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""" |
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KnowYourData |
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============ |
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A rapid and lightweight module to describe the statistics and structure of |
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data arrays for interactive use. |
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The most simple use case to display data is if you have a numpy array 'x': |
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>>> from knowyourdata import kyd |
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>>> kyd(x) |
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""" |
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import sys |
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import numpy as np |
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class KYD(object): |
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"""The Central Class for KYD""" |
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# Variable for Data Vector |
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data = None |
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# Initial Flags |
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f_allfinite = False |
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f_allnonfinite = False |
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f_hasnan = False |
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f_hasinf = False |
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# Initialized Numbers |
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num_nan = 0 |
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num_inf = 0 |
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# Display Settings |
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col_width = 10 |
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precision = 4 |
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def check_finite(self): |
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"""Checking to see if all elements are finite and setting flags""" |
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if np.all(np.isfinite(self.data)): |
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self.filt_data = self.data |
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self.f_allfinite = True |
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else: |
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finite_inds = np.where(np.isfinite(self.data)) |
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self.filt_data = self.data[finite_inds] |
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if self.filt_data.size == 0: |
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self.f_allnonfinite = True |
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if np.any(np.isnan(self.data)): |
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self.f_hasnan = True |
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self.num_nan = np.sum(np.isnan(self.data)) |
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if np.any(np.isinf(self.data)): |
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self.f_hasinf = True |
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self.num_inf = np.sum(np.isinf(self.data)) |
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def check_struct(self): |
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"""Determining the Structure of the Numpy Array""" |
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self.dtype = self.data.dtype |
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self.ndim = self.data.ndim |
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self.shape = self.data.shape |
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self.size = self.data.size |
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self.memsize = sys.getsizeof(self.data) |
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self.human_memsize = sizeof_fmt(self.memsize) |
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def get_basic_stats(self): |
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"""Get basic statistics about array""" |
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if self.f_allnonfinite: |
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self.min = self.max = self.range = np.nan |
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self.mean = self.std = self.median = np.nan |
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self.firstquartile = self.thirdquartile = np.nan |
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self.ci_68 = self.ci_95 = self.ci_99 = np.array([np.nan, np.nan]) |
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return |
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self.min = np.float_(np.min(self.filt_data)) |
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self.max = np.float_(np.max(self.filt_data)) |
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self.range = self.max - self.min |
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self.mean = np.mean(self.filt_data) |
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self.std = np.std(self.filt_data) |
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self.median = np.float_(np.median(self.filt_data)) |
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self.firstquartile = np.float_(np.percentile(self.filt_data, 25)) |
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self.thirdquartile = np.float_(np.percentile(self.filt_data, 75)) |
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self.ci_99 = np.float_( |
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np.percentile(self.filt_data, np.array([0.5, 99.5]))) |
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self.ci_95 = np.float_( |
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np.percentile(self.filt_data, np.array([2.5, 97.5]))) |
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self.ci_68 = np.float_( |
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np.percentile(self.filt_data, np.array([16.0, 84.0]))) |
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def display_basic_stats_new(self): |
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"""Display Basic Statistics""" |
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pstr_list = [] |
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pstr_struct_header1 = "Basic Statistics " |
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pstr_struct_header2 = '' |
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pstr_list.append(pstr_struct_header1) |
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pstr_list.append(pstr_struct_header2) |
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template_str = ( |
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" {0:^10} " |
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" {1:>8} " |
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" {2:<10} " |
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" {3:>8} " |
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" {4:<10} " |
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) |
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tmp_data = [ |
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[ |
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"Mean:", "{self.mean:.{self.precision}}".format(self=self), |
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"", |
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"Std Dev:", "{self.std:.{self.precision}}".format(self=self) |
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], |
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["Min:", "1Q:", "Median:", "3Q:", "Max:"], |
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[ |
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"{self.min: .{self.precision}}".format(self=self), |
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"{self.firstquartile: .{self.precision}}".format(self=self), |
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"{self.median: .{self.precision}}".format(self=self), |
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"{self.thirdquartile: .{self.precision}}".format(self=self), |
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"{self.max: .{self.precision}}".format(self=self), |
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], |
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['-99 CI:', '-95 CI:', '-68 CI:', '+68 CI:', '+95 CI:', '+99 CI:'], |
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[ |
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"{self.ci_99[0]: .{self.precision}}".format(self=self), |
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"{self.ci_95[0]: .{self.precision}}".format(self=self), |
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"{self.ci_68[0]: .{self.precision}}".format(self=self), |
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"{self.ci_68[1]: .{self.precision}}".format(self=self), |
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"{self.ci_95[1]: .{self.precision}}".format(self=self), |
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"{self.ci_99[1]: .{self.precision}}".format(self=self), |
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], |
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] |
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n_tmp_data = len(tmp_data) |
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num_rows_in_cols = [len(i) for i in tmp_data] |
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num_rows = np.max(num_rows_in_cols) |
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for i in range(n_tmp_data): |
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tmp_col = tmp_data[i] |
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for j in range(num_rows_in_cols[i], num_rows): |
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tmp_col.append("") |
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for i in range(num_rows): |
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pstr_list.append( |
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template_str.format( |
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tmp_data[0][i], |
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tmp_data[1][i], |
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tmp_data[2][i], |
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tmp_data[3][i], |
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tmp_data[4][i], |
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) |
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) |
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return pstr_list |
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def display_struct(self): |
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"""Display information about array structure""" |
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pstr_list = [] |
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# pstr_struct_header0 = "................." |
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# Commenting out Ansi Coloured Version |
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# pstr_struct_header1 = '\033[1m' + "Array Structure " + '\033[0m' |
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pstr_struct_header1 = "Array Structure " |
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pstr_struct_header2 = " " |
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# pstr_list.append(pstr_struct_header0) |
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pstr_list.append(pstr_struct_header1) |
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pstr_list.append(pstr_struct_header2) |
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pstr_n_dim = ( |
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"Number of Dimensions:\t" |
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"{self.ndim}").format( |
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self=self) |
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pstr_list.append(pstr_n_dim) |
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pstr_shape = ( |
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"Shape of Dimensions:\t" |
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"{self.shape}").format( |
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self=self) |
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pstr_list.append(pstr_shape) |
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pstr_dtype = ( |
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"Array Data Type:\t" |
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"{self.dtype}").format( |
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self=self) |
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pstr_list.append(pstr_dtype) |
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pstr_memsize = ( |
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"Memory Size:\t\t" |
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"{self.human_memsize}").format( |
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self=self) |
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pstr_list.append(pstr_memsize) |
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pstr_spacer = ("") |
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pstr_list.append(pstr_spacer) |
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pstr_numnan = ( |
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"Number of NaN:\t" |
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"{self.num_nan}").format( |
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self=self) |
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pstr_list.append(pstr_numnan) |
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pstr_numinf = ( |
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"Number of Inf:\t" |
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"{self.num_inf}").format( |
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self=self) |
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pstr_list.append(pstr_numinf) |
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return pstr_list |
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def display(self, short=False): |
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"""Displaying all relevant statistics""" |
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if short: |
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pass |
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print("") |
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pstr_basic = self.display_basic_stats_new() |
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pstr_struct = self.display_struct() |
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n_basic = len(pstr_basic) |
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n_struct = len(pstr_struct) |
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l_colwidth = max([len(x) for x in pstr_basic]) + 1 |
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r_colwidth = max([len(x) for x in pstr_struct]) + 2 |
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# new_colwidth = self.col_width + 20 |
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# Finding the longest string |
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len_list = max([n_basic, n_struct]) |
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for i in range(len_list): |
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tmp_str = '| ' |
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if i < n_basic: |
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tmp_str += (pstr_basic[i].ljust(l_colwidth)) |
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else: |
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tmp_str += ''.ljust(l_colwidth) |
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tmp_str += ' | ' |
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if i < n_struct: |
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tmp_str += (pstr_struct[i].expandtabs().ljust(r_colwidth)) |
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else: |
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tmp_str += ''.ljust(r_colwidth) |
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tmp_str += '\t|' |
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print(tmp_str) |
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print("") |
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def clear_memory(self): |
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"""Ensuring the Numpy Array does not exist in memory""" |
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del self.data |
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del self.filt_data |
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def __init__(self, data): |
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super(KYD, self).__init__() |
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# Ensuring that the array is a numpy array |
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if not isinstance(data, np.ndarray): |
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data = np.array(data) |
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self.data = data |
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self.check_finite() |
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self.check_struct() |
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self.get_basic_stats() |
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self.clear_memory() |
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def sizeof_fmt(num, suffix='B'): |
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"""Return human readable version of in-memory size. |
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Code from Fred Cirera from Stack Overflow: |
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https://stackoverflow.com/questions/1094841/reusable-library-to-get-human-readable-version-of-file-size |
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""" |
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for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']: |
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if abs(num) < 1024.0: |
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return "%3.1f%s%s" % (num, unit, suffix) |
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num /= 1024.0 |
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return "%.1f%s%s" % (num, 'Yi', suffix) |
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def kyd(data, full_statistics=False): |
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"""Print statistics of any numpy array |
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data -- Numpy Array of Data |
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Keyword arguments: |
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full_statistics -- printing all detailed statistics of the sources |
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(Currently Not Implemented) |
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""" |
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data_kyd = KYD(data) |
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if full_statistics: |
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data_kyd.display() |
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else: |
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data_kyd.display(short=True) |
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return data_kyd |
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