pyrsona 1.0

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pyrsona

Text data file validation and structure management using the pydantic and parse Python packages.

Installation

Install using pip install pyrsona.

A Simple Example

For the text file example.txt:

operator name: Jane Smith
country: NZ
year: 2022

ID,Time,Duration (sec),Reading
1,20:04:05,12.2,2098
2,20:05:00,2.35,4328

The following pyrsona file structure model can be defined:

from pyrsona import BaseStructure
from pydantic import BaseModel
from datetime import time


class ExampleStructure(BaseStructure):

    structure = (
        "operator name: {operator_name}\n"
        "country: {country}\n"
        "year: {}\n"
        "\n"
        "ID,Time,Duration (sec),Reading\n"
    )

    class meta_model(BaseModel):
        operator_name: str
        country: str

    class row_model(BaseModel):
        id: int
        time: time
        duration_sec: float
        value: float

The read() method can then be used to read the file, parse its contents and validate the meta data and table rows:

meta, table_rows, structure_id = ExampleStructure.read("example.txt")

print(meta)
#> {'operator_name': 'Jane Smith', 'country': 'NZ'}

print(table_rows)
#> [{'id': 1, 'time': datetime.time(20, 4, 5), 'value': 2098.0}, {'id': 2,
# 'time': datetime.time(20, 5), 'value': 4328.0}]

print(structure_id)
#> ExampleStructure

What's going on here:

• The structure class attribute contains a definition of the basic file structure. This definition includes the meta data lines and table header lines. Any variable text of interest is replaced with curly brackets and a field name, E.g. '{operator_name}', while any variable text that should be ignored is replaced with empty curly brackets, E.g. '{}'. The structure definition must contain all spaces, tabs and new line characters in order for a file to successfully match it. The named fields in the structure definition will be passed to meta_model.

• meta_model is simply a pydantic model with field names that match the named fields in the structure definition. All values sent to meta_model will be strings and these will be converted to the field types defined in meta_model. Custom pydantic validators can be included in the meta_model definition as per standard pydantic models.

• row_model is also a pydantic model. This time the field names do not need to match the header line in the structure definition; however, the row_model fields do need to be provided in the same order as the table columns. This allows the table column names to be customised/standardised where the user does not control the file structure itself. Again, custom pydantic validators can be included in the row_model definition if required.

Another Example

Should the file structure change at some point in the future a new model can be created based on the original model. This is referred to as a sub-model, where the original model is the parent model.

Given the slightly modified file structure of new_example.txt:

operator name: Jane Smith
country: NZ
city: Auckland
year: 2022

ID,Time,Duration (sec),Reading
1,20:04:05,12.2,2098
2,20:05:00,2.35,4328

Attempting to parse this file using the original ExampleStructure model will raise a PyrsonaError due to the addition of the 'city: Auckland' line. In order to successfully parse the file and capture the new 'city' field the following sub-model should be defined.

from pyrsona import BaseStructure
from pydantic import BaseModel
from datetime import time


class NewExampleStructure(ExampleStructure):

    structure = (
        "operator name: {operator_name}\n"
        "country: {country}\n"
        "city: {city}\n"
        "year: {}\n"
        "\n"
        "ID,Time,Duration (sec),Reading\n"
    )

    class meta_model(BaseModel):
        operator_name: str
        country: str
        city: str

ExampleStructure is still used as the entry point; however, pyrsona will attempt to parse the file using any sub-models that exist (in this case NewExampleStructure) before using ExampleStructure itself.

meta, table_rows, structure_id = ExampleStructure.read("new_example.txt")

print(meta)
#> {'operator_name': 'Jane Smith', 'country': 'NZ', 'city': 'Auckland'}

print(table_rows)
#> [{'id': 1, 'time': datetime.time(20, 4, 5), 'value': 2098.0}, {'id': 2,
# 'time': datetime.time(20, 5), 'value': 4328.0}]

print(structure_id)
#> NewExampleStructure

What's going on here:

• A new pyrsona file structure model is defined based on the original ExampleStructure model. This means that structure, meta_model and row_model will be inherited from ExampleStructure. This also provides a single entry point (I.e. ExampleStructure.read()) when attempting to read the different file versions.

• structure and meta_model are redefined to include the new "city: Auckland" meta data line. Alternatively, the original meta_model in ExampleStructure could have been updated to include an optional city field.

Post-processors

It is sometimes necessary to modify some of the data following parsing by the meta_model and row_model. Two post-processing methods are available for this purpose.

Using the ExampleStructure class above, meta_postprocessor and table_postprocessor static methods are defined for post-processing the meta data and table_rows, respectively:

class ExampleStructure(BaseStructure):

    # Lines omitted for brevity

    @staticmethod
    def meta_postprocessor(meta):
        meta["version"] = 3
        return meta

    @staticmethod
    def table_postprocessor(table_rows, meta):
        # Add a cumulative total and delete the "id" field:
        total = 0
        for ii, row in enumerate(table_rows):
            total += row["value"]
            row["total"] = total
            del(row["id"])
            table_rows[ii] = row
        return table_rows

The meta data and table_rows are now run through the post-processing stages before being returned, resulting in the following changes:

• A new version field is added to the meta data.
• The id field is deleted from the table_rows and a cumulative total field is added.

meta, table_rows, structure_id = ExampleStructure.read("example.txt")

print(meta)
#> {'operator_name': 'Jane Smith', 'country': 'NZ', 'version': 3}

print(table_rows)
#> [{'time': datetime.time(20, 4, 5), 'duration_sec': 12.2, 'value': 2098.0,
# 'total': 2098.0}, {'time': datetime.time(20, 5), 'duration_sec': 2.35, 'value': 4328.0,
# 'total': 6426.0}]

print(structure_id)
#> NewExampleStructure

Array data in field

Sometimes the table rows contain array data that is not easily converted to a pydantic model. In this case, the row_model can be omitted and the table_postprocessor method can be used to convert the table rows into a more suitable format.

class ExampleStructure(BaseStructure):

    structure = (
        "operator name: {operator_name}\n"
        "country: {country}\n"
        "year: {}\n"
        "\n"
        "ID,Time,Duration (sec),Reading\n"
    )

    class meta_model(BaseModel):
        operator_name: str
        country: str

    @staticmethod
    def table_postprocessor(table_rows, meta):

        class row_model(BaseModel):
            id: int
            array_data: list[str]

        ids = [row[0] for row in table_rows]
        array_data = [row[1:] for row in table_rows]

        table_rows = [
            row_model(id=row_id, array_data=row_array_data).dict()
            for row_id, row_array_data in zip(ids, array_data)
        ]

        return table_rows

With an undefined row_model the table row data would be returned as a list of strings. The table_postprocessor method can then be used to convert the data into a more suitable format using custom logic.

print(table_rows)
#> [{'id': 1, 'array_data': ['20:04:05', '12.2', '2098']}, {'id': 2, 'array_data': ['20:05:00','2.35','4328']}]

Extra details

All meta lines MUST be included

While the parse package allows a wildcard '{}' to be used to ignore several lines this can cause a named field to be unexpectedly included in the wildcard section. pyrsona therefore checks for the presence of a new line character '\n' in the named field values and fails if one is found.

Sub-sub-models

Calling the read() method will first build a list of pyrsona file structure models from the parent model down.

Any sub-models of the parent model will themselves be checked for sub-models, meaning that every model in the tree below the parent model will be used when attempting to parse a file.

Each branch of models will be ordered bottom-up so that the deepest nested model in a branch will be used first. The parent model will be the final model used if all others fail.

Model names

The read() method returns a structure_id variable that matches the model name. This structure_id can be useful when creating automated tests that sit alongside the pyrsona models as it provides a mechanism for confirming that a text file was parsed using the expected pyrsona model where multiple sub-models exist.

As the number of sub-models grows a naming convention becomes more important. One option is to set the names of any sub-models to a random hexadecimal value prefixed with a single underscore (in case the value begins with a number), E.g. '_a4c15356'. The initial underscore will be removed from model name when returning the structure_id value.

parse formats

The parse package allows format specifications to be included alongside the fields, E.g. '{year:d}'. While including these format types in the structure definition is valid, more complex format conversions can be made using meta_model. Keeping all format conversions in meta_model means that all conversions are defined in one place.