All classes:
- numpy._globals._NoValueType
- numpy._globals._CopyMode
- numpy._core.numerictypes._PreprocessDTypeError
- numpy._core._exceptions.UFuncTypeError
- numpy._core._exceptions._UFuncNoLoopError
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- numpy._core._exceptions._UFuncCastingError
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- numpy._core._ufunc_config._unspecified
- numpy._core._ufunc_config.errstate
- numpy.lib._stride_tricks_impl.DummyArray
- numpy.lib.mixins.NDArrayOperatorsMixin
- numpy.lib._utils_impl._Deprecate
- numpy.lib._datasource._FileOpeners
- numpy.lib._datasource.DataSource
- numpy.lib._datasource.Repository
- numpy.lib._iotools.LineSplitter
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- numpy.lib._iotools.StringConverter
- numpy.lib._npyio_impl.BagObj
- numpy.lib._npyio_impl.NpzFile
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- numpy.ma.core.MaskedArrayFutureWarning
- numpy.ma.core.MAError
- numpy.ma.core.MaskError
- numpy.ma.core._DomainCheckInterval
- numpy.ma.core._DomainTan
- numpy.ma.core._DomainSafeDivide
- numpy.ma.core._DomainGreater
- numpy.ma.core._DomainGreaterEqual
- numpy.ma.core._MaskedUFunc
- numpy.ma.core._MaskedUnaryOperation
- numpy.ma.core._MaskedBinaryOperation
- numpy.ma.core._DomainedBinaryOperation
- numpy.ma.core._MaskedPrintOption
- numpy.ma.core.MaskedIterator
- numpy.ma.core.MaskedArray
- numpy.ma.core.mvoid
- numpy.ma.core.MaskedConstant
- numpy.ma.core._extrema_operation
- numpy.ma.core._frommethod
- numpy.ma.core._convert2ma
- numpy.lib._function_base_impl.vectorize
- numpy._typing.NBitBase
- numpy._typing._256Bit
- numpy._typing._128Bit
- numpy._typing._96Bit
- numpy._typing._80Bit
- numpy._typing._64Bit
- numpy._typing._32Bit
- numpy._typing._16Bit
- numpy._typing._8Bit
- numpy._typing._nested_sequence._NestedSequence
- numpy._typing._dtype_like._DTypeDictBase
- numpy._typing._dtype_like._DTypeDict
- numpy._typing._dtype_like._SupportsDType
- numpy._typing._array_like._SupportsArray
- numpy._typing._array_like._SupportsArrayFunc
- numpy.linalg._linalg.EigResult
- numpy.linalg._linalg.EighResult
- numpy.linalg._linalg.QRResult
- numpy.linalg._linalg.SlogdetResult
- numpy.linalg._linalg.SVDResult
- numpy.linalg._linalg.LinAlgError
- numpy._pytesttester.PytestTester
- numpy.distutils.cpuinfo.CPUInfoBase
- numpy.distutils.cpuinfo.LinuxCPUInfo
- numpy.distutils.cpuinfo.IRIXCPUInfo
- numpy.distutils.cpuinfo.DarwinCPUInfo
- numpy.distutils.cpuinfo.SunOSCPUInfo
- numpy.distutils.cpuinfo.Win32CPUInfo
- numpy.distutils.misc_util.InstallableLib
- numpy.distutils.extension.Extension
- numpy.distutils.numpy_distribution.NumpyDistribution
- numpy.distutils.mingw32ccompiler.Mingw32CCompiler
- numpy.distutils.command.config.config
- numpy.distutils._shell_utils.CommandLineParser
- numpy.distutils._shell_utils.WindowsParser
- numpy.distutils._shell_utils.PosixParser
- numpy.distutils.fcompiler.environment.EnvironmentConfig
- numpy.distutils.fcompiler.CompilerNotFound
- numpy.distutils.fcompiler.FCompiler
- numpy.distutils.command.config_compiler.config_fc
- numpy.distutils.command.config_compiler.config_cc
- numpy.distutils.command.config.GrabStdout
- numpy.distutils.command.build.build
- numpy.distutils.command.build_py.build_py
- numpy.distutils.system_info.NotFoundError
- numpy.distutils.system_info.AliasedOptionError
- numpy.distutils.system_info.AtlasNotFoundError
- numpy.distutils.system_info.FlameNotFoundError
- numpy.distutils.system_info.LapackNotFoundError
- numpy.distutils.system_info.LapackSrcNotFoundError
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- numpy.distutils.system_info.BlasOptNotFoundError
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- numpy.distutils.system_info.BlasILP64NotFoundError
- numpy.distutils.system_info.BlasSrcNotFoundError
- numpy.distutils.system_info.FFTWNotFoundError
- numpy.distutils.system_info.DJBFFTNotFoundError
- numpy.distutils.system_info.NumericNotFoundError
- numpy.distutils.system_info.X11NotFoundError
- numpy.distutils.system_info.UmfpackNotFoundError
- numpy.distutils.system_info.system_info
- numpy.distutils.system_info.fft_opt_info
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- numpy.distutils.system_info.fftw2_info
- numpy.distutils.system_info.fftw3_info
- numpy.distutils.system_info.fftw3_armpl_info
- numpy.distutils.system_info.dfftw_info
- numpy.distutils.system_info.sfftw_info
- numpy.distutils.system_info.fftw_threads_info
- numpy.distutils.system_info.dfftw_threads_info
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- numpy.distutils.system_info.blas64__opt_info
- numpy.distutils.system_info.cblas_info
- numpy.distutils.system_info.blas_info
- numpy.distutils.system_info.openblas_info
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- numpy.distutils.system_info.blis_info
- numpy.distutils.system_info.flame_info
- numpy.distutils.system_info.accelerate_info
- numpy.distutils.system_info.accelerate_lapack_info
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- numpy.distutils.system_info.x11_info
- numpy.distutils.system_info._numpy_info
- numpy.distutils.system_info.numarray_info
- numpy.distutils.system_info.Numeric_info
- numpy.distutils.system_info.numpy_info
- numpy.distutils.system_info.numerix_info
- numpy.distutils.system_info.f2py_info
- numpy.exceptions.ComplexWarning
- numpy.exceptions.ModuleDeprecationWarning
- numpy.exceptions.VisibleDeprecationWarning
- numpy.exceptions.RankWarning
- numpy.exceptions.TooHardError
- numpy.exceptions.AxisError
- numpy.exceptions.DTypePromotionError
- numpy.f2py.auxfuncs.F2PYError
- numpy.f2py.auxfuncs.throw_error
- numpy.f2py.symbolic.Language
- numpy.f2py.symbolic.Op
- numpy.f2py.symbolic.RelOp
- numpy.f2py.symbolic.ArithOp
- numpy.f2py.symbolic.OpError
- numpy.f2py.symbolic.Precedence
- numpy.f2py.symbolic.ExprWarning
- numpy.f2py.symbolic.Expr
- numpy.f2py.symbolic._Pair
- numpy.f2py.symbolic._FromStringWorker
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- numpy.f2py._backends._meson.MesonTemplate
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- numpy.f2py._backends._distutils.DistutilsBackend
- numpy.f2py.f2py2e.CombineIncludePaths
- numpy.distutils.system_info.boost_python_info
- numpy.distutils.system_info.agg2_info
- numpy.distutils.system_info._pkg_config_info
- numpy.distutils.system_info.wx_info
- numpy.distutils.system_info.gdk_pixbuf_xlib_2_info
- numpy.distutils.system_info.gdk_pixbuf_2_info
- numpy.distutils.system_info.gdk_x11_2_info
- numpy.distutils.system_info.gdk_2_info
- numpy.distutils.system_info.gdk_info
- numpy.distutils.system_info.gtkp_x11_2_info
- numpy.distutils.system_info.gtkp_2_info
- numpy.distutils.system_info.xft_info
- numpy.distutils.system_info.freetype2_info
- numpy.distutils.system_info.amd_info
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- numpy.distutils.command.build_ext.build_ext
- numpy.distutils.command.build_clib.build_clib
- numpy.distutils.command.build_src.build_src
- numpy.distutils.command.build_scripts.build_scripts
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- numpy.distutils.command.install_data.install_data
- numpy.distutils.command.install_headers.install_headers
- numpy.distutils.command.install.install
- numpy.distutils.command.bdist_rpm.bdist_rpm
- numpy.distutils.command.install_clib.install_clib
- numpy.distutils.command.develop.develop
- numpy.distutils.command.egg_info.egg_info
- numpy.distutils.misc_util.Configuration
- numpy.distutils.npy_pkg_config.FormatError
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- numpy.testing._private.utils.clear_and_catch_warnings
- numpy.testing._private.utils.suppress_warnings
- numpy._core.arrayprint.FloatingFormat
- numpy._core.arrayprint.IntegerFormat
- numpy._core.arrayprint.BoolFormat
- numpy._core.arrayprint.ComplexFloatingFormat
- numpy._core.arrayprint._TimelikeFormat
- numpy._core.arrayprint.DatetimeFormat
- numpy._core.arrayprint.TimedeltaFormat
- numpy._core.arrayprint.SubArrayFormat
- numpy._core.arrayprint.StructuredVoidFormat
- numpy._core.records.format_parser
- numpy._core.records.record
- numpy._core.records.recarray
- numpy.ma.mrecords.MaskedRecords
- numpy.matrixlib.defmatrix.matrix
- numpy.lib._index_tricks_impl.nd_grid
- numpy.lib._index_tricks_impl.MGridClass
- numpy.lib._index_tricks_impl.OGridClass
- numpy.lib._index_tricks_impl.AxisConcatenator
- numpy.lib._index_tricks_impl.RClass
- numpy.lib._index_tricks_impl.CClass
- numpy.lib._index_tricks_impl.ndenumerate
- numpy.lib._index_tricks_impl.ndindex
- numpy.lib._index_tricks_impl.IndexExpression
- numpy.ma.extras._fromnxfunction
- numpy.ma.extras._fromnxfunction_single
- numpy.ma.extras._fromnxfunction_seq
- numpy.ma.extras._fromnxfunction_args
- numpy.ma.extras._fromnxfunction_allargs
- numpy.ma.extras.MAxisConcatenator
- numpy.ma.extras.mr_class
- numpy._core._machar.MachAr
- numpy._core.getlimits.MachArLike
- numpy._core.getlimits.finfo
- numpy._core.getlimits.iinfo
- numpy.lib._arraysetops_impl.UniqueAllResult
- numpy.lib._arraysetops_impl.UniqueCountsResult
- numpy.lib._arraysetops_impl.UniqueInverseResult
- numpy.lib._polynomial_impl.poly1d
- numpy.lib._arrayterator_impl.Arrayterator
- numpy.lib._version.NumpyVersion
- numpy._core.memmap.memmap
- numpy._core._internal.dummy_ctype
- numpy._core._internal._missing_ctypes
- numpy._core._internal._missing_ctypes.c_void_p
- numpy._core._internal._ctypes
- numpy._core._internal._Stream
- numpy.__config__.DisplayModes
- numpy._array_api_info.__array_namespace_info__
- numpy.polynomial._polybase.ABCPolyBase
- numpy.polynomial.polynomial.Polynomial
- numpy.polynomial.chebyshev.Chebyshev
- numpy.polynomial.legendre.Legendre
- numpy.polynomial.hermite.Hermite
- numpy.polynomial.hermite_e.HermiteE
- numpy.polynomial.laguerre.Laguerre
- numpy.ctypeslib._ndptr
- numpy.ctypeslib._concrete_ndptr
- numpy._core.defchararray.chararray
- bottleneck._pytesttester.PytestTester
- numpy.ma.timer_comparison.ModuleTester
- numpy.typing.mypy_plugin._NumpyPlugin
- numpy.distutils.fujitsuccompiler.FujitsuCCompiler
- numpy.distutils.msvccompiler.MSVCCompiler
- numpy.distutils.msvc9compiler.MSVCCompiler
- numpy.distutils.armccompiler.ArmCCompiler
- numpy.distutils.intelccompiler.IntelCCompiler
- numpy.distutils.intelccompiler.IntelItaniumCCompiler
- numpy.distutils.intelccompiler.IntelEM64TCCompiler
- numpy.distutils.intelccompiler.IntelCCompilerW
- numpy.distutils.intelccompiler.IntelEM64TCCompilerW
- numpy.distutils.pathccompiler.PathScaleCCompiler
- numpy.distutils.fcompiler.hpux.HPUXFCompiler
- numpy.distutils.fcompiler.compaq.CompaqFCompiler
- numpy.distutils.fcompiler.compaq.CompaqVisualFCompiler
- numpy.distutils.fcompiler.ibm.IBMFCompiler
- numpy.distutils.fcompiler.gnu.GnuFCompiler
- numpy.distutils.fcompiler.gnu.Gnu95FCompiler
- numpy.distutils.fcompiler.nv.NVHPCFCompiler
- numpy.distutils.fcompiler.intel.BaseIntelFCompiler
- numpy.distutils.fcompiler.intel.IntelFCompiler
- numpy.distutils.fcompiler.intel.IntelItaniumFCompiler
- numpy.distutils.fcompiler.intel.IntelEM64TFCompiler
- numpy.distutils.fcompiler.intel.IntelVisualFCompiler
- numpy.distutils.fcompiler.intel.IntelItaniumVisualFCompiler
- numpy.distutils.fcompiler.intel.IntelEM64VisualFCompiler
- numpy.distutils.fcompiler.arm.ArmFlangCompiler
- numpy.distutils.fcompiler.absoft.AbsoftFCompiler
- numpy.distutils.fcompiler.g95.G95FCompiler
- numpy.distutils.fcompiler.fujitsu.FujitsuFCompiler
- numpy.distutils.fcompiler.mips.MIPSFCompiler
- numpy.distutils.fcompiler.pathf95.PathScaleFCompiler
- numpy.distutils.fcompiler.vast.VastFCompiler
- numpy.distutils.fcompiler.nag.BaseNAGFCompiler
- numpy.distutils.fcompiler.nag.NAGFCompiler
- numpy.distutils.fcompiler.nag.NAGFORCompiler
- numpy.distutils.fcompiler.pg.PGroupFCompiler
- numpy.distutils.fcompiler.pg.PGroupFlangCompiler
- numpy.distutils.fcompiler.none.NoneFCompiler
- numpy.distutils.fcompiler.lahey.LaheyFCompiler
- numpy.distutils.fcompiler.sun.SunFCompiler
- numpy.compat.py3k.contextlib_nullcontext
- numpy._utils._pep440.Infinity
- numpy._utils._pep440.NegativeInfinity
- numpy._utils._pep440.InvalidVersion
- numpy._utils._pep440._BaseVersion
- numpy._utils._pep440.LegacyVersion
- numpy._utils._pep440.Version
- numpy.testing.print_coercion_tables.GenericObject
- numpy.lib._user_array_impl.container
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    IfCount: 0
    argCount: 0
    argDefaultValues: []
    argNames: []
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    functionDepth: 0
    functionLinenumber: -1
    functionName: bottleneck.src
    functionSourceFile: bottleneck.src
    functionUses: 0
    functionsReached: []
    linkageType: pythonLinkage
    raised: []
    returnType: N/A
  Function list name: All functions
Function coverage:
  '...fuzz_bn':
  - 98
  - 99
  - 15
  '...fuzz_bn.TestOneInput':
  - 36
  - 37
  - 38
  - 68
  - 69
  - 71
  - 74
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  - 82
  - 86
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  - 58
  - 59
  - 60
  '...fuzz_bn.gen_random_array':
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  - 27
  - 28
  - 29
  - 30
  - 31
  '...fuzz_bn.main':
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  - 94
  - 95
  bottleneck:
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  - 20
  - 15
  bottleneck._pytesttester:
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  bottleneck._pytesttester.PytestTester:
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  bottleneck._pytesttester.PytestTester.__call__:
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  - 75
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  - 53
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  - 61
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  - 63
  bottleneck._pytesttester._pytest_has_xdist:
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  bottleneck._version.get_versions:
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  bottleneck.benchmark.autotimeit.autoscaler:
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  - 15
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  bottleneck.benchmark.bench.benchsuite:
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  bottleneck.benchmark.bench.getarray:
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  bottleneck.benchmark.bench.timer:
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  bottleneck.benchmark.bench_detailed.bench_detailed:
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  bottleneck.benchmark.bench_detailed.timer:
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  bottleneck.conftest:
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  bottleneck.conftest.pytest_configure:
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  bottleneck.slow.move:
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  bottleneck.slow.move.move_argmax.argmax:
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  bottleneck.slow.move.move_argmin:
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  bottleneck.slow.move.move_argmin.argmin:
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  bottleneck.slow.move.move_func:
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  bottleneck.slow.move.move_max:
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  bottleneck.slow.move.move_mean:
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  bottleneck.slow.move.move_median:
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  bottleneck.slow.move.move_min:
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  bottleneck.slow.move.move_rank:
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  bottleneck.slow.move.move_std:
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  bottleneck.slow.move.move_var:
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  bottleneck.slow.nonreduce.replace:
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  bottleneck.slow.nonreduce_axis._rank:
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  bottleneck.slow.nonreduce_axis.nanrankdata:
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  bottleneck.slow.nonreduce_axis.rankdata:
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  bottleneck.slow.nonreduce_axis.scipy_rankdata:
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  bottleneck.slow.reduce.ss:
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  bottleneck.src.bn_config:
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  bottleneck.src.bn_template:
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  numpy.f2py.crackfortran._calc_depend_dict:
  - 2400
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  numpy.f2py.crackfortran._ensure_exprdict:
  - 3208
  - 3202
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  numpy.f2py.crackfortran._eval_length:
  - 2570
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  numpy.f2py.crackfortran._eval_scalar:
  - 2579
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  - 2583
  - 2584
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  numpy.f2py.crackfortran._get_depend_dict:
  - 2375
  - 2376
  - 2378
  - 2379
  - 2382
  - 2383
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  - 2385
  - 2386
  - 2387
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  numpy.f2py.crackfortran._is_intent_callback:
  - 941
  - 942
  numpy.f2py.crackfortran._is_visit_pair:
  - 3548
  - 3549
  - 3550
  numpy.f2py.crackfortran._kind_func:
  - 2424
  - 2426
  - 2428
  numpy.f2py.crackfortran._resolvenameargspattern:
  - 968
  - 969
  - 970
  - 971
  - 972
  - 973
  - 974
  - 975
  - 977
  - 978
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  numpy.f2py.crackfortran._resolvetypedefpattern:
  - 948
  - 949
  - 950
  - 951
  - 952
  - 953
  - 954
  numpy.f2py.crackfortran._selected_int_kind_func:
  - 2436
  - 2438
  - 2440
  - 2442
  - 2444
  numpy.f2py.crackfortran._selected_real_kind_func:
  - 2452
  - 2454
  - 2456
  - 2457
  - 2458
  - 2461
  - 2463
  numpy.f2py.crackfortran._simplifyargs:
  - 678
  - 679
  - 680
  - 681
  - 682
  numpy.f2py.crackfortran.analyzeargs:
  - 3168
  - 3169
  - 3170
  - 3173
  - 3174
  - 3175
  - 3177
  - 3178
  - 3179
  - 3180
  - 3183
  - 3184
  - 3185
  - 3187
  - 3188
  - 3189
  numpy.f2py.crackfortran.analyzebody:
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  - 2245
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  - 2249
  - 2251
  - 2256
  - 2257
  - 2258
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  - 2228
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  numpy.f2py.crackfortran.analyzecommon:
  - 2183
  - 2186
  - 2188
  - 2189
  - 2191
  - 2193
  - 2194
  - 2195
  - 2196
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  - 2200
  - 2203
  - 2205
  - 2207
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  - 2214
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  numpy.f2py.crackfortran.analyzeline:
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  - 1028
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  - 1019
  - 1020
  - 1533
  - 1022
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  numpy.f2py.crackfortran.analyzevars:
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  - 2602
  - 2603
  - 2604
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  - 2715
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  - 2747
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  - 2769
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  - 2975
  - 2977
  - 2978
  - 2979
  - 2980
  numpy.f2py.crackfortran.analyzevars.compute_deps:
  - 2816
  - 2813
  - 2814
  - 2815
  numpy.f2py.crackfortran.appenddecl:
  - 896
  - 897
  - 898
  - 900
  - 901
  - 902
  - 903
  - 904
  - 905
  - 906
  - 907
  - 908
  - 910
  - 912
  - 914
  - 890
  - 892
  - 894
  numpy.f2py.crackfortran.appendmultiline:
  - 1584
  - 1579
  - 1582
  numpy.f2py.crackfortran.buildimplicitrules:
  - 2274
  - 2275
  - 2277
  - 2278
  - 2279
  - 2281
  - 2282
  - 2271
  numpy.f2py.crackfortran.character_backward_compatibility_hook:
  - 3653
  - 3621
  - 3655
  - 3656
  - 3657
  - 3658
  - 3661
  - 3662
  - 3665
  - 3668
  - 3669
  - 3674
  - 3643
  - 3646
  numpy.f2py.crackfortran.character_backward_compatibility_hook.fix_usage:
  - 3638
  - 3639
  numpy.f2py.crackfortran.common2fortran:
  - 3336
  - 3337
  - 3339
  - 3335
  numpy.f2py.crackfortran.crack2fortran:
  - 3535
  numpy.f2py.crackfortran.crack2fortrangen:
  - 3261
  - 3263
  - 3264
  - 3265
  - 3266
  - 3268
  - 3270
  - 3276
  - 3279
  - 3281
  - 3283
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  - 3285
  - 3286
  - 3287
  - 3288
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  - 3300
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  - 3310
  - 3312
  - 3313
  - 3314
  - 3315
  - 3318
  - 3320
  - 3322
  - 3324
  - 3326
  numpy.f2py.crackfortran.crackfortran:
  - 3520
  - 3522
  - 3523
  - 3524
  - 3525
  - 3526
  - 3527
  - 3528
  - 3518
  - 3519
  numpy.f2py.crackfortran.crackline:
  - 769
  - 771
  - 773
  - 774
  - 775
  - 777
  - 779
  - 780
  - 781
  - 782
  - 783
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  - 816
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  - 822
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  - 700
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  - 704
  - 831
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  - 833
  - 708
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  - 710
  - 834
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  - 835
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  - 844
  - 724
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  - 729
  - 730
  - 731
  - 733
  - 737
  - 738
  - 742
  - 748
  - 751
  - 765
  - 766
  numpy.f2py.crackfortran.cracktypespec:
  - 1852
  - 1853
  - 1854
  - 1855
  - 1856
  - 1857
  - 1859
  - 1862
  - 1863
  - 1865
  - 1866
  - 1867
  - 1868
  - 1869
  - 1870
  - 1871
  - 1873
  - 1876
  - 1877
  - 1878
  - 1879
  - 1880
  - 1881
  - 1885
  - 1889
  - 1890
  - 1892
  - 1893
  - 1894
  - 1895
  - 1896
  - 1897
  - 1899
  - 1900
  - 1902
  - 1903
  numpy.f2py.crackfortran.cracktypespec0:
  - 1602
  - 1603
  - 1604
  - 1605
  - 1608
  - 1609
  - 1610
  - 1591
  - 1593
  - 1596
  - 1597
  - 1598
  - 1599
  numpy.f2py.crackfortran.determineexprtype:
  - 3214
  - 3215
  - 3216
  - 3217
  - 3219
  - 3220
  - 3221
  - 3222
  - 3223
  - 3225
  - 3226
  - 3227
  - 3228
  - 3229
  - 3231
  - 3232
  - 3233
  - 3234
  - 3236
  - 3237
  - 3239
  - 3240
  - 3241
  - 3242
  - 3243
  - 3245
  - 3246
  - 3247
  - 3248
  - 3250
  - 3251
  - 3252
  numpy.f2py.crackfortran.expr2name:
  - 3137
  - 3138
  - 3139
  - 3142
  - 3149
  - 3151
  - 3153
  - 3154
  - 3157
  - 3158
  - 3162
  - 3163
  - 3132
  - 3133
  - 3134
  - 3135
  numpy.f2py.crackfortran.get_parameters:
  - 2560
  - 2561
  - 2564
  - 2565
  - 2469
  - 2470
  - 2471
  - 2474
  - 2477
  - 2478
  - 2479
  - 2480
  - 2481
  - 2483
  - 2485
  - 2486
  - 2488
  - 2489
  - 2490
  - 2495
  - 2497
  - 2498
  - 2506
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  - 2510
  - 2511
  - 2514
  - 2516
  - 2517
  - 2518
  - 2520
  - 2521
  - 2531
  - 2532
  - 2533
  - 2534
  - 2535
  - 2536
  - 2538
  - 2539
  - 2542
  - 2544
  - 2548
  - 2550
  - 2553
  - 2556
  - 2558
  - 2559
  numpy.f2py.crackfortran.get_sorted_names:
  - 2405
  - 2407
  - 2408
  - 2409
  - 2412
  - 2414
  - 2415
  numpy.f2py.crackfortran.get_usedict:
  - 1984
  - 1985
  - 1986
  - 1983
  numpy.f2py.crackfortran.get_useparameters:
  - 1993
  - 1995
  - 1996
  - 1998
  - 1999
  - 2000
  - 2001
  - 2002
  - 2005
  - 2006
  - 2009
  - 2010
  - 2011
  - 2012
  - 2013
  - 2014
  numpy.f2py.crackfortran.getblockname:
  - 1965
  numpy.f2py.crackfortran.getlincoef:
  - 2321
  - 2325
  - 2327
  - 2328
  - 2329
  - 2331
  - 2334
  - 2335
  - 2336
  - 2338
  - 2341
  - 2342
  - 2343
  - 2344
  - 2347
  - 2348
  - 2349
  - 2350
  - 2353
  - 2354
  - 2355
  - 2357
  - 2360
  - 2361
  - 2362
  - 2363
  - 2300
  numpy.f2py.crackfortran.is_free_format:
  - 353
  - 333
  - 337
  - 339
  - 340
  - 342
  - 344
  - 348
  - 350
  numpy.f2py.crackfortran.markinnerspaces:
  - 1667
  - 1669
  - 1671
  - 1643
  - 1662
  - 1663
  numpy.f2py.crackfortran.markoutercomma:
  - 868
  - 871
  - 875
  - 877
  - 879
  - 881
  numpy.f2py.crackfortran.markouterparen:
  - 850
  - 851
  - 853
  - 856
  - 858
  numpy.f2py.crackfortran.myeval:
  - 2290
  - 2291
  - 2292
  numpy.f2py.crackfortran.openhook:
  - 320
  - 322
  - 324
  - 329
  - 302
  - 312
  - 313
  - 317
  - 318
  - 319
  numpy.f2py.crackfortran.outmess:
  - 239
  - 241
  - 242
  - 243
  - 244
  numpy.f2py.crackfortran.param_eval:
  - 2989
  - 3007
  - 3009
  - 3012
  - 3017
  - 3019
  - 3020
  - 3022
  - 3024
  - 3025
  - 3026
  - 3028
  - 3029
  - 3030
  - 3036
  - 3038
  - 3040
  - 3042
  - 3043
  - 3045
  numpy.f2py.crackfortran.param_parse:
  - 3107
  - 3109
  - 3110
  - 3113
  - 3114
  - 3051
  - 3115
  - 3116
  - 3118
  - 3119
  - 3122
  - 3124
  - 3125
  - 3126
  numpy.f2py.crackfortran.parse_name_for_bind:
  - 961
  - 962
  - 964
  - 958
  - 959
  numpy.f2py.crackfortran.postcrack:
  - 2053
  - 2060
  - 2063
  - 2064
  - 2065
  - 2067
  - 2068
  - 2070
  - 2072
  - 2073
  - 2076
  - 2077
  - 2078
  - 2079
  - 2080
  - 2081
  - 2082
  - 2084
  - 2087
  - 2089
  - 2090
  - 2091
  - 2095
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  - 2100
  - 2102
  - 2103
  - 2107
  - 2114
  - 2115
  - 2118
  - 2120
  - 2122
  - 2124
  - 2125
  - 2128
  - 2129
  - 2131
  - 2133
  - 2135
  - 2137
  - 2142
  - 2143
  numpy.f2py.crackfortran.postcrack2:
  - 2023
  - 2025
  - 2026
  - 2029
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  numpy.lib._function_base_impl.vectorize.__call__:
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  numpy.lib._npyio_impl.genfromtxt.tobytes_first:
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  numpy.lib._npyio_impl.loadtxt:
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  numpy.lib._npyio_impl.recfromtxt:
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  numpy.lib._npyio_impl.savetxt.WriteWrap.first_write:
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  - 970
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  - 907
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  - 775
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  - 1345
  - 1858
  - 2624
  - 2377
  - 2893
  - 3277
  - 3535
  - 1240
  - 859
  - 1502
  - 746
  - 237
  - 2286
  - 623
  - 499
  - 2163
  - 1141
  - 2037
  - 3446
  - 1912
  - 1659
  numpy.linalg._linalg.LinAlgError:
  - 75
  numpy.linalg._linalg._assert_2d:
  - 187
  - 188
  numpy.linalg._linalg._assert_finite:
  - 205
  - 206
  numpy.linalg._linalg._assert_stacked_2d:
  - 193
  - 194
  numpy.linalg._linalg._assert_stacked_square:
  - 201
  - 199
  numpy.linalg._linalg._commonType:
  - 160
  - 166
  - 152
  - 154
  - 155
  - 157
  - 158
  numpy.linalg._linalg._complexType:
  - 146
  numpy.linalg._linalg._convertarray:
  - 1337
  - 1338
  numpy.linalg._linalg._is_empty_2d:
  - 211
  numpy.linalg._linalg._makearray:
  - 124
  - 125
  numpy.linalg._linalg._multi_dot:
  - 3086
  - 3085
  - 3078
  - 3079
  numpy.linalg._linalg._multi_dot_matrix_chain_order:
  - 3053
  - 3059
  - 3062
  - 3064
  - 3065
  - 3068
  - 3037
  - 3070
  numpy.linalg._linalg._multi_dot_three:
  - 3016
  - 3033
  - 3030
  - 3031
  numpy.linalg._linalg._multi_svd_norm:
  - 2616
  - 2593
  - 2615
  numpy.linalg._linalg._multidot_dispatcher:
  - 2889
  - 2890
  numpy.linalg._linalg._realType:
  - 143
  numpy.linalg._linalg._to_native_byte_order:
  - 175
  - 176
  - 177
  - 179
  - 180
  numpy.linalg._linalg.cholesky:
  - 841
  - 842
  - 843
  - 748
  - 844
  - 845
  - 846
  - 848
  - 849
  numpy.linalg._linalg.cond:
  - 1994
  - 1995
  - 1997
  - 1998
  - 1999
  - 2000
  - 2007
  - 2008
  - 2009
  - 2010
  - 2011
  - 2012
  - 2013
  - 2014
  - 2017
  - 2018
  - 2019
  - 2020
  - 2021
  - 2023
  - 2027
  - 1914
  numpy.linalg._linalg.cross:
  - 3337
  - 3338
  - 3340
  - 3279
  - 3347
  numpy.linalg._linalg.det:
  - 2432
  - 2433
  - 2379
  - 2427
  - 2428
  - 2429
  - 2430
  - 2431
  numpy.linalg._linalg.diagonal:
  - 3098
  - 3183
  numpy.linalg._linalg.eig:
  - 1347
  - 1479
  - 1480
  - 1481
  - 1482
  - 1483
  - 1485
  - 1486
  - 1489
  - 1491
  - 1494
  - 1496
  - 1498
  - 1499
  numpy.linalg._linalg.eigh:
  - 1504
  - 1633
  - 1634
  - 1635
  - 1636
  - 1638
  - 1643
  - 1644
  - 1647
  - 1648
  - 1649
  - 1650
  - 1629
  - 1630
  numpy.linalg._linalg.eigvals:
  - 1216
  - 1217
  - 1218
  - 1220
  - 1221
  - 1224
  - 1226
  - 1227
  - 1229
  - 1231
  - 1233
  - 1143
  - 1214
  - 1215
  numpy.linalg._linalg.eigvalsh:
  - 1316
  - 1317
  - 1320
  - 1325
  - 1326
  - 1327
  - 1328
  - 1329
  - 1330
  - 1333
  - 1334
  - 1242
  numpy.linalg._linalg.inv:
  - 608
  - 609
  - 610
  - 612
  - 613
  - 615
  - 616
  - 501
  - 607
  numpy.linalg._linalg.isComplexType:
  - 129
  numpy.linalg._linalg.lstsq:
  - 2561
  - 2563
  - 2565
  - 2567
  - 2569
  - 2445
  - 2575
  - 2576
  - 2581
  - 2582
  - 2585
  - 2586
  - 2588
  - 2589
  - 2540
  - 2541
  - 2543
  - 2545
  - 2548
  - 2551
  - 2552
  - 2554
  - 2555
  - 2557
  - 2558
  numpy.linalg._linalg.matmul:
  - 3437
  - 3358
  numpy.linalg._linalg.matrix_norm:
  - 3474
  - 3526
  - 3527
  numpy.linalg._linalg.matrix_power:
  - 689
  - 690
  - 691
  - 694
  - 700
  - 702
  - 708
  - 709
  - 710
  - 713
  - 714
  - 715
  - 718
  - 721
  - 722
  - 724
  - 725
  - 732
  - 733
  - 734
  - 735
  - 625
  numpy.linalg._linalg.matrix_rank:
  - 2145
  - 2146
  - 2147
  - 2149
  - 2150
  - 2152
  - 2154
  - 2039
  - 2137
  - 2140
  - 2141
  - 2142
  - 2143
  numpy.linalg._linalg.matrix_transpose:
  - 3461
  numpy.linalg._linalg.multi_dot:
  - 3009
  - 3010
  - 2980
  - 2982
  - 2984
  - 2985
  - 2987
  - 2895
  - 2993
  - 2994
  - 2995
  - 2996
  - 2997
  - 3000
  - 3001
  - 3003
  - 3004
  - 3007
  numpy.linalg._linalg.norm:
  - 2816
  - 2817
  - 2818
  - 2819
  - 2820
  - 2823
  - 2827
  - 2829
  - 2830
  - 2832
  - 2833
  - 2836
  - 2839
  - 2841
  - 2842
  - 2844
  - 2846
  - 2847
  - 2848
  - 2850
  - 2851
  - 2852
  - 2853
  - 2854
  - 2855
  - 2857
  - 2858
  - 2859
  - 2861
  - 2862
  - 2863
  - 2865
  - 2866
  - 2867
  - 2869
  - 2870
  - 2871
  - 2872
  - 2873
  - 2876
  - 2877
  - 2880
  - 2626
  - 2777
  - 2779
  - 2780
  - 2783
  - 2785
  - 2790
  - 2791
  - 2794
  - 2796
  - 2797
  - 2798
  - 2799
  - 2804
  - 2805
  - 2806
  - 2808
  - 2815
  numpy.linalg._linalg.outer:
  - 920
  - 921
  - 922
  - 861
  - 927
  numpy.linalg._linalg.pinv:
  - 2274
  - 2276
  - 2279
  - 2280
  - 2251
  - 2252
  - 2253
  - 2255
  - 2256
  - 2259
  - 2165
  - 2265
  - 2266
  - 2268
  - 2269
  - 2270
  - 2271
  numpy.linalg._linalg.qr:
  - 939
  - 1071
  - 1072
  - 1074
  - 1077
  - 1079
  - 1082
  - 1087
  - 1088
  - 1090
  - 1091
  - 1092
  - 1093
  - 1095
  - 1096
  - 1098
  - 1101
  - 1102
  - 1103
  - 1104
  - 1106
  - 1107
  - 1108
  - 1109
  - 1110
  - 1112
  - 1113
  - 1114
  - 1120
  - 1127
  - 1128
  - 1130
  - 1131
  - 1133
  - 1134
  - 1136
  numpy.linalg._linalg.slogdet:
  - 2368
  - 2369
  - 2370
  - 2371
  - 2372
  - 2373
  - 2374
  - 2288
  - 2365
  - 2366
  - 2367
  numpy.linalg._linalg.solve:
  - 322
  - 397
  - 398
  - 399
  - 400
  - 401
  - 405
  - 410
  - 411
  - 413
  - 415
  numpy.linalg._linalg.svd:
  - 1803
  - 1805
  - 1809
  - 1810
  - 1811
  - 1812
  - 1813
  - 1814
  - 1815
  - 1816
  - 1818
  - 1819
  - 1821
  - 1822
  - 1823
  - 1825
  - 1826
  - 1829
  - 1830
  - 1835
  - 1836
  - 1839
  - 1840
  - 1841
  - 1842
  - 1843
  - 1845
  - 1846
  - 1849
  - 1850
  - 1661
  numpy.linalg._linalg.svdvals:
  - 1905
  - 1860
  numpy.linalg._linalg.tensordot:
  - 3448
  numpy.linalg._linalg.tensorinv:
  - 482
  - 484
  - 424
  - 488
  - 489
  - 490
  - 479
  numpy.linalg._linalg.tensorsolve:
  - 288
  - 291
  - 292
  - 293
  - 294
  - 295
  - 296
  - 300
  - 239
  - 303
  - 309
  - 310
  - 311
  - 287
  numpy.linalg._linalg.trace:
  - 3194
  - 3268
  numpy.linalg._linalg.transpose:
  - 229
  - 215
  numpy.linalg._linalg.vecdot:
  - 3681
  - 3637
  numpy.linalg._linalg.vector_norm:
  - 3617
  - 3620
  - 3621
  - 3623
  - 3593
  - 3594
  - 3595
  - 3625
  - 3597
  - 3599
  - 3537
  - 3602
  - 3603
  - 3605
  - 3607
  - 3615
  numpy.linalg.linalg.__getattr__:
  - 8
  - 4
  - 5
  numpy.ma:
  - 1
  - 53
  numpy.ma.core:
  - 1
  - 7183
  - 7184
  - 8237
  - 8255
  - 8783
  - 80
  - 8785
  - 8789
  - 8791
  - 8795
  - 8797
  - 8801
  - 8806
  - 8811
  - 8816
  - 8818
  - 8822
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  - 7069
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  - 7071
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  - 7073
  - 7074
  - 7075
  - 7076
  - 7077
  - 7078
  - 7079
  - 7080
  - 7081
  - 7082
  - 2475
  - 7083
  - 7084
  - 7085
  - 7086
  - 7087
  - 7088
  - 7089
  - 7090
  - 7091
  - 7093
  - 7094
  - 7096
  - 2496
  - 2497
  - 2504
  - 2512
  - 2517
  numpy.ma.core.MAError:
  - 147
  numpy.ma.core.MaskError:
  - 155
  numpy.ma.core.MaskedArray:
  - 6240
  - 6241
  - 6242
  - 6243
  - 6244
  - 2757
  - 6245
  - 6246
  - 3407
  - 3773
  - 3774
  - 6239
  numpy.ma.core.MaskedArray.<lambda1>:
  - 6245
  numpy.ma.core.MaskedArray.__add__:
  - 4306
  - 4308
  - 4302
  numpy.ma.core.MaskedArray.__array_finalize__:
  - 3040
  - 3073
  - 3107
  - 3076
  - 3077
  - 3045
  - 3079
  - 3110
  - 3130
  - 3116
  - 3088
  - 3127
  - 3126
  - 3095
  - 3128
  - 3098
  - 3100
  - 3102
  numpy.ma.core.MaskedArray.__array_wrap__:
  - 3170
  - 3139
  - 3173
  - 3142
  - 3143
  - 3145
  - 3146
  - 3180
  - 3150
  - 3152
  - 3153
  - 3155
  - 3156
  - 3158
  - 3133
  numpy.ma.core.MaskedArray.__deepcopy__:
  - 6504
  - 6505
  - 6507
  - 6508
  - 6509
  - 6514
  - 6515
  numpy.ma.core.MaskedArray.__div__:
  - 4355
  - 4357
  - 4351
  numpy.ma.core.MaskedArray.__eq__:
  - 4273
  - 4263
  numpy.ma.core.MaskedArray.__float__:
  - 4545
  - 4546
  - 4548
  - 4538
  - 4542
  numpy.ma.core.MaskedArray.__floordiv__:
  - 4376
  - 4380
  - 4382
  numpy.ma.core.MaskedArray.__ge__:
  - 4296
  numpy.ma.core.MaskedArray.__getitem__:
  - 3328
  - 3332
  - 3335
  - 3337
  - 3341
  - 3344
  - 3345
  - 3348
  - 3349
  - 3355
  - 3361
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  - 3365
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  - 3371
  - 3381
  - 3382
  - 3384
  - 3394
  - 3397
  - 3399
  - 3279
  - 3318
  - 3322
  - 3327
  numpy.ma.core.MaskedArray.__getitem__._is_scalar:
  - 3293
  numpy.ma.core.MaskedArray.__getitem__._scalar_heuristic:
  - 3296
  - 3301
  - 3305
  - 3306
  - 3313
  numpy.ma.core.MaskedArray.__getstate__:
  - 6474
  - 6475
  - 6469
  numpy.ma.core.MaskedArray.__gt__:
  - 4299
  numpy.ma.core.MaskedArray.__iadd__:
  - 4418
  - 4420
  - 4421
  - 4422
  - 4408
  - 4412
  - 4413
  - 4414
  - 4415
  numpy.ma.core.MaskedArray.__idiv__:
  - 4460
  - 4464
  - 4465
  - 4466
  - 4467
  - 4469
  - 4471
  - 4472
  - 4474
  - 4475
  numpy.ma.core.MaskedArray.__ifloordiv__:
  - 4483
  - 4484
  - 4485
  - 4486
  - 4488
  - 4490
  - 4491
  - 4493
  - 4494
  - 4479
  numpy.ma.core.MaskedArray.__imul__:
  - 4448
  - 4449
  - 4450
  - 4452
  - 4454
  - 4455
  - 4456
  - 4443
  - 4447
  numpy.ma.core.MaskedArray.__int__:
  - 4560
  - 4555
  - 4558
  - 4551
  numpy.ma.core.MaskedArray.__ipow__:
  - 4517
  - 4521
  - 4522
  - 4523
  - 4524
  - 4525
  - 4526
  - 4527
  - 4528
  - 4532
  - 4533
  - 4534
  numpy.ma.core.MaskedArray.__isub__:
  - 4426
  - 4430
  - 4431
  - 4432
  - 4433
  - 4435
  - 4437
  - 4438
  - 4439
  numpy.ma.core.MaskedArray.__itruediv__:
  - 4512
  - 4513
  - 4498
  - 4502
  - 4503
  - 4504
  - 4505
  - 4507
  - 4509
  - 4510
  numpy.ma.core.MaskedArray.__le__:
  - 4290
  numpy.ma.core.MaskedArray.__lt__:
  - 4293
  numpy.ma.core.MaskedArray.__mul__:
  - 4336
  - 4337
  - 4339
  numpy.ma.core.MaskedArray.__ne__:
  - 4276
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  numpy.ma.core.MaskedArray.__new__:
  - 2947
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  - 2891
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  - 2904
  - 2907
  - 2909
  - 2913
  - 2915
  - 2918
  - 2919
  - 2925
  - 2930
  - 2931
  - 2933
  - 2939
  numpy.ma.core.MaskedArray.__new__._recursive_or:
  - 2985
  - 2986
  - 2988
  - 2989
  numpy.ma.core.MaskedArray.__pow__:
  - 4392
  - 4396
  - 4398
  numpy.ma.core.MaskedArray.__radd__:
  - 4317
  - 4311
  numpy.ma.core.MaskedArray.__reduce__:
  - 6500
  - 6495
  numpy.ma.core.MaskedArray.__repr__:
  - 4097
  - 4098
  - 4107
  - 4108
  - 4114
  - 4115
  - 4118
  - 4122
  - 4127
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  - 4076
  - 4080
  - 4087
  - 4089
  - 4091
  - 4092
  - 4093
  - 4094
  - 4095
  numpy.ma.core.MaskedArray.__rfloordiv__:
  - 4385
  - 4389
  numpy.ma.core.MaskedArray.__rmul__:
  - 4348
  - 4342
  numpy.ma.core.MaskedArray.__rpow__:
  - 4401
  - 4405
  numpy.ma.core.MaskedArray.__rsub__:
  - 4329
  - 4333
  numpy.ma.core.MaskedArray.__rtruediv__:
  - 4369
  - 4373
  numpy.ma.core.MaskedArray.__setitem__:
  - 3460
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  - 3464
  - 3467
  - 3469
  - 3470
  - 3471
  - 3409
  - 3416
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  - 3422
  - 3423
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  - 3429
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  - 3432
  - 3434
  - 3435
  - 3441
  - 3443
  - 3444
  - 3445
  - 3446
  - 3449
  - 3450
  - 3452
  - 3454
  - 3455
  numpy.ma.core.MaskedArray.__setmask__:
  - 3507
  - 3513
  - 3516
  - 3519
  - 3521
  - 3523
  - 3526
  - 3531
  - 3539
  - 3541
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  - 3544
  - 3548
  - 3554
  - 3557
  - 3560
  - 3561
  - 3566
  - 3572
  numpy.ma.core.MaskedArray.__setstate__:
  - 6490
  - 6491
  - 6478
  numpy.ma.core.MaskedArray.__str__:
  - 4073
  numpy.ma.core.MaskedArray.__sub__:
  - 4320
  - 4324
  - 4326
  numpy.ma.core.MaskedArray.__truediv__:
  - 4360
  - 4364
  - 4366
  numpy.ma.core.MaskedArray._comparison:
  - 4229
  - 4231
  - 4234
  - 4235
  - 4240
  - 4242
  - 4245
  - 4247
  - 4248
  - 4253
  - 4255
  - 4257
  - 4189
  - 4199
  - 4201
  - 4203
  - 4204
  - 4207
  - 4214
  - 4215
  - 4217
  - 4220
  - 4222
  - 4223
  numpy.ma.core.MaskedArray._delegate_binop:
  - 4177
  - 4179
  - 4180
  - 4181
  numpy.ma.core.MaskedArray._get_data:
  - 3771
  - 3757
  numpy.ma.core.MaskedArray._insert_masked_print:
  - 4065
  - 4066
  - 4067
  - 4069
  - 4042
  - 4046
  - 4048
  - 4057
  - 4058
  - 4060
  - 4061
  - 4062
  - 4063
  numpy.ma.core.MaskedArray._update_from:
  - 3014
  - 3018
  - 3019
  - 3024
  - 3025
  - 3026
  - 3027
  - 3028
  - 3029
  - 3030
  - 3031
  - 3032
  - 3035
  - 3036
  numpy.ma.core.MaskedArray.all:
  - 5033
  - 5034
  - 5035
  - 5036
  - 5037
  - 5038
  - 5007
  - 5041
  - 5042
  - 5043
  - 5044
  numpy.ma.core.MaskedArray.anom:
  - 5496
  - 5465
  - 5497
  - 5500
  numpy.ma.core.MaskedArray.any:
  - 5063
  - 5064
  - 5065
  - 5066
  - 5067
  - 5068
  - 5071
  - 5072
  - 5073
  - 5074
  - 5048
  numpy.ma.core.MaskedArray.argmax:
  - 5776
  - 5809
  - 5810
  - 5811
  - 5808
  - 5812
  numpy.ma.core.MaskedArray.argmin:
  - 5728
  - 5768
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  numpy.ma.core.is_string_or_list_of_strings:
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  numpy.ma.core.left_shift:
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  - 7437
  - 7438
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  numpy.ma.core.make_mask:
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  - 1673
  - 1677
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  - 1680
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  numpy.ma.core.make_mask_descr:
  - 1394
  - 1364
  numpy.ma.core.make_mask_none:
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  - 1729
  - 1731
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  numpy.ma.core.mask_or:
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  - 1794
  - 1796
  - 1797
  - 1799
  - 1746
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  - 1785
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  numpy.ma.core.masked_greater:
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  numpy.ma.core.masked_greater_equal:
  - 2045
  - 2022
  numpy.ma.core.masked_inside:
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  - 2163
  - 2196
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  numpy.ma.core.masked_invalid:
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  - 2415
  - 2418
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  - 2387
  numpy.ma.core.masked_less:
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  - 2049
  numpy.ma.core.masked_less_equal:
  - 2099
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  numpy.ma.core.masked_not_equal:
  - 2126
  - 2103
  numpy.ma.core.masked_object:
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  - 2307
  - 2309
  - 2245
  - 2310
  - 2303
  numpy.ma.core.masked_outside:
  - 2241
  - 2204
  - 2237
  - 2239
  numpy.ma.core.masked_values:
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  - 2376
  - 2377
  - 2314
  - 2379
  - 2380
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  numpy.ma.core.masked_where:
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  - 1986
  - 1989
  - 1990
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  - 1972
  - 1973
  - 1976
  - 1979
  - 1980
  - 1981
  numpy.ma.core.max:
  - 7002
  - 7006
  numpy.ma.core.maximum_fill_value:
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  - 375
  numpy.ma.core.min:
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  - 6990
  numpy.ma.core.minimum_fill_value:
  - 371
  - 323
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  - 6530
  numpy.ma.core.mvoid.__getitem__:
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  numpy.ma.core.mvoid.__iter__:
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  - 6606
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  numpy.ma.core.mvoid.__len__:
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  numpy.ma.core.mvoid.__new__:
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  - 6538
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  - 6546
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  numpy.ma.core.mvoid.__setitem__:
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  numpy.ma.core.mvoid.filled:
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  numpy.ma.core.mvoid.tolist:
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  - 6658
  - 6661
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  - 6642
  - 6653
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  numpy.ma.core.outer:
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  - 8245
  - 8246
  - 8247
  - 8248
  - 8249
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  - 8251
  - 8252
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  - 7170
  - 7171
  - 7173
  - 7175
  - 7176
  - 7178
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  - 7153
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  - 7159
  - 7160
  - 7164
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  numpy.ma.core.ptp:
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  - 7014
  numpy.ma.core.put:
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  - 7517
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  numpy.ma.core.putmask:
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  - 7560
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  - 7566
  - 7567
  - 7568
  - 7569
  - 7572
  - 7573
  - 7574
  - 7575
  numpy.ma.core.reshape:
  - 7664
  - 7665
  - 7619
  - 7662
  numpy.ma.core.resize:
  - 7724
  - 7725
  - 7726
  - 7727
  - 7728
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  numpy.ma.core.right_shift:
  - 7472
  - 7473
  - 7474
  - 7475
  - 7445
  - 7477
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  - 8112
  - 8114
  - 8115
  - 8116
  numpy.ma.core.set_fill_value:
  - 563
  - 564
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  numpy.ma.core.shape:
  - 7744
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  numpy.ma.core.size:
  - 7750
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  numpy.ma.core.sort:
  - 7232
  - 7233
  - 7234
  - 7204
  - 7237
  - 7238
  - 7241
  numpy.ma.core.take:
  - 7099
  - 7101
  - 7102
  numpy.ma.core.transpose:
  - 7580
  - 7613
  - 7615
  numpy.ma.core.where:
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  - 7954
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  - 7974
  - 7975
  - 7976
  - 7978
  - 7979
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  - 7983
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  - 2344
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  - 364
  - 366
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  - 499
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  - 500
  numpy.ma.extras.MAxisConcatenator:
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  numpy.ma.extras.MAxisConcatenator.__getitem__:
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  numpy.ma.extras.MAxisConcatenator.makemat:
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  numpy.ma.extras._covhelper:
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  - 1577
  - 1579
  - 1582
  - 1585
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  - 1588
  - 1590
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  - 1604
  - 1606
  - 1607
  - 1609
  - 1610
  - 1614
  - 1618
  - 1622
  - 1625
  numpy.ma.extras._ezclump:
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  - 2189
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  - 2193
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  - 2195
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  - 2201
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  numpy.ma.extras._fromnxfunction:
  - 232
  numpy.ma.extras._fromnxfunction.__init__:
  - 253
  numpy.ma.extras._fromnxfunction.getdoc:
  - 256
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  - 269
  - 270
  - 271
  - 272
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  numpy.ma.extras._fromnxfunction_allargs:
  - 340
  numpy.ma.extras._fromnxfunction_allargs.__call__:
  - 353
  - 354
  - 355
  - 356
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  - 351
  numpy.ma.extras._fromnxfunction_args:
  - 315
  numpy.ma.extras._fromnxfunction_args.__call__:
  - 324
  - 326
  - 327
  - 328
  - 330
  - 331
  - 332
  - 333
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  numpy.ma.extras._fromnxfunction_seq:
  - 302
  numpy.ma.extras._fromnxfunction_seq.__call__:
  - 308
  - 309
  - 310
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  numpy.ma.extras._fromnxfunction_single:
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  numpy.ma.extras._fromnxfunction_single.__call__:
  - 290
  - 291
  - 292
  - 293
  - 294
  - 296
  - 297
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  numpy.ma.extras._median:
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  - 803
  - 804
  - 805
  - 806
  - 808
  - 811
  - 813
  - 816
  - 818
  - 821
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  - 823
  - 824
  - 826
  - 827
  - 829
  - 831
  - 832
  - 833
  - 834
  - 836
  - 841
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  - 845
  - 850
  - 852
  - 855
  - 867
  - 869
  - 871
  - 872
  - 874
  - 876
  numpy.ma.extras._median.replace_masked:
  - 864
  - 862
  - 863
  numpy.ma.extras.apply_along_axis:
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  - 394
  - 396
  - 398
  - 399
  - 400
  - 401
  - 402
  - 403
  - 404
  - 406
  - 407
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  - 430
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  - 457
  - 458
  - 459
  - 460
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  - 463
  - 464
  - 465
  - 467
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  numpy.ma.extras.apply_over_axes:
  - 481
  - 482
  - 485
  - 486
  - 489
  - 490
  - 474
  - 477
  - 479
  numpy.ma.extras.average:
  - 661
  - 662
  - 664
  - 665
  - 667
  - 673
  - 674
  - 675
  - 677
  - 550
  - 679
  - 680
  - 682
  - 685
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  - 690
  - 696
  - 697
  - 698
  - 700
  - 704
  - 705
  - 708
  - 709
  - 710
  numpy.ma.extras.clump_masked:
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  - 2252
  - 2285
  - 2286
  numpy.ma.extras.clump_unmasked:
  - 2212
  - 2245
  - 2246
  - 2247
  - 2248
  numpy.ma.extras.compress_cols:
  - 1072
  - 1035
  - 1069
  - 1070
  numpy.ma.extras.compress_nd:
  - 929
  - 930
  - 933
  - 934
  - 935
  - 882
  - 917
  - 918
  - 920
  - 921
  - 923
  - 926
  numpy.ma.extras.compress_rowcols:
  - 992
  - 940
  - 990
  numpy.ma.extras.compress_rows:
  - 1029
  - 996
  - 1028
  - 1031
  numpy.ma.extras.corrcoef:
  - 1728
  - 1793
  - 1796
  - 1798
  - 1799
  - 1789
  - 1791
  numpy.ma.extras.count_masked:
  - 50
  - 99
  - 100
  numpy.ma.extras.cov:
  - 1701
  - 1704
  - 1705
  - 1710
  - 1711
  - 1712
  - 1713
  - 1714
  - 1715
  - 1716
  - 1718
  - 1719
  - 1720
  - 1721
  - 1722
  - 1630
  numpy.ma.extras.ediff1d:
  - 1290
  - 1294
  - 1295
  - 1296
  - 1297
  - 1299
  - 1270
  - 1302
  numpy.ma.extras.flatnotmasked_contiguous:
  - 2091
  - 2092
  - 2093
  - 2096
  - 2097
  - 2098
  - 2099
  - 2047
  numpy.ma.extras.flatnotmasked_edges:
  - 1984
  - 1985
  - 1986
  - 1987
  - 1988
  - 1939
  numpy.ma.extras.flatten_inplace:
  - 384
  - 381
  - 383
  numpy.ma.extras.in1d:
  - 1473
  - 1456
  - 1457
  - 1458
  - 1428
  - 1460
  - 1464
  - 1466
  - 1470
  - 1471
  numpy.ma.extras.intersect1d:
  - 1381
  - 1382
  - 1385
  - 1386
  - 1358
  numpy.ma.extras.isin:
  - 1480
  - 1507
  - 1508
  numpy.ma.extras.issequence:
  - 42
  - 46
  numpy.ma.extras.mask_cols:
  - 1216
  - 1256
  - 1259
  - 1262
  numpy.ma.extras.mask_rowcols:
  - 1153
  - 1155
  - 1156
  - 1157
  - 1158
  - 1159
  - 1160
  - 1076
  - 1148
  - 1149
  - 1151
  numpy.ma.extras.mask_rows:
  - 1209
  - 1212
  - 1165
  - 1206
  numpy.ma.extras.masked_all:
  - 104
  - 160
  - 159
  numpy.ma.extras.masked_all_like:
  - 224
  - 165
  - 223
  numpy.ma.extras.median:
  - 717
  - 783
  - 784
  - 787
  - 788
  - 792
  numpy.ma.extras.mr_class:
  - 1839
  numpy.ma.extras.mr_class.__init__:
  - 1860
  numpy.ma.extras.ndenumerate:
  - 1931
  - 1932
  - 1933
  - 1934
  - 1870
  - 1935
  numpy.ma.extras.notmasked_contiguous:
  - 2176
  - 2178
  - 2163
  - 2165
  - 2167
  - 2168
  - 2105
  - 2174
  numpy.ma.extras.notmasked_edges:
  - 1995
  - 2037
  - 2038
  - 2039
  - 2040
  - 2041
  numpy.ma.extras.polyfit:
  - 2311
  - 2315
  - 2316
  - 2318
  - 2319
  - 2320
  - 2321
  - 2322
  - 2323
  - 2324
  - 2328
  - 2329
  - 2330
  - 2332
  - 2334
  - 2336
  - 2338
  - 2340
  - 2342
  numpy.ma.extras.setdiff1d:
  - 1537
  - 1557
  - 1558
  - 1560
  - 1561
  - 1562
  numpy.ma.extras.setxor1d:
  - 1411
  - 1412
  - 1413
  - 1415
  - 1416
  - 1418
  - 1419
  - 1421
  - 1391
  numpy.ma.extras.union1d:
  - 1513
  - 1533
  numpy.ma.extras.unique:
  - 1345
  - 1348
  - 1349
  - 1350
  - 1351
  - 1353
  - 1308
  numpy.ma.extras.vander:
  - 2297
  - 2301
  - 2302
  - 2303
  numpy.ma.mrecords:
  - 1
  numpy.ma.mrecords.MaskedRecords:
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  numpy.ma.mrecords.MaskedRecords.__array_finalize__:
  - 145
  - 146
  - 147
  - 148
  - 149
  - 150
  - 152
  - 153
  - 157
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  numpy.ma.mrecords.MaskedRecords.__getattribute__:
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  - 196
  - 228
  - 234
  - 203
  - 204
  - 205
  - 206
  - 212
  - 213
  - 219
  - 220
  - 221
  - 222
  numpy.ma.mrecords.MaskedRecords.__getitem__:
  - 321
  - 322
  - 294
  - 301
  - 302
  - 304
  - 309
  - 313
  - 316
  numpy.ma.mrecords.MaskedRecords.__getstate__:
  - 441
  - 450
  - 451
  numpy.ma.mrecords.MaskedRecords.__len__:
  - 185
  - 186
  - 188
  - 180
  numpy.ma.mrecords.MaskedRecords.__new__:
  - 131
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  - 110
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  - 116
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  - 118
  - 120
  - 121
  - 123
  - 124
  - 125
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  numpy.ma.mrecords.MaskedRecords.__reduce__:
  - 483
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  numpy.ma.mrecords.MaskedRecords.__repr__:
  - 354
  - 355
  - 356
  - 357
  - 359
  - 349
  numpy.ma.mrecords.MaskedRecords.__setattr__:
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  - 287
  - 261
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  - 263
  - 265
  - 269
  - 238
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  - 243
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  - 247
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  - 252
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  - 472
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  - 473
  - 471
  numpy.ma.mrecords.MaskedRecords.__str__:
  - 335
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  - 340
  - 341
  - 342
  - 344
  - 345
  - 346
  numpy.ma.mrecords.MaskedRecords._data:
  - 169
  - 165
  numpy.ma.mrecords.MaskedRecords._fieldmask:
  - 173
  numpy.ma.mrecords.MaskedRecords.copy:
  - 416
  - 420
  - 421
  numpy.ma.mrecords.MaskedRecords.harden_mask:
  - 402
  numpy.ma.mrecords.MaskedRecords.soften_mask:
  - 409
  numpy.ma.mrecords.MaskedRecords.tolist:
  - 425
  - 433
  - 434
  - 435
  - 436
  - 438
  numpy.ma.mrecords.MaskedRecords.view:
  - 385
  - 387
  - 388
  - 390
  - 393
  - 362
  - 395
  - 396
  - 397
  - 367
  - 368
  - 369
  - 371
  - 373
  - 375
  - 376
  - 378
  - 381
  numpy.ma.mrecords._checknames:
  - 64
  - 67
  - 68
  - 69
  - 70
  - 72
  - 74
  - 75
  - 45
  - 52
  - 53
  - 54
  - 57
  - 59
  - 60
  - 63
  numpy.ma.mrecords._get_fieldmask:
  - 80
  - 81
  numpy.ma.mrecords._guessvartypes:
  - 640
  - 642
  - 612
  - 644
  - 622
  - 623
  - 625
  - 628
  - 630
  - 633
  - 636
  - 638
  numpy.ma.mrecords._mrreconstruct:
  - 491
  - 492
  - 493
  - 487
  numpy.ma.mrecords.addfield:
  - 769
  - 770
  - 773
  - 774
  - 776
  - 777
  - 779
  - 749
  - 758
  - 759
  - 760
  - 763
  - 764
  - 766
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  numpy.ma.mrecords.fromarrays:
  - 534
  - 535
  - 536
  - 506
  - 540
  - 541
  numpy.ma.mrecords.fromrecords:
  - 580
  - 549
  - 582
  - 584
  - 585
  - 587
  - 589
  - 590
  - 594
  - 597
  - 598
  - 599
  - 600
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  - 603
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  numpy.ma.mrecords.fromtextfile:
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  - 695
  - 699
  - 706
  - 710
  - 711
  - 712
  - 713
  - 715
  - 719
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  - 728
  - 729
  - 732
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  - 736
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  - 742
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  - 745
  numpy.ma.mrecords.openfile:
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  - 654
  - 658
  - 661
  - 662
  - 664
  numpy.ma.testutils:
  - 1
  numpy.ma.testutils._assert_equal_on_sequences:
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  - 92
  - 93
  - 87
  numpy.ma.testutils.almost:
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  - 73
  - 74
  - 75
  - 76
  - 77
  - 78
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  - 81
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  numpy.ma.testutils.approx:
  - 42
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  - 54
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  numpy.ma.testutils.assert_almost_equal:
  - 192
  - 181
  - 187
  - 188
  - 190
  numpy.ma.testutils.assert_array_almost_equal:
  - 260
  - 269
  numpy.ma.testutils.assert_array_almost_equal.compare:
  - 267
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  numpy.ma.testutils.assert_array_approx_equal:
  - 246
  - 255
  numpy.ma.testutils.assert_array_approx_equal.compare:
  - 253
  - 254
  numpy.ma.testutils.assert_array_compare:
  - 201
  - 208
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  - 210
  - 211
  - 213
  - 217
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  numpy.ma.testutils.assert_array_equal:
  - 225
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  numpy.ma.testutils.assert_array_less:
  - 274
  - 278
  numpy.ma.testutils.assert_equal:
  - 128
  - 129
  - 130
  - 131
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  - 136
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  - 121
  - 122
  - 123
  - 125
  numpy.ma.testutils.assert_equal_records:
  - 98
  - 104
  - 105
  - 106
  - 107
  - 108
  numpy.ma.testutils.assert_mask_equal:
  - 288
  - 289
  - 290
  - 291
  - 292
  - 284
  numpy.ma.testutils.fail_if_array_equal:
  - 241
  - 235
  numpy.ma.testutils.fail_if_array_equal.compare:
  - 240
  numpy.ma.testutils.fail_if_equal:
  - 160
  - 161
  - 163
  - 165
  - 166
  - 167
  - 168
  - 170
  - 171
  - 172
  - 173
  - 152
  - 156
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  - 159
  numpy.ma.timer_comparison:
  - 429
  - 436
  - 438
  - 439
  - 440
  - 441
  - 442
  numpy.ma.timer_comparison.ModuleTester:
  - 192
  - 289
  - 422
  - 236
  - 110
  - 212
  - 149
  - 341
  - 376
  - 121
  numpy.ma.timer_comparison.ModuleTester.assert_array_compare:
  - 49
  - 53
  - 54
  - 55
  - 57
  - 58
  - 59
  - 60
  - 61
  - 62
  - 63
  - 65
  - 66
  - 67
  - 68
  - 69
  - 73
  - 74
  - 80
  - 81
  - 82
  - 83
  - 87
  - 88
  - 89
  - 90
  - 91
  - 92
  - 99
  numpy.ma.timer_comparison.ModuleTester.assert_array_equal:
  - 107
  - 103
  numpy.ma.timer_comparison.ModuleTester.test_0:
  - 112
  - 116
  - 118
  - 119
  numpy.ma.timer_comparison.ModuleTester.test_1:
  - 128
  - 131
  - 132
  - 133
  - 134
  - 136
  - 138
  - 139
  - 141
  - 147
  - 123
  - 127
  numpy.ma.timer_comparison.ModuleTester.test_2:
  - 151
  - 155
  - 156
  - 157
  - 158
  - 160
  - 161
  - 163
  - 167
  - 174
  - 175
  - 176
  - 177
  - 178
  - 179
  - 181
  - 182
  - 186
  - 187
  - 189
  numpy.ma.timer_comparison.ModuleTester.test_3:
  - 194
  - 198
  - 200
  - 201
  - 202
  - 203
  - 204
  - 205
  - 206
  - 207
  - 208
  - 209
  - 210
  numpy.ma.timer_comparison.ModuleTester.test_4:
  - 224
  - 225
  - 226
  - 227
  - 228
  - 229
  - 231
  - 214
  - 218
  - 219
  - 221
  - 222
  - 223
  numpy.ma.timer_comparison.ModuleTester.test_5:
  - 257
  - 259
  - 260
  - 263
  - 265
  - 267
  - 268
  - 271
  - 273
  - 275
  - 276
  - 279
  - 280
  - 281
  - 283
  - 284
  - 287
  - 238
  - 242
  - 243
  - 244
  - 247
  - 249
  - 251
  - 252
  - 255
  numpy.ma.timer_comparison.ModuleTester.test_6:
  - 291
  - 295
  - 296
  - 297
  - 300
  - 304
  - 305
  - 306
  - 308
  - 309
  - 312
  - 316
  - 317
  - 318
  - 320
  - 321
  - 324
  - 328
  - 329
  - 330
  - 332
  - 333
  - 336
  numpy.ma.timer_comparison.ModuleTester.test_7:
  - 366
  - 368
  - 369
  - 371
  - 372
  - 373
  - 374
  - 343
  - 344
  - 345
  - 346
  numpy.ma.timer_comparison.ModuleTester.test_99:
  - 386
  - 387
  - 388
  - 390
  - 391
  - 392
  - 393
  - 394
  - 397
  - 398
  - 399
  - 400
  - 401
  - 402
  - 403
  - 404
  - 405
  - 406
  - 407
  - 410
  - 412
  - 413
  - 414
  - 415
  - 416
  - 417
  - 418
  - 419
  - 420
  - 379
  - 380
  - 381
  - 382
  - 383
  numpy.ma.timer_comparison.ModuleTester.test_A:
  - 424
  - 426
  numpy.matlib:
  - 5
  numpy.matlib.empty:
  - 25
  - 63
  numpy.matlib.eye:
  - 232
  - 191
  numpy.matlib.identity:
  - 185
  - 186
  - 155
  numpy.matlib.ones:
  - 66
  - 107
  - 106
  numpy.matlib.rand:
  - 235
  - 276
  - 278
  numpy.matlib.randn:
  - 329
  - 281
  - 331
  numpy.matlib.repmat:
  - 334
  - 370
  - 372
  - 374
  - 380
  - 381
  numpy.matlib.zeros:
  - 151
  - 150
  - 111
  numpy.matrixlib:
  - 1
  - 10
  numpy.matrixlib.defmatrix:
  - 35
  - 1038
  numpy.matrixlib.defmatrix._convert_from_string:
  - 16
  - 17
  - 19
  - 21
  - 22
  - 24
  - 25
  - 26
  - 27
  - 28
  - 29
  - 31
  numpy.matrixlib.defmatrix._from_string:
  - 1024
  - 1033
  - 1034
  - 1035
  - 1014
  - 1016
  - 1017
  - 1019
  - 1020
  - 1023
  numpy.matrixlib.defmatrix.asmatrix:
  - 69
  - 37
  numpy.matrixlib.defmatrix.bmat:
  - 1093
  - 1094
  - 1096
  - 1103
  - 1040
  - 1105
  - 1108
  - 1109
  - 1110
  - 1112
  - 1113
  - 1114
  - 1115
  numpy.matrixlib.defmatrix.matrix:
  - 74
  numpy.matrixlib.defmatrix.matrix.A:
  - 842
  - 868
  numpy.matrixlib.defmatrix.matrix.A1:
  - 872
  - 897
  numpy.matrixlib.defmatrix.matrix.H:
  - 1001
  - 1002
  - 1004
  - 973
  numpy.matrixlib.defmatrix.matrix.I:
  - 834
  - 797
  - 838
  numpy.matrixlib.defmatrix.matrix.T:
  - 969
  - 940
  numpy.matrixlib.defmatrix.matrix.__array_finalize__:
  - 172
  - 174
  - 176
  - 177
  - 178
  - 179
  - 182
  - 186
  - 188
  numpy.matrixlib.defmatrix.matrix.__getitem__:
  - 196
  - 200
  - 203
  - 205
  - 209
  - 212
  numpy.matrixlib.defmatrix.matrix.__mul__:
  - 219
  - 221
  - 222
  - 223
  numpy.matrixlib.defmatrix.matrix.__new__:
  - 128
  - 130
  - 132
  - 133
  - 136
  - 137
  - 138
  - 139
  - 140
  - 143
  - 144
  - 148
  - 151
  - 153
  - 155
  - 159
  - 162
  - 163
  - 165
  - 118
  - 124
  - 126
  numpy.matrixlib.defmatrix.matrix.__pow__:
  - 234
  numpy.matrixlib.defmatrix.matrix.__rmul__:
  - 227
  numpy.matrixlib.defmatrix.matrix._align:
  - 244
  - 247
  - 249
  - 251
  - 252
  numpy.matrixlib.defmatrix.matrix._collapse:
  - 257
  - 260
  numpy.matrixlib.defmatrix.matrix.all:
  - 612
  - 575
  numpy.matrixlib.defmatrix.matrix.any:
  - 552
  - 572
  numpy.matrixlib.defmatrix.matrix.argmax:
  - 650
  - 686
  numpy.matrixlib.defmatrix.matrix.argmin:
  - 760
  - 724
  numpy.matrixlib.defmatrix.matrix.flatten:
  - 380
  - 414
  numpy.matrixlib.defmatrix.matrix.max:
  - 647
  - 615
  numpy.matrixlib.defmatrix.matrix.mean:
  - 448
  - 417
  numpy.matrixlib.defmatrix.matrix.min:
  - 689
  - 721
  numpy.matrixlib.defmatrix.matrix.prod:
  - 549
  - 519
  numpy.matrixlib.defmatrix.matrix.ptp:
  - 793
  - 763
  numpy.matrixlib.defmatrix.matrix.ravel:
  - 936
  - 901
  numpy.matrixlib.defmatrix.matrix.squeeze:
  - 375
  - 327
  numpy.matrixlib.defmatrix.matrix.std:
  - 482
  - 451
  numpy.matrixlib.defmatrix.matrix.sum:
  - 322
  - 291
  numpy.matrixlib.defmatrix.matrix.tolist:
  - 268
  - 287
  numpy.matrixlib.defmatrix.matrix.var:
  - 516
  - 485
  numpy.polynomial:
  - 1
  - 186
  numpy.polynomial._polybase:
  - 1
  numpy.polynomial._polybase.ABCPolyBase:
  - 81
  - 20
  - 93
  numpy.polynomial._polybase.ABCPolyBase.__add__:
  - 546
  - 548
  - 551
  numpy.polynomial._polybase.ABCPolyBase.__call__:
  - 526
  - 527
  numpy.polynomial._polybase.ABCPolyBase.__divmod__:
  - 600
  - 593
  - 595
  - 601
  numpy.polynomial._polybase.ABCPolyBase.__eq__:
  - 664
  - 665
  - 667
  - 663
  numpy.polynomial._polybase.ABCPolyBase.__floordiv__:
  - 581
  - 582
  numpy.polynomial._polybase.ABCPolyBase.__format__:
  - 352
  - 353
  - 354
  - 344
  - 345
  - 346
  numpy.polynomial._polybase.ABCPolyBase.__getstate__:
  - 513
  - 514
  - 515
  - 516
  numpy.polynomial._polybase.ABCPolyBase.__init__:
  - 323
  - 306
  - 309
  - 310
  - 311
  - 315
  - 316
  - 317
  numpy.polynomial._polybase.ABCPolyBase.__iter__:
  - 530
  numpy.polynomial._polybase.ABCPolyBase.__len__:
  - 533
  numpy.polynomial._polybase.ABCPolyBase.__mod__:
  - 587
  - 588
  numpy.polynomial._polybase.ABCPolyBase.__mul__:
  - 562
  - 564
  - 567
  numpy.polynomial._polybase.ABCPolyBase.__ne__:
  - 672
  numpy.polynomial._polybase.ABCPolyBase.__neg__:
  - 538
  numpy.polynomial._polybase.ABCPolyBase.__pow__:
  - 605
  - 606
  numpy.polynomial._polybase.ABCPolyBase.__radd__:
  - 611
  - 614
  numpy.polynomial._polybase.ABCPolyBase.__rdiv__:
  - 632
  numpy.polynomial._polybase.ABCPolyBase.__rdivmod__:
  - 658
  - 659
  - 653
  numpy.polynomial._polybase.ABCPolyBase.__repr__:
  - 336
  - 337
  - 338
  numpy.polynomial._polybase.ABCPolyBase.__rfloordiv__:
  - 640
  - 641
  numpy.polynomial._polybase.ABCPolyBase.__rmod__:
  - 646
  - 647
  numpy.polynomial._polybase.ABCPolyBase.__rmul__:
  - 625
  - 628
  numpy.polynomial._polybase.ABCPolyBase.__rsub__:
  - 618
  - 621
  numpy.polynomial._polybase.ABCPolyBase.__str__:
  - 357
  - 358
  - 359
  numpy.polynomial._polybase.ABCPolyBase.__sub__:
  - 554
  - 556
  - 559
  numpy.polynomial._polybase.ABCPolyBase.__truediv__:
  - 578
  - 573
  numpy.polynomial._polybase.ABCPolyBase._format_term:
  - 450
  - 451
  - 454
  - 455
  - 457
  - 458
  - 462
  - 463
  numpy.polynomial._polybase.ABCPolyBase._generate_string:
  - 387
  - 388
  - 390
  - 362
  - 394
  - 396
  - 367
  - 368
  - 399
  - 370
  - 402
  - 372
  - 403
  - 374
  - 376
  - 379
  - 381
  numpy.polynomial._polybase.ABCPolyBase._get_coefficients:
  - 293
  - 294
  - 296
  - 298
  - 268
  - 300
  numpy.polynomial._polybase.ABCPolyBase._repr_latex_:
  - 480
  - 481
  - 482
  - 483
  - 484
  - 486
  - 489
  - 490
  - 495
  - 496
  - 498
  - 500
  - 501
  - 470
  - 471
  - 477
  - 479
  numpy.polynomial._polybase.ABCPolyBase._repr_latex_scalar:
  - 447
  numpy.polynomial._polybase.ABCPolyBase._repr_latex_term:
  - 436
  numpy.polynomial._polybase.ABCPolyBase._str_term_ascii:
  - 427
  - 423
  numpy.polynomial._polybase.ABCPolyBase._str_term_unicode:
  - 409
  - 418
  - 413
  numpy.polynomial._polybase.ABCPolyBase.basis:
  - 1173
  - 1175
  - 1144
  - 1177
  - 1179
  - 1181
  numpy.polynomial._polybase.ABCPolyBase.cast:
  - 1185
  - 1219
  - 1221
  - 1223
  numpy.polynomial._polybase.ABCPolyBase.convert:
  - 832
  - 802
  - 834
  - 836
  - 830
  numpy.polynomial._polybase.ABCPolyBase.copy:
  - 687
  - 679
  numpy.polynomial._polybase.ABCPolyBase.cutdeg:
  - 747
  - 725
  numpy.polynomial._polybase.ABCPolyBase.degree:
  - 722
  - 690
  numpy.polynomial._polybase.ABCPolyBase.deriv:
  - 920
  - 901
  - 918
  - 919
  numpy.polynomial._polybase.ABCPolyBase.fit:
  - 1058
  - 1062
  - 972
  - 1042
  - 1043
  - 1044
  - 1047
  - 1050
  - 1053
  - 1054
  - 1055
  numpy.polynomial._polybase.ABCPolyBase.fromroots:
  - 1092
  - 1093
  - 1094
  - 1095
  - 1066
  - 1098
  - 1101
  - 1102
  - 1104
  - 1105
  numpy.polynomial._polybase.ABCPolyBase.has_samecoef:
  - 208
  - 206
  - 191
  numpy.polynomial._polybase.ABCPolyBase.has_samedomain:
  - 229
  - 214
  numpy.polynomial._polybase.ABCPolyBase.has_sametype:
  - 265
  - 250
  numpy.polynomial._polybase.ABCPolyBase.has_samewindow:
  - 232
  - 247
  numpy.polynomial._polybase.ABCPolyBase.identity:
  - 1134
  - 1136
  - 1138
  - 1139
  - 1140
  - 1109
  numpy.polynomial._polybase.ABCPolyBase.integ:
  - 897
  - 898
  - 868
  - 892
  - 893
  numpy.polynomial._polybase.ABCPolyBase.linspace:
  - 938
  - 963
  - 965
  - 966
  numpy.polynomial._polybase.ABCPolyBase.mapparms:
  - 865
  - 839
  numpy.polynomial._polybase.ABCPolyBase.roots:
  - 923
  - 934
  - 935
  numpy.polynomial._polybase.ABCPolyBase.trim:
  - 769
  - 770
  - 750
  numpy.polynomial._polybase.ABCPolyBase.truncate:
  - 773
  - 792
  - 793
  - 795
  - 799
  numpy.polynomial.chebyshev:
  - 1
  - 464
  - 467
  - 470
  - 473
  numpy.polynomial.chebyshev.Chebyshev:
  - 2084
  - 2085
  - 2028
  - 2029
  - 2030
  - 2031
  - 2000
  - 2032
  - 2033
  - 2034
  - 2035
  - 2036
  - 2037
  - 2038
  - 2039
  numpy.polynomial.chebyshev.Chebyshev.interpolate:
  - 2080
  - 2081
  - 2043
  - 2077
  - 2079
  numpy.polynomial.chebyshev.Chebyshev.interpolate.<lambda1>:
  - 2079
  numpy.polynomial.chebyshev._cseries_to_zseries:
  - 153
  - 134
  numpy.polynomial.chebyshev._zseries_der:
  - 302
  - 303
  - 304
  - 305
  - 278
  numpy.polynomial.chebyshev._zseries_div:
  - 256
  - 273
  - 211
  - 244
  - 245
  - 246
  - 243
  - 250
  - 247
  numpy.polynomial.chebyshev._zseries_int:
  - 333
  - 334
  - 335
  - 336
  - 310
  numpy.polynomial.chebyshev._zseries_mul:
  - 184
  - 207
  numpy.polynomial.chebyshev._zseries_to_cseries:
  - 178
  - 159
  numpy.polynomial.chebyshev.cheb2poly:
  - 451
  - 453
  - 454
  - 455
  - 398
  - 443
  - 444
  - 445
  numpy.polynomial.chebyshev.chebadd:
  - 608
  - 570
  numpy.polynomial.chebyshev.chebcompanion:
  - 1705
  - 1706
  - 1708
  - 1709
  - 1679
  - 1711
  - 1712
  - 1713
  - 1714
  - 1715
  - 1716
  numpy.polynomial.chebyshev.chebder:
  - 960
  - 963
  - 967
  - 939
  - 940
  - 941
  - 942
  - 943
  - 880
  - 944
  - 946
  - 948
  - 951
  - 952
  - 953
  - 956
  - 959
  numpy.polynomial.chebyshev.chebdiv:
  - 801
  - 802
  - 806
  - 807
  - 808
  - 810
  - 813
  - 814
  - 815
  - 816
  - 817
  - 755
  numpy.polynomial.chebyshev.chebfit:
  - 1552
  - 1675
  numpy.polynomial.chebyshev.chebfromroots:
  - 515
  - 566
  numpy.polynomial.chebyshev.chebgauss:
  - 1890
  - 1891
  - 1852
  - 1886
  - 1887
  numpy.polynomial.chebyshev.chebgrid2d:
  - 1232
  - 1281
  numpy.polynomial.chebyshev.chebgrid3d:
  - 1336
  - 1388
  numpy.polynomial.chebyshev.chebint:
  - 1056
  - 1057
  - 1058
  - 1059
  - 1061
  - 1062
  - 1063
  - 1065
  - 1067
  - 1069
  - 1071
  - 1073
  - 1076
  - 1077
  - 1078
  - 1079
  - 1081
  - 1084
  - 1087
  - 1089
  - 1092
  - 1094
  - 972
  numpy.polynomial.chebyshev.chebinterpolate:
  - 1832
  - 1835
  - 1837
  - 1841
  - 1842
  - 1843
  - 1844
  - 1785
  numpy.polynomial.chebyshev.chebline:
  - 509
  - 508
  - 477
  - 511
  numpy.polynomial.chebyshev.chebmul:
  - 706
  - 746
  - 747
  - 748
  - 749
  - 750
  - 751
  numpy.polynomial.chebyshev.chebmulx:
  - 656
  - 690
  - 692
  - 695
  - 698
  numpy.polynomial.chebyshev.chebpow:
  - 865
  - 866
  - 867
  - 872
  - 874
  - 875
  - 876
  - 822
  - 859
  - 860
  - 861
  - 863
  numpy.polynomial.chebyshev.chebpts1:
  - 1952
  - 1954
  - 1957
  - 1925
  - 1958
  - 1951
  numpy.polynomial.chebyshev.chebpts2:
  - 1985
  - 1986
  - 1988
  - 1991
  - 1992
  - 1962
  numpy.polynomial.chebyshev.chebroots:
  - 1771
  - 1772
  - 1773
  - 1774
  - 1775
  - 1778
  - 1779
  - 1780
  - 1724
  numpy.polynomial.chebyshev.chebsub:
  - 652
  - 612
  numpy.polynomial.chebyshev.chebval:
  - 1157
  - 1158
  - 1159
  - 1160
  - 1161
  - 1162
  - 1099
  - 1163
  - 1165
  - 1168
  - 1175
  numpy.polynomial.chebyshev.chebval2d:
  - 1228
  - 1183
  numpy.polynomial.chebyshev.chebval3d:
  - 1332
  - 1285
  numpy.polynomial.chebyshev.chebvander:
  - 1441
  - 1392
  - 1426
  - 1427
  - 1430
  - 1433
  - 1436
  - 1439
  numpy.polynomial.chebyshev.chebvander2d:
  - 1445
  - 1494
  numpy.polynomial.chebyshev.chebvander3d:
  - 1498
  - 1548
  numpy.polynomial.chebyshev.chebweight:
  - 1920
  - 1897
  numpy.polynomial.chebyshev.poly2cheb:
  - 389
  - 390
  - 392
  - 393
  - 348
  numpy.polynomial.hermite:
  - 1
  - 206
  - 209
  - 212
  - 215
  numpy.polynomial.hermite.Hermite:
  - 1792
  - 1783
  - 1777
  - 1778
  - 1779
  - 1780
  - 1749
  - 1781
  - 1782
  - 1784
  - 1785
  - 1786
  - 1787
  - 1788
  - 1791
  numpy.polynomial.hermite._normed_hermite_n:
  - 1632
  - 1593
  - 1621
  - 1622
  - 1625
  - 1626
  - 1627
  - 1629
  - 1630
  numpy.polynomial.hermite.herm2poly:
  - 193
  - 195
  - 196
  - 197
  - 143
  - 182
  - 183
  - 184
  - 186
  numpy.polynomial.hermite.hermadd:
  - 315
  - 351
  numpy.polynomial.hermite.hermcompanion:
  - 1477
  - 1510
  - 1511
  - 1513
  - 1514
  - 1516
  - 1517
  - 1518
  - 1519
  - 1520
  - 1521
  - 1522
  numpy.polynomial.hermite.hermder:
  - 673
  - 674
  - 677
  - 653
  - 654
  - 655
  - 656
  - 657
  - 658
  - 660
  - 662
  - 599
  - 665
  - 666
  - 667
  - 670
  numpy.polynomial.hermite.hermdiv:
  - 514
  - 558
  numpy.polynomial.hermite.hermfit:
  - 1473
  - 1343
  numpy.polynomial.hermite.hermfromroots:
  - 259
  - 311
  numpy.polynomial.hermite.hermgauss:
  - 1636
  - 1703
  - 1677
  - 1678
  - 1683
  - 1684
  - 1685
  - 1688
  - 1689
  - 1694
  - 1695
  numpy.polynomial.hermite.hermgrid2d:
  - 1018
  - 958
  numpy.polynomial.hermite.hermgrid3d:
  - 1083
  - 1148
  numpy.polynomial.hermite.hermint:
  - 769
  - 770
  - 771
  - 773
  - 775
  - 777
  - 779
  - 781
  - 784
  - 785
  - 786
  - 787
  - 789
  - 792
  - 795
  - 797
  - 799
  - 682
  - 764
  - 765
  - 766
  - 767
  numpy.polynomial.hermite.hermline:
  - 219
  - 252
  - 253
  - 255
  numpy.polynomial.hermite.hermmul:
  - 448
  - 486
  - 488
  - 495
  - 498
  - 502
  - 505
  - 508
  - 509
  - 510
  numpy.polynomial.hermite.hermmulx:
  - 395
  - 433
  - 435
  - 438
  - 441
  numpy.polynomial.hermite.hermpow:
  - 562
  - 595
  numpy.polynomial.hermite.hermroots:
  - 1579
  - 1580
  - 1581
  - 1582
  - 1583
  - 1586
  - 1587
  - 1588
  - 1529
  numpy.polynomial.hermite.hermsub:
  - 355
  - 391
  numpy.polynomial.hermite.hermval:
  - 804
  - 872
  - 873
  - 874
  - 875
  - 876
  - 877
  - 878
  - 881
  - 884
  - 888
  - 891
  numpy.polynomial.hermite.hermval2d:
  - 954
  - 900
  numpy.polynomial.hermite.hermval3d:
  - 1022
  - 1079
  numpy.polynomial.hermite.hermvander:
  - 1152
  - 1196
  - 1197
  - 1200
  - 1203
  - 1205
  - 1208
  - 1210
  numpy.polynomial.hermite.hermvander2d:
  - 1274
  - 1214
  numpy.polynomial.hermite.hermvander3d:
  - 1339
  - 1278
  numpy.polynomial.hermite.hermweight:
  - 1740
  - 1709
  numpy.polynomial.hermite.poly2herm:
  - 97
  - 134
  - 135
  - 137
  - 138
  numpy.polynomial.hermite_e:
  - 1
  - 207
  - 210
  - 213
  - 216
  numpy.polynomial.hermite_e.HermiteE:
  - 1696
  - 1692
  - 1697
  - 1698
  - 1701
  - 1702
  - 1687
  - 1688
  - 1689
  - 1690
  - 1659
  - 1691
  - 1693
  - 1694
  - 1695
  numpy.polynomial.hermite_e._normed_hermite_e_n:
  - 1546
  - 1547
  - 1518
  - 1551
  - 1550
  - 1552
  - 1554
  - 1555
  numpy.polynomial.hermite_e.herme2poly:
  - 194
  - 196
  - 197
  - 198
  - 145
  - 184
  - 185
  - 186
  - 188
  numpy.polynomial.hermite_e.hermeadd:
  - 315
  - 351
  numpy.polynomial.hermite_e.hermecompanion:
  - 1408
  - 1441
  - 1442
  - 1443
  - 1444
  - 1445
  - 1446
  - 1447
  - 1435
  - 1436
  - 1438
  - 1439
  numpy.polynomial.hermite_e.hermeder:
  - 672
  - 675
  - 651
  - 652
  - 653
  - 654
  - 655
  - 656
  - 658
  - 660
  - 597
  - 663
  - 664
  - 665
  - 668
  - 671
  numpy.polynomial.hermite_e.hermediv:
  - 514
  - 556
  numpy.polynomial.hermite_e.hermefit:
  - 1274
  - 1404
  numpy.polynomial.hermite_e.hermefromroots:
  - 259
  - 311
  numpy.polynomial.hermite_e.hermegauss:
  - 1602
  - 1603
  - 1604
  - 1607
  - 1608
  - 1613
  - 1614
  - 1622
  - 1561
  - 1596
  - 1597
  numpy.polynomial.hermite_e.hermegrid2d:
  - 946
  - 995
  numpy.polynomial.hermite_e.hermegrid3d:
  - 1050
  - 1102
  numpy.polynomial.hermite_e.hermeint:
  - 768
  - 769
  - 771
  - 773
  - 775
  - 777
  - 779
  - 782
  - 783
  - 784
  - 785
  - 787
  - 790
  - 793
  - 795
  - 797
  - 680
  - 762
  - 763
  - 764
  - 765
  - 767
  numpy.polynomial.hermite_e.hermeline:
  - 253
  - 220
  - 252
  - 255
  numpy.polynomial.hermite_e.hermemul:
  - 448
  - 486
  - 488
  - 495
  - 498
  - 502
  - 505
  - 508
  - 509
  - 510
  numpy.polynomial.hermite_e.hermemulx:
  - 395
  - 433
  - 435
  - 438
  - 441
  numpy.polynomial.hermite_e.hermepow:
  - 560
  - 593
  numpy.polynomial.hermite_e.hermeroots:
  - 1504
  - 1505
  - 1506
  - 1507
  - 1508
  - 1511
  - 1512
  - 1513
  - 1454
  numpy.polynomial.hermite_e.hermesub:
  - 355
  - 391
  numpy.polynomial.hermite_e.hermeval:
  - 802
  - 870
  - 871
  - 872
  - 873
  - 874
  - 875
  - 876
  - 878
  - 881
  - 885
  - 888
  numpy.polynomial.hermite_e.hermeval2d:
  - 897
  - 942
  numpy.polynomial.hermite_e.hermeval3d:
  - 1046
  - 999
  numpy.polynomial.hermite_e.hermevander:
  - 1154
  - 1157
  - 1159
  - 1161
  - 1163
  - 1106
  - 1150
  - 1151
  numpy.polynomial.hermite_e.hermevander2d:
  - 1216
  - 1167
  numpy.polynomial.hermite_e.hermevander3d:
  - 1220
  - 1270
  numpy.polynomial.hermite_e.hermeweight:
  - 1650
  - 1628
  numpy.polynomial.hermite_e.poly2herme:
  - 98
  - 136
  - 137
  - 139
  - 140
  numpy.polynomial.laguerre:
  - 1
  - 203
  - 206
  - 209
  - 212
  numpy.polynomial.laguerre.Laguerre:
  - 1719
  - 1710
  - 1711
  - 1712
  - 1713
  - 1682
  - 1714
  - 1715
  - 1716
  - 1717
  - 1718
  - 1720
  - 1721
  - 1724
  - 1725
  numpy.polynomial.laguerre.lag2poly:
  - 192
  - 193
  - 194
  - 143
  - 182
  - 183
  - 184
  - 190
  numpy.polynomial.laguerre.lagadd:
  - 346
  - 310
  numpy.polynomial.laguerre.lagcompanion:
  - 1504
  - 1492
  - 1460
  - 1493
  - 1495
  - 1496
  - 1498
  - 1499
  - 1500
  - 1501
  - 1502
  - 1503
  numpy.polynomial.laguerre.lagder:
  - 674
  - 647
  - 648
  - 649
  - 651
  - 652
  - 653
  - 655
  - 593
  - 657
  - 660
  - 661
  - 662
  - 665
  - 668
  - 669
  numpy.polynomial.laguerre.lagdiv:
  - 552
  - 510
  numpy.polynomial.laguerre.lagfit:
  - 1456
  - 1326
  numpy.polynomial.laguerre.lagfromroots:
  - 306
  - 254
  numpy.polynomial.laguerre.laggauss:
  - 1633
  - 1634
  - 1635
  - 1575
  - 1639
  - 1616
  - 1617
  - 1622
  - 1623
  - 1624
  - 1627
  - 1628
  numpy.polynomial.laguerre.laggrid2d:
  - 953
  - 1010
  numpy.polynomial.laguerre.laggrid3d:
  - 1072
  - 1134
  numpy.polynomial.laguerre.lagint:
  - 768
  - 769
  - 771
  - 773
  - 775
  - 777
  - 779
  - 782
  - 783
  - 784
  - 785
  - 787
  - 790
  - 793
  - 796
  - 798
  - 679
  - 762
  - 763
  - 764
  - 765
  - 767
  numpy.polynomial.laguerre.lagline:
  - 216
  - 248
  - 250
  - 247
  numpy.polynomial.laguerre.lagmul:
  - 482
  - 484
  - 491
  - 494
  - 498
  - 501
  - 504
  - 505
  - 506
  - 444
  numpy.polynomial.laguerre.lagmulx:
  - 390
  - 428
  - 430
  - 433
  - 436
  numpy.polynomial.laguerre.lagpow:
  - 556
  - 589
  numpy.polynomial.laguerre.lagroots:
  - 1568
  - 1569
  - 1570
  - 1511
  - 1561
  - 1562
  - 1563
  - 1564
  - 1565
  numpy.polynomial.laguerre.lagsub:
  - 386
  - 350
  numpy.polynomial.laguerre.lagval:
  - 803
  - 871
  - 872
  - 873
  - 874
  - 875
  - 876
  - 877
  - 879
  - 882
  - 886
  - 889
  numpy.polynomial.laguerre.lagval2d:
  - 898
  - 949
  numpy.polynomial.laguerre.lagval3d:
  - 1068
  - 1014
  numpy.polynomial.laguerre.lagvander:
  - 1186
  - 1189
  - 1191
  - 1193
  - 1195
  - 1138
  - 1182
  - 1183
  numpy.polynomial.laguerre.lagvander2d:
  - 1257
  - 1199
  numpy.polynomial.laguerre.lagvander3d:
  - 1322
  - 1261
  numpy.polynomial.laguerre.lagweight:
  - 1674
  - 1645
  numpy.polynomial.laguerre.poly2lag:
  - 97
  - 137
  - 138
  - 135
  numpy.polynomial.legendre:
  - 1
  - 226
  - 217
  - 220
  - 223
  numpy.polynomial.legendre.Legendre:
  - 1664
  - 1665
  - 1655
  - 1650
  - 1651
  - 1652
  - 1653
  - 1622
  - 1654
  - 1656
  - 1657
  - 1658
  - 1659
  - 1660
  - 1661
  numpy.polynomial.legendre.leg2poly:
  - 196
  - 197
  - 198
  - 204
  - 206
  - 207
  - 208
  - 150
  numpy.polynomial.legendre.legadd:
  - 363
  - 325
  numpy.polynomial.legendre.legcompanion:
  - 1444
  - 1445
  - 1447
  - 1448
  - 1418
  - 1450
  - 1451
  - 1452
  - 1453
  - 1454
  - 1455
  numpy.polynomial.legendre.legder:
  - 674
  - 675
  - 676
  - 677
  - 678
  - 615
  - 679
  - 681
  - 683
  - 686
  - 687
  - 688
  - 691
  - 694
  - 695
  - 698
  - 702
  numpy.polynomial.legendre.legdiv:
  - 580
  - 535
  numpy.polynomial.legendre.legfit:
  - 1289
  - 1414
  numpy.polynomial.legendre.legfromroots:
  - 321
  - 270
  numpy.polynomial.legendre.leggauss:
  - 1569
  - 1570
  - 1575
  - 1576
  - 1577
  - 1585
  - 1523
  - 1558
  - 1559
  - 1564
  - 1565
  - 1566
  numpy.polynomial.legendre.leggrid2d:
  - 969
  - 1018
  numpy.polynomial.legendre.leggrid3d:
  - 1073
  - 1125
  numpy.polynomial.legendre.legint:
  - 791
  - 792
  - 793
  - 794
  - 796
  - 797
  - 798
  - 800
  - 802
  - 804
  - 806
  - 808
  - 811
  - 812
  - 813
  - 814
  - 816
  - 819
  - 822
  - 824
  - 828
  - 830
  - 707
  numpy.polynomial.legendre.legline:
  - 264
  - 266
  - 230
  - 263
  numpy.polynomial.legendre.legmul:
  - 516
  - 519
  - 523
  - 526
  - 529
  - 530
  - 467
  - 531
  - 507
  - 509
  numpy.polynomial.legendre.legmulx:
  - 449
  - 451
  - 454
  - 457
  - 411
  numpy.polynomial.legendre.legpow:
  - 584
  - 611
  numpy.polynomial.legendre.legroots:
  - 1509
  - 1510
  - 1511
  - 1512
  - 1513
  - 1516
  - 1517
  - 1518
  - 1462
  numpy.polynomial.legendre.legsub:
  - 407
  - 367
  numpy.polynomial.legendre.legval:
  - 896
  - 897
  - 898
  - 835
  - 899
  - 901
  - 904
  - 908
  - 911
  - 893
  - 894
  - 895
  numpy.polynomial.legendre.legval2d:
  - 920
  - 965
  numpy.polynomial.legendre.legval3d:
  - 1069
  - 1022
  numpy.polynomial.legendre.legvander:
  - 1129
  - 1163
  - 1164
  - 1167
  - 1170
  - 1174
  - 1176
  - 1178
  numpy.polynomial.legendre.legvander2d:
  - 1182
  - 1231
  numpy.polynomial.legendre.legvander3d:
  - 1235
  - 1285
  numpy.polynomial.legendre.legweight:
  - 1591
  numpy.polynomial.legendre.poly2leg:
  - 101
  - 141
  - 142
  - 144
  - 145
  numpy.polynomial.polynomial:
  - 1
  - 98
  - 101
  - 104
  - 107
  numpy.polynomial.polynomial.Polynomial:
  - 1632
  - 1633
  - 1634
  - 1627
  - 1637
  - 1638
  - 1623
  - 1624
  - 1625
  - 1626
  - 1595
  - 1628
  - 1629
  - 1630
  - 1631
  numpy.polynomial.polynomial.Polynomial._repr_latex_term:
  - 1657
  - 1659
  - 1661
  numpy.polynomial.polynomial.Polynomial._str_term_ascii:
  - 1650
  numpy.polynomial.polynomial.Polynomial._str_term_unicode:
  - 1643
  - 1646
  numpy.polynomial.polynomial.polyadd:
  - 217
  - 249
  numpy.polynomial.polynomial.polycompanion:
  - 1480
  - 1513
  - 1514
  - 1516
  - 1517
  - 1519
  - 1520
  - 1521
  numpy.polynomial.polynomial.polyder:
  - 545
  - 546
  - 549
  - 523
  - 524
  - 528
  - 529
  - 530
  - 532
  - 534
  - 472
  - 537
  - 538
  - 539
  - 542
  numpy.polynomial.polynomial.polydiv:
  - 417
  - 429
  - 375
  - 408
  - 409
  - 413
  - 414
  - 415
  numpy.polynomial.polynomial.polyfit:
  - 1476
  - 1324
  numpy.polynomial.polynomial.polyfromroots:
  - 153
  - 213
  numpy.polynomial.polynomial.polygrid2d:
  - 916
  - 973
  numpy.polynomial.polynomial.polygrid3d:
  - 1035
  - 1095
  numpy.polynomial.polynomial.polyint:
  - 640
  - 642
  - 644
  - 646
  - 648
  - 650
  - 653
  - 654
  - 655
  - 656
  - 658
  - 661
  - 664
  - 666
  - 668
  - 554
  - 631
  - 632
  - 636
  - 638
  - 639
  numpy.polynomial.polynomial.polyline:
  - 146
  - 115
  - 147
  - 149
  numpy.polynomial.polynomial.polymul:
  - 337
  - 370
  - 371
  - 369
  numpy.polynomial.polynomial.polymulx:
  - 290
  - 330
  - 325
  - 327
  numpy.polynomial.polynomial.polypow:
  - 433
  - 468
  numpy.polynomial.polynomial.polyroots:
  - 1577
  - 1578
  - 1579
  - 1580
  - 1581
  - 1584
  - 1585
  - 1586
  - 1528
  numpy.polynomial.polynomial.polysub:
  - 253
  - 286
  numpy.polynomial.polynomial.polyval:
  - 673
  - 754
  - 755
  - 758
  - 759
  - 760
  - 761
  - 764
  numpy.polynomial.polynomial.polyval2d:
  - 912
  - 859
  numpy.polynomial.polynomial.polyval3d:
  - 977
  - 1031
  numpy.polynomial.polynomial.polyvalfromroots:
  - 770
  - 845
  - 846
  - 847
  - 848
  - 849
  - 850
  - 851
  - 852
  - 853
  - 855
  numpy.polynomial.polynomial.polyvander:
  - 1153
  - 1156
  - 1158
  - 1160
  - 1162
  - 1099
  - 1149
  - 1150
  numpy.polynomial.polynomial.polyvander2d:
  - 1241
  - 1166
  numpy.polynomial.polynomial.polyvander3d:
  - 1320
  - 1245
  numpy.polynomial.polyutils:
  - 1
  numpy.polynomial.polyutils._add:
  - 556
  - 565
  - 558
  - 559
  numpy.polynomial.polyutils._as_int:
  - 704
  - 720
  numpy.polynomial.polyutils._div:
  - 545
  - 547
  - 548
  - 520
  - 552
  - 534
  - 535
  - 538
  - 539
  - 540
  - 542
  numpy.polynomial.polyutils._fit:
  - 640
  - 641
  - 643
  - 647
  - 651
  - 652
  - 653
  - 655
  - 660
  - 662
  - 664
  - 583
  - 593
  - 594
  - 595
  - 598
  - 600
  - 602
  - 604
  - 606
  - 608
  - 611
  - 614
  - 616
  - 618
  - 619
  - 624
  - 625
  - 626
  - 628
  - 636
  - 637
  numpy.polynomial.polyutils._fromroots:
  - 456
  - 457
  - 459
  - 460
  - 461
  - 462
  - 464
  - 465
  - 466
  - 467
  - 444
  numpy.polynomial.polyutils._gridnd:
  - 504
  - 514
  - 515
  numpy.polynomial.polyutils._nth_slice:
  - 360
  - 361
  numpy.polynomial.polyutils._pow:
  - 684
  - 685
  - 686
  - 688
  - 690
  - 691
  - 692
  - 698
  - 699
  - 671
  numpy.polynomial.polyutils._sub:
  - 569
  - 579
  - 571
  - 572
  numpy.polynomial.polyutils._valnd:
  - 484
  - 486
  - 487
  - 489
  - 493
  - 494
  - 497
  - 498
  - 499
  - 474
  numpy.polynomial.polyutils._vander_nd:
  - 416
  - 420
  - 425
  - 426
  - 365
  - 430
  - 409
  - 410
  - 413
  numpy.polynomial.polyutils._vander_nd_flat:
  - 440
  - 434
  - 439
  numpy.polynomial.polyutils.as_series:
  - 128
  - 129
  - 130
  - 67
  - 132
  - 134
  - 137
  - 140
  - 117
  - 118
  - 119
  - 121
  - 123
  - 124
  - 126
  numpy.polynomial.polyutils.format_float:
  - 737
  - 738
  - 739
  - 743
  - 746
  - 747
  - 750
  - 753
  - 726
  - 727
  - 729
  - 731
  - 733
  numpy.polynomial.polyutils.getdomain:
  - 195
  - 233
  - 234
  - 235
  - 236
  - 237
  - 239
  numpy.polynomial.polyutils.mapdomain:
  - 352
  - 289
  - 354
  - 353
  numpy.polynomial.polyutils.mapparms:
  - 242
  numpy.polynomial.polyutils.trimcoef:
  - 192
  - 145
  - 184
  - 187
  - 188
  - 189
  numpy.polynomial.polyutils.trimseq:
  - 57
  - 60
  - 61
  - 38
  numpy.polynomial.set_default_printstyle:
  - 137
  - 179
  - 173
  numpy.random:
  - 1
  - 214
  numpy.random.__RandomState_ctor:
  - 210
  - 198
  numpy.random._examples.cffi.extending:
  - 32
  - 1
  - 37
  - 39
  - 8
  - 10
  - 13
  - 19
  - 21
  - 25
  - 26
  - 31
  numpy.random._examples.cffi.parse.parse_distributions_h:
  - 5
  - 12
  - 14
  - 16
  - 18
  - 19
  - 21
  - 25
  - 27
  - 28
  - 31
  - 35
  - 41
  - 42
  - 44
  - 45
  - 46
  - 50
  - 51
  - 52
  - 53
  numpy.random._examples.numba.extending:
  - 35
  - 7
  - 72
  - 41
  - 42
  - 75
  - 46
  - 47
  - 48
  - 49
  - 82
  - 56
  - 28
  numpy.random._examples.numba.extending.bounded_uint:
  - 65
  - 67
  numpy.random._examples.numba.extending.bounded_uints:
  - 77
  - 78
  - 79
  numpy.random._examples.numba.extending.normals:
  - 12
  - 13
  - 14
  - 15
  - 18
  - 19
  - 21
  - 23
  numpy.random._examples.numba.extending.numbacall:
  - 33
  numpy.random._examples.numba.extending.numpycall:
  - 38
  numpy.random._examples.numba.extending_distributions:
  - 64
  - 1
  - 66
  - 35
  - 36
  - 37
  - 38
  - 39
  - 67
  - 43
  - 46
  - 60
  numpy.random._examples.numba.extending_distributions.normals:
  - 56
  - 54
  - 55
  numpy.random._pickle.__bit_generator_ctor:
  - 32
  - 41
  - 34
  - 19
  numpy.random._pickle.__generator_ctor:
  - 64
  - 66
  - 46
  - 63
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ep:
  func_name: TestOneInput
  module: '...fuzz_bn'
