Not sure if I can post a question like this here so please redirect me if I'm in the wrong place.
I've bought a Wahoo TICKR X to monitor my heart rate during exercise. Also I would like to get more familiar with python so i decided I would like do do the analyses of my heart rate myself in python instead of in the wahoo app. I thought this would also give more freedom in the choice of visualization, testing etc.
I've recorded my heart rate for 5 minutes or so and exported the .fit file. However I can't even find a suitable library to read the .fit file. Can anyone recommend a library that works with .fit file from wahoo?
I'm using ubuntu, anaconda, python 3.7
import pyfits
# Load the FITS file into the program
hdulist = pyfits.open('/home/bradmin/Downloads/2020-03-26.fit')
# Load table data as tbdata
tbdata = hdulist[1].data
OSError Traceback (most recent call last)
<ipython-input-3-a970e2cd9dee> in <module>
2
3 # Load the FITS file into the program
----> 4 hdulist = pyfits.open('/home/bradmin/Downloads/2020-03-26.fit')
5
6 # Load table data as tbdata
~/anaconda3/lib/python3.7/site-packages/pyfits/hdu/hdulist.py in fitsopen(name, mode, memmap, save_backup, **kwargs)
122 raise ValueError('Empty filename: %s' % repr(name))
123
--> 124 return HDUList.fromfile(name, mode, memmap, save_backup, **kwargs)
125
126
~/anaconda3/lib/python3.7/site-packages/pyfits/hdu/hdulist.py in fromfile(cls, fileobj, mode, memmap, save_backup, **kwargs)
264
265 return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,
--> 266 save_backup=save_backup, **kwargs)
267
268 #classmethod
~/anaconda3/lib/python3.7/site-packages/pyfits/hdu/hdulist.py in _readfrom(cls, fileobj, data, mode, memmap, save_backup, **kwargs)
853 # raise and exception
854 if mode in ('readonly', 'denywrite') and len(hdulist) == 0:
--> 855 raise IOError('Empty or corrupt FITS file')
856
857 # initialize/reset attributes to be used in "update/append" mode
OSError: Empty or corrupt FITS file
link to the file: https://wetransfer.com/downloads/6d054a5d52899aefcb1bcd22bda92ba120200326161849/b9831a
EDIT
I've tried this now but i get an error:
import fitdecode
src_file = "/home/bradmin/Downloads/2020-03-26.fit"
with fitdecode.FitReader(src_file) as fit:
for frame in fit:
# The yielded frame object is of one of the following types:
# * fitdecode.FitHeader
# * fitdecode.FitDefinitionMessage
# * fitdecode.FitDataMessage
# * fitdecode.FitCRC
if isinstance(frame, fitdecode.FitDataMessage):
# Here, frame is a FitDataMessage object.
# A FitDataMessage object contains decoded values that
# are directly usable in your script logic.
print(frame.name)
file_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
developer_data_id
field_description
field_description
field_description
field_description
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-e8d95d3087dc> in <module>
2
3 with fitdecode.FitReader(src_file) as fit:
----> 4 for frame in fit:
5 # The yielded frame object is of one of the following types:
6 # * fitdecode.FitHeader
~/anaconda3/lib/python3.7/site-packages/fitdecode/reader.py in __iter__(self)
191
192 def __iter__(self):
--> 193 yield from self._read_next()
194
195 #property
~/anaconda3/lib/python3.7/site-packages/fitdecode/reader.py in _read_next(self)
298 assert self._header
299
--> 300 record = self._read_record()
301 if not record:
302 break
~/anaconda3/lib/python3.7/site-packages/fitdecode/reader.py in _read_record(self)
443 self._add_dev_data_id(message)
444 elif message.mesg_type.mesg_num == profile.MESG_NUM_FIELD_DESCRIPTION:
--> 445 self._add_dev_field_description(message)
446
447 return message
~/anaconda3/lib/python3.7/site-packages/fitdecode/reader.py in _add_dev_field_description(self, message)
780 base_type_id = message.get_field('fit_base_type_id').raw_value
781 field_name = message.get_field('field_name').raw_value
--> 782 units = message.get_field('units').raw_value
783
784 try:
~/anaconda3/lib/python3.7/site-packages/fitdecode/records.py in get_field(self, field_name_or_num, idx)
188 raise KeyError(
189 f'field "{field_name_or_num}" (idx #{idx}) not found in ' +
--> 190 f'message "{self.name}"')
191
192 def get_fields(self, field_name_or_num):
KeyError: 'field "units" (idx #0) not found in message "field_description"'
The format seems to be this FIT format. pyfits is for an entirely different format, it seems.
The article above refers to a gpsbabel tool, which you could use to convert the FIT file to something more interoperable and usable, e.g. GPX (an XML-based format that's easy to parse).
Or, of course, if you want a pure-Python solution, you can port the FIT format reading bits from gpsbabel to Python use the fitdecode library.
Related
I am trying to create a feature extractor using from torchvision.models.feature_extraction import create_feature_extractor.
The model I am trying to use is from the vit_pytorch (link: https://github.com/lucidrains/vit-pytorch). The problem I face is that when I create a model from this lib:
from vit_pytorch import ViT
from torchvision.models.feature_extraction import create_feature_extractor
model = ViT(image_size=28,
patch_size=7,
num_classes=10,
dim=16,
depth=6,
heads=16,
mlp_dim=256,
dropout=0.1,
emb_dropout=0.1,
channels=1)
random_layer_name = 'transformer.layers.1.1.fn.net.4'
feature_extractor = create_feature_extractor(model,
return_nodes=random_layer_name)
and when trying to use the create_feature_extractor() on this model I always get this error:
RuntimeError Traceback (most recent call last)
Cell In[17], line 2
1 # torch.fx.wrap('len')
----> 2 feature_extractor = create_feature_extractor(model,
3 return_nodes=['transformer.layers.1.1.fn.net.4'])
File ~/Mokslas/AI/venv/lib/python3.10/site-packages/torchvision/models/feature_extraction.py:485, in create_feature_extractor(model, return_nodes, train_return_nodes, eval_return_nodes, tracer_kwargs, suppress_diff_warning)
483 # Instantiate our NodePathTracer and use that to trace the model
484 tracer = NodePathTracer(**tracer_kwargs)
--> 485 graph = tracer.trace(model)
487 name = model.__class__.__name__ if isinstance(model, nn.Module) else model.__name__
488 graph_module = fx.GraphModule(tracer.root, graph, name)
File ~/Mokslas/AI/venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:756, in Tracer.trace(self, root, concrete_args)
749 for module in self._autowrap_search:
750 _autowrap_check(
751 patcher, module.__dict__, self._autowrap_function_ids
752 )
753 self.create_node(
754 "output",
755 "output",
--> 756 (self.create_arg(fn(*args)),),
757 {},
758 type_expr=fn.__annotations__.get("return", None),
759 )
761 self.submodule_paths = None
762 finally:
File ~/Mokslas/AI/venv/lib/python3.10/site-packages/vit_pytorch/vit.py:115, in ViT.forward(self, img)
114 def forward(self, img):
--> 115 x = self.to_patch_embedding(img)
116 b, n, _ = x.shape
118 cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
File ~/Mokslas/AI/venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py:734, in Tracer.trace.<locals>.module_call_wrapper(mod, *args, **kwargs)
727 return _orig_module_call(mod, *args, **kwargs)
729 _autowrap_check(
730 patcher,
731 getattr(getattr(mod, "forward", mod), "__globals__", {}),
732 self._autowrap_function_ids,
733 )
--> 734 return self.call_module(mod, forward, args, kwargs)
File ~/Mokslas/AI/venv/lib/python3.10/site-packages/torchvision/models/feature_extraction.py:83, in NodePathTracer.call_module(self, m, forward, args, kwargs)
...
--> 396 raise RuntimeError("'len' is not supported in symbolic tracing by default. If you want "
397 "this call to be recorded, please call torch.fx.wrap('len') at "
398 "module scope")
RuntimeError: 'len' is not supported in symbolic tracing by default. If you want this call to be recorded, please call torch.fx.wrap('len') at module scope
It doesn't matter which model I choose from that library or which layer or layers I choose to be outputed I always get the same error.
I have tried to add torch.fx.wrap('len') but the same problem persisted. I know I could try to solve it by using the hook methods, but is there a way to solve this problem so that I could still use the create_feature_extractor() functionality?
I have a scikit learn pipeline that I serialize using:
with lzma.open('outputs/baseModel_LR.joblib',"wb") as f:
dill.dump(pipeline, f)
When I try to open the file and load the pipeline using:
with lzma.open('outputs/baseModel_LR.joblib',"rb") as f:
model = dill.load(f)
it gives error:
---------------------------------------------------------------------------
EOFError Traceback (most recent call last)
somePath/notebooks/test.ipynb Cell 5 in <cell line: 1>()
1 with lzma.open('outputs/baseModel_LR.joblib',"rb") as f:
----> 2 model = dill.load(f)
3 model
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/dill/_dill.py:373, in load(file, ignore, **kwds)
367 def load(file, ignore=None, **kwds):
368 """
369 Unpickle an object from a file.
370
371 See :func:`loads` for keyword arguments.
372 """
--> 373 return Unpickler(file, ignore=ignore, **kwds).load()
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/dill/_dill.py:646, in Unpickler.load(self)
645 def load(self): #NOTE: if settings change, need to update attributes
--> 646 obj = StockUnpickler.load(self)
647 if type(obj).__module__ == getattr(_main_module, '__name__', '__main__'):
648 if not self._ignore:
649 # point obj class to main
File /anaconda/envs/azureml_py38/lib/python3.8/lzma.py:200, in LZMAFile.read(self, size)
194 """Read up to size uncompressed bytes from the file.
...
100 "end-of-stream marker was reached")
101 else:
102 rawblock = b""
**EOFError: Compressed file ended before the end-of-stream marker was reached**
Has anyone faced this problem and solved it? I use lzma because otherwise the joblib size is 27GB and with lzma its just 20MB
I'm trying to download the MNIST dataset from openml, using the openml library.
I tried using Jupyter notebooks because I don't want to download the same dataset every time.
Problem is, after running the following code, I get an error:
from openml.datasets import get_dataset
mnist = get_dataset(554)
x, y, p, q = mnist.get_data(
dataset_format="dataframe", target=mnist.default_target_attribute
)
I'm pasting the whole error message I get, the problem occurs when I try assigning the .get_data to x, y, p and q.
The environment I'm running this on is called Oceanic.
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:491, in OpenMLDataset._cache_compressed_file_from_file(self, data_file)
490 try:
--> 491 data = pd.read_parquet(data_file)
492 except Exception as e:
File ~\anaconda3\envs\Oceanic\lib\site-packages\pandas\io\parquet.py:493, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, **kwargs)
491 impl = get_engine(engine)
--> 493 return impl.read(
494 path,
495 columns=columns,
496 storage_options=storage_options,
497 use_nullable_dtypes=use_nullable_dtypes,
498 **kwargs,
499 )
File ~\anaconda3\envs\Oceanic\lib\site-packages\pandas\io\parquet.py:240, in PyArrowImpl.read(self, path, columns, use_nullable_dtypes, storage_options, **kwargs)
239 try:
--> 240 result = self.api.parquet.read_table(
241 path_or_handle, columns=columns, **kwargs
242 ).to_pandas(**to_pandas_kwargs)
243 if manager == "array":
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\parquet.py:1731, in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, read_dictionary, filesystem, filters, buffer_size, partitioning, use_legacy_dataset, ignore_prefixes)
1727 dataset = ParquetFile(
1728 source, metadata=metadata, read_dictionary=read_dictionary,
1729 memory_map=memory_map, buffer_size=buffer_size)
-> 1731 return dataset.read(columns=columns, use_threads=use_threads,
1732 use_pandas_metadata=use_pandas_metadata)
1734 if ignore_prefixes is not None:
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\parquet.py:1608, in _ParquetDatasetV2.read(self, columns, use_threads, use_pandas_metadata)
1606 use_threads = False
-> 1608 table = self._dataset.to_table(
1609 columns=columns, filter=self._filter_expression,
1610 use_threads=use_threads
1611 )
1613 # if use_pandas_metadata, restore the pandas metadata (which gets
1614 # lost if doing a specific `columns` selection in to_table)
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\_dataset.pyx:458, in pyarrow._dataset.Dataset.to_table()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\_dataset.pyx:2889, in pyarrow._dataset.Scanner.to_table()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\error.pxi:141, in pyarrow.lib.pyarrow_internal_check_status()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\error.pxi:112, in pyarrow.lib.check_status()
OSError: NotImplemented: Support for codec 'snappy' not built
The above exception was the direct cause of the following exception:
Exception Traceback (most recent call last)
Input In [10], in <cell line: 1>()
----> 1 x, y, p, q = mnist.get_data(
2 dataset_format="dataframe", target=mnist.default_target_attribute
3 )
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:698, in OpenMLDataset.get_data(self, target, include_row_id, include_ignore_attribute, dataset_format)
658 def get_data(
659 self,
660 target: Optional[Union[List[str], str]] = None,
(...)
668 List[str],
669 ]:
670 """ Returns dataset content as dataframes or sparse matrices.
671
672 Parameters
(...)
696 List of attribute names.
697 """
--> 698 data, categorical, attribute_names = self._load_data()
700 to_exclude = []
701 if not include_row_id and self.row_id_attribute is not None:
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:531, in OpenMLDataset._load_data(self)
528 self._download_data()
530 file_to_load = self.data_file if self.parquet_file is None else self.parquet_file
--> 531 return self._cache_compressed_file_from_file(file_to_load)
533 # helper variable to help identify where errors occur
534 fpath = self.data_feather_file if self.cache_format == "feather" else self.data_pickle_file
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:493, in OpenMLDataset._cache_compressed_file_from_file(self, data_file)
491 data = pd.read_parquet(data_file)
492 except Exception as e:
--> 493 raise Exception(f"File: {data_file}") from e
495 categorical = [data[c].dtype.name == "category" for c in data.columns]
496 attribute_names = list(data.columns)
Exception: File: C:\Users\Irving\.openml\org\openml\www\datasets\554\dataset.pq
Now, I've written the same code on Pycharm and it works just fine, I managed to correctly assign the dataframes and show them to me. I've got no idea why this isn't working and I would like to know why because I would prefer to work with Jupyter notebooks.
Any help is appreciated, thanks in advance.
Trying to export and convert my data to a parquet file. Data is sba data from kaggle that we've transformed bit. Trying to covert it to parquet to load onto a hfds server.
Data link
https://www.kaggle.com/mirbektoktogaraev/should-this-loan-be-approved-or-denied
tryin to use the code:
sba.to_parquet('sba.parquet.gzip', compression = 'gzip', partition_cols= 'State')
but get the error:
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-39-377ee6551e44> in <module>
----> 1 sba.to_parquet('sba.parquet.gzip', compression = 'gzip', partition_cols= 'State')
/opt/conda/lib/python3.8/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
197 else:
198 kwargs[new_arg_name] = new_arg_value
--> 199 return func(*args, **kwargs)
200
201 return cast(F, wrapper)
/opt/conda/lib/python3.8/site-packages/pandas/core/frame.py in to_parquet(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)
2453 from pandas.io.parquet import to_parquet
2454
-> 2455 return to_parquet(
2456 self,
2457 path,
/opt/conda/lib/python3.8/site-packages/pandas/io/parquet.py in to_parquet(df, path, engine, compression, index, storage_options, partition_cols, **kwargs)
388 path_or_buf: FilePathOrBuffer = io.BytesIO() if path is None else path
389
--> 390 impl.write(
391 df,
392 path_or_buf,
/opt/conda/lib/python3.8/site-packages/pandas/io/parquet.py in write(self, df, path, compression, index, storage_options, partition_cols, **kwargs)
150 from_pandas_kwargs["preserve_index"] = index
151
--> 152 table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
153
154 path_or_handle, handles, kwargs["filesystem"] = _get_path_or_handle(
/opt/conda/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pandas()
/opt/conda/lib/python3.8/site-packages/pyarrow/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)
600 for i, maybe_fut in enumerate(arrays):
601 if isinstance(maybe_fut, futures.Future):
--> 602 arrays[i] = maybe_fut.result()
603
604 types = [x.type for x in arrays]
/opt/conda/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433
434 self._condition.wait(timeout)
/opt/conda/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
386 def __get_result(self):
387 if self._exception:
--> 388 raise self._exception
389 else:
390 return self._result
/opt/conda/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
/opt/conda/lib/python3.8/site-packages/pyarrow/pandas_compat.py in convert_column(col, field)
572 e.args += ("Conversion failed for column {!s} with type {!s}"
573 .format(col.name, col.dtype),)
--> 574 raise e
575 if not field_nullable and result.null_count > 0:
576 raise ValueError("Field {} was non-nullable but pandas column "
/opt/conda/lib/python3.8/site-packages/pyarrow/pandas_compat.py in convert_column(col, field)
566
567 try:
--> 568 result = pa.array(col, type=type_, from_pandas=True, safe=safe)
569 except (pa.ArrowInvalid,
570 pa.ArrowNotImplementedError,
/opt/conda/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/opt/conda/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._ndarray_to_array()
/opt/conda/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: ('Could not convert 2004 with type str: tried to convert to int', 'Conversion failed for column ApprovalFY with type object')
Any help would be amazing.
#Micah Kornfield is correct. Here is more specific answer.
If you look at your data, more specifically between rows 688127 and 688128 you find the following
df.loc[688127,'ApprovalFY']
2004
vs
df.loc[688128,'ApprovalFY']
'2004'
This type of change in data causes issue when parsing as parquet file. I am not an expert on parquet file, however the way that I understood is that parquet files identify the type of data in order to store them more efficiently. Therefore if you have a two different type of data in the same column you will receive the error. A lot of people run to this type of issue when they save their data into csv and then try to read csv file and concatenate the csv data with new data that they get from API,etc.
Every time you save your data in the csv format it converts it to text and when you read it it can change it from 2004 to '2004'.
Back to original question, it is a good idea to perform some data type checking before saving your data as parquet.
I'm taking an online python course (EpiSkills, which uses the Jupyter notebook) that was written in Python 2.7, and I'm on Python 3.6.4 so I have run into a few compatibility issues along the way. Most of the time I've been able to stumble through, but can't figure out this one, so was hoping someone might be able to help.
I start with the following packages:
import pandas as pd
import epipy
import seaborn as sns
%pylab inline
import statsmodels.api as sm
from scipy import stats
import numpy as np
And use the following code to create a pandas series and model:
multivar_model = sm.formula.glm('age ~ onset_to_hospital + onset_to_death +
data=my_data).fit()
new_data = pd.Series([6, 8, 'male'], index=['onset_to_hospital', 'onset_to_death', 'sex'])
When I try to use this to the following code, I throw the error that I've attached:
multivar_model.predict(new_data)
NameError part1
NameError part2
The intended output is meant to be this:
array([ 60.6497459])
I know that a lot of NameErrors are because something has been specified in the local, not global, environment but I'm unsure how to correct it in this instance. Any help is much appreciated.
Thanks!
C
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\compat.py in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
116 try:
--> 117 return f(*args, **kwargs)
118 except Exception as e:
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\eval.py in eval(self, expr, source_name, inner_namespace)
165 return eval(code, {}, VarLookupDict([inner_namespace]
--> 166 + self._namespaces))
167
<string> in <module>()
NameError: name 'onset_to_death' is not defined
The above exception was the direct cause of the following exception:
PatsyError Traceback (most recent call last)
<ipython-input-79-e0364e267da7> in <module>()
----> 1 multivar_model.predict(new_data)
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\statsmodels\base\model.py in predict(self, exog, transform, *args, **kwargs)
774 exog_index = exog.index
775 exog = dmatrix(self.model.data.design_info.builder,
--> 776 exog, return_type="dataframe")
777 if len(exog) < len(exog_index):
778 # missing values, rows have been dropped
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\highlevel.py in dmatrix(formula_like, data, eval_env, NA_action, return_type)
289 eval_env = EvalEnvironment.capture(eval_env, reference=1)
290 (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
--> 291 NA_action, return_type)
292 if lhs.shape[1] != 0:
293 raise PatsyError("encountered outcome variables for a model "
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\highlevel.py in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
167 return build_design_matrices(design_infos, data,
168 NA_action=NA_action,
--> 169 return_type=return_type)
170 else:
171 # No builders, but maybe we can still get matrices
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\build.py in build_design_matrices(design_infos, data, NA_action, return_type, dtype)
886 for factor_info in six.itervalues(design_info.factor_infos):
887 if factor_info not in factor_info_to_values:
--> 888 value, is_NA = _eval_factor(factor_info, data, NA_action)
889 factor_info_to_isNAs[factor_info] = is_NA
890 # value may now be a Series, DataFrame, or ndarray
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\build.py in _eval_factor(factor_info, data, NA_action)
61 def _eval_factor(factor_info, data, NA_action):
62 factor = factor_info.factor
---> 63 result = factor.eval(factor_info.state, data)
64 # Returns either a 2d ndarray, or a DataFrame, plus is_NA mask
65 if factor_info.type == "numerical":
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\eval.py in eval(self, memorize_state, data)
564 return self._eval(memorize_state["eval_code"],
565 memorize_state,
--> 566 data)
567
568 __getstate__ = no_pickling
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\eval.py in _eval(self, code, memorize_state, data)
549 memorize_state["eval_env"].eval,
550 code,
--> 551 inner_namespace=inner_namespace)
552
553 def memorize_chunk(self, state, which_pass, data):
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\compat.py in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
122 origin)
123 # Use 'exec' to hide this syntax from the Python 2 parser:
--> 124 exec("raise new_exc from e")
125 else:
126 # In python 2, we just let the original exception escape -- better
~\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\patsy\compat.py in <module>()
PatsyError: Error evaluating factor: NameError: name 'onset_to_death' is not defined
age ~ onset_to_hospital + onset_to_death + sex
^^^^^^^^^^^^^^