ISMN_Interface date format error (v1.4.0)

[AndresTerrer]

I downloaded Soild_moisture, soil_temperature, surface_temperature, snow_depth and air_temperature for all stations betwen 2017-01-01 and 2017-12-31.

When loading the zip file and generating metadata for the first time the code outputed this error.

ValueError: time data "2017-01-01" doesn't match format "%Y-%m-%d %H:%M:%S", at position 462. You might want to try: - passing format if your strings have a consistent format; - passing format='ISO8601' if your strings are all ISO8601 but not necessarily in exactly the same format; - passing format='mixed', and the format will be inferred for each element individually. You might want to use dayfirst alongside this.

I tryed with ismn==1.2.0 and then got the "No objects to concatenate error" (see: https://github.com/TUW-GEO/ismn/issues/52)

Then I downgraded to ismn==1.3.0 and tryed to load a sinpler dataset contatining only Soild_moisture, soil_temperature, surface_temperature. It worked fine.

[AndresTerrer]

This is the full error message

---

ValueError Traceback (most recent call last) Cell In[6], line 2 1 # This will take a while the first time, while the interface creates all the indeces. ----> 2 ismn_data = ISMN_Interface(data_path, parallel=True)

File ...\ismn\interface.py:162, in ISMN_Interface.init(self, data_path, meta_path, network, parallel, keep_loaded_data, temp_root, custom_meta_reader, force_metadata_collection) 159 self.meta_path = meta_path 160 self.temp_root = temp_root --> 162 self.activate_network( 163 network=network, 164 meta_path=self.meta_path, 165 temp_root=self.temp_root)

File ...\ismn\interface.py:196, in ISMN_Interface.activate_network(self, network, meta_path, temp_root) 187 self.__file_collection = IsmnFileCollection.build_from_scratch( 188 self.root, 189 parallel=self.parallel, (...) 192 custom_meta_readers=self.custom_meta_reader, 193 ) 194 self.__file_collection.to_metadata_csv(meta_csv_file) --> 196 self.__file_collection = IsmnFileCollection.from_metadata_csv( 197 self.root, meta_csv_file, network=network) 199 networks = self._collect() 200 self.collection = NetworkCollection(networks)

File ...\ismn\filecollection.py:405, in IsmnFileCollection.from_metadata_csv(cls, data_root, meta_csv_file, network, temp_root) 401 network = np.atleast_1d(network) 403 print(f"Found existing ismn metadata in {meta_csv_file}.") --> 405 metadata_df = _load_metadata_df(meta_csv_file) 407 if network is not None: 408 metadata_df = metadata_df[np.isin(metadata_df["network"].values, 409 network)]

File ...\ismn\filecollection.py:136, in _load_metadata_df(meta_csv_file) 134 # parse date cols as datetime 135 for col in ["timerange_from", "timerange_to"]: --> 136 metadata_df[col, "val"] = pd.to_datetime(metadata_df[col, "val"]) 138 lvars = [] 139 for c in metadata_df.columns:

File ...\pandas\core\tools\datetimes.py:1063, in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache) 1061 result = arg.tz_localize("utc") 1062 elif isinstance(arg, ABCSeries): -> 1063 cache_array = _maybe_cache(arg, format, cache, convert_listlike) 1064 if not cache_array.empty: 1065 result = arg.map(cache_array)

File ...\pandas\core\tools\datetimes.py:247, in _maybe_cache(arg, format, cache, convert_listlike) 245 unique_dates = unique(arg) 246 if len(unique_dates) < len(arg): --> 247 cache_dates = convert_listlike(unique_dates, format) 248 # GH#45319 249 try:

File ...\pandas\core\tools\datetimes.py:433, in _convert_listlike_datetimes(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact) 431 # format could be inferred, or user didn't ask for mixed-format parsing. 432 if format is not None and format != "mixed": --> 433 return _array_strptime_with_fallback(arg, name, utc, format, exact, errors) 435 result, tz_parsed = objects_to_datetime64( 436 arg, 437 dayfirst=dayfirst, (...) 441 allow_object=True, 442 ) 444 if tz_parsed is not None: 445 # We can take a shortcut since the datetime64 numpy array 446 # is in UTC

File ...\pandas\core\tools\datetimes.py:467, in _array_strptime_with_fallback(arg, name, utc, fmt, exact, errors) 456 def _array_strptime_with_fallback( 457 arg, 458 name, (...) 462 errors: str, 463 ) -> Index: 464 """ 465 Call array_strptime, with fallback behavior depending on 'errors'. 466 """ --> 467 result, tz_out = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc) 468 if tz_out is not None: 469 unit = np.datetime_data(result.dtype)[0]

File strptime.pyx:501, in pandas._libs.tslibs.strptime.array_strptime()

File strptime.pyx:451, in pandas._libs.tslibs.strptime.array_strptime()

File strptime.pyx:583, in pandas._libs.tslibs.strptime._parse_with_format()

ValueError: time data "2017-01-01" doesn't match format "%Y-%m-%d %H:%M:%S", at position 462. You might want to try: - passing format if your strings have a consistent format; - passing format='ISO8601' if your strings are all ISO8601 but not necessarily in exactly the same format; - passing format='mixed', and the format will be inferred for each element individually. You might want to use dayfirst alongside this.

[wpreimes]

Hi, thanks for reporting this. Unfortunately I could not reproduce the error so far (I have downloaded all data for 2017 from the ismn platform yesterday, maybe there was a temporary hiccup in the database and you got a corrupt file?).

• What OS and python version are you on?
• Can you check if your python_metadata folder (should be in the same directory as your data) has any contents and if yes, send me the csv and log file there if they exist? You can drop them here if you want https://cloud.geo.tuwien.ac.at/s/edAnagjt7rfJrJb
• Can you delete the python_metadata folder stored with your data and try to re-generate it and see if that helps?
• Can you figure out if only a specific network or file makes problems? Maybe the .csv or .log can give us a hint
• Can you send me a list of your installed dependencies. I.e in the environment where you installed the ismn package do conda list or pip list.
• Do you remember which options you selected when downloading the data?

[wpreimes]

After some testing, I think there might be a bug with parallel extraction from the zip archive. I'm looking into this. I suggest to use parallel=False (Default) for now when reading data directly from zip. Or extract the archive first and pass the directory that contains the extracted networks instead of the zip file.