earthscopestraintools.tiledbtools module

class earthscopestraintools.tiledbtools.ProcessedStrainReader(uri: str, period=300)

Bases: object

check_query_result(epochs, start, end)

reindex_df(df: DataFrame, columns: list = [], attr='data')

to_df(data_types: list | str, timeseries: list | str, attrs: list | str, start_ts: int | None = None, end_ts: int | None = None, start_str: str | None = None, end_str: str | None = None, start_dt: datetime | None = None, end_dt: datetime | None = None, reindex=True, print_array_range=False)

to_ts(data_types: list | str, timeseries: str, attrs: list | None = None, name: str = 'timeseries', units: str | None = None, start_ts: int | None = None, end_ts: int | None = None, start_str: str | None = None, end_str: str | None = None, start_dt: datetime | None = None, end_dt: datetime | None = None, print_array_range=False, to_na=True)

class earthscopestraintools.tiledbtools.ProcessedStrainWriter(uri: str)

Bases: object

create(schema_type: str = '3D', schema_source: str = 's3')

df_2_tiledb(df: DataFrame, data_types: list, timeseries: str, level: str, quality_df: DataFrame | None = None, print_it: bool = False)

prepares a dataframe to write to array schema df - dataframe with time index, columns are timeseries data (one column per data_type) uri - string. which tiledb array to write to data_types - list of strings. data_types to map columns into. ie CH0, 2Ene, pressure, time_index timeseries - string. name of timeseries. counts, microstrain, offset_c, tide_c, atmp_c, atmp, pore, mjd, doy level - 2char string. ‘0a’, ‘1a’, ‘2a’, ‘2b’ quality_df- dataframe, optional. if not included, quality flags will be set to ‘g’ print_it - bool, optional. show the constructed dataframe as it is being written to tiledb.

ts_2_tiledb(ts: Timeseries, cleanup: bool = False)

write_df_to_tiledb(df: DataFrame)

class earthscopestraintools.tiledbtools.RawStrainReader(uri: str, period=None)

Bases: object

check_query_result(df, start, end)

reindex_df(df: DataFrame, columns: list = [], attr='data')

to_df(channels: list | str, start_ts: int | None = None, end_ts: int | None = None, start_str: str | None = None, end_str: str | None = None, start_dt: datetime | None = None, end_dt: datetime | None = None, print_array_range=False)

to_ts(channels: list, units: str, start_ts: int | None = None, end_ts: int | None = None, start_str: str | None = None, end_str: str | None = None, start_dt: datetime | None = None, end_dt: datetime | None = None, to_nan: bool = True, name: str = '')

class earthscopestraintools.tiledbtools.RawStrainWriter(uri: str, array_type='int')

Bases: object

create(schema_type: str, schema_source: str = 's3')

df_2_tiledb(df: DataFrame, print_it: bool = False)

prepares a dataframe to write to array schema df - dataframe with time index, columns are timeseries data (one column per channel) print_it - bool, optional. show the constructed dataframe as it is being written to tiledb.

ts_2_tiledb(ts: Timeseries, cleanup: bool = False)

write_df_to_tiledb(df: DataFrame)

class earthscopestraintools.tiledbtools.StrainArray(uri: str, period: float | None = None)

Bases: object

cleanup()

cleanup_meta()

consolidate_array_meta(print_it=True)

consolidate_fragment_meta(print_it=True)

consolidate_fragments(print_it=True)

create(schema_type: str, schema_source: str = 's3')

default_config()

delete()

exists()

get_channels()

get_data_types()

get_network()

get_nonempty_domain()

get_period()

get_schema(schema_type)

get_schema_from_s3(schema_type)

get_station()

get_timeseries()

print_schema()

set_array_meta(network: str | None = None, station: str | None = None, period: float | None = None)

set_default_ctx()

update_array_meta(network: str | None = None, station: str | None = None, period: float | None = None)

vacuum_array_meta(print_it=True)

vacuum_fragment_meta(print_it=True)

vacuum_fragments(print_it=True)

earthscopestraintools.tiledbtools.lookup_s3_uri(network, station, period)

earthscopestraintools.tiledbtools.str_to_unix_ms(time_string: str)