Wastewater proxy merge #29
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…y and summarized the data that feed into the emis
…ote: there is an issue where the proxy emis do not match with the GHGI emis
…ote: there is an issue where the proxy emis do not match with the GHGI emis
… code is integrated into existing ECHO / GHGRP data allocation
… up final proxy creation step.
…ndle proxy allocation. meat and poultry is the last remaining proxy sector to finalize
…y complete other than potential geospatial de-deduplication improvements.
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I went through the updated script to address merge conflicts with the existing script, it should be ready for merge now. |
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| def convert_state_names_to_codes(df, state_column): |
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us_state_to_abbrev <- check out this function, I think John wrote it and this might be a duplicate of that function?
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| # Extract coordinates | ||
| echo_coords = np.array([(lat, lon) for lat, lon in |
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I'd like to revise this using the geospatial functions in geopandas instead of doing it manually. I can advise on it.
| dist = np.sqrt((ghgrp_row['latitude'] - echo_row['Facility Latitude'])**2 + | ||
| (ghgrp_row['longitude'] - echo_row['Facility Longitude'])**2) | ||
| if dist < 0.025 and ghgrp_row['Year'] == echo_row['Year']: | ||
| dist = np.sqrt((ghgrp_row['latitude'] - echo_row['latitude'])**2 + |
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I think it's fine to continue to use RSS here, but I do wonder for readability if it makes sense to use the CRS defined in config.py (EQ_AREA_CRS = "ESRI:102003") or an equidistant CRS to do calculations in meters? This might also help the clarity of the ECHO vs FRS distance tolerance / filtering above doing it in meters instead of DD.
| return result_df | ||
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| def geocode_address(df, address_column): |
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Love this. Could you move it to utils.py?
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| # %% | |||
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Let's get ahead of John's code review an add a header for this file.
Pull completed wastewater proxy script into main. This script writes all of the industrial wastewater proxies and the nonseptic domestic proxy.