Glenn, could you try the attached weeimport.py, it should support text imports and be compatible with v4.5.1.
Gary On Friday, 4 March 2022 at 14:51:33 UTC+10 gjr80 wrote: > Ah, didn't notice you were on 4.5.1. There was a change to units.py under > 4.6.x that forced a change in weeimport.py, consequently the weeimport.py > version I provided does not get on with WeeWX 4.5.x. Let me get you a 4.5.x > compatible version. > > Gary > > On Friday, 4 March 2022 at 13:16:57 UTC+10 Glenn McKechnie wrote: > >> On 04/03/2022, gjr80 <[email protected]> wrote: >> > Glenn, >> > >> > Have you had a chance to try the revised wee_import? I am working on a >> > number of other changes to the same piece of wee_import code and I would >> > prefer to do them all at once if possible. >> >> Whoops. Sorry about that. It slid down the Todo list a little too >> far. ie;- I got somewhat sidetracked. >> >> Re-visiting this and I find I've either forgotten something critical, >> or it just plain doesn't work... >> >> I'm stll using the weewx.451 installation where I was developing this. >> I've fetched and replaced weeimport/weeimport.py as per your instructions. >> >> sudo bin/wee_import --import-config=/home/weewx/csv.conf >> --config=/home/weewx/weewx.conf --verbose --dry-run >> >> and I get the following error (with dry-run and without) ... >> >> Using WeeWX configuration file /home/weewx/weewx.conf >> Starting wee_import... >> A CSV import from source file '/var/tmp/weewxaddnotes.csv' has been >> requested. >> The following options will be used: >> config=/home/weewx/weewx.conf, import-config=/home/weewx/csv.conf >> source=/var/tmp/weewxaddnotes.csv, from=None, to=None >> dry-run=True, calc_missing=True, ignore_invalid_data=True >> tranche=250, interval=conf, date/time_string_format=%Y-%m-%d %H:%M:%S >> delimiter=',', rain=cumulative, wind_direction=[-360.0, 360.0] >> UV=False, radiation=False >> Using database binding 'wx_binding', which is bound to database >> 'weewx.sdb' >> Destination table 'archive' unit system is '0x10' (METRIC). >> Missing derived observations will be calculated. >> All WeeWX UV fields will be set to None. >> All WeeWX radiation fields will be set to None. >> This is a dry run, imported data will not be saved to archive. >> Starting dry run import ... >> Obtaining raw import data for period 1 ... >> Traceback (most recent call last): >> File "bin/wee_import", line 899, in <module> >> main() >> File "bin/wee_import", line 829, in main >> source_obj.run() >> File "/home/weewx451/bin/weeimport/weeimport.py", line 372, in run >> _raw_data = self.getRawData(period) >> File "/home/weewx451/bin/weeimport/csvimport.py", line 246, in getRawData >> self.map = self.parseMap('CSV', _csv_reader, self.csv_config_dict) >> File "/home/weewx451/bin/weeimport/weeimport.py", line 635, in parseMap >> and _val['units'] not in weewx.units.USUnits.values() \ >> AttributeError: 'ListOfDicts' object has no attribute 'values' >> >> >> The database field is named ANComment, the field I'm importing is >> named ANComment >> >> The attached files will show the full details. weewx.conf is unedited, >> it's a throw away installation. >> >> And, I've just cloned the wee_import_text branch - gives the same error. >> >> -- >> >> >> Cheers >> Glenn >> >> rorpi - read only raspberry pi & various weewx addons >> https://github.com/glennmckechnie >> > -- You received this message because you are subscribed to the Google Groups "weewx-user" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-user/030ad74a-557c-465f-a7c3-8d24a46b7773n%40googlegroups.com.
# # Copyright (c) 2009-2019 Tom Keffer <[email protected]> and # Gary Roderick # # See the file LICENSE.txt for your full rights. # """Module providing the base classes and API for importing observational data into WeeWX. """ from __future__ import with_statement from __future__ import print_function from __future__ import absolute_import # Python imports import datetime import logging import numbers import re import sys import time from datetime import datetime as dt import six from six.moves import input # WeeWX imports import weecfg import weecfg.database import weewx import weewx.qc import weewx.wxservices from weewx.manager import open_manager_with_config from weewx.units import unit_constants, unit_nicknames, convertStd, to_std_system, ValueTuple from weeutil.weeutil import timestamp_to_string, option_as_list, to_int, tobool, get_object, max_with_none log = logging.getLogger(__name__) # List of sources we support SUPPORTED_SOURCES = ['CSV', 'WU', 'Cumulus', 'WD', 'WeatherCat'] # Minimum requirements in any explicit or implicit WeeWX field-to-import field # map MINIMUM_MAP = {'dateTime': {'units': 'unix_epoch'}, 'usUnits': {'units': None}, 'interval': {'units': 'minute'}} # ============================================================================ # Error Classes # ============================================================================ class WeeImportOptionError(Exception): """Base class of exceptions thrown when encountering an error with a command line option. """ class WeeImportMapError(Exception): """Base class of exceptions thrown when encountering an error with an external source-to-WeeWX field map. """ class WeeImportIOError(Exception): """Base class of exceptions thrown when encountering an I/O error with an external source. """ class WeeImportFieldError(Exception): """Base class of exceptions thrown when encountering an error with a field from an external source. """ class WeeImportDecodeError(Exception): """Base class of exceptions thrown when encountering a decode error with an external source. """ # ============================================================================ # class Source # ============================================================================ class Source(object): """ Abstract base class used for interacting with an external data source to import records into the WeeWX archive. __init__() must define the following properties: dry_run - Is this a dry run (ie do not save imported records to archive). [True|False]. calc_missing - Calculate any missing derived observations. [True|False]. ignore_invalid_data - Ignore any invalid data found in a source field. [True|False]. tranche - Number of records to be written to archive in a single transaction. Integer. interval - Method of determining interval value if interval field not included in data source. ['config'|'derive'|x] where x is an integer. Child classes are used to interact with a specific source (eg CSV file, WU). Any such child classes must define a getRawData() method which: - gets the raw observation data and returns an iterable yielding data dicts whose fields can be mapped to a WeeWX archive field - defines an import data field-to-WeeWX archive field map (self.map) self.raw_datetime_format - Format of date time data field from which observation timestamp is to be derived. A string in Python datetime string format such as '%Y-%m-%d %H:%M:%S'. If the date time data field cannot be interpreted as a string wee_import attempts to interpret the field as a unix timestamp. If the field is not a valid unix timestamp an error is raised. """ # reg expression to match any HTML tag of the form <...> _tags = re.compile(r'\<.*\>') def __init__(self, config_dict, import_config_dict, options): """A generic initialisation. Set some realistic default values for options read from the import config file. Obtain objects to handle missing derived obs (if required) and QC on imported data. Parse any --date command line option so we know what records to import. """ # save our WeeWX config dict self.config_dict = config_dict # get our import config dict settings # interval, default to 'derive' self.interval = import_config_dict.get('interval', 'derive') # do we ignore invalid data, default to True self.ignore_invalid_data = tobool(import_config_dict.get('ignore_invalid_data', True)) # tranche, default to 250 self.tranche = to_int(import_config_dict.get('tranche', 250)) # apply QC, default to True self.apply_qc = tobool(import_config_dict.get('qc', True)) # calc-missing, default to True self.calc_missing = tobool(import_config_dict.get('calc_missing', True)) # Some sources include UV index and solar radiation values even if no # sensor was present. The WeeWX convention is to store the None value # when a sensor or observation does not exist. Record whether UV and/or # solar radiation sensor was present. # UV, default to True self.UV_sensor = tobool(import_config_dict.get('UV_sensor', True)) # solar, default to True self.solar_sensor = tobool(import_config_dict.get('solar_sensor', True)) # initialise ignore extreme > 255.0 values for temperature and # humidity fields for WD imports self.ignore_extr_th = False self.db_binding_wx = get_binding(config_dict) self.dbm = open_manager_with_config(config_dict, self.db_binding_wx, initialize=True, default_binding_dict={'table_name': 'archive', 'manager': 'weewx.wxmanager.DaySummaryManager', 'schema': 'schemas.wview_extended.schema'}) # get the unit system used in our db if self.dbm.std_unit_system is None: # we have a fresh archive (ie no records) so cannot deduce # the unit system in use, so go to our config_dict self.archive_unit_sys = unit_constants[self.config_dict['StdConvert'].get('target_unit', 'US')] else: # get our unit system from the archive db self.archive_unit_sys = self.dbm.std_unit_system # get ourselves a QC object to do QC on imported records self.import_QC = weewx.qc.QC(config_dict, parent='weeimport') # Process our command line options self.dry_run = options.dry_run self.verbose = options.verbose self.no_prompt = options.no_prompt self.suppress = options.suppress # By processing any --date, --from and --to options we need to derive # self.first_ts and self.last_ts; the earliest (exclusive) and latest # (inclusive) timestamps of data to be imported. If we have no --date, # --from or --to then set both to None (we then get the default action # for each import type). # First we see if we have a valid --date, if not then we look for # --from and --to. if options.date or options.date == "": # there is a --date but is it valid try: _first_dt = dt.strptime(options.date, "%Y-%m-%d") except ValueError: # Could not convert --date. If we have a --date it must be # valid otherwise we can't continue so raise it. _msg = "Invalid --date option specified." raise WeeImportOptionError(_msg) else: # we have a valid date so do some date arithmetic _last_dt = _first_dt + datetime.timedelta(days=1) self.first_ts = time.mktime(_first_dt.timetuple()) self.last_ts = time.mktime(_last_dt.timetuple()) elif options.date_from or options.date_to or options.date_from == '' or options.date_to == '': # There is a --from and/or a --to, but do we have both and are # they valid. # try --from first try: if 'T' in options.date_from: _from_dt = dt.strptime(options.date_from, "%Y-%m-%dT%H:%M") else: _from_dt = dt.strptime(options.date_from, "%Y-%m-%d") _from_ts = time.mktime(_from_dt.timetuple()) except TypeError: # --from not specified we can't continue so raise it _msg = "Missing --from option. Both --from and --to must be specified." raise WeeImportOptionError(_msg) except ValueError: # could not convert --from, we can't continue so raise it _msg = "Invalid --from option." raise WeeImportOptionError(_msg) # try --to try: if 'T' in options.date_to: _to_dt = dt.strptime(options.date_to, "%Y-%m-%dT%H:%M") else: _to_dt = dt.strptime(options.date_to, "%Y-%m-%d") # since it is just a date we want the end of the day _to_dt += datetime.timedelta(days=1) _to_ts = time.mktime(_to_dt.timetuple()) except TypeError: # --to not specified , we can't continue so raise it _msg = "Missing --to option. Both --from and --to must be specified." raise WeeImportOptionError(_msg) except ValueError: # could not convert --to, we can't continue so raise it _msg = "Invalid --to option." raise WeeImportOptionError(_msg) # If we made it here we have a _from_ts and _to_ts. Do a simple # error check first. if _from_ts > _to_ts: # from is later than to, raise it _msg = "--from value is later than --to value." raise WeeImportOptionError(_msg) self.first_ts = _from_ts self.last_ts = _to_ts else: # no --date or --from/--to so we take the default, set all to None self.first_ts = None self.last_ts = None # initialise a few properties we will need during the import # answer flags self.ans = None self.interval_ans = None # properties to help with processing multi-period imports self.period_no = None # total records processed self.total_rec_proc = 0 # total unique records identified self.total_unique_rec = 0 # total duplicate records identified self.total_duplicate_rec = 0 # time we started to first save self.t1 = None # time taken to process self.tdiff = None # earliest timestamp imported self.earliest_ts = None # latest timestamp imported self.latest_ts = None # initialise two sets to hold timestamps of records for which we # encountered duplicates # duplicates seen over all periods self.duplicates = set() # duplicates seen over the current period self.period_duplicates = set() @staticmethod def sourceFactory(options, args): """Factory to produce a Source object. Returns an appropriate object depending on the source type. Raises a weewx.UnsupportedFeature error if an object could not be created. """ # get some key WeeWX parameters # first the config dict to use config_path, config_dict = weecfg.read_config(options.config_path, args) # get wee_import config dict if it exists import_config_path, import_config_dict = weecfg.read_config(None, args, file_name=options.import_config_path) # we should have a source parameter at the root of out import config # file, try to get it but be prepared to catch the error. try: source = import_config_dict['source'] except KeyError: # we have no source parameter so check if we have a single source # config stanza, if we do then proceed using that _source_keys = [s for s in SUPPORTED_SOURCES if s in import_config_dict.keys] if len(_source_keys) == 1: # we have a single source config stanza so use that source = _source_keys[0] else: # there is no source parameter and we do not have a single # source config stanza so raise an error _msg = "Invalid 'source' parameter or no 'source' parameter specified in %s" % import_config_path raise weewx.UnsupportedFeature(_msg) # if we made it this far we have all we need to create an object module_class = '.'.join(['weeimport', source.lower() + 'import', source + 'Source']) return get_object(module_class)(config_dict, config_path, import_config_dict.get(source, {}), import_config_path, options) def run(self): """Main entry point for importing from an external source. Source data may be provided as a group of records over a single period (eg a single CSV file) or as a number of groups of records covering multiple periods(eg a WU multi-day import). Step through each group of records, getting the raw data, mapping the data and saving the data for each period. """ # setup a counter to count the periods of records self.period_no = 1 # obtain the lastUpdate meta data value before we import anything last_update = to_int(self.dbm._read_metadata('lastUpdate')) with self.dbm as archive: if self.first_period: # collect the time for some stats reporting later self.t1 = time.time() # it's convenient to give this message now if self.dry_run: print('Starting dry run import ...') else: print('Starting import ...') if self.first_period and not self.last_period: # there are more periods so say so print("Records covering multiple periods have been identified for import.") # step through our periods of records until we reach the end. A # 'period' of records may comprise the contents of a file, a day # of WU obs or a month of Cumulus obs for period in self.period_generator(): # if we are importing multiple periods of data then tell the # user what period we are up to if not (self.first_period and self.last_period): print("Period %d ..." % self.period_no) # get the raw data _msg = 'Obtaining raw import data for period %d ...' % self.period_no if self.verbose: print(_msg) log.info(_msg) try: _raw_data = self.getRawData(period) except WeeImportIOError as e: print("**** Unable to load source data for period %d." % self.period_no) log.info("**** Unable to load source data for period %d." % self.period_no) print("**** %s" % e) log.info("**** %s" % e) print("**** Period %d will be skipped. Proceeding to next period." % self.period_no) log.info("**** Period %d will be skipped. Proceeding to next period." % self.period_no) # increment our period counter self.period_no += 1 continue except WeeImportDecodeError as e: print("**** Unable to decode source data for period %d." % self.period_no) log.info("**** Unable to decode source data for period %d." % self.period_no) print("**** %s" % e) log.info("**** %s" % e) print("**** Period %d will be skipped. Proceeding to next period." % self.period_no) log.info("**** Period %d will be skipped. Proceeding to next period." % self.period_no) print("**** Consider specifying the source file encoding using the 'source_encoding' config option.") log.info("**** Consider specifying the source file encoding using the 'source_encoding' config option.") # increment our period counter self.period_no += 1 continue _msg = 'Raw import data read successfully for period %d.' % self.period_no if self.verbose: print(_msg) log.info(_msg) # map the raw data to a WeeWX archive compatible dictionary _msg = 'Mapping raw import data for period %d ...' % self.period_no if self.verbose: print(_msg) log.info(_msg) _mapped_data = self.mapRawData(_raw_data, self.archive_unit_sys) _msg = 'Raw import data mapped successfully for period %d.' % self.period_no if self.verbose: print(_msg) log.info(_msg) # save the mapped data to archive # first advise the user and log, but only if its not a dry run if not self.dry_run: _msg = 'Saving mapped data to archive for period %d ...' % self.period_no if self.verbose: print(_msg) log.info(_msg) self.saveToArchive(archive, _mapped_data) # advise the user and log, but only if its not a dry run if not self.dry_run: _msg = 'Mapped data saved to archive successfully for period %d.' % self.period_no if self.verbose: print(_msg) log.info(_msg) # increment our period counter self.period_no += 1 # The source data has been processed and any records saved to # archive (except if it was a dry run). # now update the lastUpdate meta data field, set it to the max of # the timestamp of the youngest record imported and the value of # lastUpdate from before we started new_last_update = max_with_none((last_update, self.latest_ts)) if new_last_update is not None: self.dbm._write_metadata('lastUpdate', str(int(new_last_update))) # If necessary, calculate any missing derived fields and provide # the user with suitable summary output. if self.total_rec_proc == 0: # nothing was imported so no need to calculate any missing # fields just inform the user what was done _msg = 'No records were identified for import. Exiting. Nothing done.' print(_msg) log.info(_msg) else: # We imported something, but was it a dry run or not? total_rec = self.total_rec_proc + self.total_duplicate_rec if self.dry_run: # It was a dry run. Skip calculation of missing derived # fields (since there are no archive records to process), # just provide the user with a summary of what we did. _msg = "Finished dry run import" print(_msg) log.info(_msg) _msg = "%d records were processed and %d unique records would "\ "have been imported." % (total_rec, self.total_rec_proc) print(_msg) log.info(_msg) if self.total_duplicate_rec > 1: _msg = "%d duplicate records were ignored." % self.total_duplicate_rec print(_msg) log.info(_msg) elif self.total_duplicate_rec == 1: _msg = "1 duplicate record was ignored." print(_msg) log.info(_msg) else: # It was not a dry run so calculate any missing derived # fields and provide the user with a summary of what we did. if self.calc_missing: # We were asked to calculate missing derived fields, so # get a CalcMissing object. # First construct a CalcMissing config dict # (self.dry_run will never be true). Subtract 0.5 # seconds from earliest timestamp as calc_missing only # calculates missing derived obs for records # timestamped after start_ts. calc_missing_config_dict = {'name': 'Calculate Missing Derived Observations', 'binding': self.db_binding_wx, 'start_ts': self.earliest_ts-0.5, 'stop_ts': self.latest_ts, 'trans_days': 1, 'dry_run': self.dry_run is True} # now obtain a CalcMissing object self.calc_missing_obj = weecfg.database.CalcMissing(self.config_dict, calc_missing_config_dict) _msg = "Calculating missing derived observations ..." print(_msg) log.info(_msg) # do the calculations self.calc_missing_obj.run() _msg = "Finished calculating missing derived observations" print(_msg) log.info(_msg) # now provide the summary report _msg = "Finished import" print(_msg) log.info(_msg) _msg = "%d records were processed and %d unique records " \ "imported in %.2f seconds." % (total_rec, self.total_rec_proc, self.tdiff) print(_msg) log.info(_msg) if self.total_duplicate_rec > 1: _msg = "%d duplicate records were ignored." % self.total_duplicate_rec print(_msg) log.info(_msg) elif self.total_duplicate_rec == 1: _msg = "1 duplicate record was ignored." print(_msg) log.info(_msg) print("Those records with a timestamp already in the archive will not have been") print("imported. Confirm successful import in the WeeWX log file.") def parseMap(self, source_type, source, import_config_dict): """Produce a source field-to-WeeWX archive field map. Data from an external source can be mapped to the WeeWX archive using: - a fixed field map (WU), - a fixed field map with user specified source units (Cumulus), or - a user defined field/units map. All user defined mapping is specified in the import config file. To generate the field map first look to see if we have a fixed map, if we do validate it and return the resulting map. Otherwise look for user specified mapping in the import config file, construct the field map and return it. If there is neither a fixed map or user specified mapping then raise an error. Input parameters: source_type: String holding name of the section in import_config_dict that holds config details for the source being used. source: Iterable holding the source data. Used if import field names are included in the source data (eg CSV). import_config_dict: config dict from import config file. Returns a map as a dictionary of elements with each element structured as follows: 'archive_field_name': {'field_name': 'source_field_name', 'units': 'unit_name'} where: - archive_field_name is an observation name in the WeeWX database schema - source_field_name is the name of a field from the external source - unit_name is the WeeWX unit name of the units used by source_field_name """ # start with the minimum map _map = dict(MINIMUM_MAP) # Do the easy one first, do we have a fixed mapping, if so validate it if self._header_map: # We have a static map that maps header fields to WeeWX (eg WU). # Our static map may have entries for fields that don't exist in our # source data so step through each field name in our source data and # only add those that exist to our resulting map. # first get a list of fields, source could be a DictReader object # or a list of dicts, a DictReader will have a fieldnames property try: _field_names = source.fieldnames except AttributeError: # Not a DictReader so need to obtain the dict keys, could just # pick a record and extract its keys but some records may have # different keys to others. Use sets and a generator # comprehension. _field_names = set().union(*(list(d.keys()) for d in source)) # now iterate over the field names for _key in _field_names: # if we know about the field name add it to our map if _key in self._header_map: _map[self._header_map[_key]['map_to']] = {'field_name': _key, 'units': self._header_map[_key]['units']} # Do we have a user specified map, if so construct our field map elif 'FieldMap' in import_config_dict: # we have a user specified map so construct our map dict for _key, _item in six.iteritems(import_config_dict['FieldMap']): _entry = option_as_list(_item) # expect 2 parameters for each option: source field, units if len(_entry) == 2: # we have 2 parameter so that's field and units _map[_key] = {'field_name': _entry[0], 'units': _entry[1]} # if the entry is not empty then it might be valid ie just a # field name (eg if usUnits is specified) elif _entry != [''] and len(_entry) == 1: # we have 1 parameter so it must be just name _map[_key] = {'field_name': _entry[0]} else: # otherwise its invalid so ignore it pass # now do some crude error checking # dateTime. We must have a dateTime mapping. Check for a 'field_name' # field under 'dateTime' and be prepared to catch the error if it # does not exist. try: if _map['dateTime']['field_name']: # we have a 'field_name' entry so continue pass else: # something is wrong, we have a 'field_name' entry but it # is not valid so raise an error _msg = "Invalid mapping specified in '%s' for " \ "field 'dateTime'." % self.import_config_path raise WeeImportMapError(_msg) except KeyError: _msg = "No mapping specified in '%s' for field " \ "'dateTime'." % self.import_config_path raise WeeImportMapError(_msg) # usUnits. We don't have to have a mapping for usUnits but if we # don't then we must have 'units' specified for each field mapping. if 'usUnits' not in _map: # no unit system mapping do we have units specified for # each individual field for _key, _val in six.iteritems(_map): # we don't need to check dateTime and usUnits if _key not in ['dateTime', 'usUnits']: if 'units' in _val: # we have a units field, do we know about it if _val['units'] not in weewx.units.default_unit_format_dict \ and _val['units'] != 'text': # we have an invalid unit string so tell the # user and exit _msg = "Unknown units '%s' specified for " \ "field '%s' in %s." % (_val['units'], _key, self.import_config_path) raise weewx.UnitError(_msg) else: # we don't have a units field, that's not allowed # so raise an error _msg = "No units specified for source field " \ "'%s' in %s." % (_key, self.import_config_path) raise WeeImportMapError(_msg) # if we got this far we have a usable map, advise the user what we # will use _msg = "The following imported field-to-WeeWX field map will be used:" if self.verbose: print(_msg) log.info(_msg) for _key, _val in six.iteritems(_map): if 'field_name' in _val: _units_msg = "" if 'units' in _val: if _val['units'] == 'text': _units_msg = " as text" else: _units_msg = " in units '%s'" % _val['units'] _msg = " source field '%s'%s --> WeeWX field '%s'" % (_val['field_name'], _units_msg, _key) if self.verbose: print(_msg) log.info(_msg) else: # no [[FieldMap]] stanza and no _header_map so raise an error as we # don't know what to map _msg = "No '%s' field map found in %s." % (source_type, self.import_config_path) raise WeeImportMapError(_msg) return _map def mapRawData(self, data, unit_sys=weewx.US): """Maps raw data to WeeWX archive record compatible dictionaries. Takes an iterable source of raw data observations, maps the fields of each row to a list of WeeWX compatible archive records and performs any necessary unit conversion. Input parameters: data: iterable that yields the data records to be processed. unit_sys: WeeWX unit system in which the generated records will be provided. Omission will result in US customary (weewx.US) being used. Returns a list of dicts of WeeWX compatible archive records. """ # initialise our list of mapped records _records = [] # initialise some rain variables _last_ts = None _last_rain = None # list of fields we have given the user a warning over, prevents us # giving multiple warnings for the same field. _warned = [] # step through each row in our data for _row in data: _rec = {} # first off process the fields that require special processing # dateTime if 'field_name' in self.map['dateTime']: # we have a map for dateTime try: _raw_dateTime = _row[self.map['dateTime']['field_name']] except KeyError: _msg = "Field '%s' not found in source data." % self.map['dateTime']['field_name'] raise WeeImportFieldError(_msg) # now process the raw date time data if isinstance(_raw_dateTime, numbers.Number) or _raw_dateTime.isdigit(): # Our dateTime is a number, is it a timestamp already? # Try to use it and catch the error if there is one and # raise it higher. try: _rec_dateTime = int(_raw_dateTime) except ValueError: _msg = "Invalid '%s' field. Cannot convert '%s' to " \ "timestamp." % (self.map['dateTime']['field_name'], _raw_dateTime) raise ValueError(_msg) else: # it's a non-numeric string so try to parse it and catch # the error if there is one and raise it higher try: _datetm = time.strptime(_raw_dateTime, self.raw_datetime_format) _rec_dateTime = int(time.mktime(_datetm)) except ValueError: _msg = "Invalid '%s' field. Cannot convert '%s' to " \ "timestamp." % (self.map['dateTime']['field_name'], _raw_dateTime) raise ValueError(_msg) # if we have a timeframe of concern does our record fall within # it if (self.first_ts is None and self.last_ts is None) or \ self.first_ts < _rec_dateTime <= self.last_ts: # we have no timeframe or if we do it falls within it so # save the dateTime _rec['dateTime'] = _rec_dateTime # update earliest and latest record timstamps if self.earliest_ts is None or _rec_dateTime < self.earliest_ts: self.earliest_ts = _rec_dateTime if self.latest_ts is None or _rec_dateTime > self.earliest_ts: self.latest_ts = _rec_dateTime else: # it is not so skip to the next record continue else: # there is no mapped field for dateTime so raise an error raise ValueError("No mapping for WeeWX field 'dateTime'.") # usUnits _units = None if 'field_name' in self.map['usUnits']: # we have a field map for a unit system try: # The mapped field is in _row so try to get the raw data. # If its not there then raise an error. _raw_units = int(_row[self.map['usUnits']['field_name']]) except KeyError: _msg = "Field '%s' not found in source data." % self.map['usUnits']['field_name'] raise WeeImportFieldError(_msg) # we have a value but is it valid if _raw_units in unit_nicknames: # it is valid so use it _units = _raw_units else: # the units value is not valid so raise an error _msg = "Invalid unit system '%s'(0x%02x) mapped from data source. " \ "Check data source or field mapping." % (_raw_units, _raw_units) raise weewx.UnitError(_msg) # interval if 'field_name' in self.map['interval']: # We have a map for interval so try to get the raw data. If # its not there then raise an error. try: _tfield = _row[self.map['interval']['field_name']] except KeyError: _msg = "Field '%s' not found in source data." % self.map['interval']['field_name'] raise WeeImportFieldError(_msg) # now process the raw interval data if _tfield is not None and _tfield != '': try: interval = int(_tfield) except ValueError: _msg = "Invalid '%s' field. Cannot convert '%s' to " \ "an integer." % (self.map['interval']['field_name'], _tfield) raise ValueError(_msg) else: # if it happens to be None then raise an error _msg = "Invalid value '%s' for mapped field '%s' at " \ "timestamp '%s'." % (_tfield, self.map['interval']['field_name'], timestamp_to_string(_rec['dateTime'])) raise ValueError(_msg) else: # we have no mapping so try to calculate it interval = self.getInterval(_last_ts, _rec['dateTime']) _rec['interval'] = interval # now step through the rest of the fields in our map and process # the fields that don't require special processing for _field in self.map: # skip those that have had special processing if _field in MINIMUM_MAP: continue # process everything else else: # is our mapped field in the record if self.map[_field]['field_name'] in _row: # yes it is # first check to see if this is a text field if 'units' in self.map[_field] and self.map[_field]['units'] == 'text': # we have a text field, so accept the field # contents as is _rec[_field] = _row[self.map[_field]['field_name']] else: # we have a non-text field so try to get a value # for the obs but if we can't catch the error try: _temp = float(_row[self.map[_field]['field_name']].strip()) except AttributeError: # the data has no strip() attribute so chances are # it's a number already if isinstance(_row[self.map[_field]['field_name']], numbers.Number): _temp = _row[self.map[_field]['field_name']] elif _row[self.map[_field]['field_name']] is None: _temp = None else: # it's not a string and its not a number so raise an error _msg = "%s: cannot convert '%s' to float at " \ "timestamp '%s'." % (_field, _row[self.map[_field]['field_name']], timestamp_to_string(_rec['dateTime'])) raise ValueError(_msg) except TypeError: # perhaps we have a None, so return None for our field _temp = None except ValueError: # most likely have non-numeric, non-None data # if this is a csv import and we are mapping to a # direction field perhaps we have a string # representation of a cardinal, intercardinal or # secondary intercardinal direction that we can # convert to degrees # set a flag to indicate whether we matched the data to a value matched = False if hasattr(self, 'wind_dir_map') and self.map[_field]['units'] == 'degree_compass': # we have a csv import and we are mapping to a # direction field # first strip any whitespace and hyphens from # the data _stripped = re.sub(r'[\s-]+', '', _row[self.map[_field]['field_name']]) # try to use the data as the key in a dict # mapping directions to degrees, if there is no # match we will have None returned dir_degrees = self.wind_dir_map.get(_stripped.upper()) # if we have a non-None value use it if dir_degrees is not None: _temp = dir_degrees # we have a match so set our flag matched = True # if we did not get a match perhaps we can ignore # the invalid data, that will depend on the # ignore_invalid_data property if not matched and self.ignore_invalid_data: # we ignore the invalid data so set our result # to None _temp = None # set our matched flag matched = True # if we did not find a match raise the error if not matched: _msg = "%s: cannot convert '%s' to float at " \ "timestamp '%s'." % (_field, _row[self.map[_field]['field_name']], timestamp_to_string(_rec['dateTime'])) raise ValueError(_msg) # some fields need some special processing # rain - if our imported 'rain' field is cumulative # (self.rain == 'cumulative') then we need to calculate # the discrete rainfall for this archive period if _field == "rain" and self.rain == "cumulative": _rain = self.getRain(_last_rain, _temp) _last_rain = _temp _temp = _rain # wind - check any wind direction fields are within our # bounds and convert to 0 to 360 range if _field == "windDir" or _field == "windGustDir": if _temp is not None and (self.wind_dir[0] <= _temp <= self.wind_dir[1]): # normalise to 0 to 360 _temp %= 360 else: # outside our bounds so set to None _temp = None # UV - if there was no UV sensor used to create the # imported data then we need to set the imported value # to None if _field == 'UV' and not self.UV_sensor: _temp = None # solar radiation - if there was no solar radiation # sensor used to create the imported data then we need # to set the imported value to None if _field == 'radiation' and not self.solar_sensor: _temp = None # check and ignore if required temperature and humidity # values of 255.0 and greater if self.ignore_extr_th \ and self.map[_field]['units'] in ['degree_C', 'degree_F', 'percent'] \ and _temp >= 255.0: _temp = None # if no mapped field for a unit system we have to do # field by field unit conversions if _units is None: _temp_vt = ValueTuple(_temp, self.map[_field]['units'], weewx.units.obs_group_dict[_field]) _conv_vt = convertStd(_temp_vt, unit_sys) _rec[_field] = _conv_vt.value else: # we do have a mapped field for a unit system so # save the field in our record and continue, any # unit conversion will be done in bulk later _rec[_field] = _temp else: # No it's not. Set the field in our output to None _rec[_field] = None # now warn the user about this field if we have not # already done so if self.map[_field]['field_name'] not in _warned: _msg = "Warning: Import field '%s' is mapped to WeeWX " \ "field '%s' but the" % (self.map[_field]['field_name'], _field) if not self.suppress: print(_msg) log.info(_msg) _msg = " import field '%s' could not be found " \ "in one or more records." % self.map[_field]['field_name'] if not self.suppress: print(_msg) log.info(_msg) _msg = " WeeWX field '%s' will be set to 'None' in these records." % _field if not self.suppress: print(_msg) log.info(_msg) # make sure we do this warning once only _warned.append(self.map[_field]['field_name']) # if we have a mapped field for a unit system with a valid value, # then all we need do is set 'usUnits', bulk conversion is taken # care of by saveToArchive() if _units is not None: # we have a mapped field for a unit system with a valid value _rec['usUnits'] = _units else: # no mapped field for unit system but we have already converted # any necessary fields on a field by field basis so all we need # do is set 'usUnits', any bulk conversion will be taken care of # by saveToArchive() _rec['usUnits'] = unit_sys # If interval is being derived from record timestamps our first # record will have an interval of None. In this case we wait until # we have the second record and then we use the interval between # records 1 and 2 as the interval for record 1. if len(_records) == 1 and _records[0]['interval'] is None: _records[0]['interval'] = _rec['interval'] _last_ts = _rec['dateTime'] # this record is done, add it to our list of records to return _records.append(_rec) # If we have more than 1 unique value for interval in our records it # could be a sign of missing data and impact the integrity of our data, # so do the check and see if the user wants to continue if len(_records) > 0: # if we have any records to return do the unique interval check # before we return the records _start_interval = _records[0]['interval'] _diff_interval = False for _rec in _records: if _rec['interval'] != _start_interval: _diff_interval = True break if _diff_interval and self.interval_ans != 'y': # we had more than one unique value for interval, warn the user _msg = "Warning: Records to be imported contain multiple " \ "different 'interval' values." print(_msg) log.info(_msg) print(" This may mean the imported data is missing some records and it may lead") print(" to data integrity issues. If the raw data has a known, fixed interval") print(" value setting the relevant 'interval' setting in wee_import config to") print(" this value may give a better result.") while self.interval_ans not in ['y', 'n']: if self.no_prompt: self.interval_ans = 'y' else: self.interval_ans = input('Are you sure you want to proceed (y/n)? ') if self.interval_ans == 'n': # the user chose to abort, but we may have already # processed some records. So log it then raise a SystemExit() if self.dry_run: print("Dry run import aborted by user. %d records were processed." % self.total_rec_proc) else: if self.total_rec_proc > 0: print("Those records with a timestamp already in the archive will not have been") print("imported. As the import was aborted before completion refer to the WeeWX log") print("file to confirm which records were imported.") raise SystemExit('Exiting.') else: print("Import aborted by user. No records saved to archive.") _msg = "User chose to abort import. %d records were processed. " \ "Exiting." % self.total_rec_proc log.info(_msg) raise SystemExit('Exiting. Nothing done.') _msg = "Mapped %d records." % len(_records) if self.verbose: print(_msg) log.info(_msg) # the user wants to continue or we have only one unique value for # interval so return the records return _records else: _msg = "Mapped 0 records." if self.verbose: print(_msg) log.info(_msg) # we have no records to return so return None return None def getInterval(self, last_ts, current_ts): """Determine an interval value for a record. The interval field can be determined in one of the following ways: - Derived from the raw data. The interval is calculated as the difference between the timestamps of consecutive records rounded to the nearest minute. In this case interval can change between records if the records are not evenly spaced in time or if there are missing records. This method is the default and is used when the interval parameter in wee_import.conf is 'derive'. - Read from weewx.conf. The interval value is read from the archive_interval parameter in [StdArchive] in weewx.conf. In this case interval may or may not be the same as the difference in time between consecutive records. This method may be of use when the import source has a known interval but may be missing a number of records which makes deriving the interval from the imported data problematic. This method is used when the interval parameter in wee_import.conf is 'conf'. Input parameters: last_ts. timestamp of the previous record. current_rain. timestamp of the current record. Returns the interval (in minutes) for the current record. """ # did we have a number specified in wee_import.conf, if so use that try: return float(self.interval) except ValueError: pass # how are we getting interval if self.interval.lower() == 'conf': # get interval from weewx.conf return to_int(float(self.config_dict['StdArchive'].get('archive_interval')) / 60.0) elif self.interval.lower() == 'derive': # get interval from the timestamps of consecutive records try: _interval = int((current_ts - last_ts) / 60.0) # but if _interval < 0 our records are not in date time order if _interval < 0: # so raise an error _msg = "Cannot derive 'interval' for record timestamp: %s." % timestamp_to_string(current_ts) print(_msg) log.info(_msg) raise ValueError("Raw data is not in ascending date time order.") except TypeError: _interval = None return _interval else: # we don't know what to do so raise an error _msg = "Cannot derive 'interval'. Unknown 'interval' setting in %s." % self.import_config_path raise ValueError(_msg) @staticmethod def getRain(last_rain, current_rain): """Determine period rainfall from two cumulative rainfall values. If the data source provides rainfall as a cumulative value then the rainfall in a period is the simple difference between the two values. But we need to take into account some special cases: No last_rain value. Will occur for very first record or maybe in an error condition. Need to return 0.0. last_rain > current_rain. Occurs when rain counter was reset (maybe daily or some other period). Need to return current_rain. current_rain is None. Could occur if imported rainfall value could not be converted to a numeric and config option ignore_invalid_data is set. Input parameters: last_rain. Previous rainfall total. current_rain. Current rainfall total. Returns the rainfall in the period. """ if last_rain is not None: # we have a value for the previous period if current_rain is not None and current_rain >= last_rain: # just return the difference return current_rain - last_rain else: # we are at a cumulative reset point or we current_rain is None, # either way we just want current_rain return current_rain else: # we have no previous rain value so return zero return 0.0 def qc(self, data_dict, data_type): """ Apply weewx.conf QC to a record. If qc option is set in the import config file then apply any StdQC min/max checks specified in weewx.conf. Input parameters: data_dict: A WeeWX compatible archive record. Returns nothing. data_dict is modified directly with obs outside of QC limits set to None. """ if self.apply_qc: self.import_QC.apply_qc(data_dict, data_type=data_type) def saveToArchive(self, archive, records): """ Save records to the WeeWX archive. Supports saving one or more records to archive. Each collection of records is processed and saved to archive in transactions of self.tranche records at a time. if the import config file qc option was set quality checks on the imported record are performed using the WeeWX StdQC configuration from weewx.conf. Any missing derived observations are then added to the archive record using the WeeWX WXCalculate class if the import config file calc_missing option was set. WeeWX API addRecord() method is used to add archive records. If --dry-run was set then every aspect of the import is carried out but nothing is saved to archive. If --dry-run was not set then the user is requested to confirm the import before any records are saved to archive. Input parameters: archive: database manager object for the WeeWX archive. records: iterable that provides WeeWX compatible archive records (in dict form) to be written to archive """ # do we have any records? if records and len(records) > 0: # if this is the first period then give a little summary about what # records we have # TODO. Check that a single period shows correct and consistent console output if self.first_period and self.last_period: # there is only 1 period, so we can count them print("%s records identified for import." % len(records)) # we do, confirm the user actually wants to save them while self.ans not in ['y', 'n'] and not self.dry_run: if self.no_prompt: self.ans = 'y' else: print("Proceeding will save all imported records in the WeeWX archive.") self.ans = input("Are you sure you want to proceed (y/n)? ") if self.ans == 'y' or self.dry_run: # we are going to save them # reset record counter nrecs = 0 # initialise our list of records for this tranche _tranche = [] # initialise a set for use in our dry run, this lets us # give some better stats on records imported unique_set = set() # step through each record in this period for _rec in records: # convert our record _conv_rec = to_std_system(_rec, self.archive_unit_sys) # perform any any required QC checks self.qc(_conv_rec, 'Archive') # add the record to our tranche and increment our count _tranche.append(_conv_rec) nrecs += 1 # if we have a full tranche then save to archive and reset # the tranche if len(_tranche) >= self.tranche: # add the record only if it is not a dry run if not self.dry_run: # add the record only if it is not a dry run archive.addRecord(_tranche) # add our the dateTime for each record in our tranche # to the dry run set for _trec in _tranche: unique_set.add(_trec['dateTime']) # tell the user what we have done _msg = "Unique records processed: %d; Last timestamp: %s\r" % (nrecs, timestamp_to_string(_rec['dateTime'])) print(_msg, end='', file=sys.stdout) sys.stdout.flush() _tranche = [] # we have processed all records but do we have any records left # in the tranche? if len(_tranche) > 0: # we do so process them if not self.dry_run: # add the record only if it is not a dry run archive.addRecord(_tranche) # add our the dateTime for each record in our tranche to # the dry run set for _trec in _tranche: unique_set.add(_trec['dateTime']) # tell the user what we have done _msg = "Unique records processed: %d; Last timestamp: %s\r" % (nrecs, timestamp_to_string(_rec['dateTime'])) print(_msg, end='', file=sys.stdout) print() sys.stdout.flush() # update our counts self.total_rec_proc += nrecs self.total_unique_rec += len(unique_set) # mention any duplicates we encountered num_duplicates = len(self.period_duplicates) self.total_duplicate_rec += num_duplicates if num_duplicates > 0: if num_duplicates == 1: _msg = " 1 duplicate record was identified in period %d:" % self.period_no else: _msg = " %d duplicate records were identified in period %d:" % (num_duplicates, self.period_no) if not self.suppress: print(_msg) log.info(_msg) for ts in sorted(self.period_duplicates): _msg = " %s" % timestamp_to_string(ts) if not self.suppress: print(_msg) log.info(_msg) # add the period duplicates to the overall duplicates self.duplicates |= self.period_duplicates # reset the period duplicates self.period_duplicates = set() elif self.ans == 'n': # user does not want to import so display a message and then # ask to exit _msg = "User chose not to import records. Exiting. Nothing done." print(_msg) log.info(_msg) raise SystemExit('Exiting. Nothing done.') else: # we have no records to import, advise the user but what we say # will depend if there are any more periods to import if self.first_period and self.last_period: # there was only 1 period _msg = 'No records identified for import.' else: # multiple periods _msg = 'Period %d - no records identified for import.' % self.period_no print(_msg) # if we have finished record the time taken for our summary if self.last_period: self.tdiff = time.time() - self.t1 # ============================================================================ # Utility functions # ============================================================================ def get_binding(config_dict): """Get the binding for the WeeWX database.""" # Extract our binding from the StdArchive section of the config file. If # it's missing, return None. if 'StdArchive' in config_dict: db_binding_wx = config_dict['StdArchive'].get('data_binding', 'wx_binding') else: db_binding_wx = None return db_binding_wx
