def test_LearnDataProxyModel():
"""Test class RadioButtonModel"""
from clair.qtgui import LearnDataProxyModel, ListingsModel
from clair.coredata import make_listing_frame, DataStore
csr = Qt.CheckStateRole
#Create test data
listings = make_listing_frame(1)
#Product "foo" is present, "bar" absent, nothing is known about "baz"
listings["expected_products"][0] = ["foo", "bar", "baz"]
listings["products"][0] = ["foo"]
listings["products_absent"][0] = ["bar"]
data_store = DataStore()
data_store.merge_listings(listings)
#Create listings model that we can adapt
lsmod = ListingsModel()
lsmod.setDataStore(data_store)
mo = LearnDataProxyModel()
mo.setListingsModel(lsmod, 3, 4, 5)
mo.setRow(lsmod.index(0, 0))
print mo.values
#Test size
assert mo.columnCount() == 4
assert mo.rowCount() == 4 #includes additional empty row
#Test data conversion
#1st row: [True, False, "foo", ...]
assert bool(mo.data(mo.index(0, 0), csr)) == True
assert bool(mo.data(mo.index(0, 1), csr)) == False
assert mo.data(mo.index(0, 2)) == "foo"
#2nd row: [False, True, "bar", ...]
assert bool(mo.data(mo.index(1, 0), csr)) == False
assert bool(mo.data(mo.index(1, 1), csr)) == True
assert mo.data(mo.index(1, 2)) == "bar"
#3rd row: [False, False, "baz", ...]
assert bool(mo.data(mo.index(2, 0), csr)) == False
assert bool(mo.data(mo.index(2, 1), csr)) == False
assert mo.data(mo.index(2, 2)) == "baz"
#Change the data
mo.setData(mo.index(0, 0), False, csr)
mo.setData(mo.index(1, 0), True, csr)
mo.setData(mo.index(2, 1), True, csr)
#Test conversion back to internal format
assert data_store.listings["expected_products"][0] == ["foo", "bar", "baz"]
assert data_store.listings["products"][0] == ["bar"]
assert data_store.listings["products_absent"][0] == ["baz"]
print "finished successfully."
def test_LearnDataProxyModel_GUI():
"""Test class RadioButtonModel"""
from clair.qtgui import LearnDataProxyModel, ListingsModel
from clair.coredata import make_listing_frame, DataStore
print "Start"
app = QApplication(sys.argv)
#Create test data
listings = make_listing_frame(1)
#Product "foo" is present, "bar" absent, nothing is known about "baz"
listings["expected_products"][0] = ["foo", "bar", "baz"]
listings["products"][0] = ["foo"]
listings["products_absent"][0] = ["bar"]
data_store = DataStore()
data_store.merge_listings(listings)
#Create listings model that we can adapt
lsmod = ListingsModel()
lsmod.setDataStore(data_store)
mo = LearnDataProxyModel()
mo.setListingsModel(lsmod, 3, 4, 5)
mo.setRow(lsmod.index(0, 0))
view = QTreeView()
view.setModel(mo)
view.show()
app.exec_()
print mo.values
print "expectedProducts:", data_store.listings["expected_products"][0]
print "products: ", data_store.listings["products"][0]
print "productsAbsent: ", data_store.listings["products_absent"][0]
print "End"
def create_models():
"""
Create a Qt-model-view models for listings, products and tasks.
Additionally returns the related ``DataStore``.
Returns
-------
listings_model, product_model, task_model, price_model, data_store
"""
from clair.qtgui import TaskModel, ProductModel, ListingsModel, PriceModel
from clair.coredata import Product, SearchTask, DataStore, \
make_listing_frame, make_price_frame
fr = make_listing_frame(3)
#All listings need unique ids
fr["id"] = ["eb-110685959294", "eb-111014122908", "eb-457"]
fr["training_sample"] = [1.0, 0.0, nan]
fr["search_tasks"] = ["s-nikon-d90", "s-nikon-d70", "s-nikon-d90"]
fr["expected_products"][0] = ["nikon-d90", "nikon-sb-24"]
fr["expected_products"][1] = ["nikon-d70"]
fr["expected_products"][2] = ["nikon-d90", "nikon-sb-24"]
fr["products"] = [["nikon-d90"], ["nikon-d70"], ["nikon-d90"]]
fr["products_absent"][0] = ["nikon-sb-24"]
fr["thumbnail"][0] = "www.some.site/dir/to/thumb.pg"
fr["image"][0] = "www.some.site/dir/to/img.pg"
fr["title"] = [u"Nikon D90 super duper!", u"Süper Camera", None]
fr["description"][0] = "Buy my old Nikon D90 camera <b>now</b>!"
fr["prod_spec"][0] = {"Marke":"Nikon", "Modell":"D90"}
fr["active"][0] = False
fr["sold"] = [1., 1., 0.]
fr["currency"][0] = "EUR"
fr["price"] = [400., 150, 300]
fr["shipping"][0] = 12.
fr["type"][0] = "auction"
fr["time"] = [datetime(2013,1,10), datetime(2013,2,2), datetime(2013,2,3)]
fr["location"][0] = u"Köln"
fr["postcode"][0] = u"50667"
fr["country"][0] = "DE"
fr["condition"][0] = 0.7
fr["server"][0] = "Ebay-Germany"
fr["server_id"] = ["110685959294", "111014122908", "457"] #ID of listing on server
fr["final_price"][0] = True
# fr["data_directory"] = ""
fr["url_webui"][0] = "www.some.site/dir/to/web-page.html"
# fr["server_repr"][0] = nan
#Put our IDs into index
fr.set_index("id", drop=False, inplace=True, verify_integrity=True)
tasks = [SearchTask("s-nikon-d90", datetime(2000, 1, 1), "ebay-de",
"Nikon D90", "daily", 100, 150, 300, "EUR",
["nikon-d90", "nikon-18-105-f/3.5-5.6--1"]),
SearchTask("s-nikon-d70", datetime(2000, 1, 1), "ebay-de",
"Nikon D70", "daily", 100, 75, 150, "EUR",
["nikon-d70", "nikon-18-105-f/3.5-5.6--1"]),]
products = [Product("nikon-d90", "Nikon D90", "Nikon D90 DSLR camera.",
["Nikon", "D 90"], ["photo.system.nikon.camera",
"photo.camera.system.nikon"]),
Product("nikon-d70", "Nikon D70", "Nikon D70 DSLR camera.",
["Nikon", "D 70"], ["photo.system.nikon.camera",
"photo.camera.system.nikon"])]
pri = make_price_frame(3)
pri["id"] = ["pri-123", "pri-456", "pri-457"]
pri["price"] = [310., 150., 300.]
pri["currency"] = ["EUR", "EUR", "EUR"]
pri["condition"] = [0.7, 0.7, 0.7]
pri["time"] = [datetime(2013,1,10), datetime(2013,2,2), datetime(2013,2,3)]
pri["product"] = ["nikon-d90", "nikon-d70", "nikon-d90"]
pri["listing"] = ["eb-123", "eb-456", "eb-457"]
pri["type"] = ["observed", "observed", "observed"]
pri["avg_period"] = None
pri["avg_num_listings"] = None
conf_dir = relative("../../example-data")
data_store = DataStore(conf_dir, None)
data_store.merge_listings(fr)
data_store.set_products(products)
data_store.add_tasks(tasks)
data_store.merge_prices(pri)
#The models are tested here, creating them may fail.
#Don't break all test, because a single model is broken.
try:
listings_model = ListingsModel()
listings_model.setDataStore(data_store)
except: #IGNORE:W0702
print "Error! ``listings_model`` could not be initialized!"
listings_model = None
try:
task_model = TaskModel()
task_model.setDataStore(data_store)
except: #IGNORE:W0702
print "Error! ``task_model`` could not be initialized!"
task_model = None
try:
product_model = ProductModel()
product_model.setDataStore(data_store)
except: #IGNORE:W0702
print "Error! ``product_model`` could not be initialized!"
product_model = None
try:
price_model = PriceModel()
price_model.setDataStore(data_store)
except: #IGNORE:W0702
print "Error! ``price_model`` could not be initialized!"
price_model = None
return listings_model, product_model, task_model, price_model, data_store
def test_ListingsModel():
"""Test ListingsModel"""
from clair.coredata import make_listing_frame
from clair.qtgui import ListingsModel, DataStore
listings = make_listing_frame(4)
data_store = DataStore()
data_store.merge_listings(listings)
model = ListingsModel()
model.setDataStore(data_store)
#Get table dimensions
assert model.rowCount() == 4
assert model.columnCount() == len(listings.columns)
#Get data from model - Currently it contains only None and nan
index17 = model.createIndex(1, 7)
data = model.data(index17, Qt.DisplayRole)
assert data == "None"
#Change data in model
model.setData(index17, "foo", Qt.EditRole)
#Test if data was really changed
data = model.data(index17, Qt.EditRole)
assert data == "foo"
#Try to get data in edit role
data = model.data(index17, Qt.EditRole)
assert data == "foo"
print listings
print listings.icol(7)
