Odoo_import_scaffold speeds up the development of import projects using odoo_csv_tools. It comes with two main functions:
- the creation of the folders structure and a basic set of files organizing the project,
- the creation of skeleton codes applying transformations on a client file before importing it into Odoo.
The skeleton code well documents the fields so that it's not necessary to look at their definition with an external tool. A field analysis automatically suggests to exclude some of them when they shoud not be imported. Each new skeleton code is fully integrated in the other project files.
- 1. Installation
- 2. How To Use It
- 3. Folders Structure and Project Files
- 4. Model Skeleton Codes
- 5. Command Line Tricks
- 6. How-To
- 7. Requirements
- 8. Known Issues
git clone [email protected]:jad-odoo/odoo_import_scaffold.git
Run the command:
odoo_import_scaffold.py -s -p project_dir [-d my_db] [-t my_db.odoo.com] [-u 2]
This will create the needed folders (conf/, data/, ...) and a set of files (conf/connection.conf, prefix.py, ...) in the folder project_dir. The complete structure is described here.
Main options:
-s | --scaffold creates the directory structure and the basic project files.
-p | --path sets the project path. Default is the current path.
-d | --db sets the target database. If omitted, it is the first part of the hostname.
-t | -- host sets the hostname of the database. Default is "localhost".
-u | --userid sets the user id used by RPC calls. Default is "1".
More information on project scaffolding here.
From your project folder, verify the connection parameters in conf/connection.conf and generate the skeleton code for a model:
odoo_import_scaffold.py -m my.model -a [--map-selection] [--with-xmlid]
This will create the python script my_model.py containing a basic skeleton code suited to import data into the model my.model thanks to the mappers of odoo_csv_tools. This will also update the other project files to take this new script into account.
Main options:
-m | --model sets the model to generate (ex: res.partner).
-a | --append adds the created references to the project files. Use this option one time per model. Don't use it if you regenerate an existing skeleton code.
--map-selection generates mapping dictionaries for selection fields. Use this option if your client file uses selection values different than the technical values of the selection fields. Use it one time per model. Don't use it if you regenerate an existing skeleton code.
--with-xmlid indicates that the identifier fields in your client file contains XML_IDs. Typically, use this option if your client file comes from an export.
The complete set of options is described here.
-
Put the client file in CSV format in the folder
origin/. Put binary files (images, documents, ...), if any, in the folderorigin/binary/. -
Check the name of the client file in
files.py. If this file is already named as the model with dots '.' replaced by underscores '_' (ie. the file is my_model.csv for the model my.model) you don't need to change anything.# Model my.model src_my_model = os.path.join(data_src_dir, 'my_model.csv') -
Review the python script my_model.py. In the generated code, you should at least:
-
verify the column names. By default their name is the same as the field, which is probably not correct for all columns of the client file. When the tag 'CSV_COLUMN' appears, you also have to replace it by the right column name,
-
apply the right date formats, if any. You always need to replace the tag 'CSV_DATE_FORMAT' with the directives reflecting the field format in your client file,
-
comment or remove the fields you don't need.
mapping_my_model = { # ID (#2841): stored, optional, readonly, integer 'id': mapper.m2o_map(PREFIX_MY_MODEL, mapper.concat('_', 'CSV_COLUMN1','CSV_COLUMN2')), # Company (#2851): stored, required, many2one -> res.company - with xml_id in module(s): base(1) # DEFAULT: 1 'company_id/id': mapper.m2o(PREFIX_RES_COMPANY, 'company_id'), # Date of Transfer (#2832): stored, optional, readonly, datetime 'date_done': mapper.val('date_done', postprocess=lambda x: datetime.strptime(x, 'CSV_DATE_FORMAT').strftime('%Y-%m-%d 00:00:00')), -
By default the delimiter of your CSV file is set to a semicolon ';'. If you use another delimiter you need to change it at the line:
processor = Processor(src_my_model, delimiter=';', preprocess=preprocess_MyModel)
-
-
All other project files are automatically set up. Although it's always advised to review:
-
mapping.pyif you used the option --map-selection, -
prefix.pyif you import boolean fields or if you didn't use the option --with-xmlid, -
the transform script
transform.sh(transform.cmdon Windows) and the load scriptload.sh(load.cmdon Windows) to be sure that all shell commands you need will be launched.See the options --map-selection and -a | --append for more details.
-
Do it for all models and client files you have to deal with.
This step transforms the client files from the folder origin/ to Odoo import files in the folder data/. No data is imported yet.
On Windows:
transform.cmd
On other platforms:
./transform.sh
Check the log files transform_my_model_out.log and transform_my_model_err.log in the folder log/. In normal situation, the log files _err.log are empty and the folder data contains all the destination files mentioned in files.py.
# Model my.model
dest_my_model = os.path.join(data_dest_dir, 'my.model.csv')
This step imports the files from the folder data/ into the database as described in the file conf/connection.conf.
On Windows:
load.cmd
On other platforms:
./load.sh
Check the log files load_my_model_out.log and load_my_model_err.log in the folder log/.
Note: On Windows, the log files could reveal some errors but actually there are not.
For each model, the files my.model.csv.fail and my.model.csv.fail.bis are created in the folder data/. At the end of the load, the files .fail.bis contain rejected records that need your attention. If these files are empty, it means all the data was imported.
Run odoo_import_scaffold.py --help for all options.
The scaffolding is started with the option -s | --scaffold. It creates the folders and the project files in the location set with the option -p | --path. By default, it's your current path.
- path/conf/: contains different files aiming to store different connection presets.
- connection.conf: default file used by RPC calls.
- connection.local: preset to import in a local database.
- connection.staging: preset to import in a staging database (encrypted connection).
- connection.master: preset to import in the master database (encrypted connection).
- path/origin/: stores the client files in CSV format.
- path/origin/binary/: stores the client binary files (ie. images, documents, ...).
- path/data/: stores the files to import after running the transform script.
- path/log/: stores the logs of the transform and load scripts.
- path/transform.sh | .cmd: launches all transformations.
- path/cleanup_data_dir.sh |.cmd: resets the data folder at each new transformation.
- path/load.sh | .cmd: launches all imports.
- path/files.py: defines all client files to transform and transformed files to import.
- path/prefix.py: defines all external ID prefixes (module names) and constants used in the project.
- path/funclib.py: common functions.
- path/mapping.py: common mapping dictionaries.
Note: All shell scripts have the extension
.cmdon Windows, or.shon other platforms.
The other scaffolding options preconfigure the connection files.
When the options -d | --db and -t | --host are used, the database name and the hostname are stored in the local and current connection files. The default hostname is localhost and the default credentials are admin/admin.
The default userid is 1 and can be changed with the option -u | --userid (ie. 2 if you target an Odoo 12 instance with the admin user).
The current and local connection files are automatically set up to establish encrypted connections if you target a remote host. Actually, the connection won't be encrypted only if the database is on your localhost.
Scaffolding in an existing path preserves the files and directories if they already exist. They can be overridden with the option -f | --force.
This function works with the option -m | --model. It generates a python script with all the necessary instructions to apply transformations on one client file and to import the result into one model.
The model definition is fetched by RPC calls using the connection parameters defined in the file conf/connection.conf by default. You can select another connection file with the option -c | --config.
The skeleton code contains mainly a mapping dictionnary with all the fields assigned to a suited mapper according to their type.
You can alter the generated code mapping the fields with the option -k | --skeleton:
- -k | --skeleton dict (default): generates a mapping dictionary suited for transformations fully handled by the mapper function.
mapping_my_model = {
'char_field1': mapper.val('char_field1'),
'integer_field2': mapper.num('integer_field2'),
...
}- -k | --skeleton map: generates a mapping dictionary with a map function for each field, which is more handy when some columns of the client file need more complex transformations. By default, the created map functions do nothing more than the original mappers used with dict.
def handle_my_model_char_field1(line):
return mapper.val('char_field1')(line)
def handle_my_model_integer_field2(line):
return mapper.num('integer_field2')(line)
mapping_my_model = {
'char_field1': handle_my_model_char_field1,
'integer_field2': handle_my_model_integer_field2,
...
}In both skeleton types, the column names of the client file are supposed to be the same as the field names.
The first displayed field is always the "id" field, then the required ones, then the optional ones, both sorted by name. Their map functions, if any, follow the same order.
You can use the option -n | --offline to get a skeleton code without field.
mapping_my_model = {
'id': ,
}This way you keep the benefits of the basic skeleton and the automatic integration of your new script in the project. But you're not annoyed with a lot of fields you probably don't need.
Note: You may also consider the option -r | --required.
The offline mode is automatically set when no database is provided. When working offline, all functions based on the fields definition are deactivated.
You can exclude the non stored fields with the option --stored. It is usually advisable but it avoids to import through non stored inherited fields.
One2many and metadata fields are excluded by default but they can be included respectively with the options --with-o2m and --with-metadata.
For each field, some properties are shown in comment such as: id, type, required, readonly, related, stored, selection values, default value, tracking options, relationship and computing. This avoids to look at the fields definition with an external tool. The many2one fields are provided with a summary of XML_IDs available for their relation model: how many in which module. This can avoid wasted researches for (un)existing external IDs to reuse.
Some default values or compute methods may be long and annoying when reading the code. By default they are limited to 10 lines and if so, the tag [truncated] indicates a partial description. You can change this limit with the option --max-descr followed by the number of lines to display. The value -1 shows the full descriptions.
# State (#969): stored, optional, many2one -> res.country.state - with xml_id in module(s): base(645)
'state_id/id': mapper.m2o(PREFIX_RES_COUNTRY_STATE, 'state_id'),
# Sales Order (#5305): stored, required, selection
# SELECTION: 'no-message': No Message, 'warning': Warning, 'block': Blocking Message,
# DEFAULT: no-message
'sale_warn': mapper.val('sale_warn'),
# Rml header (#1069): stored, required, text
# DEFAULT:
#
# <header>
# <pageTemplate>
# [truncated...]
'rml_header': mapper.val('rml_header'),
The option --map-selection it to use when the client file contains custom values for selection fields instead of their technical values. To do so, a mapping dictionary is added in the file mapping.py with all the possible field values. These are mapped from their visible values by defaut. In addition, the mapper of this field is automatically set to that dictionary. This is done for all selection fields of the model.
Exemple on the model res.partner and the field sale_warn:
mapping.py
res_partner_sale_warn_map = {
"No Message": 'no-message',
"Warning": 'warning',
"Blocking Message": 'block',
}
res_partner.py
mapping_res_partner = {
...
# Sales Order (#5305): stored, required, selection
# SELECTION: 'no-message': No Message, 'warning': Warning, 'block': Blocking Message
# DEFAULT: no-message
'sale_warn': mapper.map_val('sale_warn', res_partner_sale_warn_map),
...
This option should be called only one time per model. If you use it twice, you must remove manually the duplicate dictionaries from the file mapping.py.
The option --with-xmlid is to use when the client file contains ready to use external IDs in identifier fields. In this case no XML_ID prefix is added in the file prefix.py, and the mapper of the "id" and relation fields is adapted to take their value stricktly from the client file column.
Here is a sample of the default mapping, without option --with-xmlid.
'id': mapper.m2o_map(OBJECT_XMLID_PREFIX, mapper.concat('_', 'CSV_COLUMN1','CSV_COLUMN2')),
'field_id/id': mapper.m2o(NEW_XMLID_PREFIX, 'field_id'),
Now with --with-xmlid.
'id': mapper.val('id'),
'field_id/id': mapper.val('field_id'),
With the option -r | --required, all the skeleton code related to optional fields is commented. It's handy when you have a few fields to import into a model that has a lot. You still have all the fields described, but you don't need to (un)comment or remove lots of them.
Note: Some fields are always commented because they should not be imported. It's namely the case with related stored, computed and non stored (and non related) fields.
At the end of the skeleton code stands the command line that launches a transformation to one import file (here: dest_my_model).
processor.process(mapping_my_model, dest_my_model, {'model': 'my.model', 'context': "{'some_key': True|False}", 'groupby': '', 'worker': 1, 'batch_size': 10}, 'set', verbose=False)
This line is preset with some options: groupby, worker and batch_size you may want to change. By default, no context is provided, letting the import script from odoo_csv_tools (odoo_import_thread.py) manage a default one. Meanwhile, under certain conditions, a context is prefilled here with:
- 'tracking_disable': True if a tracked field was found in the model.
- 'defer_fields_computation': True if a computed field was found in the model.
- 'write_metadata': True if the option --with-metadata was used (and even if there is no audit fields).
By default, the generated python script is located in the current path and named as the model with dots '.' replaced by underscores '_' (my.model -> my_model.py). You can set another file name (and location) with the option -o | --outfile.
When a model is added to the project, the needed references can be automatically added in files.py, prefixes.py, the transform and the load scripts with the option -a | --append.
-
In
files.py: the names of the client file and the import file.# Model my.model src_my_model = os.path.join(data_src_dir, 'my_model.csv') dest_my_model = os.path.join(data_src_dir, 'my.model.csv') -
In
prefix.py: the XML_ID prefix of the generated model. This prefix is combined with a project_name that is set by default to the last folder of your project_path.PREFIX_MY_MODEL = '%s_my_model' % project_name -
In the transfom script: the command line to launch the new transformation.
On Windows:
echo Transform my_model python my_model.py > %LOGDIR%\transform_my_model_out.log 2> %LOGDIR%\transform_my_model_err.logOn other platforms:
load_script my_model -
In the load script: the command line to launch the new import.
On Windows:
echo Load my_model call my_model.cmd > %LOGDIR%\load_my_model_out.log 2> %LOGDIR%\load_my_model_err.logOn other platforms:
load_script my_model
The option -a | --append should be used only one time per model. If you use it twice, you must remove manually the duplicate references in all these files.
The skeleton code is preserved if its python script already exists. You can recreate the script with the option -f | --force.
Note: this option is only related to the current functions: project scaffolding and/or model skeletoning. If you "--force" the generation of a model without scaffolding the project, only the skeleton code is recreated while the project structure is left as is. In the same way, if you "--force" the scaffolding without the option -m|--model, the skeleton codes are preserved if any.
It is possible to scaffold the project structure and to generate the first skeleton code in one command line. You can only do that with a database where the default credentials admin/admin are valid for the userid. Also, if the database is not on your local computer, encrypted connections must be allowed on port 443.
odoo_import_scaffold.py -s -p project_dir -d my_db -t my_db.odoo.com -m my.model -a
It is possible to reduce the command line. If the option -t | --host is used without -d | --db, the database will be the first part of the hostname. So, this command line is equivalent to the previous one.
odoo_import_scaffold.py -s -p project_dir -t my_db.odoo.com -m my.model -a
When no option is used, you are prompted to scaffold a project in your current directory. This path can be changed with the only option -p | --path So a project can be quickly scaffolded into your working directory with the command:
odoo_import_scaffold.py
or in another directory with the command:
odoo_import_scaffold.py -p project_dir
- To quickly create a new project:
odoo_import_scaffold.py -s -p my_project
Assuming the file conf/connection.conf is valid and you are in the project folder.
- To generate a new model:
odoo_import_scaffold.py -m my.model -a
- To generate a new model without adding its references to the python and action scripts (say, you just need the mapping code to integrate in an existing script):
odoo_import_scaffold.py -m my.model
- To only add the references of an already generated model (because you previously omitted the option -a):
odoo_import_scaffold.py -m my.model -a
-
To change the skeleton of an already generated model (YOUR CHANGES IN THE PYTHON SCRIPT WILL BE LOST !!!):
Change some options between brackets []:
odoo_import_scaffold.py -m my.model -f [-k dict|map] [-r] [--map-selection] [--max-descr MAXDESCR] [--with-xmid] [--with_o2m] [--with-metadata] [--stored]Generate a minimal skeleton (offline mode):
odoo_import_scaffold.py -m my.model -f -n -
To list all the models from the target Odoo instance:
odoo_import_scaffold.py -l
-
odoo_csv_tools. Install it with the command:
[sudo] pip install odoo-import-export-client
-
The module import_metadata must be installed to consider the context key "write_metadata".
-
The module defer_fields_computation must be installed to consider the context key "defer_fields_computation".
- The option -a | --append adds the model references event if they already exist in their respective files.
- With the option --map-selection, the mapping dictionaries of the selection fields are added even they already exist in
mapping.py.