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"pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "sphinx", "sphinx-autosummary-accessors", "xarray[complete]"] +etc = ["sparse"] +io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"] +parallel = ["dask[complete]"] +viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"] + [[package]] name = "zipp" version = "3.21.0" @@ -2895,5 +3932,5 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" -python-versions = "^3.10" -content-hash = "0e3ce24bf8a1f077ed69b69eabb228c49b071f51d89d16bd651f6e2305e33a77" +python-versions = ">=3.10,<3.13" +content-hash = "2fd97730190d0fa167efdcba5bd1414d5d48e148b70fb5be5e2cf601fe3c5cb2" diff --git a/pyproject.toml b/pyproject.toml index e0dd5b41..88e2ad6e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,13 +9,15 @@ license = "TBD" include = ["LICENCE"] # poetry uses US English so assumes it will be spelt LICENSE [tool.poetry.dependencies] -python = "^3.10" +python = ">=3.10,<3.13" selenium = "^4.26.1" webdriver-manager = "^4.0.2" beautifulsoup4 = "^4.12.3" types-beautifulsoup4 = "^4.12.0.20241020" types-requests = "^2.32.0.20241016" pandas = "^2.2.3" +pycountry = "^24.6.1" +primap2 = "^0.11.2" [tool.poetry.group.tests.dependencies] diff --git a/requirements.txt b/requirements.txt index 2bf5df74..1600dcee 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,13 @@ -attrs==24.2.0 ; python_version >= "3.10" and python_version < "4.0" \ +attrs==24.2.0 ; python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346 \ --hash=sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2 -beautifulsoup4==4.12.3 ; python_version >= "3.10" and python_version < "4.0" \ +beautifulsoup4==4.12.3 ; python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051 \ --hash=sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed -certifi==2024.8.30 ; python_version >= "3.10" and python_version < "4.0" \ +certifi==2024.8.30 ; python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8 \ --hash=sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9 -cffi==1.17.1 ; os_name == "nt" and implementation_name != "pypy" and python_version >= "3.10" and python_version < "4.0" \ +cffi==1.17.1 ; os_name == "nt" and implementation_name != "pypy" and python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8 \ --hash=sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2 \ --hash=sha256:0e2b1fac190ae3ebfe37b979cc1ce69c81f4e4fe5746bb401dca63a9062cdaf1 \ @@ -75,7 +75,7 @@ cffi==1.17.1 ; os_name == "nt" and implementation_name != "pypy" and python_vers --hash=sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99 \ --hash=sha256:f7f5baafcc48261359e14bcd6d9bff6d4b28d9103847c9e136694cb0501aef87 \ --hash=sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b -charset-normalizer==3.4.0 ; python_version >= "3.10" and python_version < "4.0" \ +charset-normalizer==3.4.0 ; python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:0099d79bdfcf5c1f0c2c72f91516702ebf8b0b8ddd8905f97a8aecf49712c621 \ --hash=sha256:0713f3adb9d03d49d365b70b84775d0a0d18e4ab08d12bc46baa6132ba78aaf6 \ --hash=sha256:07afec21bbbbf8a5cc3651aa96b980afe2526e7f048fdfb7f1014d84acc8b6d8 \ @@ -181,78 +181,490 @@ charset-normalizer==3.4.0 ; python_version >= "3.10" and python_version < "4.0" --hash=sha256:f606a1881d2663630ea5b8ce2efe2111740df4b687bd78b34a8131baa007f79b \ --hash=sha256:fe9f97feb71aa9896b81973a7bbada8c49501dc73e58a10fcef6663af95e5079 \ --hash=sha256:ffc519621dce0c767e96b9c53f09c5d215578e10b02c285809f76509a3931482 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--hash=sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad +wsproto==1.2.0 ; python_version >= "3.10" and python_version < "3.13" \ --hash=sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065 \ --hash=sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736 +xarray==2024.10.0 ; python_version >= "3.10" and python_version < "3.13" \ + --hash=sha256:ae1d38cb44a0324dfb61e492394158ae22389bf7de9f3c174309c17376df63a0 \ + --hash=sha256:e369e2bac430e418c2448e5b96f07da4635f98c1319aa23cfeb3fbcb9a01d2e0 diff --git a/src/faostat_data_primap/read.py b/src/faostat_data_primap/read.py index 7940c2cd..0da11b3c 100644 --- a/src/faostat_data_primap/read.py +++ b/src/faostat_data_primap/read.py @@ -1,25 +1,218 @@ """read data set""" -import os + import pandas as pd +import primap2 as pm2 +import pycountry from src.faostat_data_primap.helper.definitions import downloaded_data_path -# read everything into data frames -# loop through all domains, but not the relases -# always use the latest release -domain = "farm_gate_agriculture_energy" -release = "2024-11-14" -dataset_path = downloaded_data_path / domain / release -all_data = {} -for item in os.listdir(dataset_path): - if item.endswith(".csv"): - all_data[item] = pd.read_csv(dataset_path / item) - -# Steps: -# delete columns that are not needed -# map country names to country ISO3 codes -# convert to interchange format +custom_country_mapping_code = {} + +custom_country_mapping_name = { + # FAO + "Bolivia (Plurinational State of)": "BOL", + "China, Hong Kong SAR": "HKG", + "China, Macao SAR": "MAC", + "China, mainland": "CHN", + "China, Taiwan Province of": "TWN", + "Iran (Islamic Republic of)": "IRN", + "Czechoslovakia": "CSK", + "Ethiopia PDR": "ETH", + "Netherlands (Kingdom of the)": "NLD", + "Netherlands Antilles (former)": "ANT", + # todo is former Sudan same as the new (north) Sudan + "Sudan (former)": "SDN", + "USSR": "SUN", + "Venezuela (Bolivarian Republic of)": "VEN", + "Yugoslav SFR": "YUG", + "World": "EARTH", + # Andrew cement (probably not needed) + "Bonaire, Saint Eustatius and Saba": "BES", + "Cape Verde": "CPV", + "Democratic Republic of the Congo": "COD", + "Faeroe Islands": "FRO", + "Micronesia (Federated States of)": "FSM", + "Iran": "IRN", + "Laos": "LAO", + "Occupied Palestinian Territory": "PSE", + "Swaziland": "SWZ", + "Taiwan": "TWN", + "Wallis and Futuna Islands": "WLF", +} + + +def get_country_code( + country_name: str, +) -> str: + """ + Get country code for country name. + + If the input is a code it will be returned, + if the input is not a three-letter code a search will be performed + + Parameters + ---------- + country_name: str + Country code or name to get the three-letter code for. + + Returns + ------- + country_code: str + + """ + # First check if it's in the list of custom codes + if country_name in custom_country_mapping_code: + country_code = country_name + elif country_name in custom_country_mapping_name: + country_code = custom_country_mapping_name[country_name] + else: + try: + # check if it's a 3 letter UNFCCC_GHG_data + country = pycountry.countries.get(alpha_3=country_name) + country_code = country.alpha_3 + except: + try: + country = pycountry.countries.search_fuzzy( + country_name.replace("_", " ") + ) + except: + msg = f"Cannot map country {country_name} to country code." + raise ValueError(msg) + if len(country) > 1: + country_code = None + for current_country in country: + if current_country.name == country_name: + country_code = current_country.alpha_3 + if country_code is None: + msg = ( + f"Country name {country_name} has {len(country)} " + "possible results for country codes." + ) + raise ValueError(msg) + + country_code = country[0].alpha_3 + + return country_code + + +files_to_read = ( + ( + "farm_gate_agriculture_energy", + "2024-11-14", + "Emissions_Agriculture_Energy_E_All_Data_NOFLAG.csv", + ), + ( + "farm_gate_emissions_crops", + "2024-11-14", + "Emissions_crops_E_All_Data_NOFLAG.csv", + ), +) + +for domain, release, filename in files_to_read: + dataset_path = downloaded_data_path / domain / release / filename + data_pd = pd.read_csv(dataset_path) + + # remove in entries with unit TJ + data_pd = data_pd[data_pd["Unit"] != "TJ"] + + # remove the country aggegrates + areas_to_remove = [ + "World", + "Africa", + "Eastern Africa", + "Middle Africa", + "Northern Africa", + "Southern Africa", + "Western Africa", + "Americas", + "Northern America", + "Central America", + "Caribbean", + "South America", + "Asia", + "Central Asia", + "Eastern Asia", + "Southern Asia", + "South-eastern Asia", + "Western Asia", + "Europe", + "Eastern Europe", + "Northern Europe", + "Southern Europe", + "Western Europe", + "Oceania", + "Australia and New Zealand", + "Melanesia", + "Micronesia", + "Polynesia", + "Least Developed Countries", + "Land Locked Developing Countries", + "Small Island Developing States", + "Low Income Food Deficit Countries", + "Net Food Importing Developing Countries", + "Annex I countries", + "Non-Annex I countries", + "OECD", + ] + + data_pd = data_pd[~data_pd["Area"].isin(areas_to_remove)] + country_mapping = {c: get_country_code(c) for c in data_pd["Area"].unique()} + + data_pd["country (ISO3)"] = data_pd["Area"].map(country_mapping) + + entity_mapping = { + "Emissions (CO2)": "CO2", + "Emissions (CH4)": "CH4", + "Emissions (N2O)": "N2O", + } + + data_pd["entity"] = data_pd["Element"].map(entity_mapping) + + # todo can we do this in primap2 function? + data_pd = data_pd.drop( + ["Element", "Element Code", "Item Code", "Area Code (M49)", "Area Code"], axis=1 + ) + + +coords_cols = { + "area": "country (ISO3)", + "category": "Item", + "unit": "Unit", + "entity": "entity", +} + +coords_terminologies = {"area": "ISO3", "category": "FAOSTAT"} + +coords_defaults = { + "source": "FAO", + "scenario": release, +} + +coords_value_mapping = {} +filter_keep = {} +filter_remove = {} +meta_data = { + "references": "tbd", + "rights": "tbd", + "contact": "tbd", + "title": "tbd", + "comment": "tbd", + "institution": "tbd", +} + +data_if = pm2.pm2io.convert_wide_dataframe_if( + data_pd, + coords_cols=coords_cols, + coords_defaults=coords_defaults, + coords_terminologies=coords_terminologies, + coords_value_mapping=coords_value_mapping, + filter_keep=filter_keep, + filter_remove=filter_remove, + meta_data=meta_data, +) + +# steps: # convert to primap2 format # save raw data set # convert categories to IPCC2006_PRIMAP standard