease the linter
krivard Oct 29, 2020
ab3ebf2
Merge pull request #404 from cmu-delphi/fix/deploy-jhu-linter
krivard Oct 29, 2020
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8 changes: 7 additions & 1 deletion _delphi_utils_python/data_proc/geomap/geo_data_proc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,6 +217,12 @@ def create_jhu_uid_fips_crosswalk():
{"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})
Expand All @@ -234,7 +240,7 @@ def create_jhu_uid_fips_crosswalk():
# Drop the JHU UIDs that were hand-modified
dup_ind = jhu_df["jhu_uid"].isin(
pd.concat(
[hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned]
[hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned, cruise_ships]
)["jhu_uid"].values
)
jhu_df.drop(jhu_df.index[dup_ind], inplace=True)
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2 changes: 0 additions & 2 deletions _delphi_utils_python/delphi_utils/data/jhu_uid_fips_table.csv
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Expand Up @@ -82,8 +82,6 @@ jhu_uid,fips,weight
63072149,72149,1.0
63072151,72151,1.0
63072153,72153,1.0
84088888,88888,1.0
84099999,99999,1.0
84000001,01000,1.0
84000002,02000,1.0
84000004,04000,1.0
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2 changes: 1 addition & 1 deletion jhu/delphi_jhu/pull.py
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Expand Up @@ -14,7 +14,7 @@ def download_data(base_url: str, metric: str) -> pd.DataFrame:
df = pd.read_csv(base_url.format(metric=metric))
# Keep the UID and the time series columns only
# The regex filters for columns with the date format MM-DD-YY or M-D-YY
df = df.filter(regex="\d{1,2}\/\d{1,2}\/\d{2}|UID").melt(
df = df.filter(regex=r"\d{1,2}\/\d{1,2}\/\d{2}|UID").melt(
id_vars=["UID"], var_name="timestamp", value_name="cumulative_counts"
)
df["timestamp"] = pd.to_datetime(df["timestamp"])
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2 changes: 0 additions & 2 deletions jhu/delphi_jhu/run.py
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Expand Up @@ -7,10 +7,8 @@
from datetime import datetime
from itertools import product
from functools import partial
from os.path import join

import numpy as np
import pandas as pd
from delphi_utils import (
read_params,
create_export_csv,
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