How to use cat method in stryker-parent

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catalogtests.py

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1import os2import re3import unittest4import gisdata5from geoserver.catalog import Catalog, ConflictingDataError, UploadError, \6 FailedRequestError7from geoserver.support import ResourceInfo, url8from geoserver.support import DimensionInfo9from geoserver.layergroup import LayerGroup10from geoserver.util import shapefile_and_friends11DBPARAMS = dict(host="localhost", port="5432", dbtype="postgis",12 database=os.getenv("DATABASE", "db"),13 user=os.getenv("DBUSER", "postgres"),14 passwd=os.getenv("DBPASS", "password")15)16try:17 import psycopg218 # only used for connection sanity if present19 conn = psycopg2.connect(('dbname=%(database)s user=%(user)s password=%(passwd)s'20 ' port=%(port)s host=%(host)s'21 ) % DBPARAMS)22except ImportError:23 pass24# support resetting geoserver datadir25GEOSERVER_HOME = os.getenv('GEOSERVER_HOME')26if GEOSERVER_HOME:27 dest = os.getenv('DATA_DIR')28 data = os.path.join(GEOSERVER_HOME, 'data/release', '')29 if dest:30 os.system('rsync -v -a --delete %s %s' % (data, os.path.join(dest, '')))31 else:32 os.system('git clean -dxf -- %s' % data)33 os.system('curl -XPOST --user admin:geoserver http://localhost:8080/geoserver/rest/reload')34def drop_table(table):35 def outer(func):36 def inner(*args):37 try: func(*args)38 finally:39 try:40 if conn:41 conn.cursor().execute('DROP TABLE %s' % table)42 except Exception,e:43 print 'ERROR dropping table'44 print e45 return inner46 return outer47class NonCatalogTests(unittest.TestCase):48 def testDimensionInfo(self):49 inf = DimensionInfo( * (None,) * 6 )50 # make sure these work with no resolution set51 self.assertTrue(inf.resolution_millis() is None)52 self.assertTrue(inf.resolution_str() is None)53 inf = lambda r: DimensionInfo(None, None, None, r, None, None)54 def assertEqualResolution(spec, millis):55 self.assertEqual(millis, inf(spec).resolution_millis())56 self.assertEqual(spec, inf(millis).resolution_str())57 assertEqualResolution('0.5 seconds', 500)58 assertEqualResolution('7 days', 604800000)59 assertEqualResolution('10 years', 315360000000000)60class CatalogTests(unittest.TestCase):61 def setUp(self):62 self.cat = Catalog("http://localhost:8080/geoserver/rest")63 def testAbout(self):64 about_html = self.cat.about()65 self.assertTrue('<html xmlns="http://www.w3.org/1999/xhtml"' in about_html)66 def testGSVersion(self):67 version = self.cat.gsversion()68 pat = re.compile('\d\.\d(\.[\dx]|-SNAPSHOT)')69 self.assertTrue(pat.match('2.2.x'))70 self.assertTrue(pat.match('2.3.2'))71 self.assertTrue(pat.match('2.3-SNAPSHOT'))72 self.assertFalse(pat.match('2.3.y'))73 self.assertFalse(pat.match('233'))74 self.assertTrue(pat.match(version))75 def testWorkspaces(self):76 self.assertEqual(7, len(self.cat.get_workspaces()))77 # marking out test since geoserver default workspace is not consistent 78 # self.assertEqual("cite", self.cat.get_default_workspace().name)79 self.assertEqual("topp", self.cat.get_workspace("topp").name)80 def testStores(self):81 topp = self.cat.get_workspace("topp")82 sf = self.cat.get_workspace("sf")83 self.assertEqual(9, len(self.cat.get_stores()))84 self.assertEqual(2, len(self.cat.get_stores(topp)))85 self.assertEqual(2, len(self.cat.get_stores(sf)))86 self.assertEqual("states_shapefile", self.cat.get_store("states_shapefile", topp).name)87 self.assertEqual("states_shapefile", self.cat.get_store("states_shapefile").name)88 self.assertEqual("states_shapefile", self.cat.get_store("states_shapefile").name)89 self.assertEqual("sfdem", self.cat.get_store("sfdem", sf).name)90 self.assertEqual("sfdem", self.cat.get_store("sfdem").name)91 92 def testResources(self):93 topp = self.cat.get_workspace("topp")94 sf = self.cat.get_workspace("sf")95 states = self.cat.get_store("states_shapefile", topp)96 sfdem = self.cat.get_store("sfdem", sf)97 self.assertEqual(19, len(self.cat.get_resources()))98 self.assertEqual(1, len(self.cat.get_resources(states)))99 self.assertEqual(5, len(self.cat.get_resources(workspace=topp)))100 self.assertEqual(1, len(self.cat.get_resources(sfdem)))101 self.assertEqual(6, len(self.cat.get_resources(workspace=sf)))102 self.assertEqual("states", self.cat.get_resource("states", states).name)103 self.assertEqual("states", self.cat.get_resource("states", workspace=topp).name)104 self.assertEqual("states", self.cat.get_resource("states").name)105 states = self.cat.get_resource("states")106 fields = [107 states.title,108 states.abstract,109 states.native_bbox,110 states.latlon_bbox,111 states.projection,112 states.projection_policy113 ]114 self.assertFalse(None in fields, str(fields))115 self.assertFalse(len(states.keywords) == 0)116 self.assertFalse(len(states.attributes) == 0)117 self.assertTrue(states.enabled)118 self.assertEqual("sfdem", self.cat.get_resource("sfdem", sfdem).name)119 self.assertEqual("sfdem", self.cat.get_resource("sfdem", workspace=sf).name)120 self.assertEqual("sfdem", self.cat.get_resource("sfdem").name)121 def testLayers(self):122 expected = set(["Arc_Sample", "Pk50095", "Img_Sample", "mosaic", "sfdem",123 "bugsites", "restricted", "streams", "archsites", "roads",124 "tasmania_roads", "tasmania_water_bodies", "tasmania_state_boundaries",125 "tasmania_cities", "states", "poly_landmarks", "tiger_roads", "poi",126 "giant_polygon"127 ])128 actual = set(l.name for l in self.cat.get_layers())129 missing = expected - actual130 extras = actual - expected131 message = "Actual layer list did not match expected! (Extras: %s) (Missing: %s)" % (extras, missing)132 self.assert_(len(expected ^ actual) == 0, message)133 states = self.cat.get_layer("states")134 self.assert_("states", states.name)135 self.assert_(isinstance(states.resource, ResourceInfo))136 self.assertEqual(set(s.name for s in states.styles), set(['pophatch', 'polygon']))137 self.assertEqual(states.default_style.name, "population")138 def testLayerGroups(self):139 expected = set(["tasmania", "tiger-ny", "spearfish"])140 actual = set(l.name for l in self.cat.get_layergroups())141 missing = expected - actual142 extras = actual - expected143 message = "Actual layergroup list did not match expected! (Extras: %s) (Missing: %s)" % (extras, missing)144 self.assert_(len(expected ^ actual) == 0, message)145 tas = self.cat.get_layergroup("tasmania")146 self.assert_("tasmania", tas.name)147 self.assert_(isinstance(tas, LayerGroup))148 self.assertEqual(tas.layers, ['tasmania_state_boundaries', 'tasmania_water_bodies', 'tasmania_roads', 'tasmania_cities'], tas.layers)149 self.assertEqual(tas.styles, [None, None, None, None], tas.styles)150 def testStyles(self):151 self.assertEqual(20, len(self.cat.get_styles()))152 self.assertEqual("population", self.cat.get_style("population").name)153 self.assertEqual("popshade.sld", self.cat.get_style("population").filename)154 self.assertEqual("population", self.cat.get_style("population").sld_name)155 self.assert_(self.cat.get_style('non-existing-style') is None)156 def testEscaping(self):157 # GSConfig is inconsistent about using exceptions vs. returning None158 # when a resource isn't found.159 # But the basic idea is that none of them should throw HTTP errors from160 # misconstructed URLS161 self.cat.get_style("best style ever")162 self.cat.get_workspace("best workspace ever")163 try:164 self.cat.get_store(workspace="best workspace ever",165 name="best store ever")166 self.fail('expected exception')167 except FailedRequestError, fre:168 self.assertEqual('No store found named: best store ever', fre.message)169 try:170 self.cat.get_resource(workspace="best workspace ever",171 store="best store ever",172 name="best resource ever")173 except FailedRequestError, fre:174 self.assertEqual('No store found named: best store ever', fre.message)175 self.cat.get_layer("best layer ever")176 self.cat.get_layergroup("best layergroup ever")177 def testUnicodeUrl(self):178 """179 Tests that the geoserver.support.url function support unicode strings.180 """181 # Test the url function with unicode182 seg = ['workspaces', 'test', 'datastores', u'operaci\xf3n_repo', 'featuretypes.xml']183 u = url(base=self.cat.service_url, seg=seg)184 self.assertEqual(u, self.cat.service_url + "/workspaces/test/datastores/operaci%C3%B3n_repo/featuretypes.xml")185 # Test the url function with normal string186 seg = ['workspaces', 'test', 'datastores', 'test-repo', 'featuretypes.xml']187 u = url(base=self.cat.service_url, seg=seg)188 self.assertEqual(u, self.cat.service_url + "/workspaces/test/datastores/test-repo/featuretypes.xml")189class ModifyingTests(unittest.TestCase):190 def setUp(self):191 self.cat = Catalog("http://localhost:8080/geoserver/rest")192 def testFeatureTypeSave(self):193 # test saving round trip194 rs = self.cat.get_resource("bugsites")195 old_abstract = rs.abstract196 new_abstract = "Not the original abstract"197 enabled = rs.enabled198 # Change abstract on server199 rs.abstract = new_abstract200 self.cat.save(rs)201 rs = self.cat.get_resource("bugsites")202 self.assertEqual(new_abstract, rs.abstract)203 self.assertEqual(enabled, rs.enabled)204 # Change keywords on server205 rs.keywords = ["bugsites", "gsconfig"]206 enabled = rs.enabled207 self.cat.save(rs)208 rs = self.cat.get_resource("bugsites")209 self.assertEqual(["bugsites", "gsconfig"], rs.keywords)210 self.assertEqual(enabled, rs.enabled)211 212 # Change metadata links on server213 rs.metadata_links = [("text/xml", "TC211", "http://example.com/gsconfig.test.metadata")]214 enabled = rs.enabled215 self.cat.save(rs)216 rs = self.cat.get_resource("bugsites")217 self.assertEqual(218 [("text/xml", "TC211", "http://example.com/gsconfig.test.metadata")],219 rs.metadata_links)220 self.assertEqual(enabled, rs.enabled)221 # Restore abstract222 rs.abstract = old_abstract223 self.cat.save(rs)224 rs = self.cat.get_resource("bugsites")225 self.assertEqual(old_abstract, rs.abstract)226 def testDataStoreCreate(self):227 ds = self.cat.create_datastore("vector_gsconfig")228 ds.connection_parameters.update(**DBPARAMS)229 self.cat.save(ds)230 def testPublishFeatureType(self):231 # Use the other test and store creation to load vector data into a database232 # @todo maybe load directly to database?233 try:234 self.testDataStoreCreateAndThenAlsoImportData()235 except FailedRequestError:236 pass237 try:238 lyr = self.cat.get_layer('import')239 # Delete the existing layer and resource to allow republishing.240 self.cat.delete(lyr)241 self.cat.delete(lyr.resource)242 ds = self.cat.get_store("gsconfig_import_test")243 # make sure it's gone244 self.assert_(self.cat.get_layer('import') is None)245 self.cat.publish_featuretype("import", ds, native_crs="EPSG:4326")246 # and now it's not247 self.assert_(self.cat.get_layer('import') is not None)248 finally:249 # tear stuff down to allow the other test to pass if we run first250 ds = self.cat.get_store("gsconfig_import_test")251 lyr = self.cat.get_layer('import')252 # Delete the existing layer and resource to allow republishing.253 self.cat.delete(lyr)254 self.cat.delete(lyr.resource)255 self.cat.delete(ds)256 def testDataStoreModify(self):257 ds = self.cat.get_store("sf")258 self.assertFalse("foo" in ds.connection_parameters)259 ds.connection_parameters = ds.connection_parameters260 ds.connection_parameters["foo"] = "bar"261 orig_ws = ds.workspace.name262 self.cat.save(ds)263 ds = self.cat.get_store("sf")264 self.assertTrue("foo" in ds.connection_parameters)265 self.assertEqual("bar", ds.connection_parameters["foo"])266 self.assertEqual(orig_ws, ds.workspace.name)267 @drop_table('import')268 def testDataStoreCreateAndThenAlsoImportData(self):269 ds = self.cat.create_datastore("gsconfig_import_test")270 ds.connection_parameters.update(**DBPARAMS)271 self.cat.save(ds)272 ds = self.cat.get_store("gsconfig_import_test")273 self.cat.add_data_to_store(ds, "import", {274 'shp': 'test/data/states.shp',275 'shx': 'test/data/states.shx',276 'dbf': 'test/data/states.dbf',277 'prj': 'test/data/states.prj'278 })279 def testCoverageStoreCreate(self):280 ds = self.cat.create_coveragestore2("coverage_gsconfig")281 ds.data_url = "file:data/mytiff.tiff"282 self.cat.save(ds)283 def testCoverageStoreModify(self):284 cs = self.cat.get_store("sfdem")285 self.assertEqual("GeoTIFF", cs.type)286 cs.type = "WorldImage"287 self.cat.save(cs)288 cs = self.cat.get_store("sfdem")289 self.assertEqual("WorldImage", cs.type)290 # not sure about order of test runs here, but it might cause problems291 # for other tests if this layer is misconfigured292 cs.type = "GeoTIFF"293 self.cat.save(cs) 294 def testCoverageSave(self):295 # test saving round trip296 rs = self.cat.get_resource("Arc_Sample")297 old_abstract = rs.abstract298 new_abstract = "Not the original abstract"299 # # Change abstract on server300 rs.abstract = new_abstract301 self.cat.save(rs)302 rs = self.cat.get_resource("Arc_Sample")303 self.assertEqual(new_abstract, rs.abstract)304 # Restore abstract305 rs.abstract = old_abstract306 self.cat.save(rs)307 rs = self.cat.get_resource("Arc_Sample")308 self.assertEqual(old_abstract, rs.abstract)309 # Change metadata links on server310 rs.metadata_links = [("text/xml", "TC211", "http://example.com/gsconfig.test.metadata")]311 enabled = rs.enabled312 self.cat.save(rs)313 rs = self.cat.get_resource("Arc_Sample")314 self.assertEqual(315 [("text/xml", "TC211", "http://example.com/gsconfig.test.metadata")],316 rs.metadata_links)317 self.assertEqual(enabled, rs.enabled)318 srs_before = set(['EPSG:4326'])319 srs_after = set(['EPSG:4326', 'EPSG:3785'])320 formats = set(['ARCGRID', 'ARCGRID-GZIP', 'GEOTIFF', 'PNG', 'GIF', 'TIFF'])321 formats_after = set(["PNG", "GIF", "TIFF"])322 # set and save request_srs_list323 self.assertEquals(set(rs.request_srs_list), srs_before, str(rs.request_srs_list))324 rs.request_srs_list = rs.request_srs_list + ['EPSG:3785']325 self.cat.save(rs)326 rs = self.cat.get_resource("Arc_Sample")327 self.assertEquals(set(rs.request_srs_list), srs_after, str(rs.request_srs_list))328 # set and save response_srs_list329 self.assertEquals(set(rs.response_srs_list), srs_before, str(rs.response_srs_list))330 rs.response_srs_list = rs.response_srs_list + ['EPSG:3785']331 self.cat.save(rs)332 rs = self.cat.get_resource("Arc_Sample")333 self.assertEquals(set(rs.response_srs_list), srs_after, str(rs.response_srs_list))334 # set and save supported_formats335 self.assertEquals(set(rs.supported_formats), formats, str(rs.supported_formats))336 rs.supported_formats = ["PNG", "GIF", "TIFF"]337 self.cat.save(rs)338 rs = self.cat.get_resource("Arc_Sample")339 self.assertEquals(set(rs.supported_formats), formats_after, str(rs.supported_formats))340 def testWmsStoreCreate(self):341 ws = self.cat.create_wmsstore("wmsstore_gsconfig")342 ws.capabilitiesURL = "http://suite.opengeo.org/geoserver/ows?service=wms&version=1.1.1&request=GetCapabilities"343 ws.type = "WMS"344 self.cat.save(ws)345 346 def testWmsLayer(self):347 self.cat.create_workspace("wmstest", "http://example.com/wmstest")348 wmstest = self.cat.get_workspace("wmstest")349 wmsstore = self.cat.create_wmsstore("wmsstore", wmstest)350 wmsstore.capabilitiesURL = "http://suite.opengeo.org/geoserver/ows?service=wms&version=1.1.1&request=GetCapabilities"351 wmsstore.type = "WMS"352 self.cat.save(wmsstore)353 wmsstore = self.cat.get_store("wmsstore")354 self.assertEqual(1, len(self.cat.get_stores(wmstest)))355 available_layers = wmsstore.get_resources(available=True)356 for layer in available_layers:357 # sanitize the layer name - validation will fail on newer geoservers358 name = layer.replace(':', '_')359 new_layer = self.cat.create_wmslayer(wmstest, wmsstore, name, nativeName=layer)360 added_layers = wmsstore.get_resources()361 self.assertEqual(len(available_layers), len(added_layers))362 changed_layer = added_layers[0]363 self.assertEqual(True, changed_layer.advertised)364 self.assertEqual(True, changed_layer.enabled)365 changed_layer.advertised = False366 changed_layer.enabled = False367 self.cat.save(changed_layer)368 self.cat._cache.clear()369 changed_layer = wmsstore.get_resources()[0]370 changed_layer.fetch()371 self.assertEqual(False, changed_layer.advertised)372 self.assertEqual(False, changed_layer.enabled)373 def testFeatureTypeCreate(self):374 shapefile_plus_sidecars = shapefile_and_friends("test/data/states")375 expected = {376 'shp': 'test/data/states.shp',377 'shx': 'test/data/states.shx',378 'dbf': 'test/data/states.dbf',379 'prj': 'test/data/states.prj'380 }381 self.assertEqual(len(expected), len(shapefile_plus_sidecars))382 for k, v in expected.iteritems():383 self.assertEqual(v, shapefile_plus_sidecars[k])384 385 sf = self.cat.get_workspace("sf")386 self.cat.create_featurestore("states_test", shapefile_plus_sidecars, sf)387 self.assert_(self.cat.get_resource("states_test", workspace=sf) is not None)388 self.assertRaises(389 ConflictingDataError, 390 lambda: self.cat.create_featurestore("states_test", shapefile_plus_sidecars, sf)391 )392 self.assertRaises(393 UploadError,394 lambda: self.cat.create_coveragestore("states_raster_test", shapefile_plus_sidecars, sf)395 )396 bogus_shp = {397 'shp': 'test/data/Pk50095.tif',398 'shx': 'test/data/Pk50095.tif',399 'dbf': 'test/data/Pk50095.tfw',400 'prj': 'test/data/Pk50095.prj'401 }402 self.assertRaises(403 UploadError,404 lambda: self.cat.create_featurestore("bogus_shp", bogus_shp, sf)405 )406 lyr = self.cat.get_layer("states_test")407 self.cat.delete(lyr)408 self.assert_(self.cat.get_layer("states_test") is None)409 def testCoverageCreate(self):410 tiffdata = {411 'tiff': 'test/data/Pk50095.tif',412 'tfw': 'test/data/Pk50095.tfw',413 'prj': 'test/data/Pk50095.prj'414 }415 sf = self.cat.get_workspace("sf")416 # TODO: Uploading WorldImage file no longer works???417 # ft = self.cat.create_coveragestore("Pk50095", tiffdata, sf)418 # self.assert_(self.cat.get_resource("Pk50095", workspace=sf) is not None)419 # self.assertRaises(420 # ConflictingDataError, 421 # lambda: self.cat.create_coveragestore("Pk50095", tiffdata, sf)422 # )423 self.assertRaises(424 UploadError, 425 lambda: self.cat.create_featurestore("Pk50095_vector", tiffdata, sf)426 )427 bogus_tiff = {428 'tiff': 'test/data/states.shp',429 'tfw': 'test/data/states.shx',430 'prj': 'test/data/states.prj'431 }432 self.assertRaises(433 UploadError,434 lambda: self.cat.create_coveragestore("states_raster", bogus_tiff)435 )436 def testLayerSave(self):437 # test saving round trip438 lyr = self.cat.get_layer("states")439 old_attribution = lyr.attribution440 new_attribution = "Not the original attribution"441 # change attribution on server442 lyr.attribution = new_attribution443 self.cat.save(lyr)444 lyr = self.cat.get_layer("states")445 self.assertEqual(new_attribution, lyr.attribution)446 # Restore attribution447 lyr.attribution = old_attribution448 self.cat.save(lyr)449 lyr = self.cat.get_layer("states")450 self.assertEqual(old_attribution, lyr.attribution)451 self.assertEqual(lyr.default_style.name, "population")452 453 old_default_style = lyr.default_style454 lyr.default_style = (s for s in lyr.styles if s.name == "pophatch").next()455 lyr.styles = [old_default_style]456 self.cat.save(lyr)457 lyr = self.cat.get_layer("states")458 self.assertEqual(lyr.default_style.name, "pophatch")459 self.assertEqual([s.name for s in lyr.styles], ["population"])460 def testStyles(self):461 # upload new style, verify existence462 self.cat.create_style("fred", open("test/fred.sld").read())463 fred = self.cat.get_style("fred")464 self.assert_(fred is not None)465 self.assertEqual("Fred", fred.sld_title)466 # replace style, verify changes467 self.cat.create_style("fred", open("test/ted.sld").read(), overwrite=True)468 fred = self.cat.get_style("fred")469 self.assert_(fred is not None)470 self.assertEqual("Ted", fred.sld_title)471 # delete style, verify non-existence472 self.cat.delete(fred, purge=True)473 self.assert_(self.cat.get_style("fred") is None)474 # attempt creating new style475 self.cat.create_style("fred", open("test/fred.sld").read())476 fred = self.cat.get_style("fred")477 self.assertEqual("Fred", fred.sld_title)478 # verify it can be found via URL and check the name479 f = self.cat.get_style_by_url(fred.href)480 self.assert_(f is not None)481 self.assertEqual(f.name, fred.name)482 def testWorkspaceStyles(self):483 # upload new style, verify existence484 self.cat.create_style("jed", open("test/fred.sld").read(), workspace="topp")485 jed = self.cat.get_style("jed", workspace="blarny")486 self.assert_(jed is None)487 jed = self.cat.get_style("jed", workspace="topp")488 self.assert_(jed is not None)489 self.assertEqual("Fred", jed.sld_title)490 jed = self.cat.get_style("topp:jed")491 self.assert_(jed is not None)492 self.assertEqual("Fred", jed.sld_title)493 # replace style, verify changes494 self.cat.create_style("jed", open("test/ted.sld").read(), overwrite=True, workspace="topp")495 jed = self.cat.get_style("jed", workspace="topp")496 self.assert_(jed is not None)497 self.assertEqual("Ted", jed.sld_title)498 # delete style, verify non-existence499 self.cat.delete(jed, purge=True)500 self.assert_(self.cat.get_style("jed", workspace="topp") is None)501 # attempt creating new style502 self.cat.create_style("jed", open("test/fred.sld").read(), workspace="topp")503 jed = self.cat.get_style("jed", workspace="topp")504 self.assertEqual("Fred", jed.sld_title)505 # verify it can be found via URL and check the full name506 f = self.cat.get_style_by_url(jed.href)507 self.assert_(f is not None)508 self.assertEqual(f.fqn, jed.fqn)509 def testLayerWorkspaceStyles(self):510 # upload new style, verify existence511 self.cat.create_style("ned", open("test/fred.sld").read(), overwrite=True, workspace="topp")512 self.cat.create_style("zed", open("test/ted.sld").read(), overwrite=True, workspace="topp")513 ned = self.cat.get_style("ned", workspace="topp")514 zed = self.cat.get_style("zed", workspace="topp")515 self.assert_(ned is not None)516 self.assert_(zed is not None)517 lyr = self.cat.get_layer("states")518 lyr.default_style = ned519 lyr.styles = [zed]520 self.cat.save(lyr)521 self.assertEqual("topp:ned", lyr.default_style)522 self.assertEqual([zed], lyr.styles)523 lyr.refresh()524 self.assertEqual("topp:ned", lyr.default_style.fqn)525 self.assertEqual([zed.fqn], [s.fqn for s in lyr.styles])526 def testWorkspaceCreate(self):527 ws = self.cat.get_workspace("acme")528 self.assertEqual(None, ws)529 self.cat.create_workspace("acme", "http://example.com/acme")530 ws = self.cat.get_workspace("acme")531 self.assertEqual("acme", ws.name)532 def testWorkspaceDelete(self): 533 self.cat.create_workspace("foo", "http://example.com/foo")534 ws = self.cat.get_workspace("foo")535 self.cat.delete(ws)536 ws = self.cat.get_workspace("foo")537 self.assert_(ws is None)538 def testWorkspaceDefault(self):539 # save orig540 orig = self.cat.get_default_workspace()541 neu = self.cat.create_workspace("neu", "http://example.com/neu")542 try:543 # make sure setting it works544 self.cat.set_default_workspace("neu")545 ws = self.cat.get_default_workspace()546 self.assertEqual('neu', ws.name)547 finally:548 # cleanup and reset to the way things were549 self.cat.delete(neu)550 self.cat.set_default_workspace(orig.name)551 ws = self.cat.get_default_workspace()552 self.assertEqual(orig.name, ws.name)553 def testFeatureTypeDelete(self):554 pass555 def testCoverageDelete(self):556 pass557 def testDataStoreDelete(self):558 states = self.cat.get_store('states_shapefile')559 self.assert_(states.enabled == True)560 states.enabled = False561 self.assert_(states.enabled == False)562 self.cat.save(states)563 states = self.cat.get_store('states_shapefile')564 self.assert_(states.enabled == False)565 states.enabled = True566 self.cat.save(states)567 states = self.cat.get_store('states_shapefile')568 self.assert_(states.enabled == True)569 def testLayerGroupSave(self):570 tas = self.cat.get_layergroup("tasmania")571 self.assertEqual(tas.layers, ['tasmania_state_boundaries', 'tasmania_water_bodies', 'tasmania_roads', 'tasmania_cities'], tas.layers)572 self.assertEqual(tas.styles, [None, None, None, None], tas.styles)573 tas.layers = tas.layers[:-1]574 tas.styles = tas.styles[:-1]575 self.cat.save(tas)576 # this verifies the local state577 self.assertEqual(tas.layers, ['tasmania_state_boundaries', 'tasmania_water_bodies', 'tasmania_roads'], tas.layers)578 self.assertEqual(tas.styles, [None, None, None], tas.styles)579 # force a refresh to check the remote state580 tas.refresh()581 self.assertEqual(tas.layers, ['tasmania_state_boundaries', 'tasmania_water_bodies', 'tasmania_roads'], tas.layers)582 self.assertEqual(tas.styles, [None, None, None], tas.styles)583 def testTimeDimension(self):584 sf = self.cat.get_workspace("sf")585 files = shapefile_and_friends(os.path.join(gisdata.GOOD_DATA, "time", "boxes_with_end_date"))586 self.cat.create_featurestore("boxes_with_end_date", files, sf)587 get_resource = lambda: self.cat._cache.clear() or self.cat.get_layer('boxes_with_end_date').resource588 # configure time as LIST589 resource = get_resource()590 timeInfo = DimensionInfo("time", "true", "LIST", None, "ISO8601", None, attribute="date")591 resource.metadata = {'time':timeInfo}592 self.cat.save(resource)593 # and verify594 resource = get_resource()595 timeInfo = resource.metadata['time']596 self.assertEqual("LIST", timeInfo.presentation)597 self.assertEqual(True, timeInfo.enabled)598 self.assertEqual("date", timeInfo.attribute)599 self.assertEqual("ISO8601", timeInfo.units)600 # disable time dimension601 timeInfo = resource.metadata['time']602 timeInfo.enabled = False603 # since this is an xml property, it won't get written unless we modify it604 resource.metadata = {'time' : timeInfo}605 self.cat.save(resource)606 # and verify607 resource = get_resource()608 timeInfo = resource.metadata['time']609 self.assertEqual(False, timeInfo.enabled)610 # configure with interval, end_attribute and enable again611 timeInfo.enabled = True612 timeInfo.presentation = 'DISCRETE_INTERVAL'613 timeInfo.resolution = '3 days'614 timeInfo.end_attribute = 'enddate'615 resource.metadata = {'time' : timeInfo}616 self.cat.save(resource)617 # and verify618 resource = get_resource()619 timeInfo = resource.metadata['time']620 self.assertEqual(True, timeInfo.enabled)621 self.assertEqual('DISCRETE_INTERVAL', timeInfo.presentation)622 self.assertEqual('3 days', timeInfo.resolution_str())623 self.assertEqual('enddate', timeInfo.end_attribute)624 def testImageMosaic(self):625 # testing the mosaic creation626 name = 'cea_mosaic'627 data = open('test/data/mosaic/cea.zip', 'rb')628 self.cat.create_imagemosaic(name, data)629 # get the layer resource back630 self.cat._cache.clear()631 resource = self.cat.get_layer(name).resource632 self.assert_(resource is not None)633 # delete granule from mosaic634 coverage = name635 store = name636 granule_id = name + '.1'637 self.cat.mosaic_delete_granule(coverage, store, granule_id)638if __name__ == "__main__":...

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test_operators.py

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1import warnings2import numpy as np3import pytest4import pandas as pd5from pandas import (6 Categorical,7 DataFrame,8 Series,9 date_range,10)11import pandas._testing as tm12class TestCategoricalOpsWithFactor:13 def test_categories_none_comparisons(self):14 factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)15 tm.assert_categorical_equal(factor, factor)16 def test_comparisons(self, factor):17 result = factor[factor == "a"]18 expected = factor[np.asarray(factor) == "a"]19 tm.assert_categorical_equal(result, expected)20 result = factor[factor != "a"]21 expected = factor[np.asarray(factor) != "a"]22 tm.assert_categorical_equal(result, expected)23 result = factor[factor < "c"]24 expected = factor[np.asarray(factor) < "c"]25 tm.assert_categorical_equal(result, expected)26 result = factor[factor > "a"]27 expected = factor[np.asarray(factor) > "a"]28 tm.assert_categorical_equal(result, expected)29 result = factor[factor >= "b"]30 expected = factor[np.asarray(factor) >= "b"]31 tm.assert_categorical_equal(result, expected)32 result = factor[factor <= "b"]33 expected = factor[np.asarray(factor) <= "b"]34 tm.assert_categorical_equal(result, expected)35 n = len(factor)36 other = factor[np.random.permutation(n)]37 result = factor == other38 expected = np.asarray(factor) == np.asarray(other)39 tm.assert_numpy_array_equal(result, expected)40 result = factor == "d"41 expected = np.zeros(len(factor), dtype=bool)42 tm.assert_numpy_array_equal(result, expected)43 # comparisons with categoricals44 cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True)45 cat_rev_base = Categorical(46 ["b", "b", "b"], categories=["c", "b", "a"], ordered=True47 )48 cat = Categorical(["a", "b", "c"], ordered=True)49 cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True)50 # comparisons need to take categories ordering into account51 res_rev = cat_rev > cat_rev_base52 exp_rev = np.array([True, False, False])53 tm.assert_numpy_array_equal(res_rev, exp_rev)54 res_rev = cat_rev < cat_rev_base55 exp_rev = np.array([False, False, True])56 tm.assert_numpy_array_equal(res_rev, exp_rev)57 res = cat > cat_base58 exp = np.array([False, False, True])59 tm.assert_numpy_array_equal(res, exp)60 # Only categories with same categories can be compared61 msg = "Categoricals can only be compared if 'categories' are the same"62 with pytest.raises(TypeError, match=msg):63 cat > cat_rev64 cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"])65 with pytest.raises(TypeError, match=msg):66 cat_rev > cat_rev_base267 # Only categories with same ordering information can be compared68 cat_unorderd = cat.set_ordered(False)69 assert not (cat > cat).any()70 with pytest.raises(TypeError, match=msg):71 cat > cat_unorderd72 # comparison (in both directions) with Series will raise73 s = Series(["b", "b", "b"])74 msg = (75 "Cannot compare a Categorical for op __gt__ with type "76 r"<class 'numpy\.ndarray'>"77 )78 with pytest.raises(TypeError, match=msg):79 cat > s80 with pytest.raises(TypeError, match=msg):81 cat_rev > s82 with pytest.raises(TypeError, match=msg):83 s < cat84 with pytest.raises(TypeError, match=msg):85 s < cat_rev86 # comparison with numpy.array will raise in both direction, but only on87 # newer numpy versions88 a = np.array(["b", "b", "b"])89 with pytest.raises(TypeError, match=msg):90 cat > a91 with pytest.raises(TypeError, match=msg):92 cat_rev > a93 # Make sure that unequal comparison take the categories order in94 # account95 cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True)96 exp = np.array([True, False, False])97 res = cat_rev > "b"98 tm.assert_numpy_array_equal(res, exp)99 # check that zero-dim array gets unboxed100 res = cat_rev > np.array("b")101 tm.assert_numpy_array_equal(res, exp)102class TestCategoricalOps:103 def test_compare_frame(self):104 # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame105 data = ["a", "b", 2, "a"]106 cat = Categorical(data)107 df = DataFrame(cat)108 result = cat == df.T109 expected = DataFrame([[True, True, True, True]])110 tm.assert_frame_equal(result, expected)111 result = cat[::-1] != df.T112 expected = DataFrame([[False, True, True, False]])113 tm.assert_frame_equal(result, expected)114 def test_compare_frame_raises(self, comparison_op):115 # alignment raises unless we transpose116 op = comparison_op117 cat = Categorical(["a", "b", 2, "a"])118 df = DataFrame(cat)119 msg = "Unable to coerce to Series, length must be 1: given 4"120 with pytest.raises(ValueError, match=msg):121 op(cat, df)122 def test_datetime_categorical_comparison(self):123 dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True)124 tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True]))125 tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True]))126 def test_reflected_comparison_with_scalars(self):127 # GH8658128 cat = Categorical([1, 2, 3], ordered=True)129 tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True]))130 tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True]))131 def test_comparison_with_unknown_scalars(self):132 # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057133 # and following comparisons with scalars not in categories should raise134 # for unequal comps, but not for equal/not equal135 cat = Categorical([1, 2, 3], ordered=True)136 msg = "Invalid comparison between dtype=category and int"137 with pytest.raises(TypeError, match=msg):138 cat < 4139 with pytest.raises(TypeError, match=msg):140 cat > 4141 with pytest.raises(TypeError, match=msg):142 4 < cat143 with pytest.raises(TypeError, match=msg):144 4 > cat145 tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))146 tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))147 def test_comparison_with_tuple(self):148 cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))149 result = cat == "foo"150 expected = np.array([True, False, False, False], dtype=bool)151 tm.assert_numpy_array_equal(result, expected)152 result = cat == (0, 1)153 expected = np.array([False, True, False, True], dtype=bool)154 tm.assert_numpy_array_equal(result, expected)155 result = cat != (0, 1)156 tm.assert_numpy_array_equal(result, ~expected)157 def test_comparison_of_ordered_categorical_with_nan_to_scalar(158 self, compare_operators_no_eq_ne159 ):160 # https://github.com/pandas-dev/pandas/issues/26504161 # BUG: fix ordered categorical comparison with missing values (#26504 )162 # and following comparisons with scalars in categories with missing163 # values should be evaluated as False164 cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)165 scalar = 2166 with warnings.catch_warnings():167 warnings.simplefilter("ignore", RuntimeWarning)168 expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)169 actual = getattr(cat, compare_operators_no_eq_ne)(scalar)170 tm.assert_numpy_array_equal(actual, expected)171 def test_comparison_of_ordered_categorical_with_nan_to_listlike(172 self, compare_operators_no_eq_ne173 ):174 # https://github.com/pandas-dev/pandas/issues/26504175 # and following comparisons of missing values in ordered Categorical176 # with listlike should be evaluated as False177 cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)178 other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)179 with warnings.catch_warnings():180 warnings.simplefilter("ignore", RuntimeWarning)181 expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)182 actual = getattr(cat, compare_operators_no_eq_ne)(other)183 tm.assert_numpy_array_equal(actual, expected)184 @pytest.mark.parametrize(185 "data,reverse,base",186 [(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])],187 )188 def test_comparisons(self, data, reverse, base):189 cat_rev = Series(Categorical(data, categories=reverse, ordered=True))190 cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True))191 cat = Series(Categorical(data, ordered=True))192 cat_base = Series(193 Categorical(base, categories=cat.cat.categories, ordered=True)194 )195 s = Series(base)196 a = np.array(base)197 # comparisons need to take categories ordering into account198 res_rev = cat_rev > cat_rev_base199 exp_rev = Series([True, False, False])200 tm.assert_series_equal(res_rev, exp_rev)201 res_rev = cat_rev < cat_rev_base202 exp_rev = Series([False, False, True])203 tm.assert_series_equal(res_rev, exp_rev)204 res = cat > cat_base205 exp = Series([False, False, True])206 tm.assert_series_equal(res, exp)207 scalar = base[1]208 res = cat > scalar209 exp = Series([False, False, True])210 exp2 = cat.values > scalar211 tm.assert_series_equal(res, exp)212 tm.assert_numpy_array_equal(res.values, exp2)213 res_rev = cat_rev > scalar214 exp_rev = Series([True, False, False])215 exp_rev2 = cat_rev.values > scalar216 tm.assert_series_equal(res_rev, exp_rev)217 tm.assert_numpy_array_equal(res_rev.values, exp_rev2)218 # Only categories with same categories can be compared219 msg = "Categoricals can only be compared if 'categories' are the same"220 with pytest.raises(TypeError, match=msg):221 cat > cat_rev222 # categorical cannot be compared to Series or numpy array, and also223 # not the other way around224 msg = (225 "Cannot compare a Categorical for op __gt__ with type "226 r"<class 'numpy\.ndarray'>"227 )228 with pytest.raises(TypeError, match=msg):229 cat > s230 with pytest.raises(TypeError, match=msg):231 cat_rev > s232 with pytest.raises(TypeError, match=msg):233 cat > a234 with pytest.raises(TypeError, match=msg):235 cat_rev > a236 with pytest.raises(TypeError, match=msg):237 s < cat238 with pytest.raises(TypeError, match=msg):239 s < cat_rev240 with pytest.raises(TypeError, match=msg):241 a < cat242 with pytest.raises(TypeError, match=msg):243 a < cat_rev244 @pytest.mark.parametrize(245 "ctor",246 [247 lambda *args, **kwargs: Categorical(*args, **kwargs),248 lambda *args, **kwargs: Series(Categorical(*args, **kwargs)),249 ],250 )251 def test_unordered_different_order_equal(self, ctor):252 # https://github.com/pandas-dev/pandas/issues/16014253 c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)254 c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)255 assert (c1 == c2).all()256 c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)257 c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False)258 assert (c1 != c2).all()259 c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)260 c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False)261 assert (c1 != c2).all()262 c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)263 c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)264 result = c1 == c2265 tm.assert_numpy_array_equal(np.array(result), np.array([True, False]))266 def test_unordered_different_categories_raises(self):267 c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False)268 c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False)269 with pytest.raises(TypeError, match=("Categoricals can only be compared")):270 c1 == c2271 def test_compare_different_lengths(self):272 c1 = Categorical([], categories=["a", "b"])273 c2 = Categorical([], categories=["a"])274 msg = "Categoricals can only be compared if 'categories' are the same."275 with pytest.raises(TypeError, match=msg):276 c1 == c2277 def test_compare_unordered_different_order(self):278 # https://github.com/pandas-dev/pandas/issues/16603#issuecomment-279 # 349290078280 a = Categorical(["a"], categories=["a", "b"])281 b = Categorical(["b"], categories=["b", "a"])282 assert not a.equals(b)283 def test_numeric_like_ops(self):284 df = DataFrame({"value": np.random.randint(0, 10000, 100)})285 labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]286 cat_labels = Categorical(labels, labels)287 df = df.sort_values(by=["value"], ascending=True)288 df["value_group"] = pd.cut(289 df.value, range(0, 10500, 500), right=False, labels=cat_labels290 )291 # numeric ops should not succeed292 for op, str_rep in [293 ("__add__", r"\+"),294 ("__sub__", "-"),295 ("__mul__", r"\*"),296 ("__truediv__", "/"),297 ]:298 msg = f"Series cannot perform the operation {str_rep}|unsupported operand"299 with pytest.raises(TypeError, match=msg):300 getattr(df, op)(df)301 # reduction ops should not succeed (unless specifically defined, e.g.302 # min/max)303 s = df["value_group"]304 for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:305 msg = f"does not support reduction '{op}'"306 with pytest.raises(TypeError, match=msg):307 getattr(s, op)(numeric_only=False)308 # mad technically works because it takes always the numeric data309 def test_numeric_like_ops_series(self):310 # numpy ops311 s = Series(Categorical([1, 2, 3, 4]))312 with pytest.raises(TypeError, match="does not support reduction 'sum'"):313 np.sum(s)314 @pytest.mark.parametrize(315 "op, str_rep",316 [317 ("__add__", r"\+"),318 ("__sub__", "-"),319 ("__mul__", r"\*"),320 ("__truediv__", "/"),321 ],322 )323 def test_numeric_like_ops_series_arith(self, op, str_rep):324 # numeric ops on a Series325 s = Series(Categorical([1, 2, 3, 4]))326 msg = f"Series cannot perform the operation {str_rep}|unsupported operand"327 with pytest.raises(TypeError, match=msg):328 getattr(s, op)(2)329 def test_numeric_like_ops_series_invalid(self):330 # invalid ufunc331 s = Series(Categorical([1, 2, 3, 4]))332 msg = "Object with dtype category cannot perform the numpy op log"333 with pytest.raises(TypeError, match=msg):...

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sandwich_metrics_unittest.py

Source:sandwich_metrics_unittest.py Github

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1# Copyright 2016 The Chromium Authors. All rights reserved.2# Use of this source code is governed by a BSD-style license that can be3# found in the LICENSE file.4import copy5import json6import os7import shutil8import subprocess9import tempfile10import unittest11import loading_trace12import page_track13import sandwich_metrics as puller14import sandwich_runner15import request_track16import tracing17_BLINK_CAT = 'blink.user_timing'18_MEM_CAT = sandwich_runner.MEMORY_DUMP_CATEGORY19_START = 'requestStart'20_LOADS = 'loadEventStart'21_LOADE = 'loadEventEnd'22_NAVIGATION_START = 'navigationStart'23_PAINT = 'firstContentfulPaint'24_LAYOUT = 'firstLayout'25_MINIMALIST_TRACE_EVENTS = [26 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _NAVIGATION_START, 'ts': 10000,27 'args': {'frame': '0'}},28 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _START, 'ts': 20000,29 'args': {}},30 {'cat': _MEM_CAT, 'name': 'periodic_interval', 'pid': 1, 'ph': 'v',31 'ts': 1, 'args': {'dumps': {'allocators': {'malloc': {'attrs': {'size':{32 'units': 'bytes', 'value': '1af2', }}}}}}},33 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _LAYOUT, 'ts': 24000,34 'args': {'frame': '0'}},35 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _PAINT, 'ts': 31000,36 'args': {'frame': '0'}},37 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _LOADS, 'ts': 35000,38 'args': {'frame': '0'}},39 {'ph': 'R', 'cat': _BLINK_CAT, 'name': _LOADE, 'ts': 40000,40 'args': {'frame': '0'}},41 {'cat': _MEM_CAT, 'name': 'periodic_interval', 'pid': 1, 'ph': 'v',42 'ts': 1, 'args': {'dumps': {'allocators': {'malloc': {'attrs': {'size':{43 'units': 'bytes', 'value': 'd704', }}}}}}},44 {'ph': 'M', 'cat': '__metadata', 'pid': 1, 'name': 'process_name', 'ts': 1,45 'args': {'name': 'Browser'}}]46def TracingTrack(events):47 return tracing.TracingTrack.FromJsonDict({48 'events': events,49 'categories': (sandwich_runner._TRACING_CATEGORIES +50 [sandwich_runner.MEMORY_DUMP_CATEGORY])})51def LoadingTrace(events):52 return loading_trace.LoadingTrace('http://a.com/', {},53 page_track.PageTrack(None),54 request_track.RequestTrack(None),55 TracingTrack(events))56class PageTrackTest(unittest.TestCase):57 def testGetBrowserPID(self):58 def RunHelper(expected, events):59 self.assertEquals(expected, puller._GetBrowserPID(TracingTrack(events)))60 RunHelper(123, [61 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': 'whatever0'},62 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': 'whatever1'},63 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': '__metadata',64 'name': 'thread_name'},65 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': '__metadata',66 'name': 'process_name', 'args': {'name': 'Renderer'}},67 {'ph': 'M', 'ts': 0, 'pid': 123, 'cat': '__metadata',68 'name': 'process_name', 'args': {'name': 'Browser'}},69 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': 'whatever0'}])70 with self.assertRaises(ValueError):71 RunHelper(123, [72 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': 'whatever0'},73 {'ph': 'M', 'ts': 0, 'pid': 354, 'cat': 'whatever1'}])74 def testGetBrowserDumpEvents(self):75 NAME = 'periodic_interval'76 def RunHelper(trace_events, browser_pid):77 trace_events = copy.copy(trace_events)78 trace_events.append({79 'pid': browser_pid,80 'cat': '__metadata',81 'name': 'process_name',82 'ph': 'M',83 'ts': 0,84 'args': {'name': 'Browser'}})85 return puller._GetBrowserDumpEvents(TracingTrack(trace_events))86 TRACE_EVENTS = [87 {'pid': 354, 'ts': 1000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME},88 {'pid': 354, 'ts': 2000, 'cat': _MEM_CAT, 'ph': 'V'},89 {'pid': 672, 'ts': 3000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME},90 {'pid': 123, 'ts': 4000, 'cat': _MEM_CAT, 'ph': 'v', 'name': 'foo'},91 {'pid': 123, 'ts': 5000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME},92 {'pid': 123, 'ts': 6000, 'cat': _MEM_CAT, 'ph': 'V'},93 {'pid': 672, 'ts': 7000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME},94 {'pid': 354, 'ts': 8000, 'cat': _MEM_CAT, 'ph': 'v', 'name': 'foo'},95 {'pid': 123, 'ts': 9000, 'cat': 'whatever1', 'ph': 'v', 'name': NAME},96 {'pid': 123, 'ts': 10000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME},97 {'pid': 354, 'ts': 11000, 'cat': 'whatever0', 'ph': 'R'},98 {'pid': 672, 'ts': 12000, 'cat': _MEM_CAT, 'ph': 'v', 'name': NAME}]99 bump_events = RunHelper(TRACE_EVENTS, 123)100 self.assertEquals(2, len(bump_events))101 self.assertEquals(5, bump_events[0].start_msec)102 self.assertEquals(10, bump_events[1].start_msec)103 bump_events = RunHelper(TRACE_EVENTS, 354)104 self.assertEquals(1, len(bump_events))105 self.assertEquals(1, bump_events[0].start_msec)106 bump_events = RunHelper(TRACE_EVENTS, 672)107 self.assertEquals(3, len(bump_events))108 self.assertEquals(3, bump_events[0].start_msec)109 self.assertEquals(7, bump_events[1].start_msec)110 self.assertEquals(12, bump_events[2].start_msec)111 with self.assertRaises(ValueError):112 RunHelper(TRACE_EVENTS, 895)113 def testGetWebPageTrackedEvents(self):114 trace_events = puller._GetWebPageTrackedEvents(TracingTrack([115 {'ph': 'R', 'ts': 0000, 'args': {}, 'cat': 'whatever',116 'name': _START},117 {'ph': 'R', 'ts': 1000, 'args': {'frame': '0'}, 'cat': 'whatever',118 'name': _LOADS},119 {'ph': 'R', 'ts': 2000, 'args': {'frame': '0'}, 'cat': 'whatever',120 'name': _LOADE},121 {'ph': 'R', 'ts': 3000, 'args': {}, 'cat': _BLINK_CAT,122 'name': _START},123 {'ph': 'R', 'ts': 4000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,124 'name': _LOADS},125 {'ph': 'R', 'ts': 5000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,126 'name': _LOADE},127 {'ph': 'R', 'ts': 7000, 'args': {}, 'cat': _BLINK_CAT,128 'name': _START},129 {'ph': 'R', 'ts': 8000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,130 'name': _LOADS},131 {'ph': 'R', 'ts': 9000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,132 'name': _LOADE},133 {'ph': 'R', 'ts': 11000, 'args': {'frame': '0'}, 'cat': 'whatever',134 'name': _START},135 {'ph': 'R', 'ts': 12000, 'args': {'frame': '0'}, 'cat': 'whatever',136 'name': _LOADS},137 {'ph': 'R', 'ts': 13000, 'args': {'frame': '0'}, 'cat': 'whatever',138 'name': _LOADE},139 {'ph': 'R', 'ts': 14000, 'args': {}, 'cat': _BLINK_CAT,140 'name': _START},141 {'ph': 'R', 'ts': 10000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,142 'name': _NAVIGATION_START}, # Event out of |start_msec| order.143 {'ph': 'R', 'ts': 6000, 'args': {'frame': '0'}, 'cat': 'whatever',144 'name': _NAVIGATION_START},145 {'ph': 'R', 'ts': 15000, 'args': {}, 'cat': _BLINK_CAT,146 'name': _START},147 {'ph': 'R', 'ts': 16000, 'args': {'frame': '1'}, 'cat': _BLINK_CAT,148 'name': _LOADS},149 {'ph': 'R', 'ts': 17000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,150 'name': _LOADS},151 {'ph': 'R', 'ts': 18000, 'args': {'frame': '1'}, 'cat': _BLINK_CAT,152 'name': _LOADE},153 {'ph': 'R', 'ts': 19000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,154 'name': _LOADE},155 {'ph': 'R', 'ts': 20000, 'args': {}, 'cat': 'whatever',156 'name': _START},157 {'ph': 'R', 'ts': 21000, 'args': {'frame': '0'}, 'cat': 'whatever',158 'name': _LOADS},159 {'ph': 'R', 'ts': 22000, 'args': {'frame': '0'}, 'cat': 'whatever',160 'name': _LOADE},161 {'ph': 'R', 'ts': 23000, 'args': {}, 'cat': _BLINK_CAT,162 'name': _START},163 {'ph': 'R', 'ts': 24000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,164 'name': _LOADS},165 {'ph': 'R', 'ts': 25000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,166 'name': _LOADE}]))167 self.assertEquals(3, len(trace_events))168 self.assertEquals(14, trace_events['requestStart'].start_msec)169 self.assertEquals(17, trace_events['loadEventStart'].start_msec)170 self.assertEquals(19, trace_events['loadEventEnd'].start_msec)171 def testExtractDefaultMetrics(self):172 metrics = puller._ExtractDefaultMetrics(LoadingTrace(173 _MINIMALIST_TRACE_EVENTS))174 self.assertEquals(4, len(metrics))175 self.assertEquals(20, metrics['total_load'])176 self.assertEquals(5, metrics['js_onload_event'])177 self.assertEquals(4, metrics['first_layout'])178 self.assertEquals(11, metrics['first_contentful_paint'])179 def testExtractDefaultMetricsBestEffort(self):180 metrics = puller._ExtractDefaultMetrics(LoadingTrace([181 {'ph': 'R', 'ts': 10000, 'args': {'frame': '0'}, 'cat': _BLINK_CAT,182 'name': _NAVIGATION_START},183 {'ph': 'R', 'ts': 11000, 'args': {'frame': '0'}, 'cat': 'whatever',184 'name': _START}]))185 self.assertEquals(4, len(metrics))186 self.assertEquals(puller._FAILED_CSV_VALUE, metrics['total_load'])187 self.assertEquals(puller._FAILED_CSV_VALUE, metrics['js_onload_event'])188 self.assertEquals(puller._FAILED_CSV_VALUE, metrics['first_layout'])189 self.assertEquals(puller._FAILED_CSV_VALUE,190 metrics['first_contentful_paint'])191 def testExtractMemoryMetrics(self):192 metrics = puller._ExtractMemoryMetrics(LoadingTrace(193 _MINIMALIST_TRACE_EVENTS))194 self.assertEquals(2, len(metrics))195 self.assertEquals(30971, metrics['browser_malloc_avg'])196 self.assertEquals(55044, metrics['browser_malloc_max'])197 def testComputeSpeedIndex(self):198 def point(time, frame_completeness):199 return puller.CompletenessPoint(time=time,200 frame_completeness=frame_completeness)201 completness_record = [202 point(0, 0.0),203 point(120, 0.4),204 point(190, 0.75),205 point(280, 1.0),206 point(400, 1.0),207 ]208 self.assertEqual(120 + 70 * 0.6 + 90 * 0.25,209 puller._ComputeSpeedIndex(completness_record))210 completness_record = [211 point(70, 0.0),212 point(150, 0.3),213 point(210, 0.6),214 point(220, 0.9),215 point(240, 1.0),216 ]217 self.assertEqual(80 + 60 * 0.7 + 10 * 0.4 + 20 * 0.1,218 puller._ComputeSpeedIndex(completness_record))219 completness_record = [220 point(90, 0.0),221 point(200, 0.6),222 point(150, 0.3),223 point(230, 1.0),224 ]225 with self.assertRaises(ValueError):226 puller._ComputeSpeedIndex(completness_record)227if __name__ == '__main__':...

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prepare_cocofied_lvis.py

Source:prepare_cocofied_lvis.py Github

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1#!/usr/bin/env python32# -*- coding: utf-8 -*-3# Copyright (c) Facebook, Inc. and its affiliates.4import copy5import json6import os7from collections import defaultdict8# This mapping is extracted from the official LVIS mapping:9# https://github.com/lvis-dataset/lvis-api/blob/master/data/coco_to_synset.json10COCO_SYNSET_CATEGORIES = [11 {"synset": "person.n.01", "coco_cat_id": 1},12 {"synset": "bicycle.n.01", "coco_cat_id": 2},13 {"synset": "car.n.01", "coco_cat_id": 3},14 {"synset": "motorcycle.n.01", "coco_cat_id": 4},15 {"synset": "airplane.n.01", "coco_cat_id": 5},16 {"synset": "bus.n.01", "coco_cat_id": 6},17 {"synset": "train.n.01", "coco_cat_id": 7},18 {"synset": "truck.n.01", "coco_cat_id": 8},19 {"synset": "boat.n.01", "coco_cat_id": 9},20 {"synset": "traffic_light.n.01", "coco_cat_id": 10},21 {"synset": "fireplug.n.01", "coco_cat_id": 11},22 {"synset": "stop_sign.n.01", "coco_cat_id": 13},23 {"synset": "parking_meter.n.01", "coco_cat_id": 14},24 {"synset": "bench.n.01", "coco_cat_id": 15},25 {"synset": "bird.n.01", "coco_cat_id": 16},26 {"synset": "cat.n.01", "coco_cat_id": 17},27 {"synset": "dog.n.01", "coco_cat_id": 18},28 {"synset": "horse.n.01", "coco_cat_id": 19},29 {"synset": "sheep.n.01", "coco_cat_id": 20},30 {"synset": "beef.n.01", "coco_cat_id": 21},31 {"synset": "elephant.n.01", "coco_cat_id": 22},32 {"synset": "bear.n.01", "coco_cat_id": 23},33 {"synset": "zebra.n.01", "coco_cat_id": 24},34 {"synset": "giraffe.n.01", "coco_cat_id": 25},35 {"synset": "backpack.n.01", "coco_cat_id": 27},36 {"synset": "umbrella.n.01", "coco_cat_id": 28},37 {"synset": "bag.n.04", "coco_cat_id": 31},38 {"synset": "necktie.n.01", "coco_cat_id": 32},39 {"synset": "bag.n.06", "coco_cat_id": 33},40 {"synset": "frisbee.n.01", "coco_cat_id": 34},41 {"synset": "ski.n.01", "coco_cat_id": 35},42 {"synset": "snowboard.n.01", "coco_cat_id": 36},43 {"synset": "ball.n.06", "coco_cat_id": 37},44 {"synset": "kite.n.03", "coco_cat_id": 38},45 {"synset": "baseball_bat.n.01", "coco_cat_id": 39},46 {"synset": "baseball_glove.n.01", "coco_cat_id": 40},47 {"synset": "skateboard.n.01", "coco_cat_id": 41},48 {"synset": "surfboard.n.01", "coco_cat_id": 42},49 {"synset": "tennis_racket.n.01", "coco_cat_id": 43},50 {"synset": "bottle.n.01", "coco_cat_id": 44},51 {"synset": "wineglass.n.01", "coco_cat_id": 46},52 {"synset": "cup.n.01", "coco_cat_id": 47},53 {"synset": "fork.n.01", "coco_cat_id": 48},54 {"synset": "knife.n.01", "coco_cat_id": 49},55 {"synset": "spoon.n.01", "coco_cat_id": 50},56 {"synset": "bowl.n.03", "coco_cat_id": 51},57 {"synset": "banana.n.02", "coco_cat_id": 52},58 {"synset": "apple.n.01", "coco_cat_id": 53},59 {"synset": "sandwich.n.01", "coco_cat_id": 54},60 {"synset": "orange.n.01", "coco_cat_id": 55},61 {"synset": "broccoli.n.01", "coco_cat_id": 56},62 {"synset": "carrot.n.01", "coco_cat_id": 57},63 {"synset": "frank.n.02", "coco_cat_id": 58},64 {"synset": "pizza.n.01", "coco_cat_id": 59},65 {"synset": "doughnut.n.02", "coco_cat_id": 60},66 {"synset": "cake.n.03", "coco_cat_id": 61},67 {"synset": "chair.n.01", "coco_cat_id": 62},68 {"synset": "sofa.n.01", "coco_cat_id": 63},69 {"synset": "pot.n.04", "coco_cat_id": 64},70 {"synset": "bed.n.01", "coco_cat_id": 65},71 {"synset": "dining_table.n.01", "coco_cat_id": 67},72 {"synset": "toilet.n.02", "coco_cat_id": 70},73 {"synset": "television_receiver.n.01", "coco_cat_id": 72},74 {"synset": "laptop.n.01", "coco_cat_id": 73},75 {"synset": "mouse.n.04", "coco_cat_id": 74},76 {"synset": "remote_control.n.01", "coco_cat_id": 75},77 {"synset": "computer_keyboard.n.01", "coco_cat_id": 76},78 {"synset": "cellular_telephone.n.01", "coco_cat_id": 77},79 {"synset": "microwave.n.02", "coco_cat_id": 78},80 {"synset": "oven.n.01", "coco_cat_id": 79},81 {"synset": "toaster.n.02", "coco_cat_id": 80},82 {"synset": "sink.n.01", "coco_cat_id": 81},83 {"synset": "electric_refrigerator.n.01", "coco_cat_id": 82},84 {"synset": "book.n.01", "coco_cat_id": 84},85 {"synset": "clock.n.01", "coco_cat_id": 85},86 {"synset": "vase.n.01", "coco_cat_id": 86},87 {"synset": "scissors.n.01", "coco_cat_id": 87},88 {"synset": "teddy.n.01", "coco_cat_id": 88},89 {"synset": "hand_blower.n.01", "coco_cat_id": 89},90 {"synset": "toothbrush.n.01", "coco_cat_id": 90},91]92def cocofy_lvis(input_filename, output_filename):93 """94 Filter LVIS instance segmentation annotations to remove all categories that are not included in95 COCO. The new json files can be used to evaluate COCO AP using `lvis-api`. The category ids in96 the output json are the incontiguous COCO dataset ids.97 Args:98 input_filename (str): path to the LVIS json file.99 output_filename (str): path to the COCOfied json file.100 """101 with open(input_filename, "r") as f:102 lvis_json = json.load(f)103 lvis_annos = lvis_json.pop("annotations")104 cocofied_lvis = copy.deepcopy(lvis_json)105 lvis_json["annotations"] = lvis_annos106 # Mapping from lvis cat id to coco cat id via synset107 lvis_cat_id_to_synset = {cat["id"]: cat["synset"] for cat in lvis_json["categories"]}108 synset_to_coco_cat_id = {x["synset"]: x["coco_cat_id"] for x in COCO_SYNSET_CATEGORIES}109 # Synsets that we will keep in the dataset110 synsets_to_keep = set(synset_to_coco_cat_id.keys())111 coco_cat_id_with_instances = defaultdict(int)112 new_annos = []113 ann_id = 1114 for ann in lvis_annos:115 lvis_cat_id = ann["category_id"]116 synset = lvis_cat_id_to_synset[lvis_cat_id]117 if synset not in synsets_to_keep:118 continue119 coco_cat_id = synset_to_coco_cat_id[synset]120 new_ann = copy.deepcopy(ann)121 new_ann["category_id"] = coco_cat_id122 new_ann["id"] = ann_id123 ann_id += 1124 new_annos.append(new_ann)125 coco_cat_id_with_instances[coco_cat_id] += 1126 cocofied_lvis["annotations"] = new_annos127 for image in cocofied_lvis["images"]:128 for key in ["not_exhaustive_category_ids", "neg_category_ids"]:129 new_category_list = []130 for lvis_cat_id in image[key]:131 synset = lvis_cat_id_to_synset[lvis_cat_id]132 if synset not in synsets_to_keep:133 continue134 coco_cat_id = synset_to_coco_cat_id[synset]135 new_category_list.append(coco_cat_id)136 coco_cat_id_with_instances[coco_cat_id] += 1137 image[key] = new_category_list138 coco_cat_id_with_instances = set(coco_cat_id_with_instances.keys())139 new_categories = []140 for cat in lvis_json["categories"]:141 synset = cat["synset"]142 if synset not in synsets_to_keep:143 continue144 coco_cat_id = synset_to_coco_cat_id[synset]145 if coco_cat_id not in coco_cat_id_with_instances:146 continue147 new_cat = copy.deepcopy(cat)148 new_cat["id"] = coco_cat_id149 new_categories.append(new_cat)150 cocofied_lvis["categories"] = new_categories151 with open(output_filename, "w") as f:152 json.dump(cocofied_lvis, f)153 print("{} is COCOfied and stored in {}.".format(input_filename, output_filename))154if __name__ == "__main__":155 dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "lvis")156 for s in ["lvis_v0.5_train", "lvis_v0.5_val"]:157 print("Start COCOfing {}.".format(s))158 cocofy_lvis(159 os.path.join(dataset_dir, "{}.json".format(s)),160 os.path.join(dataset_dir, "{}_cocofied.json".format(s)),...

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evaluation.py

Source:evaluation.py Github

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1import numpy as np2import keras3from sklearn import metrics4from mlxtend.evaluate import confusion_matrix5from sklearn.metrics import roc_auc_score, precision_recall_curve, auc6# one is agitation and zero is not-agitation7# here positive is agitation and negative is not-agitation8def decision(y_pred, thresh=0.5):9 return np.where(y_pred[:,1] >= thresh, 1, 0)10 #return np.argmax(y_pred, axis=1)11# number of positive cases which have been predicted negative / number of positive cases12def false_rejection_rate(y_true, y_pred, thresh=0.5):13 pred = decision(y_pred, thresh)14 num_p = np.count_nonzero(y_true == 1.)15 FN = [i for i in range(0, len(pred)) if y_true[i] == 1. and pred[i] == 0.]16 num_f_n = len(FN)17 return num_f_n/num_p18# 1 - (number of negative cases which predicted negative / number of negative cases)19def false_acceptance_rate(y_true, y_pred, thresh=0.5):20 pred = decision(y_pred, thresh)21 num_n = np.count_nonzero(y_true == 0.)22 TN = [i for i in range(0, len(pred)) if pred[i] == 0. and y_true[i] == 0.]23 num_t_n = len(TN)24 return 1-(num_t_n/num_n)25def HalfTotalErrorRate(y_true, y_pred, thresh=0.5):26 alpha = 0.527 if y_true.ndim != 1:28 y_true = np.argmax(y_true, axis=1)29 far = false_acceptance_rate(y_true, y_pred, thresh)30 frr = false_rejection_rate(y_true, y_pred, thresh)31 return (alpha * far) + (alpha * frr)32def CategoricalTruePositive(y_true, y_pred, thresh=0.5):33 y_true = np.argmax(y_true, axis=1)34 y_pred = decision(y_pred, thresh)35 true_poss = np.logical_and(np.equal(y_true, 1), np.equal(y_pred, 1))36 true_poss = np.sum(true_poss)37 return true_poss38def CategoricalTrueNegative(y_true, y_pred, thresh=0.5):39 y_true = np.argmax(y_true, axis=1)40 y_pred = decision(y_pred, thresh)41 42 true_neg = np.logical_and(np.equal(y_true, 0), np.equal(y_pred, 0))43 true_neg = np.sum(true_neg)44 return true_neg45 46def CategoricalFalseNegative(y_true, y_pred, thresh=0.5):47 y_true = np.argmax(y_true, axis=1)48 y_pred = decision(y_pred, thresh)49 false_neg = np.logical_and(np.equal(y_true, 1), np.equal(y_pred, 0))50 false_neg = np.sum(false_neg)51 return false_neg52def CategoricalFalsePositive(y_true, y_pred, thresh=0.5):53 y_true = np.argmax(y_true, axis=1)54 y_pred = decision(y_pred, thresh)55 false_poss = np.logical_and(np.equal(y_true, 0), np.equal(y_pred, 1))56 false_poss = np.sum(false_poss)57 return false_poss58class CategoricalEvaluation(keras.callbacks.Callback):59 def __init__(self, model, validation_data, thresh=0.5):60 super().__init__()61 self.model = model62 self.y_true = validation_data[1]63 self.x_val = validation_data[0]64 self.thresh = thresh65 self.CTP = []66 self.CTN = []67 self.CFN = []68 self.CFP = []69 self.HTER = []70 self.recall = []71 self.specificity = []72 self.precision = []73 def on_epoch_end(self, epoch, logs={}):74 y_pred = self.model.predict(self.x_val)75 cat_true_positive = CategoricalTruePositive(self.y_true, y_pred, self.thresh)76 cat_true_negative = CategoricalTrueNegative(self.y_true, y_pred, self.thresh)77 cat_false_negative = CategoricalFalseNegative(self.y_true, y_pred, self.thresh)78 cat_false_positive = CategoricalFalsePositive(self.y_true, y_pred, self.thresh)79 half_total_error_rate = HalfTotalErrorRate(self.y_true, y_pred, self.thresh)80 cat_recall = cat_true_positive / (cat_true_positive + cat_false_negative)81 cat_spec = cat_true_negative / (cat_true_negative + cat_false_positive)82 cat_prec = cat_true_positive / (cat_true_positive + cat_false_positive)83 84 self.CTP.append(cat_true_positive)85 self.CTN.append(cat_true_negative)86 self.CFN.append(cat_false_negative)87 self.CFP.append(cat_false_positive)88 self.HTER.append(half_total_error_rate)89 self.recall.append(cat_recall)90 self.specificity.append(cat_spec)91 self.precision.append(cat_prec)92 93 print("True Positive:", cat_true_positive)94 print("True Negative:", cat_true_negative)95 print("False Negative:", cat_false_negative)96 print("False Positive:", cat_false_positive)97 print("Recall/Sensitivity:", cat_recall)98 print("Specificity:", cat_spec)99 print("Precision:", cat_prec)100 print("Half Total Error Rate:", half_total_error_rate)101def CategoricalAccuracy(thresh=0.5):102 def cat_accuracy(y_true, y_pred):103 y_pred = tf.cast(tf.greater_equal(y_pred[:, 1], thresh), tf.float32)104 y_true = tf.cast(tf.argmax(y_true, axis=1), tf.float32)105 return K.mean(K.equal(y_true, K.round(y_pred)))106 107 return cat_accuracy108def evaluation(y_true, y_pred):109 true_positive = CategoricalTruePositive(y_true, y_pred)110 true_negative = CategoricalTrueNegative(y_true, y_pred)111 false_negative = CategoricalFalseNegative(y_true, y_pred)112 false_positive = CategoricalFalsePositive(y_true, y_pred)113 recall = true_positive / (true_positive + false_negative)114 spec = true_negative / (true_negative + false_positive)115 prec = true_positive / (true_positive + false_positive)116 roc_auc = roc_auc_score(y_true, y_pred[:, 1])117 precision, rec, _ = precision_recall_curve(y_true, y_pred[:, 1])118 pr_rec = auc(rec, precision)119 f1 = 2 * (recall * prec) / (prec + recall)120 acc = (true_positive + true_negative)/(true_negative + true_positive + false_negative + false_positive)121 return {'TP': true_positive,122 'TN': true_negative,123 'FN': false_negative,124 'FP': false_positive,125 'Accuracy': acc,126 'Recall': recall,127 'Specificity': spec,128 'Precision': prec,129 'F1': f1,130 'AUC': roc_auc,131 'prec-rec': pr_rec132 }133def categorical_evaluation(model, x, true, thresh=0.5):134 y_pred = model.predict_proba(x)135 cat_true_positive = CategoricalTruePositive(true, y_pred, thresh)136 cat_true_negative = CategoricalTrueNegative(true, y_pred, thresh)137 cat_false_negative = CategoricalFalseNegative(true, y_pred, thresh)138 cat_false_positive = CategoricalFalsePositive(true, y_pred, thresh)139 half_total_error_rate = HalfTotalErrorRate(true, y_pred, thresh)140 cat_recall = cat_true_positive / (cat_true_positive + cat_false_negative)141 cat_spec = cat_true_negative / (cat_true_negative + cat_false_positive)142 cat_prec = cat_true_positive / (cat_true_positive + cat_false_positive)143 auc_roc = roc_auc_score(true, y_pred[:, 1])144 precision, recall, thresholds = precision_recall_curve(true, y_pred[:, 1])145 pr_rec = auc(recall, precision)146 acc = (cat_true_positive + cat_true_negative)/(cat_true_negative + cat_true_positive + cat_false_negative + cat_false_positive)147 f1 = 2 * (cat_recall * cat_prec)/(cat_recall + cat_prec)148 return {'TP': cat_true_positive,149 'TN': cat_true_negative,150 'FN': cat_false_negative,151 'FP': cat_false_positive,152 'Accuracy': acc,153 'Recall': cat_recall,154 'Specificity': cat_spec,155 'Precision': cat_prec,156 'F1': f1,157 'HTER': half_total_error_rate,158 'auc': auc_roc,159 'prec-rec': pr_rec...

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category.py

Source:category.py Github

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1# -*- coding: utf-8 -*-2from requests.auth import HTTPBasicAuth3from sqlalchemy.orm import sessionmaker4from data_updater.models import server as server_models5from config import CLIENT_SETTINGS6from .utils import request_data, timestamp_to_date_str, updated_request, timestamp_cur, print_json7from .cache import update_cache8url = CLIENT_SETTINGS['root_url'].rstrip('/') + '/' + CLIENT_SETTINGS['category_url'].lstrip('/')9category_problem_url = CLIENT_SETTINGS['category_problem_url'].rstrip('/')10username = CLIENT_SETTINGS['username']11password = CLIENT_SETTINGS['password']12auth = HTTPBasicAuth(username, password)13cache_name = 'category_updater_cache'14Session = sessionmaker(bind=server_models.engine)15session = Session()16def request_category_list(request_url):17 return request_data(url=request_url, auth=auth)18def request_category_problem_list(request_url):19 return request_data(url=request_url, auth=auth)20def write_category(cat_json):21 cat = session.query(server_models.Category).filter_by(id=cat_json['id']).first()22 if cat is None:23 cat_model = server_models.Category(id=cat_json['id'],24 title=cat_json['title'],25 introduction=cat_json['introduction'],26 source=cat_json['source'],27 author=cat_json['author'],28 number_problem=cat_json['number_problem'])29 session.add(cat_model)30 print('created cat %s: %s' % (cat_json['id'], cat_json['title']))31 else:32 cat.title = cat_json['title']33 cat.introduction = cat_json['introduction']34 cat.source = cat_json['source']35 cat.author = cat_json['author']36 cat.number_problem = cat_json['number_problem']37 print('updated env %s: %s' % (cat_json['id'], cat_json['title']))38 session.commit()39def write_category_problems(cat_id, cp_json):40 cp = session.query(41 server_models.CategoryProblemRelation).filter_by(id=cp_json['id']).first()42 problem = session.query(43 server_models.Problem).filter_by(id=cp_json['problem']['id']).first()44 if problem is None:45 if cp is not None:46 session.delete(cp)47 elif cp is None:48 cat_model = server_models.CategoryProblemRelation(id=cp_json['id'],49 category_id=cat_id,50 problem_id=cp_json['problem']['id'],51 directory=cp_json['directory'])52 session.add(cat_model)53 print('created cat_problem %s: %s' % (cp_json['id'], cp_json['problem']['title']))54 else:55 cp.category_id = cat_id,56 cp.problem_id = cp_json['problem']['id']57 cp.directory = cp_json['directory']58 print('updated cat_problem %s: %s' % (cp_json['id'], cp_json['problem']['title']))59 session.commit()60def update_category(cat_json):61 write_category(cat_json)62 cat_id = int(cat_json['id'])63 request_url = url.rstrip('/') + ('/%s/' % (cat_json['id'])) + category_problem_url + '/'64 while request_url is not None:65 chance_left = 366 cp_list = None67 while cp_list is None and chance_left > 0:68 print('requesting %s, tried %s ......' % (request_url, 3-chance_left))69 cp_list = request_category_problem_list(request_url)70 chance_left -= 171 if cp_list is None:72 request_url = None73 else:74 for cp in cp_list['results']:75 write_category_problems(cat_id, cp)76 request_url = cp_list['next']77def update_categories(update_all=False):78 time = timestamp_cur()79 request_url = url if update_all else updated_request(url, cache_name)80 while request_url is not None:81 chance_left = 382 cat_list = None83 while cat_list is None and chance_left > 0:84 print('requesting %s, tried %s ......' % (request_url, 3-chance_left))85 cat_list = request_category_list(request_url)86 chance_left -= 187 if cat_list is None:88 request_url = None89 else:90 for c in cat_list['results']:91 update_category(c)92 request_url = cat_list['next']...

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test_dataset_wrapper.py

Source:test_dataset_wrapper.py Github

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1import bisect2import math3from collections import defaultdict4from unittest.mock import MagicMock5import numpy as np6from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset,7 RepeatDataset)8def test_dataset_wrapper():9 CustomDataset.load_annotations = MagicMock()10 CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)11 dataset_a = CustomDataset(12 ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')13 len_a = 1014 cat_ids_list_a = [15 np.random.randint(0, 80, num).tolist()16 for num in np.random.randint(1, 20, len_a)17 ]18 dataset_a.data_infos = MagicMock()19 dataset_a.data_infos.__len__.return_value = len_a20 dataset_a.get_cat_ids = MagicMock(21 side_effect=lambda idx: cat_ids_list_a[idx])22 dataset_b = CustomDataset(23 ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')24 len_b = 2025 cat_ids_list_b = [26 np.random.randint(0, 80, num).tolist()27 for num in np.random.randint(1, 20, len_b)28 ]29 dataset_b.data_infos = MagicMock()30 dataset_b.data_infos.__len__.return_value = len_b31 dataset_b.get_cat_ids = MagicMock(32 side_effect=lambda idx: cat_ids_list_b[idx])33 concat_dataset = ConcatDataset([dataset_a, dataset_b])34 assert concat_dataset[5] == 535 assert concat_dataset[25] == 1536 assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5]37 assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15]38 assert len(concat_dataset) == len(dataset_a) + len(dataset_b)39 repeat_dataset = RepeatDataset(dataset_a, 10)40 assert repeat_dataset[5] == 541 assert repeat_dataset[15] == 542 assert repeat_dataset[27] == 743 assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5]44 assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5]45 assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7]46 assert len(repeat_dataset) == 10 * len(dataset_a)47 category_freq = defaultdict(int)48 for cat_ids in cat_ids_list_a:49 cat_ids = set(cat_ids)50 for cat_id in cat_ids:51 category_freq[cat_id] += 152 for k, v in category_freq.items():53 category_freq[k] = v / len(cat_ids_list_a)54 mean_freq = np.mean(list(category_freq.values()))55 repeat_thr = mean_freq56 category_repeat = {57 cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))58 for cat_id, cat_freq in category_freq.items()59 }60 repeat_factors = []61 for cat_ids in cat_ids_list_a:62 cat_ids = set(cat_ids)63 repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids})64 repeat_factors.append(math.ceil(repeat_factor))65 repeat_factors_cumsum = np.cumsum(repeat_factors)66 repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)67 assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1]68 for idx in np.random.randint(0, len(repeat_factor_dataset), 3):69 assert repeat_factor_dataset[idx] == bisect.bisect_right(...

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coco_annotation.py

Source:coco_annotation.py Github

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1import json2from collections import defaultdict3name_box_id = defaultdict(list)4id_name = dict()5f = open(6 "mscoco2017/annotations/instances_train2017.json",7 encoding='utf-8')8data = json.load(f)9annotations = data['annotations']10for ant in annotations:11 id = ant['image_id']12 name = 'mscoco2017/train2017/%012d.jpg' % id13 cat = ant['category_id']14 if cat >= 1 and cat <= 11:15 cat = cat - 116 elif cat >= 13 and cat <= 25:17 cat = cat - 218 elif cat >= 27 and cat <= 28:19 cat = cat - 320 elif cat >= 31 and cat <= 44:21 cat = cat - 522 elif cat >= 46 and cat <= 65:23 cat = cat - 624 elif cat == 67:25 cat = cat - 726 elif cat == 70:27 cat = cat - 928 elif cat >= 72 and cat <= 82:29 cat = cat - 1030 elif cat >= 84 and cat <= 90:31 cat = cat - 1132 name_box_id[name].append([ant['bbox'], cat])33f = open('train.txt', 'w')34for key in name_box_id.keys():35 f.write(key)36 box_infos = name_box_id[key]37 for info in box_infos:38 x_min = int(info[0][0])39 y_min = int(info[0][1])40 x_max = x_min + int(info[0][2])41 y_max = y_min + int(info[0][3])42 box_info = " %d,%d,%d,%d,%d" % (43 x_min, y_min, x_max, y_max, int(info[1]))44 f.write(box_info)45 f.write('\n')...

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Using AI Code Generation

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1const strykerParent = require('stryker-parent');2strykerParent.cat();3const strykerParent = require('stryker-parent');4strykerParent.dog();5const strykerParent = require('stryker-parent');6strykerParent.cat();7const strykerParent = require('stryker-parent');8strykerParent.dog();9const strykerParent = require('stryker-parent');10strykerParent.cat();11const strykerParent = require('stryker-parent');12strykerParent.dog();13const strykerParent = require('stryker-parent');14strykerParent.cat();15const strykerParent = require('stryker-parent');16strykerParent.dog();17const strykerParent = require('stryker-parent');18strykerParent.cat();19const strykerParent = require('stryker-parent');20strykerParent.dog();21const strykerParent = require('stryker-parent');22strykerParent.cat();23const strykerParent = require('stryker-parent');24strykerParent.dog();25const strykerParent = require('stryker-parent');26strykerParent.cat();27const strykerParent = require('stryker-parent

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Using AI Code Generation

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1var strykerParent = require('stryker-parent');2var stryker = new strykerParent();3stryker.cat();4var strykerChild = require('stryker-child');5var stryker = new strykerChild();6stryker.cat();7var strykerChild2 = require('stryker-child2');8var stryker = new strykerChild2();9stryker.cat();10var strykerChild3 = require('stryker-child3');11var stryker = new strykerChild3();12stryker.cat();13var strykerChild4 = require('stryker-child4');14var stryker = new strykerChild4();15stryker.cat();16var strykerChild5 = require('stryker-child5');17var stryker = new strykerChild5();18stryker.cat();19var strykerChild6 = require('stryker-child6');20var stryker = new strykerChild6();21stryker.cat();22var strykerChild7 = require('stryker-child7');23var stryker = new strykerChild7();24stryker.cat();25var strykerChild8 = require('stryker-child8');26var stryker = new strykerChild8();27stryker.cat();28var strykerChild9 = require('stryker-child9');29var stryker = new strykerChild9();30stryker.cat();31var strykerChild10 = require('stryker-child10');

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Using AI Code Generation

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1var strykerParent = require('stryker-parent');2console.log(strykerParent.cat('Hello', 'World'));3var strykerParent = require('stryker-parent');4console.log(strykerParent.cat('Hello', 'World'));5import strykerParent = require('stryker-parent');6console.log(strykerParent.cat('Hello', 'World'));

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Using AI Code Generation

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1var cat = require('stryker-parent').cat;2console.log('The cat says: ' + cat());3var cat = require('stryker-parent').cat;4console.log('The cat says: ' + cat());5var cat = require('stryker-parent').cat;6console.log('The cat says: ' + cat());7var cat = require('stryker-parent').cat;8console.log('The cat says: ' + cat());9var cat = require('stryker-parent').cat;10console.log('The cat says: ' + cat());11var cat = require('stryker-parent').cat;12console.log('The cat says: ' + cat());13var cat = require('stryker-parent').cat;14console.log('The cat says: ' + cat());15var cat = require('stryker-parent').cat;16console.log('The cat says: ' + cat());17var cat = require('stryker-parent').cat;18console.log('The cat says: ' + cat());19var cat = require('stryker-parent').cat;20console.log('The cat says: ' + cat());21var cat = require('stryker-parent').cat;22console.log('The cat says: ' + cat());23var cat = require('stryker-parent').cat;24console.log('The cat says: ' + cat());25var cat = require('stryker-parent').cat;26console.log('The cat says: '

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Using AI Code Generation

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1var stryker = require('stryker-parent');2var strykerCat = new stryker.Cat();3strykerCat.meow();4var stryker = require('stryker-parent');5var strykerCat = new stryker.Cat();6strykerCat.meow();7var stryker = require('stryker-parent');8var strykerCat = new stryker.Cat();9strykerCat.meow();10var stryker = require('stryker-parent');11var strykerCat = new stryker.Cat();12strykerCat.meow();13var stryker = require('stryker-parent');14var strykerCat = new stryker.Cat();15strykerCat.meow();16var stryker = require('stryker-parent');17var strykerCat = new stryker.Cat();18strykerCat.meow();19var stryker = require('stryker-parent');20var strykerCat = new stryker.Cat();21strykerCat.meow();22var stryker = require('stryker-parent');23var strykerCat = new stryker.Cat();24strykerCat.meow();25var stryker = require('stryker-parent');26var strykerCat = new stryker.Cat();27strykerCat.meow();28var stryker = require('stryker-parent');29var strykerCat = new stryker.Cat();30strykerCat.meow();31var stryker = require('stryker-parent');

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Using AI Code Generation

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1var strykerParent = require('stryker-parent');2var cat = strykerParent.cat;3var cat = strykerParent.cat;4cat('meow');5var strykerParent = require('stryker-parent');6var cat = strykerParent.cat;7var cat = strykerParent.cat;8cat('meow');9var strykerParent = require('stryker-parent');10var cat = strykerParent.cat;11var cat = strykerParent.cat;12cat('meow');13var strykerParent = require('stryker-parent');14var cat = strykerParent.cat;15var cat = strykerParent.cat;16cat('meow');17var strykerParent = require('stryker-parent');18var cat = strykerParent.cat;19var cat = strykerParent.cat;20cat('meow');21var strykerParent = require('stryker-parent');22var cat = strykerParent.cat;23var cat = strykerParent.cat;24cat('meow');25var strykerParent = require('stryker-parent');26var cat = strykerParent.cat;27var cat = strykerParent.cat;28cat('meow');29var strykerParent = require('stryker-parent');30var cat = strykerParent.cat;31var cat = strykerParent.cat;32cat('meow');33var strykerParent = require('stryker-parent');34var cat = strykerParent.cat;35var cat = strykerParent.cat;36cat('meow');37var strykerParent = require('stryker-parent');38var cat = strykerParent.cat;

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Using AI Code Generation

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1const strykerParent = require('stryker-parent');2console.log(strykerParent.cat());3{4 "dependencies": {5 }6}7const strykerParent = require('stryker-parent');8console.log(strykerParent.cat());9const strykerParent = require('stryker-parent');10console.log(strykerParent.cat());11{12 "dependencies": {13 }14}15const strykerParent = require('stryker-parent');16console.log(strykerParent.cat());17const strykerParent = require('stryker-parent');18console.log(strykerParent.cat());19{20 "dependencies": {21 }22}23const strykerParent = require('stryker-parent');24console.log(strykerParent.cat());25const strykerParent = require('stryker-parent');26console.log(strykerParent.cat());27{

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Using AI Code Generation

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1var parent = require('stryker-parent');2var str = 'foo';3var parent = require('stryker-parent');4var str = 'foo';5var parent = require('stryker-parent');6var str = 'foo';7var parent = require('stryker-parent');8var str = 'foo';9var parent = require('stryker-parent');10var str = 'foo';11var parent = require('stryker-parent');12var str = 'foo';13var parent = require('stryker-parent');14var str = 'foo';15var parent = require('stryker-parent');16var str = 'foo';17var parent = require('stryker-parent');18var str = 'foo';19var parent = require('stryker-parent');20var str = 'foo';21var parent = require('stryker-parent');22var str = 'foo';

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