Python numpy.trunc() Examples
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Example #1
Source File: utils.py From VIP with MIT License | 6 votes |
def idl_round(x): """ Round to the *nearest* integer, half-away-from-zero. Parameters ---------- x : array-like Number or array to be rounded Returns ------- r_rounded : array-like note that the returned values are floats Notes ----- IDL ``ROUND`` rounds to the *nearest* integer (commercial rounding), unlike numpy's round/rint, which round to the nearest *even* value (half-to-even, financial rounding) as defined in IEEE-754 standard. """ return np.trunc(x + np.copysign(0.5, x))
Example #2
Source File: image_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def test_adjust_gamma_less_one(self): """Verifying the output with expected results for gamma correction with gamma equal to half""" with self.test_session(): x_np = np.arange(0, 255, 4, np.uint8).reshape(8,8) y = image_ops.adjust_gamma(x_np, gamma=0.5) y_tf = np.trunc(y.eval()) y_np = np.array([[ 0, 31, 45, 55, 63, 71, 78, 84], [ 90, 95, 100, 105, 110, 115, 119, 123], [127, 131, 135, 139, 142, 146, 149, 153], [156, 159, 162, 165, 168, 171, 174, 177], [180, 183, 186, 188, 191, 194, 196, 199], [201, 204, 206, 209, 211, 214, 216, 218], [221, 223, 225, 228, 230, 232, 234, 236], [238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.float32) self.assertAllClose(y_tf, y_np, 1e-6)
Example #3
Source File: image_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def test_adjust_gamma_greater_one(self): """Verifying the output with expected results for gamma correction with gamma equal to two""" with self.test_session(): x_np = np.arange(0, 255, 4, np.uint8).reshape(8,8) y = image_ops.adjust_gamma(x_np, gamma=2) y_tf = np.trunc(y.eval()) y_np = np.array([[ 0, 0, 0, 0, 1, 1, 2, 3], [ 4, 5, 6, 7, 9, 10, 12, 14], [ 16, 18, 20, 22, 25, 27, 30, 33], [ 36, 39, 42, 45, 49, 52, 56, 60], [ 64, 68, 72, 76, 81, 85, 90, 95], [100, 105, 110, 116, 121, 127, 132, 138], [144, 150, 156, 163, 169, 176, 182, 189], [196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.float32) self.assertAllClose(y_tf, y_np, 1e-6)
Example #4
Source File: RQGA.py From QuantumGeneticAlgorithms with MIT License | 6 votes |
def RQGA(n, string_num): psi_=psi(string_num) H=hadamard(n) psi_=np.dot(H,psi_) print(psi_) print() iter=np.trunc(maxiter(n)) iter=int(round(iter)) for i in range (1,iter): U_O=U_Oracle(n) print(U_O) print() psi_=np.dot(U_O,psi_) print(psi_) print() D=ia(n) psi_=np.dot(D,psi_) print(psi_) ######################################################### # # # MAIN PROGRAM # # # #########################################################
Example #5
Source File: virtual_pwsMagnet.py From qkit with GNU General Public License v2.0 | 6 votes |
def check_for_quench(self, wait=5, threshold=0.95, repetitions=1000): ''' Checks the magent for quench Input: wait (float) : waiting time in sec, default=5 threshold (float) : maximum voltage in volts, default=0.95 repetitions (int) : number of times quenching is checked, default=1000 Output: None ''' logging.debug(__name__ + 'check_for_quench()') for i in range(repetitions): time.sleep(wait) voltage = self.do_get_voltage() current = self.do_get_current() print "V=" + str(np.trunc(voltage * 1000) / 1000.) + "V, I=" + str( np.trunc(current * 1000) / 1000.) + "A, R=" + str(int(np.trunc(voltage / current * 1000))) + "mOhm" if voltage > threshold: print "WARNING! Magnet quench!! ramping down the coil..." self.ramp_current(0., 2e-3, wait=0.2, showvalue=True) return
Example #6
Source File: hope_numpy.py From hope with GNU General Public License v3.0 | 6 votes |
def numpy_math(x): # **Trigonometric functions** a = np.sin(x) # Trigonometric sine, element-wise. a = np.cos(x) # Cosine elementwise. a = np.tan(x) # Compute tangent element-wise. a = np.arcsin(x) #Inverse sine, element-wise. a = np.arccos(x) #Trigonometric inverse cosine, element-wise. a = np.arctan(x) #Trigonometric inverse tangent, element-wise. # **Hyperbolic functions** a = np.sinh(x) # Hyperbolic sine, element-wise. a = np.cosh(x) # Hyperbolic cosine, element-wise. a = np.tanh(x) # Compute hyperbolic tangent element-wise. # **Miscellaneous** a = np.exp(x) # Calculate the exponential of all elements in the input array. a = np.sum(x) # Return the sum of array elements. a = np.sqrt(x) # Return the positive square-root of an array, element-wise. a = np.ceil(x) # Return the ceiling of the input, element-wise. a = np.floor(x) # Return the floor of the input, element-wise. a = np.trunc(x) # Return the truncated value of the input, element-wise. a = np.fabs(x) # Compute the absolute values element-wise a = np.pi # Returns the pi constant
Example #7
Source File: test_op_level3.py From incubator-tvm with Apache License 2.0 | 6 votes |
def test_unary_identity(): for op, ref in [(relay.zeros_like, np.zeros_like), (relay.ones_like, np.ones_like), (relay.ceil, np.ceil), (relay.floor, np.floor), (relay.trunc, np.trunc), (relay.round, np.round), (relay.abs, np.abs), (relay.copy, None), # np.copy (relay.negative, np.negative), (relay.sign, np.sign)]: shape = (8, 9, 4) x = relay.var("x", relay.TensorType(shape, "float32")) y = op(x) yy = run_infer_type(y) assert yy.checked_type == relay.TensorType(shape, "float32") if ref is not None: data = np.random.rand(*shape).astype('float32') intrp = create_executor() op_res = intrp.evaluate(y, { x: relay.const(data) }) ref_res = ref(data) np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
Example #8
Source File: mini_batch_handler.py From recnet with MIT License | 5 votes |
def check_out_data_set(self): for set in ['train', 'valid', 'test']: if self.prm.data[set + "_data_name"] != None: file_name = self.prm.data["data_location"] + self.prm.data[set + "_data_name"] try: d = klepto.archives.file_archive(file_name, cached=True,serialized=True) d.load() data_set_x = d['x'] data_set_y = d['y'] d.clear() self.prm.data[set + "_set_len"] = data_set_x.__len__() if data_set_x.__len__() != data_set_y.__len__(): raise Warning("x and y " + set + "_data_name have not the same length") self.prm.data["x_size"] = data_set_x[0].shape[1] if self.prm.data["x_size"] != int(self.prm.struct["net_size"][0]): raise Warning(set + " data x size and net input size are unequal") if self.prm.optimize['CTC'] == False: self.prm.data["y_size"] = data_set_y[0].shape[1] if self.prm.data["y_size"] != int(self.prm.struct["net_size"][-1]): raise Warning(set + " data y size and net input size are unequal") else: self.prm.data["y_size"] = self.prm.struct["net_size"][-1] del data_set_x del data_set_y self.prm.data[set + "_batch_quantity"] = int(np.trunc(self.prm.data[set + "_set_len" ]/self.prm.data["batch_size"])) self.prm.data["checked_data"][set] = True except KeyError: raise Warning("data_location or " + set + "_data_name wrong") ###### Create mini batches and storage them in klepto files ########################################
Example #9
Source File: cross_val.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
def __init__(self, n, k): """ K-Folds cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements k: int number of folds Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]] >>> y = [1, 2, 3, 4] >>> kf = cross_val.KFold(4, k=2) >>> for train_index, test_index in kf: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) TRAIN: [False False True True] TEST: [ True True False False] TRAIN: [ True True False False] TEST: [False False True True] Notes ----- All the folds have size trunc(n/k), the last one has the complementary """ assert k>0, ValueError('cannot have k below 1') assert k<n, ValueError('cannot have k=%d greater than %d'% (k, n)) self.n = n self.k = k
Example #10
Source File: test_functions.py From hope with GNU General Public License v3.0 | 5 votes |
def test_func_trunc(a, b, c): return np.trunc(a)
Example #11
Source File: formats.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
def set_jds(self, val1, val2): self._check_scale(self._scale) # Validate scale. sum12, err12 = two_sum(val1, val2) iy_start = np.trunc(sum12).astype(int) extra, y_frac = two_sum(sum12, -iy_start) y_frac += extra + err12 val = (val1 + val2).astype(np.double) iy_start = np.trunc(val).astype(int) imon = np.ones_like(iy_start) iday = np.ones_like(iy_start) ihr = np.zeros_like(iy_start) imin = np.zeros_like(iy_start) isec = np.zeros_like(y_frac) # Possible enhancement: use np.unique to only compute start, stop # for unique values of iy_start. scale = self.scale.upper().encode('ascii') jd1_start, jd2_start = erfa.dtf2d(scale, iy_start, imon, iday, ihr, imin, isec) jd1_end, jd2_end = erfa.dtf2d(scale, iy_start + 1, imon, iday, ihr, imin, isec) t_start = Time(jd1_start, jd2_start, scale=self.scale, format='jd') t_end = Time(jd1_end, jd2_end, scale=self.scale, format='jd') t_frac = t_start + (t_end - t_start) * y_frac self.jd1, self.jd2 = day_frac(t_frac.jd1, t_frac.jd2)
Example #12
Source File: anomaly_detect_ts.py From AnomalyDetection with Apache License 2.0 | 5 votes |
def _get_max_outliers(data, max_percent_anomalies): """ Calculates the max_outliers for an input data set data : pandas DataFrame the input data set max_percent_anomalies : float the input maximum number of anomalies per percent of data set values """ max_outliers = int(np.trunc(data.size * max_percent_anomalies)) assert max_outliers, 'With longterm=True, AnomalyDetection splits the data into 2 week periods by default. You have {0} observations in a period, which is too few. Set a higher piecewise_median_period_weeks.'.format( data.size) return max_outliers
Example #13
Source File: test_operations.py From myia with MIT License | 5 votes |
def test_elemwise_trunc(a): return np.trunc(a)
Example #14
Source File: test_operations.py From myia with MIT License | 5 votes |
def test_trunc(a): return math.trunc(a)
Example #15
Source File: circlefit.py From qkit with GNU General Public License v2.0 | 5 votes |
def _periodic_boundary(self,x,bound): return np.fmod(x,bound)-np.trunc(x/bound)*bound
Example #16
Source File: woebin.py From scorecardpy with MIT License | 5 votes |
def pretty(low, high, n): ''' pretty breakpoints, the same as pretty function in R Params ------ low: minimal value low: maximal value n: number of intervals Returns ------ numpy.ndarray returns a breakpoints array ''' # nicenumber def nicenumber(x): exp = np.trunc(np.log10(abs(x))) f = abs(x) / 10**exp if f < 1.5: nf = 1. elif f < 3.: nf = 2. elif f < 7.: nf = 5. else: nf = 10. return np.sign(x) * nf * 10.**exp # pretty breakpoints d = abs(nicenumber((high-low)/(n-1))) miny = np.floor(low / d) * d maxy = np.ceil (high / d) * d return np.arange(miny, maxy+0.5*d, d) # required in woebin2 # return initial binning
Example #17
Source File: circlefit.py From resonator_tools with GNU General Public License v2.0 | 5 votes |
def _periodic_boundary(self,x,bound): return np.fmod(x,bound)-np.trunc(x/bound)*bound
Example #18
Source File: loda.py From ad_examples with MIT License | 5 votes |
def pdf_hist_equal_bins(x, h, minpdf=1e-8): # here we are assuming a regular histogram where # h.breaks[1] - h.breaks[0] would return the width of the bin p = (x - h.breaks[0]) / (h.breaks[1] - h.breaks[0]) ndensity = len(h.density) p = np.array([min(int(np.trunc(v)), ndensity-1) for v in p]) d = h.density[p] # quick hack to make sure d is never 0 d = np.array([max(v, minpdf) for v in d]) return d
Example #19
Source File: loda.py From ad_examples with MIT License | 5 votes |
def get_bin_for_equal_hist(breaks, x): if x < breaks[0]: return 0 if x > breaks[len(breaks)-1]: return len(breaks)-1 i = np.trunc((x - breaks[0]) / (breaks[1] - breaks[0])) # get integral value return int(i)
Example #20
Source File: topography.py From typhon with MIT License | 5 votes |
def get_native_grids(lat_min, lon_min, lat_max, lon_max): """ Returns the latitude and longitude grid at native SRTM30 resolution that are included in the given rectangle. Args: lat_min: The latitude coordinate of the lower left corner. lon_min: The longitude coordinate of the lower left corner. lat_max: The latitude coordinate of the upper right corner. lon_max: The latitude coordinate of the upper right corner. Returns: Tuple :code:`(lats, lons)` of 1D-arrays containing the latitude and longitude coordinates of the SRTM30 data points within the given rectangle. """ i = (90 - lat_max) / SRTM30._dlat i_max = np.trunc(i) if not i_max < i: i_max = i_max + 1 i = (90 - lat_min) / SRTM30._dlat i_min = np.trunc(i) lat_grid = 90 + 0.5 * SRTM30._dlat - np.arange(i_max, i_min + 1) * SRTM30._dlat j = (lon_max + 180) / SRTM30._dlon j_max = np.trunc((lon_max + 180.0) / SRTM30._dlon) if not j_max < j: j_max = j_max - 1 j_min = np.trunc((lon_min + 180.0) / SRTM30._dlon) lon_grid = -180 + 0.5 * SRTM30._dlon lon_grid += np.arange(j_min, j_max + 1) * SRTM30._dlon return lat_grid, lon_grid
Example #21
Source File: rasterlayer.py From Pyspatialml with GNU General Public License v3.0 | 5 votes |
def __trunc__(self): """Truncating to an integral using math.trunc(), i.e. math.trunc(layer) """ def func(arr): return np.trunc(arr) return self._arith(func)
Example #22
Source File: test_var.py From attention-lvcsr with MIT License | 5 votes |
def test_numpy_method(): # This type of code is used frequently by PyMC3 users x = tt.dmatrix('x') data = np.random.rand(5, 5) x.tag.test_value = data for fct in [np.arccos, np.arccosh, np.arcsin, np.arcsinh, np.arctan, np.arctanh, np.ceil, np.cos, np.cosh, np.deg2rad, np.exp, np.exp2, np.expm1, np.floor, np.log, np.log10, np.log1p, np.log2, np.rad2deg, np.sin, np.sinh, np.sqrt, np.tan, np.tanh, np.trunc]: y = fct(x) f = theano.function([x], y) utt.assert_allclose(np.nan_to_num(f(data)), np.nan_to_num(fct(data)))
Example #23
Source File: Unit.py From PySpice with GNU General Public License v3.0 | 5 votes |
def __trunc__(self): """trunc(self): Truncates self to an Integral. Returns an Integral i such that: * i>0 iff self>0; * abs(i) <= abs(self); * for any Integral j satisfying the first two conditions, abs(i) >= abs(j) [i.e. i has "maximal" abs among those]. i.e. "truncate towards 0". """ raise NotImplementedError ##############################################
Example #24
Source File: trunc.py From mars with Apache License 2.0 | 5 votes |
def trunc(x, out=None, where=None, **kwargs): """ Return the truncated value of the input, element-wise. The truncated value of the scalar `x` is the nearest integer `i` which is closer to zero than `x` is. In short, the fractional part of the signed number `x` is discarded. Parameters ---------- x : array_like Input data. out : Tensor, None, or tuple of Tensor and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or `None`, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs Returns ------- y : Tensor or scalar The truncated value of each element in `x`. See Also -------- ceil, floor, rint Examples -------- >>> import mars.tensor as mt >>> a = mt.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) >>> mt.trunc(a).execute() array([-1., -1., -0., 0., 1., 1., 2.]) """ op = TensorTrunc(**kwargs) return op(x, out=out, where=where)
Example #25
Source File: test_var.py From D-VAE with MIT License | 5 votes |
def test_numpy_method(): # This type of code is used frequently by PyMC3 users x = tt.dmatrix('x') data = np.random.rand(5, 5) x.tag.test_value = data for fct in [np.arccos, np.arccosh, np.arcsin, np.arcsinh, np.arctan, np.arctanh, np.ceil, np.cos, np.cosh, np.deg2rad, np.exp, np.exp2, np.expm1, np.floor, np.log, np.log10, np.log1p, np.log2, np.rad2deg, np.sin, np.sinh, np.sqrt, np.tan, np.tanh, np.trunc]: y = fct(x) f = theano.function([x], y) utt.assert_allclose(np.nan_to_num(f(data)), np.nan_to_num(fct(data)))
Example #26
Source File: basic.py From D-VAE with MIT License | 5 votes |
def impl(self, x): return numpy.trunc(x)
Example #27
Source File: cross_val.py From vnpy_crypto with MIT License | 5 votes |
def __init__(self, n, k): """ K-Folds cross validation iterator: Provides train/test indexes to split data in train test sets Parameters ---------- n: int Total number of elements k: int number of folds Examples -------- >>> from scikits.learn import cross_val >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]] >>> y = [1, 2, 3, 4] >>> kf = cross_val.KFold(4, k=2) >>> for train_index, test_index in kf: ... print "TRAIN:", train_index, "TEST:", test_index ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y) TRAIN: [False False True True] TEST: [ True True False False] TRAIN: [ True True False False] TEST: [False False True True] Notes ----- All the folds have size trunc(n/k), the last one has the complementary """ assert k>0, ValueError('cannot have k below 1') assert k<n, ValueError('cannot have k=%d greater than %d'% (k, n)) self.n = n self.k = k
Example #28
Source File: thresholding.py From nistats with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _compute_hommel_value(z_vals, alpha, verbose=False): """Compute the All-Resolution Inference hommel-value""" if alpha < 0 or alpha > 1: raise ValueError('alpha should be between 0 and 1') z_vals_ = - np.sort(- z_vals) p_vals = norm.sf(z_vals_) n_samples = len(p_vals) if len(p_vals) == 1: return p_vals[0] > alpha if p_vals[0] > alpha: return n_samples slopes = (alpha - p_vals[: - 1]) / np.arange(n_samples, 1, -1) slope = np.max(slopes) hommel_value = np.trunc(n_samples + (alpha - slope * n_samples) / slope) if verbose: try: from matplotlib import pyplot as plt except ImportError: warnings.warn('"verbose" option requires the package Matplotlib.' 'Please install it using `pip install matplotlib`.') else: plt.figure() plt.plot(p_vals, 'o') plt.plot([n_samples - hommel_value, n_samples], [0, alpha]) plt.plot([0, n_samples], [0, 0], 'k') plt.show(block=False) return np.minimum(hommel_value, n_samples)
Example #29
Source File: report_card.py From nussl with MIT License | 5 votes |
def truncate(values, decs=2): return np.trunc(values*10**decs)/(10**decs)
Example #30
Source File: numeric_functions.py From nufhe with GNU General Public License v3.0 | 5 votes |
def double_to_t32(d: float): return ((d - numpy.trunc(d)) * 2**32).astype(Torus32)