diff --git a/elt-common/src/elt_common/runner.py b/elt-common/src/elt_common/runner.py index 7a71310d..afe41553 100644 --- a/elt-common/src/elt_common/runner.py +++ b/elt-common/src/elt_common/runner.py @@ -76,6 +76,10 @@ def run_ingest(job: ELTJobManifest) -> dict[str, int]: watermarks: list[Watermark] = [] for data in table_props.extractor(watermark_before_extract): + if not data: + LOGGER.info("No rows extracted, skipping") + continue + # 'replace' really means delete the contents of the table, then append the new data. # Extractors can return multiple chunks of data, in which case only the first chunk # should cause a deletion, whilst the remaining chunks should be appended. diff --git a/elt-common/tests/unit_tests/test_runner.py b/elt-common/tests/unit_tests/test_runner.py index bde37c42..90d61667 100644 --- a/elt-common/tests/unit_tests/test_runner.py +++ b/elt-common/tests/unit_tests/test_runner.py @@ -80,16 +80,16 @@ def test_run_ingest_extract_using_all_write_modes( run_ingest(elt_job) call_args_list = mock_iceberg_io.write_table.call_args_list - assert len(call_args_list) == 4 expected_table_names = [ "table_default_write", "table_replace_mode", "table_merge_mode", - "empty", ] - expected_write_modes = ("append", "replace", "merge", "append") - expected_merge_on = ([], [], ["name"], []) + assert len(call_args_list) == len(expected_table_names) + + expected_write_modes = ("append", "replace", "merge") + expected_merge_on = ([], [], ["name"]) for index, call in enumerate(call_args_list): call_args, call_kwargs = call.args, call.kwargs @@ -97,10 +97,7 @@ def test_run_ingest_extract_using_all_write_modes( data = call_args[1] assert isinstance(data, pa.Table) expected_table_name = expected_table_names[index] - if expected_table_name != "empty": - assert data["name"][0].as_py() == expected_table_name - else: - assert data.num_rows == 0 + assert data["name"][0].as_py() == expected_table_name assert call_args[2] == expected_write_modes[index] assert call_kwargs["merge_on"] == expected_merge_on[index] diff --git a/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/electricity_sharepoint.py b/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/electricity_sharepoint.py new file mode 100644 index 00000000..18cdafaf --- /dev/null +++ b/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/electricity_sharepoint.py @@ -0,0 +1,141 @@ +"""Extracts measurements of ISIS's electricity usage from the ISIS Sustainability Sharepoint site""" + +import concurrent.futures +import datetime as dt +import io +import os +import logging +import pathlib + +import pandas as pd +import pyarrow as pa + +from parsing import read_power_consumption_csv, read_power_consumption_excel + +from elt_common.extract import ( + BaseExtract, + ResourceProperties, + Watermark, + ResourceWriteProperties, +) +from elt_common.sources.m365.client import SPListClient, M365File +from elt_common.sources.m365.credentials import M365Credentials + +SITE_URL = "https://stfc365.sharepoint.com/sites/ISISSustainability" +MAX_WORKERS = min(8, (os.cpu_count() or 1) + 4) + +LOGGER = logging.getLogger(__name__) + +_root_path = "/General/RDM Data" +_default_backfill_globs = [ + "**/*.xlsx", + "**/*-daily.csv", + "**/*-manual-export.csv", +] + + +class Configuration(M365Credentials): + backfill: bool = False + backfill_globs: list[str] = [] + + @property + def glob_patterns(self): + if not self.backfill: + return ["*-ISIS.csv"] + return self.backfill_globs if self.backfill_globs else _default_backfill_globs + + +class Extract(BaseExtract): + config_cls = Configuration + + def __init__(self, cfg: Configuration): + super().__init__(cfg) + self._client = SPListClient(SITE_URL, cfg) + self._backfilling = cfg.backfill + self._glob_patterns = cfg.glob_patterns + + LOGGER.debug(f"Searching for files matching: {self._glob_patterns}") + + def extract_resource_properties(self): + yield ( + "rdm_data", + ResourceProperties( + extractor=self._extract_electricity_usage, + write_properties=ResourceWriteProperties( + merge_on=["date_time"], write_mode="merge" + ), + watermark_column="date_time", + ), + ) + + def _extract_electricity_usage(self, w: Watermark | None): + watermark_value: dt.datetime | None = None + if w and self._backfilling: + LOGGER.debug("Ignoring watermark because this is a backfill") + elif w: + if not isinstance(w.value, dt.datetime): + LOGGER.warning( + f"Ignoring watermark for electricity SP because it was '{w.value}', not a datetime" + ) + else: + watermark_value = w.value + LOGGER.debug(f"Only fetching files modified after {watermark_value}") + + files = [] + for pattern in self._glob_patterns: + files.extend( + self._client.glob( + _root_path, pattern=pattern, modified_after=watermark_value + ) + ) + + LOGGER.debug(f"Matched {len(files)} files") + + resolved = self._read_files(files) + df = read_contents_to_dataframe(resolved) + if df is not None and df.size > 0: + yield pa.Table.from_pandas(df, preserve_index=False) + else: + yield pa.Table.from_pylist([]) + + def _read_files(self, files: list[M365File]) -> list[tuple[str, bytes]]: + results = [] + with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: + future_to_file_item = { + executor.submit(self._client.read_file, f.path): f for f in files + } + + for future in concurrent.futures.as_completed(future_to_file_item): + file = future_to_file_item[future] + results.append((file.name, future.result())) + return results + + +def read_contents_to_dataframe(files: list[tuple[str, bytes]]): + """Extracts data from files into a single, combined dataframe + + :param files: (name, content) pairs for the files to extract data from + """ + + def read_as_dataframe(file_name, file_bytes) -> pd.DataFrame | None: + file_content = io.BytesIO(file_bytes) + LOGGER.debug(f"File '{file_name}' has size {len(file_bytes)} bytes.") + match pathlib.Path(file_name).suffix: + case ".csv": + df = read_power_consumption_csv(file_name, file_content) + case ".xlsx": + df = read_power_consumption_excel(file_content) + case _: + raise RuntimeError(f"Unsupported file extension in '{file_name}'") + + if df is not None: + df["file_name"] = file_name + return df + + df_batch: pd.DataFrame | None = None + for name, file_contents in files: + df = read_as_dataframe(name, file_contents) + if df is not None: + df_batch = df if df_batch is None else pd.concat((df_batch, df)) + + return df_batch diff --git a/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/parsing.py b/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/parsing.py new file mode 100644 index 00000000..90790ae5 --- /dev/null +++ b/elt-pipelines/facility_ops/ingest/estates/electricity_sharepoint/parsing.py @@ -0,0 +1,150 @@ +"""Functions for parsing data from the electricity files + +There are 3 known formats for the data. Each contains a preamble of the form: + +Site Information: +RAL ISIS RDM +RAL ISIS RDM +Controller: ISIS +Controller description: ISIS Energy Totals +Status: Online + +This preamble is discarded from all formats. + +1. Excel (old, manual export format) +------------------------------------ + +These were produced by manual export from the original system. + +There are two columns: "Time,Total Power (MW)". Time is in the format "YYYY-mm-DDTHH:MM:SS". + +2. Automated CSV export +----------------------- + +An automation deposits .csv files at regular intervals. + +There are three columns: "Time,Date,ISIS Elec Total Power", where Time is in format HH:MM:SS and Date is in format DD/mm/YY. +Several of these files can be concatenated together to form larger timespan records - they are concatenated as is and repeated +preamble sections are not discarded. The variable CSV_PREAMBLE_ANCHOR defines the line at which a new section occurs. + +3. Manual CSV export +-------------------- + +These were produced more recently by manual export from the original system. +There are three columns: "Time,ISIS Elec Total Energy,ISIS Elec Total Power", where Time is in format "DD/mm/YY HH:MM:SS". +The second column is discarded. +""" + +import io +import logging +import pandas as pd + +from typing import Sequence + +CSV_PREAMBLE_ANCHOR = "time" +COL_DATE_TIME = "date_time" +COL_TOTAL_POWER = "isis_elec_total_power_mw" +EXCEL_SKIP_ROWS = 7 +RDM_TIMEZONE = "Europe/London" + +LOGGER = logging.getLogger(__name__) + + +def read_power_consumption_excel(file_content: io.BytesIO) -> pd.DataFrame: + # See module comment for description of the format + df_raw = pd.read_excel(file_content, skiprows=EXCEL_SKIP_ROWS) + df_raw = df_raw.rename( + columns={"Time": COL_DATE_TIME, "ISIS Elec Total Power {MW}": COL_TOTAL_POWER} + ) + df_raw[COL_DATE_TIME] = _to_utc(df_raw[COL_DATE_TIME]) # type: ignore + return df_raw + + +def read_power_consumption_csv( + file_name: str, file_content: io.BytesIO +) -> pd.DataFrame | None: + # See module comment for description of the format + + def _append_if_not_none(seq, df): + if df is not None: + seq.append(df) + + metadata_anchor = "site information" + sections = [] + current_lines: list[str] = [] + in_data = False + for line in file_content.getvalue().decode().splitlines(): + line = line.strip() + line_lower = line.lower() + + if line_lower.startswith(CSV_PREAMBLE_ANCHOR): + # Save any previous section + if current_lines: + _append_if_not_none( + sections, _csv_section_to_df(file_name, current_lines) + ) + # start new section with header + current_lines = [line] + in_data = True + + elif in_data: + if line_lower.startswith(metadata_anchor): + # Metadata block + in_data = False + continue + else: + current_lines.append(line) + + # the last section + if current_lines: + _append_if_not_none(sections, _csv_section_to_df(file_name, current_lines)) + + # concatenate + if sections: + return pd.concat(sections) + + return None + + +def _csv_section_to_df(file_name: str, lines: Sequence[str]) -> pd.DataFrame | None: + """Parse csv and return a DataFrame if the times are valid. None if not""" + buff = io.StringIO("\n".join(lines)) + df_raw: pd.DataFrame = pd.read_csv(buff) # type: ignore + + # clean up column names (strip any whitespace) + df_raw.columns = df_raw.columns.str.strip() + cols = [c for c in df_raw.columns] + if len(cols) != 3 or "power" not in cols[2].lower(): + LOGGER.warning(f"Columns in {file_name} are an unexpected format: {cols}") + return None + + try: + if cols[1].strip() == "Date": + # Automated CSV format + raw_datetime = df_raw["Date"] + " " + df_raw["Time"] + else: + # Manual CSV format + raw_datetime = df_raw["Time"] + + df_datetime: pd.Series = pd.to_datetime( # type: ignore + raw_datetime, format="%d/%m/%y %H:%M:%S" + ) + df = _to_utc(df_datetime).to_frame(name=COL_DATE_TIME) + except ValueError as exc: + # Pandas 3 uses ValueError for conversions that produce ambiguous/non-existent times + msg = str(exc) + if "ambiguous" in msg or "nonexistent" in msg: + LOGGER.warning( + f"'Error loading section of {file_name}'. DST issues detected: {str(exc)}" + ) + return None + else: + raise + + df[COL_TOTAL_POWER] = df_raw[cols[2]] + return df + + +def _to_utc(ts: pd.Series) -> pd.Series: + """Assumes timezone unaware data and converts to UTC""" + return ts.dt.tz_localize(RDM_TIMEZONE).dt.tz_convert("UTC") diff --git a/elt-pipelines/pyproject.toml b/elt-pipelines/pyproject.toml index 55b21802..4de03c6f 100644 --- a/elt-pipelines/pyproject.toml +++ b/elt-pipelines/pyproject.toml @@ -15,7 +15,7 @@ statusdisplay = [ "requests>=2.34.2", ] -accelerator_sharepoint = [ +sharepoint = [ "pandas >= 3.0.3", "openpyxl >= 3.1.5", "elt-common[m365]" diff --git a/elt-pipelines/uv.lock b/elt-pipelines/uv.lock index 54b6e89a..55e08fa3 100644 --- a/elt-pipelines/uv.lock +++ b/elt-pipelines/uv.lock @@ -14,7 +14,7 @@ resolution-markers = [ [[package]] name = "aiobotocore" -version = "3.7.0" +version = "3.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohttp" }, @@ -25,23 +25,23 @@ 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