diff --git a/_sources/numpy_guide.rst.txt b/_sources/numpy_guide.rst.txt index 2483cb95..f350765f 100644 --- a/_sources/numpy_guide.rst.txt +++ b/_sources/numpy_guide.rst.txt @@ -184,14 +184,13 @@ Arrays of numbers with uncertainties can be directly :ref:`pickled `, saved to file and read from a file. Pickling has the advantage of preserving correlations between errors. -Storing instead arrays in **text format** loses correlations between -errors but has the advantage of being both computer- and -human-readable. This can be done through NumPy's :func:`savetxt` and -:func:`loadtxt`. +Storing arrays in **text format** loses correlations between errors but has the +advantage of being both computer- and human-readable. This can be done through +NumPy's :func:`savetxt` and :func:`loadtxt`. Writing the array to file can be done by asking NumPy to use the -*representation* of numbers with uncertainties (instead of the default -float conversion): +*representation* of numbers with uncertainties (instead of the default float +conversion): >>> numpy.savetxt('arr.txt', arr, fmt='%r') @@ -201,27 +200,18 @@ the array:: 1.0+/-0.01 2.0+/-0.002 -The file can then be read back by instructing NumPy to convert all the -columns with :func:`uncertainties.ufloat_fromstr`. The number -:data:`num_cols` of columns in the input file (1, in our example) must -be determined in advance, because NumPy requires a converter for each -column separately. For Python 2: - ->>> converters = dict.fromkeys(range(num_cols), uncertainties.ufloat_fromstr) - -For Python 3, since :func:`numpy.loadtxt` passes bytes to converters, -they must first be converted into a string: - ->>> converters = dict.fromkeys( - range(num_cols), - lambda col_bytes: uncertainties.ufloat_fromstr(col_bytes.decode("latin1"))) - -(Latin 1 appears to in fact be the encoding used in -:func:`numpy.savetxt` [as of NumPy 1.12]. This encoding seems -to be the one hardcoded in :func:`numpy.compat.asbytes`.) - -The array can then be loaded: - +The file can then be read back by instructing NumPy with :meth:`numpy.loadtxt`, +but for object arrays, this requires a converter function for each column +separately. We can use func:`uncertainties.ufloat_fromstr`, but +:meth:`numpy.loadtxt` passes bytes to converters, they must first be converted +into a string. In addition the number of maximum number of columns must be +known. An example of using all of this to unpack the data saved with +:meth:`numpy.savetxt` would be: + +>>> from uncertainties import ufloat_fromstr +>>> max_cols = 1 +>>> converters = {col: lambda dat: ufloat_fromstr(dat.decode("utf-8")) +.... for col in range(max_cols)} >>> arr = numpy.loadtxt('arr.txt', converters=converters, dtype=object) .. index:: linear algebra; additional functions, ulinalg diff --git a/numpy_guide.html b/numpy_guide.html index 987e39b4..d5b85ab3 100644 --- a/numpy_guide.html +++ b/numpy_guide.html @@ -310,13 +310,12 @@

Creation and manipulation of arrays and matrices

Storing arrays in text format

Arrays of numbers with uncertainties can be directly pickled, saved to file and read from a file. Pickling has the advantage of preserving correlations between errors.

-

Storing instead arrays in text format loses correlations between -errors but has the advantage of being both computer- and -human-readable. This can be done through NumPy’s savetxt() and -loadtxt().

+

Storing arrays in text format loses correlations between errors but has the +advantage of being both computer- and human-readable. This can be done through +NumPy’s savetxt() and loadtxt().

Writing the array to file can be done by asking NumPy to use the -representation of numbers with uncertainties (instead of the default -float conversion):

+representation of numbers with uncertainties (instead of the default float +conversion):

>>> numpy.savetxt('arr.txt', arr, fmt='%r')
@@ -326,26 +325,18 @@

Creation and manipulation of arrays and matrices2.0+/-0.002 -

The file can then be read back by instructing NumPy to convert all the -columns with uncertainties.ufloat_fromstr(). The number -num_cols of columns in the input file (1, in our example) must -be determined in advance, because NumPy requires a converter for each -column separately. For Python 2:

-
>>> converters = dict.fromkeys(range(num_cols), uncertainties.ufloat_fromstr)
-
-
-

For Python 3, since numpy.loadtxt() passes bytes to converters, -they must first be converted into a string:

-
>>> converters = dict.fromkeys(
-        range(num_cols),
-        lambda col_bytes: uncertainties.ufloat_fromstr(col_bytes.decode("latin1")))
-
-
-

(Latin 1 appears to in fact be the encoding used in -numpy.savetxt() [as of NumPy 1.12]. This encoding seems -to be the one hardcoded in numpy.compat.asbytes().)

-

The array can then be loaded:

-
>>> arr = numpy.loadtxt('arr.txt', converters=converters, dtype=object)
+
The file can then be read back by instructing NumPy with numpy.loadtxt(),
+but for object arrays, this requires a converter function for each column
+separately.  We can use func:uncertainties.ufloat_fromstr, but
+numpy.loadtxt() passes bytes to converters, they must first be converted
+into a string.  In addition the number of maximum number of columns must be
+known.  An example of using all of this to unpack the data saved with
+numpy.savetxt() would be:

+
>>> from uncertainties import ufloat_fromstr
+>>> max_cols = 1
+>>> converters = {col: lambda dat: ufloat_fromstr(dat.decode("utf-8"))
+....                              for col in range(max_cols)}
+>>> arr = numpy.loadtxt('arr.txt', converters=converters, dtype=object)
 

 

 
diff --git a/searchindex.js b/searchindex.js
index 44ddc331..31f4a1ef 100644
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\ No newline at end of file
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