{
"metadata": {
"name": "",
"signature": "sha256:7d01da7300982ebf3acd799b54b93beda7dec63ba8f164356465e08a34dc3311"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from node.models import Node"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root1 = Node.add_root(name=\"Documents of user1\", user_id=1, type_id=2)\n",
"root2 = Node.add_root(name=\"Documents of user2\", user_id=2, type_id=2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root1.add_child(name=\"Project for Alexandre\", user_id=1, type_id=1)\n",
"root1.add_child(name=\"Other project for Alexandre\", user_id=1, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<Node: Other project for Alexandre>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root2.add_child(name=\"Project for Mat\", user_id=2, type_id=1)\n",
"root2.add_child(name=\"Other project for Mat\", user_id=2, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"<Node: Other project for Mat>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for node in Node.objects.filter(user_id__gt=0):\n",
" print(\"[%3d] \" % node.id + node.depth*\" \" + node.name)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 1] Documents of user1\n",
"[ 3] Project for Alexandre\n",
"[ 4] Other project for Alexandre\n",
"[ 2] Documents of user2\n",
"[ 5] Project for Mat\n",
"[ 6] Other project for Mat\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root1.get_children().first().add_child(name=\"Un doc au pif\", user_id=1, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<Node: Un doc au pif>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for node in root1.get_descendants():\n",
" print(node.depth*\" \" + node.name)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Project for Alexandre\n",
" Un doc au pif\n",
" Other project for Alexandre\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Node.dump_bulk()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"[{'data': {'file': '',\n",
" 'type': 2,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Documents of user1',\n",
" 'user': 1},\n",
" 'children': [{'data': {'file': '',\n",
" 'type': 1,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Project for Alexandre',\n",
" 'user': 1},\n",
" 'children': [{'data': {'file': '',\n",
" 'type': 1,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Un doc au pif',\n",
" 'user': 1},\n",
" 'id': 7}],\n",
" 'id': 3},\n",
" {'data': {'file': '',\n",
" 'type': 1,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Other project for Alexandre',\n",
" 'user': 1},\n",
" 'id': 4}],\n",
" 'id': 1},\n",
" {'data': {'file': '',\n",
" 'type': 2,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Documents of user2',\n",
" 'user': 2},\n",
" 'children': [{'data': {'file': '',\n",
" 'type': 1,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Project for Mat',\n",
" 'user': 2},\n",
" 'id': 5},\n",
" {'data': {'file': '',\n",
" 'type': 1,\n",
" 'metadata': {},\n",
" 'date': datetime.date(2014, 10, 8),\n",
" 'name': 'Other project for Mat',\n",
" 'user': 2},\n",
" 'id': 6}],\n",
" 'id': 2}]"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Node.load_bulk([{\"data\":{\"name\":\"test\",\"user_id\":1,\"type_id\":1}}])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"[8]"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = Node.objects.all()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"0"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"NodeType.objects.all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"[<NodeType: Root>, <NodeType: Project>, <NodeType: Document>]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r = NodeType.objects.get(name='Root')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Node.objects.filter(type=r)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"SELECT \"node_node\".\"id\", \"node_node\".\"path\", \"node_node\".\"depth\", \"node_node\".\"numchild\", \"node_node\".\"user_id\", \"node_node\".\"type_id\", \"node_node\".\"name\", \"node_node\".\"date\", \"node_node\".\"file\", \"node_node\".\"metadata\" FROM \"node_node\" WHERE \"node_node\".\"type_id\" = 26 ORDER BY \"node_node\".\"path\" ASC\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from django.contrib.auth.models import User"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"User.objects.all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"[<User: alexandre>, <User: mat>, <User: User #0>, <User: User #1>]"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"User.objects.filter(id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"[<User: alexandre>]"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from django.contrib import admin"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(admin.site.register)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on method register in module django.contrib.admin.sites:\n",
"\n",
"register(model_or_iterable, admin_class=None, **options) method of django.contrib.admin.sites.AdminSite instance\n",
" Registers the given model(s) with the given admin class.\n",
" \n",
" The model(s) should be Model classes, not instances.\n",
" \n",
" If an admin class isn't given, it will use ModelAdmin (the default\n",
" admin options). If keyword arguments are given -- e.g., list_display --\n",
" they'll be applied as options to the admin class.\n",
" \n",
" If a model is already registered, this will raise AlreadyRegistered.\n",
" \n",
" If a model is abstract, this will raise ImproperlyConfigured.\n",
"\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 104,
"text": [
"[]"
]
}
],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root.add_child(name=\"Corpus Press\", user_id=1, type_id=2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 107,
"text": [
"<Node: Corpus Press>"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"parent = Node.add_root(name=\"Test parent\", user_id=1, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child1 = Node(name=\"Test child\", user_id=1, type_id=2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"parent.add_child(instance=child1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"<Node: Test child>"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"parent.get_children()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 117,
"text": [
"[<Node: Corpus Presse>, <Node: Test child>]"
]
}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child2= Node(name=\"Test child\", user_id=1, type_id=2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child1.add_child(instance=child2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 120,
"text": [
"<Node: Test child>"
]
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child1.get_children()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 121,
"text": [
"[<Node: Test child>]"
]
}
],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root.get_children()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 112,
"text": [
"[<Node: Corpus Twitter>, <Node: Corpus Twitter>]"
]
}
],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"root.save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s = root.get_siblings()\n",
"s"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"[<Node: Projet 4>, <Node: Projet 4>, <Node: Projet 5>, <Node: Projet 1>, <Node: Projet 2>, <Node: Projet 3>]"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"project = s[0]\n",
"project.get_siblings()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 99,
"text": [
"[<Node: Projet 2>, <Node: Projet 3>, <Node: Projet 4>, <Node: Projet 4>, <Node: Projet 5>, <Node: Corpus Press>, <Node: Corpus Twitter>, <Node: Projet 1>]"
]
}
],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"project.add_sibling(name=\"Corpus Twitter\", user_id=1, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"<Node: Corpus Twitter>"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"project.add_child(name=\"Corpus Twitter\", user_id=1, type_id=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 101,
"text": [
"<Node: Corpus Twitter>"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"project.save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"project.get_children()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 103,
"text": [
"[<Node: Corpus 1>, <Node: Corpus 3>, <Node: Corpus 2>, <Node: Corpus 2>, <Node: Corpus 2>, <Node: Corpus 3>, <Node: Corpus Presse>]"
]
}
],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"children = r.get_children()\n",
"children"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"[<Node: Corpus Twitter>]"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child = children[1]\n",
"child"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"text": [
"<Node: Corpus Science>"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child.delete()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"child.save()\n",
"child"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"<Node: Corpus Science>"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Node.dump_bulk()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "3",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-66-a6f171e65441>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mNode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump_bulk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/env/lib/python3.4/site-packages/treebeard/ns_tree.py\u001b[0m in \u001b[0;36mdump_bulk\u001b[1;34m(cls, parent, keep_ids)\u001b[0m\n\u001b[0;32m 590\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 591\u001b[0m \u001b[0mparentobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_parent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m \u001b[0mparentser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlnk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mparentobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 593\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'children'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparentser\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 594\u001b[0m \u001b[0mparentser\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'children'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 3"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(root.get_siblings)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on method get_siblings in module treebeard.ns_tree:\n",
"\n",
"get_siblings() method of node.models.Node instance\n",
" :returns: A queryset of all the node's siblings, including the node\n",
" itself.\n",
"\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = s[0]\n",
"n.get_parent()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Node.get_root_nodes()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"[<Node: Projet 3>, <Node: Projet 2>, <Node: Projet 1>]"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(Node.get_root_nodes())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on HStoreQuerySet in module django_hstore.query object:\n",
"\n",
"class HStoreQuerySet(django.db.models.query.QuerySet)\n",
" | Method resolution order:\n",
" | HStoreQuerySet\n",
" | django.db.models.query.QuerySet\n",
" | builtins.object\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, model=None, query=None, using=None, *args, **kwargs)\n",
" | \n",
" | hkeys = selector(self, *args, **params)\n",
" | \n",
" | hpeek = selector(self, *args, **params)\n",
" | \n",
" | hremove = updater(self, *args, **params)\n",
" | \n",
" | hslice = selector(self, *args, **params)\n",
" | \n",
" | hupdate = updater(self, *args, **params)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Methods inherited from django.db.models.query.QuerySet:\n",
" | \n",
" | __and__(self, other)\n",
" | \n",
" | __deepcopy__(self, memo)\n",
" | Deep copy of a QuerySet doesn't populate the cache\n",
" | \n",
" | __getitem__(self, k)\n",
" | Retrieves an item or slice from the set of results.\n",
" | \n",
" | __getstate__(self)\n",
" | Allows the QuerySet to be pickled.\n",
" | \n",
" | __iter__(self)\n",
" | The queryset iterator protocol uses three nested iterators in the\n",
" | default case:\n",
" | 1. sql.compiler:execute_sql()\n",
" | - Returns 100 rows at time (constants.GET_ITERATOR_CHUNK_SIZE)\n",
" | using cursor.fetchmany(). This part is responsible for\n",
" | doing some column masking, and returning the rows in chunks.\n",
" | 2. sql/compiler.results_iter()\n",
" | - Returns one row at time. At this point the rows are still just\n",
" | tuples. In some cases the return values are converted to\n",
" | Python values at this location (see resolve_columns(),\n",
" | resolve_aggregate()).\n",
" | 3. self.iterator()\n",
" | - Responsible for turning the rows into model objects.\n",
" | \n",
" | __len__(self)\n",
" | \n",
" | __nonzero__(self)\n",
" | \n",
" | __or__(self, other)\n",
" | \n",
" | __repr__(self)\n",
" | \n",
" | aggregate(self, *args, **kwargs)\n",
" | Returns a dictionary containing the calculations (aggregation)\n",
" | over the current queryset\n",
" | \n",
" | If args is present the expression is passed as a kwarg using\n",
" | the Aggregate object's default alias.\n",
" | \n",
" | all(self)\n",
" | Returns a new QuerySet that is a copy of the current one. This allows a\n",
" | QuerySet to proxy for a model manager in some cases.\n",
" | \n",
" | annotate(self, *args, **kwargs)\n",
" | Return a query set in which the returned objects have been annotated\n",
" | with data aggregated from related fields.\n",
" | \n",
" | bulk_create(self, objs, batch_size=None)\n",
" | Inserts each of the instances into the database. This does *not* call\n",
" | save() on each of the instances, does not send any pre/post save\n",
" | signals, and does not set the primary key attribute if it is an\n",
" | autoincrement field.\n",
" | \n",
" | complex_filter(self, filter_obj)\n",
" | Returns a new QuerySet instance with filter_obj added to the filters.\n",
" | \n",
" | filter_obj can be a Q object (or anything with an add_to_query()\n",
" | method) or a dictionary of keyword lookup arguments.\n",
" | \n",
" | This exists to support framework features such as 'limit_choices_to',\n",
" | and usually it will be more natural to use other methods.\n",
" | \n",
" | count(self)\n",
" | Performs a SELECT COUNT() and returns the number of records as an\n",
" | integer.\n",
" | \n",
" | If the QuerySet is already fully cached this simply returns the length\n",
" | of the cached results set to avoid multiple SELECT COUNT(*) calls.\n",
" | \n",
" | create(self, **kwargs)\n",
" | Creates a new object with the given kwargs, saving it to the database\n",
" | and returning the created object.\n",
" | \n",
" | dates(self, field_name, kind, order='ASC')\n",
" | Returns a list of date objects representing all available dates for\n",
" | the given field_name, scoped to 'kind'.\n",
" | \n",
" | datetimes(self, field_name, kind, order='ASC', tzinfo=None)\n",
" | Returns a list of datetime objects representing all available\n",
" | datetimes for the given field_name, scoped to 'kind'.\n",
" | \n",
" | defer(self, *fields)\n",
" | Defers the loading of data for certain fields until they are accessed.\n",
" | The set of fields to defer is added to any existing set of deferred\n",
" | fields. The only exception to this is if None is passed in as the only\n",
" | parameter, in which case all deferrals are removed (None acts as a\n",
" | reset option).\n",
" | \n",
" | delete(self)\n",
" | Deletes the records in the current QuerySet.\n",
" | \n",
" | distinct(self, *field_names)\n",
" | Returns a new QuerySet instance that will select only distinct results.\n",
" | \n",
" | earliest(self, field_name=None)\n",
" | \n",
" | exclude(self, *args, **kwargs)\n",
" | Returns a new QuerySet instance with NOT (args) ANDed to the existing\n",
" | set.\n",
" | \n",
" | exists(self)\n",
" | \n",
" | extra(self, select=None, where=None, params=None, tables=None, order_by=None, select_params=None)\n",
" | Adds extra SQL fragments to the query.\n",
" | \n",
" | filter(self, *args, **kwargs)\n",
" | Returns a new QuerySet instance with the args ANDed to the existing\n",
" | set.\n",
" | \n",
" | first(self)\n",
" | Returns the first object of a query, returns None if no match is found.\n",
" | \n",
" | get(self, *args, **kwargs)\n",
" | Performs the query and returns a single object matching the given\n",
" | keyword arguments.\n",
" | \n",
" | get_or_create(self, **kwargs)\n",
" | Looks up an object with the given kwargs, creating one if necessary.\n",
" | Returns a tuple of (object, created), where created is a boolean\n",
" | specifying whether an object was created.\n",
" | \n",
" | in_bulk(self, id_list)\n",
" | Returns a dictionary mapping each of the given IDs to the object with\n",
" | that ID.\n",
" | \n",
" | iterator(self)\n",
" | An iterator over the results from applying this QuerySet to the\n",
" | database.\n",
" | \n",
" | last(self)\n",
" | Returns the last object of a query, returns None if no match is found.\n",
" | \n",
" | latest(self, field_name=None)\n",
" | \n",
" | none(self)\n",
" | Returns an empty QuerySet.\n",
" | \n",
" | only(self, *fields)\n",
" | Essentially, the opposite of defer. Only the fields passed into this\n",
" | method and that are not already specified as deferred are loaded\n",
" | immediately when the queryset is evaluated.\n",
" | \n",
" | order_by(self, *field_names)\n",
" | Returns a new QuerySet instance with the ordering changed.\n",
" | \n",
" | prefetch_related(self, *lookups)\n",
" | Returns a new QuerySet instance that will prefetch the specified\n",
" | Many-To-One and Many-To-Many related objects when the QuerySet is\n",
" | evaluated.\n",
" | \n",
" | When prefetch_related() is called more than once, the list of lookups to\n",
" | prefetch is appended to. If prefetch_related(None) is called, the\n",
" | the list is cleared.\n",
" | \n",
" | reverse(self)\n",
" | Reverses the ordering of the QuerySet.\n",
" | \n",
" | select_for_update(self, **kwargs)\n",
" | Returns a new QuerySet instance that will select objects with a\n",
" | FOR UPDATE lock.\n",
" | \n",
" | select_related(self, *fields, **kwargs)\n",
" | Returns a new QuerySet instance that will select related objects.\n",
" | \n",
" | If fields are specified, they must be ForeignKey fields and only those\n",
" | related objects are included in the selection.\n",
" | \n",
" | If select_related(None) is called, the list is cleared.\n",
" | \n",
" | update(self, **kwargs)\n",
" | Updates all elements in the current QuerySet, setting all the given\n",
" | fields to the appropriate values.\n",
" | \n",
" | using(self, alias)\n",
" | Selects which database this QuerySet should excecute its query against.\n",
" | \n",
" | values(self, *fields)\n",
" | \n",
" | values_list(self, *fields, **kwargs)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors inherited from django.db.models.query.QuerySet:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | db\n",
" | Return the database that will be used if this query is executed now\n",
" | \n",
" | ordered\n",
" | Returns True if the QuerySet is ordered -- i.e. has an order_by()\n",
" | clause or a default ordering on the model.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data and other attributes inherited from django.db.models.query.QuerySet:\n",
" | \n",
" | value_annotation = True\n",
"\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import nltk\n",
"from nltk.stem.snowball import EnglishStemmer\n",
"stemmer = EnglishStemmer()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import zipfile"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"zipfile.is_zipfile(\"/tmp/date.zip\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"False"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with zipfile.ZipFile(\"/tmp/date.zip\", 'r') as f:\n",
" for x in f.namelist():\n",
" print(x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/tmp/date.zip'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-62bd2ffbe177>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/tmp/date.zip\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnamelist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python3.4/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, file, mode, compression, allowZip64)\u001b[0m\n\u001b[0;32m 921\u001b[0m \u001b[0mmodeDict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'r'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'a'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m'r+b'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 922\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 923\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 924\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 925\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/tmp/date.zip'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from lxml import etree"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(etree.parse)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function parse in module lxml.etree:\n",
"\n",
"parse(...)\n",
" parse(source, parser=None, base_url=None)\n",
" \n",
" Return an ElementTree object loaded with source elements. If no parser\n",
" is provided as second argument, the default parser is used.\n",
" \n",
" The ``source`` can be any of the following:\n",
" \n",
" - a file name/path\n",
" - a file object\n",
" - a file-like object\n",
" - a URL using the HTTP or FTP protocol\n",
" \n",
" To parse from a string, use the ``fromstring()`` function instead.\n",
" \n",
" Note that it is generally faster to parse from a file path or URL\n",
" than from an open file object or file-like object. Transparent\n",
" decompression from gzip compressed sources is supported (unless\n",
" explicitly disabled in libxml2).\n",
" \n",
" The ``base_url`` keyword allows setting a URL for the document\n",
" when parsing from a file-like object. This is needed when looking\n",
" up external entities (DTD, XInclude, ...) with relative paths.\n",
"\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importation\n",
"## Europresse"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c = gargantext.bdd.Europresse()\n",
"c.add(\"/home/alexandre/projets/abeilles/documents/Europresse/html/\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for doc in c:\n",
" d = documents.models.Document()\n",
" d.project_id = \"1\"\n",
" d.corpus_id = \"1\"\n",
" d.analyst_id = \"1\"\n",
" try:\n",
" d.uniqu_id = doc[\"object_id\"]\n",
" d.date = doc[\"date\"]\n",
" d.title = doc[\"title\"]\n",
" d.authors = doc[\"authors\"]\n",
" d.text = doc[\"text\"]\n",
" d.source = doc[\"source\"]\n",
" d.save()\n",
" except:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 88
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ISI (todo)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"projects = documents.models.Project.objects.all()\n",
"for p in projects:\n",
" corpora = documents.models.Corpus.objects.filter(project_id=p.id)\n",
" print(p.id, p.title)\n",
" for c in corpora:\n",
" print(\"|_\", c.id,\":\", c.title)\n",
" print(\"\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3 Hola Ebola\n",
"\n",
"2 Fukushima again\n",
"|_ 7 : Test\n",
"\n",
"4 Thanks anthrax\n",
"\n",
"1 Bees swarm\n",
"|_ 9 : Bees tweets\n",
"|_ 4 : Health academic publications\n",
"|_ 2 : bees and (pesticides or chemicals or neocotinoids)\n",
"|_ 1 : Quand les abeilles meurent, les articles sont compt\u00e9s\n",
"\n",
"6 CIRDEM\n",
"|_ 8 : Zotero fichier du 9 sept.\n",
"\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def extractNgrams(corpus_pk=1):\n",
" corpus = documents.models.Corpus.objects.get(pk=1)\n",
" data = gargantext.Corpus()\n",
" docs = data.query('''select * from documents_document\n",
" where corpus_id = %d\n",
" limit 90;''' % corpus_pk)\n",
" words = gargantext.Ngrams()\n",
" words.get(docs, key='text', unique_id=\"unique_id\")\n",
" return(words)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = gargantext.Corpus()\n",
"docs = data.query('''select * from documents_document\n",
" where corpus_id = %d\n",
" limit 9;''' % 1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def ngram(terms):\n",
" stems = stemmer.stem(terms)\n",
" n = len(stems.split(\" \"))\n",
" ngram = documents.models.Ngram.objects.get_or_create(terms = terms,\\\n",
" stem = stems,\\\n",
" n= n)\n",
" return(ngram[0])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"doc = documents.models.Document.objects.get(pk = 9103)\n",
"doc.title"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"'France/R\u00e9gent TS: contr\u00f4le judiciaire annul\u00e9 pour BASF, confirm\u00e9 pour Bayer'"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gram = ngram(\"de\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def addNgram2doc(gram, doc):\n",
" ngramDoc = documents.models.NgramDocument.objects.get_or_create(terms=gram,\\\n",
" document = doc,\\\n",
" defaults={'occurrences':0})[0]\n",
" ngramDoc.occurrences = F('occurrences') + 1\n",
" ngramDoc.save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Insert ngrams"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l = set()\n",
"d = defaultdict(lambda : defaultdict(lambda: defaultdict(lambda: defaultdict(int))))\n",
"\n",
"docs = documents.models.Document.objects.all().filter(project_id=1)[:1000] \n",
"\n",
"for doc in docs:\n",
" sentences = nltk.sent_tokenize(doc.text)\n",
" for sentence in sentences:\n",
" words = nltk.wordpunct_tokenize(sentence)\n",
" #print(len(words))\n",
" for word in words:\n",
" stems = stemmer.stem(word)\n",
" new = (word, stems, len(stems.split(\" \")))\n",
" l.add(new)\n",
" \n",
" d[word][doc.id]['count'] = d[word][doc.id].get('count', 0) + 1\n",
" \n",
"\n",
"new_grams = [documents.models.Ngram(terms=x[0], stem=x[1], n=x[2]) for x in l]\n",
"new_gramDoc = [ documents.models.NgramDocumentTemporary(terms=k, document=pk, occurrences=d[k][pk]['count']) \\\n",
" for k in d.keys() \\\n",
" for pk in d[k].keys()\\\n",
" ]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"documents.models.NgramTemporary.objects.bulk_create(new_grams)\n",
"documents.models.NgramDocumentTemporary.objects.bulk_create(new_gramDoc)\n",
"\n",
"from django.db import connection\n",
"cursor = connection.cursor()\n",
"# LOCK TABLE documents_ngramtemporary IN EXCLUSIVE MODE;\n",
"query_string = \"\"\"\n",
" INSERT INTO documents_ngram \n",
" SELECT * FROM documents_ngramtemporary WHERE NOT EXISTS \n",
" ( SELECT 1 FROM documents_ngram WHERE \n",
" documents_ngram.terms = documents_ngramtemporary.terms);\n",
" \n",
" delete from documents_ngramtemporary;\n",
" \n",
" INSERT INTO \n",
" documents_ngramdocument (terms_id, document_id, occurrences)\n",
" SELECT \n",
" GT.id, DT.id, NDT.occurrences \n",
" FROM \n",
" documents_ngramdocumenttemporary as NDT \n",
" INNER JOIN documents_document AS DT ON DT.id = NDT.document \n",
" INNER JOIN documents_ngram AS GT ON GT.terms = NDT.terms ;\n",
" \n",
" delete from documents_ngramdocumenttemporary;\n",
" \"\"\"\n",
"cursor.execute(query_string)\n",
"\n",
"try:\n",
" while True:\n",
" row = cursor.fetchone()\n",
" if row is None:\n",
" break\n",
" print(row)\n",
"except:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Insert NgramsDoc"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"SELECT t1.terms_id, t2.terms_id, COUNT(*) AS c, t3.project_id\n",
"FROM documents_ngramdocument AS t1\n",
"\n",
"INNER JOIN documents_ngramdocument AS t2\n",
"ON t1.document_id = t2.document_id\n",
"\n",
"INNER JOIN documents_corpus\n",
"\n",
"GROUP BY t1.terms_id, t2.terms_id;\n",
"\n",
"# add corpus_id in column !"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Coocurrences"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"curs.execute(\"select * from documents_project;\")\n",
"curs.fetchone()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 77,
"text": [
"(3, datetime.date(2014, 9, 8), 1, 'Hola Ebola', 'Dance with the risks', {})"
]
}
],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# anything can be used as a file if it has .read() and .readline() methods\n",
"import io\n",
"data = io.StringIO()\n",
"data.write('\\n'.join(['Test\\tretest\\t2',\n",
" 'Madonna\\tMado\\t45',\n",
" 'Federico\\tDi Gregorio\\t3']))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 81,
"text": [
"52"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.seek(0)\n",
"curs.copy_from(data, 'documents_ngramtemporary', columns=('terms', 'stem', 'n'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "InternalError",
"evalue": "ERREUR: la transaction est annul\u00e9e, les commandes sont ignor\u00e9es jusqu'\u00e0 la fin du bloc\nde la transaction\n",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mInternalError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-82-a9cf2ba7718f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcurs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'documents_ngramtemporary'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'terms'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'stem'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mInternalError\u001b[0m: ERREUR: la transaction est annul\u00e9e, les commandes sont ignor\u00e9es jusqu'\u00e0 la fin du bloc\nde la transaction\n"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 0
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#REDIS"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"BROKER_URL = 'redis://localhost:6379/0'\n",
"# redis://:password@hostname:port/db_number"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 3600} # 1 hour."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"BROKER_TRANSPORT_OPTIONS = {'fanout_prefix': True}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 43200}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import absolute_import\n",
"\n",
"from celery import Celery\n",
"\n",
"app = Celery('proj',\n",
" broker='redis://localhost:6379/0',\n",
" backend='redis://localhost:6379/0',\n",
" include=['proj.tasks'])\n",
"\n",
"# Optional configuration, see the application user guide.\n",
"app.conf.update(\n",
" CELERY_TASK_RESULT_EXPIRES=3600,\n",
")\n",
"\n",
"if __name__ == '__main__':\n",
" pass#app.start()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import absolute_import\n",
"\n",
"\n",
"@app.task\n",
"def add(x, y):\n",
" return x + y\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"app.send_task(add(3, 1000))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<AsyncResult: c4807752-eb28-4e0f-b8b9-fce8267bddd3>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"celery -A proj worker --loglevel=info"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-16-5806eb0c4fe2>, line 1)",
"output_type": "pyerr",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-16-5806eb0c4fe2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m celery -A proj worker --loglevel=info\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}