This repository will hold and be the source of any open-source TYMLEZ CET & CRU Policies for Hedera Guardian.
TYMLEZ have attempted to create a universal schema that can be used as the basis for insetting / avoidance claims made against any GHG Compatible project.
Whilst these schemas will not be able to be used Verbatim for all registries, they form an important base for dMRV recording against a particular project.
| def write_to_quest(df_row): | |
| HOST = args['questdb_host'] | |
| PORT = int(args['questdb_port']) | |
| sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | |
| try: | |
| sock.connect((HOST, PORT)) | |
| sock.sendall(_row_to_line_protocol(df_row).encode()) | |
| except socket.error as e: |
| mapped_readings_df = mapped_readings_df \ | |
| .withColumn("week_of_year", F.weekofyear(reading_time)) \ | |
| .withColumn("date_str", F.regexp_replace(F.col("period_end").substr(1,10), "-", "")) \ | |
| .withColumn("day_of_month", F.dayofmonth(reading_time)) \ | |
| .withColumn("month", F.month(reading_time)) \ | |
| .withColumn("year", F.year(reading_time)) \ | |
| .withColumn("hour", F.hour(reading_time)) \ | |
| .withColumn("minute", F.minute(reading_time)) \ | |
| .withColumn("reading_date_time", reading_time) \ | |
| .withColumn("ingestion_date", F.current_timestamp()) \ |
| mapped_readings = ApplyMapping.apply(frame=datasource, mappings=[ | |
| ("meter_id", "string", "meter_id", "string"), | |
| ("period_start", "string", "period_start", "string"), | |
| ("period_end", "string", "period_end", "string"), | |
| ("e1", "double", "export_interval.e1", "double"), | |
| ("e2", "double", "export_interval.e2", "double"), | |
| ("e3", "double", "export_interval.e3", "double"), #NB: this can technically have many more circuits | |
| ("quality_method", "string", "quality_method", "string"), | |
| ("event", "string", "event", "string"), | |
| ("b1", "double", "import_interval.b1", "double"), |
| SELECT count(distinct(id)) from datatable d | |
| LEFT JOIN polygons p ON ST_DWITHIN(ST_MAKEPOINT(d.lon,d.lat), p.geom, 0.1) | |
| WHERE p.place_cat IN ('beauty', 'sports') |
| int pnpoly(int nvert, float *vertx, float *verty, float testx, float testy) | |
| { | |
| int i, j, c = 0; | |
| for (i = 0, j = nvert-1; i < nvert; j = i++) { | |
| if ( ((verty[i]>testy) != (verty[j]>testy)) && | |
| (testx < (vertx[j]-vertx[i]) * (testy-verty[i]) / (verty[j]-verty[i]) + vertx[i]) ) | |
| c = !c; | |
| } | |
| return c; | |
| } |
| SELECT m.id, d.name, d.brand, d.wkt from dealerships d | |
| LEFT JOIN movement m ON ST_CONTAINS(d.wkt, ST_POINT(m.lng, m.lat)) | |
| WHERE d.brand IN ('AUDI', 'BMW') |
| %%time | |
| ddf.to_orc('/data/Data Files/Vegas/datafiles/orc/') |
| %%time | |
| ddf = ddf.repartition(npartitions=3000) |
| %%time | |
| import dask_cudf as dc | |
| ddf = dc.read_csv('/data/Data Files/Vegas/datafiles/csv/*.csv.gz', compression='gzip') |