Combines most popular python parsers (json, jprops, pickle...) with user-defined parsers and type converters to read objects from files. Supports multifile & multiparser objects, typically useful to organize test data. Leverages PEP484 type hints in order to intelligently use the best parser/converter chain, and to try several combinations if relevant
Project description
A declarative framework that combines many popular python parsers (json, jprops, yaml, pickle, pandas…) with your user-defined parsers and type converters, in order to easily read objects from files. It is able to read an object even if the object’s content comes from several files requiring several parsers. This is typically useful to read test data where you want to combine datasets, parameters, expected results and what not.
This library provides a framework, not a specific parser for a specific file format. By default several classic parsers from the python world are already registered, but it is also extremely easy to add more if your favourite parser is missing. Even better: if you just need to parse a derived type of a type that can already be parsed, you may simply need to register a type converter, the framework will link it to any compliant parser for you.
Contents
ToC created by `gh-md-toc <https://github.com/ekalinin/github-markdown-toc>`__
Overview
1- Intended audience
developers looking for an easy way to parse dictionaries and simple objects from various formats (json, properties, cfg, csv…) using standard python libraries. They can combine this library with classtools_autocode or attrs to preserve compact, readable and content-validated classes.
developers who already know how to parse their various files independently, but looking for a higher-level tool to read complex objects made of several files/folders and potentially requiring to combine several parsers.
2- Typical use cases
read collections of test cases on the file system - each test case being composed of several files (for example 2 ‘test inputs’ .csv files, 1 ‘test configuration’ .cfg file, and one ‘reference test results’ json or yaml file)
more generally, read complex objects that for some reason are not represented by a single file representation, for example objects made of several csv files (timeseries + descriptive data), combinations of csv and xml/json files, configuration files, pickle files, etc.
Note on the Intelligence/Speed tradeoff: This framework contains a bit of nontrivial logic in order to transparently infer which parser and conversion chain to use, and even in some cases to try several alternatives in order to guess what was your intent. This makes it quite powerful but will certainly be slower and more memory-consuming than writing a dedicated parser tailored for your specific case. However if you are looking for a tool to speedup your development so that you may focus on what’s important (your business logic, algorithm implementation, test logic, etc) then have a try, it might do the job.
3- Main features
Declarative (class-based): you first define the objects to parse - by creating or importing their class -, then you use parse_item or parse_collection on the appropriate folder or file path.
Simple objects out-of-the-box: if you’re interested in parsing singlefile objects only requiring simple types in their constructor, then the framework is already able to parse them, for many singlefile formats (json, properties, txt, csv, yaml and more.).
Multifile collections out-of-the-box: the framework is able to parse collections of objects, each represented by a file. Parsing may optionally be done in a lazy fashion (each item is only read if needed).
Serialization: pickle files (.pyc) are supported too. Base64-encoded pickle objects can also be included in any simple file content.
Multiparser: the library will use the best parser adapted to each file format and desired type. At the time of writing the library:
knows 44 ways to parse a file
is able to parse 15 object types (including ‘AnyObject’ for generic parsers)
from 13 file extensions
Multifile+Multiparser objects: You may therefore parse complex objects requiring a combination of several parsers to be built from several files. The framework will introspect the object constructor in order to know the list of required attributes to parse as well as their their type, and if they are mandatory or optional.
Recursive: attributes may themselves be collections or complex types.
Supports two main file mapping flavours for Multifile objects: the library comes with two ways to organize multifile objects such as collections: wrapped (each multifile object is a folder), or flat (all files are in the same folder, files belonging to the same multifile object have the same prefix)
In addition the library is
Extensible. You may register any number of additional file parsers, or type converters, or both. When registering a parser you just have to declare the object types that it can parse, and the file extensions it can read. The same goes for converters: you declare the object type it can read, and the object type it can convert to.
Intelligent Since several parsers may be registered for the same file extension, and more generally several parsing chains (parser + converters) may be eligible to a given task, the library has a built-in set of rules to select the relevant parsing chains and test them in most plausible order. This provides you with several ways to parse the same object. This might be useful for example if some of your data comes from nominal tests, some other from field tests, some other from web service calls, etc. You don’t need anymore to convert all of these to the same format before using it.
No annotations required: as opposed to some data binding frameworks, this library is meant to parse object types that may already exist, and potentially only for tests. Therefore the framework does not require annotations on the type if there is there is a registered way to parse it. However if you wish to build higher-level objects encapsulating the result of several parsers, then PEP484 type hints are required. But that’s probably less a problem since these objects are yours (they are part of your tests for example)
Installation
1- Recommended : create a clean virtual environment
We strongly recommend that you use conda environment or pip virtualenv/venv in order to better manage packages. Once you are in your virtual environment, open a terminal and check that the python interpreter is correct:
(Windows)> where python
(Linux) > which pythonThe first executable that should show up should be the one from the virtual environment.
2- Installation steps
This package is available on PyPI. You may therefore use pip to install from a release
> pip install parsyfiles3- Uninstalling
As usual :
> pip uninstall parsyfilesUsage
1- Collections of known types
(a) Example: parsing a list of DataFrame
The most simple case of all: you wish to parse a collection of files that all have the same type, and for which a parser is already registered. For example your wish to parse a list of DataFrame for a data folder that looks like this:
./demo/simple_collection
├── a.csv
├── b.txt
├── c.xls
├── d.xlsx
└── e.xlsmNote: you may find this example data folder in the `project sources <https://github.com/smarie/python-simple-file-collection-parsing-framework/tree/master/parsyfiles/test_data>`__
Parsing all of these dataframes is straightforward:
from pprint import pprint
from parsyfiles import parse_collection
from pandas import DataFrame
dfs = parse_collection('./demo/simple_collection', DataFrame)
pprint(dfs)Here is the result
**** Starting to parse collection of <DataFrame> at location ./demo/simple_collection ****
Checking all files under ./demo/simple_collection
./demo/simple_collection (multifile)
./demo/simple_collection\a (singlefile, .csv)
(...)
./demo/simple_collection\e (singlefile, .xlsm)
File checks done
Building a parsing plan to parse ./demo/simple_collection (multifile) into a Dict[str, DataFrame]
./demo/simple_collection (multifile) > Dict[str, DataFrame] ------- using Multifile Dict parser (based on 'parsyfiles defaults' to find the parser for each item)
./demo/simple_collection\a (singlefile, .csv) > DataFrame ------- using <read_df_or_series_from_csv(stream mode)>
(...)
./demo/simple_collection\e (singlefile, .xlsm) > DataFrame ------- using <read_dataframe_from_xls(file mode)>
Parsing Plan created successfully
Executing Parsing Plan for ./demo/simple_collection (multifile) > Dict[str, DataFrame] ------- using Multifile Dict parser (based on 'parsyfiles defaults' to find the parser for each item)
Parsing ./demo/simple_collection (multifile) > Dict[str, DataFrame] ------- using Multifile Dict parser (based on 'parsyfiles defaults' to find the parser for each item)
Parsing ./demo/simple_collection\a (singlefile, .csv) > DataFrame ------- using <read_df_or_series_from_csv(stream mode)>
--> Successfully parsed a DataFrame from ./demo/simple_collection\a
(...)
Parsing ./demo/simple_collection\e (singlefile, .xlsm) > DataFrame ------- using <read_dataframe_from_xls(file mode)>
--> Successfully parsed a DataFrame from ./demo/simple_collection\e
Assembling all parsed child items into a Dict[str, DataFrame] to build ./demo/simple_collection (multifile)
--> Successfully parsed a Dict[str, DataFrame] from ./demo/simple_collection
Completed parsing successfully
{'a': a b c d
0 1 2 3 4,
'b': a b c d
0 1 2 3 4,
'c': c 5
0 d 8
1 e 12
2 f 3,
'd': c 5
0 d 8
1 e 12
2 f 3,
'e': c 5
0 d 8
1 e 12
2 f 3}
Note: the above capture was slightly ‘improved’ for readability, because unfortunately pprint does not display dictionaries of dataframes as nicely as this.
(b) Understanding the log output
By default the library uses a Logger that has an additional handler to print to stdout. If you do not want to see all these messages printed to the console, or if you want to use a different logging configuration, you may provide a custom logger to the function:
from logging import getLogger
dfs = parse_collection('./demo/simple_collection', DataFrame, logger=getLogger('my_logger'))In the log output you see a couple hints on how the parsing framework works:
first it recursively checks your folder to check that it is entirely compliant with the file mapping format. That is the log section beginning with “Checking all files under ./demo/simple_collection”. If the same item appears twice (e.g. a.csv and a.txt) it will throw an error at this stage (an ObjectPresentMultipleTimesOnFileSystemError).
then it recursively creates a parsing plan that is able to produce an object the required type. That’s the section beginning with “Building a parsing plan to parse ./demo/simple_collection (multifile) into a Dict[str, DataFrame]”. Here you may note that by default, a collection of items is actually parsed as an object of type dictionary, where the key is the name of the file without extension, and the value is the object that is parsed from the file. If at this stage it does not find a way to parse a given file into the required object type, it will fail. For example if you add a file in the folder, named unknown_ext_for_dataframe.ukn, you will get an error (a NoParserFoundForObjectExt).
finally it executes the parsing plan. That’s the section beginning with “Executing Parsing Plan for ./demo/simple_collection (multifile) > Dict[str, DataFrame] (...)”.
It is important to understand these 3 log sections, since the main issue with complex frameworks is debugging when something unexpected happens :-).
(c) Parsing a single file only
The following code may be used to parse a single file explicitly:
from pprint import pprint
from parsyfiles import parse_item
from pandas import DataFrame
df = parse_item('./demo/simple_collection/c', DataFrame)
pprint(df)Important : note that the file extension does not appear in the argument of the parse_item function.
(d) Default collection type and other supported types
You might have noticed that the demonstrated collection example returned a dict of dataframes, not a list. This is the default behaviour of the parse_collection method - it has the advantage of not making any assumption on the sorting order.
Behind the scenes, parse_collection redirects to the parse_item command. So the following code leads to the exact same results:
from parsyfiles import parse_item
from pandas import DataFrame
from typing import Dict
df = parse_item('./demo/simple_collection/c', Dict[str, DataFrame])The typing module is used here to entirely specify the type of item that you want to parse (Dict[str, DataFrame]). The parsed item will be a dictionary with string keys (the file names) and DataFrame values (the parsed file contents).
You may parse a list, a set, or a tuple exactly the same way, using the corresponding typing class:
from parsyfiles import parse_item
from pandas import DataFrame
from typing import List, Set, Tuple
dfl = parse_item('./demo/simple_collection', List[DataFrame])
# dfs = parse_item('./demo/simple_collection', Set[DataFrame])
dft = parse_item('./demo/simple_collection', Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame])For List and Tuple the implied order is alphabetical on the file names (similar to using sorted() on the items of the dictionary). Note that DataFrame objects are not mutable, so in this particular case the collection cannot be parsed as a Set.
Finally, note that it is not possible to mix collection and non-collection items together (for example, Union[int, List[int]] is not supported).
2- Simple user-defined types
(a) Example: parsing a collection of test cases
Suppose that you want to test the following exec_op function, and you want to read your test datasets from a bunch of files.
def exec_op(x: float, y: float, op: str) -> float:
if op is '+':
return x+y
elif op is '-':
return x-y
else:
raise ValueError('Unsupported operation : \'' + op + '\'')Each test dataset could be represented as an object, containing the inputs and expected outputs for exec_op. For example:
class ExecOpTest(object):
def __init__(self, x: float, y: float, op: str, expected_result: float):
self.x = x
self.y = y
self.op = op
self.expected_result = expected_result
def __str__(self):
return self.__repr__()
def __repr__(self):
return str(self.x) + ' ' + self.op + ' ' + str(self.y) + ' =? ' + str(self.expected_result)Obviously this class is not known by the parsyfiles framework: there is no registered parser for the ExecOpTest type. However the type is fairly simple, so it can actually fit into a dictionary containing the values for x, y, op, and expected_results. parsyfiles knows a couple ways to parse dictionaries, using python standard libraries:
From a .cfg or .ini file using the configparser module
From a .json file using the json module
From a .properties or .txt file using the jprops module
From a .yaml or .yml file using the yaml module
From a .csv, .txt, .xls, .xlsx, .xlsm file using the pandas module
etc.
It also knows how to convert a dictionary into an object, as long as the object constructor contains the right information about expected types. For example in the example above, the constructor has explicit PEP484 annotations x: float, y: float, op: str, expected_result: float.
So let’s try to parse instances of ExecOpTest from various files. Our test data folder looks like this (available in the project sources):
./demo/simple_objects
├── test_diff_1.cfg
├── test_diff_2.ini
├── test_diff_3_csv_format.txt
├── test_sum_1.json
├── test_sum_2.properties
├── test_sum_3_properties_format.txt
├── test_sum_4.yaml
├── test_sum_5.xls
├── test_sum_6.xlsx
└── test_sum_7.xlsmAs usual, we tell the framework that we want to parse a collection of objects of type ExecOpTest:
from pprint import pprint
from parsyfiles import parse_collection
sf_tests = parse_collection('./demo/simple_objects', ExecOpTest)
pprint(sf_tests)Here is the result:
**** Starting to parse collection of <ExecOpTest> at location ./demo/simple_objects ****
Checking all files under ./demo/simple_objects
(...)
File checks done
Building a parsing plan to parse ./demo/simple_objects (multifile) into a Dict[str, ExecOpTest]
(...)
Parsing Plan created successfully
Executing Parsing Plan for ./demo/simple_objects (multifile) > Dict[str, ExecOpTest] ------- using Multifile Dict parser (based on 'parsyfiles defaults' to find the parser for each item)
(...)
--> Successfully parsed a Dict[str, ExecOpTest] from ./demo/simple_objects
Completed parsing successfully
{'test_diff_1': 1.0 - 1.0 =? 0.0,
'test_diff_2': 0.0 - 1.0 =? -1.0,
'test_diff_3_csv_format': 5.0 - 4.0 =? 1.0,
'test_diff_4_csv_format2': 4.0 - 4.0 =? 0.0,
'test_sum_1': 1.0 + 2.0 =? 3.0,
'test_sum_2': 0.0 + 1.0 =? 1.0,
'test_sum_3_properties_format': 1.0 + 1.0 =? 2.0,
'test_sum_4': 2.0 + 5.0 =? 7.0,
'test_sum_5': 56.0 + 12.0 =? 68.0,
'test_sum_6': 56.0 + 13.0 =? 69.0,
'test_sum_7': 56.0 + 14.0 =? 70.0}
(b) Under the hood : why does it work, even on ambiguous files?
In the example above, three files were actually quite difficult to parse into a dict before being converted to an ExecOpTest: test_diff_3_csv_format.txt, test_diff_4_csv_format2.txt and test_sum_4.yaml. Let’s look at both cases in details.
Solved Difficulty 1 - Several formats/parsers for the same file extension
test_diff_3_csv_format.txt and test_diff_4_csv_format2.txt are both .txt file that contains csv-format data. But
there are several way to write a dictionary in a csv format (one row of header + one row of values, or one column of names + one column of values).
.txt files may also contain many other formats such as for example, the ‘properties’ format.
How does the framework manage to parse these files ? Lets look at the log output for test_diff_3_csv_format.txt:
Parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_dict_from_properties> => <dict_to_object>$
!! Caught error during execution !!
File "C:\W_dev\_pycharm_workspace\python-parsyfiles\parsyfiles\support_for_objects.py", line 273, in dict_to_object
attr_name)
ParsingException : Error while parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) as a ExecOpTest with parser '$<read_dict_from_properties> => <dict_to_object>$' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) : caught
InvalidAttributeNameForConstructorError : Cannot parse object of type <ExecOpTest> using the provided configuration file: configuration contains a property name ('5,4,-,1')that is not an attribute of the object constructor. <ExecOpTest> constructor attributes are : ['y', 'x', 'expected_result', 'op']
Rebuilding local parsing plan with next candidate parser: $<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$
./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$
Parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$
!! Caught error during execution !!
File "C:\Anaconda3\envs\azuremlbricks\lib\base64.py", line 88, in b64decode
return binascii.a2b_base64(s)
ParsingException : Error while parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) as a ExecOpTest with parser '$<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) : caught
Error : Incorrect padding
Rebuilding local parsing plan with next candidate parser: $<read_str_from_txt> => <constructor_with_str_arg>$
./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_str_from_txt> => <constructor_with_str_arg>$
Parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_str_from_txt> => <constructor_with_str_arg>$
!! Caught error during execution !!
File "C:\W_dev\_pycharm_workspace\python-parsyfiles\parsyfiles\support_for_primitive_types.py", line 98, in constructor_with_str_arg
return desired_type(source)
ParsingException : Error while parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) as a ExecOpTest with parser '$<read_str_from_txt> => <constructor_with_str_arg>$' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) : caught
CaughtTypeError : Caught TypeError while calling conversion function 'constructor_with_str_arg'. Note that the conversion function signature should be 'def my_convert_fun(desired_type: Type[T], source: S, logger: Logger, **kwargs) -> T' (unpacked options mode - default) or def my_convert_fun(desired_type: Type[T], source: S, logger: Logger, options: Dict[str, Dict[str, Any]]) -> T (unpack_options = False).Caught error message is : TypeError : __init__() missing 3 required positional arguments: 'y', 'op', and 'expected_result'
Rebuilding local parsing plan with next candidate parser: $<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$
./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$
Parsing ./demo/simple_objects\test_diff_3_csv_format (singlefile, .txt) > ExecOpTest ------- using $<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$
--> Successfully parsed a ExecOpTest from ./demo/simple_objects\test_diff_3_csv_format
You can see from the logs that the framework successively tries several ways to parse this file :
$<read_dict_from_properties> => <dict_to_object>$: the txt file is read in the ‘properties’ format (using jprops) into a dictionary, and then the dictionary is converted to a ExecOpTest object. This fails.
$<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$ : the txt file is read as a string, and then the string is interpreted as a base64-encoded pickle ExecOpTest object (!). This fails.
$<read_str_from_txt> => <constructor_with_str_arg>$: the txt file is read as a string, and then the constructor of ExecOpTest is called with that string as unique argument. This fails again.
$<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$: the txt file is read as a csv into a DataFrame, then the DataFrame is converted to a dictionary, and finally the dictionary is converted into a ExecOpTest object. This finally succeeds.
The same goes for the other file test_diff_4_csv_format2.txt.
Solved Difficulty 2 - Generic parsers
For test_sum_4.yaml, the difficulty is that yaml format may contain a dictionary directly, but is also able to contain any typed object thanks to the YAML object directive. Therefore it could contain a ExecOpTest.
The parsing logs are the following:
Parsing ./demo/simple_objects\test_sum_4 (singlefile, .yaml) > ExecOpTest ------- using <read_object_from_yaml>
!! Caught error during execution !!
File "C:\W_dev\_pycharm_workspace\python-parsyfiles\parsyfiles\parsing_core_api.py", line 403, in execute
res, options)
ParsingException : Error while parsing ./demo/simple_objects\test_sum_4 (singlefile, .yaml) as a <class 'test_parsyfiles.DemoTests.test_simple_objects.<locals>.ExecOpTest'> with parser '<read_object_from_yaml>' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) :
parser returned {'y': 5, 'x': 2, 'op': '+', 'expected_result': 7} of type <class 'dict'> which is not an instance of <class 'test_parsyfiles.DemoTests.test_simple_objects.<locals>.ExecOpTest'>
Rebuilding local parsing plan with next candidate parser: $<read_collection_from_yaml> => <dict_to_object>$
./demo/simple_objects\test_sum_4 (singlefile, .yaml) > ExecOpTest ------- using $<read_collection_from_yaml> => <dict_to_object>$
Parsing ./demo/simple_objects\test_sum_4 (singlefile, .yaml) > ExecOpTest ------- using $<read_collection_from_yaml> => <dict_to_object>$
--> Successfully parsed a ExecOpTest from ./demo/simple_objects\test_sum_4
You can see from the logs that the framework successively tries several ways to parse this file :
<read_object_from_yaml>: the file is read according to the yaml format, as an ExecOpTest object directly. This fails.
$<read_collection_from_yaml> => <dict_to_object>$: the file is read according to the yaml format, as a dictionary. Then this dictionary is converted into a ExecOpTest object. This succeeds
Understanding the inference logic - in which order are the parsers tried ?
These example show how parsyfiles intelligently combines all registered parsers and converters to create parsing chains that make sense. These parsing chains are tried in order until a solution is found. Note that the order is deterministic:
First all exact match parsers. This includes combinations of {parser + converter chain} that lead to an exact match, sorted by converter chain size: first the small conversion chains, last the large conversion chains.
Then all approximative match parsers. This is similar to the “exact match” except that these are parsers able to parse a subclass of what you’re asking for.
Finally all generic parsers. This includes combinations of {parser + converter chain} that end with a generic converter (for example the “dict to object” converter seen in the example above)
In order to know in advance which file extensions and formats the framework will be able to parse, you may wish to use the following command to ask the framework:
from parsyfiles import RootParser
RootParser().print_capabilities_for_type(typ=ExecOpTest)The result is a dictionary where each entry is a file extension:
{'.cfg': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_config> => <merge_all_config_sections_into_a_single_dict> -> <dict_to_object>$,
$<read_config> => <config_to_dict_of_dict> -> <dict_of_dict_to_object>$,
$<read_config> => <config_to_dict_of_dict> -> <dict_to_object>$]},
'.csv': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$]},
'.ini': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_config> => <merge_all_config_sections_into_a_single_dict> -> <dict_to_object>$,
$<read_config> => <config_to_dict_of_dict> -> <dict_of_dict_to_object>$,
$<read_config> => <config_to_dict_of_dict> -> <dict_to_object>$]},
'.json': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dict_or_list_from_json> => <dict_to_object>$]},
'.properties': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dict_from_properties> => <dict_to_object>$]},
'.pyc': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [<read_object_from_pickle>]},
'.txt': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dict_from_properties> => <dict_to_object>$,
$<read_str_from_txt> => <base64_ascii_str_pickle_to_object>$,
$<read_str_from_txt> => <constructor_with_str_arg>$,
$<read_df_or_series_from_csv> => <single_row_or_col_df_to_dict> -> <dict_to_object>$]},
'.xls': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dataframe_from_xls> => <single_row_or_col_df_to_dict> -> <dict_to_object>$]},
'.xlsm': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dataframe_from_xls> => <single_row_or_col_df_to_dict> -> <dict_to_object>$]},
'.xlsx': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [$<read_dataframe_from_xls> => <single_row_or_col_df_to_dict> -> <dict_to_object>$]},
'.yaml': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [<read_object_from_yaml>,
$<read_collection_from_yaml> => <dict_to_object>$]},
'.yml': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [<read_object_from_yaml>,
$<read_collection_from_yaml> => <dict_to_object>$]},
'<multifile>': {'1_exact_match': [],
'2_approx_match': [],
'3_generic': [Multifile Object parser (parsyfiles defaults)]}}
Looking at the entries for .txt and .yaml, we can find back the ordered list of parsers that were automatically tried in the above examples.
3- Multifile objects: combining several parsers
This ‘the’ typical use case for this library. Suppose that you want to test the following exec_op_series function, that uses complex types Series and AlgoConf as inputs and AlgoResults as output:
class AlgoConf(object):
def __init__(self, foo_param: str, bar_param: int):
self.foo_param = foo_param
self.bar_param = bar_param
class AlgoResults(object):
def __init__(self, score: float, perf: float):
self.score = score
self.perf = perf
from pandas import Series
def exec_op_series(x: Series, y: AlgoConf) -> AlgoResults:
# ... intelligent stuff here...
passSimilar to what we’ve done in previous chapter, each test dataset can be represented as an object, containing the inputs and expected outputs. For example with this class:
class ExecOpSeriesTest(object):
def __init__(self, x: Series, y: AlgoConf, expected_results: AlgoResults):
self.x = x
self.y = y
self.expected_results = expected_resultsOur test data folder look like this :
./demo/complex_objects
├── case1
│ ├── expected_results.txt
│ ├── x.csv
│ └── y.txt
└── case2
├── expected_results.txt
├── x.csv
└── y.txt
You may notice that in this case, we chose to represent each instance of ExecOpSeriesTest as a folder. This makes them ‘multifile’. The default multifile object parser in the framework will try to parse each attribute of the constructor as an independent file in the folder.
The code for parsing remains the same - we tell the framework that we want to parse a collection of objects of type ExecOpSeriesTest. The rest is handled automatically by the framework:
from pprint import pprint
from parsyfiles import parse_collection
mf_tests = parse_collection('./demo/complex_objects', ExecOpSeriesTest)
pprint(mf_tests)Here are the results :
**** Starting to parse collection of <ExecOpSeriesTest> at location ./demo/complex_objects ****
Checking all files under ./demo/complex_objects
./demo/complex_objects (multifile)
(...)
File checks done
Building a parsing plan to parse ./demo/complex_objects (multifile) into a Dict[str, ExecOpSeriesTest]
(...)
Parsing Plan created successfully
Executing Parsing Plan for ./demo/complex_objects (multifile) > Dict[str, ExecOpSeriesTest] ------- using Multifile Collection parser (parsyfiles defaults)
(...)
--> Successfully parsed a Dict[str, ExecOpSeriesTest] from ./demo/complex_objects
Completed parsing successfully
{'case1': <ExecOpSeriesTest object at 0x00000000087DDF98>,
'case2': <ExecOpSeriesTest object at 0x000000000737FBE0>}
Note that multifile objects and singlefile objects may coexist in the same folder, and that parsing is recursive - meaning that multifile objects or collections may contain multifile children as well.
4- Advanced topics
The parse_collection and parse_item that we have used in most examples are actually just helper methods to build a parser registry (RootParser()) and use it. Most of the advanced topics below use this object directly.
(a) Lazy parsing
The multifile collection parser included in the library provides an option to return a lazy collection instead of a standard set, list, dict or tuple. This collection will trigger parsing of each element only when that element is required. In addition to better controlling the parsing time, this feature is especially useful if you want to parse the most items possible, even if one item in the list fails parsing.
from pprint import pprint
from parsyfiles import parse_collection
from pandas import DataFrame
dfs = parse_collection('./demo/simple_collection', DataFrame, lazy_mfcollection_parsing=True)
print('dfs length : ' + str(len(dfs)))
print('dfs keys : ' + str(dfs.keys()))
print('Is b in dfs : ' + str('b' in dfs))
pprint(dfs.get('b'))The result log shows that parse_collection returned without parsing, and that parsing is executed when item 'b' is read from the dictionary:
Executing Parsing Plan for ./demo/simple_collection (multifile) > Dict[str, DataFrame] ------- using Multifile Collection parser (parsyfiles defaults)
Parsing ./demo/simple_collection (multifile) > Dict[str, DataFrame] ------- using Multifile Collection parser (parsyfiles defaults)
Assembling a Dict[str, DataFrame] from all children of ./demo/simple_collection (multifile) (lazy parsing: children will be parsed when used)
--> Successfully parsed a Dict[str, DataFrame] from ./demo/simple_collection
Completed parsing successfully
dfs length : 5
dfs keys : {'a', 'e', 'd', 'c', 'b'}
Is b in dfs : True
Executing Parsing Plan for ./demo/simple_collection\b (singlefile, .txt) > DataFrame ------- using <read_df_or_series_from_csv>
Parsing ./demo/simple_collection\b (singlefile, .txt) > DataFrame ------- using <read_df_or_series_from_csv>
--> Successfully parsed a DataFrame from ./demo/simple_collection\b
Completed parsing successfully
a b c d
0 1 2 3 4
(b) Passing options to existing parsers
Parsers and converters support options. In order to know which options are available for a specific parser, the best is to identify it and ask it. For example if you want to know what are the options available for the parsers reading DataFrame objects :
from pandas import DataFrame
from parsyfiles import RootParser
# create a root parser
parser = RootParser()
# retrieve the parsers of interest
parsers = parser.get_capabilities_for_type(DataFrame, strict_type_matching=False)
df_csv_parser = parsers['.csv']['1_exact_match'][0]
p_id_csv = df_csv_parser.get_id_for_options()
print('Parser id for csv is : ' + p_id_csv + ', implementing function is ' + repr(df_csv_parser._parser_func))
print(' * ' + df_csv_parser.options_hints())
df_xls_parser = parsers['.xls']['1_exact_match'][0]
p_id_xls = df_xls_parser.get_id_for_options()
print('Parser id for csv is : ' + p_id_xls + ', implementing function is ' + repr(df_xls_parser._parser_func))
print(' * ' + df_xls_parser.options_hints())The result is:
Parser id for csv is : read_df_or_series_from_csv, implementing function is <function read_df_or_series_from_csv at 0x0000000007391378> * read_df_or_series_from_csv: all options from read_csv are supported, see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html Parser id for csv is : read_dataframe_from_xls, implementing function is <function read_dataframe_from_xls at 0x0000000007391158> * read_dataframe_from_xls: all options from read_excel are supported, see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html
Then you may set the options accordingly on the root parser before calling it
from parsyfiles import create_parser_options, add_parser_options
# configure the DataFrame parsers to automatically parse dates and use the first column as index
opts = create_parser_options()
opts = add_parser_options(opts, 'read_df_or_series_from_csv', {'parse_dates': True, 'index_col': 0})
opts = add_parser_options(opts, 'read_dataframe_from_xls', {'index_col': 0})
dfs = parser.parse_collection('./test_data/demo/ts_collection', DataFrame, options=opts)
print(dfs)Results:
{'a': a b c d
time
2015-08-28 23:30:00 1 2 3 4
2015-08-29 00:00:00 1 2 3 5,
'c': a b
date
2015-01-01 1 2
2015-01-02 4 3,
'b': a b c d
time
2015-08-28 23:30:00 1 2 3 4
2015-08-29 00:00:00 1 2 3 5}
(c) Parsing subclasses of existing types - registering converters
Imagine that you want to parse a subtype of something the framework already knows to parse. For example a TimeSeries class of your own, that extends DataFrame:
from pandas import DataFrame, DatetimeIndex
class TimeSeries(DataFrame):
"""
A dummy timeseries class that extends DataFrame
"""
def __init__(self, df: DataFrame):
"""
Constructor from a DataFrame. The DataFrame index should be an instance of DatetimeIndex
:param df:
"""
if isinstance(df, DataFrame) and isinstance(df.index, DatetimeIndex):
if df.index.tz is None:
df.index = df.index.tz_localize(tz='UTC')# use the UTC hypothesis in absence of other hints
self._df = df
else:
raise ValueError('Error creating TimeSeries from DataFrame: provided DataFrame does not have a '
'valid DatetimeIndex')
def __getattr__(self, item):
# Redirects anything that is not implemented here to the base dataframe.
# this is called only if the attribute was not found the usual way
# easy version of the dynamic proxy just to save time :)
# see http://code.activestate.com/recipes/496741-object-proxying/ for "the answer"
df = object.__getattribute__(self, '_df')
if hasattr(df, item):
return getattr(df, item)
else:
raise AttributeError('\'' + self.__class__.__name__ + '\' object has no attribute \'' + item + '\'')
def update(self, other, join='left', overwrite=True, filter_func=None, raise_conflict=False):
""" For some reason this method was abstract in DataFrame so we have to implement it """
return self._df.update(other, join=join, overwrite=overwrite, filter_func=filter_func,
raise_conflict=raise_conflict)It is relatively easy to write a converter between a DataFrame and a TimeSeries. parsyfiles provides classes that you should use to define your converters, for example here ConverterFunction, that takes as argument a conversion method with a specific signature - hence the extra unused arguments in df_to_ts:
from typing import Type
from logging import Logger
from parsyfiles.converting_core import ConverterFunction
def df_to_ts(desired_type: Type[TimeSeries], df: DataFrame, logger: Logger) -> TimeSeries:
""" Converter from DataFrame to TimeSeries """
return TimeSeries(df)
my_converter = ConverterFunction(from_type=DataFrame, to_type=TimeSeries, conversion_method=df_to_ts)You have to create the parser manually in order to register your converter:
from parsyfiles import RootParser, create_parser_options, add_parser_options
# create a parser
parser = RootParser('parsyfiles with timeseries')
parser.register_converter(my_converter)In some cases you may wish to change the underlying parsers options. This is possible provided that you know the identifier of the parser you wish to configure (typically it is the one appearing in the logs):
# configure the DataFrame parsers to read the first column as an datetime index
opts = create_parser_options()
opts = add_parser_options(opts, 'read_df_or_series_from_csv', {'parse_dates': True, 'index_col': 0})
opts = add_parser_options(opts, 'read_dataframe_from_xls', {'index_col': 0})Finally, parsing is done the same way than before:
dfs = parser.parse_collection('./test_data/demo/ts_collection', TimeSeries, options=opts)Note: you might have noticed that ``TimeSeries`` is a dynamic proxy. The ``TimeSeries`` class extends the ``DataFrame`` class, but delegates everything to the underlying ``DataFrame`` implementation provided in the constructor. This pattern is a good way to create specialized versions of generic objects created by your favourite parsers. For example two ``DataFrame`` might represent a training set, and a prediction table. Both objects, although similar (both are tables with rows and columns), might have very different contents (column names, column types, number of rows, etc.). We can make this fundamental difference appear at parsing level, by creating two classes.
(d) Registering a new parser
Parsyfiles offers several ways to register a parser. Here is a simple example, where we register a basic ‘singlefile’ xml parser:
from typing import Type
from parsyfiles import RootParser
from parsyfiles.parsing_core import SingleFileParserFunction, T
from logging import Logger
from xml.etree.ElementTree import ElementTree, parse, tostring
def read_xml(desired_type: Type[T], file_path: str, encoding: str,
logger: Logger, **kwargs):
"""
Opens an XML file and returns the tree parsed from it as an ElementTree.
:param desired_type:
:param file_path:
:param encoding:
:param logger:
:param kwargs:
:return:
"""
return parse(file_path)
my_parser = SingleFileParserFunction(parser_function=read_xml,
streaming_mode=False,
supported_exts={'.xml'},
supported_types={ElementTree})
parser = RootParser('parsyfiles with timeseries')
parser.register_parser(my_parser)
xmls = parser.parse_collection('./test_data/demo/xml_collection', ElementTree)
print({name: tostring(x.getroot()) for name, x in xmls.items()})For more examples on how the parser API can be used, please have a look at the core and optional plugins.
(e) Contract validation for parsed objects : combo with classtools-autocode and attrs
Users may wish to use classtools_autocode or attrs in order to create very compact classes representing their objects while at the same time ensuring that parsed data is valid according to some contract. Parsyfiles is totally compliant with such classes, as shown in the examples below
classtools-autocode example
from classtools_autocode import autoprops, autoargs
from contracts import contract, new_contract
# custom PyContract used in the class
new_contract('allowed_op', lambda x: x in {'+','*'})
@autoprops
class ExecOpTest(object):
@autoargs
@contract(x='int|float', y='int|float', op='str,allowed_op', expected_result='int|float')
def __init__(self, x: float, y: float, op: str, expected_result: float):
pass
def __str__(self):
return self.__repr__()
def __repr__(self):
return str(self.x) + ' ' + self.op + ' ' + str(self.y) + ' =? ' + str(self.expected_result)
sf_tests = parse_collection('./demo/simple_objects', ExecOpTest)The above code has a contract associated to allowed_op that checks that it must be in {'+','*'}. When '-' is found in a test file, it fails:
ParsingException : Error while parsing ./demo/simple_objects\test_diff_1 (singlefile, .cfg) as a ExecOpTest with parser '$<read_config> => <merge_all_config_sections_into_a_single_dict> -> <dict_to_object>$' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) : caught
ObjectInstantiationException : Error while building object of type <ExecOpTest> using its constructor and parsed contents : {'y': 1.0, 'x': 1.0, 'expected_result': 0.0, 'op': '-'} :
<class 'contracts.interface.ContractNotRespected'> Breach for argument 'op' to ExecOpTest:generated_setter_fun().
Value does not pass criteria of <lambda>()() (module: test_parsyfiles).
checking: callable() for value: Instance of <class 'str'>: '-'
checking: allowed_op for value: Instance of <class 'str'>: '-'
checking: str,allowed_op for value: Instance of <class 'str'>: '-'attrs example
In order for parsyfiles to find the required type for each attribute declared using attrs, you will have to use attr.validators.instance_of. However, since you may wish to also implement some custom validation logic, we provide (until it is offically added in attrs) a chaining operator. The code below shows how to create a similar example than the previous one:
import attr
from attr.validators import instance_of
from parsyfiles.plugins_optional.support_for_attrs import chain
# custom contract used in the class
def validate_op(instance, attribute, value):
allowed = {'+','*'}
if value not in allowed:
raise ValueError('\'op\' has to be a string, in ' + str(allowed) + '!')
@attr.s
class ExecOpTest(object):
x = attr.ib(convert=float, validator=instance_of(float))
y = attr.ib(convert=float, validator=instance_of(float))
# we use the 'chain' validator here to keep using instance_of
op = attr.ib(convert=str, validator=chain(instance_of(str), validate_op))
expected_result = attr.ib(convert=float, validator=instance_of(float))
# with self.assertRaises(ParsingException):
sf_tests = parse_collection('./test_data/demo/simple_objects', ExecOpTest)When '-' is found in a test file, it also fails with a nice error message:
ParsingException : Error while parsing ./test_data/demo/simple_objects\test_diff_1 (singlefile, .cfg) as a ExecOpTest with parser '$<read_config> => <merge_all_config_sections_into_a_single_dict> -> <dict_to_object>$' using options=({'MultifileCollectionParser': {'lazy_parsing': False}}) : caught
ObjectInstantiationException : Error while building object of type <ExecOpTest> using its constructor and parsed contents : {'y': 1.0, 'x': 1.0, 'expected_result': 0.0, 'op': '-'} :
<class 'ValueError'> 'op' has to be a string, in {'*', '+'}!Note: unfortunately, as of today (version 16.3), attrs does not validate attribute contents when fields are later modified on the object directly. A pull request is ongoing.
(e) File mappings: Wrapped/Flat and encoding
In 3- Multifile objects: combining several parsers we used folders to encapsulate objects. In previous examples we also used the root folder to encapsulate the main item collection. This default setting is known as ‘Wrapped’ mode and correspond behind the scenes to a WrappedFileMappingConfiguration being used, with default python encoding.
Alternatively you may wish to use flat mode. In this case the folder structure should be flat, as shown below. Item names and field names are separated by a configurable character string. For example to parse the same example as in 3- Multifile objects: combining several parsers but with the following flat tree structure:
.
├── case1--expected_results.txt
├── case1--x.csv
├── case1--y.txt
├── case2--expected_results.txt
├── case2--x.csv
└── case2--y.txtyou’ll need to call
from parsyfiles import FlatFileMappingConfiguration
dfs = parse_collection('./demo/complex_objects_flat', DataFrame, file_mapping_conf=FlatFileMappingConfiguration())Note that FlatFileMappingConfiguration may be configured to use another separator sequence than '--' by passing it to the constructor: e.g. FlatFileMappingConfiguration(separator='_'). A dot '.' may be safely used as a separator too.
Finally you may change the file encoding used by both file mapping configurations : WrappedFileMappingConfiguration(encoding='utf-16') FlatFileMappingConfiguration(encoding='utf-16').
(f) Recursivity: Multifile children of Multifile objects
As said earlier in this tutorial, parsyfiles is able to parse multifile recursively, for example multifile collections of multifile objects, multifile objects containing attributes, etc.
Example recursivity in flat mode
./custom_old_demo_flat_coll ├── case1--input_a.txt ├── case1--input_b.txt ├── case1--output.txt ├── case2--input_a.txt ├── case2--input_b.txt ├── case2--options.txt ├── case2--output.txt ├── case3--input_a.txt ├── case3--input_b.txt ├── case3--input_c--keyA--item1.txt ├── case3--input_c--keyA--item2.txt ├── case3--input_c--keyB--item1.txt ├── case3--options.cfg └── case3--output.txt
Example recursivity in wrapped mode
./custom_old_demo_coll
├── case1
│ ├── input_a.txt
│ ├── input_b.txt
│ └── output.txt
├── case2
│ ├── input_a.txt
│ ├── input_b.txt
│ ├── options.txt
│ └── output.txt
└── case3
├── input_a.txt
├── input_b.txt
├── input_c
│ ├── keyA
│ │ ├── item1.txt
│ │ └── item2.txt
│ └── keyB
│ └── item1.txt
├── options.cfg
└── output.txt
(g) Diversity of formats supported: DataFrames - revisited
Now that we’ve seen that parsyfiles is able to combine parsers and converters, we can try to parse DataFrame objects from many more sources:
./demo/simple_collection_dataframe_inference ├── a.csv ├── b.txt ├── c.xls ├── d.xlsx ├── s_b64_pickle.txt ├── t_pickle.pyc ├── u.json ├── v_properties.txt ├── w.properties ├── x.yaml ├── y.cfg └── z.ini
Note: once again you may find this example data folder in the `project sources <https://github.com/smarie/python-simple-file-collection-parsing-framework/tree/master/parsyfiles/test_data>`__
The code is the same:
from pprint import pprint
from parsyfiles import parse_collection
from pandas import DataFrame
dfs = parse_collection('./demo/simple_collection_dataframe_inference', DataFrame)
pprint(dfs)And here is the result
TODO
See Also
Developers
Packaging
This project uses setuptools_scm to synchronise the version number. Therefore the following command should be used for development snapshots as well as official releases:
python setup.py egg_info bdist_wheel rotate -m.whl -k3Releasing memo
twine upload dist/* -r pypitest
twine upload dist/*Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for parsyfiles-2.0.2-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a5ae9d178e91c8785435bc32840b87ad02cf4a0ca71a03ddcec56b19f97e782d |
|
| MD5 | 0f19826733421411ccda7ba390f35121 |
|
| BLAKE2b-256 | ca2aba17333801542a169f92d40422136311571f436ae334c40dba01ce8de997 |
