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cost = numel(output).", "numpy_ref": "np.random.f", "slug": "random-f", "status": "supported", "summary": "Draw samples from an F distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/gamma", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/gamma/", "detail_json_href": "/api-data/ops/random-gamma.json", "display_type": "custom", "flopscope_ref": "fnp.random.gamma", "free": false, "module": "numpy.random", "name": "random.gamma", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.gamma", "slug": "random-gamma", "status": "supported", "summary": "Draw samples from a Gamma distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/geometric", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/geometric/", "detail_json_href": "/api-data/ops/random-geometric.json", "display_type": "custom", "flopscope_ref": "fnp.random.geometric", "free": false, "module": "numpy.random", "name": "random.geometric", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.geometric", "slug": "random-geometric", "status": "supported", "summary": "Draw samples from the geometric distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/get-state", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/random/get-state/", "detail_json_href": "/api-data/ops/random-get_state.json", "display_type": "free", "flopscope_ref": "fnp.random.get_state", "free": true, "module": "numpy.random", "name": "random.get_state", "notes": "Return tuple representing internal state of generator.", "numpy_ref": "np.random.get_state", "slug": "random-get_state", "status": "supported", "summary": "Return a tuple representing the internal state of the generator.", "weight": 0.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/gumbel", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/gumbel/", "detail_json_href": "/api-data/ops/random-gumbel.json", "display_type": "custom", "flopscope_ref": "fnp.random.gumbel", "free": false, "module": "numpy.random", "name": "random.gumbel", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.gumbel", "slug": "random-gumbel", "status": "supported", "summary": "Draw samples from a Gumbel distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/hypergeometric", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/hypergeometric/", "detail_json_href": "/api-data/ops/random-hypergeometric.json", "display_type": "custom", "flopscope_ref": "fnp.random.hypergeometric", "free": false, "module": "numpy.random", "name": "random.hypergeometric", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.hypergeometric", "slug": "random-hypergeometric", "status": "supported", "summary": "Draw samples from a Hypergeometric distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/laplace", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/laplace/", "detail_json_href": "/api-data/ops/random-laplace.json", "display_type": "custom", "flopscope_ref": "fnp.random.laplace", "free": false, "module": "numpy.random", "name": "random.laplace", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.laplace", "slug": "random-laplace", "status": "supported", "summary": "Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/logistic", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/logistic/", "detail_json_href": "/api-data/ops/random-logistic.json", "display_type": "custom", "flopscope_ref": "fnp.random.logistic", "free": false, "module": "numpy.random", "name": "random.logistic", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.logistic", "slug": "random-logistic", "status": "supported", "summary": "Draw samples from a logistic distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/lognormal", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/lognormal/", "detail_json_href": "/api-data/ops/random-lognormal.json", "display_type": "custom", "flopscope_ref": "fnp.random.lognormal", "free": false, "module": "numpy.random", "name": "random.lognormal", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.lognormal", "slug": "random-lognormal", "status": "supported", "summary": "Draw samples from a log-normal distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/logseries", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/logseries/", "detail_json_href": "/api-data/ops/random-logseries.json", "display_type": "custom", "flopscope_ref": "fnp.random.logseries", "free": false, "module": "numpy.random", "name": "random.logseries", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.logseries", "slug": "random-logseries", "status": "supported", "summary": "Draw samples from a logarithmic series distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/multinomial", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/multinomial/", "detail_json_href": "/api-data/ops/random-multinomial.json", "display_type": "custom", "flopscope_ref": "fnp.random.multinomial", "free": false, "module": "numpy.random", "name": "random.multinomial", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.multinomial", "slug": "random-multinomial", "status": "supported", "summary": "Draw samples from a multinomial distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/multivariate-normal", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/multivariate-normal/", "detail_json_href": "/api-data/ops/random-multivariate_normal.json", "display_type": "custom", "flopscope_ref": "fnp.random.multivariate_normal", "free": false, "module": "numpy.random", "name": "random.multivariate_normal", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.multivariate_normal", "slug": "random-multivariate_normal", "status": "supported", "summary": "Draw random samples from a multivariate normal distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/negative-binomial", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/negative-binomial/", "detail_json_href": "/api-data/ops/random-negative_binomial.json", "display_type": "custom", "flopscope_ref": "fnp.random.negative_binomial", "free": false, "module": "numpy.random", "name": "random.negative_binomial", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.negative_binomial", "slug": "random-negative_binomial", "status": "supported", "summary": "Draw samples from a negative binomial distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/noncentral-chisquare", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/noncentral-chisquare/", "detail_json_href": "/api-data/ops/random-noncentral_chisquare.json", "display_type": "custom", "flopscope_ref": "fnp.random.noncentral_chisquare", "free": false, "module": "numpy.random", "name": "random.noncentral_chisquare", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.noncentral_chisquare", "slug": "random-noncentral_chisquare", "status": "supported", "summary": "Draw samples from a noncentral chi-square distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/noncentral-f", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/noncentral-f/", "detail_json_href": "/api-data/ops/random-noncentral_f.json", "display_type": "custom", "flopscope_ref": "fnp.random.noncentral_f", "free": false, "module": "numpy.random", "name": "random.noncentral_f", "notes": "Sampling; 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cost = numel(output).", "numpy_ref": "np.random.poisson", "slug": "random-poisson", "status": "supported", "summary": "Draw samples from a Poisson distribution.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/power", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/power/", "detail_json_href": "/api-data/ops/random-power.json", "display_type": "custom", "flopscope_ref": "fnp.random.power", "free": false, "module": "numpy.random", "name": "random.power", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.power", "slug": "random-power", "status": "supported", "summary": "Draws samples in [0, 1] from a power distribution with positive exponent a - 1.", "weight": 16.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/rand", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/rand/", "detail_json_href": "/api-data/ops/random-rand.json", "display_type": "custom", "flopscope_ref": "fnp.random.rand", "free": false, "module": "numpy.random", "name": "random.rand", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.rand", "slug": "random-rand", "status": "supported", "summary": "Random values in a given shape.", "weight": 1.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/randint", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/randint/", "detail_json_href": "/api-data/ops/random-randint.json", "display_type": "custom", "flopscope_ref": "fnp.random.randint", "free": false, "module": "numpy.random", "name": "random.randint", "notes": "Sampling; cost = numel(output).", "numpy_ref": "np.random.randint", "slug": "random-randint", "status": "supported", "summary": "Return random integers from `low` (inclusive) to `high` (exclusive).", "weight": 1.0 }, { "area": "random", "blocked": false, "canonical_path": "numpy/random/randn", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/random/randn/", "detail_json_href": "/api-data/ops/random-randn.json", "display_type": "custom", "flopscope_ref": "fnp.random.randn", "free": false, "module": "numpy.random", "name": "random.randn", "notes": "Sampling; 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cost = numel(input).", "numpy_ref": "scipy.stats.norm.cdf", "slug": "stats-norm-cdf", "status": "supported", "summary": "Evaluate the cumulative distribution function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/norm/pdf", "category": "counted_custom", "cost_formula": "10n", "cost_formula_latex": "$10n$", "detail_href": "/docs/api/stats/norm/pdf/", "detail_json_href": "/api-data/ops/stats-norm-pdf.json", "display_type": "custom", "flopscope_ref": "flops.stats.norm.pdf", "free": false, "module": "flopscope.stats", "name": "stats.norm.pdf", "notes": "Normal PDF; cost = numel(input).", "numpy_ref": "scipy.stats.norm.pdf", "slug": "stats-norm-pdf", "status": "supported", "summary": "Evaluate the probability density function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/norm/ppf", "category": "counted_custom", "cost_formula": "40n", "cost_formula_latex": "$40n$", "detail_href": "/docs/api/stats/norm/ppf/", "detail_json_href": "/api-data/ops/stats-norm-ppf.json", "display_type": "custom", "flopscope_ref": "flops.stats.norm.ppf", "free": false, "module": "flopscope.stats", "name": "stats.norm.ppf", "notes": "Normal PPF (inverse CDF); cost = numel(input).", "numpy_ref": "scipy.stats.norm.ppf", "slug": "stats-norm-ppf", "status": "supported", "summary": "Evaluate the percent-point function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/truncnorm/cdf", "category": "counted_custom", "cost_formula": "30n", "cost_formula_latex": "$30n$", "detail_href": "/docs/api/stats/truncnorm/cdf/", "detail_json_href": "/api-data/ops/stats-truncnorm-cdf.json", "display_type": "custom", "flopscope_ref": "flops.stats.truncnorm.cdf", "free": false, "module": "flopscope.stats", "name": "stats.truncnorm.cdf", "notes": "Truncated normal CDF; cost = numel(input).", "numpy_ref": "scipy.stats.truncnorm.cdf", "slug": "stats-truncnorm-cdf", "status": "supported", "summary": "Evaluate the cumulative distribution function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/truncnorm/pdf", "category": "counted_custom", "cost_formula": "30n", "cost_formula_latex": "$30n$", "detail_href": "/docs/api/stats/truncnorm/pdf/", "detail_json_href": "/api-data/ops/stats-truncnorm-pdf.json", "display_type": "custom", "flopscope_ref": "flops.stats.truncnorm.pdf", "free": false, "module": "flopscope.stats", "name": "stats.truncnorm.pdf", "notes": "Truncated normal PDF; cost = numel(input).", "numpy_ref": "scipy.stats.truncnorm.pdf", "slug": "stats-truncnorm-pdf", "status": "supported", "summary": "Evaluate the probability density function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/truncnorm/ppf", "category": "counted_custom", "cost_formula": "50n", "cost_formula_latex": "$50n$", "detail_href": "/docs/api/stats/truncnorm/ppf/", "detail_json_href": "/api-data/ops/stats-truncnorm-ppf.json", "display_type": "custom", "flopscope_ref": "flops.stats.truncnorm.ppf", "free": false, "module": "flopscope.stats", "name": "stats.truncnorm.ppf", "notes": "Truncated normal PPF; cost = numel(input).", "numpy_ref": "scipy.stats.truncnorm.ppf", "slug": "stats-truncnorm-ppf", "status": "supported", "summary": "Evaluate the percent-point function.", "weight": 16.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/uniform/cdf", "category": "counted_custom", "cost_formula": "3n", "cost_formula_latex": "$3n$", "detail_href": "/docs/api/stats/uniform/cdf/", "detail_json_href": "/api-data/ops/stats-uniform-cdf.json", "display_type": "custom", "flopscope_ref": "flops.stats.uniform.cdf", "free": false, "module": "flopscope.stats", "name": "stats.uniform.cdf", "notes": "Uniform CDF; cost = numel(input).", "numpy_ref": "scipy.stats.uniform.cdf", "slug": "stats-uniform-cdf", "status": "supported", "summary": "Evaluate the cumulative distribution function.", "weight": 1.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/uniform/pdf", "category": "counted_custom", "cost_formula": "3n", "cost_formula_latex": "$3n$", "detail_href": "/docs/api/stats/uniform/pdf/", "detail_json_href": "/api-data/ops/stats-uniform-pdf.json", "display_type": "custom", "flopscope_ref": "flops.stats.uniform.pdf", "free": false, "module": "flopscope.stats", "name": "stats.uniform.pdf", "notes": "Uniform PDF; cost = numel(input).", "numpy_ref": "scipy.stats.uniform.pdf", "slug": "stats-uniform-pdf", "status": "supported", "summary": "Evaluate the probability density function.", "weight": 1.0 }, { "area": "stats", "blocked": false, "canonical_path": "stats/uniform/ppf", "category": "counted_custom", "cost_formula": "3n", "cost_formula_latex": "$3n$", "detail_href": "/docs/api/stats/uniform/ppf/", "detail_json_href": "/api-data/ops/stats-uniform-ppf.json", "display_type": "custom", "flopscope_ref": "flops.stats.uniform.ppf", "free": false, "module": "flopscope.stats", "name": "stats.uniform.ppf", "notes": "Uniform PPF; cost = numel(input).", "numpy_ref": "scipy.stats.uniform.ppf", "slug": "stats-uniform-ppf", "status": "supported", "summary": "Evaluate the percent-point function.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/std", "category": "counted_reduction", "cost_formula": "numel(input)", "cost_formula_latex": "$\\text{numel}(\\text{input})$", "detail_href": "/docs/api/numpy/std/", "detail_json_href": "/api-data/ops/std.json", "display_type": "counted", "flopscope_ref": "fnp.std", "free": false, "module": "numpy", "name": "std", "notes": "Standard deviation; cost_multiplier=2 (two passes).", "numpy_ref": "np.std", "slug": "std", "status": "supported", "summary": "Compute the standard deviation along the specified axis.", "weight": 2.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/subtract", "category": "counted_binary", "cost_formula": "numel(output)", "cost_formula_latex": "$\\text{numel}(\\text{output})$", "detail_href": "/docs/api/numpy/subtract/", "detail_json_href": "/api-data/ops/subtract.json", "display_type": "counted", "flopscope_ref": "fnp.subtract", "free": false, "module": "numpy", "name": "subtract", "notes": "Element-wise subtraction.", "numpy_ref": "np.subtract", "slug": "subtract", "status": "supported", "summary": "Subtract arguments, element-wise.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/sum", "category": "counted_reduction", "cost_formula": "numel(input)", "cost_formula_latex": "$\\text{numel}(\\text{input})$", "detail_href": "/docs/api/numpy/sum/", "detail_json_href": "/api-data/ops/sum.json", "display_type": "counted", "flopscope_ref": "fnp.sum", "free": false, "module": "numpy", "name": "sum", "notes": "Sum of array elements.", "numpy_ref": "np.sum", "slug": "sum", "status": "supported", "summary": "Sum of array elements over a given axis.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/swapaxes", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/swapaxes/", "detail_json_href": "/api-data/ops/swapaxes.json", "display_type": "free", "flopscope_ref": "fnp.swapaxes", "free": true, "module": "numpy", "name": "swapaxes", "notes": "Interchange two axes of an array.", "numpy_ref": "np.swapaxes", "slug": "swapaxes", "status": "supported", "summary": "Interchange two axes of an array.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/take", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/take/", "detail_json_href": "/api-data/ops/take.json", "display_type": "custom", "flopscope_ref": "fnp.take", "free": false, "module": "numpy", "name": "take", "notes": "Take elements from array along axis. Cost: numel(output).", "numpy_ref": "np.take", "slug": "take", "status": "supported", "summary": "Take elements from an array along an axis.", "weight": 4.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/take-along-axis", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/take-along-axis/", "detail_json_href": "/api-data/ops/take_along_axis.json", "display_type": "custom", "flopscope_ref": "fnp.take_along_axis", "free": false, "module": "numpy", "name": "take_along_axis", "notes": "Take values from input array by matching 1-D index. Cost: numel(output).", "numpy_ref": "np.take_along_axis", "slug": "take_along_axis", "status": "supported", "summary": "Take values from the input array by matching 1d index and data slices.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tan", "category": "counted_unary", "cost_formula": "numel(output)", "cost_formula_latex": "$\\text{numel}(\\text{output})$", "detail_href": "/docs/api/numpy/tan/", "detail_json_href": "/api-data/ops/tan.json", "display_type": "counted", "flopscope_ref": "fnp.tan", "free": false, "module": "numpy", "name": "tan", "notes": "Element-wise tangent.", "numpy_ref": "np.tan", "slug": "tan", "status": "supported", "summary": "Compute tangent element-wise.", "weight": 16.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tanh", "category": "counted_unary", "cost_formula": "numel(output)", "cost_formula_latex": "$\\text{numel}(\\text{output})$", "detail_href": "/docs/api/numpy/tanh/", "detail_json_href": "/api-data/ops/tanh.json", "display_type": "counted", "flopscope_ref": "fnp.tanh", "free": false, "module": "numpy", "name": "tanh", "notes": "Element-wise hyperbolic tangent.", "numpy_ref": "np.tanh", "slug": "tanh", "status": "supported", "summary": "Compute hyperbolic tangent element-wise.", "weight": 16.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tensordot", "category": "counted_custom", "cost_formula": "product of contracted dims * output size", "cost_formula_latex": "$\\prod_i d_i$", "detail_href": "/docs/api/numpy/tensordot/", "detail_json_href": "/api-data/ops/tensordot.json", "display_type": "custom", "flopscope_ref": "fnp.tensordot", "free": false, "module": "numpy", "name": "tensordot", "notes": "Tensor dot product along specified axes.", "numpy_ref": "np.tensordot", "slug": "tensordot", "status": "supported", "summary": "Compute tensor dot product along specified axes.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tile", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/tile/", "detail_json_href": "/api-data/ops/tile.json", "display_type": "custom", "flopscope_ref": "fnp.tile", "free": false, "module": "numpy", "name": "tile", "notes": "Repeat array by tiling. Cost: numel(output).", "numpy_ref": "np.tile", "slug": "tile", "status": "supported", "summary": "Construct an array by repeating A the number of times given by reps.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/trace", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/trace/", "detail_json_href": "/api-data/ops/trace.json", "display_type": "custom", "flopscope_ref": "fnp.trace", "free": false, "module": "numpy", "name": "trace", "notes": "Diagonal sum; cost = min(n,m).", "numpy_ref": "np.trace", "slug": "trace", "status": "supported", "summary": "Return the sum along diagonals of the array.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/transpose", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/transpose/", "detail_json_href": "/api-data/ops/transpose.json", "display_type": "free", "flopscope_ref": "fnp.transpose", "free": true, "module": "numpy", "name": "transpose", "notes": "Permute array dimensions.", "numpy_ref": "np.transpose", "slug": "transpose", "status": "supported", "summary": "Returns an array with axes transposed.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/trapezoid", "category": "counted_custom", "cost_formula": "numel(input)", "cost_formula_latex": "$\\text{numel}(\\text{input})$", "detail_href": "/docs/api/numpy/trapezoid/", "detail_json_href": "/api-data/ops/trapezoid.json", "display_type": "custom", "flopscope_ref": "fnp.trapezoid", "free": false, "module": "numpy", "name": "trapezoid", "notes": "Integrate using the trapezoidal rule.", "numpy_ref": "np.trapezoid", "slug": "trapezoid", "status": "supported", "summary": "Integrate along the given axis using the composite trapezoidal rule.", "weight": 1.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tri", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/tri/", "detail_json_href": "/api-data/ops/tri.json", "display_type": "free", "flopscope_ref": "fnp.tri", "free": true, "module": "numpy", "name": "tri", "notes": "Array with ones at and below given diagonal.", "numpy_ref": "np.tri", "slug": "tri", "status": "supported", "summary": "An array with ones at and below the given diagonal and zeros elsewhere.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tril", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/tril/", "detail_json_href": "/api-data/ops/tril.json", "display_type": "free", "flopscope_ref": "fnp.tril", "free": true, "module": "numpy", "name": "tril", "notes": "Lower triangle of array.", "numpy_ref": "np.tril", "slug": "tril", "status": "supported", "summary": "Lower triangle of an array.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tril-indices", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/tril-indices/", "detail_json_href": "/api-data/ops/tril_indices.json", "display_type": "free", "flopscope_ref": "fnp.tril_indices", "free": true, "module": "numpy", "name": "tril_indices", "notes": "Return lower-triangle indices for n x n array.", "numpy_ref": "np.tril_indices", "slug": "tril_indices", "status": "supported", "summary": "Return the indices for the lower-triangle of an (n, m) array.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/tril-indices-from", "category": "free", "cost_formula": "0", "cost_formula_latex": "$0$", "detail_href": "/docs/api/numpy/tril-indices-from/", "detail_json_href": "/api-data/ops/tril_indices_from.json", "display_type": "free", "flopscope_ref": "fnp.tril_indices_from", "free": true, "module": "numpy", "name": "tril_indices_from", "notes": "Return lower-triangle indices for given array.", "numpy_ref": "np.tril_indices_from", "slug": "tril_indices_from", "status": "supported", "summary": "Return the indices for the lower-triangle of arr.", "weight": 0.0 }, { "area": "core", "blocked": false, "canonical_path": "numpy/trim-zeros", "category": "counted_custom", "cost_formula": "per-operation", "cost_formula_latex": "varies", "detail_href": "/docs/api/numpy/trim-zeros/", "detail_json_href": "/api-data/ops/trim_zeros.json", "display_type": "custom", "flopscope_ref": "fnp.trim_zeros", "free": false, "module": "numpy", "name": "trim_zeros", "notes": "Trim leading/trailing zeros from 1-D array. 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