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Python numpy 模块-get_printoptions() 实例源码（python的numpy模块）

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如果您想了解Pythonnumpy模块-get_printoptions()实例源码的相关知识，那么本文是一篇不可错过的文章，我们将对python的numpy模块进行全面详尽的解释，并且为您提供关于J

如果您想了解Python numpy 模块-get_printoptions() 实例源码的相关知识，那么本文是一篇不可错过的文章，我们将对python的numpy模块进行全面详尽的解释，并且为您提供关于Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable、numpy.random.random & numpy.ndarray.astype & numpy.arange、numpy.ravel()/numpy.flatten()/numpy.squeeze()、Numpy：数组创建 numpy.arrray() , numpy.arange()、np.linspace ()、数组基本属性的有价值的信息。

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Python numpy 模块-get_printoptions() 实例源码（python的numpy模块）

Python numpy 模块-get_printoptions() 实例源码（python的numpy模块）

Python numpy 模块，get_printoptions() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.get_printoptions()。

项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_mean(self, file_list):
  2.  
  3.         logger = logging.getLogger("acoustic_norm")
  4.  
  5.         mean_vector = numpy.zeros((1, self.feature_dimension))
  6.         all_frame_number = 0
  7.  
  8.         io_funcs = BinaryIOCollection()
  9.         for file_name in file_list:
  10.             features = io_funcs.load_binary_file(file_name, self.feature_dimension)
  11.             current_frame_number = features.size // self.feature_dimension
  12.             mean_vector += numpy.reshape(numpy.sum(features, axis=0), (1, self.feature_dimension))
  13.             all_frame_number += current_frame_number
  14.  
  15.         mean_vector /= float(all_frame_number)
  16.  
  17.         # po=numpy.get_printoptions()
  18.         # numpy.set_printoptions(precision=2,threshold=20,linewidth=1000,edgeitems=4)
  19.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  20.         logger.info('' mean: %s'' % mean_vector)
  21.         # restore the print options
  22.         # numpy.set_printoptions(po)
  23.  
  24.         return  mean_vector
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_mean(self, file_list, start_index, end_index):
  2.  
  3.         local_feature_dimension = end_index - start_index
  4.  
  5.         mean_vector = numpy.zeros((1, local_feature_dimension))
  6.         all_frame_number = 0
  7.  
  8.         io_funcs = BinaryIOCollection()
  9.         for file_name in file_list:
  10.             features, current_frame_number = io_funcs.load_binary_file_frame(file_name, self.feature_dimension)
  11.  
  12.             mean_vector += numpy.reshape(numpy.sum(features[:, start_index:end_index], local_feature_dimension))
  13.             all_frame_number += current_frame_number
  14.  
  15.         mean_vector /= float(all_frame_number)
  16.  
  17.         # po=numpy.get_printoptions()
  18.         # numpy.set_printoptions(precision=2,edgeitems=4)
  19.         self.logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  20.         self.logger.info('' mean: %s'' % mean_vector)
  21.         # restore the print options
  22.         # numpy.set_printoptions(po)
  23.  
  24.         return  mean_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_mean(self, self.feature_dimension)
  2.             current_frame_number = features.size / self.feature_dimension
  3.             mean_vector += numpy.reshape(numpy.sum(features,edgeitems=4)
  4.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  5.         logger.info('' mean: %s'' % mean_vector)
  6.         # restore the print options
  7.         # numpy.set_printoptions(po)
  8.  
  9.         return  mean_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         self.logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         self.logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         return  mean_vector
项目：mlens    作者：flennerhag    | 项目源码 | 文件源码 
  1. def pformat(obj, indent=0, depth=3):
  2.     if ''numpy'' in sys.modules:
  3.         import numpy as np
  4.         print_options = np.get_printoptions()
  5.         np.set_printoptions(precision=6, threshold=64, edgeitems=1)
  6.     else:
  7.         print_options = None
  8.     out = pprint.pformat(obj, depth=depth, indent=indent)
  9.     if print_options:
  10.         np.set_printoptions(**print_options)
  11.     return out
  12.  
  13.  
  14. ###############################################################################
  15. # class `Logger`
  16. ###############################################################################
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         return  mean_vector
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         self.logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         self.logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         return  mean_vector
项目：rankpy    作者：dmitru    | 项目源码 | 文件源码 
  1. def pformat(obj, indent=indent)
  2.     if print_options:
  3.         np.set_printoptions(**print_options)
  4.     return out
  5.  
  6.  
  7. ###############################################################################
  8. # class `Logger`
  9. ###############################################################################
项目：polyaxon    作者：polyaxon    | 项目源码 | 文件源码 
  1. def after_run(self, run_context, run_values):
  2.         global_episode = run_values.results[''global_episode'']
  3.         if can_run_hook(run_context):
  4.             if self._timer.should_trigger_for_episode(global_episode):
  5.                 original = np.get_printoptions()
  6.                 np.set_printoptions(suppress=True)
  7.                 elapsed_secs, _ = self._timer.update_last_triggered_episode(global_episode)
  8.                 if self._formatter:
  9.                     logging.info(self._formatter(run_values.results))
  10.                 else:
  11.                     stats = []
  12.                     for tag in self._tag_order:
  13.                         stats.append("%s = %s" % (tag, run_values.results[tag]))
  14.                     if elapsed_secs is not None:
  15.                         logging.info("%s (%.3f sec)", ",".join(stats), elapsed_secs)
  16.                     else:
  17.                         logging.info("%s",".join(stats))
  18.                 np.set_printoptions(**original)
项目：Parallel-SGD    作者：angadgill    | 项目源码 | 文件源码 
  1. def pformat(obj, indent=indent)
  2.     if print_options:
  3.         np.set_printoptions(**print_options)
  4.     return out
  5.  
  6.  
  7. ###############################################################################
  8. # class `Logger`
  9. ###############################################################################
项目：treecat    作者：posterior    | 项目源码 | 文件源码 
  1. def np_printoptions(**kwargs):
  2.     """Context manager to temporarily set numpy print options."""
  3.     old = np.get_printoptions()
  4.     np.set_printoptions(**kwargs)
  5.     yield
  6.     np.set_printoptions(**old)
项目：radar    作者：amoose136    | 项目源码 | 文件源码 
  1. def setUp(self):
  2.         self.oldopts = np.get_printoptions()
项目：IDNNs    作者：ravidziv    | 项目源码 | 文件源码 
  1. def printoptions(*args, **kwargs):
  2.     original = np.get_printoptions()
  3.     np.set_printoptions(*args, **kwargs)
  4.     try:
  5.         yield
  6.     finally:
  7.         np.set_printoptions(**original)
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_mean(self, end_index):
  2.  
  3.         logger = logging.getLogger(''feature_normalisation'')
  4.  
  5.         local_feature_dimension = end_index - start_index
  6.  
  7.         mean_vector = numpy.zeros((1, local_feature_dimension))
  8.         all_frame_number = 0
  9.  
  10.         io_funcs = HTKFeat_read()
  11.         for file_name in file_list:
  12.             features, current_frame_number = io_funcs.getall(file_name)
  13. #            io_funcs = HTK_Parm_IO()
  14. #            io_funcs.read_htk(file_name)
  15. #            features = io_funcs.data
  16. #            current_frame_number = io_funcs.n_samples
  17.  
  18.             mean_vector += numpy.reshape(numpy.sum(features[:, local_feature_dimension))
  19.             all_frame_number += current_frame_number
  20.  
  21.         mean_vector /= float(all_frame_number)
  22.  
  23.         # setting the print options in this way seems to break subsequent printing of numpy float32 types
  24.         # no idea what is going on - removed until this can be solved
  25.         # po=numpy.get_printoptions()
  26.         # numpy.set_printoptions(precision=2,edgeitems=4)
  27.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  28.         logger.info('' mean: %s'' % mean_vector)
  29.         # restore the print options
  30.         # numpy.set_printoptions(po)
  31.  
  32.         self.mean_vector = mean_vector
  33.  
  34.         return  mean_vector
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_std(self, mean_vector, end_index):
  2.  
  3.         logger = logging.getLogger(''feature_normalisation'')
  4.  
  5.         local_feature_dimension = end_index - start_index
  6.  
  7.         std_vector = numpy.zeros((1, self.feature_dimension))
  8.         all_frame_number = 0
  9.  
  10.         io_funcs = HTKFeat_read()
  11.         for file_name in file_list:
  12.             features, current_frame_number = io_funcs.getall(file_name)
  13.  
  14.             mean_matrix = numpy.tile(mean_vector, (current_frame_number, 1))
  15.  
  16.             std_vector += numpy.reshape(numpy.sum((features[:, start_index:end_index] - mean_matrix) ** 2, local_feature_dimension))
  17.             all_frame_number += current_frame_number
  18.  
  19.         std_vector /= float(all_frame_number)
  20.  
  21.         std_vector = std_vector ** 0.5
  22.  
  23.         # setting the print options in this way seems to break subsequent printing of numpy float32 types
  24.         # no idea what is going on - removed until this can be solved
  25.         # po=numpy.get_printoptions()
  26.         # numpy.set_printoptions(precision=2,edgeitems=4)
  27.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  28.         logger.info(''  std: %s'' % std_vector)
  29.         # restore the print options
  30.         # numpy.set_printoptions(po)
  31.  
  32.         self.std_vector = std_vector
  33.  
  34.         return  std_vector
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def printoptions(*args, **kwargs):
  2.     original = numpy.get_printoptions()
  3.     numpy.set_printoptions(*args, **kwargs)
  4.     yield
  5.     numpy.set_printoptions(**original)
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def find_min_max_values(self, in_file_list):
  2.  
  3.         logger = logging.getLogger("acoustic_norm")
  4.  
  5.         file_number = len(in_file_list)
  6.         min_value_matrix = numpy.zeros((file_number, self.feature_dimension))
  7.         max_value_matrix = numpy.zeros((file_number, self.feature_dimension))
  8.         io_funcs = BinaryIOCollection()
  9.         for i in range(file_number):
  10.             features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension)
  11.  
  12.             temp_min = numpy.amin(features, axis = 0)
  13.             temp_max = numpy.amax(features, axis = 0)
  14.  
  15.             min_value_matrix[i, ] = temp_min;
  16.             max_value_matrix[i, ] = temp_max;
  17.  
  18.         self.min_vector = numpy.amin(min_value_matrix, axis = 0)
  19.         self.max_vector = numpy.amax(max_value_matrix, axis = 0)
  20.         self.min_vector = numpy.reshape(self.min_vector, self.feature_dimension))
  21.         self.max_vector = numpy.reshape(self.max_vector, self.feature_dimension))
  22.  
  23.         # po=numpy.get_printoptions()
  24.         # numpy.set_printoptions(precision=2,edgeitems=4)
  25.         logger.info(''across %d files found min/max values of length %d:'' % (file_number,self.feature_dimension) )
  26.         logger.info(''  min: %s'' % self.min_vector)
  27.         logger.info(''  max: %s'' % self.max_vector)
  28.         # restore the print options
  29.         # numpy.set_printoptions(po)
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def find_min_max_values(self, in_file_list, end_index):
  2.  
  3.         local_feature_dimension = end_index - start_index
  4.  
  5.         file_number = len(in_file_list)
  6.         min_value_matrix = numpy.zeros((file_number, local_feature_dimension))
  7.         max_value_matrix = numpy.zeros((file_number, local_feature_dimension))
  8.         io_funcs = BinaryIOCollection()
  9.         for i in range(file_number):
  10.             features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension)
  11.  
  12.             temp_min = numpy.amin(features[:, axis = 0)
  13.             temp_max = numpy.amax(features[:, local_feature_dimension))
  14.         self.max_vector = numpy.reshape(self.max_vector, local_feature_dimension))
  15.  
  16.         # po=numpy.get_printoptions()
  17.         # numpy.set_printoptions(precision=2,edgeitems=4)
  18.         self.logger.info(''found min/max values of length %d:'' % local_feature_dimension)
  19.         self.logger.info(''  min: %s'' % self.min_vector)
  20.         self.logger.info(''  max: %s'' % self.max_vector)
  21.         # restore the print options
  22.         # numpy.set_printoptions(po)
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_std(self, end_index):
  2.         local_feature_dimension = end_index - start_index
  3.  
  4.         std_vector = numpy.zeros((1, self.feature_dimension))
  5.         all_frame_number = 0
  6.  
  7.         io_funcs = BinaryIOCollection()
  8.         for file_name in file_list:
  9.             features, self.feature_dimension)
  10.  
  11.             mean_matrix = numpy.tile(mean_vector, local_feature_dimension))
  12.             all_frame_number += current_frame_number
  13.  
  14.         std_vector /= float(all_frame_number)
  15.  
  16.         std_vector = std_vector ** 0.5
  17.  
  18.         # po=numpy.get_printoptions()
  19.         # numpy.set_printoptions(precision=2,edgeitems=4)
  20.         self.logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  21.         self.logger.info(''  std: %s'' % std_vector)
  22.         # restore the print options
  23.         # numpy.set_printoptions(po)
  24.  
  25.         return  std_vector
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.mean_vector = mean_vector
  8.  
  9.         return  mean_vector
项目：merlin    作者：CSTR-Edinburgh    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.std_vector = std_vector
  8.  
  9.         return  std_vector
项目：leetcode    作者：thomasyimgit    | 项目源码 | 文件源码 
  1. def test_precision():
  2.     """test varIoUs values for float_precision."""
  3.     f = Plaintextformatter()
  4.     nt.assert_equal(f(pi), repr(pi))
  5.     f.float_precision = 0
  6.     if numpy:
  7.         po = numpy.get_printoptions()
  8.         nt.assert_equal(po[''precision''], 0)
  9.     nt.assert_equal(f(pi), ''3'')
  10.     f.float_precision = 2
  11.     if numpy:
  12.         po = numpy.get_printoptions()
  13.         nt.assert_equal(po[''precision''], 2)
  14.     nt.assert_equal(f(pi), ''3.14'')
  15.     f.float_precision = ''%g''
  16.     if numpy:
  17.         po = numpy.get_printoptions()
  18.         nt.assert_equal(po[''precision''], ''3.14159'')
  19.     f.float_precision = ''%e''
  20.     nt.assert_equal(f(pi), ''3.141593e+00'')
  21.     f.float_precision = ''''
  22.     if numpy:
  23.         po = numpy.get_printoptions()
  24.         nt.assert_equal(po[''precision''], 8)
  25.     nt.assert_equal(f(pi), repr(pi))
项目：phoebe2    作者：phoebe-project    | 项目源码 | 文件源码 
  1. def __repr__(self):
  2.         """
  3.         FloatArrayParameter needs to "truncate" the array by temporarily
  4.         overriding np.set_printoptions
  5.         """
  6.         opt = np.get_printoptions()
  7.         # <Parameter:_qualifier= takes 13+len(qualifier) characters
  8.         np.set_printoptions(threshold=8, edgeitems=3, linewidth=opt[''linewidth'']-(13+len(self.qualifier)))
  9.         repr_ = super(FloatArrayParameter, self).__repr__()
  10.         np.set_printoptions(**opt)
  11.         return repr_
项目：phoebe2    作者：phoebe-project    | 项目源码 | 文件源码 
  1. def __str__(self):
  2.         """
  3.         FloatArrayParameter needs to "truncate" the array by temporarily
  4.         overriding np.set_printoptions
  5.         """
  6.         opt = np.get_printoptions()
  7.         # Value:_ takes 7 characters
  8.         np.set_printoptions(threshold=8, linewidth=opt[''linewidth'']-7)
  9.         str_ = super(FloatArrayParameter, self).__str__()
  10.         np.set_printoptions(**opt)
  11.         return str_
项目：phoebe2    作者：phoebe-project    | 项目源码 | 文件源码 
  1. def to_string_short(self):
  2.         """
  3.         see also :meth:`to_string`
  4.  
  5.         :return: a shorter abreviated string reprentation of the parameter
  6.         """
  7.         opt = np.get_printoptions()
  8.         np.set_printoptions(threshold=8, linewidth=opt[''linewidth'']-len(self.uniquetwig)-2)
  9.         str_ = super(FloatArrayParameter, self).to_string_short()
  10.         np.set_printoptions(**opt)
  11.         return str_
项目：phoebe2    作者：phoebe-project    | 项目源码 | 文件源码 
  1. def __repr__(self):
  2.         """
  3.         IntArrayParameter needs to "truncate" the array by temporarily
  4.         overriding np.set_printoptions
  5.         """
  6.         opt = np.get_printoptions()
  7.         # <Parameter:_qualifier= takes 13+len(qualifier) characters
  8.         np.set_printoptions(threshold=8, linewidth=opt[''linewidth'']-(13+len(self.qualifier)))
  9.         repr_ = super(IntArrayParameter, self).__repr__()
  10.         np.set_printoptions(**opt)
  11.         return repr_
项目：phoebe2    作者：phoebe-project    | 项目源码 | 文件源码 
  1. def __str__(self):
  2.         """
  3.         IntArrayParameter needs to "truncate" the array by temporarily
  4.         overriding np.set_printoptions
  5.         """
  6.         opt = np.get_printoptions()
  7.         # Value:_ takes 7 characters
  8.         np.set_printoptions(threshold=8, linewidth=opt[''linewidth'']-7)
  9.         str_ = super(IntArrayParameter, self).__str__()
  10.         np.set_printoptions(**opt)
  11.         return str_
项目：krpcScripts    作者：jwvanderbeck    | 项目源码 | 文件源码 
  1. def setUp(self):
  2.         self.oldopts = np.get_printoptions()
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def printoptions(*args, **kwargs)
  2.     yield 
  3.     numpy.set_printoptions(**original)
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def find_min_max_values(self, self.feature_dimension))
  2.         io_funcs = BinaryIOCollection()
  3.         for i in xrange(file_number):
  4.             features = io_funcs.load_binary_file(in_file_list[i],self.feature_dimension) )
  5.         logger.info(''  min: %s'' % self.min_vector)
  6.         logger.info(''  max: %s'' % self.max_vector)
  7.         # restore the print options
  8.         # numpy.set_printoptions(po)
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_std(self, mean_vector):
  2.  
  3.         logger = logging.getLogger("acoustic_norm")
  4.  
  5.         std_vector = numpy.zeros((1, self.feature_dimension)
  6.             current_frame_number = features.size / self.feature_dimension
  7.             mean_matrix = numpy.tile(mean_vector, 1))
  8.  
  9.             std_vector += numpy.reshape(numpy.sum((features - mean_matrix) ** 2, self.feature_dimension))
  10.             all_frame_number += current_frame_number
  11.  
  12.         std_vector /= float(all_frame_number)
  13.  
  14.         std_vector = std_vector ** 0.5
  15.  
  16.         # po=numpy.get_printoptions()
  17.         # numpy.set_printoptions(precision=2,edgeitems=4)
  18.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  19.         logger.info(''  std: %s'' % std_vector)
  20.         # restore the print options
  21.         # numpy.set_printoptions(po)
  22.  
  23.         return  std_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def find_min_max_values(self, local_feature_dimension))
  2.         io_funcs = BinaryIOCollection()
  3.         for i in xrange(file_number):
  4.             features = io_funcs.load_binary_file(in_file_list[i],edgeitems=4)
  5.         self.logger.info(''found min/max values of length %d:'' % local_feature_dimension)
  6.         self.logger.info(''  min: %s'' % self.min_vector)
  7.         self.logger.info(''  max: %s'' % self.max_vector)
  8.         # restore the print options
  9.         # numpy.set_printoptions(po)
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         self.logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         self.logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         return  std_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.mean_vector = mean_vector
  8.  
  9.         return  mean_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.std_vector = std_vector
  8.  
  9.         return  std_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def find_min_max_values(self,edgeitems=4)
  2.         self.logger.info(''found min/max values of length %d:'' % local_feature_dimension)
  3.         self.logger.info(''  min: %s'' % self.min_vector)
  4.         self.logger.info(''  max: %s'' % self.max_vector)
  5.         # restore the print options
  6.         # numpy.set_printoptions(po)
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.mean_vector = mean_vector
  8.  
  9.         return  mean_vector
项目：world_merlin    作者：pbaljeka    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.std_vector = std_vector
  8.  
  9.         return  std_vector
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda    作者：SignalMedia    | 项目源码 | 文件源码 
  1. def setUp(self):
  2.         self.oldopts = np.get_printoptions()
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.mean_vector = mean_vector
  8.  
  9.         return  mean_vector
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.std_vector = std_vector
  8.  
  9.         return  std_vector
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def printoptions(*args, **kwargs)
  2.     yield 
  3.     numpy.set_printoptions(**original)
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def find_min_max_values(self,self.feature_dimension) )
  2.         logger.info(''  min: %s'' % self.min_vector)
  3.         logger.info(''  max: %s'' % self.max_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def find_min_max_values(self,edgeitems=4)
  2.         self.logger.info(''found min/max values of length %d:'' % local_feature_dimension)
  3.         self.logger.info(''  min: %s'' % self.min_vector)
  4.         self.logger.info(''  max: %s'' % self.max_vector)
  5.         # restore the print options
  6.         # numpy.set_printoptions(po)
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         self.logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         self.logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         return  std_vector
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_mean(self,edgeitems=4)
  2.         logger.info(''computed mean vector of length %d :'' % mean_vector.shape[1] )
  3.         logger.info('' mean: %s'' % mean_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.mean_vector = mean_vector
  8.  
  9.         return  mean_vector
项目：mimicry.ai    作者：fizerkhan    | 项目源码 | 文件源码 
  1. def compute_std(self,edgeitems=4)
  2.         logger.info(''computed  std vector of length %d'' % std_vector.shape[1] )
  3.         logger.info(''  std: %s'' % std_vector)
  4.         # restore the print options
  5.         # numpy.set_printoptions(po)
  6.  
  7.         self.std_vector = std_vector
  8.  
  9.         return  std_vector
项目：cohda    作者：ambimanus    | 项目源码 | 文件源码 
  1. def _printoptions(*args, **kwargs)
  2.     yield
  3.     np.set_printoptions(**original)
  4.  
  5.  
  6. # http://code.activestate.com/recipes/577586-converts-from-decimal-to-any-base-between-2-and-26/
项目：aws-lambda-numpy    作者：vitolimandibhrata    | 项目源码 | 文件源码 
  1. def setUp(self):
  2.         self.oldopts = np.get_printoptions()
项目：MobileNet    作者：Zehaos    | 项目源码 | 文件源码 
  1. def parse_numpy_printoption(kv_str):
  2.     """Sets a single numpy printoption from a string of the form ''x=y''.
  3.  
  4.     See documentation on numpy.set_printoptions() for details about what values
  5.     x and y can take. x can be any option listed there other than ''formatter''.
  6.  
  7.     Args:
  8.         kv_str: A string of the form ''x=y'',such as ''threshold=100000''
  9.  
  10.     Raises:
  11.         argparse.ArgumentTypeError: If the string Couldn''t be used to set any
  12.                 nump printoption.
  13.     """
  14.     k_v_str = kv_str.split("=", 1)
  15.     if len(k_v_str) != 2 or not k_v_str[0]:
  16.         raise argparse.ArgumentTypeError("''%s'' is not in the form k=v." % kv_str)
  17.     k, v_str = k_v_str
  18.     printoptions = np.get_printoptions()
  19.     if k not in printoptions:
  20.         raise argparse.ArgumentTypeError("''%s'' is not a valid printoption." % k)
  21.     v_type = type(printoptions[k])
  22.     if v_type is type(None):
  23.         raise argparse.ArgumentTypeError(
  24.                 "Setting ''%s'' from the command line is not supported." % k)
  25.     try:
  26.         v = (v_type(v_str) if v_type is not bool
  27.              else flags.BooleanParser().Parse(v_str))
  28.     except ValueError as e:
  29.         raise argparse.ArgumentTypeError(e.message)
  30.     np.set_printoptions(**{k: v})

Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable

Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable

如何解决Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: ''numpy.ndarray'' object is not callable？

晚安， 尝试打印以下内容时，我在 jupyter 中遇到了 numpy 问题，并且得到了一个 错误： 需要注意的是python版本是3.8.8。 我先用 spyder 测试它，它运行正确，它给了我预期的结果

使用 Spyder：

import numpy as np
    for i in range (5):
        n = np.random.rand ()
    print (n)

Results
0.6604903457995978
0.8236300859753154
0.16067650689842816
0.6967868357083673
0.4231597934445466

现在有了 jupyter

import numpy as np
    for i in range (5):
        n = np.random.rand ()
    print (n)

-------------------------------------------------- ------
TypeError Traceback (most recent call last)
<ipython-input-78-0c6a801b3ea9> in <module>
       2 for i in range (5):
       3 n = np.random.rand ()
---->  4 print (n)

       TypeError: ''numpy.ndarray'' object is not callable

感谢您对我如何在 Jupyter 中解决此问题的帮助。

非常感谢您抽出宝贵时间。

阿特，约翰”

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

小编邮箱:dio#foxmail.com (将#修改为@）

numpy.random.random & numpy.ndarray.astype & numpy.arange

numpy.random.random & numpy.ndarray.astype & numpy.arange

今天看到这样一句代码：

xb = np.random.random((nb, d)).astype(''float32'') #创建一个二维随机数矩阵（nb行d列）
xb[:, 0] += np.arange(nb) / 1000. #将矩阵第一列的每个数加上一个值

要理解这两句代码需要理解三个函数

1、生成随机数

numpy.random.random(size=None) 

size为None时，返回float。

size不为None时，返回numpy.ndarray。例如numpy.random.random((1,2))，返回1行2列的numpy数组

 

2、对numpy数组中每一个元素进行类型转换

numpy.ndarray.astype(dtype)

返回numpy.ndarray。例如 numpy.array([1, 2, 2.5]).astype(int)，返回numpy数组 [1, 2, 2]

 

3、获取等差数列

numpy.arange([start,]stop,[step,]dtype=None)

功能类似python中自带的range()和numpy中的numpy.linspace

返回numpy数组。例如numpy.arange(3)，返回numpy数组[0, 1, 2]

numpy.ravel()/numpy.flatten()/numpy.squeeze()

numpy.ravel()/numpy.flatten()/numpy.squeeze()

numpy.ravel(a, order=''C'')

　　Return a flattened array

numpy.chararray.flatten(order=''C'')

　　Return a copy of the array collapsed into one dimension

numpy.squeeze(a, axis=None)

　　Remove single-dimensional entries from the shape of an array.

 

相同点: 将多维数组 降为 一维数组

不同点:

　　ravel() 返回的是视图(view)，意味着改变元素的值会影响原始数组元素的值;

　　flatten() 返回的是拷贝，意味着改变元素的值不会影响原始数组;

　　squeeze()返回的是视图(view)，仅仅是将shape中dimension为1的维度去掉;

 

ravel()示例:

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 
 4 def log_type(name,arr):
 5     print("数组{}的大小:{}".format(name,arr.size))
 6     print("数组{}的维度:{}".format(name,arr.shape))
 7     print("数组{}的维度:{}".format(name,arr.ndim))
 8     print("数组{}元素的数据类型:{}".format(name,arr.dtype))
 9     #print("数组:{}".format(arr.data))
10     
11 a = np.floor(10*np.random.random((3,4)))
12 print(a)
13 log_type(''a'',a)
14 
15 a1 = a.ravel()
16 print("a1:{}".format(a1))
17 log_type(''a1'',a1)
18 a1[2] = 100
19 
20 print(a)
21 log_type(''a'',a)

 

flatten()示例

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 
 4 def log_type(name,arr):
 5     print("数组{}的大小:{}".format(name,arr.size))
 6     print("数组{}的维度:{}".format(name,arr.shape))
 7     print("数组{}的维度:{}".format(name,arr.ndim))
 8     print("数组{}元素的数据类型:{}".format(name,arr.dtype))
 9     #print("数组:{}".format(arr.data))
10     
11 a = np.floor(10*np.random.random((3,4)))
12 print(a)
13 log_type(''a'',a)
14 
15 a1 = a.flatten()
16 print("修改前a1:{}".format(a1))
17 log_type(''a1'',a1)
18 a1[2] = 100
19 print("修改后a1:{}".format(a1))
20 
21 print("a:{}".format(a))
22 log_type(''a'',a)

 

squeeze()示例：

1. 没有single-dimensional entries的情况

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 
 4 def log_type(name,arr):
 5     print("数组{}的大小:{}".format(name,arr.size))
 6     print("数组{}的维度:{}".format(name,arr.shape))
 7     print("数组{}的维度:{}".format(name,arr.ndim))
 8     print("数组{}元素的数据类型:{}".format(name,arr.dtype))
 9     #print("数组:{}".format(arr.data))
10     
11 a = np.floor(10*np.random.random((3,4)))
12 print(a)
13 log_type(''a'',a)
14 
15 a1 = a.squeeze()
16 print("修改前a1:{}".format(a1))
17 log_type(''a1'',a1)
18 a1[2] = 100
19 print("修改后a1:{}".format(a1))
20 
21 print("a:{}".format(a))
22 log_type(''a'',a)

从结果中可以看到，当没有single-dimensional entries时，squeeze()返回额数组对象是一个view,而不是copy。

 

2. 有single-dimentional entries 的情况

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 
 4 def log_type(name,arr):
 5     print("数组{}的大小:{}".format(name,arr.size))
 6     print("数组{}的维度:{}".format(name,arr.shape))
 7     print("数组{}的维度:{}".format(name,arr.ndim))
 8     print("数组{}元素的数据类型:{}".format(name,arr.dtype))
 9     #print("数组:{}".format(arr.data))
10 
11 a = np.floor(10*np.random.random((1,3,4)))
12 print(a)
13 log_type(''a'',a)
14 
15 a1 = a.squeeze()
16 print("修改前a1:{}".format(a1))
17 log_type(''a1'',a1)
18 a1[2] = 100
19 print("修改后a1:{}".format(a1))
20 
21 print("a:{}".format(a))
22 log_type(''a'',a)

 

Numpy：数组创建 numpy.arrray() , numpy.arange()、np.linspace ()、数组基本属性

Numpy：数组创建 numpy.arrray() , numpy.arange()、np.linspace ()、数组基本属性

一、Numpy数组创建

 part 1：np.linspace(起始值，终止值，元素总个数

 

import numpy as np
''''''
numpy中的ndarray数组
''''''

ary = np.array([1, 2, 3, 4, 5])
print(ary)
ary = ary * 10
print(ary)

''''''
ndarray对象的创建
''''''
# 创建二维数组
# np.array([[],[],...])
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print(a)

# np.arange(起始值, 结束值, 步长（默认1）)
b = np.arange(1, 10, 1)
print(b)

print("-------------np.zeros(数组元素个数, dtype=''数组元素类型'')-----")
# 创建一维数组：
c = np.zeros(10)
print(c, ''; c.dtype:'', c.dtype)

# 创建二维数组：
print(np.zeros ((3,4)))

print("----------np.ones(数组元素个数, dtype=''数组元素类型'')--------")
# 创建一维数组：
d = np.ones(10, dtype=''int64'')
print(d, ''; d.dtype:'', d.dtype)

# 创建三维数组：
print(np.ones( (2,3,4), dtype=np.int32 ))
# 打印维度
print(np.ones( (2,3,4), dtype=np.int32 ).ndim)  # 返回：3（维）

 

结果图：

 

part 2 :np.linspace ( 起始值，终止值，元素总个数）

 

import numpy as np
a = np.arange( 10, 30, 5 )

b = np.arange( 0, 2, 0.3 )

c = np.arange(12).reshape(4,3)

d = np.random.random((2,3))  # 取-1到1之间的随机数，要求设置为诶2行3列的结构

print(a)
print(b)
print(c)
print(d)

print("-----------------")
from numpy import pi
print(np.linspace( 0, 2*pi, 100 ))

print("-------------np.linspace(起始值，终止值，元素总个数）------------------")
print(np.sin(np.linspace( 0, 2*pi, 100 )))

 

结果图：

 

 

 

 

二、Numpy的ndarray对象属性：

数组的结构：array.shape

数组的维度：array.ndim

元素的类型：array.dtype

数组元素的个数：array.size

数组的索引（下标）:array[0]

 

''''''
数组的基本属性
''''''
import numpy as np

print("--------------------案例1：------------------------------")
a = np.arange(15).reshape(3, 5)
print(a)
print(a.shape)     # 打印数组结构
print(len(a))      # 打印有多少行
print(a.ndim)     # 打印维度
print(a.dtype)    # 打印a数组内的元素的数据类型
# print(a.dtype.name)
print(a.size)    # 打印数组的总元素个数


print("-------------------案例2：---------------------------")
a = np.array([[1, 2, 3], [4, 5, 6]])
print(a)

# 测试数组的基本属性
print(''a.shape:'', a.shape)
print(''a.size:'', a.size)
print(''len(a):'', len(a))
# a.shape = (6, )  # 此格式可将原数组结构变成1行6列的数据结构
# print(a, ''a.shape:'', a.shape)

# 数组元素的索引
ary = np.arange(1, 28)
ary.shape = (3, 3, 3)   # 创建三维数组
print("ary.shape:",ary.shape,"\n",ary )

print("-----------------")
print(''ary[0]:'', ary[0])
print(''ary[0][0]:'', ary[0][0])
print(''ary[0][0][0]:'', ary[0][0][0])
print(''ary[0,0,0]:'', ary[0, 0, 0])

print("-----------------")


# 遍历三维数组:遍历出数组里的每个元素
for i in range(ary.shape[0]):
    for j in range(ary.shape[1]):
        for k in range(ary.shape[2]):
            print(ary[i, j, k], end='' '')

 

结果图：

 

今天关于Python numpy 模块-get_printoptions() 实例源码和python的numpy模块的讲解已经结束，谢谢您的阅读，如果想了解更多关于Jupyter 中的 Numpy 在打印时出错（Python 版本 3.8.8）：TypeError: 'numpy.ndarray' object is not callable、numpy.random.random & numpy.ndarray.astype & numpy.arange、numpy.ravel()/numpy.flatten()/numpy.squeeze()、Numpy：数组创建 numpy.arrray() , numpy.arange()、np.linspace ()、数组基本属性的相关知识，请在本站搜索。

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