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How to use yes method in Testify

Best Python code snippet using Testify_python

dataset_forVD2D3D.spec

Source:dataset_forVD2D3D.spec

1# samples example2# the [image] sections indicate the network inputs3# format should be gray images with any bit depth.4#5# [image1]6# fnames =  path/of/image1.tif/h5,7#           path/of/image2.tif/h58# pp_types = standard2D, none9# is_auto_crop = yes10#11# the [label] sections indicate ground truth of network outputs12# format could be 24bit RGB or gray image with any bit depth.13# the mask images should be binary image with any bit depth.14# only the voxels with gray value greater than 0 is effective for training.15#16# [label1]17# fnames = path/of/image3.tif/h5,18#          path/of/image4.tif/h519# preprocessing type: one_class, binary_class, none, affinity20# pp_types = binary_class, binary_class21# fmasks = path/of/mask1.tif/h5,22#      path/of/mask2.tif/h523#24# [sample] section indicates the group of the corresponding input and output labels25# the name should be the same with the one in the network config file26#27# [sample1]28# input1 = 129# input2 = 230# output1 = 131# output2 = 232# DENOISED MICROSCOPE STACKS33[image1]34fnames =../../vesselNN_dataset/denoised/burgess2014_bbbDisruption_BM4D_denoised.tif35pp_types = standard2D36is_auto_crop = yes37[image2]38fnames =../../vesselNN_dataset/denoised/burgess2014_lowerRes_hiSNR_BM4D_denoised.tif39pp_types = standard2D40is_auto_crop = yes41[image3]42fnames =../../vesselNN_dataset/denoised/burgess2014_noisySparseVessels_BM4D_denoised.tif43pp_types = standard2D44is_auto_crop = yes45[image4]46fnames =../../vesselNN_dataset/denoised/burgess2014_tgMouse_BM4D_denoised.tif47pp_types = standard2D48is_auto_crop = yes49[image5]50fnames =../../vesselNN_dataset/denoised/poon2015_BBB_Leakage_BM4D_denoised.tif51pp_types = standard2D52is_auto_crop = yes53[image6]54fnames =../../vesselNN_dataset/denoised/poon2015_BBB_noLeakage_BM4D_denoised.tif55pp_types = standard2D56is_auto_crop = yes57[image7]58fnames =../../vesselNN_dataset/denoised/poon2015_mixedSize1_BM4D_denoised.tif59pp_types = standard2D60is_auto_crop = yes61[image8]62fnames =../../vesselNN_dataset/denoised/poon2015_mixedSize2_BM4D_denoised.tif63pp_types = standard2D64is_auto_crop = yes65[image9]66fnames =../../vesselNN_dataset/denoised/poon2015_plaqueVessels_BM4D_denoised.tif67pp_types = standard2D68is_auto_crop = yes69[image10]70fnames =../../vesselNN_dataset/denoised/santos2015_lowContrastVessels_BM4D_denoised.tif71pp_types = standard2D72is_auto_crop = yes73[image11]74fnames =../../vesselNN_dataset/denoised/santos2015_mixedSizelowContrastVessels_BM4D_denoised.tif75pp_types = standard2D76is_auto_crop = yes77[image12]78fnames =../../vesselNN_dataset/denoised/santos2015_tumor_BM4D_denoised.tif79pp_types = standard2D80is_auto_crop = yes81# RECURSIVE OUTPUTS from VD2D82[image13]83fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample1_output_0.tif84pp_types = symetric_rescale85is_auto_crop = yes86[image14]87fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample2_output_0.tif88pp_types = symetric_rescale89is_auto_crop = yes90[image15]91fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample3_output_0.tif92pp_types = symetric_rescale93is_auto_crop = yes94[image16]95fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample4_output_0.tif96pp_types = symetric_rescale97is_auto_crop = yes98[image17]99fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample5_output_0.tif100pp_types = symetric_rescale101is_auto_crop = yes102[image18]103fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample6_output_0.tif104pp_types = symetric_rescale105is_auto_crop = yes106[image19]107fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample7_output_0.tif108pp_types = symetric_rescale109is_auto_crop = yes110[image20]111fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample8_output_0.tif112pp_types = symetric_rescale113is_auto_crop = yes114[image21]115fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample9_output_0.tif116pp_types = symetric_rescale117is_auto_crop = yes118[image22]119fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample10_output_0.tif120pp_types = symetric_rescale121is_auto_crop = yes122[image23]123fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample11_output_0.tif124pp_types = symetric_rescale125is_auto_crop = yes126[image24]127fnames =../../vesselNN_dataset/experiments/VD2D_tanh/out_sample12_output_0.tif128pp_types = symetric_rescale129is_auto_crop = yes130# LABELS131[label1]132fnames =../../vesselNN_dataset/labels/burgess2014_bbbDisruption_labels_v1.tif133pp_types = binary_class134is_auto_crop = yes135fmasks =136[label2]137fnames =../../vesselNN_dataset/labels/burgess2014_lowerRes_hiSNR_labels_v1.tif138pp_types = binary_class139is_auto_crop = yes140fmasks =141[label3]142fnames =../../vesselNN_dataset/labels/burgess2014_noisySparseVessels_labels_v1.tif143pp_types = binary_class144is_auto_crop = yes145fmasks =146[label4]147fnames =../../vesselNN_dataset/labels/burgess2014_tgMouse_labels_v1.tif148pp_types = binary_class149is_auto_crop = yes150fmasks =151[label5]152fnames =../../vesselNN_dataset/labels/poon2015_BBB_Leakage_manualLabels_v2.tif153pp_types = binary_class154is_auto_crop = yes155fmasks =156[label6]157fnames =../../vesselNN_dataset/labels/poon2015_BBB_noLeakage_manualLabel_v2.tif158pp_types = binary_class159is_auto_crop = yes160fmasks =161[label7]162fnames =../../vesselNN_dataset/labels/poon2015_mixedSize1_manualLabel_v2.tif163pp_types = binary_class164is_auto_crop = yes165fmasks =166[label8]167fnames =../../vesselNN_dataset/labels/poon2015_mixedSize2_labels_v1.tif168pp_types = binary_class169is_auto_crop = yes170fmasks =171[label9]172fnames =../../vesselNN_dataset/labels/poon2015_plaqueVessels_label_underSegmented.tif173pp_types = binary_class174is_auto_crop = yes175fmasks =176[label10]177fnames =../../vesselNN_dataset/labels/santos2015_lowContrastVessels_labelsMarc.tif178pp_types = binary_class179is_auto_crop = yes180fmasks =181[label11]182fnames =../../vesselNN_dataset/labels/santos2015_mixedSizelowContrastVessels_labelsMarc.tif183pp_types = binary_class184is_auto_crop = yes185fmasks =186[label12]187fnames =../../vesselNN_dataset/labels/santos2015_tumor_initLabel_v1.tif188pp_types = binary_class189is_auto_crop = yes190fmasks =191# INPUT-OUTPUT DEFINITIONS192[sample1]193input = 1194input-r = 13195output = 1196[sample2]197input = 2198input-r = 14199output = 2200[sample3]201input = 3202input-r = 15203output = 3204[sample4]205input = 4206input-r = 16207output = 4208[sample5]209input = 5210input-r = 17211output = 5212[sample6]213input = 6214input-r = 18215output = 6216[sample7]217input = 7218input-r = 19219output = 7220[sample8]221input = 8222input-r = 20223output = 8224[sample9]225input = 9226input-r = 21227output = 9228[sample10]229input = 10230input-r = 22231output = 10232[sample11]233input = 11234input-r = 23235output = 11236[sample12]237input = 12238input-r = 24...

drvsupport.py

Source:drvsupport.py

1# -*- coding: utf-8 -*-2from fiona._drivers import GDALEnv3# Here is the list of available drivers as (name, modes) tuples. Currently,4# we only expose the defaults (excepting FileGDB). We also don't expose5# the CSV or GeoJSON drivers. Use Python's csv and json modules instead.6# Might still exclude a few more of these after making a pass through the7# entries for each at http://www.gdal.org/ogr/ogr_formats.html to screen8# out the multi-layer formats.9supported_drivers = dict([10#OGR Vector Formats11#Format Name 	Code 	Creation 	Georeferencing 	Compiled by default12#Aeronav FAA files 	AeronavFAA 	No 	Yes 	Yes13    ("AeronavFAA", "r"),14#ESRI ArcObjects 	ArcObjects 	No 	Yes 	No, needs ESRI ArcObjects15#Arc/Info Binary Coverage 	AVCBin 	No 	Yes 	Yes16# multi-layer17#   ("AVCBin", "r"),18#Arc/Info .E00 (ASCII) Coverage 	AVCE00 	No 	Yes 	Yes19# multi-layer20#    ("AVCE00", "r"),21#Arc/Info Generate 	ARCGEN 	No 	No 	Yes22    ("ARCGEN", "r"),23#Atlas BNA 	BNA 	Yes 	No 	Yes24    ("BNA", "raw"),25#AutoCAD DWG 	DWG 	No 	No 	No26#AutoCAD DXF 	DXF 	Yes 	No 	Yes27    ("DXF", "raw"),28#Comma Separated Value (.csv) 	CSV 	Yes 	No 	Yes29#CouchDB / GeoCouch 	CouchDB 	Yes 	Yes 	No, needs libcurl30#DODS/OPeNDAP 	DODS 	No 	Yes 	No, needs libdap31#EDIGEO 	EDIGEO 	No 	Yes 	Yes32# multi-layer? Hard to tell from the OGR docs33#   ("EDIGEO", "r"),34#ElasticSearch 	ElasticSearch 	Yes (write-only) 	- 	No, needs libcurl35#ESRI FileGDB 	FileGDB 	Yes 	Yes 	No, needs FileGDB API library36# multi-layer37    ("FileGDB", "raw"),38    ("OpenFileGDB", "r"),39#ESRI Personal GeoDatabase 	PGeo 	No 	Yes 	No, needs ODBC library40#ESRI ArcSDE 	SDE 	No 	Yes 	No, needs ESRI SDE41#ESRI Shapefile 	ESRI Shapefile 	Yes 	Yes 	Yes42    ("ESRI Shapefile", "raw"),43#FMEObjects Gateway 	FMEObjects Gateway 	No 	Yes 	No, needs FME44#GeoJSON 	GeoJSON 	Yes 	Yes 	Yes45    ("GeoJSON", "rw"),46#GÃ©oconcept Export 	Geoconcept 	Yes 	Yes 	Yes47# multi-layers48#   ("Geoconcept", "raw"),49#Geomedia .mdb 	Geomedia 	No 	No 	No, needs ODBC library50#GeoPackage	GPKG	Yes	Yes	No, needs libsqlite351    ("GPKG", "rw"),52#GeoRSS 	GeoRSS 	Yes 	Yes 	Yes (read support needs libexpat)53#Google Fusion Tables 	GFT 	Yes 	Yes 	No, needs libcurl54#GML 	GML 	Yes 	Yes 	Yes (read support needs Xerces or libexpat)55#GMT 	GMT 	Yes 	Yes 	Yes56    ("GMT", "raw"),57#GPSBabel 	GPSBabel 	Yes 	Yes 	Yes (needs GPSBabel and GPX driver)58#GPX 	GPX 	Yes 	Yes 	Yes (read support needs libexpat)59    ("GPX", "raw"),60#GRASS 	GRASS 	No 	Yes 	No, needs libgrass61#GPSTrackMaker (.gtm, .gtz) 	GPSTrackMaker 	Yes 	Yes 	Yes62    ("GPSTrackMaker", "raw"),63#Hydrographic Transfer Format 	HTF 	No 	Yes 	Yes64# TODO: Fiona is not ready for multi-layer formats: ("HTF", "r"),65#Idrisi Vector (.VCT) 	Idrisi 	No 	Yes 	Yes66    ("Idrisi", "r"),67#Informix DataBlade 	IDB 	Yes 	Yes 	No, needs Informix DataBlade68#INTERLIS 	"Interlis 1" and "Interlis 2" 	Yes 	Yes 	No, needs Xerces (INTERLIS model reading needs ili2c.jar)69#INGRES 	INGRES 	Yes 	No 	No, needs INGRESS70#KML 	KML 	Yes 	Yes 	Yes (read support needs libexpat)71#LIBKML 	LIBKML 	Yes 	Yes 	No, needs libkml72#Mapinfo File 	MapInfo File 	Yes 	Yes 	Yes73    ("MapInfo File", "raw"),74#Microstation DGN 	DGN 	Yes 	No 	Yes75    ("DGN", "raw"),76#Access MDB (PGeo and Geomedia capable) 	MDB 	No 	Yes 	No, needs JDK/JRE77#Memory 	Memory 	Yes 	Yes 	Yes78#MySQL 	MySQL 	No 	Yes 	No, needs MySQL library79#NAS - ALKIS 	NAS 	No 	Yes 	No, needs Xerces80#Oracle Spatial 	OCI 	Yes 	Yes 	No, needs OCI library81#ODBC 	ODBC 	No 	Yes 	No, needs ODBC library82#MS SQL Spatial 	MSSQLSpatial 	Yes 	Yes 	No, needs ODBC library83#Open Document Spreadsheet 	ODS 	Yes 	No 	No, needs libexpat84#OGDI Vectors (VPF, VMAP, DCW) 	OGDI 	No 	Yes 	No, needs OGDI library85#OpenAir 	OpenAir 	No 	Yes 	Yes86# multi-layer87#   ("OpenAir", "r"),88#PCI Geomatics Database File 	PCIDSK 	No 	No 	Yes, using internal PCIDSK SDK (from GDAL 1.7.0)89    ("PCIDSK", "r"),90#PDS 	PDS 	No 	Yes 	Yes91    ("PDS", "r"),92#PGDump 	PostgreSQL SQL dump 	Yes 	Yes 	Yes93#PostgreSQL/PostGIS 	PostgreSQL/PostGIS 	Yes 	Yes 	No, needs PostgreSQL client library (libpq)94#EPIInfo .REC 	REC 	No 	No 	Yes95#S-57 (ENC) 	S57 	No 	Yes 	Yes96# multi-layer97#   ("S57", "r"),98#SDTS 	SDTS 	No 	Yes 	Yes99# multi-layer100#   ("SDTS", "r"),101#SEG-P1 / UKOOA P1/90 	SEGUKOOA 	No 	Yes 	Yes102# multi-layers103#   ("SEGUKOOA", "r"),104#SEG-Y 	SEGY 	No 	No 	Yes105    ("SEGY", "r"),106#Norwegian SOSI Standard 	SOSI 	No 	Yes 	No, needs FYBA library107#SQLite/SpatiaLite 	SQLite 	Yes 	Yes 	No, needs libsqlite3 or libspatialite108#SUA 	SUA 	No 	Yes 	Yes109    ("SUA", "r"),110#SVG 	SVG 	No 	Yes 	No, needs libexpat111#UK .NTF 	UK. NTF 	No 	Yes 	Yes112# multi-layer113#   ("UK. NTF", "r"),114#U.S. Census TIGER/Line 	TIGER 	No 	Yes 	Yes115# multi-layer116#   ("TIGER", "r"),117#VFK data 	VFK 	No 	Yes 	Yes118# multi-layer119#   ("VFK", "r"),120#VRT - Virtual Datasource 	VRT 	No 	Yes 	Yes121# multi-layer122#   ("VRT", "r"),123#OGC WFS (Web Feature Service) 	WFS 	Yes 	Yes 	No, needs libcurl124#MS Excel format 	XLS 	No 	No 	No, needs libfreexl125#Office Open XML spreadsheet 	XLSX 	Yes 	No 	No, needs libexpat126#X-Plane/Flighgear aeronautical data 	XPLANE 	No 	Yes 	Yes127# multi-layer128#   ("XPLANE", "r") 129])130# Removes drivers in the supported_drivers dictionary that the   131# machine's installation of OGR due to how it is compiled.132# OGR may not have optional libararies compiled or installed.133def _filter_supported_drivers():134    global supported_drivers135    gdalenv = GDALEnv()136    ogrdrv_names = gdalenv.start().drivers().keys()137    supported_drivers_copy = supported_drivers.copy()138    for drv in supported_drivers.keys():139        if drv not in ogrdrv_names:140            del supported_drivers_copy[drv]141    gdalenv.stop()142    supported_drivers = supported_drivers_copy...

6-16.py

Source:6-16.py

1import numpy as np2import matplotlib.pyplot as plt3import random4import time5# 6-16a6# num_points = 100007# xes = []8# yes = []9# centers = [random.randint(0,num_points-1)]10# for i in range(0, num_points):11# 	xes.append(random.uniform(0,1))12# 	yes.append(random.uniform(0,1))13# 6-16b14num_points = 1000015xes = []16yes = []17centers = [random.randint(0,num_points-1)]18mean = 019for i in range(0, num_points):20	if i % num_points/10 == 0:21		mean += 0.1 22	xes.append(np.random.normal(mean, 0.1, 1))23	yes.append(np.random.normal(mean, 0.1, 1))24start_time = time.time() # calculating starttime for clusters25# greedily get 10 clusters (already have one)26for i in range(0, 9):27	max_dist = 028	max_index = -129	for j in range(0, num_points):30		min_dist = 10031		for k in range(0, len(centers)):32			dist = np.power(np.power(xes[centers[k]] - xes[j], 2) + np.power(yes[centers[k]] - yes[j], 2), 0.5)33			if dist < min_dist:34				min_dist = dist # we want to maximize min distance to space out the clusters35		if min_dist > max_dist:36			max_dist = min_dist37			max_index = j38	centers.append(max_index)39for i in range(0, 9):40	print xes[centers[i]], yes[centers[i]]41# assign points to clusters (0-9)42clusters = []43cluster_xes = []44cluster_yes = []45cluster_radii = []46for i in range(0,10):47	clusters.append([])48	cluster_xes.append(0)49	cluster_yes.append(0)50	cluster_radii.append(0)51for i in range(0, num_points):52	min_dist = 10053	center = -154	for k in range(0, len(centers)):55		dist = np.power(np.power(xes[centers[k]] - xes[i], 2) + np.power(yes[centers[k]] - yes[i], 2), 0.5)56		if dist < min_dist:57			min_dist = dist58			center = k59	clusters[center].append(i)60	if cluster_radii[center] < min_dist:61		cluster_radii[center] = min_dist62	cluster_xes[center] += xes[i]63	cluster_yes[center] += yes[i]64for i in range(0, 10):65	print len(clusters[i])66	if len(clusters[i]) == 0:67		continue68	cluster_xes[i] /= len(clusters[i])69	cluster_yes[i] /= len(clusters[i])70query_x = []71query_y = []72for i in range(0, num_points):73	query_x.append(random.uniform(0,1))74	query_y.append(random.uniform(0,1))75# BRUTE FORCE76# import time77# start_time = time.time()78# for i in range(0, num_points):79# 	if i % 100 == 0:80# 		print "Points completed:", i81# 	min_dist = 10082# 	min_index = -183# 	for j in range(0, num_points):84# 		dist = np.power(np.power(xes[j] - query_x[i], 2) + np.power(yes[j] - query_y[i], 2), 0.5)85# 		if dist < min_dist:86# 			min_dist = dist87# 			min_index = j88# # 668.26699996 seconds89# print("--- %s seconds ---" % (time.time() - start_time))90# start_time = time.time()91# BRANCH AND BOUND92# import time93# start_time = time.time()94for i in range(0, num_points):95	if i % 100 == 0:96		print "Points completed:", i97	min_clust_dist = 10098	min_clust = -199	for j in range(0, 10): # for each point, first find minimum cluster100		dist = np.power(np.power(xes[i] - cluster_xes[j], 2) + np.power(yes[i] - cluster_yes[j], 2), 0.5)101		if dist < min_clust_dist:102			min_clust_dist = dist103			min_clust = j104	min_dist = 100105	for j in range(0, len(clusters[min_clust])):106		dist = np.power(np.power(xes[i] - xes[clusters[min_clust][j]], 2) + np.power(yes[i] - yes[clusters[min_clust][j]], 2), 0.5)107		if dist < min_dist:108			min_dist = dist109	for j in range(0, 10):110		if j != min_clust:111			dist = np.power(np.power(xes[i] - cluster_xes[j], 2) + np.power(yes[i] - cluster_yes[j], 2), 0.5) - cluster_radii[j]112			if dist < min_dist:113				for k in range(0, len(clusters[j])):114					dist = np.power(np.power(xes[i] - xes[clusters[j][k]], 2) + np.power(yes[i] - yes[clusters[j][k]], 2), 0.5)115					if dist < min_dist:116						min_dist = dist117# 145.251000166 seconds...

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