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How to use allocate_id method in Slash

Best Python code snippet using slash

kits_reel_allocate_line.py

Source:kits_reel_allocate_line.py

1from odoo import models,fields,api2class kits_reel_allocate_line(models.Model):3    _name = 'kits.reel.allocate.line'4    _description = 'Reel Allocate Lines'5    _rec_name = 'product_id'6    allocate_id = fields.Many2one('kits.reel.allocate','Reel Allocation',ondelete="cascade")7    alternate_allocate_id = fields.Many2one('kits.reel.allocate','Alternate Reel Allocation',ondelete="cascade")8    9    product_id = fields.Many2one('product.product','Product',ondelete='restrict')10    mo_id = fields.Many2one('mrp.production','Manufacturing',ondelete='restrict')11    lot_id = fields.Many2one("stock.production.lot",'Lot Number',ondelete='restrict')12    component_line_id = fields.Many2one('stock.move','Component Line',ondelete='cascade')13    qty_needed = fields.Float('Quantity Needed',related="component_line_id.product_qty")14    qty_allocated = fields.Float('Allocated Quantity')15    # wh_quantity = fields.Float('Available Qty',compute="_compute_wh_quantity")16    wh_quantity = fields.Float('Available Qty',related='lot_id.product_qty')17    allowed_alternate_product_ids = fields.Many2many('product.product','reel_allocate_line_alternate_products_rel','reel_allocate_line_id','product_product_id','Alternate products',compute="_compute_allowed_alternate_product_ids",store=True,compute_sudo=True)18    # For Lot Allocation19    quant_id = fields.Many2one('stock.quant','Allocated Stock')20    location_src_id = fields.Many2one('stock.location','Source Location',ondelete="restrict")21    move_line_id = fields.Many2one('stock.move.line','Move Line',ondelete="restrict")22    # @api.depends('product_id','allocate_id','alternate_allocate_id')23    # def _compute_wh_quantity(self):24    #     for record in self:25    #         allocate_id = record.allocate_id or record.alternate_allocate_id or False26    #         total_available = 027    #         if allocate_id:28    #             quant_ids = self.env['stock.quant'].search([('product_id','=',allocate_id.product_id.id),('on_hand','=',True),('location_id','=',allocate_id.replenishment_id.location_id.id),('quantity','>=',1)])29    #             total_available = sum(quant_ids.mapped('quantity'))30    #         record.wh_quantity = total_available31                32    @api.depends('product_id','product_id.mt_product_alternative_ids')33    def _compute_allowed_alternate_product_ids(self):34        for record in self:35            record.allowed_alternate_product_ids = [(6,0,record.product_id.mt_product_alternative_ids.mapped('mt_product_id').ids)]36    @api.onchange('product_id')37    def _onchange_product_id(self):38        self.ensure_one()39        template_product_ids = self.env['mrp.bom.line'].search([('product_id','=',self.product_id.id)]).mapped('bom_id').mapped('product_tmpl_id')40        41        mo_ids = self.env['mrp.production'].search([('state','in',('draft','confirmed','progress')),('product_id','in',template_product_ids.product_variant_ids.ids)])42        return {43            'domain':{'mo_id':[('id','in',mo_ids)]}44        }45    @api.model46    def create(self,vals):47        res = super(kits_reel_allocate_line,self).create(vals)48        # if res and res.component_line_id:49        #     move_line_id = self.env['stock.move.line'].create({50        #         'move_id':res.component_line_id.id,51        #         'company_id':res.component_line_id.company_id.id,52        #         'date':fields.Datetime.now(),53        #         'location_dest_id':res.component_line_id.location_dest_id.id,54        #         'location_id':res.quant_id.location_id.id,55        #         'product_uom_id':res.product_id.uom_id.id,56        #         'product_uom_qty':res.qty_allocated,57        #         'product_id':res.product_id.id,58        #         'lot_id':res.lot_id.id,59        #         })60        #     res.move_line_id = move_line_id61        return res62    def write(self,vals):63        res = super(kits_reel_allocate_line,self).write(vals)64        return res65    def unlink(self):66        # to_delete = self.env['stock.move.line']67        # for record in self:68        #     to_delete |= record.move_line_id69        res = super(kits_reel_allocate_line,self).unlink()70        # if res:71        #     to_delete.unlink()...

test_create_tensor.py

Source:test_create_tensor.py

...4from pandas.util.testing import assert_frame_equal5from tensorly.decomposition import tucker6from recommender_systems.tensor_decomposition import create_tensor7class TestCreateTensor(unittest.TestCase):8    def test_allocate_id(self):9        df = pd.DataFrame([['hoge_a', 'foo_a', 'bar_a'],10                           ['hoge_b', 'foo_b', 'bar_a'],11                           ['hoge_b', 'foo_c', 'bar_b']12                          ]13                         )14        df.columns = ['hoge', 'foo', 'bar']15        actual_df = df.copy()16        for col_name in ['hoge', 'foo', 'bar']:17            actual_label, actual_unique = create_tensor.allocate_id(actual_df, col_name)18        df['hoge_id'] = [0, 1, 1]19        df['foo_id'] = [0, 1, 2]20        df['bar_id'] = [0, 0, 1]21                                                                                   22        assert_frame_equal(actual_df, df)23    def test_allocate_id(self):24        df = pd.DataFrame([['hoge_a', 'foo_a', 'bar_a'],25                           ['hoge_b', 'foo_b', 'bar_a'],26                           ['hoge_b', 'foo_c', 'bar_b']27                          ]28                         )29        df.columns = ['hoge', 'foo', 'bar']30        actual_df = df.copy()31        col_name = 'hoge'32        mode_member = np.array(['hoge_a', 'hoge_b', 'hoge_c'])33        actual_label, actual_unique = create_tensor.allocate_id(actual_df, col_name, mode_member)34        df['hoge_id'] = [0, 1, 1]35                                                                                   36        assert_frame_equal(actual_df, df)37    def test_melting_matrix(self):38        list_x = [np.array([[5. ,  4.],39                            [-2.5, 0.]])40                 ]41        list_expected = [np.array([[0, 0, 5.],42                                   [0, 1, 4.],43                                   [1, 0, -2.5],44                                   [1, 1, 0.]])45                        ]46        for x, expected in zip(list_x, list_expected):47            actual = create_tensor.melting_matrix(x)48            self.assertEqual(actual.all(), expected.all())49    def test_create_tensor_from_df(self):50        df = pd.DataFrame([['hoge_a', 'foo_a',  0],51                           ['hoge_b', 'foo_b',  1],52                           ['hoge_b', 'foo_c',  2]53                          ]54                         )55        df.columns = ['hoge', 'foo', 'val']56        list_df = [df]57        list_mode = [['hoge', 'foo',]]58        list_shape = [(2, 3)]59        list_value_name = ['val']60              61        for df, mode, shape, value_name in zip(list_df, list_mode, list_shape, list_value_name):62            ## Make an actual tensor ############################63            ct = create_tensor.CreateTensor(df, value_name, mode)64            actual = ct.create_tensor_from_df(missing_val=0)65            #####################################################66            ## Make an expected tansor ##########################67            for col_name in mode:68                actual_label, actual_unique = create_tensor.allocate_id(df, col_name)69            expected = np.array([[0, 0, 0],70                                 [0, 1, 2]71                                ]72                               )73            #####################################################74            self.assertEqual(actual.all(), expected.all())75if __name__== '__main__':...

create_tensor.py

Source:create_tensor.py

1import numpy as np2import pandas as pd3import tensorly as tl4def allocate_id(df, col_name, mode_member=None):5    col_name_id = col_name + '_id'6    if mode_member is not None:7        mode_member = mode_member.astype(object)8        unique = mode_member9        label = np.array([np.where(data==mode_member)[0][0] for data in df[col_name]]) 10        df[col_name_id] = list(label)11    else:12        label, unique = pd.factorize(df[col_name])13        df[col_name_id] = list(label)14    df[col_name_id] = df[col_name_id].astype(np.int64)15    return label, unique16def melting_matrix(x):17    melted = np.array([[int(i), int(j), x[i][j]] for i in range(x.shape[0]) for j in range(x.shape[1])])18    return melted19class CreateTensor:20    """21    To create the tensor to decomposite22    Attributes23    ----------24        value_name : str25            the name of data to be entries of the tensor26        mode : list 27            A column name of the dataframe28        mode_members : list of numpy.array29            The entry's label of each mode30        31        tensor : numpy.array32            tensor to be decomposed 33        dataset : pandas.dataframe34            trimed dataset that has columns of mode and entries of the tensor35        indexes : pandas.dataframe36            labels of the tensor's mode37        values : pandas.series38            entries of the tensor39    Examples40    --------41    >>> mode = ['dog', 'cat', 'monkey'] 42    >>> dog_breeds = np.array(['shiba', 'husky', 'retriever'])43    ...44    >>> mode_members = [dog_breeds, cat_breeds, monkey_breeds] 45    >>> ct = CreateTensor(df, 'average_weight', mode, mode_member)46    """47    def __init__(self, df, value_name, mode=[None], mode_members=None):48        self.df = df49        self.value_name = value_name50        self.mode = mode51        self.mode_members = mode_members 52        ## Allocate id to each mode ######################53        if mode_members != None:54            self.labels = [allocate_id(self.df, col_name, mode_member)[0] for col_name, mode_member in zip(mode, mode_members)]55            self.uniques = [allocate_id(self.df, col_name, mode_member)[1] for col_name, mode_member in zip(mode, mode_members)]56        else:57            self.labels = [allocate_id(self.df, col_name)[0] for col_name in mode]58            self.uniques = [allocate_id(self.df, col_name)[1] for col_name in mode]59        ##################################################60        self.shape = tuple([len(unique) for unique in self.uniques])61        self.tensor = np.full(self.shape, np.nan)62    def create_tensor_from_df(self, missing_val='mean'):63        self.missing_val = missing_val64        self.mode_ids = [col_name + '_id' for col_name in self.mode]65        self.dataset = self.df[self.mode_ids + [self.value_name]]66        self.indexes = self.df[self.mode_ids]67        self.values = self.df[self.value_name]68        69        self.tensor[tuple(self.indexes.values.T)] = self.values.values70        if self.missing_val == 'mean':71            self.tensor[np.isnan(self.tensor)] = np.nanmean(self.tensor)72        else:...

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