8. Importing data¶

8.1. data files¶

As of now, you can only import CSV files. LIAM2 currently supports two kinds of data files: tables and multi-dimensional arrays.

table files are used for entities data and optionally for globals. They should have one column per field and their first row should contain the name of the fields. These names should not contain any special character (accents, etc.).

For entities data, you need at least two integer columns: “id” and “period” (though they do not necessarily need to be named like that in the csv file).

array files are used for other external data (alignment data for example). They are arrays of any number of dimensions of a single homogeneous type. The first row should contain the dimension names (one dimension by cell). The second row should contain the possible values for the last dimension. Each subsequent row should start by the values for the first dimensions then the actual data.

example

gender |  work | civilstate |      |      |
       |       |          1 |    2 |    3 |    4
 False | False |       5313 | 1912 |  695 | 1222
 False |  True |        432 |  232 |   51 |   87
  True | False |       4701 | 2185 | 1164 | 1079
  True |  True |        369 |  155 |  101 |  116

This is the same format that groupby() generates except for totals.

8.2. description file¶

To import CSV files, you need to create a description file. Those description files have the following general format:

output: <path_of_hdf5_file>.csv

# compression is optional. compression type can be 'zlib', 'bzip2' or 'lzo'
# level is a digit from 1 to 9 and is optional (defaults to 5).
# Examples of valid compression strings are: zlib, lzo-1, bzip2-9.
# You should experiment to see which compression scheme (if any) offers the
# best trade-off for your dataset.
compression: <type>-<level>

# globals are entirely optional
globals:
    periodic:
        path: <path_of_file>.csv
        # if the csv file is transposed (each field is on a row instead of
        # a column and the field names are in the first column, instead of
        # the first row), you can use "transpose: True". You do not need to
        # specify anything if the file is not transposed.
        transposed: True

        # fields are optional (if not specified, all fields are imported)
        fields:
            # PERIOD is implicit
            - <field1_name>: <field1_type>
            - <field2_name>: <field2_type>
            - ...

    other_table:
        # same options than for periodic: path, fields, transpose, ...

    other_array:
        path: <path_of_file>.csv
        type: <field_type>

entities:
    <entity1_name>:
        path: <path_to_entity1_data>.csv

        # defaults to False if not present
        transposed: True

        # if you want to manually select the fields to be used, and/or
        # specify their types, you can do so in the following section.
        # If you want to use all the fields present in the csv file, you
        # can simply omit this section. The field types will be
        # automatically detected.
        fields:
            # period and id are implicit
            - <field1_name>: <field1_type>
            - <field2_name>: <field2_type>
            - ...

        # if you want to keep your csv files intact but use different
        # names in your simulation than in the csv files, you can specify
        # name changes here.
        oldnames:
            <fieldX_newname>: <fieldX_oldname>
            <fieldY_newname>: <fieldY_oldname>
            ...

        # another option to specify name changes (takes precedence over
        # oldnames in case of conflicts).
        newnames:
            <fieldX_oldname>: <fieldX_newname>
            <fieldY_oldname>: <fieldY_newname>
            ...

        # if you want to merge several files, use this format:
        files:
            - <path>\<to>\<file1>.<ext>:
                # any option (renamings, ...) specified here will override
                # the corresponding options defined at the level of the
                # entity
                transposed: True|False
                newnames:
                    <fieldX_oldname>: <fieldX_newname>
                    <fieldY_oldname>: <fieldY_newname>

            # if you don't have any specific option for a file, use "{}"
            - <path>\<to>\<file2>.<ext>: {}
            - ...

        # OR, if all the files use the global options (the options defined
        # at the level of the entity):
        files:
            - <path>\<to>\<file1>.<ext>
            - <path>\<to>\<file2>.<ext>
            - ...

        # if you want to fill missing values for some fields (this only
        # works when "files" is used).
        interpolate:
            <fieldX_name>: previous_value

        # if you want to invert the value of some boolean fields
        # (True -> False and False -> True), add them to the "invert" list
        # below.
        invert: [list, of, boolean, fields, to, invert]

    <entity2_name>:
        ...

Most elements of this description file are optional. The only required elements are “output” and “entities”. If an element is not specified, it uses the following default value:

if path is omitted, it defaults to a file named after the entity in the same directory than the description file (ie local_path\name_of_the_entity.csv).
if the fields section is omitted, all columns of the csv file will be imported and their type will be detected automatically.
if compression is omitted, the output will not be compressed.

Note that if an “entity section” is entirely empty, you need to use the special code: “{}”.

simplest example

output: simplest.h5

entities:
    household: {}
    person: {}

This will try to load all the fields of the household and person entities in “household.csv” and “person.csv” in the same directory than the description file.

simple example

output: simple.h5

globals:
    periodic:
        path: input\globals.csv

entities:
    household:
        path: input/household.csv

    person:
        path: input/person.csv

This will try to load all the fields of the household and person entities in “household.csv” and “person.csv” in the “input” sub-directory of the directory where the description file is.

example 3

output: example3.h5

globals:
    periodic:
        path: input/globals_transposed.csv
        transposed: True

entities:
    household:
        path: input/household.csv

    person:
        path: input/person.csv
        fields:
            - age:        int
            - gender:     bool
            - workstate:  int
            - civilstate: int
            - partner_id: int

        oldnames:
            gender: male

This will load all the fields of the household entity in “household.csv” and load from “person.csv” only the fields listed above. The data will be converted (if necessary) to the type declared. In this case, person.csv should contain at least the following columns (not necessarily in this order): period, id, age, male, workstate, civilstate, partner_id.

If the fields of an entity are scattered in several files, you can use the “files” key to list them, as in this fourth example :

output: example4.h5

entities:
    person:
        fields:
            - age:        int
            - gender:     bool
            - workstate:  int
            - civilstate: int

        # renamings applying to all files of this entity
        newnames:
            time: period

        files:
            - param\p_age.csv:
                # additional renamings for this file only
                newnames:
                    value: age
            - param\p_workstate.csv:
                newnames:
                    value: workstate
            # person.csv should have at least 4 columns:
            # period, id, age and gender
            - param/person.csv:
                newnames:
                    # we override the "global" renaming
                    period: period

        interpolate:
            workstate: previous_value
            civilstate: previous_value

But this can become tedious if you have a lot of files to import and they all have the same column names. If the name of the field can be extracted from the name of the file, you can automate the process like this:

example 5

output: example5.h5

entities:
    person:
        fields:
            - age:  int
            - work: bool

        newnames:
            time: period
            # {basename} evaluates to the name of the file without
            # extension. In the examples below, that would be
            # 'p_age' and 'p_work'. We then use the "replace" method
            # on the string we got, to get rid of 'p_'.
            value: eval('{basename}'.replace('p_', ''))

        files:
            - param\p_age.csv
            - param\p_work.csv

        interpolate:
            work: previous_value

8.3. importing the data¶

Once you have your data as CSV files and created a description file, you can import your data.

If you are using the bundled editor, simply open the description file and press F5.

If you are using the command line, use:

[BUNDLEPATH]\liam2\main import <path_to_description_file>

8. Importing data¶

8.1. data files¶

8.2. description file¶

8.3. importing the data¶

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