diff --git a/.gitignore b/.gitignore
index 5dc0d6c5b..ad1b45bd3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -176,3 +176,5 @@ gldcore/converters/autotest/table2glm_input.glm
 gldcore/geodata/docs/geodata.conf
 gldcore/gridlabd.in
 geodata.conf
+gldcore/autotest/output.csv
+python_extras/example/output.csv
diff --git a/docs/GLM/Directive/Clock.md b/docs/GLM/Directive/Clock.md
index d6e53a3bb..4b9d40ab1 100644
--- a/docs/GLM/Directive/Clock.md
+++ b/docs/GLM/Directive/Clock.md
@@ -10,6 +10,7 @@ clock
     timezone "<specification>";
     starttime "<datetime>";
     stoptime "<datetime>";
+    runtime "<deltatime>";
 }
 ~~~
 
@@ -21,9 +22,17 @@ The `clock` directive specifies how the internal clock will operate while a simu
 
 The `timezone` property sets the time zone for the simulation.  The time zone may be specified either as a ISO timezone, e.g., `PST+8PDT`, or a locale, e.g., `US/CA/San Francisco`.  If the time zone is not specified the simulation will use `UTC`.
 
+## `runtime`
+
+The `runtime` property sets the duration of the simulation run.  This will always be relative to the `starttime` and set the `stoptime` accordingly.  Valid units are `s`, `m`, `h`, `d`, and `w` for seconds, minutes, hours, days, and weeks, respectively.
+
+Note that runtime units of months and years are not supported because they are not well-defined units.
+
 ## `starttime`
 
-The `starttime` property specifies when the simulation start.  If no time zone is specified, the current time zone is used if it has been specified. Otherwise `UTC` is assumed. If no start time is specified, the current wall clock time is used. Note, `ISO8601` is supported.
+The `starttime` property specifies when the simulation start.  If no time zone is specified, the current time zone is used if it has been specified. Otherwise `UTC` is assumed. If no start time is specified, the current wall clock time is used. Note, `ISO8601` is supported.  
+
+Note that the parameter `timestamp` is a synonym for `starttime`.
 
 ## `stoptime`
 
diff --git a/docs/GLM/Directive/Filter.md b/docs/GLM/Directive/Filter.md
index 2c3290666..fad765b92 100644
--- a/docs/GLM/Directive/Filter.md
+++ b/docs/GLM/Directive/Filter.md
@@ -12,6 +12,20 @@ filter <name>(<domain>[,<timestep>[,<timeskew>[,<option>=<value>]]]) = <polynomi
 
 The filter directive defines a filter that can be used to connect a signal source property to a output signal property. 
 
+Filter may be used to output values to an object property of type `double`.  Outputs are summed so that multiple filter may output to a single property, e.g.,
+
+~~~
+object example
+{
+  output1 filter11(input1);
+  output1 filter21(input2);
+  output2 filter12(input1);
+  output2 filter22(input2);
+}
+~~~
+
+Would represent a MIMO system with two inputs going to two outputs through 4 different filters with outputs summed.
+
 ## Name
 
 Any unique alphabetic name may be used.
@@ -46,7 +60,7 @@ The numerator and denominator are specified as a polynomial of the form $a_n z^n
   an z^n + ... + a1 z + a0
 ~~~
 
-The order of the numerator must be strictly less than the order of the denominator. 
+The order of the numerator must be less than or equal to the order of the denominator. 
 
 # Example
 
diff --git a/docs/Global/Output_message_context.md b/docs/Global/Output_message_context.md
index 2d26c61b1..9abf1a644 100644
--- a/docs/Global/Output_message_context.md
+++ b/docs/Global/Output_message_context.md
@@ -17,7 +17,62 @@ bash$ gridlabd --define output_message_context=ALL
 
 # Description
 
-Control context from which debug messages are allowed
+Control context from which core debug messages are allowed.  Valid message contexts are
+
+ - `AGGREGATE`: aggregation debug messages only
+ - `ALL`: all debug messages allowed
+ - `CLASS`: class debug messages only
+ - `CMDARG`: command line subsystem debug messages only
+ - `COMPARE`: compare debug messages only
+ - `CONVERT`: converter debug messages only
+ - `CREATE`: create debug messages only
+ - `DELTAMODE`: deltamode debug messages only
+ - `ENDUSE`: enduse debug messages only
+ - `ENVIRONMENT`: environment debug messages only
+ - `EXCEPTION`: exception handling debug messages only
+ - `EXEC`: exec debug messages only
+ - `FIND`: find subsystem debug messages only
+ - `GLOBALS`: globals debug messages only
+ - `GUI`: graphical user interface debug messages only
+ - `HTTP`: HTTP server debug messages only
+ - `HTTPCLIENT`: HTTP client debug messages only
+ - `INDEX`: index debug messages only
+ - `INSTANCE`: instance manager debug messages only
+ - `INTERPOLATE`: interpolation debug messages only
+ - `JOB`: job control debug messages only
+ - `KML`: KML interface debug messages only
+ - `LEGAL`: legal debug messages only
+ - `LINK`: link debug messages only
+ - `LIST`: list debug messages only
+ - `LOAD`: loader debug messages only
+ - `LOADSHAPE`: loadshape debug messages only
+ - `LOCALE`: locale debug messages only
+ - `LOCK`: memory locking debug messages only
+ - `MAIN`: main module debug messages only
+ - `MATCH`: match subsystem debug messages only
+ - `MATLAB`: Matlab interface debug messages only
+ - `MODULE`: module debug messages only
+ - `NONE`: no debug messages allowed
+ - `OBJECT`: object debug messages only
+ - `PROPERTY`: property debug messages only
+ - `PYTHON`: python module debug messages only
+ - `RANDOM`: random variable debug messages only
+ - `REALTIME`: realtime subsystem debug messages only
+ - `SANITIZE`: model sanitizer subsystem debug messages only
+ - `SAVE`: save debug messages only
+ - `SCHEDULE`: schedule debug messages only
+ - `SERVER`: server subsystem debug messages only
+ - `SETUP`: setup debug messages only
+ - `STREAM`: streaming data debug messages only
+ - `TEST`: testing subsystem debug messages only
+ - `THREADPOOL`: thread pool debug messages only
+ - `TIME`: time subsystem debug messages only
+ - `TRANSFORM`: transform subsystem debug messages only
+ - `UNIT`: unit manager subsystem debug messages only
+ - `VALIDATE`: validation subsystem debug messages only
+ - `VERSION`: version debug messages only
+ - `XCORE`: X11 core debug messages only
+ - `XML`: XML interface debug messages only
 
 # Example
 
diff --git a/docs/Subcommand/Matrix/Absolute.md b/docs/Subcommand/Matrix/Absolute.md
new file mode 100644
index 000000000..1155c9588
--- /dev/null
+++ b/docs/Subcommand/Matrix/Absolute.md
@@ -0,0 +1,37 @@
+[[/Absolute]] -- Calculate the absolute value element-wise.
+
+~~~
+Syntax
+------
+
+matrix absolute <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+absolute : ndarray
+    An ndarray containing the absolute value of
+    each element in `x`.  For complex input, ``a + ib``, the
+    absolute value is :math:`\sqrt{ a^2 + b^2 }`.
+    This is a scalar if `x` is a scalar.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Add.md b/docs/Subcommand/Matrix/Add.md
new file mode 100644
index 000000000..4972cdd19
--- /dev/null
+++ b/docs/Subcommand/Matrix/Add.md
@@ -0,0 +1,41 @@
+[[/Add]] -- Add arguments element-wise.
+
+~~~
+Syntax
+------
+
+matrix add <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays to be added.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+add : ndarray or scalar
+    The sum of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+Equivalent to `x1` + `x2` in terms of array broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Angle.md b/docs/Subcommand/Matrix/Angle.md
new file mode 100644
index 000000000..42dc5c39d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Angle.md
@@ -0,0 +1,45 @@
+[[/Angle]]
+
+~~~
+Syntax
+------
+
+matrix angle <matrix> deg=<boolstr>
+
+
+    Return the angle of the complex argument.
+
+    Parameters
+    ----------
+    z : array_like
+        A complex number or sequence of complex numbers.
+    deg : bool, optional
+        Return angle in degrees if True, radians if False (default).
+
+    Returns
+    -------
+    angle : ndarray or scalar
+        The counterclockwise angle from the positive real axis on the complex
+        plane in the range ``(-pi, pi]``, with dtype as numpy.float64.
+
+        .. versionchanged:: 1.16.0
+            This function works on subclasses of ndarray like `ma.array`.
+
+    See Also
+    --------
+    arctan2
+    absolute
+
+    Notes
+    -----
+    Although the angle of the complex number 0 is undefined, ``numpy.angle(0)``
+    returns the value 0.
+
+    Examples
+    --------
+    >>> np.angle([1.0, 1.0j, 1+1j])               # in radians
+    array([ 0.        ,  1.57079633,  0.78539816]) # may vary
+    >>> np.angle(1+1j, deg=True)                  # in degrees
+    45.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arange.md b/docs/Subcommand/Matrix/Arange.md
new file mode 100644
index 000000000..f371059dd
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arange.md
@@ -0,0 +1,72 @@
+[[/Arange]]
+
+~~~
+Syntax
+------
+
+matrix arange <float> stop=<float> step=<float> [subok=<boolstr>]
+
+arange([start,] stop[, step,], dtype=None, *, like=None)
+
+    Return evenly spaced values within a given interval.
+
+    Values are generated within the half-open interval ``[start, stop)``
+    (in other words, the interval including `start` but excluding `stop`).
+    For integer arguments the function is equivalent to the Python built-in
+    `range` function, but returns an ndarray rather than a list.
+
+    When using a non-integer step, such as 0.1, the results will often not
+    be consistent.  It is better to use `numpy.linspace` for these cases.
+
+    Parameters
+    ----------
+    start : integer or real, optional
+        Start of interval.  The interval includes this value.  The default
+        start value is 0.
+    stop : integer or real
+        End of interval.  The interval does not include this value, except
+        in some cases where `step` is not an integer and floating point
+        round-off affects the length of `out`.
+    step : integer or real, optional
+        Spacing between values.  For any output `out`, this is the distance
+        between two adjacent values, ``out[i+1] - out[i]``.  The default
+        step size is 1.  If `step` is specified as a position argument,
+        `start` must also be given.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    like : array_like
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument.
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    arange : ndarray
+        Array of evenly spaced values.
+
+        For floating point arguments, the length of the result is
+        ``ceil((stop - start)/step)``.  Because of floating point overflow,
+        this rule may result in the last element of `out` being greater
+        than `stop`.
+
+    See Also
+    --------
+    numpy.linspace : Evenly spaced numbers with careful handling of endpoints.
+    numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.
+    numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
+
+    Examples
+    --------
+    >>> np.arange(3)
+    array([0, 1, 2])
+    >>> np.arange(3.0)
+    array([ 0.,  1.,  2.])
+    >>> np.arange(3,7)
+    array([3, 4, 5, 6])
+    >>> np.arange(3,7,2)
+~~~
diff --git a/docs/Subcommand/Matrix/Arccos.md b/docs/Subcommand/Matrix/Arccos.md
new file mode 100644
index 000000000..998bc86f4
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arccos.md
@@ -0,0 +1,62 @@
+[[/Arccos]] -- Trigonometric inverse cosine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix arccos <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    `x`-coordinate on the unit circle.
+    For real arguments, the domain is [-1, 1].
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+angle : ndarray
+    The angle of the ray intersecting the unit circle at the given
+    `x`-coordinate in radians [0, pi].
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+cos, arctan, arcsin, emath.arccos
+
+Notes
+-----
+`arccos` is a multivalued function: for each `x` there are infinitely
+many numbers `z` such that ``cos(z) = x``. The convention is to return
+the angle `z` whose real part lies in `[0, pi]`.
+
+For real-valued input data types, `arccos` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arccos` is a complex analytic function that
+has branch cuts ``[-inf, -1]`` and `[1, inf]` and is continuous from
+above on the former and from below on the latter.
+
+The inverse `cos` is also known as `acos` or cos^-1.
+
+References
+----------
+M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+10th printing, 1964, pp. 79. http://www.math.sfu.ca/~cbm/aands/
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arccosh.md b/docs/Subcommand/Matrix/Arccosh.md
new file mode 100644
index 000000000..4492c2815
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arccosh.md
@@ -0,0 +1,61 @@
+[[/Arccosh]] -- Inverse hyperbolic cosine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix arccosh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+arccosh : ndarray
+    Array of the same shape as `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+
+cosh, arcsinh, sinh, arctanh, tanh
+
+Notes
+-----
+`arccosh` is a multivalued function: for each `x` there are infinitely
+many numbers `z` such that `cosh(z) = x`. The convention is to return the
+`z` whose imaginary part lies in ``[-pi, pi]`` and the real part in
+``[0, inf]``.
+
+For real-valued input data types, `arccosh` always returns real output.
+For each value that cannot be expressed as a real number or infinity, it
+yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arccosh` is a complex analytical function that
+has a branch cut `[-inf, 1]` and is continuous from above on it.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Inverse hyperbolic function",
+       https://en.wikipedia.org/wiki/Arccosh
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arcsin.md b/docs/Subcommand/Matrix/Arcsin.md
new file mode 100644
index 000000000..b10190967
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arcsin.md
@@ -0,0 +1,62 @@
+[[/Arcsin]] -- Inverse sine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix arcsin <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    `y`-coordinate on the unit circle.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+angle : ndarray
+    The inverse sine of each element in `x`, in radians and in the
+    closed interval ``[-pi/2, pi/2]``.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+sin, cos, arccos, tan, arctan, arctan2, emath.arcsin
+
+Notes
+-----
+`arcsin` is a multivalued function: for each `x` there are infinitely
+many numbers `z` such that :math:`sin(z) = x`.  The convention is to
+return the angle `z` whose real part lies in [-pi/2, pi/2].
+
+For real-valued input data types, *arcsin* always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arcsin` is a complex analytic function that
+has, by convention, the branch cuts [-inf, -1] and [1, inf]  and is
+continuous from above on the former and from below on the latter.
+
+The inverse sine is also known as `asin` or sin^{-1}.
+
+References
+----------
+Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
+10th printing, New York: Dover, 1964, pp. 79ff.
+http://www.math.sfu.ca/~cbm/aands/
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arcsinh.md b/docs/Subcommand/Matrix/Arcsinh.md
new file mode 100644
index 000000000..44bf8928c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arcsinh.md
@@ -0,0 +1,58 @@
+[[/Arcsinh]] -- Inverse hyperbolic sine element-wise.
+
+~~~
+Syntax
+------
+
+matrix arcsinh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Array of the same shape as `x`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+`arcsinh` is a multivalued function: for each `x` there are infinitely
+many numbers `z` such that `sinh(z) = x`. The convention is to return the
+`z` whose imaginary part lies in `[-pi/2, pi/2]`.
+
+For real-valued input data types, `arcsinh` always returns real output.
+For each value that cannot be expressed as a real number or infinity, it
+returns ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arccos` is a complex analytical function that
+has branch cuts `[1j, infj]` and `[-1j, -infj]` and is continuous from
+the right on the former and from the left on the latter.
+
+The inverse hyperbolic sine is also known as `asinh` or ``sinh^-1``.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Inverse hyperbolic function",
+       https://en.wikipedia.org/wiki/Arcsinh
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arctan.md b/docs/Subcommand/Matrix/Arctan.md
new file mode 100644
index 000000000..b6fe77192
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arctan.md
@@ -0,0 +1,63 @@
+[[/Arctan]] -- Trigonometric inverse tangent, element-wise.
+
+~~~
+Syntax
+------
+
+matrix arctan <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Out has the same shape as `x`.  Its real part is in
+    ``[-pi/2, pi/2]`` (``arctan(+/-inf)`` returns ``+/-pi/2``).
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+arctan2 : The "four quadrant" arctan of the angle formed by (`x`, `y`)
+    and the positive `x`-axis.
+angle : Argument of complex values.
+
+Notes
+-----
+`arctan` is a multi-valued function: for each `x` there are infinitely
+many numbers `z` such that tan(`z`) = `x`.  The convention is to return
+the angle `z` whose real part lies in [-pi/2, pi/2].
+
+For real-valued input data types, `arctan` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arctan` is a complex analytic function that
+has [``1j, infj``] and [``-1j, -infj``] as branch cuts, and is continuous
+from the left on the former and from the right on the latter.
+
+The inverse tangent is also known as `atan` or tan^{-1}.
+
+References
+----------
+Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*,
+10th printing, New York: Dover, 1964, pp. 79.
+http://www.math.sfu.ca/~cbm/aands/
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arctan2.md b/docs/Subcommand/Matrix/Arctan2.md
new file mode 100644
index 000000000..2bba42ba9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arctan2.md
@@ -0,0 +1,67 @@
+[[/Arctan2]] -- Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.
+
+~~~
+Syntax
+------
+
+matrix arctan2 <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like, real-valued
+    `y`-coordinates.
+x2 : array_like, real-valued
+    `x`-coordinates.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+angle : ndarray
+    Array of angles in radians, in the range ``[-pi, pi]``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+arctan, tan, angle
+
+Notes
+-----
+*arctan2* is identical to the `atan2` function of the underlying
+C library.  The following special values are defined in the C
+standard: [1]_
+
+====== ====== ================
+`x1`   `x2`   `arctan2(x1,x2)`
+====== ====== ================
++/- 0  +0     +/- 0
++/- 0  -0     +/- pi
+ > 0   +/-inf +0 / +pi
+ < 0   +/-inf -0 / -pi
++/-inf +inf   +/- (pi/4)
++/-inf -inf   +/- (3*pi/4)
+====== ====== ================
+
+Note that +0 and -0 are distinct floating point numbers, as are +inf
+and -inf.
+
+References
+----------
+.. [1] ISO/IEC standard 9899:1999, "Programming language C."
+
+~~~
diff --git a/docs/Subcommand/Matrix/Arctanh.md b/docs/Subcommand/Matrix/Arctanh.md
new file mode 100644
index 000000000..37bb4db4d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Arctanh.md
@@ -0,0 +1,62 @@
+[[/Arctanh]] -- Inverse hyperbolic tangent element-wise.
+
+~~~
+Syntax
+------
+
+matrix arctanh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Array of the same shape as `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+emath.arctanh
+
+Notes
+-----
+`arctanh` is a multivalued function: for each `x` there are infinitely
+many numbers `z` such that ``tanh(z) = x``. The convention is to return
+the `z` whose imaginary part lies in `[-pi/2, pi/2]`.
+
+For real-valued input data types, `arctanh` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `arctanh` is a complex analytical function
+that has branch cuts `[-1, -inf]` and `[1, inf]` and is continuous from
+above on the former and from below on the latter.
+
+The inverse hyperbolic tangent is also known as `atanh` or ``tanh^-1``.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Inverse hyperbolic function",
+       https://en.wikipedia.org/wiki/Arctanh
+
+~~~
diff --git a/docs/Subcommand/Matrix/Around.md b/docs/Subcommand/Matrix/Around.md
new file mode 100644
index 000000000..b06647401
--- /dev/null
+++ b/docs/Subcommand/Matrix/Around.md
@@ -0,0 +1,100 @@
+[[/Around]]
+
+~~~
+Syntax
+------
+
+matrix around <matrix> decimals=<int>
+
+
+    Evenly round to the given number of decimals.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output, but the type of the output
+        values will be cast if necessary. See :ref:`ufuncs-output-type` for more
+        details.
+
+    Returns
+    -------
+    rounded_array : ndarray
+        An array of the same type as `a`, containing the rounded values.
+        Unless `out` was specified, a new array is created.  A reference to
+        the result is returned.
+
+        The real and imaginary parts of complex numbers are rounded
+        separately.  The result of rounding a float is a float.
+
+    See Also
+    --------
+    ndarray.round : equivalent method
+
+    ceil, fix, floor, rint, trunc
+
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    ``np.around`` uses a fast but sometimes inexact algorithm to round
+    floating-point datatypes. For positive `decimals` it is equivalent to
+    ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has
+    error due to the inexact representation of decimal fractions in the IEEE
+    floating point standard [1]_ and errors introduced when scaling by powers
+    of ten. For instance, note the extra "1" in the following:
+
+        >>> np.round(56294995342131.5, 3)
+        56294995342131.51
+
+    If your goal is to print such values with a fixed number of decimals, it is
+    preferable to use numpy's float printing routines to limit the number of
+    printed decimals:
+
+        >>> np.format_float_positional(56294995342131.5, precision=3)
+        '56294995342131.5'
+
+    The float printing routines use an accurate but much more computationally
+    demanding algorithm to compute the number of digits after the decimal
+    point.
+
+    Alternatively, Python's builtin `round` function uses a more accurate
+    but slower algorithm for 64-bit floating point values:
+
+        >>> round(56294995342131.5, 3)
+        56294995342131.5
+        >>> np.round(16.055, 2), round(16.055, 2)  # equals 16.0549999999999997
+        (16.06, 16.05)
+
+
+    References
+    ----------
+    .. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
+           https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
+    .. [2] "How Futile are Mindless Assessments of
+           Roundoff in Floating-Point Computation?", William Kahan,
+           https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf
+
+    Examples
+    --------
+    >>> np.around([0.37, 1.64])
+    array([0.,  2.])
+    >>> np.around([0.37, 1.64], decimals=1)
+    array([0.4,  1.6])
+    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    array([0.,  2.,  2.,  4.,  4.])
+    >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
+    array([ 1,  2,  3, 11])
+    >>> np.around([1,2,3,11], decimals=-1)
+    array([ 0,  0,  0, 10])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cbrt.md b/docs/Subcommand/Matrix/Cbrt.md
new file mode 100644
index 000000000..450850fb4
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cbrt.md
@@ -0,0 +1,38 @@
+[[/Cbrt]] -- Return the cube-root of an array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix cbrt <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    The values whose cube-roots are required.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    An array of the same shape as `x`, containing the cube
+    cube-root of each element in `x`.
+    If `out` was provided, `y` is a reference to it.
+    This is a scalar if `x` is a scalar.
+
+
+~~~
diff --git a/docs/Subcommand/Matrix/Ceil.md b/docs/Subcommand/Matrix/Ceil.md
new file mode 100644
index 000000000..b5ba4319d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Ceil.md
@@ -0,0 +1,39 @@
+[[/Ceil]] -- Return the ceiling of the input, element-wise.
+
+~~~
+Syntax
+------
+
+matrix ceil <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input data.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The ceiling of each element in `x`, with `float` dtype.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+floor, trunc, rint, fix
+
+~~~
diff --git a/docs/Subcommand/Matrix/Clip.md b/docs/Subcommand/Matrix/Clip.md
new file mode 100644
index 000000000..047220f08
--- /dev/null
+++ b/docs/Subcommand/Matrix/Clip.md
@@ -0,0 +1,75 @@
+[[/Clip]]
+
+~~~
+Syntax
+------
+
+matrix clip <matrix> a_min=<matrix> a_max=<matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+
+    Clip (limit) the values in an array.
+
+    Given an interval, values outside the interval are clipped to
+    the interval edges.  For example, if an interval of ``[0, 1]``
+    is specified, values smaller than 0 become 0, and values larger
+    than 1 become 1.
+
+    Equivalent to but faster than ``np.minimum(a_max, np.maximum(a, a_min))``.
+
+    No check is performed to ensure ``a_min < a_max``.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing elements to clip.
+    a_min, a_max : array_like or None
+        Minimum and maximum value. If ``None``, clipping is not performed on
+        the corresponding edge. Only one of `a_min` and `a_max` may be
+        ``None``. Both are broadcast against `a`.
+    out : ndarray, optional
+        The results will be placed in this array. It may be the input
+        array for in-place clipping.  `out` must be of the right shape
+        to hold the output.  Its type is preserved.
+    **kwargs
+        For other keyword-only arguments, see the
+        :ref:`ufunc docs <ufuncs.kwargs>`.
+
+        .. versionadded:: 1.17.0
+
+    Returns
+    -------
+    clipped_array : ndarray
+        An array with the elements of `a`, but where values
+        < `a_min` are replaced with `a_min`, and those > `a_max`
+        with `a_max`.
+
+    See Also
+    --------
+    :ref:`ufuncs-output-type`
+
+    Notes
+    -----
+    When `a_min` is greater than `a_max`, `clip` returns an 
+    array in which all values are equal to `a_max`, 
+    as shown in the second example.  
+
+    Examples
+    --------
+    >>> a = np.arange(10)
+    >>> a
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> np.clip(a, 1, 8)
+    array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
+    >>> np.clip(a, 8, 1)
+    array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
+    >>> np.clip(a, 3, 6, out=a)
+    array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
+    >>> a
+    array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
+    >>> a = np.arange(10)
+    >>> a
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
+    array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Conj.md b/docs/Subcommand/Matrix/Conj.md
new file mode 100644
index 000000000..bf582fac9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Conj.md
@@ -0,0 +1,42 @@
+[[/Conj]] -- Return the complex conjugate, element-wise.
+
+~~~
+Syntax
+------
+
+matrix conj <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input value.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The complex conjugate of `x`, with same dtype as `y`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+`conj` is an alias for `conjugate`:
+
+>>> np.conj is np.conjugate
+True
+
+~~~
diff --git a/docs/Subcommand/Matrix/Conjugate.md b/docs/Subcommand/Matrix/Conjugate.md
new file mode 100644
index 000000000..fa9b1a915
--- /dev/null
+++ b/docs/Subcommand/Matrix/Conjugate.md
@@ -0,0 +1,42 @@
+[[/Conjugate]] -- Return the complex conjugate, element-wise.
+
+~~~
+Syntax
+------
+
+matrix conjugate <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input value.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The complex conjugate of `x`, with same dtype as `y`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+`conj` is an alias for `conjugate`:
+
+>>> np.conj is np.conjugate
+True
+
+~~~
diff --git a/docs/Subcommand/Matrix/Convolve.md b/docs/Subcommand/Matrix/Convolve.md
new file mode 100644
index 000000000..c111f33f7
--- /dev/null
+++ b/docs/Subcommand/Matrix/Convolve.md
@@ -0,0 +1,95 @@
+[[/Convolve]]
+
+~~~
+Syntax
+------
+
+matrix convolve <array> <array> mode=<str>
+
+
+    Returns the discrete, linear convolution of two one-dimensional sequences.
+
+    The convolution operator is often seen in signal processing, where it
+    models the effect of a linear time-invariant system on a signal [1]_.  In
+    probability theory, the sum of two independent random variables is
+    distributed according to the convolution of their individual
+    distributions.
+
+    If `v` is longer than `a`, the arrays are swapped before computation.
+
+    Parameters
+    ----------
+    a : (N,) array_like
+        First one-dimensional input array.
+    v : (M,) array_like
+        Second one-dimensional input array.
+    mode : {'full', 'valid', 'same'}, optional
+        'full':
+          By default, mode is 'full'.  This returns the convolution
+          at each point of overlap, with an output shape of (N+M-1,). At
+          the end-points of the convolution, the signals do not overlap
+          completely, and boundary effects may be seen.
+
+        'same':
+          Mode 'same' returns output of length ``max(M, N)``.  Boundary
+          effects are still visible.
+
+        'valid':
+          Mode 'valid' returns output of length
+          ``max(M, N) - min(M, N) + 1``.  The convolution product is only given
+          for points where the signals overlap completely.  Values outside
+          the signal boundary have no effect.
+
+    Returns
+    -------
+    out : ndarray
+        Discrete, linear convolution of `a` and `v`.
+
+    See Also
+    --------
+    scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier
+                               Transform.
+    scipy.linalg.toeplitz : Used to construct the convolution operator.
+    polymul : Polynomial multiplication. Same output as convolve, but also
+              accepts poly1d objects as input.
+
+    Notes
+    -----
+    The discrete convolution operation is defined as
+
+    .. math:: (a * v)[n] = \sum_{m = -\infty}^{\infty} a[m] v[n - m]
+
+    It can be shown that a convolution :math:`x(t) * y(t)` in time/space
+    is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier
+    domain, after appropriate padding (padding is necessary to prevent
+    circular convolution).  Since multiplication is more efficient (faster)
+    than convolution, the function `scipy.signal.fftconvolve` exploits the
+    FFT to calculate the convolution of large data-sets.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Convolution",
+        https://en.wikipedia.org/wiki/Convolution
+
+    Examples
+    --------
+    Note how the convolution operator flips the second array
+    before "sliding" the two across one another:
+
+    >>> np.convolve([1, 2, 3], [0, 1, 0.5])
+    array([0. , 1. , 2.5, 4. , 1.5])
+
+    Only return the middle values of the convolution.
+    Contains boundary effects, where zeros are taken
+    into account:
+
+    >>> np.convolve([1,2,3],[0,1,0.5], 'same')
+    array([1. ,  2.5,  4. ])
+
+    The two arrays are of the same length, so there
+    is only one position where they completely overlap:
+
+    >>> np.convolve([1,2,3],[0,1,0.5], 'valid')
+    array([2.5])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Copy.md b/docs/Subcommand/Matrix/Copy.md
new file mode 100644
index 000000000..9cfa1ab62
--- /dev/null
+++ b/docs/Subcommand/Matrix/Copy.md
@@ -0,0 +1,84 @@
+[[/Copy]]
+
+~~~
+Syntax
+------
+
+matrix copy <matrix>
+
+
+    Return an array copy of the given object.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout of the copy. 'C' means C-order,
+        'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
+        'C' otherwise. 'K' means match the layout of `a` as closely
+        as possible. (Note that this function and :meth:`ndarray.copy` are very
+        similar, but have different default values for their order=
+        arguments.)
+    subok : bool, optional
+        If True, then sub-classes will be passed-through, otherwise the
+        returned array will be forced to be a base-class array (defaults to False).
+
+        .. versionadded:: 1.19.0
+
+    Returns
+    -------
+    arr : ndarray
+        Array interpretation of `a`.
+
+    See Also
+    --------
+    ndarray.copy : Preferred method for creating an array copy
+
+    Notes
+    -----
+    This is equivalent to:
+
+    >>> np.array(a, copy=True)  #doctest: +SKIP
+
+    Examples
+    --------
+    Create an array x, with a reference y and a copy z:
+
+    >>> x = np.array([1, 2, 3])
+    >>> y = x
+    >>> z = np.copy(x)
+
+    Note that, when we modify x, y changes, but not z:
+
+    >>> x[0] = 10
+    >>> x[0] == y[0]
+    True
+    >>> x[0] == z[0]
+    False
+
+    Note that np.copy is a shallow copy and will not copy object
+    elements within arrays. This is mainly important for arrays
+    containing Python objects. The new array will contain the
+    same object which may lead to surprises if that object can
+    be modified (is mutable):
+
+    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
+    >>> b = np.copy(a)
+    >>> b[2][0] = 10
+    >>> a
+    array([1, 'm', list([10, 3, 4])], dtype=object)
+
+    To ensure all elements within an ``object`` array are copied,
+    use `copy.deepcopy`:
+
+    >>> import copy
+    >>> a = np.array([1, 'm', [2, 3, 4]], dtype=object)
+    >>> c = copy.deepcopy(a)
+    >>> c[2][0] = 10
+    >>> c
+    array([1, 'm', list([10, 3, 4])], dtype=object)
+    >>> a
+    array([1, 'm', list([2, 3, 4])], dtype=object)
+
+~~~
diff --git a/docs/Subcommand/Matrix/Copysign.md b/docs/Subcommand/Matrix/Copysign.md
new file mode 100644
index 000000000..9caf47529
--- /dev/null
+++ b/docs/Subcommand/Matrix/Copysign.md
@@ -0,0 +1,39 @@
+[[/Copysign]] -- Change the sign of x1 to that of x2, element-wise.
+
+~~~
+Syntax
+------
+
+matrix copysign <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Values to change the sign of.
+x2 : array_like
+    The sign of `x2` is copied to `x1`.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    The values of `x1` with the sign of `x2`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cos.md b/docs/Subcommand/Matrix/Cos.md
new file mode 100644
index 000000000..2c3c06b7d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cos.md
@@ -0,0 +1,45 @@
+[[/Cos]] -- Cosine element-wise.
+
+~~~
+Syntax
+------
+
+matrix cos <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array in radians.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding cosine values.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+If `out` is provided, the function writes the result into it,
+and returns a reference to `out`.  (See Examples)
+
+References
+----------
+M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
+New York, NY: Dover, 1972.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cosh.md b/docs/Subcommand/Matrix/Cosh.md
new file mode 100644
index 000000000..56cd75737
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cosh.md
@@ -0,0 +1,35 @@
+[[/Cosh]] -- Hyperbolic cosine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix cosh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Output array of same shape as `x`.
+    This is a scalar if `x` is a scalar.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cross.md b/docs/Subcommand/Matrix/Cross.md
new file mode 100644
index 000000000..2735f8be4
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cross.md
@@ -0,0 +1,120 @@
+[[/Cross]]
+
+~~~
+Syntax
+------
+
+matrix cross <matrix> <matrix> axisa=<int> axisb=<int> axisc=<int> axis=<int>
+
+
+    Return the cross product of two (arrays of) vectors.
+
+    The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular
+    to both `a` and `b`.  If `a` and `b` are arrays of vectors, the vectors
+    are defined by the last axis of `a` and `b` by default, and these axes
+    can have dimensions 2 or 3.  Where the dimension of either `a` or `b` is
+    2, the third component of the input vector is assumed to be zero and the
+    cross product calculated accordingly.  In cases where both input vectors
+    have dimension 2, the z-component of the cross product is returned.
+
+    Parameters
+    ----------
+    a : array_like
+        Components of the first vector(s).
+    b : array_like
+        Components of the second vector(s).
+    axisa : int, optional
+        Axis of `a` that defines the vector(s).  By default, the last axis.
+    axisb : int, optional
+        Axis of `b` that defines the vector(s).  By default, the last axis.
+    axisc : int, optional
+        Axis of `c` containing the cross product vector(s).  Ignored if
+        both input vectors have dimension 2, as the return is scalar.
+        By default, the last axis.
+    axis : int, optional
+        If defined, the axis of `a`, `b` and `c` that defines the vector(s)
+        and cross product(s).  Overrides `axisa`, `axisb` and `axisc`.
+
+    Returns
+    -------
+    c : ndarray
+        Vector cross product(s).
+
+    Raises
+    ------
+    ValueError
+        When the dimension of the vector(s) in `a` and/or `b` does not
+        equal 2 or 3.
+
+    See Also
+    --------
+    inner : Inner product
+    outer : Outer product.
+    ix_ : Construct index arrays.
+
+    Notes
+    -----
+    .. versionadded:: 1.9.0
+
+    Supports full broadcasting of the inputs.
+
+    Examples
+    --------
+    Vector cross-product.
+
+    >>> x = [1, 2, 3]
+    >>> y = [4, 5, 6]
+    >>> np.cross(x, y)
+    array([-3,  6, -3])
+
+    One vector with dimension 2.
+
+    >>> x = [1, 2]
+    >>> y = [4, 5, 6]
+    >>> np.cross(x, y)
+    array([12, -6, -3])
+
+    Equivalently:
+
+    >>> x = [1, 2, 0]
+    >>> y = [4, 5, 6]
+    >>> np.cross(x, y)
+    array([12, -6, -3])
+
+    Both vectors with dimension 2.
+
+    >>> x = [1,2]
+    >>> y = [4,5]
+    >>> np.cross(x, y)
+    array(-3)
+
+    Multiple vector cross-products. Note that the direction of the cross
+    product vector is defined by the `right-hand rule`.
+
+    >>> x = np.array([[1,2,3], [4,5,6]])
+    >>> y = np.array([[4,5,6], [1,2,3]])
+    >>> np.cross(x, y)
+    array([[-3,  6, -3],
+           [ 3, -6,  3]])
+
+    The orientation of `c` can be changed using the `axisc` keyword.
+
+    >>> np.cross(x, y, axisc=0)
+    array([[-3,  3],
+           [ 6, -6],
+           [-3,  3]])
+
+    Change the vector definition of `x` and `y` using `axisa` and `axisb`.
+
+    >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
+    >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
+    >>> np.cross(x, y)
+    array([[ -6,  12,  -6],
+           [  0,   0,   0],
+           [  6, -12,   6]])
+    >>> np.cross(x, y, axisa=0, axisb=0)
+    array([[-24,  48, -24],
+           [-30,  60, -30],
+           [-36,  72, -36]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cumprod.md b/docs/Subcommand/Matrix/Cumprod.md
new file mode 100644
index 000000000..eba6a6133
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cumprod.md
@@ -0,0 +1,67 @@
+[[/Cumprod]]
+
+~~~
+Syntax
+------
+
+matrix cumprod <matrix> axis=<int> [dtype=<str>]
+
+
+    Return the cumulative product of elements along a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative product is computed.  By default
+        the input is flattened.
+    dtype : dtype, optional
+        Type of the returned array, as well as of the accumulator in which
+        the elements are multiplied.  If *dtype* is not specified, it
+        defaults to the dtype of `a`, unless `a` has an integer dtype with
+        a precision less than that of the default platform integer.  In
+        that case, the default platform integer is used instead.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type of the resulting values will be cast if necessary.
+
+    Returns
+    -------
+    cumprod : ndarray
+        A new array holding the result is returned unless `out` is
+        specified, in which case a reference to out is returned.
+
+    See Also
+    --------
+    :ref:`ufuncs-output-type`
+
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.
+
+    Examples
+    --------
+    >>> a = np.array([1,2,3])
+    >>> np.cumprod(a) # intermediate results 1, 1*2
+    ...               # total product 1*2*3 = 6
+    array([1, 2, 6])
+    >>> a = np.array([[1, 2, 3], [4, 5, 6]])
+    >>> np.cumprod(a, dtype=float) # specify type of output
+    array([   1.,    2.,    6.,   24.,  120.,  720.])
+
+    The cumulative product for each column (i.e., over the rows) of `a`:
+
+    >>> np.cumprod(a, axis=0)
+    array([[ 1,  2,  3],
+           [ 4, 10, 18]])
+
+    The cumulative product for each row (i.e. over the columns) of `a`:
+
+    >>> np.cumprod(a,axis=1)
+    array([[  1,   2,   6],
+           [  4,  20, 120]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Cumsum.md b/docs/Subcommand/Matrix/Cumsum.md
new file mode 100644
index 000000000..c57864df8
--- /dev/null
+++ b/docs/Subcommand/Matrix/Cumsum.md
@@ -0,0 +1,80 @@
+[[/Cumsum]]
+
+~~~
+Syntax
+------
+
+matrix cumsum <matrix> axis=<int> [dtype=<str>]
+
+
+    Return the cumulative sum of the elements along a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default
+        (None) is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the
+        elements are summed.  If `dtype` is not specified, it defaults
+        to the dtype of `a`, unless `a` has an integer dtype with a
+        precision less than that of the default platform integer.  In
+        that case, the default platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary. See :ref:`ufuncs-output-type` for
+        more details.
+
+    Returns
+    -------
+    cumsum_along_axis : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which case a reference to `out` is returned. The
+        result has the same size as `a`, and the same shape as `a` if
+        `axis` is not None or `a` is a 1-d array.
+
+    See Also
+    --------
+    sum : Sum array elements.
+    trapz : Integration of array values using the composite trapezoidal rule.
+    diff : Calculate the n-th discrete difference along given axis.
+
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.
+
+    ``cumsum(a)[-1]`` may not be equal to ``sum(a)`` for floating-point
+    values since ``sum`` may use a pairwise summation routine, reducing
+    the roundoff-error. See `sum` for more information.
+
+    Examples
+    --------
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> a
+    array([[1, 2, 3],
+           [4, 5, 6]])
+    >>> np.cumsum(a)
+    array([ 1,  3,  6, 10, 15, 21])
+    >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
+    array([  1.,   3.,   6.,  10.,  15.,  21.])
+
+    >>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
+    array([[1, 2, 3],
+           [5, 7, 9]])
+    >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
+    array([[ 1,  3,  6],
+           [ 4,  9, 15]])
+
+    ``cumsum(b)[-1]`` may not be equal to ``sum(b)``
+
+    >>> b = np.array([1, 2e-9, 3e-9] * 1000000)
+    >>> b.cumsum()[-1]
+    1000000.0050045159
+    >>> b.sum()                    
+    1000000.0050000029
+
+~~~
diff --git a/docs/Subcommand/Matrix/Deg2Rad.md b/docs/Subcommand/Matrix/Deg2Rad.md
new file mode 100644
index 000000000..f9e9a3035
--- /dev/null
+++ b/docs/Subcommand/Matrix/Deg2Rad.md
@@ -0,0 +1,46 @@
+[[/Deg2Rad]] -- Convert angles from degrees to radians.
+
+~~~
+Syntax
+------
+
+matrix deg2rad <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Angles in degrees.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding angle in radians.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+rad2deg : Convert angles from radians to degrees.
+unwrap : Remove large jumps in angle by wrapping.
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+``deg2rad(x)`` is ``x * pi / 180``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Degrees.md b/docs/Subcommand/Matrix/Degrees.md
new file mode 100644
index 000000000..c4a643772
--- /dev/null
+++ b/docs/Subcommand/Matrix/Degrees.md
@@ -0,0 +1,40 @@
+[[/Degrees]] -- Convert angles from radians to degrees.
+
+~~~
+Syntax
+------
+
+matrix degrees <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array in radians.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray of floats
+    The corresponding degree values; if `out` was supplied this is a
+    reference to it.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+rad2deg : equivalent function
+
+~~~
diff --git a/docs/Subcommand/Matrix/Diff.md b/docs/Subcommand/Matrix/Diff.md
new file mode 100644
index 000000000..a91640b30
--- /dev/null
+++ b/docs/Subcommand/Matrix/Diff.md
@@ -0,0 +1,91 @@
+[[/Diff]]
+
+~~~
+Syntax
+------
+
+matrix diff <matrix> n=<int> axis=<int> prepend=<matrix> append=<matrix>
+
+
+    Calculate the n-th discrete difference along the given axis.
+
+    The first difference is given by ``out[i] = a[i+1] - a[i]`` along
+    the given axis, higher differences are calculated by using `diff`
+    recursively.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input
+        is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the
+        last axis.
+    prepend, append : array_like, optional
+        Values to prepend or append to `a` along axis prior to
+        performing the difference.  Scalar values are expanded to
+        arrays with length 1 in the direction of axis and the shape
+        of the input array in along all other axes.  Otherwise the
+        dimension and shape must match `a` except along axis.
+
+        .. versionadded:: 1.16.0
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences. The shape of the output is the same as `a`
+        except along `axis` where the dimension is smaller by `n`. The
+        type of the output is the same as the type of the difference
+        between any two elements of `a`. This is the same as the type of
+        `a` in most cases. A notable exception is `datetime64`, which
+        results in a `timedelta64` output array.
+
+    See Also
+    --------
+    gradient, ediff1d, cumsum
+
+    Notes
+    -----
+    Type is preserved for boolean arrays, so the result will contain
+    `False` when consecutive elements are the same and `True` when they
+    differ.
+
+    For unsigned integer arrays, the results will also be unsigned. This
+    should not be surprising, as the result is consistent with
+    calculating the difference directly:
+
+    >>> u8_arr = np.array([1, 0], dtype=np.uint8)
+    >>> np.diff(u8_arr)
+    array([255], dtype=uint8)
+    >>> u8_arr[1,...] - u8_arr[0,...]
+    255
+
+    If this is not desirable, then the array should be cast to a larger
+    integer type first:
+
+    >>> i16_arr = u8_arr.astype(np.int16)
+    >>> np.diff(i16_arr)
+    array([-1], dtype=int16)
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+           [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    >>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
+    >>> np.diff(x)
+    array([1, 1], dtype='timedelta64[D]')
+
+~~~
diff --git a/docs/Subcommand/Matrix/Divide.md b/docs/Subcommand/Matrix/Divide.md
new file mode 100644
index 000000000..dc0d4d999
--- /dev/null
+++ b/docs/Subcommand/Matrix/Divide.md
@@ -0,0 +1,44 @@
+[[/Divide]] -- Returns a true division of the inputs, element-wise.
+
+~~~
+Syntax
+------
+
+matrix divide <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend array.
+x2 : array_like
+    Divisor array.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+In Python, ``//`` is the floor division operator and ``/`` the
+true division operator.  The ``true_divide(x1, x2)`` function is
+equivalent to true division in Python.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Divmod.md b/docs/Subcommand/Matrix/Divmod.md
new file mode 100644
index 000000000..09c20f403
--- /dev/null
+++ b/docs/Subcommand/Matrix/Divmod.md
@@ -0,0 +1,49 @@
+[[/Divmod]] -- Return element-wise quotient and remainder simultaneously.
+
+~~~
+Syntax
+------
+
+matrix divmod <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend array.
+x2 : array_like
+    Divisor array.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out1 : ndarray
+    Element-wise quotient resulting from floor division.
+    This is a scalar if both `x1` and `x2` are scalars.
+out2 : ndarray
+    Element-wise remainder from floor division.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+floor_divide : Equivalent to Python's ``//`` operator.
+remainder : Equivalent to Python's ``%`` operator.
+modf : Equivalent to ``divmod(x, 1)`` for positive ``x`` with the return
+       values switched.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Dot.md b/docs/Subcommand/Matrix/Dot.md
new file mode 100644
index 000000000..b4824ed15
--- /dev/null
+++ b/docs/Subcommand/Matrix/Dot.md
@@ -0,0 +1,92 @@
+[[/Dot]]
+
+~~~
+Syntax
+------
+
+matrix dot <matrix> <matrix>
+
+
+    dot(a, b, out=None)
+
+    Dot product of two arrays. Specifically,
+
+    - If both `a` and `b` are 1-D arrays, it is inner product of vectors
+      (without complex conjugation).
+
+    - If both `a` and `b` are 2-D arrays, it is matrix multiplication,
+      but using :func:`matmul` or ``a @ b`` is preferred.
+
+    - If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
+      and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
+
+    - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
+      the last axis of `a` and `b`.
+
+    - If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
+      sum product over the last axis of `a` and the second-to-last axis of `b`::
+
+        dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
+
+    Parameters
+    ----------
+    a : array_like
+        First argument.
+    b : array_like
+        Second argument.
+    out : ndarray, optional
+        Output argument. This must have the exact kind that would be returned
+        if it was not used. In particular, it must have the right type, must be
+        C-contiguous, and its dtype must be the dtype that would be returned
+        for `dot(a,b)`. This is a performance feature. Therefore, if these
+        conditions are not met, an exception is raised, instead of attempting
+        to be flexible.
+
+    Returns
+    -------
+    output : ndarray
+        Returns the dot product of `a` and `b`.  If `a` and `b` are both
+        scalars or both 1-D arrays then a scalar is returned; otherwise
+        an array is returned.
+        If `out` is given, then it is returned.
+
+    Raises
+    ------
+    ValueError
+        If the last dimension of `a` is not the same size as
+        the second-to-last dimension of `b`.
+
+    See Also
+    --------
+    vdot : Complex-conjugating dot product.
+    tensordot : Sum products over arbitrary axes.
+    einsum : Einstein summation convention.
+    matmul : '@' operator as method with out parameter.
+    linalg.multi_dot : Chained dot product.
+
+    Examples
+    --------
+    >>> np.dot(3, 4)
+    12
+
+    Neither argument is complex-conjugated:
+
+    >>> np.dot([2j, 3j], [2j, 3j])
+    (-13+0j)
+
+    For 2-D arrays it is the matrix product:
+
+    >>> a = [[1, 0], [0, 1]]
+    >>> b = [[4, 1], [2, 2]]
+    >>> np.dot(a, b)
+    array([[4, 1],
+           [2, 2]])
+
+    >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
+    >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
+    >>> np.dot(a, b)[2,3,2,1,2,2]
+    499128
+    >>> sum(a[2,3,2,:] * b[1,2,:,2])
+    499128
+
+~~~
diff --git a/docs/Subcommand/Matrix/Exp.md b/docs/Subcommand/Matrix/Exp.md
new file mode 100644
index 000000000..901300ea3
--- /dev/null
+++ b/docs/Subcommand/Matrix/Exp.md
@@ -0,0 +1,61 @@
+[[/Exp]] -- Calculate the exponential of all elements in the input array.
+
+~~~
+Syntax
+------
+
+matrix exp <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Output array, element-wise exponential of `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+expm1 : Calculate ``exp(x) - 1`` for all elements in the array.
+exp2  : Calculate ``2**x`` for all elements in the array.
+
+Notes
+-----
+The irrational number ``e`` is also known as Euler's number.  It is
+approximately 2.718281, and is the base of the natural logarithm,
+``ln`` (this means that, if :math:`x = \ln y = \log_e y`,
+then :math:`e^x = y`. For real input, ``exp(x)`` is always positive.
+
+For complex arguments, ``x = a + ib``, we can write
+:math:`e^x = e^a e^{ib}`.  The first term, :math:`e^a`, is already
+known (it is the real argument, described above).  The second term,
+:math:`e^{ib}`, is :math:`\cos b + i \sin b`, a function with
+magnitude 1 and a periodic phase.
+
+References
+----------
+.. [1] Wikipedia, "Exponential function",
+       https://en.wikipedia.org/wiki/Exponential_function
+.. [2] M. Abramovitz and I. A. Stegun, "Handbook of Mathematical Functions
+       with Formulas, Graphs, and Mathematical Tables," Dover, 1964, p. 69,
+       http://www.math.sfu.ca/~cbm/aands/page_69.htm
+
+~~~
diff --git a/docs/Subcommand/Matrix/Exp2.md b/docs/Subcommand/Matrix/Exp2.md
new file mode 100644
index 000000000..418b425b5
--- /dev/null
+++ b/docs/Subcommand/Matrix/Exp2.md
@@ -0,0 +1,45 @@
+[[/Exp2]] -- Calculate `2**p` for all `p` in the input array.
+
+~~~
+Syntax
+------
+
+matrix exp2 <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Element-wise 2 to the power `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+power
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+
+
+~~~
diff --git a/docs/Subcommand/Matrix/Expm1.md b/docs/Subcommand/Matrix/Expm1.md
new file mode 100644
index 000000000..c3c091402
--- /dev/null
+++ b/docs/Subcommand/Matrix/Expm1.md
@@ -0,0 +1,45 @@
+[[/Expm1]] -- Calculate ``exp(x) - 1`` for all elements in the array.
+
+~~~
+Syntax
+------
+
+matrix expm1 <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Element-wise exponential minus one: ``out = exp(x) - 1``.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+log1p : ``log(1 + x)``, the inverse of expm1.
+
+
+Notes
+-----
+This function provides greater precision than ``exp(x) - 1``
+for small values of ``x``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Eye.md b/docs/Subcommand/Matrix/Eye.md
new file mode 100644
index 000000000..6238caaac
--- /dev/null
+++ b/docs/Subcommand/Matrix/Eye.md
@@ -0,0 +1,59 @@
+[[/Eye]]
+
+~~~
+Syntax
+------
+
+matrix eye <int> M=<int> k=<int> dtype=<str> order=<str>
+
+
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+      Number of rows in the output.
+    M : int, optional
+      Number of columns in the output. If None, defaults to `N`.
+    k : int, optional
+      Index of the diagonal: 0 (the default) refers to the main diagonal,
+      a positive value refers to an upper diagonal, and a negative value
+      to a lower diagonal.
+    dtype : data-type, optional
+      Data-type of the returned array.
+    order : {'C', 'F'}, optional
+        Whether the output should be stored in row-major (C-style) or
+        column-major (Fortran-style) order in memory.
+
+        .. versionadded:: 1.14.0
+    like : array_like
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument.
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+      An array where all elements are equal to zero, except for the `k`-th
+      diagonal, whose values are equal to one.
+
+    See Also
+    --------
+    identity : (almost) equivalent function
+    diag : diagonal 2-D array from a 1-D array specified by the user.
+
+    Examples
+    --------
+    >>> np.eye(2, dtype=int)
+    array([[1, 0],
+           [0, 1]])
+    >>> np.eye(3, k=1)
+    array([[0.,  1.,  0.],
+           [0.,  0.,  1.],
+           [0.,  0.,  0.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Fabs.md b/docs/Subcommand/Matrix/Fabs.md
new file mode 100644
index 000000000..c3f5d16ff
--- /dev/null
+++ b/docs/Subcommand/Matrix/Fabs.md
@@ -0,0 +1,40 @@
+[[/Fabs]] -- Compute the absolute values element-wise.
+
+~~~
+Syntax
+------
+
+matrix fabs <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    The array of numbers for which the absolute values are required. If
+    `x` is a scalar, the result `y` will also be a scalar.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The absolute values of `x`, the returned values are always floats.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+absolute : Absolute values including `complex` types.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Fix.md b/docs/Subcommand/Matrix/Fix.md
new file mode 100644
index 000000000..90825d7ed
--- /dev/null
+++ b/docs/Subcommand/Matrix/Fix.md
@@ -0,0 +1,48 @@
+[[/Fix]]
+
+~~~
+Syntax
+------
+
+matrix fix <matrix>
+
+
+    Round to nearest integer towards zero.
+
+    Round an array of floats element-wise to nearest integer towards zero.
+    The rounded values are returned as floats.
+
+    Parameters
+    ----------
+    x : array_like
+        An array of floats to be rounded
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the input broadcasts to. If not provided or None, a
+        freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray of floats
+        A float array with the same dimensions as the input.
+        If second argument is not supplied then a float array is returned
+        with the rounded values.
+
+        If a second argument is supplied the result is stored there.
+        The return value `out` is then a reference to that array.
+
+    See Also
+    --------
+    rint, trunc, floor, ceil
+    around : Round to given number of decimals
+
+    Examples
+    --------
+    >>> np.fix(3.14)
+    3.0
+    >>> np.fix(3)
+    3.0
+    >>> np.fix([2.1, 2.9, -2.1, -2.9])
+    array([ 2.,  2., -2., -2.])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Float_Power.md b/docs/Subcommand/Matrix/Float_Power.md
new file mode 100644
index 000000000..c2cc4ce87
--- /dev/null
+++ b/docs/Subcommand/Matrix/Float_Power.md
@@ -0,0 +1,43 @@
+[[/Float_Power]] -- First array elements raised to powers from second array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix float_power <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    The bases.
+x2 : array_like
+    The exponents.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The bases in `x1` raised to the exponents in `x2`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+power : power function that preserves type
+
+~~~
diff --git a/docs/Subcommand/Matrix/Floor.md b/docs/Subcommand/Matrix/Floor.md
new file mode 100644
index 000000000..915633e4c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Floor.md
@@ -0,0 +1,46 @@
+[[/Floor]] -- Return the floor of the input, element-wise.
+
+~~~
+Syntax
+------
+
+matrix floor <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input data.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The floor of each element in `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+ceil, trunc, rint, fix
+
+Notes
+-----
+Some spreadsheet programs calculate the "floor-towards-zero", where
+``floor(-2.5) == -2``.  NumPy instead uses the definition of
+`floor` where `floor(-2.5) == -3`. The "floor-towards-zero"
+function is called ``fix`` in NumPy.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Floor_Divide.md b/docs/Subcommand/Matrix/Floor_Divide.md
new file mode 100644
index 000000000..75983cfaf
--- /dev/null
+++ b/docs/Subcommand/Matrix/Floor_Divide.md
@@ -0,0 +1,47 @@
+[[/Floor_Divide]] -- Return the largest integer smaller or equal to the division of the inputs.
+
+~~~
+Syntax
+------
+
+matrix floor_divide <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Numerator.
+x2 : array_like
+    Denominator.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    y = floor(`x1`/`x2`)
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+remainder : Remainder complementary to floor_divide.
+divmod : Simultaneous floor division and remainder.
+divide : Standard division.
+floor : Round a number to the nearest integer toward minus infinity.
+ceil : Round a number to the nearest integer toward infinity.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Fmax.md b/docs/Subcommand/Matrix/Fmax.md
new file mode 100644
index 000000000..a24d1fd46
--- /dev/null
+++ b/docs/Subcommand/Matrix/Fmax.md
@@ -0,0 +1,57 @@
+[[/Fmax]] -- Element-wise maximum of array elements.
+
+~~~
+Syntax
+------
+
+matrix fmax <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays holding the elements to be compared.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The maximum of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+fmin :
+    Element-wise minimum of two arrays, ignores NaNs.
+maximum :
+    Element-wise maximum of two arrays, propagates NaNs.
+amax :
+    The maximum value of an array along a given axis, propagates NaNs.
+nanmax :
+    The maximum value of an array along a given axis, ignores NaNs.
+
+minimum, amin, nanmin
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither
+x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Fmin.md b/docs/Subcommand/Matrix/Fmin.md
new file mode 100644
index 000000000..cbbe14f3d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Fmin.md
@@ -0,0 +1,57 @@
+[[/Fmin]] -- Element-wise minimum of array elements.
+
+~~~
+Syntax
+------
+
+matrix fmin <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays holding the elements to be compared.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The minimum of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+fmax :
+    Element-wise maximum of two arrays, ignores NaNs.
+minimum :
+    Element-wise minimum of two arrays, propagates NaNs.
+amin :
+    The minimum value of an array along a given axis, propagates NaNs.
+nanmin :
+    The minimum value of an array along a given axis, ignores NaNs.
+
+maximum, amax, nanmax
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither
+x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Fmod.md b/docs/Subcommand/Matrix/Fmod.md
new file mode 100644
index 000000000..fec84fad4
--- /dev/null
+++ b/docs/Subcommand/Matrix/Fmod.md
@@ -0,0 +1,52 @@
+[[/Fmod]] -- Return the element-wise remainder of division.
+
+~~~
+Syntax
+------
+
+matrix fmod <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend.
+x2 : array_like
+    Divisor.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : array_like
+    The remainder of the division of `x1` by `x2`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+remainder : Equivalent to the Python ``%`` operator.
+divide
+
+Notes
+-----
+The result of the modulo operation for negative dividend and divisors
+is bound by conventions. For `fmod`, the sign of result is the sign of
+the dividend, while for `remainder` the sign of the result is the sign
+of the divisor. The `fmod` function is equivalent to the Matlab(TM)
+``rem`` function.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Frexp.md b/docs/Subcommand/Matrix/Frexp.md
new file mode 100644
index 000000000..bf32c3d30
--- /dev/null
+++ b/docs/Subcommand/Matrix/Frexp.md
@@ -0,0 +1,50 @@
+[[/Frexp]] -- Decompose the elements of x into mantissa and twos exponent.
+
+~~~
+Syntax
+------
+
+matrix frexp <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Array of numbers to be decomposed.
+out1 : ndarray, optional
+    Output array for the mantissa. Must have the same shape as `x`.
+out2 : ndarray, optional
+    Output array for the exponent. Must have the same shape as `x`.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+mantissa : ndarray
+    Floating values between -1 and 1.
+    This is a scalar if `x` is a scalar.
+exponent : ndarray
+    Integer exponents of 2.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`.
+
+Notes
+-----
+Complex dtypes are not supported, they will raise a TypeError.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Gcd.md b/docs/Subcommand/Matrix/Gcd.md
new file mode 100644
index 000000000..80586d396
--- /dev/null
+++ b/docs/Subcommand/Matrix/Gcd.md
@@ -0,0 +1,26 @@
+[[/Gcd]] -- Returns the greatest common divisor of ``|x1|`` and ``|x2|``
+
+~~~
+Syntax
+------
+
+matrix gcd <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like, int
+    Arrays of values.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+
+Returns
+-------
+y : ndarray or scalar
+    The greatest common divisor of the absolute value of the inputs
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+lcm : The lowest common multiple
+
+~~~
diff --git a/docs/Subcommand/Matrix/Gradient.md b/docs/Subcommand/Matrix/Gradient.md
new file mode 100644
index 000000000..cfd64b90e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Gradient.md
@@ -0,0 +1,181 @@
+[[/Gradient]]
+
+~~~
+Syntax
+------
+
+matrix gradient <matrix> spacing=<matrix> edge_order=<int> [axis=<inttuple>]
+
+
+    Return the gradient of an N-dimensional array.
+
+    The gradient is computed using second order accurate central differences
+    in the interior points and either first or second order accurate one-sides
+    (forward or backwards) differences at the boundaries.
+    The returned gradient hence has the same shape as the input array.
+
+    Parameters
+    ----------
+    f : array_like
+        An N-dimensional array containing samples of a scalar function.
+    varargs : list of scalar or array, optional
+        Spacing between f values. Default unitary spacing for all dimensions.
+        Spacing can be specified using:
+
+        1. single scalar to specify a sample distance for all dimensions.
+        2. N scalars to specify a constant sample distance for each dimension.
+           i.e. `dx`, `dy`, `dz`, ...
+        3. N arrays to specify the coordinates of the values along each
+           dimension of F. The length of the array must match the size of
+           the corresponding dimension
+        4. Any combination of N scalars/arrays with the meaning of 2. and 3.
+
+        If `axis` is given, the number of varargs must equal the number of axes.
+        Default: 1.
+
+    edge_order : {1, 2}, optional
+        Gradient is calculated using N-th order accurate differences
+        at the boundaries. Default: 1.
+
+        .. versionadded:: 1.9.1
+
+    axis : None or int or tuple of ints, optional
+        Gradient is calculated only along the given axis or axes
+        The default (axis = None) is to calculate the gradient for all the axes
+        of the input array. axis may be negative, in which case it counts from
+        the last to the first axis.
+
+        .. versionadded:: 1.11.0
+
+    Returns
+    -------
+    gradient : ndarray or list of ndarray
+        A list of ndarrays (or a single ndarray if there is only one dimension)
+        corresponding to the derivatives of f with respect to each dimension.
+        Each derivative has the same shape as f.
+
+    Examples
+    --------
+    >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=float)
+    >>> np.gradient(f)
+    array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
+    >>> np.gradient(f, 2)
+    array([0.5 ,  0.75,  1.25,  1.75,  2.25,  2.5 ])
+
+    Spacing can be also specified with an array that represents the coordinates
+    of the values F along the dimensions.
+    For instance a uniform spacing:
+
+    >>> x = np.arange(f.size)
+    >>> np.gradient(f, x)
+    array([1. ,  1.5,  2.5,  3.5,  4.5,  5. ])
+
+    Or a non uniform one:
+
+    >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=float)
+    >>> np.gradient(f, x)
+    array([1. ,  3. ,  3.5,  6.7,  6.9,  2.5])
+
+    For two dimensional arrays, the return will be two arrays ordered by
+    axis. In this example the first array stands for the gradient in
+    rows and the second one in columns direction:
+
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float))
+    [array([[ 2.,  2., -1.],
+           [ 2.,  2., -1.]]), array([[1. , 2.5, 4. ],
+           [1. , 1. , 1. ]])]
+
+    In this example the spacing is also specified:
+    uniform for axis=0 and non uniform for axis=1
+
+    >>> dx = 2.
+    >>> y = [1., 1.5, 3.5]
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), dx, y)
+    [array([[ 1. ,  1. , -0.5],
+           [ 1. ,  1. , -0.5]]), array([[2. , 2. , 2. ],
+           [2. , 1.7, 0.5]])]
+
+    It is possible to specify how boundaries are treated using `edge_order`
+
+    >>> x = np.array([0, 1, 2, 3, 4])
+    >>> f = x**2
+    >>> np.gradient(f, edge_order=1)
+    array([1.,  2.,  4.,  6.,  7.])
+    >>> np.gradient(f, edge_order=2)
+    array([0., 2., 4., 6., 8.])
+
+    The `axis` keyword can be used to specify a subset of axes of which the
+    gradient is calculated
+
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), axis=0)
+    array([[ 2.,  2., -1.],
+           [ 2.,  2., -1.]])
+
+    Notes
+    -----
+    Assuming that :math:`f\in C^{3}` (i.e., :math:`f` has at least 3 continuous
+    derivatives) and let :math:`h_{*}` be a non-homogeneous stepsize, we
+    minimize the "consistency error" :math:`\eta_{i}` between the true gradient
+    and its estimate from a linear combination of the neighboring grid-points:
+
+    .. math::
+
+        \eta_{i} = f_{i}^{\left(1\right)} -
+                    \left[ \alpha f\left(x_{i}\right) +
+                            \beta f\left(x_{i} + h_{d}\right) +
+                            \gamma f\left(x_{i}-h_{s}\right)
+                    \right]
+
+    By substituting :math:`f(x_{i} + h_{d})` and :math:`f(x_{i} - h_{s})`
+    with their Taylor series expansion, this translates into solving
+    the following the linear system:
+
+    .. math::
+
+        \left\{
+            \begin{array}{r}
+                \alpha+\beta+\gamma=0 \\
+                \beta h_{d}-\gamma h_{s}=1 \\
+                \beta h_{d}^{2}+\gamma h_{s}^{2}=0
+            \end{array}
+        \right.
+
+    The resulting approximation of :math:`f_{i}^{(1)}` is the following:
+
+    .. math::
+
+        \hat f_{i}^{(1)} =
+            \frac{
+                h_{s}^{2}f\left(x_{i} + h_{d}\right)
+                + \left(h_{d}^{2} - h_{s}^{2}\right)f\left(x_{i}\right)
+                - h_{d}^{2}f\left(x_{i}-h_{s}\right)}
+                { h_{s}h_{d}\left(h_{d} + h_{s}\right)}
+            + \mathcal{O}\left(\frac{h_{d}h_{s}^{2}
+                                + h_{s}h_{d}^{2}}{h_{d}
+                                + h_{s}}\right)
+
+    It is worth noting that if :math:`h_{s}=h_{d}`
+    (i.e., data are evenly spaced)
+    we find the standard second order approximation:
+
+    .. math::
+
+        \hat f_{i}^{(1)}=
+            \frac{f\left(x_{i+1}\right) - f\left(x_{i-1}\right)}{2h}
+            + \mathcal{O}\left(h^{2}\right)
+
+    With a similar procedure the forward/backward approximations used for
+    boundaries can be derived.
+
+    References
+    ----------
+    .. [1]  Quarteroni A., Sacco R., Saleri F. (2007) Numerical Mathematics
+            (Texts in Applied Mathematics). New York: Springer.
+    .. [2]  Durran D. R. (1999) Numerical Methods for Wave Equations
+            in Geophysical Fluid Dynamics. New York: Springer.
+    .. [3]  Fornberg B. (1988) Generation of Finite Difference Formulas on
+            Arbitrarily Spaced Grids,
+            Mathematics of Computation 51, no. 184 : 699-706.
+            `PDF <http://www.ams.org/journals/mcom/1988-51-184/
+            S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_.
+~~~
diff --git a/docs/Subcommand/Matrix/Heaviside.md b/docs/Subcommand/Matrix/Heaviside.md
new file mode 100644
index 000000000..af0b113cc
--- /dev/null
+++ b/docs/Subcommand/Matrix/Heaviside.md
@@ -0,0 +1,48 @@
+[[/Heaviside]] -- Compute the Heaviside step function.
+
+~~~
+Syntax
+------
+
+matrix heaviside <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Input values.
+x2 : array_like
+    The value of the function when x1 is 0.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    The output array, element-wise Heaviside step function of `x1`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+.. versionadded:: 1.13.0
+
+References
+----------
+.. Wikipedia, "Heaviside step function",
+   https://en.wikipedia.org/wiki/Heaviside_step_function
+
+~~~
diff --git a/docs/Subcommand/Matrix/Hstack.md b/docs/Subcommand/Matrix/Hstack.md
new file mode 100644
index 000000000..97a5d826d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Hstack.md
@@ -0,0 +1,55 @@
+[[/Hstack]]
+
+~~~
+Syntax
+------
+
+matrix hstack <matrix> <matrix>
+
+
+    Stack arrays in sequence horizontally (column wise).
+
+    This is equivalent to concatenation along the second axis, except for 1-D
+    arrays where it concatenates along the first axis. Rebuilds arrays divided
+    by `hsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the second axis,
+        except 1-D arrays which can be any length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays.
+
+    See Also
+    --------
+    concatenate : Join a sequence of arrays along an existing axis.
+    stack : Join a sequence of arrays along a new axis.
+    block : Assemble an nd-array from nested lists of blocks.
+    vstack : Stack arrays in sequence vertically (row wise).
+    dstack : Stack arrays in sequence depth wise (along third axis).
+    column_stack : Stack 1-D arrays as columns into a 2-D array.
+    hsplit : Split an array into multiple sub-arrays horizontally (column-wise).
+
+    Examples
+    --------
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((4,5,6))
+    >>> np.hstack((a,b))
+    array([1, 2, 3, 4, 5, 6])
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[4],[5],[6]])
+    >>> np.hstack((a,b))
+    array([[1, 4],
+           [2, 5],
+           [3, 6]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Hypot.md b/docs/Subcommand/Matrix/Hypot.md
new file mode 100644
index 000000000..ff495350c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Hypot.md
@@ -0,0 +1,37 @@
+[[/Hypot]] -- Given the "legs" of a right triangle, return its hypotenuse.
+
+~~~
+Syntax
+------
+
+matrix hypot <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    Leg of the triangle(s).
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+z : ndarray
+    The hypotenuse of the triangle(s).
+    This is a scalar if both `x1` and `x2` are scalars.
+
+~~~
diff --git a/docs/Subcommand/Matrix/I0.md b/docs/Subcommand/Matrix/I0.md
new file mode 100644
index 000000000..5afcd98d7
--- /dev/null
+++ b/docs/Subcommand/Matrix/I0.md
@@ -0,0 +1,57 @@
+[[/I0]]
+
+~~~
+Syntax
+------
+
+matrix i0 <array>
+
+
+    Modified Bessel function of the first kind, order 0.
+
+    Usually denoted :math:`I_0`.
+
+    Parameters
+    ----------
+    x : array_like of float
+        Argument of the Bessel function.
+
+    Returns
+    -------
+    out : ndarray, shape = x.shape, dtype = float
+        The modified Bessel function evaluated at each of the elements of `x`.
+
+    See Also
+    --------
+    scipy.special.i0, scipy.special.iv, scipy.special.ive
+
+    Notes
+    -----
+    The scipy implementation is recommended over this function: it is a
+    proper ufunc written in C, and more than an order of magnitude faster.
+
+    We use the algorithm published by Clenshaw [1]_ and referenced by
+    Abramowitz and Stegun [2]_, for which the function domain is
+    partitioned into the two intervals [0,8] and (8,inf), and Chebyshev
+    polynomial expansions are employed in each interval. Relative error on
+    the domain [0,30] using IEEE arithmetic is documented [3]_ as having a
+    peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000).
+
+    References
+    ----------
+    .. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in
+           *National Physical Laboratory Mathematical Tables*, vol. 5, London:
+           Her Majesty's Stationery Office, 1962.
+    .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical
+           Functions*, 10th printing, New York: Dover, 1964, pp. 379.
+           http://www.math.sfu.ca/~cbm/aands/page_379.htm
+    .. [3] https://metacpan.org/pod/distribution/Math-Cephes/lib/Math/Cephes.pod#i0:-Modified-Bessel-function-of-order-zero
+
+    Examples
+    --------
+    >>> np.i0(0.)
+    array(1.0)
+    >>> np.i0([0, 1, 2, 3])
+    array([1.        , 1.26606588, 2.2795853 , 4.88079259])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Identity.md b/docs/Subcommand/Matrix/Identity.md
new file mode 100644
index 000000000..415c098a5
--- /dev/null
+++ b/docs/Subcommand/Matrix/Identity.md
@@ -0,0 +1,43 @@
+[[/Identity]]
+
+~~~
+Syntax
+------
+
+matrix identity <int> dtype=<str>
+
+
+    Return the identity array.
+
+    The identity array is a square array with ones on
+    the main diagonal.
+
+    Parameters
+    ----------
+    n : int
+        Number of rows (and columns) in `n` x `n` output.
+    dtype : data-type, optional
+        Data-type of the output.  Defaults to ``float``.
+    like : array_like
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument.
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        `n` x `n` array with its main diagonal set to one,
+        and all other elements 0.
+
+    Examples
+    --------
+    >>> np.identity(3)
+    array([[1.,  0.,  0.],
+           [0.,  1.,  0.],
+           [0.,  0.,  1.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Imag.md b/docs/Subcommand/Matrix/Imag.md
new file mode 100644
index 000000000..4a46801c1
--- /dev/null
+++ b/docs/Subcommand/Matrix/Imag.md
@@ -0,0 +1,39 @@
+[[/Imag]]
+
+~~~
+Syntax
+------
+
+matrix imag <matrix>
+
+
+    Return the imaginary part of the complex argument.
+
+    Parameters
+    ----------
+    val : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The imaginary component of the complex argument. If `val` is real,
+        the type of `val` is used for the output.  If `val` has complex
+        elements, the returned type is float.
+
+    See Also
+    --------
+    real, angle, real_if_close
+
+    Examples
+    --------
+    >>> a = np.array([1+2j, 3+4j, 5+6j])
+    >>> a.imag
+    array([2.,  4.,  6.])
+    >>> a.imag = np.array([8, 10, 12])
+    >>> a
+    array([1. +8.j,  3.+10.j,  5.+12.j])
+    >>> np.imag(1 + 1j)
+    1.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Interp.md b/docs/Subcommand/Matrix/Interp.md
new file mode 100644
index 000000000..7a6f3cf36
--- /dev/null
+++ b/docs/Subcommand/Matrix/Interp.md
@@ -0,0 +1,110 @@
+[[/Interp]]
+
+~~~
+Syntax
+------
+
+matrix interp <array> <array> <array> left=<float> right=<float> period=<float>
+
+
+    One-dimensional linear interpolation for monotonically increasing sample points.
+
+    Returns the one-dimensional piecewise linear interpolant to a function
+    with given discrete data points (`xp`, `fp`), evaluated at `x`.
+
+    Parameters
+    ----------
+    x : array_like
+        The x-coordinates at which to evaluate the interpolated values.
+
+    xp : 1-D sequence of floats
+        The x-coordinates of the data points, must be increasing if argument
+        `period` is not specified. Otherwise, `xp` is internally sorted after
+        normalizing the periodic boundaries with ``xp = xp % period``.
+
+    fp : 1-D sequence of float or complex
+        The y-coordinates of the data points, same length as `xp`.
+
+    left : optional float or complex corresponding to fp
+        Value to return for `x < xp[0]`, default is `fp[0]`.
+
+    right : optional float or complex corresponding to fp
+        Value to return for `x > xp[-1]`, default is `fp[-1]`.
+
+    period : None or float, optional
+        A period for the x-coordinates. This parameter allows the proper
+        interpolation of angular x-coordinates. Parameters `left` and `right`
+        are ignored if `period` is specified.
+
+        .. versionadded:: 1.10.0
+
+    Returns
+    -------
+    y : float or complex (corresponding to fp) or ndarray
+        The interpolated values, same shape as `x`.
+
+    Raises
+    ------
+    ValueError
+        If `xp` and `fp` have different length
+        If `xp` or `fp` are not 1-D sequences
+        If `period == 0`
+
+    See Also
+    --------
+    scipy.interpolate
+
+    Warnings
+    --------
+    The x-coordinate sequence is expected to be increasing, but this is not
+    explicitly enforced.  However, if the sequence `xp` is non-increasing,
+    interpolation results are meaningless.
+
+    Note that, since NaN is unsortable, `xp` also cannot contain NaNs.
+
+    A simple check for `xp` being strictly increasing is::
+
+        np.all(np.diff(xp) > 0)
+
+    Examples
+    --------
+    >>> xp = [1, 2, 3]
+    >>> fp = [3, 2, 0]
+    >>> np.interp(2.5, xp, fp)
+    1.0
+    >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
+    array([3.  , 3.  , 2.5 , 0.56, 0.  ])
+    >>> UNDEF = -99.0
+    >>> np.interp(3.14, xp, fp, right=UNDEF)
+    -99.0
+
+    Plot an interpolant to the sine function:
+
+    >>> x = np.linspace(0, 2*np.pi, 10)
+    >>> y = np.sin(x)
+    >>> xvals = np.linspace(0, 2*np.pi, 50)
+    >>> yinterp = np.interp(xvals, x, y)
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(x, y, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.plot(xvals, yinterp, '-x')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.show()
+
+    Interpolation with periodic x-coordinates:
+
+    >>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
+    >>> xp = [190, -190, 350, -350]
+    >>> fp = [5, 10, 3, 4]
+    >>> np.interp(x, xp, fp, period=360)
+    array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])
+
+    Complex interpolation:
+
+    >>> x = [1.5, 4.0]
+    >>> xp = [2,3,5]
+    >>> fp = [1.0j, 0, 2+3j]
+    >>> np.interp(x, xp, fp)
+    array([0.+1.j , 1.+1.5j])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Lcm.md b/docs/Subcommand/Matrix/Lcm.md
new file mode 100644
index 000000000..813c56223
--- /dev/null
+++ b/docs/Subcommand/Matrix/Lcm.md
@@ -0,0 +1,26 @@
+[[/Lcm]] -- Returns the lowest common multiple of ``|x1|`` and ``|x2|``
+
+~~~
+Syntax
+------
+
+matrix lcm <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like, int
+    Arrays of values.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+
+Returns
+-------
+y : ndarray or scalar
+    The lowest common multiple of the absolute value of the inputs
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+gcd : The greatest common divisor
+
+~~~
diff --git a/docs/Subcommand/Matrix/Ldexp.md b/docs/Subcommand/Matrix/Ldexp.md
new file mode 100644
index 000000000..e22132659
--- /dev/null
+++ b/docs/Subcommand/Matrix/Ldexp.md
@@ -0,0 +1,50 @@
+[[/Ldexp]] -- Returns x1 * 2**x2, element-wise.
+
+~~~
+Syntax
+------
+
+matrix ldexp <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Array of multipliers.
+x2 : array_like, int
+    Array of twos exponents.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The result of ``x1 * 2**x2``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`.
+
+Notes
+-----
+Complex dtypes are not supported, they will raise a TypeError.
+
+`ldexp` is useful as the inverse of `frexp`, if used by itself it is
+more clear to simply use the expression ``x1 * 2**x2``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Cholesky.md b/docs/Subcommand/Matrix/Linalg/Cholesky.md
new file mode 100644
index 000000000..3ed412b3c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Cholesky.md
@@ -0,0 +1,90 @@
+[[/Linalg/Cholesky]]
+
+~~~
+Syntax
+------
+
+matrix cholesky <matrix>
+
+
+    Cholesky decomposition.
+
+    Return the Cholesky decomposition, `L * L.H`, of the square matrix `a`,
+    where `L` is lower-triangular and .H is the conjugate transpose operator
+    (which is the ordinary transpose if `a` is real-valued).  `a` must be
+    Hermitian (symmetric if real-valued) and positive-definite. No
+    checking is performed to verify whether `a` is Hermitian or not.
+    In addition, only the lower-triangular and diagonal elements of `a`
+    are used. Only `L` is actually returned.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Hermitian (symmetric if all elements are real), positive-definite
+        input matrix.
+
+    Returns
+    -------
+    L : (..., M, M) array_like
+        Upper or lower-triangular Cholesky factor of `a`.  Returns a
+        matrix object if `a` is a matrix object.
+
+    Raises
+    ------
+    LinAlgError
+       If the decomposition fails, for example, if `a` is not
+       positive-definite.
+
+    See Also
+    --------
+    scipy.linalg.cholesky : Similar function in SciPy.
+    scipy.linalg.cholesky_banded : Cholesky decompose a banded Hermitian
+                                   positive-definite matrix.
+    scipy.linalg.cho_factor : Cholesky decomposition of a matrix, to use in
+                              `scipy.linalg.cho_solve`.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The Cholesky decomposition is often used as a fast way of solving
+
+    .. math:: A \mathbf{x} = \mathbf{b}
+
+    (when `A` is both Hermitian/symmetric and positive-definite).
+
+    First, we solve for :math:`\mathbf{y}` in
+
+    .. math:: L \mathbf{y} = \mathbf{b},
+
+    and then for :math:`\mathbf{x}` in
+
+    .. math:: L.H \mathbf{x} = \mathbf{y}.
+
+    Examples
+    --------
+    >>> A = np.array([[1,-2j],[2j,5]])
+    >>> A
+    array([[ 1.+0.j, -0.-2.j],
+           [ 0.+2.j,  5.+0.j]])
+    >>> L = np.linalg.cholesky(A)
+    >>> L
+    array([[1.+0.j, 0.+0.j],
+           [0.+2.j, 1.+0.j]])
+    >>> np.dot(L, L.T.conj()) # verify that L * L.H = A
+    array([[1.+0.j, 0.-2.j],
+           [0.+2.j, 5.+0.j]])
+    >>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like?
+    >>> np.linalg.cholesky(A) # an ndarray object is returned
+    array([[1.+0.j, 0.+0.j],
+           [0.+2.j, 1.+0.j]])
+    >>> # But a matrix object is returned if A is a matrix object
+    >>> np.linalg.cholesky(np.matrix(A))
+    matrix([[ 1.+0.j,  0.+0.j],
+            [ 0.+2.j,  1.+0.j]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Cond.md b/docs/Subcommand/Matrix/Linalg/Cond.md
new file mode 100644
index 000000000..fa55bd279
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Cond.md
@@ -0,0 +1,86 @@
+[[/Linalg/Cond]]
+
+~~~
+Syntax
+------
+
+matrix cond <matrix> p=<order>
+
+
+    Compute the condition number of a matrix.
+
+    This function is capable of returning the condition number using
+    one of seven different norms, depending on the value of `p` (see
+    Parameters below).
+
+    Parameters
+    ----------
+    x : (..., M, N) array_like
+        The matrix whose condition number is sought.
+    p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional
+        Order of the norm:
+
+        =====  ============================
+        p      norm for matrices
+        =====  ============================
+        None   2-norm, computed directly using the ``SVD``
+        'fro'  Frobenius norm
+        inf    max(sum(abs(x), axis=1))
+        -inf   min(sum(abs(x), axis=1))
+        1      max(sum(abs(x), axis=0))
+        -1     min(sum(abs(x), axis=0))
+        2      2-norm (largest sing. value)
+        -2     smallest singular value
+        =====  ============================
+
+        inf means the numpy.inf object, and the Frobenius norm is
+        the root-of-sum-of-squares norm.
+
+    Returns
+    -------
+    c : {float, inf}
+        The condition number of the matrix. May be infinite.
+
+    See Also
+    --------
+    numpy.linalg.norm
+
+    Notes
+    -----
+    The condition number of `x` is defined as the norm of `x` times the
+    norm of the inverse of `x` [1]_; the norm can be the usual L2-norm
+    (root-of-sum-of-squares) or one of a number of other matrix norms.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, Orlando, FL,
+           Academic Press, Inc., 1980, pg. 285.
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]])
+    >>> a
+    array([[ 1,  0, -1],
+           [ 0,  1,  0],
+           [ 1,  0,  1]])
+    >>> LA.cond(a)
+    1.4142135623730951
+    >>> LA.cond(a, 'fro')
+    3.1622776601683795
+    >>> LA.cond(a, np.inf)
+    2.0
+    >>> LA.cond(a, -np.inf)
+    1.0
+    >>> LA.cond(a, 1)
+    2.0
+    >>> LA.cond(a, -1)
+    1.0
+    >>> LA.cond(a, 2)
+    1.4142135623730951
+    >>> LA.cond(a, -2)
+    0.70710678118654746 # may vary
+    >>> min(LA.svd(a, compute_uv=False))*min(LA.svd(LA.inv(a), compute_uv=False))
+    0.70710678118654746 # may vary
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Det.md b/docs/Subcommand/Matrix/Linalg/Det.md
new file mode 100644
index 000000000..dc96f0138
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Det.md
@@ -0,0 +1,55 @@
+[[/Linalg/Det]]
+
+~~~
+Syntax
+------
+
+matrix det <matrix>
+
+
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) array_like
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+      for large matrices where underflow/overflow may occur.
+    scipy.linalg.det : Similar function in SciPy.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine ``z/dgetrf``.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0 # may vary
+
+    Computing determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Eig.md b/docs/Subcommand/Matrix/Linalg/Eig.md
new file mode 100644
index 000000000..bba74553f
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Eig.md
@@ -0,0 +1,133 @@
+[[/Linalg/Eig]]
+
+~~~
+Syntax
+------
+
+matrix eig <matrix>
+
+
+    Compute the eigenvalues and right eigenvectors of a square array.
+
+    Parameters
+    ----------
+    a : (..., M, M) array
+        Matrices for which the eigenvalues and right eigenvectors will
+        be computed
+
+    Returns
+    -------
+    w : (..., M) array
+        The eigenvalues, each repeated according to its multiplicity.
+        The eigenvalues are not necessarily ordered. The resulting
+        array will be of complex type, unless the imaginary part is
+        zero in which case it will be cast to a real type. When `a`
+        is real the resulting eigenvalues will be real (0 imaginary
+        part) or occur in conjugate pairs
+
+    v : (..., M, M) array
+        The normalized (unit "length") eigenvectors, such that the
+        column ``v[:,i]`` is the eigenvector corresponding to the
+        eigenvalue ``w[i]``.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigvals : eigenvalues of a non-symmetric array.
+    eigh : eigenvalues and eigenvectors of a real symmetric or complex
+           Hermitian (conjugate symmetric) array.
+    eigvalsh : eigenvalues of a real symmetric or complex Hermitian
+               (conjugate symmetric) array.
+    scipy.linalg.eig : Similar function in SciPy that also solves the
+                       generalized eigenvalue problem.
+    scipy.linalg.schur : Best choice for unitary and other non-Hermitian
+                         normal matrices.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    This is implemented using the ``_geev`` LAPACK routines which compute
+    the eigenvalues and eigenvectors of general square arrays.
+
+    The number `w` is an eigenvalue of `a` if there exists a vector
+    `v` such that ``a @ v = w * v``. Thus, the arrays `a`, `w`, and
+    `v` satisfy the equations ``a @ v[:,i] = w[i] * v[:,i]``
+    for :math:`i \in \{0,...,M-1\}`.
+
+    The array `v` of eigenvectors may not be of maximum rank, that is, some
+    of the columns may be linearly dependent, although round-off error may
+    obscure that fact. If the eigenvalues are all different, then theoretically
+    the eigenvectors are linearly independent and `a` can be diagonalized by
+    a similarity transformation using `v`, i.e, ``inv(v) @ a @ v`` is diagonal.
+
+    For non-Hermitian normal matrices the SciPy function `scipy.linalg.schur`
+    is preferred because the matrix `v` is guaranteed to be unitary, which is
+    not the case when using `eig`. The Schur factorization produces an
+    upper triangular matrix rather than a diagonal matrix, but for normal
+    matrices only the diagonal of the upper triangular matrix is needed, the
+    rest is roundoff error.
+
+    Finally, it is emphasized that `v` consists of the *right* (as in
+    right-hand side) eigenvectors of `a`.  A vector `y` satisfying
+    ``y.T @ a = z * y.T`` for some number `z` is called a *left*
+    eigenvector of `a`, and, in general, the left and right eigenvectors
+    of a matrix are not necessarily the (perhaps conjugate) transposes
+    of each other.
+
+    References
+    ----------
+    G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, FL,
+    Academic Press, Inc., 1980, Various pp.
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+
+    (Almost) trivial example with real e-values and e-vectors.
+
+    >>> w, v = LA.eig(np.diag((1, 2, 3)))
+    >>> w; v
+    array([1., 2., 3.])
+    array([[1., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 1.]])
+
+    Real matrix possessing complex e-values and e-vectors; note that the
+    e-values are complex conjugates of each other.
+
+    >>> w, v = LA.eig(np.array([[1, -1], [1, 1]]))
+    >>> w; v
+    array([1.+1.j, 1.-1.j])
+    array([[0.70710678+0.j        , 0.70710678-0.j        ],
+           [0.        -0.70710678j, 0.        +0.70710678j]])
+
+    Complex-valued matrix with real e-values (but complex-valued e-vectors);
+    note that ``a.conj().T == a``, i.e., `a` is Hermitian.
+
+    >>> a = np.array([[1, 1j], [-1j, 1]])
+    >>> w, v = LA.eig(a)
+    >>> w; v
+    array([2.+0.j, 0.+0.j])
+    array([[ 0.        +0.70710678j,  0.70710678+0.j        ], # may vary
+           [ 0.70710678+0.j        , -0.        +0.70710678j]])
+
+    Be careful about round-off error!
+
+    >>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]])
+    >>> # Theor. e-values are 1 +/- 1e-9
+    >>> w, v = LA.eig(a)
+    >>> w; v
+    array([1., 1.])
+    array([[1., 0.],
+           [0., 1.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Eigh.md b/docs/Subcommand/Matrix/Linalg/Eigh.md
new file mode 100644
index 000000000..46f1da671
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Eigh.md
@@ -0,0 +1,123 @@
+[[/Linalg/Eigh]]
+
+~~~
+Syntax
+------
+
+matrix eigh <matrix> UPLO=<str>
+
+
+    Return the eigenvalues and eigenvectors of a complex Hermitian
+    (conjugate symmetric) or a real symmetric matrix.
+
+    Returns two objects, a 1-D array containing the eigenvalues of `a`, and
+    a 2-D square array or matrix (depending on the input type) of the
+    corresponding eigenvectors (in columns).
+
+    Parameters
+    ----------
+    a : (..., M, M) array
+        Hermitian or real symmetric matrices whose eigenvalues and
+        eigenvectors are to be computed.
+    UPLO : {'L', 'U'}, optional
+        Specifies whether the calculation is done with the lower triangular
+        part of `a` ('L', default) or the upper triangular part ('U').
+        Irrespective of this value only the real parts of the diagonal will
+        be considered in the computation to preserve the notion of a Hermitian
+        matrix. It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+
+    Returns
+    -------
+    w : (..., M) ndarray
+        The eigenvalues in ascending order, each repeated according to
+        its multiplicity.
+    v : {(..., M, M) ndarray, (..., M, M) matrix}
+        The column ``v[:, i]`` is the normalized eigenvector corresponding
+        to the eigenvalue ``w[i]``.  Will return a matrix object if `a` is
+        a matrix object.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigvalsh : eigenvalues of real symmetric or complex Hermitian
+               (conjugate symmetric) arrays.
+    eig : eigenvalues and right eigenvectors for non-symmetric arrays.
+    eigvals : eigenvalues of non-symmetric arrays.
+    scipy.linalg.eigh : Similar function in SciPy (but also solves the
+                        generalized eigenvalue problem).
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The eigenvalues/eigenvectors are computed using LAPACK routines ``_syevd``,
+    ``_heevd``.
+
+    The eigenvalues of real symmetric or complex Hermitian matrices are
+    always real. [1]_ The array `v` of (column) eigenvectors is unitary
+    and `a`, `w`, and `v` satisfy the equations
+    ``dot(a, v[:, i]) = w[i] * v[:, i]``.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pg. 222.
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, -2j], [2j, 5]])
+    >>> a
+    array([[ 1.+0.j, -0.-2.j],
+           [ 0.+2.j,  5.+0.j]])
+    >>> w, v = LA.eigh(a)
+    >>> w; v
+    array([0.17157288, 5.82842712])
+    array([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
+           [ 0.        +0.38268343j,  0.        -0.92387953j]])
+
+    >>> np.dot(a, v[:, 0]) - w[0] * v[:, 0] # verify 1st e-val/vec pair
+    array([5.55111512e-17+0.0000000e+00j, 0.00000000e+00+1.2490009e-16j])
+    >>> np.dot(a, v[:, 1]) - w[1] * v[:, 1] # verify 2nd e-val/vec pair
+    array([0.+0.j, 0.+0.j])
+
+    >>> A = np.matrix(a) # what happens if input is a matrix object
+    >>> A
+    matrix([[ 1.+0.j, -0.-2.j],
+            [ 0.+2.j,  5.+0.j]])
+    >>> w, v = LA.eigh(A)
+    >>> w; v
+    array([0.17157288, 5.82842712])
+    matrix([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
+            [ 0.        +0.38268343j,  0.        -0.92387953j]])
+
+    >>> # demonstrate the treatment of the imaginary part of the diagonal
+    >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
+    >>> a
+    array([[5.+2.j, 9.-2.j],
+           [0.+2.j, 2.-1.j]])
+    >>> # with UPLO='L' this is numerically equivalent to using LA.eig() with:
+    >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
+    >>> b
+    array([[5.+0.j, 0.-2.j],
+           [0.+2.j, 2.+0.j]])
+    >>> wa, va = LA.eigh(a)
+    >>> wb, vb = LA.eig(b)
+    >>> wa; wb
+    array([1., 6.])
+    array([6.+0.j, 1.+0.j])
+    >>> va; vb
+    array([[-0.4472136 +0.j        , -0.89442719+0.j        ], # may vary
+           [ 0.        +0.89442719j,  0.        -0.4472136j ]])
+    array([[ 0.89442719+0.j       , -0.        +0.4472136j],
+           [-0.        +0.4472136j,  0.89442719+0.j       ]])
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Eigvals.md b/docs/Subcommand/Matrix/Linalg/Eigvals.md
new file mode 100644
index 000000000..9c7d7abdb
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Eigvals.md
@@ -0,0 +1,77 @@
+[[/Linalg/Eigvals]]
+
+~~~
+Syntax
+------
+
+matrix eigvals <matrix>
+
+
+    Compute the eigenvalues of a general matrix.
+
+    Main difference between `eigvals` and `eig`: the eigenvectors aren't
+    returned.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        A complex- or real-valued matrix whose eigenvalues will be computed.
+
+    Returns
+    -------
+    w : (..., M,) ndarray
+        The eigenvalues, each repeated according to its multiplicity.
+        They are not necessarily ordered, nor are they necessarily
+        real for real matrices.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eig : eigenvalues and right eigenvectors of general arrays
+    eigvalsh : eigenvalues of real symmetric or complex Hermitian
+               (conjugate symmetric) arrays.
+    eigh : eigenvalues and eigenvectors of real symmetric or complex
+           Hermitian (conjugate symmetric) arrays.
+    scipy.linalg.eigvals : Similar function in SciPy.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    This is implemented using the ``_geev`` LAPACK routines which compute
+    the eigenvalues and eigenvectors of general square arrays.
+
+    Examples
+    --------
+    Illustration, using the fact that the eigenvalues of a diagonal matrix
+    are its diagonal elements, that multiplying a matrix on the left
+    by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose
+    of `Q`), preserves the eigenvalues of the "middle" matrix.  In other words,
+    if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as
+    ``A``:
+
+    >>> from numpy import linalg as LA
+    >>> x = np.random.random()
+    >>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]])
+    >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :])
+    (1.0, 1.0, 0.0)
+
+    Now multiply a diagonal matrix by ``Q`` on one side and by ``Q.T`` on the other:
+
+    >>> D = np.diag((-1,1))
+    >>> LA.eigvals(D)
+    array([-1.,  1.])
+    >>> A = np.dot(Q, D)
+    >>> A = np.dot(A, Q.T)
+    >>> LA.eigvals(A)
+    array([ 1., -1.]) # random
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Eigvalsh.md b/docs/Subcommand/Matrix/Linalg/Eigvalsh.md
new file mode 100644
index 000000000..0d5097147
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Eigvalsh.md
@@ -0,0 +1,81 @@
+[[/Linalg/Eigvalsh]]
+
+~~~
+Syntax
+------
+
+matrix eigvalsh <matrix> UPLO=<str>
+
+
+    Compute the eigenvalues of a complex Hermitian or real symmetric matrix.
+
+    Main difference from eigh: the eigenvectors are not computed.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        A complex- or real-valued matrix whose eigenvalues are to be
+        computed.
+    UPLO : {'L', 'U'}, optional
+        Specifies whether the calculation is done with the lower triangular
+        part of `a` ('L', default) or the upper triangular part ('U').
+        Irrespective of this value only the real parts of the diagonal will
+        be considered in the computation to preserve the notion of a Hermitian
+        matrix. It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+
+    Returns
+    -------
+    w : (..., M,) ndarray
+        The eigenvalues in ascending order, each repeated according to
+        its multiplicity.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigh : eigenvalues and eigenvectors of real symmetric or complex Hermitian
+           (conjugate symmetric) arrays.
+    eigvals : eigenvalues of general real or complex arrays.
+    eig : eigenvalues and right eigenvectors of general real or complex
+          arrays.
+    scipy.linalg.eigvalsh : Similar function in SciPy.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The eigenvalues are computed using LAPACK routines ``_syevd``, ``_heevd``.
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, -2j], [2j, 5]])
+    >>> LA.eigvalsh(a)
+    array([ 0.17157288,  5.82842712]) # may vary
+
+    >>> # demonstrate the treatment of the imaginary part of the diagonal
+    >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
+    >>> a
+    array([[5.+2.j, 9.-2.j],
+           [0.+2.j, 2.-1.j]])
+    >>> # with UPLO='L' this is numerically equivalent to using LA.eigvals()
+    >>> # with:
+    >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
+    >>> b
+    array([[5.+0.j, 0.-2.j],
+           [0.+2.j, 2.+0.j]])
+    >>> wa = LA.eigvalsh(a)
+    >>> wb = LA.eigvals(b)
+    >>> wa; wb
+    array([1., 6.])
+    array([6.+0.j, 1.+0.j])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Inv.md b/docs/Subcommand/Matrix/Linalg/Inv.md
new file mode 100644
index 000000000..92a0dbe4a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Inv.md
@@ -0,0 +1,68 @@
+[[/Linalg/Inv]]
+
+~~~
+Syntax
+------
+
+matrix inv <matrix>
+
+
+    Compute the (multiplicative) inverse of a matrix.
+
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Matrix to be inverted.
+
+    Returns
+    -------
+    ainv : (..., M, M) ndarray or matrix
+        (Multiplicative) inverse of the matrix `a`.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is not square or inversion fails.
+
+    See Also
+    --------
+    scipy.linalg.inv : Similar function in SciPy.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    Examples
+    --------
+    >>> from numpy.linalg import inv
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    >>> ainv = inv(a)
+    >>> np.allclose(np.dot(a, ainv), np.eye(2))
+    True
+    >>> np.allclose(np.dot(ainv, a), np.eye(2))
+    True
+
+    If a is a matrix object, then the return value is a matrix as well:
+
+    >>> ainv = inv(np.matrix(a))
+    >>> ainv
+    matrix([[-2. ,  1. ],
+            [ 1.5, -0.5]])
+
+    Inverses of several matrices can be computed at once:
+
+    >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
+    >>> inv(a)
+    array([[[-2.  ,  1.  ],
+            [ 1.5 , -0.5 ]],
+           [[-1.25,  0.75],
+            [ 0.75, -0.25]]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Lstsq.md b/docs/Subcommand/Matrix/Linalg/Lstsq.md
new file mode 100644
index 000000000..3282d659b
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Lstsq.md
@@ -0,0 +1,103 @@
+[[/Linalg/Lstsq]]
+
+~~~
+Syntax
+------
+
+matrix lstsq <matrix> <matrix> rcond=<float>
+
+
+    Return the least-squares solution to a linear matrix equation.
+
+    Computes the vector `x` that approximatively solves the equation
+    ``a @ x = b``. The equation may be under-, well-, or over-determined
+    (i.e., the number of linearly independent rows of `a` can be less than,
+    equal to, or greater than its number of linearly independent columns).
+    If `a` is square and of full rank, then `x` (but for round-off error)
+    is the "exact" solution of the equation. Else, `x` minimizes the
+    Euclidean 2-norm :math:`||b - ax||`. If there are multiple minimizing 
+    solutions, the one with the smallest 2-norm :math:`||x||` is returned.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        "Coefficient" matrix.
+    b : {(M,), (M, K)} array_like
+        Ordinate or "dependent variable" values. If `b` is two-dimensional,
+        the least-squares solution is calculated for each of the `K` columns
+        of `b`.
+    rcond : float, optional
+        Cut-off ratio for small singular values of `a`.
+        For the purposes of rank determination, singular values are treated
+        as zero if they are smaller than `rcond` times the largest singular
+        value of `a`.
+
+        .. versionchanged:: 1.14.0
+           If not set, a FutureWarning is given. The previous default
+           of ``-1`` will use the machine precision as `rcond` parameter,
+           the new default will use the machine precision times `max(M, N)`.
+           To silence the warning and use the new default, use ``rcond=None``,
+           to keep using the old behavior, use ``rcond=-1``.
+
+    Returns
+    -------
+    x : {(N,), (N, K)} ndarray
+        Least-squares solution. If `b` is two-dimensional,
+        the solutions are in the `K` columns of `x`.
+    residuals : {(1,), (K,), (0,)} ndarray
+        Sums of squared residuals: Squared Euclidean 2-norm for each column in
+        ``b - a @ x``.
+        If the rank of `a` is < N or M <= N, this is an empty array.
+        If `b` is 1-dimensional, this is a (1,) shape array.
+        Otherwise the shape is (K,).
+    rank : int
+        Rank of matrix `a`.
+    s : (min(M, N),) ndarray
+        Singular values of `a`.
+
+    Raises
+    ------
+    LinAlgError
+        If computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.lstsq : Similar function in SciPy.
+
+    Notes
+    -----
+    If `b` is a matrix, then all array results are returned as matrices.
+
+    Examples
+    --------
+    Fit a line, ``y = mx + c``, through some noisy data-points:
+
+    >>> x = np.array([0, 1, 2, 3])
+    >>> y = np.array([-1, 0.2, 0.9, 2.1])
+
+    By examining the coefficients, we see that the line should have a
+    gradient of roughly 1 and cut the y-axis at, more or less, -1.
+
+    We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
+    and ``p = [[m], [c]]``.  Now use `lstsq` to solve for `p`:
+
+    >>> A = np.vstack([x, np.ones(len(x))]).T
+    >>> A
+    array([[ 0.,  1.],
+           [ 1.,  1.],
+           [ 2.,  1.],
+           [ 3.,  1.]])
+
+    >>> m, c = np.linalg.lstsq(A, y, rcond=None)[0]
+    >>> m, c
+    (1.0 -0.95) # may vary
+
+    Plot the data along with the fitted line:
+
+    >>> import matplotlib.pyplot as plt
+    >>> _ = plt.plot(x, y, 'o', label='Original data', markersize=10)
+    >>> _ = plt.plot(x, m*x + c, 'r', label='Fitted line')
+    >>> _ = plt.legend()
+    >>> plt.show()
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Matrix_Rank.md b/docs/Subcommand/Matrix/Linalg/Matrix_Rank.md
new file mode 100644
index 000000000..18a447d61
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Matrix_Rank.md
@@ -0,0 +1,100 @@
+[[/Linalg/Matrix_Rank]]
+
+~~~
+Syntax
+------
+
+matrix matrix_rank <matrix>
+
+
+    Return matrix rank of array using SVD method
+
+    Rank of the array is the number of singular values of the array that are
+    greater than `tol`.
+
+    .. versionchanged:: 1.14
+       Can now operate on stacks of matrices
+
+    Parameters
+    ----------
+    M : {(M,), (..., M, N)} array_like
+        Input vector or stack of matrices.
+    tol : (...) array_like, float, optional
+        Threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+
+        .. versionchanged:: 1.14
+           Broadcasted against the stack of matrices
+    hermitian : bool, optional
+        If True, `M` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+        .. versionadded:: 1.14
+
+    Returns
+    -------
+    rank : (...) array_like
+        Rank of M.
+
+    Notes
+    -----
+    The default threshold to detect rank deficiency is a test on the magnitude
+    of the singular values of `M`.  By default, we identify singular values less
+    than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+    the symbols defined above). This is the algorithm MATLAB uses [1].  It also
+    appears in *Numerical recipes* in the discussion of SVD solutions for linear
+    least squares [2].
+
+    This default threshold is designed to detect rank deficiency accounting for
+    the numerical errors of the SVD computation.  Imagine that there is a column
+    in `M` that is an exact (in floating point) linear combination of other
+    columns in `M`. Computing the SVD on `M` will not produce a singular value
+    exactly equal to 0 in general: any difference of the smallest SVD value from
+    0 will be caused by numerical imprecision in the calculation of the SVD.
+    Our threshold for small SVD values takes this numerical imprecision into
+    account, and the default threshold will detect such numerical rank
+    deficiency.  The threshold may declare a matrix `M` rank deficient even if
+    the linear combination of some columns of `M` is not exactly equal to
+    another column of `M` but only numerically very close to another column of
+    `M`.
+
+    We chose our default threshold because it is in wide use.  Other thresholds
+    are possible.  For example, elsewhere in the 2007 edition of *Numerical
+    recipes* there is an alternative threshold of ``S.max() *
+    np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+    this threshold as being based on "expected roundoff error" (p 71).
+
+    The thresholds above deal with floating point roundoff error in the
+    calculation of the SVD.  However, you may have more information about the
+    sources of error in `M` that would make you consider other tolerance values
+    to detect *effective* rank deficiency.  The most useful measure of the
+    tolerance depends on the operations you intend to use on your matrix.  For
+    example, if your data come from uncertain measurements with uncertainties
+    greater than floating point epsilon, choosing a tolerance near that
+    uncertainty may be preferable.  The tolerance may be absolute if the
+    uncertainties are absolute rather than relative.
+
+    References
+    ----------
+    .. [1] MATLAB reference documention, "Rank"
+           https://www.mathworks.com/help/techdoc/ref/rank.html
+    .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+           "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+           page 795.
+
+    Examples
+    --------
+    >>> from numpy.linalg import matrix_rank
+    >>> matrix_rank(np.eye(4)) # Full rank matrix
+    4
+    >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+    >>> matrix_rank(I)
+    3
+    >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+    1
+    >>> matrix_rank(np.zeros((4,)))
+    0
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Norm.md b/docs/Subcommand/Matrix/Linalg/Norm.md
new file mode 100644
index 000000000..29666d61e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Norm.md
@@ -0,0 +1,157 @@
+[[/Linalg/Norm]]
+
+~~~
+Syntax
+------
+
+matrix norm <matrix> ord=<order> axis=<int> keepdims=<boolstr>
+
+
+    Matrix or vector norm.
+
+    This function is able to return one of eight different matrix norms,
+    or one of an infinite number of vector norms (described below), depending
+    on the value of the ``ord`` parameter.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.  If `axis` is None, `x` must be 1-D or 2-D, unless `ord`
+        is None. If both `axis` and `ord` are None, the 2-norm of
+        ``x.ravel`` will be returned.
+    ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
+        Order of the norm (see table under ``Notes``). inf means numpy's
+        `inf` object. The default is None.
+    axis : {None, int, 2-tuple of ints}, optional.
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None then either a vector norm (when `x`
+        is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default
+        is None.
+
+        .. versionadded:: 1.8.0
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in the
+        result as dimensions with size one.  With this option the result will
+        broadcast correctly against the original `x`.
+
+        .. versionadded:: 1.10.0
+
+    Returns
+    -------
+    n : float or ndarray
+        Norm of the matrix or vector(s).
+
+    See Also
+    --------
+    scipy.linalg.norm : Similar function in SciPy.
+
+    Notes
+    -----
+    For values of ``ord < 1``, the result is, strictly speaking, not a
+    mathematical 'norm', but it may still be useful for various numerical
+    purposes.
+
+    The following norms can be calculated:
+
+    =====  ============================  ==========================
+    ord    norm for matrices             norm for vectors
+    =====  ============================  ==========================
+    None   Frobenius norm                2-norm
+    'fro'  Frobenius norm                --
+    'nuc'  nuclear norm                  --
+    inf    max(sum(abs(x), axis=1))      max(abs(x))
+    -inf   min(sum(abs(x), axis=1))      min(abs(x))
+    0      --                            sum(x != 0)
+    1      max(sum(abs(x), axis=0))      as below
+    -1     min(sum(abs(x), axis=0))      as below
+    2      2-norm (largest sing. value)  as below
+    -2     smallest singular value       as below
+    other  --                            sum(abs(x)**ord)**(1./ord)
+    =====  ============================  ==========================
+
+    The Frobenius norm is given by [1]_:
+
+        :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    The nuclear norm is the sum of the singular values.
+
+    Both the Frobenius and nuclear norm orders are only defined for
+    matrices and raise a ValueError when ``x.ndim != 2``.
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4, -3, -2, ...,  2,  3,  4])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4, -3, -2],
+           [-1,  0,  1],
+           [ 2,  3,  4]])
+
+    >>> LA.norm(a)
+    7.745966692414834
+    >>> LA.norm(b)
+    7.745966692414834
+    >>> LA.norm(b, 'fro')
+    7.745966692414834
+    >>> LA.norm(a, np.inf)
+    4.0
+    >>> LA.norm(b, np.inf)
+    9.0
+    >>> LA.norm(a, -np.inf)
+    0.0
+    >>> LA.norm(b, -np.inf)
+    2.0
+
+    >>> LA.norm(a, 1)
+    20.0
+    >>> LA.norm(b, 1)
+    7.0
+    >>> LA.norm(a, -1)
+    -4.6566128774142013e-010
+    >>> LA.norm(b, -1)
+    6.0
+    >>> LA.norm(a, 2)
+    7.745966692414834
+    >>> LA.norm(b, 2)
+    7.3484692283495345
+
+    >>> LA.norm(a, -2)
+    0.0
+    >>> LA.norm(b, -2)
+    1.8570331885190563e-016 # may vary
+    >>> LA.norm(a, 3)
+    5.8480354764257312 # may vary
+    >>> LA.norm(a, -3)
+    0.0
+
+    Using the `axis` argument to compute vector norms:
+
+    >>> c = np.array([[ 1, 2, 3],
+    ...               [-1, 1, 4]])
+    >>> LA.norm(c, axis=0)
+    array([ 1.41421356,  2.23606798,  5.        ])
+    >>> LA.norm(c, axis=1)
+    array([ 3.74165739,  4.24264069])
+    >>> LA.norm(c, ord=1, axis=1)
+    array([ 6.,  6.])
+
+    Using the `axis` argument to compute matrix norms:
+
+    >>> m = np.arange(8).reshape(2,2,2)
+    >>> LA.norm(m, axis=(1,2))
+    array([  3.74165739,  11.22497216])
+    >>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
+    (3.7416573867739413, 11.224972160321824)
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Pinv.md b/docs/Subcommand/Matrix/Linalg/Pinv.md
new file mode 100644
index 000000000..8399868bb
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Pinv.md
@@ -0,0 +1,86 @@
+[[/Linalg/Pinv]]
+
+~~~
+Syntax
+------
+
+matrix pinv <matrix>
+
+
+    Compute the (Moore-Penrose) pseudo-inverse of a matrix.
+
+    Calculate the generalized inverse of a matrix using its
+    singular-value decomposition (SVD) and including all
+    *large* singular values.
+
+    .. versionchanged:: 1.14
+       Can now operate on stacks of matrices
+
+    Parameters
+    ----------
+    a : (..., M, N) array_like
+        Matrix or stack of matrices to be pseudo-inverted.
+    rcond : (...) array_like of float
+        Cutoff for small singular values.
+        Singular values less than or equal to
+        ``rcond * largest_singular_value`` are set to zero.
+        Broadcasts against the stack of matrices.
+    hermitian : bool, optional
+        If True, `a` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+        .. versionadded:: 1.17.0
+
+    Returns
+    -------
+    B : (..., N, M) ndarray
+        The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
+        is `B`.
+
+    Raises
+    ------
+    LinAlgError
+        If the SVD computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.pinv : Similar function in SciPy.
+    scipy.linalg.pinv2 : Similar function in SciPy (SVD-based).
+    scipy.linalg.pinvh : Compute the (Moore-Penrose) pseudo-inverse of a
+                         Hermitian matrix.
+
+    Notes
+    -----
+    The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
+    defined as: "the matrix that 'solves' [the least-squares problem]
+    :math:`Ax = b`," i.e., if :math:`\bar{x}` is said solution, then
+    :math:`A^+` is that matrix such that :math:`\bar{x} = A^+b`.
+
+    It can be shown that if :math:`Q_1 \Sigma Q_2^T = A` is the singular
+    value decomposition of A, then
+    :math:`A^+ = Q_2 \Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
+    orthogonal matrices, :math:`\Sigma` is a diagonal matrix consisting
+    of A's so-called singular values, (followed, typically, by
+    zeros), and then :math:`\Sigma^+` is simply the diagonal matrix
+    consisting of the reciprocals of A's singular values
+    (again, followed by zeros). [1]_
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pp. 139-142.
+
+    Examples
+    --------
+    The following example checks that ``a * a+ * a == a`` and
+    ``a+ * a * a+ == a+``:
+
+    >>> a = np.random.randn(9, 6)
+    >>> B = np.linalg.pinv(a)
+    >>> np.allclose(a, np.dot(a, np.dot(B, a)))
+    True
+    >>> np.allclose(B, np.dot(B, np.dot(a, B)))
+    True
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Qr.md b/docs/Subcommand/Matrix/Linalg/Qr.md
new file mode 100644
index 000000000..23896c289
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Qr.md
@@ -0,0 +1,123 @@
+[[/Linalg/Qr]]
+
+~~~
+Syntax
+------
+
+matrix qr <matrix> mode=<str>
+
+
+    Compute the qr factorization of a matrix.
+
+    Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is
+    upper-triangular.
+
+    Parameters
+    ----------
+    a : array_like, shape (M, N)
+        Matrix to be factored.
+    mode : {'reduced', 'complete', 'r', 'raw'}, optional
+        If K = min(M, N), then
+
+        * 'reduced'  : returns q, r with dimensions (M, K), (K, N) (default)
+        * 'complete' : returns q, r with dimensions (M, M), (M, N)
+        * 'r'        : returns r only with dimensions (K, N)
+        * 'raw'      : returns h, tau with dimensions (N, M), (K,)
+
+        The options 'reduced', 'complete, and 'raw' are new in numpy 1.8,
+        see the notes for more information. The default is 'reduced', and to
+        maintain backward compatibility with earlier versions of numpy both
+        it and the old default 'full' can be omitted. Note that array h
+        returned in 'raw' mode is transposed for calling Fortran. The
+        'economic' mode is deprecated.  The modes 'full' and 'economic' may
+        be passed using only the first letter for backwards compatibility,
+        but all others must be spelled out. See the Notes for more
+        explanation.
+
+
+    Returns
+    -------
+    q : ndarray of float or complex, optional
+        A matrix with orthonormal columns. When mode = 'complete' the
+        result is an orthogonal/unitary matrix depending on whether or not
+        a is real/complex. The determinant may be either +/- 1 in that
+        case.
+    r : ndarray of float or complex, optional
+        The upper-triangular matrix.
+    (h, tau) : ndarrays of np.double or np.cdouble, optional
+        The array h contains the Householder reflectors that generate q
+        along with r. The tau array contains scaling factors for the
+        reflectors. In the deprecated  'economic' mode only h is returned.
+
+    Raises
+    ------
+    LinAlgError
+        If factoring fails.
+
+    See Also
+    --------
+    scipy.linalg.qr : Similar function in SciPy.
+    scipy.linalg.rq : Compute RQ decomposition of a matrix.
+
+    Notes
+    -----
+    This is an interface to the LAPACK routines ``dgeqrf``, ``zgeqrf``,
+    ``dorgqr``, and ``zungqr``.
+
+    For more information on the qr factorization, see for example:
+    https://en.wikipedia.org/wiki/QR_factorization
+
+    Subclasses of `ndarray` are preserved except for the 'raw' mode. So if
+    `a` is of type `matrix`, all the return values will be matrices too.
+
+    New 'reduced', 'complete', and 'raw' options for mode were added in
+    NumPy 1.8.0 and the old option 'full' was made an alias of 'reduced'.  In
+    addition the options 'full' and 'economic' were deprecated.  Because
+    'full' was the previous default and 'reduced' is the new default,
+    backward compatibility can be maintained by letting `mode` default.
+    The 'raw' option was added so that LAPACK routines that can multiply
+    arrays by q using the Householder reflectors can be used. Note that in
+    this case the returned arrays are of type np.double or np.cdouble and
+    the h array is transposed to be FORTRAN compatible.  No routines using
+    the 'raw' return are currently exposed by numpy, but some are available
+    in lapack_lite and just await the necessary work.
+
+    Examples
+    --------
+    >>> a = np.random.randn(9, 6)
+    >>> q, r = np.linalg.qr(a)
+    >>> np.allclose(a, np.dot(q, r))  # a does equal qr
+    True
+    >>> r2 = np.linalg.qr(a, mode='r')
+    >>> np.allclose(r, r2)  # mode='r' returns the same r as mode='full'
+    True
+
+    Example illustrating a common use of `qr`: solving of least squares
+    problems
+
+    What are the least-squares-best `m` and `y0` in ``y = y0 + mx`` for
+    the following data: {(0,1), (1,0), (1,2), (2,1)}. (Graph the points
+    and you'll see that it should be y0 = 0, m = 1.)  The answer is provided
+    by solving the over-determined matrix equation ``Ax = b``, where::
+
+      A = array([[0, 1], [1, 1], [1, 1], [2, 1]])
+      x = array([[y0], [m]])
+      b = array([[1], [0], [2], [1]])
+
+    If A = qr such that q is orthonormal (which is always possible via
+    Gram-Schmidt), then ``x = inv(r) * (q.T) * b``.  (In numpy practice,
+    however, we simply use `lstsq`.)
+
+    >>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]])
+    >>> A
+    array([[0, 1],
+           [1, 1],
+           [1, 1],
+           [2, 1]])
+    >>> b = np.array([1, 0, 2, 1])
+    >>> q, r = np.linalg.qr(A)
+    >>> p = np.dot(q.T, b)
+    >>> np.dot(np.linalg.inv(r), p)
+    array([  1.1e-16,   1.0e+00])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Slogdet.md b/docs/Subcommand/Matrix/Linalg/Slogdet.md
new file mode 100644
index 000000000..929f71bd5
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Slogdet.md
@@ -0,0 +1,81 @@
+[[/Linalg/Slogdet]]
+
+~~~
+Syntax
+------
+
+matrix slogdet <matrix>
+
+
+    Compute the sign and (natural) logarithm of the determinant of an array.
+
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    sign : (...) array_like
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1. For a complex matrix, this is a complex number
+        with absolute value 1 (i.e., it is on the unit circle), or else 0.
+    logdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+
+    If the determinant is zero, then `sign` will be 0 and `logdet` will be
+    -Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    .. versionadded:: 1.6.0
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine ``z/dgetrf``.
+
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logdet) = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (-1, 0.69314718055994529) # may vary
+    >>> sign * np.exp(logdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> sign, logdet = np.linalg.slogdet(a)
+    >>> (sign, logdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+    >>> sign * np.exp(logdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1, -1151.2925464970228)
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Solve.md b/docs/Subcommand/Matrix/Linalg/Solve.md
new file mode 100644
index 000000000..009802500
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Solve.md
@@ -0,0 +1,71 @@
+[[/Linalg/Solve]]
+
+~~~
+Syntax
+------
+
+matrix solve <matrix> <matrix>
+
+
+    Solve a linear matrix equation, or system of linear scalar equations.
+
+    Computes the "exact" solution, `x`, of the well-determined, i.e., full
+    rank, linear matrix equation `ax = b`.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Coefficient matrix.
+    b : {(..., M,), (..., M, K)}, array_like
+        Ordinate or "dependent variable" values.
+
+    Returns
+    -------
+    x : {(..., M,), (..., M, K)} ndarray
+        Solution to the system a x = b.  Returned shape is identical to `b`.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular or not square.
+
+    See Also
+    --------
+    scipy.linalg.solve : Similar function in SciPy.
+
+    Notes
+    -----
+
+    .. versionadded:: 1.8.0
+
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The solutions are computed using LAPACK routine ``_gesv``.
+
+    `a` must be square and of full-rank, i.e., all rows (or, equivalently,
+    columns) must be linearly independent; if either is not true, use
+    `lstsq` for the least-squares best "solution" of the
+    system/equation.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pg. 22.
+
+    Examples
+    --------
+    Solve the system of equations ``x0 + 2 * x1 = 1`` and ``3 * x0 + 5 * x1 = 2``:
+
+    >>> a = np.array([[1, 2], [3, 5]])
+    >>> b = np.array([1, 2])
+    >>> x = np.linalg.solve(a, b)
+    >>> x
+    array([-1.,  1.])
+
+    Check that the solution is correct:
+
+    >>> np.allclose(np.dot(a, x), b)
+    True
+
+~~~
diff --git a/docs/Subcommand/Matrix/Linalg/Svd.md b/docs/Subcommand/Matrix/Linalg/Svd.md
new file mode 100644
index 000000000..3c0851c8f
--- /dev/null
+++ b/docs/Subcommand/Matrix/Linalg/Svd.md
@@ -0,0 +1,141 @@
+[[/Linalg/Svd]]
+
+~~~
+Syntax
+------
+
+matrix svd <matrix> full_matrices=<boolstr> compute_uv=<boolstr> hermitian=<boolstr>
+
+
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, it is factorized as ``u @ np.diag(s) @ vh
+    = (u * s) @ vh``, where `u` and `vh` are 2D unitary arrays and `s` is a 1D
+    array of `a`'s singular values. When `a` is higher-dimensional, SVD is
+    applied in stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) array_like
+        A real or complex array with ``a.ndim >= 2``.
+    full_matrices : bool, optional
+        If True (default), `u` and `vh` have the shapes ``(..., M, M)`` and
+        ``(..., N, N)``, respectively.  Otherwise, the shapes are
+        ``(..., M, K)`` and ``(..., K, N)``, respectively, where
+        ``K = min(M, N)``.
+    compute_uv : bool, optional
+        Whether or not to compute `u` and `vh` in addition to `s`.  True
+        by default.
+    hermitian : bool, optional
+        If True, `a` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+        .. versionadded:: 1.17.0
+
+    Returns
+    -------
+    u : { (..., M, M), (..., M, K) } array
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
+    s : (..., K) array
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    vh : { (..., N, N), (..., K, N) } array
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
+
+    Raises
+    ------
+    LinAlgError
+        If SVD computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.svd : Similar function in SciPy.
+    scipy.linalg.svdvals : Compute singular values of a matrix.
+
+    Notes
+    -----
+
+    .. versionchanged:: 1.8.0
+       Broadcasting rules apply, see the `numpy.linalg` documentation for
+       details.
+
+    The decomposition is performed using LAPACK routine ``_gesdd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U S V^H`, where :math:`A = a`, :math:`U= u`,
+    :math:`S= \mathtt{np.diag}(s)` and :math:`V^H = vh`. The 1D array `s`
+    contains the singular values of `a` and `u` and `vh` are unitary. The rows
+    of `vh` are the eigenvectors of :math:`A^H A` and the columns of `u` are
+    the eigenvectors of :math:`A A^H`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    If `a` has more than two dimensions, then broadcasting rules apply, as
+    explained in :ref:`routines.linalg-broadcasting`. This means that SVD is
+    working in "stacked" mode: it iterates over all indices of the first
+    ``a.ndim - 2`` dimensions and for each combination SVD is applied to the
+    last two indices. The matrix `a` can be reconstructed from the
+    decomposition with either ``(u * s[..., None, :]) @ vh`` or
+    ``u @ (s[..., None] * vh)``. (The ``@`` operator can be replaced by the
+    function ``np.matmul`` for python versions below 3.5.)
+
+    If `a` is a ``matrix`` object (as opposed to an ``ndarray``), then so are
+    all the return values.
+
+    Examples
+    --------
+    >>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
+    >>> b = np.random.randn(2, 7, 8, 3) + 1j*np.random.randn(2, 7, 8, 3)
+
+    Reconstruction based on full SVD, 2D case:
+
+    >>> u, s, vh = np.linalg.svd(a, full_matrices=True)
+    >>> u.shape, s.shape, vh.shape
+    ((9, 9), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(u[:, :6] * s, vh))
+    True
+    >>> smat = np.zeros((9, 6), dtype=complex)
+    >>> smat[:6, :6] = np.diag(s)
+    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    True
+
+    Reconstruction based on reduced SVD, 2D case:
+
+    >>> u, s, vh = np.linalg.svd(a, full_matrices=False)
+    >>> u.shape, s.shape, vh.shape
+    ((9, 6), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(u * s, vh))
+    True
+    >>> smat = np.diag(s)
+    >>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
+    True
+
+    Reconstruction based on full SVD, 4D case:
+
+    >>> u, s, vh = np.linalg.svd(b, full_matrices=True)
+    >>> u.shape, s.shape, vh.shape
+    ((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(u[..., :3] * s[..., None, :], vh))
+    True
+    >>> np.allclose(b, np.matmul(u[..., :3], s[..., None] * vh))
+    True
+
+    Reconstruction based on reduced SVD, 4D case:
+
+    >>> u, s, vh = np.linalg.svd(b, full_matrices=False)
+    >>> u.shape, s.shape, vh.shape
+    ((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(u * s[..., None, :], vh))
+    True
+    >>> np.allclose(b, np.matmul(u, s[..., None] * vh))
+    True
+
+~~~
diff --git a/docs/Subcommand/Matrix/Log.md b/docs/Subcommand/Matrix/Log.md
new file mode 100644
index 000000000..86d5e1a65
--- /dev/null
+++ b/docs/Subcommand/Matrix/Log.md
@@ -0,0 +1,60 @@
+[[/Log]] -- Natural logarithm, element-wise.
+
+~~~
+Syntax
+------
+
+matrix log <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input value.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The natural logarithm of `x`, element-wise.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+log10, log2, log1p, emath.log
+
+Notes
+-----
+Logarithm is a multivalued function: for each `x` there is an infinite
+number of `z` such that `exp(z) = x`. The convention is to return the
+`z` whose imaginary part lies in `[-pi, pi]`.
+
+For real-valued input data types, `log` always returns real output. For
+each value that cannot be expressed as a real number or infinity, it
+yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `log` is a complex analytical function that
+has a branch cut `[-inf, 0]` and is continuous from above on it. `log`
+handles the floating-point negative zero as an infinitesimal negative
+number, conforming to the C99 standard.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm
+
+~~~
diff --git a/docs/Subcommand/Matrix/Log10.md b/docs/Subcommand/Matrix/Log10.md
new file mode 100644
index 000000000..84a412831
--- /dev/null
+++ b/docs/Subcommand/Matrix/Log10.md
@@ -0,0 +1,61 @@
+[[/Log10]] -- Return the base 10 logarithm of the input array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix log10 <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The logarithm to the base 10 of `x`, element-wise. NaNs are
+    returned where x is negative.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+emath.log10
+
+Notes
+-----
+Logarithm is a multivalued function: for each `x` there is an infinite
+number of `z` such that `10**z = x`. The convention is to return the
+`z` whose imaginary part lies in `[-pi, pi]`.
+
+For real-valued input data types, `log10` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `log10` is a complex analytical function that
+has a branch cut `[-inf, 0]` and is continuous from above on it.
+`log10` handles the floating-point negative zero as an infinitesimal
+negative number, conforming to the C99 standard.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm
+
+~~~
diff --git a/docs/Subcommand/Matrix/Log1P.md b/docs/Subcommand/Matrix/Log1P.md
new file mode 100644
index 000000000..de467d70e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Log1P.md
@@ -0,0 +1,63 @@
+[[/Log1P]] -- Return the natural logarithm of one plus the input array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix log1p <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    Natural logarithm of `1 + x`, element-wise.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+expm1 : ``exp(x) - 1``, the inverse of `log1p`.
+
+Notes
+-----
+For real-valued input, `log1p` is accurate also for `x` so small
+that `1 + x == 1` in floating-point accuracy.
+
+Logarithm is a multivalued function: for each `x` there is an infinite
+number of `z` such that `exp(z) = 1 + x`. The convention is to return
+the `z` whose imaginary part lies in `[-pi, pi]`.
+
+For real-valued input data types, `log1p` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `log1p` is a complex analytical function that
+has a branch cut `[-inf, -1]` and is continuous from above on it.
+`log1p` handles the floating-point negative zero as an infinitesimal
+negative number, conforming to the C99 standard.
+
+References
+----------
+.. [1] M. Abramowitz and I.A. Stegun, "Handbook of Mathematical Functions",
+       10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/
+.. [2] Wikipedia, "Logarithm". https://en.wikipedia.org/wiki/Logarithm
+
+~~~
diff --git a/docs/Subcommand/Matrix/Log2.md b/docs/Subcommand/Matrix/Log2.md
new file mode 100644
index 000000000..2655e47e7
--- /dev/null
+++ b/docs/Subcommand/Matrix/Log2.md
@@ -0,0 +1,56 @@
+[[/Log2]] -- Base-2 logarithm of `x`.
+
+~~~
+Syntax
+------
+
+matrix log2 <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    Base-2 logarithm of `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+log, log10, log1p, emath.log2
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+Logarithm is a multivalued function: for each `x` there is an infinite
+number of `z` such that `2**z = x`. The convention is to return the `z`
+whose imaginary part lies in `[-pi, pi]`.
+
+For real-valued input data types, `log2` always returns real output.
+For each value that cannot be expressed as a real number or infinity,
+it yields ``nan`` and sets the `invalid` floating point error flag.
+
+For complex-valued input, `log2` is a complex analytical function that
+has a branch cut `[-inf, 0]` and is continuous from above on it. `log2`
+handles the floating-point negative zero as an infinitesimal negative
+number, conforming to the C99 standard.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Logaddexp.md b/docs/Subcommand/Matrix/Logaddexp.md
new file mode 100644
index 000000000..df28777e6
--- /dev/null
+++ b/docs/Subcommand/Matrix/Logaddexp.md
@@ -0,0 +1,45 @@
+[[/Logaddexp]] -- Logarithm of the sum of exponentiations of the inputs.
+
+~~~
+Syntax
+------
+
+matrix logaddexp <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    Input values.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+result : ndarray
+    Logarithm of ``exp(x1) + exp(x2)``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+logaddexp2: Logarithm of the sum of exponentiations of inputs in base 2.
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Logaddexp2.md b/docs/Subcommand/Matrix/Logaddexp2.md
new file mode 100644
index 000000000..cad20af5a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Logaddexp2.md
@@ -0,0 +1,45 @@
+[[/Logaddexp2]] -- Logarithm of the sum of exponentiations of the inputs in base-2.
+
+~~~
+Syntax
+------
+
+matrix logaddexp2 <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    Input values.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+result : ndarray
+    Base-2 logarithm of ``2**x1 + 2**x2``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+logaddexp: Logarithm of the sum of exponentiations of the inputs.
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matlib/Rand.md b/docs/Subcommand/Matrix/Matlib/Rand.md
new file mode 100644
index 000000000..2c24b8f0a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matlib/Rand.md
@@ -0,0 +1,49 @@
+[[/Matlib/Rand]]
+
+~~~
+Syntax
+------
+
+matrix rand <intlist_args>
+
+
+    Return a matrix of random values with given shape.
+
+    Create a matrix of the given shape and propagate it with
+    random samples from a uniform distribution over ``[0, 1)``.
+
+    Parameters
+    ----------
+    \*args : Arguments
+        Shape of the output.
+        If given as N integers, each integer specifies the size of one
+        dimension.
+        If given as a tuple, this tuple gives the complete shape.
+
+    Returns
+    -------
+    out : ndarray
+        The matrix of random values with shape given by `\*args`.
+
+    See Also
+    --------
+    randn, numpy.random.RandomState.rand
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> import numpy.matlib
+    >>> np.matlib.rand(2, 3)
+    matrix([[0.69646919, 0.28613933, 0.22685145],
+            [0.55131477, 0.71946897, 0.42310646]])
+    >>> np.matlib.rand((2, 3))
+    matrix([[0.9807642 , 0.68482974, 0.4809319 ],
+            [0.39211752, 0.34317802, 0.72904971]])
+
+    If the first argument is a tuple, other arguments are ignored:
+
+    >>> np.matlib.rand((2, 3), 4)
+    matrix([[0.43857224, 0.0596779 , 0.39804426],
+            [0.73799541, 0.18249173, 0.17545176]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matlib/Randn.md b/docs/Subcommand/Matrix/Matlib/Randn.md
new file mode 100644
index 000000000..cf723049a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matlib/Randn.md
@@ -0,0 +1,54 @@
+[[/Matlib/Randn]]
+
+~~~
+Syntax
+------
+
+matrix randn <intlist_args>
+
+
+    Return a random matrix with data from the "standard normal" distribution.
+
+    `randn` generates a matrix filled with random floats sampled from a
+    univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
+
+    Parameters
+    ----------
+    \*args : Arguments
+        Shape of the output.
+        If given as N integers, each integer specifies the size of one
+        dimension. If given as a tuple, this tuple gives the complete shape.
+
+    Returns
+    -------
+    Z : matrix of floats
+        A matrix of floating-point samples drawn from the standard normal
+        distribution.
+
+    See Also
+    --------
+    rand, numpy.random.RandomState.randn
+
+    Notes
+    -----
+    For random samples from :math:`N(\mu, \sigma^2)`, use:
+
+    ``sigma * np.matlib.randn(...) + mu``
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> import numpy.matlib
+    >>> np.matlib.randn(1)
+    matrix([[-1.0856306]])
+    >>> np.matlib.randn(1, 2, 3)
+    matrix([[ 0.99734545,  0.2829785 , -1.50629471],
+            [-0.57860025,  1.65143654, -2.42667924]])
+
+    Two-by-four matrix of samples from :math:`N(3, 6.25)`:
+
+    >>> 2.5 * np.matlib.randn((2, 4)) + 3
+    matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
+            [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matlib/Repmat.md b/docs/Subcommand/Matrix/Matlib/Repmat.md
new file mode 100644
index 000000000..1c4f590f0
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matlib/Repmat.md
@@ -0,0 +1,44 @@
+[[/Matlib/Repmat]]
+
+~~~
+Syntax
+------
+
+matrix repmat <matrix> <int> <int>
+
+
+    Repeat a 0-D to 2-D array or matrix MxN times.
+
+    Parameters
+    ----------
+    a : array_like
+        The array or matrix to be repeated.
+    m, n : int
+        The number of times `a` is repeated along the first and second axes.
+
+    Returns
+    -------
+    out : ndarray
+        The result of repeating `a`.
+
+    Examples
+    --------
+    >>> import numpy.matlib
+    >>> a0 = np.array(1)
+    >>> np.matlib.repmat(a0, 2, 3)
+    array([[1, 1, 1],
+           [1, 1, 1]])
+
+    >>> a1 = np.arange(4)
+    >>> np.matlib.repmat(a1, 2, 2)
+    array([[0, 1, 2, 3, 0, 1, 2, 3],
+           [0, 1, 2, 3, 0, 1, 2, 3]])
+
+    >>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
+    >>> np.matlib.repmat(a2, 2, 3)
+    matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
+            [3, 4, 5, 3, 4, 5, 3, 4, 5],
+            [0, 1, 2, 0, 1, 2, 0, 1, 2],
+            [3, 4, 5, 3, 4, 5, 3, 4, 5]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/All.md b/docs/Subcommand/Matrix/Matrix/All.md
new file mode 100644
index 000000000..c554537c1
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/All.md
@@ -0,0 +1,45 @@
+[[/Matrix/All]]
+
+~~~
+Syntax
+------
+
+matrix all <matrix> axis=<int>
+
+
+        Test whether all matrix elements along a given axis evaluate to True.
+
+        Parameters
+        ----------
+        See `numpy.all` for complete descriptions
+
+        See Also
+        --------
+        numpy.all
+
+        Notes
+        -----
+        This is the same as `ndarray.all`, but it returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> y = x[0]; y
+        matrix([[0, 1, 2, 3]])
+        >>> (x == y)
+        matrix([[ True,  True,  True,  True],
+                [False, False, False, False],
+                [False, False, False, False]])
+        >>> (x == y).all()
+        False
+        >>> (x == y).all(0)
+        matrix([[False, False, False, False]])
+        >>> (x == y).all(1)
+        matrix([[ True],
+                [False],
+                [False]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Any.md b/docs/Subcommand/Matrix/Matrix/Any.md
new file mode 100644
index 000000000..0e25958c1
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Any.md
@@ -0,0 +1,28 @@
+[[/Matrix/Any]]
+
+~~~
+Syntax
+------
+
+matrix any <matrix> axis=<int>
+
+
+        Test whether any array element along a given axis evaluates to True.
+
+        Refer to `numpy.any` for full documentation.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis along which logical OR is performed
+        out : ndarray, optional
+            Output to existing array instead of creating new one, must have
+            same shape as expected output
+
+        Returns
+        -------
+            any : bool, ndarray
+                Returns a single bool if `axis` is ``None``; otherwise,
+                returns `ndarray`
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Argmax.md b/docs/Subcommand/Matrix/Matrix/Argmax.md
new file mode 100644
index 000000000..3788778e9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Argmax.md
@@ -0,0 +1,44 @@
+[[/Matrix/Argmax]]
+
+~~~
+Syntax
+------
+
+matrix argmax <matrix> axis=<int>
+
+
+        Indexes of the maximum values along an axis.
+
+        Return the indexes of the first occurrences of the maximum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmax` for complete descriptions
+
+        See Also
+        --------
+        numpy.argmax
+
+        Notes
+        -----
+        This is the same as `ndarray.argmax`, but returns a `matrix` object
+        where `ndarray.argmax` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.argmax()
+        11
+        >>> x.argmax(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmax(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Argmin.md b/docs/Subcommand/Matrix/Matrix/Argmin.md
new file mode 100644
index 000000000..29b3774b8
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Argmin.md
@@ -0,0 +1,44 @@
+[[/Matrix/Argmin]]
+
+~~~
+Syntax
+------
+
+matrix argmin <matrix> axis=<int>
+
+
+        Indexes of the minimum values along an axis.
+
+        Return the indexes of the first occurrences of the minimum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmin` for complete descriptions.
+
+        See Also
+        --------
+        numpy.argmin
+
+        Notes
+        -----
+        This is the same as `ndarray.argmin`, but returns a `matrix` object
+        where `ndarray.argmin` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.argmin()
+        11
+        >>> x.argmin(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmin(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Argpartition.md b/docs/Subcommand/Matrix/Matrix/Argpartition.md
new file mode 100644
index 000000000..ad4ed0fbe
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Argpartition.md
@@ -0,0 +1,19 @@
+[[/Matrix/Argpartition]]
+
+~~~
+Syntax
+------
+
+matrix argpartition <matrix> <int> axis=<int> kind=<str>
+
+a.argpartition(kth, axis=-1, kind='introselect', order=None)
+
+    Returns the indices that would partition this array.
+
+    Refer to `numpy.argpartition` for full documentation.
+
+    .. versionadded:: 1.8.0
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Argsort.md b/docs/Subcommand/Matrix/Matrix/Argsort.md
new file mode 100644
index 000000000..36c7e456f
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Argsort.md
@@ -0,0 +1,17 @@
+[[/Matrix/Argsort]]
+
+~~~
+Syntax
+------
+
+matrix argsort <matrix> axis=<int> kind=<str>
+
+a.argsort(axis=-1, kind=None, order=None)
+
+    Returns the indices that would sort this array.
+
+    Refer to `numpy.argsort` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Astype.md b/docs/Subcommand/Matrix/Matrix/Astype.md
new file mode 100644
index 000000000..594a19f66
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Astype.md
@@ -0,0 +1,76 @@
+[[/Matrix/Astype]]
+
+~~~
+Syntax
+------
+
+matrix astype <matrix> <str> order=<str> casting=<str> subok=<boolstr>
+
+a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
+
+    Copy of the array, cast to a specified type.
+
+    Parameters
+    ----------
+    dtype : str or dtype
+        Typecode or data-type to which the array is cast.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout order of the result.
+        'C' means C order, 'F' means Fortran order, 'A'
+        means 'F' order if all the arrays are Fortran contiguous,
+        'C' order otherwise, and 'K' means as close to the
+        order the array elements appear in memory as possible.
+        Default is 'K'.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        Controls what kind of data casting may occur. Defaults to 'unsafe'
+        for backwards compatibility.
+
+          * 'no' means the data types should not be cast at all.
+          * 'equiv' means only byte-order changes are allowed.
+          * 'safe' means only casts which can preserve values are allowed.
+          * 'same_kind' means only safe casts or casts within a kind,
+            like float64 to float32, are allowed.
+          * 'unsafe' means any data conversions may be done.
+    subok : bool, optional
+        If True, then sub-classes will be passed-through (default), otherwise
+        the returned array will be forced to be a base-class array.
+    copy : bool, optional
+        By default, astype always returns a newly allocated array. If this
+        is set to false, and the `dtype`, `order`, and `subok`
+        requirements are satisfied, the input array is returned instead
+        of a copy.
+
+    Returns
+    -------
+    arr_t : ndarray
+        Unless `copy` is False and the other conditions for returning the input
+        array are satisfied (see description for `copy` input parameter), `arr_t`
+        is a new array of the same shape as the input array, with dtype, order
+        given by `dtype`, `order`.
+
+    Notes
+    -----
+    .. versionchanged:: 1.17.0
+       Casting between a simple data type and a structured one is possible only
+       for "unsafe" casting.  Casting to multiple fields is allowed, but
+       casting from multiple fields is not.
+
+    .. versionchanged:: 1.9.0
+       Casting from numeric to string types in 'safe' casting mode requires
+       that the string dtype length is long enough to store the max
+       integer/float value converted.
+
+    Raises
+    ------
+    ComplexWarning
+        When casting from complex to float or int. To avoid this,
+        one should use ``a.real.astype(t)``.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 2.5])
+    >>> x
+    array([1. ,  2. ,  2.5])
+
+    >>> x.astype(int)
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Byteswap.md b/docs/Subcommand/Matrix/Matrix/Byteswap.md
new file mode 100644
index 000000000..43412b769
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Byteswap.md
@@ -0,0 +1,56 @@
+[[/Matrix/Byteswap]]
+
+~~~
+Syntax
+------
+
+matrix byteswap <matrix>
+
+a.byteswap(inplace=False)
+
+    Swap the bytes of the array elements
+
+    Toggle between low-endian and big-endian data representation by
+    returning a byteswapped array, optionally swapped in-place.
+    Arrays of byte-strings are not swapped. The real and imaginary
+    parts of a complex number are swapped individually.
+
+    Parameters
+    ----------
+    inplace : bool, optional
+        If ``True``, swap bytes in-place, default is ``False``.
+
+    Returns
+    -------
+    out : ndarray
+        The byteswapped array. If `inplace` is ``True``, this is
+        a view to self.
+
+    Examples
+    --------
+    >>> A = np.array([1, 256, 8755], dtype=np.int16)
+    >>> list(map(hex, A))
+    ['0x1', '0x100', '0x2233']
+    >>> A.byteswap(inplace=True)
+    array([  256,     1, 13090], dtype=int16)
+    >>> list(map(hex, A))
+    ['0x100', '0x1', '0x3322']
+
+    Arrays of byte-strings are not swapped
+
+    >>> A = np.array([b'ceg', b'fac'])
+    >>> A.byteswap()
+    array([b'ceg', b'fac'], dtype='|S3')
+
+    ``A.newbyteorder().byteswap()`` produces an array with the same values
+      but different representation in memory
+
+    >>> A = np.array([1, 2, 3])
+    >>> A.view(np.uint8)
+    array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
+           0, 0], dtype=uint8)
+    >>> A.newbyteorder().byteswap(inplace=True)
+    array([1, 2, 3])
+    >>> A.view(np.uint8)
+    array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Choose.md b/docs/Subcommand/Matrix/Matrix/Choose.md
new file mode 100644
index 000000000..0eae7d335
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Choose.md
@@ -0,0 +1,17 @@
+[[/Matrix/Choose]]
+
+~~~
+Syntax
+------
+
+matrix choose <matrix> <intlist> mode=<str>
+
+a.choose(choices, out=None, mode='raise')
+
+    Use an index array to construct a new array from a set of choices.
+
+    Refer to `numpy.choose` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Clip.md b/docs/Subcommand/Matrix/Matrix/Clip.md
new file mode 100644
index 000000000..f6e595923
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Clip.md
@@ -0,0 +1,18 @@
+[[/Matrix/Clip]]
+
+~~~
+Syntax
+------
+
+matrix clip <matrix> min=<float> max=<float> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+a.clip(min=None, max=None, out=None, **kwargs)
+
+    Return an array whose values are limited to ``[min, max]``.
+    One of max or min must be given.
+
+    Refer to `numpy.clip` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Compress.md b/docs/Subcommand/Matrix/Matrix/Compress.md
new file mode 100644
index 000000000..8642af741
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Compress.md
@@ -0,0 +1,17 @@
+[[/Matrix/Compress]]
+
+~~~
+Syntax
+------
+
+matrix compress <matrix>
+
+a.compress(condition, axis=None, out=None)
+
+    Return selected slices of this array along given axis.
+
+    Refer to `numpy.compress` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Conj.md b/docs/Subcommand/Matrix/Matrix/Conj.md
new file mode 100644
index 000000000..de2db3c9a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Conj.md
@@ -0,0 +1,17 @@
+[[/Matrix/Conj]]
+
+~~~
+Syntax
+------
+
+matrix conj <matrix>
+
+a.conj()
+
+    Complex-conjugate all elements.
+
+    Refer to `numpy.conjugate` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Conjugate.md b/docs/Subcommand/Matrix/Matrix/Conjugate.md
new file mode 100644
index 000000000..4a228cf1a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Conjugate.md
@@ -0,0 +1,17 @@
+[[/Matrix/Conjugate]]
+
+~~~
+Syntax
+------
+
+matrix conjugate <matrix>
+
+a.conjugate()
+
+    Return the complex conjugate, element-wise.
+
+    Refer to `numpy.conjugate` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Cumprod.md b/docs/Subcommand/Matrix/Matrix/Cumprod.md
new file mode 100644
index 000000000..6a67c5971
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Cumprod.md
@@ -0,0 +1,17 @@
+[[/Matrix/Cumprod]]
+
+~~~
+Syntax
+------
+
+matrix cumprod <matrix>
+
+a.cumprod(axis=None, dtype=None, out=None)
+
+    Return the cumulative product of the elements along the given axis.
+
+    Refer to `numpy.cumprod` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Cumsum.md b/docs/Subcommand/Matrix/Matrix/Cumsum.md
new file mode 100644
index 000000000..24406df83
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Cumsum.md
@@ -0,0 +1,17 @@
+[[/Matrix/Cumsum]]
+
+~~~
+Syntax
+------
+
+matrix cumsum <matrix>
+
+a.cumsum(axis=None, dtype=None, out=None)
+
+    Return the cumulative sum of the elements along the given axis.
+
+    Refer to `numpy.cumsum` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Diagonal.md b/docs/Subcommand/Matrix/Matrix/Diagonal.md
new file mode 100644
index 000000000..b4ed7291d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Diagonal.md
@@ -0,0 +1,19 @@
+[[/Matrix/Diagonal]]
+
+~~~
+Syntax
+------
+
+matrix diagonal <matrix>
+
+a.diagonal(offset=0, axis1=0, axis2=1)
+
+    Return specified diagonals. In NumPy 1.9 the returned array is a
+    read-only view instead of a copy as in previous NumPy versions.  In
+    a future version the read-only restriction will be removed.
+
+    Refer to :func:`numpy.diagonal` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Dot.md b/docs/Subcommand/Matrix/Matrix/Dot.md
new file mode 100644
index 000000000..a196c4808
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Dot.md
@@ -0,0 +1,31 @@
+[[/Matrix/Dot]]
+
+~~~
+Syntax
+------
+
+matrix dot <matrix> <matrix>
+
+a.dot(b, out=None)
+
+    Dot product of two arrays.
+
+    Refer to `numpy.dot` for full documentation.
+
+    See Also
+    --------
+    numpy.dot : equivalent function
+
+    Examples
+    --------
+    >>> a = np.eye(2)
+    >>> b = np.ones((2, 2)) * 2
+    >>> a.dot(b)
+    array([[2.,  2.],
+           [2.,  2.]])
+
+    This array method can be conveniently chained:
+
+    >>> a.dot(b).dot(b)
+    array([[8.,  8.],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Fill.md b/docs/Subcommand/Matrix/Matrix/Fill.md
new file mode 100644
index 000000000..ac2ffac29
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Fill.md
@@ -0,0 +1,27 @@
+[[/Matrix/Fill]]
+
+~~~
+Syntax
+------
+
+matrix fill <matrix> <weakcomplex>
+
+a.fill(value)
+
+    Fill the array with a scalar value.
+
+    Parameters
+    ----------
+    value : scalar
+        All elements of `a` will be assigned this value.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2])
+    >>> a.fill(0)
+    >>> a
+    array([0, 0])
+    >>> a = np.empty(2)
+    >>> a.fill(1)
+    >>> a
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Flatten.md b/docs/Subcommand/Matrix/Matrix/Flatten.md
new file mode 100644
index 000000000..bd0895bea
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Flatten.md
@@ -0,0 +1,42 @@
+[[/Matrix/Flatten]]
+
+~~~
+Syntax
+------
+
+matrix flatten <matrix>
+
+
+        Return a flattened copy of the matrix.
+
+        All `N` elements of the matrix are placed into a single row.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            'C' means to flatten in row-major (C-style) order. 'F' means to
+            flatten in column-major (Fortran-style) order. 'A' means to
+            flatten in column-major order if `m` is Fortran *contiguous* in
+            memory, row-major order otherwise. 'K' means to flatten `m` in
+            the order the elements occur in memory. The default is 'C'.
+
+        Returns
+        -------
+        y : matrix
+            A copy of the matrix, flattened to a `(1, N)` matrix where `N`
+            is the number of elements in the original matrix.
+
+        See Also
+        --------
+        ravel : Return a flattened array.
+        flat : A 1-D flat iterator over the matrix.
+
+        Examples
+        --------
+        >>> m = np.matrix([[1,2], [3,4]])
+        >>> m.flatten()
+        matrix([[1, 2, 3, 4]])
+        >>> m.flatten('F')
+        matrix([[1, 3, 2, 4]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Getfield.md b/docs/Subcommand/Matrix/Matrix/Getfield.md
new file mode 100644
index 000000000..89c90e5f6
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Getfield.md
@@ -0,0 +1,44 @@
+[[/Matrix/Getfield]]
+
+~~~
+Syntax
+------
+
+matrix getfield <matrix> <str> offset=<int>
+
+a.getfield(dtype, offset=0)
+
+    Returns a field of the given array as a certain type.
+
+    A field is a view of the array data with a given data-type. The values in
+    the view are determined by the given type and the offset into the current
+    array in bytes. The offset needs to be such that the view dtype fits in the
+    array dtype; for example an array of dtype complex128 has 16-byte elements.
+    If taking a view with a 32-bit integer (4 bytes), the offset needs to be
+    between 0 and 12 bytes.
+
+    Parameters
+    ----------
+    dtype : str or dtype
+        The data type of the view. The dtype size of the view can not be larger
+        than that of the array itself.
+    offset : int
+        Number of bytes to skip before beginning the element view.
+
+    Examples
+    --------
+    >>> x = np.diag([1.+1.j]*2)
+    >>> x[1, 1] = 2 + 4.j
+    >>> x
+    array([[1.+1.j,  0.+0.j],
+           [0.+0.j,  2.+4.j]])
+    >>> x.getfield(np.float64)
+    array([[1.,  0.],
+           [0.,  2.]])
+
+    By choosing an offset of 8 bytes we can select the complex part of the
+    array for our view:
+
+    >>> x.getfield(np.float64, offset=8)
+    array([[1.,  0.],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Geth.md b/docs/Subcommand/Matrix/Matrix/Geth.md
new file mode 100644
index 000000000..b2f44b5ef
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Geth.md
@@ -0,0 +1,36 @@
+[[/Matrix/Geth]]
+
+~~~
+Syntax
+------
+
+matrix getH <matrix>
+
+
+        Returns the (complex) conjugate transpose of `self`.
+
+        Equivalent to ``np.transpose(self)`` if `self` is real-valued.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            complex conjugate transpose of `self`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4)))
+        >>> z = x - 1j*x; z
+        matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
+                [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
+                [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
+        >>> z.getH()
+        matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
+                [ 1. +1.j,  5. +5.j,  9. +9.j],
+                [ 2. +2.j,  6. +6.j, 10.+10.j],
+                [ 3. +3.j,  7. +7.j, 11.+11.j]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Geti.md b/docs/Subcommand/Matrix/Matrix/Geti.md
new file mode 100644
index 000000000..f801e5d44
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Geti.md
@@ -0,0 +1,44 @@
+[[/Matrix/Geti]]
+
+~~~
+Syntax
+------
+
+matrix getI <matrix>
+
+
+        Returns the (multiplicative) inverse of invertible `self`.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            If `self` is non-singular, `ret` is such that ``ret * self`` ==
+            ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return
+            ``True``.
+
+        Raises
+        ------
+        numpy.linalg.LinAlgError: Singular matrix
+            If `self` is singular.
+
+        See Also
+        --------
+        linalg.inv
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]'); m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getI()
+        matrix([[-2. ,  1. ],
+                [ 1.5, -0.5]])
+        >>> m.getI() * m
+        matrix([[ 1.,  0.], # may vary
+                [ 0.,  1.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Gett.md b/docs/Subcommand/Matrix/Matrix/Gett.md
new file mode 100644
index 000000000..a60b81643
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Gett.md
@@ -0,0 +1,37 @@
+[[/Matrix/Gett]]
+
+~~~
+Syntax
+------
+
+matrix getT <matrix>
+
+
+        Returns the transpose of the matrix.
+
+        Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            The (non-conjugated) transpose of the matrix.
+
+        See Also
+        --------
+        transpose, getH
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]')
+        >>> m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getT()
+        matrix([[1, 3],
+                [2, 4]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Item.md b/docs/Subcommand/Matrix/Matrix/Item.md
new file mode 100644
index 000000000..44f1c4641
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Item.md
@@ -0,0 +1,61 @@
+[[/Matrix/Item]]
+
+~~~
+Syntax
+------
+
+matrix item <matrix> <inttuple>
+
+a.item(*args)
+
+    Copy an element of an array to a standard Python scalar and return it.
+
+    Parameters
+    ----------
+    \*args : Arguments (variable number and type)
+
+        * none: in this case, the method only works for arrays
+          with one element (`a.size == 1`), which element is
+          copied into a standard Python scalar object and returned.
+
+        * int_type: this argument is interpreted as a flat index into
+          the array, specifying which element to copy and return.
+
+        * tuple of int_types: functions as does a single int_type argument,
+          except that the argument is interpreted as an nd-index into the
+          array.
+
+    Returns
+    -------
+    z : Standard Python scalar object
+        A copy of the specified element of the array as a suitable
+        Python scalar
+
+    Notes
+    -----
+    When the data type of `a` is longdouble or clongdouble, item() returns
+    a scalar array object because there is no available Python scalar that
+    would not lose information. Void arrays return a buffer object for item(),
+    unless fields are defined, in which case a tuple is returned.
+
+    `item` is very similar to a[args], except, instead of an array scalar,
+    a standard Python scalar is returned. This can be useful for speeding up
+    access to elements of the array and doing arithmetic on elements of the
+    array using Python's optimized math.
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> x = np.random.randint(9, size=(3, 3))
+    >>> x
+    array([[2, 2, 6],
+           [1, 3, 6],
+           [1, 0, 1]])
+    >>> x.item(3)
+    1
+    >>> x.item(7)
+    0
+    >>> x.item((0, 1))
+    2
+    >>> x.item((2, 2))
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Itemset.md b/docs/Subcommand/Matrix/Matrix/Itemset.md
new file mode 100644
index 000000000..ab35d6911
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Itemset.md
@@ -0,0 +1,49 @@
+[[/Matrix/Itemset]]
+
+~~~
+Syntax
+------
+
+matrix itemset <matrix> <inttuple> <weakcomplex>
+
+a.itemset(*args)
+
+    Insert scalar into an array (scalar is cast to array's dtype, if possible)
+
+    There must be at least 1 argument, and define the last argument
+    as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
+    than ``a[args] = item``.  The item should be a scalar value and `args`
+    must select a single item in the array `a`.
+
+    Parameters
+    ----------
+    \*args : Arguments
+        If one argument: a scalar, only used in case `a` is of size 1.
+        If two arguments: the last argument is the value to be set
+        and must be a scalar, the first argument specifies a single array
+        element location. It is either an int or a tuple.
+
+    Notes
+    -----
+    Compared to indexing syntax, `itemset` provides some speed increase
+    for placing a scalar into a particular location in an `ndarray`,
+    if you must do this.  However, generally this is discouraged:
+    among other problems, it complicates the appearance of the code.
+    Also, when using `itemset` (and `item`) inside a loop, be sure
+    to assign the methods to a local variable to avoid the attribute
+    look-up at each loop iteration.
+
+    Examples
+    --------
+    >>> np.random.seed(123)
+    >>> x = np.random.randint(9, size=(3, 3))
+    >>> x
+    array([[2, 2, 6],
+           [1, 3, 6],
+           [1, 0, 1]])
+    >>> x.itemset(4, 0)
+    >>> x.itemset((2, 2), 9)
+    >>> x
+    array([[2, 2, 6],
+           [1, 0, 6],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Max.md b/docs/Subcommand/Matrix/Matrix/Max.md
new file mode 100644
index 000000000..d998ea1ef
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Max.md
@@ -0,0 +1,40 @@
+[[/Matrix/Max]]
+
+~~~
+Syntax
+------
+
+matrix max <matrix> axis=<int>
+
+
+        Return the maximum value along an axis.
+
+        Parameters
+        ----------
+        See `amax` for complete descriptions
+
+        See Also
+        --------
+        amax, ndarray.max
+
+        Notes
+        -----
+        This is the same as `ndarray.max`, but returns a `matrix` object
+        where `ndarray.max` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.max()
+        11
+        >>> x.max(0)
+        matrix([[ 8,  9, 10, 11]])
+        >>> x.max(1)
+        matrix([[ 3],
+                [ 7],
+                [11]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Mean.md b/docs/Subcommand/Matrix/Matrix/Mean.md
new file mode 100644
index 000000000..0638918db
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Mean.md
@@ -0,0 +1,39 @@
+[[/Matrix/Mean]]
+
+~~~
+Syntax
+------
+
+matrix mean <matrix> axis=<int>
+
+
+        Returns the average of the matrix elements along the given axis.
+
+        Refer to `numpy.mean` for full documentation.
+
+        See Also
+        --------
+        numpy.mean
+
+        Notes
+        -----
+        Same as `ndarray.mean` except that, where that returns an `ndarray`,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.mean()
+        5.5
+        >>> x.mean(0)
+        matrix([[4., 5., 6., 7.]])
+        >>> x.mean(1)
+        matrix([[ 1.5],
+                [ 5.5],
+                [ 9.5]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Min.md b/docs/Subcommand/Matrix/Matrix/Min.md
new file mode 100644
index 000000000..36a261ff7
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Min.md
@@ -0,0 +1,40 @@
+[[/Matrix/Min]]
+
+~~~
+Syntax
+------
+
+matrix min <matrix> axis=<int>
+
+
+        Return the minimum value along an axis.
+
+        Parameters
+        ----------
+        See `amin` for complete descriptions.
+
+        See Also
+        --------
+        amin, ndarray.min
+
+        Notes
+        -----
+        This is the same as `ndarray.min`, but returns a `matrix` object
+        where `ndarray.min` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.min()
+        -11
+        >>> x.min(0)
+        matrix([[ -8,  -9, -10, -11]])
+        >>> x.min(1)
+        matrix([[ -3],
+                [ -7],
+                [-11]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Nonzero.md b/docs/Subcommand/Matrix/Matrix/Nonzero.md
new file mode 100644
index 000000000..81a0cc602
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Nonzero.md
@@ -0,0 +1,17 @@
+[[/Matrix/Nonzero]]
+
+~~~
+Syntax
+------
+
+matrix nonzero <matrix>
+
+a.nonzero()
+
+    Return the indices of the elements that are non-zero.
+
+    Refer to `numpy.nonzero` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Partition.md b/docs/Subcommand/Matrix/Matrix/Partition.md
new file mode 100644
index 000000000..000bd9ec3
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Partition.md
@@ -0,0 +1,59 @@
+[[/Matrix/Partition]]
+
+~~~
+Syntax
+------
+
+matrix partition <matrix> <inttuple> axis=<int> kind=<str> order=<strlist>
+
+a.partition(kth, axis=-1, kind='introselect', order=None)
+
+    Rearranges the elements in the array in such a way that the value of the
+    element in kth position is in the position it would be in a sorted array.
+    All elements smaller than the kth element are moved before this element and
+    all equal or greater are moved behind it. The ordering of the elements in
+    the two partitions is undefined.
+
+    .. versionadded:: 1.8.0
+
+    Parameters
+    ----------
+    kth : int or sequence of ints
+        Element index to partition by. The kth element value will be in its
+        final sorted position and all smaller elements will be moved before it
+        and all equal or greater elements behind it.
+        The order of all elements in the partitions is undefined.
+        If provided with a sequence of kth it will partition all elements
+        indexed by kth of them into their sorted position at once.
+    axis : int, optional
+        Axis along which to sort. Default is -1, which means sort along the
+        last axis.
+    kind : {'introselect'}, optional
+        Selection algorithm. Default is 'introselect'.
+    order : str or list of str, optional
+        When `a` is an array with fields defined, this argument specifies
+        which fields to compare first, second, etc. A single field can
+        be specified as a string, and not all fields need to be specified,
+        but unspecified fields will still be used, in the order in which
+        they come up in the dtype, to break ties.
+
+    See Also
+    --------
+    numpy.partition : Return a parititioned copy of an array.
+    argpartition : Indirect partition.
+    sort : Full sort.
+
+    Notes
+    -----
+    See ``np.partition`` for notes on the different algorithms.
+
+    Examples
+    --------
+    >>> a = np.array([3, 4, 2, 1])
+    >>> a.partition(3)
+    >>> a
+    array([2, 1, 3, 4])
+
+    >>> a.partition((1, 3))
+    >>> a
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Prod.md b/docs/Subcommand/Matrix/Matrix/Prod.md
new file mode 100644
index 000000000..1c9f2d939
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Prod.md
@@ -0,0 +1,38 @@
+[[/Matrix/Prod]]
+
+~~~
+Syntax
+------
+
+matrix prod <matrix> axis=<int> [dtype=<str>]
+
+
+        Return the product of the array elements over the given axis.
+
+        Refer to `prod` for full documentation.
+
+        See Also
+        --------
+        prod, ndarray.prod
+
+        Notes
+        -----
+        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
+        returns a `matrix` object instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.prod()
+        0
+        >>> x.prod(0)
+        matrix([[  0,  45, 120, 231]])
+        >>> x.prod(1)
+        matrix([[   0],
+                [ 840],
+                [7920]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Ptp.md b/docs/Subcommand/Matrix/Matrix/Ptp.md
new file mode 100644
index 000000000..86f055daa
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Ptp.md
@@ -0,0 +1,38 @@
+[[/Matrix/Ptp]]
+
+~~~
+Syntax
+------
+
+matrix ptp <matrix> axis=<int>
+
+
+        Peak-to-peak (maximum - minimum) value along the given axis.
+
+        Refer to `numpy.ptp` for full documentation.
+
+        See Also
+        --------
+        numpy.ptp
+
+        Notes
+        -----
+        Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.ptp()
+        11
+        >>> x.ptp(0)
+        matrix([[8, 8, 8, 8]])
+        >>> x.ptp(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Put.md b/docs/Subcommand/Matrix/Matrix/Put.md
new file mode 100644
index 000000000..f73faa8f0
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Put.md
@@ -0,0 +1,17 @@
+[[/Matrix/Put]]
+
+~~~
+Syntax
+------
+
+matrix put <matrix> <intlist> <matrix> mode=<str>
+
+a.put(indices, values, mode='raise')
+
+    Set ``a.flat[n] = values[n]`` for all `n` in indices.
+
+    Refer to `numpy.put` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Ravel.md b/docs/Subcommand/Matrix/Matrix/Ravel.md
new file mode 100644
index 000000000..bfa74879c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Ravel.md
@@ -0,0 +1,43 @@
+[[/Matrix/Ravel]]
+
+~~~
+Syntax
+------
+
+matrix ravel <matrix> order=<str>
+
+
+        Return a flattened matrix.
+
+        Refer to `numpy.ravel` for more documentation.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            The elements of `m` are read using this index order. 'C' means to
+            index the elements in C-like order, with the last axis index
+            changing fastest, back to the first axis index changing slowest.
+            'F' means to index the elements in Fortran-like index order, with
+            the first index changing fastest, and the last index changing
+            slowest. Note that the 'C' and 'F' options take no account of the
+            memory layout of the underlying array, and only refer to the order
+            of axis indexing.  'A' means to read the elements in Fortran-like
+            index order if `m` is Fortran *contiguous* in memory, C-like order
+            otherwise.  'K' means to read the elements in the order they occur
+            in memory, except for reversing the data when strides are negative.
+            By default, 'C' index order is used.
+
+        Returns
+        -------
+        ret : matrix
+            Return the matrix flattened to shape `(1, N)` where `N`
+            is the number of elements in the original matrix.
+            A copy is made only if necessary.
+
+        See Also
+        --------
+        matrix.flatten : returns a similar output matrix but always a copy
+        matrix.flat : a flat iterator on the array.
+        numpy.ravel : related function which returns an ndarray
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Repeat.md b/docs/Subcommand/Matrix/Matrix/Repeat.md
new file mode 100644
index 000000000..607039167
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Repeat.md
@@ -0,0 +1,17 @@
+[[/Matrix/Repeat]]
+
+~~~
+Syntax
+------
+
+matrix repeat <matrix> <inttuple> axis=<int>
+
+a.repeat(repeats, axis=None)
+
+    Repeat elements of an array.
+
+    Refer to `numpy.repeat` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Reshape.md b/docs/Subcommand/Matrix/Matrix/Reshape.md
new file mode 100644
index 000000000..55bf0cc71
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Reshape.md
@@ -0,0 +1,24 @@
+[[/Matrix/Reshape]]
+
+~~~
+Syntax
+------
+
+matrix reshape <matrix> <inttuple> order=<str>
+
+a.reshape(shape, order='C')
+
+    Returns an array containing the same data with a new shape.
+
+    Refer to `numpy.reshape` for full documentation.
+
+    See Also
+    --------
+    numpy.reshape : equivalent function
+
+    Notes
+    -----
+    Unlike the free function `numpy.reshape`, this method on `ndarray` allows
+    the elements of the shape parameter to be passed in as separate arguments.
+    For example, ``a.reshape(10, 11)`` is equivalent to
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Resize.md b/docs/Subcommand/Matrix/Matrix/Resize.md
new file mode 100644
index 000000000..b88aa399a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Resize.md
@@ -0,0 +1,94 @@
+[[/Matrix/Resize]]
+
+~~~
+Syntax
+------
+
+matrix resize <matrix>
+
+a.resize(new_shape, refcheck=True)
+
+    Change shape and size of array in-place.
+
+    Parameters
+    ----------
+    new_shape : tuple of ints, or `n` ints
+        Shape of resized array.
+    refcheck : bool, optional
+        If False, reference count will not be checked. Default is True.
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    ValueError
+        If `a` does not own its own data or references or views to it exist,
+        and the data memory must be changed.
+        PyPy only: will always raise if the data memory must be changed, since
+        there is no reliable way to determine if references or views to it
+        exist.
+
+    SystemError
+        If the `order` keyword argument is specified. This behaviour is a
+        bug in NumPy.
+
+    See Also
+    --------
+    resize : Return a new array with the specified shape.
+
+    Notes
+    -----
+    This reallocates space for the data area if necessary.
+
+    Only contiguous arrays (data elements consecutive in memory) can be
+    resized.
+
+    The purpose of the reference count check is to make sure you
+    do not use this array as a buffer for another Python object and then
+    reallocate the memory. However, reference counts can increase in
+    other ways so if you are sure that you have not shared the memory
+    for this array with another Python object, then you may safely set
+    `refcheck` to False.
+
+    Examples
+    --------
+    Shrinking an array: array is flattened (in the order that the data are
+    stored in memory), resized, and reshaped:
+
+    >>> a = np.array([[0, 1], [2, 3]], order='C')
+    >>> a.resize((2, 1))
+    >>> a
+    array([[0],
+           [1]])
+
+    >>> a = np.array([[0, 1], [2, 3]], order='F')
+    >>> a.resize((2, 1))
+    >>> a
+    array([[0],
+           [2]])
+
+    Enlarging an array: as above, but missing entries are filled with zeros:
+
+    >>> b = np.array([[0, 1], [2, 3]])
+    >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
+    >>> b
+    array([[0, 1, 2],
+           [3, 0, 0]])
+
+    Referencing an array prevents resizing...
+
+    >>> c = a
+    >>> a.resize((1, 1))
+    Traceback (most recent call last):
+    ...
+    ValueError: cannot resize an array that references or is referenced ...
+
+    Unless `refcheck` is False:
+
+    >>> a.resize((1, 1), refcheck=False)
+    >>> a
+    array([[0]])
+    >>> c
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Round.md b/docs/Subcommand/Matrix/Matrix/Round.md
new file mode 100644
index 000000000..9671d8cbc
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Round.md
@@ -0,0 +1,17 @@
+[[/Matrix/Round]]
+
+~~~
+Syntax
+------
+
+matrix round <matrix> decimals=<int>
+
+a.round(decimals=0, out=None)
+
+    Return `a` with each element rounded to the given number of decimals.
+
+    Refer to `numpy.around` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Searchsorted.md b/docs/Subcommand/Matrix/Matrix/Searchsorted.md
new file mode 100644
index 000000000..026b3e7c1
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Searchsorted.md
@@ -0,0 +1,17 @@
+[[/Matrix/Searchsorted]]
+
+~~~
+Syntax
+------
+
+matrix searchsorted <matrix>
+
+a.searchsorted(v, side='left', sorter=None)
+
+    Find indices where elements of v should be inserted in a to maintain order.
+
+    For full documentation, see `numpy.searchsorted`
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Setfield.md b/docs/Subcommand/Matrix/Matrix/Setfield.md
new file mode 100644
index 000000000..4603059fc
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Setfield.md
@@ -0,0 +1,53 @@
+[[/Matrix/Setfield]]
+
+~~~
+Syntax
+------
+
+matrix setfield <matrix> <weakcomplex> <str> offset=<int>
+
+a.setfield(val, dtype, offset=0)
+
+    Put a value into a specified place in a field defined by a data-type.
+
+    Place `val` into `a`'s field defined by `dtype` and beginning `offset`
+    bytes into the field.
+
+    Parameters
+    ----------
+    val : object
+        Value to be placed in field.
+    dtype : dtype object
+        Data-type of the field in which to place `val`.
+    offset : int, optional
+        The number of bytes into the field at which to place `val`.
+
+    Returns
+    -------
+    None
+
+    See Also
+    --------
+    getfield
+
+    Examples
+    --------
+    >>> x = np.eye(3)
+    >>> x.getfield(np.float64)
+    array([[1.,  0.,  0.],
+           [0.,  1.,  0.],
+           [0.,  0.,  1.]])
+    >>> x.setfield(3, np.int32)
+    >>> x.getfield(np.int32)
+    array([[3, 3, 3],
+           [3, 3, 3],
+           [3, 3, 3]], dtype=int32)
+    >>> x
+    array([[1.0e+000, 1.5e-323, 1.5e-323],
+           [1.5e-323, 1.0e+000, 1.5e-323],
+           [1.5e-323, 1.5e-323, 1.0e+000]])
+    >>> x.setfield(np.eye(3), np.int32)
+    >>> x
+    array([[1.,  0.,  0.],
+           [0.,  1.,  0.],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Sort.md b/docs/Subcommand/Matrix/Matrix/Sort.md
new file mode 100644
index 000000000..cb4f40f97
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Sort.md
@@ -0,0 +1,65 @@
+[[/Matrix/Sort]]
+
+~~~
+Syntax
+------
+
+matrix sort <matrix> axis=<int> kind=<str> order=<strlist>
+
+a.sort(axis=-1, kind=None, order=None)
+
+    Sort an array in-place. Refer to `numpy.sort` for full documentation.
+
+    Parameters
+    ----------
+    axis : int, optional
+        Axis along which to sort. Default is -1, which means sort along the
+        last axis.
+    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+        Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
+        and 'mergesort' use timsort under the covers and, in general, the
+        actual implementation will vary with datatype. The 'mergesort' option
+        is retained for backwards compatibility.
+
+        .. versionchanged:: 1.15.0
+           The 'stable' option was added.
+
+    order : str or list of str, optional
+        When `a` is an array with fields defined, this argument specifies
+        which fields to compare first, second, etc.  A single field can
+        be specified as a string, and not all fields need be specified,
+        but unspecified fields will still be used, in the order in which
+        they come up in the dtype, to break ties.
+
+    See Also
+    --------
+    numpy.sort : Return a sorted copy of an array.
+    numpy.argsort : Indirect sort.
+    numpy.lexsort : Indirect stable sort on multiple keys.
+    numpy.searchsorted : Find elements in sorted array.
+    numpy.partition: Partial sort.
+
+    Notes
+    -----
+    See `numpy.sort` for notes on the different sorting algorithms.
+
+    Examples
+    --------
+    >>> a = np.array([[1,4], [3,1]])
+    >>> a.sort(axis=1)
+    >>> a
+    array([[1, 4],
+           [1, 3]])
+    >>> a.sort(axis=0)
+    >>> a
+    array([[1, 3],
+           [1, 4]])
+
+    Use the `order` keyword to specify a field to use when sorting a
+    structured array:
+
+    >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
+    >>> a.sort(order='y')
+    >>> a
+    array([(b'c', 1), (b'a', 2)],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Squeeze.md b/docs/Subcommand/Matrix/Matrix/Squeeze.md
new file mode 100644
index 000000000..d737297ac
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Squeeze.md
@@ -0,0 +1,56 @@
+[[/Matrix/Squeeze]]
+
+~~~
+Syntax
+------
+
+matrix squeeze <matrix> axis=<int>
+
+
+        Return a possibly reshaped matrix.
+
+        Refer to `numpy.squeeze` for more documentation.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Selects a subset of the axes of length one in the shape.
+            If an axis is selected with shape entry greater than one,
+            an error is raised.
+
+        Returns
+        -------
+        squeezed : matrix
+            The matrix, but as a (1, N) matrix if it had shape (N, 1).
+
+        See Also
+        --------
+        numpy.squeeze : related function
+
+        Notes
+        -----
+        If `m` has a single column then that column is returned
+        as the single row of a matrix.  Otherwise `m` is returned.
+        The returned matrix is always either `m` itself or a view into `m`.
+        Supplying an axis keyword argument will not affect the returned matrix
+        but it may cause an error to be raised.
+
+        Examples
+        --------
+        >>> c = np.matrix([[1], [2]])
+        >>> c
+        matrix([[1],
+                [2]])
+        >>> c.squeeze()
+        matrix([[1, 2]])
+        >>> r = c.T
+        >>> r
+        matrix([[1, 2]])
+        >>> r.squeeze()
+        matrix([[1, 2]])
+        >>> m = np.matrix([[1, 2], [3, 4]])
+        >>> m.squeeze()
+        matrix([[1, 2],
+                [3, 4]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Std.md b/docs/Subcommand/Matrix/Matrix/Std.md
new file mode 100644
index 000000000..60c75fe11
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Std.md
@@ -0,0 +1,39 @@
+[[/Matrix/Std]]
+
+~~~
+Syntax
+------
+
+matrix std <matrix> axis=<int> ddof=<int> [dtype=<str>]
+
+
+        Return the standard deviation of the array elements along the given axis.
+
+        Refer to `numpy.std` for full documentation.
+
+        See Also
+        --------
+        numpy.std
+
+        Notes
+        -----
+        This is the same as `ndarray.std`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.std()
+        3.4520525295346629 # may vary
+        >>> x.std(0)
+        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary
+        >>> x.std(1)
+        matrix([[ 1.11803399],
+                [ 1.11803399],
+                [ 1.11803399]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Sum.md b/docs/Subcommand/Matrix/Matrix/Sum.md
new file mode 100644
index 000000000..2347a5807
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Sum.md
@@ -0,0 +1,39 @@
+[[/Matrix/Sum]]
+
+~~~
+Syntax
+------
+
+matrix sum <matrix> [axis=<inttuple>] [dtype=<str>]
+
+
+        Returns the sum of the matrix elements, along the given axis.
+
+        Refer to `numpy.sum` for full documentation.
+
+        See Also
+        --------
+        numpy.sum
+
+        Notes
+        -----
+        This is the same as `ndarray.sum`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix([[1, 2], [4, 3]])
+        >>> x.sum()
+        10
+        >>> x.sum(axis=1)
+        matrix([[3],
+                [7]])
+        >>> x.sum(axis=1, dtype='float')
+        matrix([[3.],
+                [7.]])
+        >>> out = np.zeros((2, 1), dtype='float')
+        >>> x.sum(axis=1, dtype='float', out=np.asmatrix(out))
+        matrix([[3.],
+                [7.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Swapaxes.md b/docs/Subcommand/Matrix/Matrix/Swapaxes.md
new file mode 100644
index 000000000..b2a3c76ef
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Swapaxes.md
@@ -0,0 +1,17 @@
+[[/Matrix/Swapaxes]]
+
+~~~
+Syntax
+------
+
+matrix swapaxes <matrix> <int> <int>
+
+a.swapaxes(axis1, axis2)
+
+    Return a view of the array with `axis1` and `axis2` interchanged.
+
+    Refer to `numpy.swapaxes` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Take.md b/docs/Subcommand/Matrix/Matrix/Take.md
new file mode 100644
index 000000000..38a83aa45
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Take.md
@@ -0,0 +1,17 @@
+[[/Matrix/Take]]
+
+~~~
+Syntax
+------
+
+matrix take <matrix> <matrix> axis=<int> mode=<str>
+
+a.take(indices, axis=None, out=None, mode='raise')
+
+    Return an array formed from the elements of `a` at the given indices.
+
+    Refer to `numpy.take` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Trace.md b/docs/Subcommand/Matrix/Matrix/Trace.md
new file mode 100644
index 000000000..88f094b53
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Trace.md
@@ -0,0 +1,17 @@
+[[/Matrix/Trace]]
+
+~~~
+Syntax
+------
+
+matrix trace <matrix> offset=<int> axis1=<int> axis2=<int> [dtype=<str>]
+
+a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
+
+    Return the sum along diagonals of the array.
+
+    Refer to `numpy.trace` for full documentation.
+
+    See Also
+    --------
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Transpose.md b/docs/Subcommand/Matrix/Matrix/Transpose.md
new file mode 100644
index 000000000..42bc8f7fc
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Transpose.md
@@ -0,0 +1,60 @@
+[[/Matrix/Transpose]]
+
+~~~
+Syntax
+------
+
+matrix transpose <matrix>
+
+a.transpose(*axes)
+
+    Returns a view of the array with axes transposed.
+
+    For a 1-D array this has no effect, as a transposed vector is simply the
+    same vector. To convert a 1-D array into a 2D column vector, an additional
+    dimension must be added. `np.atleast2d(a).T` achieves this, as does
+    `a[:, np.newaxis]`.
+    For a 2-D array, this is a standard matrix transpose.
+    For an n-D array, if axes are given, their order indicates how the
+    axes are permuted (see Examples). If axes are not provided and
+    ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
+    ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
+
+    Parameters
+    ----------
+    axes : None, tuple of ints, or `n` ints
+
+     * None or no argument: reverses the order of the axes.
+
+     * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
+       `i`-th axis becomes `a.transpose()`'s `j`-th axis.
+
+     * `n` ints: same as an n-tuple of the same ints (this form is
+       intended simply as a "convenience" alternative to the tuple form)
+
+    Returns
+    -------
+    out : ndarray
+        View of `a`, with axes suitably permuted.
+
+    See Also
+    --------
+    transpose : Equivalent function
+    ndarray.T : Array property returning the array transposed.
+    ndarray.reshape : Give a new shape to an array without changing its data.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> a
+    array([[1, 2],
+           [3, 4]])
+    >>> a.transpose()
+    array([[1, 3],
+           [2, 4]])
+    >>> a.transpose((1, 0))
+    array([[1, 3],
+           [2, 4]])
+    >>> a.transpose(1, 0)
+    array([[1, 3],
+~~~
diff --git a/docs/Subcommand/Matrix/Matrix/Var.md b/docs/Subcommand/Matrix/Matrix/Var.md
new file mode 100644
index 000000000..9c07da63f
--- /dev/null
+++ b/docs/Subcommand/Matrix/Matrix/Var.md
@@ -0,0 +1,39 @@
+[[/Matrix/Var]]
+
+~~~
+Syntax
+------
+
+matrix var <matrix> axis=<int> ddof=<int> [dtype=<str>]
+
+
+        Returns the variance of the matrix elements, along the given axis.
+
+        Refer to `numpy.var` for full documentation.
+
+        See Also
+        --------
+        numpy.var
+
+        Notes
+        -----
+        This is the same as `ndarray.var`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.var()
+        11.916666666666666
+        >>> x.var(0)
+        matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]]) # may vary
+        >>> x.var(1)
+        matrix([[1.25],
+                [1.25],
+                [1.25]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Maximum.md b/docs/Subcommand/Matrix/Maximum.md
new file mode 100644
index 000000000..1d4772057
--- /dev/null
+++ b/docs/Subcommand/Matrix/Maximum.md
@@ -0,0 +1,56 @@
+[[/Maximum]] -- Element-wise maximum of array elements.
+
+~~~
+Syntax
+------
+
+matrix maximum <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays holding the elements to be compared.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The maximum of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+minimum :
+    Element-wise minimum of two arrays, propagates NaNs.
+fmax :
+    Element-wise maximum of two arrays, ignores NaNs.
+amax :
+    The maximum value of an array along a given axis, propagates NaNs.
+nanmax :
+    The maximum value of an array along a given axis, ignores NaNs.
+
+fmin, amin, nanmin
+
+Notes
+-----
+The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when
+neither x1 nor x2 are nans, but it is faster and does proper
+broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Minimum.md b/docs/Subcommand/Matrix/Minimum.md
new file mode 100644
index 000000000..dd430be89
--- /dev/null
+++ b/docs/Subcommand/Matrix/Minimum.md
@@ -0,0 +1,56 @@
+[[/Minimum]] -- Element-wise minimum of array elements.
+
+~~~
+Syntax
+------
+
+matrix minimum <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays holding the elements to be compared.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The minimum of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+maximum :
+    Element-wise maximum of two arrays, propagates NaNs.
+fmin :
+    Element-wise minimum of two arrays, ignores NaNs.
+amin :
+    The minimum value of an array along a given axis, propagates NaNs.
+nanmin :
+    The minimum value of an array along a given axis, ignores NaNs.
+
+fmax, amax, nanmax
+
+Notes
+-----
+The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when
+neither x1 nor x2 are NaNs, but it is faster and does proper
+broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Mod.md b/docs/Subcommand/Matrix/Mod.md
new file mode 100644
index 000000000..e8b216afe
--- /dev/null
+++ b/docs/Subcommand/Matrix/Mod.md
@@ -0,0 +1,52 @@
+[[/Mod]] -- Return element-wise remainder of division.
+
+~~~
+Syntax
+------
+
+matrix mod <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend array.
+x2 : array_like
+    Divisor array.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The element-wise remainder of the quotient ``floor_divide(x1, x2)``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+floor_divide : Equivalent of Python ``//`` operator.
+divmod : Simultaneous floor division and remainder.
+fmod : Equivalent of the MATLAB ``rem`` function.
+divide, floor
+
+Notes
+-----
+Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of)
+integers.
+``mod`` is an alias of ``remainder``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Modf.md b/docs/Subcommand/Matrix/Modf.md
new file mode 100644
index 000000000..a88eb52dc
--- /dev/null
+++ b/docs/Subcommand/Matrix/Modf.md
@@ -0,0 +1,47 @@
+[[/Modf]] -- Return the fractional and integral parts of an array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix modf <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y1 : ndarray
+    Fractional part of `x`.
+    This is a scalar if `x` is a scalar.
+y2 : ndarray
+    Integral part of `x`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+For integer input the return values are floats.
+
+See Also
+--------
+divmod : ``divmod(x, 1)`` is equivalent to ``modf`` with the return values
+         switched, except it always has a positive remainder.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Multiply.md b/docs/Subcommand/Matrix/Multiply.md
new file mode 100644
index 000000000..76a19400b
--- /dev/null
+++ b/docs/Subcommand/Matrix/Multiply.md
@@ -0,0 +1,41 @@
+[[/Multiply]] -- Multiply arguments element-wise.
+
+~~~
+Syntax
+------
+
+matrix multiply <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    Input arrays to be multiplied.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The product of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+Equivalent to `x1` * `x2` in terms of array broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Nancumprod.md b/docs/Subcommand/Matrix/Nancumprod.md
new file mode 100644
index 000000000..6dbf0a395
--- /dev/null
+++ b/docs/Subcommand/Matrix/Nancumprod.md
@@ -0,0 +1,65 @@
+[[/Nancumprod]]
+
+~~~
+Syntax
+------
+
+matrix nancumprod <matrix> axis=<int> [dtype=<str>]
+
+
+    Return the cumulative product of array elements over a given axis treating Not a
+    Numbers (NaNs) as one.  The cumulative product does not change when NaNs are
+    encountered and leading NaNs are replaced by ones.
+
+    Ones are returned for slices that are all-NaN or empty.
+
+    .. versionadded:: 1.12.0
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative product is computed.  By default
+        the input is flattened.
+    dtype : dtype, optional
+        Type of the returned array, as well as of the accumulator in which
+        the elements are multiplied.  If *dtype* is not specified, it
+        defaults to the dtype of `a`, unless `a` has an integer dtype with
+        a precision less than that of the default platform integer.  In
+        that case, the default platform integer is used instead.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type of the resulting values will be cast if necessary.
+
+    Returns
+    -------
+    nancumprod : ndarray
+        A new array holding the result is returned unless `out` is
+        specified, in which case it is returned.
+
+    See Also
+    --------
+    numpy.cumprod : Cumulative product across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> np.nancumprod(1)
+    array([1])
+    >>> np.nancumprod([1])
+    array([1])
+    >>> np.nancumprod([1, np.nan])
+    array([1.,  1.])
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nancumprod(a)
+    array([1.,  2.,  6.,  6.])
+    >>> np.nancumprod(a, axis=0)
+    array([[1.,  2.],
+           [3.,  2.]])
+    >>> np.nancumprod(a, axis=1)
+    array([[1.,  2.],
+           [3.,  3.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Nancumsum.md b/docs/Subcommand/Matrix/Nancumsum.md
new file mode 100644
index 000000000..77d473571
--- /dev/null
+++ b/docs/Subcommand/Matrix/Nancumsum.md
@@ -0,0 +1,68 @@
+[[/Nancumsum]]
+
+~~~
+Syntax
+------
+
+matrix nancumsum <matrix> axis=<int> [dtype=<str>]
+
+
+    Return the cumulative sum of array elements over a given axis treating Not a
+    Numbers (NaNs) as zero.  The cumulative sum does not change when NaNs are
+    encountered and leading NaNs are replaced by zeros.
+
+    Zeros are returned for slices that are all-NaN or empty.
+
+    .. versionadded:: 1.12.0
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default
+        (None) is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the
+        elements are summed.  If `dtype` is not specified, it defaults
+        to the dtype of `a`, unless `a` has an integer dtype with a
+        precision less than that of the default platform integer.  In
+        that case, the default platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary. See :ref:`ufuncs-output-type` for
+        more details.
+
+    Returns
+    -------
+    nancumsum : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which it is returned. The result has the same
+        size as `a`, and the same shape as `a` if `axis` is not None
+        or `a` is a 1-d array.
+
+    See Also
+    --------
+    numpy.cumsum : Cumulative sum across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> np.nancumsum(1)
+    array([1])
+    >>> np.nancumsum([1])
+    array([1])
+    >>> np.nancumsum([1, np.nan])
+    array([1.,  1.])
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nancumsum(a)
+    array([1.,  3.,  6.,  6.])
+    >>> np.nancumsum(a, axis=0)
+    array([[1.,  2.],
+           [4.,  2.]])
+    >>> np.nancumsum(a, axis=1)
+    array([[1.,  3.],
+           [3.,  3.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Nanprod.md b/docs/Subcommand/Matrix/Nanprod.md
new file mode 100644
index 000000000..5e33966c9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Nanprod.md
@@ -0,0 +1,68 @@
+[[/Nanprod]]
+
+~~~
+Syntax
+------
+
+matrix nanprod <matrix> initial=<float> [axis=<inttuple>] [dtype=<str>] [keepdims=<boolstr>] [where=<boollist>]
+
+
+    Return the product of array elements over a given axis treating Not a
+    Numbers (NaNs) as ones.
+
+    One is returned for slices that are all-NaN or empty.
+
+    .. versionadded:: 1.10.0
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose product is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the product is computed. The default is to compute
+        the product of the flattened array.
+    dtype : data-type, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed.  By default, the dtype of `a` is used.  An
+        exception is when `a` has an integer type with less precision than
+        the platform (u)intp. In that case, the default will be either
+        (u)int32 or (u)int64 depending on whether the platform is 32 or 64
+        bits. For inexact inputs, dtype must be inexact.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``. If provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary. See
+        :ref:`ufuncs-output-type` for more details. The casting of NaN to integer
+        can yield unexpected results.
+    keepdims : bool, optional
+        If True, the axes which are reduced are left in the result as
+        dimensions with size one. With this option, the result will
+        broadcast correctly against the original `arr`.
+
+    Returns
+    -------
+    nanprod : ndarray
+        A new array holding the result is returned unless `out` is
+        specified, in which case it is returned.
+
+    See Also
+    --------
+    numpy.prod : Product across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> np.nanprod(1)
+    1
+    >>> np.nanprod([1])
+    1
+    >>> np.nanprod([1, np.nan])
+    1.0
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nanprod(a)
+    6.0
+    >>> np.nanprod(a, axis=0)
+    array([3., 2.])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Nansum.md b/docs/Subcommand/Matrix/Nansum.md
new file mode 100644
index 000000000..ddf2c9010
--- /dev/null
+++ b/docs/Subcommand/Matrix/Nansum.md
@@ -0,0 +1,96 @@
+[[/Nansum]]
+
+~~~
+Syntax
+------
+
+matrix nansum <matrix> initial=<float> [axis=<inttuple>] [dtype=<str>] [keepdims=<boolstr>] [where=<boollist>]
+
+
+    Return the sum of array elements over a given axis treating Not a
+    Numbers (NaNs) as zero.
+
+    In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or
+    empty. In later versions zero is returned.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose sum is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the sum is computed. The default is to compute the
+        sum of the flattened array.
+    dtype : data-type, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed.  By default, the dtype of `a` is used.  An
+        exception is when `a` has an integer type with less precision than
+        the platform (u)intp. In that case, the default will be either
+        (u)int32 or (u)int64 depending on whether the platform is 32 or 64
+        bits. For inexact inputs, dtype must be inexact.
+
+        .. versionadded:: 1.8.0
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``. If provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary.  See
+        :ref:`ufuncs-output-type` for more details. The casting of NaN to integer
+        can yield unexpected results.
+
+        .. versionadded:: 1.8.0
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+
+        If the value is anything but the default, then
+        `keepdims` will be passed through to the `mean` or `sum` methods
+        of sub-classes of `ndarray`.  If the sub-classes methods
+        does not implement `keepdims` any exceptions will be raised.
+
+        .. versionadded:: 1.8.0
+
+    Returns
+    -------
+    nansum : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which it is returned. The result has the same
+        size as `a`, and the same shape as `a` if `axis` is not None
+        or `a` is a 1-d array.
+
+    See Also
+    --------
+    numpy.sum : Sum across array propagating NaNs.
+    isnan : Show which elements are NaN.
+    isfinite : Show which elements are not NaN or +/-inf.
+
+    Notes
+    -----
+    If both positive and negative infinity are present, the sum will be Not
+    A Number (NaN).
+
+    Examples
+    --------
+    >>> np.nansum(1)
+    1
+    >>> np.nansum([1])
+    1
+    >>> np.nansum([1, np.nan])
+    1.0
+    >>> a = np.array([[1, 1], [1, np.nan]])
+    >>> np.nansum(a)
+    3.0
+    >>> np.nansum(a, axis=0)
+    array([2.,  1.])
+    >>> np.nansum([1, np.nan, np.inf])
+    inf
+    >>> np.nansum([1, np.nan, np.NINF])
+    -inf
+    >>> from numpy.testing import suppress_warnings
+    >>> with suppress_warnings() as sup:
+    ...     sup.filter(RuntimeWarning)
+    ...     np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
+    nan
+
+~~~
diff --git a/docs/Subcommand/Matrix/Negative.md b/docs/Subcommand/Matrix/Negative.md
new file mode 100644
index 000000000..0f641a4e0
--- /dev/null
+++ b/docs/Subcommand/Matrix/Negative.md
@@ -0,0 +1,35 @@
+[[/Negative]] -- Numerical negative, element-wise.
+
+~~~
+Syntax
+------
+
+matrix negative <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like or scalar
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    Returned array or scalar: `y = -x`.
+    This is a scalar if `x` is a scalar.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Nextafter.md b/docs/Subcommand/Matrix/Nextafter.md
new file mode 100644
index 000000000..f54526931
--- /dev/null
+++ b/docs/Subcommand/Matrix/Nextafter.md
@@ -0,0 +1,39 @@
+[[/Nextafter]] -- Return the next floating-point value after x1 towards x2, element-wise.
+
+~~~
+Syntax
+------
+
+matrix nextafter <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Values to find the next representable value of.
+x2 : array_like
+    The direction where to look for the next representable value of `x1`.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    The next representable values of `x1` in the direction of `x2`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Ones.md b/docs/Subcommand/Matrix/Ones.md
new file mode 100644
index 000000000..5d0089b78
--- /dev/null
+++ b/docs/Subcommand/Matrix/Ones.md
@@ -0,0 +1,62 @@
+[[/Ones]]
+
+~~~
+Syntax
+------
+
+matrix ones <intlist> dtype=<str> order=<str>
+
+
+    Return a new array of given shape and type, filled with ones.
+
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., `numpy.int8`.  Default is
+        `numpy.float64`.
+    order : {'C', 'F'}, optional, default: C
+        Whether to store multi-dimensional data in row-major
+        (C-style) or column-major (Fortran-style) order in
+        memory.
+    like : array_like
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument.
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the given shape, dtype, and order.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    empty : Return a new uninitialized array.
+    zeros : Return a new array setting values to zero.
+    full : Return a new array of given shape filled with value.
+
+
+    Examples
+    --------
+    >>> np.ones(5)
+    array([1., 1., 1., 1., 1.])
+
+    >>> np.ones((5,), dtype=int)
+    array([1, 1, 1, 1, 1])
+
+    >>> np.ones((2, 1))
+    array([[1.],
+           [1.]])
+
+    >>> s = (2,2)
+    >>> np.ones(s)
+    array([[1.,  1.],
+           [1.,  1.]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Positive.md b/docs/Subcommand/Matrix/Positive.md
new file mode 100644
index 000000000..86327fb60
--- /dev/null
+++ b/docs/Subcommand/Matrix/Positive.md
@@ -0,0 +1,25 @@
+[[/Positive]] -- Numerical positive, element-wise.
+
+~~~
+Syntax
+------
+
+matrix positive <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like or scalar
+    Input array.
+
+Returns
+-------
+y : ndarray or scalar
+    Returned array or scalar: `y = +x`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+Equivalent to `x.copy()`, but only defined for types that support
+arithmetic.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Power.md b/docs/Subcommand/Matrix/Power.md
new file mode 100644
index 000000000..86b64d17e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Power.md
@@ -0,0 +1,43 @@
+[[/Power]] -- First array elements raised to powers from second array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix power <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    The bases.
+x2 : array_like
+    The exponents.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The bases in `x1` raised to the exponents in `x2`.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+float_power : power function that promotes integers to float
+
+~~~
diff --git a/docs/Subcommand/Matrix/Prod.md b/docs/Subcommand/Matrix/Prod.md
new file mode 100644
index 000000000..b28ae09aa
--- /dev/null
+++ b/docs/Subcommand/Matrix/Prod.md
@@ -0,0 +1,123 @@
+[[/Prod]]
+
+~~~
+Syntax
+------
+
+matrix prod <matrix> initial=<float> [axis=<inttuple>] [dtype=<str>] [keepdims=<boolstr>] [where=<boollist>]
+
+
+    Return the product of array elements over a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a product is performed.  The default,
+        axis=None, will calculate the product of all the elements in the
+        input array. If axis is negative it counts from the last to the
+        first axis.
+
+        .. versionadded:: 1.7.0
+
+        If axis is a tuple of ints, a product is performed on all of the
+        axes specified in the tuple instead of a single axis or all the
+        axes as before.
+    dtype : dtype, optional
+        The type of the returned array, as well as of the accumulator in
+        which the elements are multiplied.  The dtype of `a` is used by
+        default unless `a` has an integer dtype of less precision than the
+        default platform integer.  In that case, if `a` is signed then the
+        platform integer is used while if `a` is unsigned then an unsigned
+        integer of the same precision as the platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output, but the type of the output
+        values will be cast if necessary.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the
+        result as dimensions with size one. With this option, the result
+        will broadcast correctly against the input array.
+
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `prod` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+    initial : scalar, optional
+        The starting value for this product. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.15.0
+
+    where : array_like of bool, optional
+        Elements to include in the product. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.17.0
+
+    Returns
+    -------
+    product_along_axis : ndarray, see `dtype` parameter above.
+        An array shaped as `a` but with the specified axis removed.
+        Returns a reference to `out` if specified.
+
+    See Also
+    --------
+    ndarray.prod : equivalent method
+    :ref:`ufuncs-output-type`
+
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.  That means that, on a 32-bit platform:
+
+    >>> x = np.array([536870910, 536870910, 536870910, 536870910])
+    >>> np.prod(x)
+    16 # may vary
+
+    The product of an empty array is the neutral element 1:
+
+    >>> np.prod([])
+    1.0
+
+    Examples
+    --------
+    By default, calculate the product of all elements:
+
+    >>> np.prod([1.,2.])
+    2.0
+
+    Even when the input array is two-dimensional:
+
+    >>> np.prod([[1.,2.],[3.,4.]])
+    24.0
+
+    But we can also specify the axis over which to multiply:
+
+    >>> np.prod([[1.,2.],[3.,4.]], axis=1)
+    array([  2.,  12.])
+
+    Or select specific elements to include:
+
+    >>> np.prod([1., np.nan, 3.], where=[True, False, True])
+    3.0
+
+    If the type of `x` is unsigned, then the output type is
+    the unsigned platform integer:
+
+    >>> x = np.array([1, 2, 3], dtype=np.uint8)
+    >>> np.prod(x).dtype == np.uint
+    True
+
+    If `x` is of a signed integer type, then the output type
+    is the default platform integer:
+
+    >>> x = np.array([1, 2, 3], dtype=np.int8)
+    >>> np.prod(x).dtype == int
+    True
+
+    You can also start the product with a value other than one:
+
+    >>> np.prod([1, 2], initial=5)
+    10
+~~~
diff --git a/docs/Subcommand/Matrix/Rad2Deg.md b/docs/Subcommand/Matrix/Rad2Deg.md
new file mode 100644
index 000000000..2bd1ae408
--- /dev/null
+++ b/docs/Subcommand/Matrix/Rad2Deg.md
@@ -0,0 +1,46 @@
+[[/Rad2Deg]] -- Convert angles from radians to degrees.
+
+~~~
+Syntax
+------
+
+matrix rad2deg <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Angle in radians.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding angle in degrees.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+deg2rad : Convert angles from degrees to radians.
+unwrap : Remove large jumps in angle by wrapping.
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+rad2deg(x) is ``180 * x / pi``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Radians.md b/docs/Subcommand/Matrix/Radians.md
new file mode 100644
index 000000000..5988002da
--- /dev/null
+++ b/docs/Subcommand/Matrix/Radians.md
@@ -0,0 +1,39 @@
+[[/Radians]] -- Convert angles from degrees to radians.
+
+~~~
+Syntax
+------
+
+matrix radians <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array in degrees.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding radian values.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+deg2rad : equivalent function
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Choice.md b/docs/Subcommand/Matrix/Random/Choice.md
new file mode 100644
index 000000000..c6d6fe0e3
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Choice.md
@@ -0,0 +1,99 @@
+[[/Random/Choice]]
+
+~~~
+Syntax
+------
+
+matrix choice <arrayorint> size=<intlist> replace=<boolstr> p=<array>
+
+
+        choice(a, size=None, replace=True, p=None)
+
+        Generates a random sample from a given 1-D array
+
+        .. versionadded:: 1.7.0
+
+        .. note::
+            New code should use the ``choice`` method of a ``default_rng()``
+            instance instead; please see the :ref:`random-quick-start`.
+
+        Parameters
+        ----------
+        a : 1-D array-like or int
+            If an ndarray, a random sample is generated from its elements.
+            If an int, the random sample is generated as if it were ``np.arange(a)``
+        size : int or tuple of ints, optional
+            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+            ``m * n * k`` samples are drawn.  Default is None, in which case a
+            single value is returned.
+        replace : boolean, optional
+            Whether the sample is with or without replacement. Default is True,
+            meaning that a value of ``a`` can be selected multiple times.
+        p : 1-D array-like, optional
+            The probabilities associated with each entry in a.
+            If not given, the sample assumes a uniform distribution over all
+            entries in ``a``.
+
+        Returns
+        -------
+        samples : single item or ndarray
+            The generated random samples
+
+        Raises
+        ------
+        ValueError
+            If a is an int and less than zero, if a or p are not 1-dimensional,
+            if a is an array-like of size 0, if p is not a vector of
+            probabilities, if a and p have different lengths, or if
+            replace=False and the sample size is greater than the population
+            size
+
+        See Also
+        --------
+        randint, shuffle, permutation
+        Generator.choice: which should be used in new code
+
+        Notes
+        -----
+        Setting user-specified probabilities through ``p`` uses a more general but less
+        efficient sampler than the default. The general sampler produces a different sample
+        than the optimized sampler even if each element of ``p`` is 1 / len(a).
+
+        Sampling random rows from a 2-D array is not possible with this function,
+        but is possible with `Generator.choice` through its ``axis`` keyword.
+
+        Examples
+        --------
+        Generate a uniform random sample from np.arange(5) of size 3:
+
+        >>> np.random.choice(5, 3)
+        array([0, 3, 4]) # random
+        >>> #This is equivalent to np.random.randint(0,5,3)
+
+        Generate a non-uniform random sample from np.arange(5) of size 3:
+
+        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
+        array([3, 3, 0]) # random
+
+        Generate a uniform random sample from np.arange(5) of size 3 without
+        replacement:
+
+        >>> np.random.choice(5, 3, replace=False)
+        array([3,1,0]) # random
+        >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]
+
+        Generate a non-uniform random sample from np.arange(5) of size
+        3 without replacement:
+
+        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
+        array([2, 3, 0]) # random
+
+        Any of the above can be repeated with an arbitrary array-like
+        instead of just integers. For instance:
+
+        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
+        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
+        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
+              dtype='<U11')
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Normal.md b/docs/Subcommand/Matrix/Random/Normal.md
new file mode 100644
index 000000000..afe31bc62
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Normal.md
@@ -0,0 +1,108 @@
+[[/Random/Normal]]
+
+~~~
+Syntax
+------
+
+matrix normal loc=<matrix> scale=<matrix> size=<intlist>
+
+
+        normal(loc=0.0, scale=1.0, size=None)
+
+        Draw random samples from a normal (Gaussian) distribution.
+
+        The probability density function of the normal distribution, first
+        derived by De Moivre and 200 years later by both Gauss and Laplace
+        independently [2]_, is often called the bell curve because of
+        its characteristic shape (see the example below).
+
+        The normal distributions occurs often in nature.  For example, it
+        describes the commonly occurring distribution of samples influenced
+        by a large number of tiny, random disturbances, each with its own
+        unique distribution [2]_.
+
+        .. note::
+            New code should use the ``normal`` method of a ``default_rng()``
+            instance instead; please see the :ref:`random-quick-start`.
+
+        Parameters
+        ----------
+        loc : float or array_like of floats
+            Mean ("centre") of the distribution.
+        scale : float or array_like of floats
+            Standard deviation (spread or "width") of the distribution. Must be
+            non-negative.
+        size : int or tuple of ints, optional
+            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+            ``m * n * k`` samples are drawn.  If size is ``None`` (default),
+            a single value is returned if ``loc`` and ``scale`` are both scalars.
+            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
+
+        Returns
+        -------
+        out : ndarray or scalar
+            Drawn samples from the parameterized normal distribution.
+
+        See Also
+        --------
+        scipy.stats.norm : probability density function, distribution or
+            cumulative density function, etc.
+        Generator.normal: which should be used for new code.
+
+        Notes
+        -----
+        The probability density for the Gaussian distribution is
+
+        .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
+                         e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
+
+        where :math:`\mu` is the mean and :math:`\sigma` the standard
+        deviation. The square of the standard deviation, :math:`\sigma^2`,
+        is called the variance.
+
+        The function has its peak at the mean, and its "spread" increases with
+        the standard deviation (the function reaches 0.607 times its maximum at
+        :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
+        normal is more likely to return samples lying close to the mean, rather
+        than those far away.
+
+        References
+        ----------
+        .. [1] Wikipedia, "Normal distribution",
+               https://en.wikipedia.org/wiki/Normal_distribution
+        .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
+               Random Variables and Random Signal Principles", 4th ed., 2001,
+               pp. 51, 51, 125.
+
+        Examples
+        --------
+        Draw samples from the distribution:
+
+        >>> mu, sigma = 0, 0.1 # mean and standard deviation
+        >>> s = np.random.normal(mu, sigma, 1000)
+
+        Verify the mean and the variance:
+
+        >>> abs(mu - np.mean(s))
+        0.0  # may vary
+
+        >>> abs(sigma - np.std(s, ddof=1))
+        0.1  # may vary
+
+        Display the histogram of the samples, along with
+        the probability density function:
+
+        >>> import matplotlib.pyplot as plt
+        >>> count, bins, ignored = plt.hist(s, 30, density=True)
+        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
+        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
+        ...          linewidth=2, color='r')
+        >>> plt.show()
+
+        Two-by-four array of samples from N(3, 6.25):
+
+        >>> np.random.normal(3, 2.5, size=(2, 4))
+        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
+               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Rand.md b/docs/Subcommand/Matrix/Random/Rand.md
new file mode 100644
index 000000000..b39e79dee
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Rand.md
@@ -0,0 +1,46 @@
+[[/Random/Rand]]
+
+~~~
+Syntax
+------
+
+matrix rand <intlist_args>
+
+
+        rand(d0, d1, ..., dn)
+
+        Random values in a given shape.
+
+        .. note::
+            This is a convenience function for users porting code from Matlab,
+            and wraps `random_sample`. That function takes a
+            tuple to specify the size of the output, which is consistent with
+            other NumPy functions like `numpy.zeros` and `numpy.ones`.
+
+        Create an array of the given shape and populate it with
+        random samples from a uniform distribution
+        over ``[0, 1)``.
+
+        Parameters
+        ----------
+        d0, d1, ..., dn : int, optional
+            The dimensions of the returned array, must be non-negative.
+            If no argument is given a single Python float is returned.
+
+        Returns
+        -------
+        out : ndarray, shape ``(d0, d1, ..., dn)``
+            Random values.
+
+        See Also
+        --------
+        random
+
+        Examples
+        --------
+        >>> np.random.rand(3,2)
+        array([[ 0.14022471,  0.96360618],  #random
+               [ 0.37601032,  0.25528411],  #random
+               [ 0.49313049,  0.94909878]]) #random
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Randint.md b/docs/Subcommand/Matrix/Random/Randint.md
new file mode 100644
index 000000000..069340a91
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Randint.md
@@ -0,0 +1,83 @@
+[[/Random/Randint]]
+
+~~~
+Syntax
+------
+
+matrix randint <int> high=<int> size=<intlist> dtype=<str>
+
+
+        randint(low, high=None, size=None, dtype=int)
+
+        Return random integers from `low` (inclusive) to `high` (exclusive).
+
+        Return random integers from the "discrete uniform" distribution of
+        the specified dtype in the "half-open" interval [`low`, `high`). If
+        `high` is None (the default), then results are from [0, `low`).
+
+        .. note::
+            New code should use the ``integers`` method of a ``default_rng()``
+            instance instead; please see the :ref:`random-quick-start`.
+
+        Parameters
+        ----------
+        low : int or array-like of ints
+            Lowest (signed) integers to be drawn from the distribution (unless
+            ``high=None``, in which case this parameter is one above the
+            *highest* such integer).
+        high : int or array-like of ints, optional
+            If provided, one above the largest (signed) integer to be drawn
+            from the distribution (see above for behavior if ``high=None``).
+            If array-like, must contain integer values
+        size : int or tuple of ints, optional
+            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+            ``m * n * k`` samples are drawn.  Default is None, in which case a
+            single value is returned.
+        dtype : dtype, optional
+            Desired dtype of the result. Byteorder must be native.
+            The default value is int.
+
+            .. versionadded:: 1.11.0
+
+        Returns
+        -------
+        out : int or ndarray of ints
+            `size`-shaped array of random integers from the appropriate
+            distribution, or a single such random int if `size` not provided.
+
+        See Also
+        --------
+        random_integers : similar to `randint`, only for the closed
+            interval [`low`, `high`], and 1 is the lowest value if `high` is
+            omitted.
+        Generator.integers: which should be used for new code.
+
+        Examples
+        --------
+        >>> np.random.randint(2, size=10)
+        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random
+        >>> np.random.randint(1, size=10)
+        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+        Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+        >>> np.random.randint(5, size=(2, 4))
+        array([[4, 0, 2, 1], # random
+               [3, 2, 2, 0]])
+
+        Generate a 1 x 3 array with 3 different upper bounds
+
+        >>> np.random.randint(1, [3, 5, 10])
+        array([2, 2, 9]) # random
+
+        Generate a 1 by 3 array with 3 different lower bounds
+
+        >>> np.random.randint([1, 5, 7], 10)
+        array([9, 8, 7]) # random
+
+        Generate a 2 by 4 array using broadcasting with dtype of uint8
+
+        >>> np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)
+        array([[ 8,  6,  9,  7], # random
+               [ 1, 16,  9, 12]], dtype=uint8)
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Randn.md b/docs/Subcommand/Matrix/Random/Randn.md
new file mode 100644
index 000000000..ba2125fb3
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Randn.md
@@ -0,0 +1,66 @@
+[[/Random/Randn]]
+
+~~~
+Syntax
+------
+
+matrix randn <intlist_args>
+
+
+        randn(d0, d1, ..., dn)
+
+        Return a sample (or samples) from the "standard normal" distribution.
+
+        .. note::
+            This is a convenience function for users porting code from Matlab,
+            and wraps `standard_normal`. That function takes a
+            tuple to specify the size of the output, which is consistent with
+            other NumPy functions like `numpy.zeros` and `numpy.ones`.
+
+        .. note::
+            New code should use the ``standard_normal`` method of a ``default_rng()``
+            instance instead; please see the :ref:`random-quick-start`.
+
+        If positive int_like arguments are provided, `randn` generates an array
+        of shape ``(d0, d1, ..., dn)``, filled
+        with random floats sampled from a univariate "normal" (Gaussian)
+        distribution of mean 0 and variance 1. A single float randomly sampled
+        from the distribution is returned if no argument is provided.
+
+        Parameters
+        ----------
+        d0, d1, ..., dn : int, optional
+            The dimensions of the returned array, must be non-negative.
+            If no argument is given a single Python float is returned.
+
+        Returns
+        -------
+        Z : ndarray or float
+            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
+            the standard normal distribution, or a single such float if
+            no parameters were supplied.
+
+        See Also
+        --------
+        standard_normal : Similar, but takes a tuple as its argument.
+        normal : Also accepts mu and sigma arguments.
+        Generator.standard_normal: which should be used for new code.
+
+        Notes
+        -----
+        For random samples from :math:`N(\mu, \sigma^2)`, use:
+
+        ``sigma * np.random.randn(...) + mu``
+
+        Examples
+        --------
+        >>> np.random.randn()
+        2.1923875335537315  # random
+
+        Two-by-four array of samples from N(3, 6.25):
+
+        >>> 3 + 2.5 * np.random.randn(2, 4)
+        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
+               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Random.md b/docs/Subcommand/Matrix/Random/Random.md
new file mode 100644
index 000000000..f2fbd7f8c
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Random.md
@@ -0,0 +1,14 @@
+[[/Random/Random]]
+
+~~~
+Syntax
+------
+
+matrix random size=<intlist>
+
+
+        random(size=None)
+
+        Return random floats in the half-open interval [0.0, 1.0). Alias for
+        `random_sample` to ease forward-porting to the new random API.
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Random_Sample.md b/docs/Subcommand/Matrix/Random/Random_Sample.md
new file mode 100644
index 000000000..d9e745c98
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Random_Sample.md
@@ -0,0 +1,57 @@
+[[/Random/Random_Sample]]
+
+~~~
+Syntax
+------
+
+matrix random_sample size=<intlist>
+
+
+        random_sample(size=None)
+
+        Return random floats in the half-open interval [0.0, 1.0).
+
+        Results are from the "continuous uniform" distribution over the
+        stated interval.  To sample :math:`Unif[a, b), b > a` multiply
+        the output of `random_sample` by `(b-a)` and add `a`::
+
+          (b - a) * random_sample() + a
+
+        .. note::
+            New code should use the ``random`` method of a ``default_rng()``
+            instance instead; please see the :ref:`random-quick-start`.
+
+        Parameters
+        ----------
+        size : int or tuple of ints, optional
+            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+            ``m * n * k`` samples are drawn.  Default is None, in which case a
+            single value is returned.
+
+        Returns
+        -------
+        out : float or ndarray of floats
+            Array of random floats of shape `size` (unless ``size=None``, in which
+            case a single float is returned).
+
+        See Also
+        --------
+        Generator.random: which should be used for new code.
+
+        Examples
+        --------
+        >>> np.random.random_sample()
+        0.47108547995356098 # random
+        >>> type(np.random.random_sample())
+        <class 'float'>
+        >>> np.random.random_sample((5,))
+        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428]) # random
+
+        Three-by-two array of random numbers from [-5, 0):
+
+        >>> 5 * np.random.random_sample((3, 2)) - 5
+        array([[-3.99149989, -0.52338984], # random
+               [-2.99091858, -0.79479508],
+               [-1.23204345, -1.75224494]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Ranf.md b/docs/Subcommand/Matrix/Random/Ranf.md
new file mode 100644
index 000000000..7b85f0759
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Ranf.md
@@ -0,0 +1,12 @@
+[[/Random/Ranf]]
+
+~~~
+Syntax
+------
+
+matrix ranf size=<intlist>
+
+
+    This is an alias of `random_sample`. See `random_sample`  for the complete
+    documentation.
+~~~
diff --git a/docs/Subcommand/Matrix/Random/Sample.md b/docs/Subcommand/Matrix/Random/Sample.md
new file mode 100644
index 000000000..1e748a8e7
--- /dev/null
+++ b/docs/Subcommand/Matrix/Random/Sample.md
@@ -0,0 +1,12 @@
+[[/Random/Sample]]
+
+~~~
+Syntax
+------
+
+matrix sample size=<intlist>
+
+
+    This is an alias of `random_sample`. See `random_sample`  for the complete
+    documentation.
+~~~
diff --git a/docs/Subcommand/Matrix/Real.md b/docs/Subcommand/Matrix/Real.md
new file mode 100644
index 000000000..19c937475
--- /dev/null
+++ b/docs/Subcommand/Matrix/Real.md
@@ -0,0 +1,42 @@
+[[/Real]]
+
+~~~
+Syntax
+------
+
+matrix real <matrix>
+
+
+    Return the real part of the complex argument.
+
+    Parameters
+    ----------
+    val : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The real component of the complex argument. If `val` is real, the type
+        of `val` is used for the output.  If `val` has complex elements, the
+        returned type is float.
+
+    See Also
+    --------
+    real_if_close, imag, angle
+
+    Examples
+    --------
+    >>> a = np.array([1+2j, 3+4j, 5+6j])
+    >>> a.real
+    array([1.,  3.,  5.])
+    >>> a.real = 9
+    >>> a
+    array([9.+2.j,  9.+4.j,  9.+6.j])
+    >>> a.real = np.array([9, 8, 7])
+    >>> a
+    array([9.+2.j,  8.+4.j,  7.+6.j])
+    >>> np.real(1 + 1j)
+    1.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Real_If_Close.md b/docs/Subcommand/Matrix/Real_If_Close.md
new file mode 100644
index 000000000..32d9870af
--- /dev/null
+++ b/docs/Subcommand/Matrix/Real_If_Close.md
@@ -0,0 +1,51 @@
+[[/Real_If_Close]]
+
+~~~
+Syntax
+------
+
+matrix real_if_close <matrix> tol=<float>
+
+
+    If input is complex with all imaginary parts close to zero, return 
+    real parts.
+
+    "Close to zero" is defined as `tol` * (machine epsilon of the type for
+    `a`).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    tol : float
+        Tolerance in machine epsilons for the complex part of the elements
+        in the array.
+
+    Returns
+    -------
+    out : ndarray
+        If `a` is real, the type of `a` is used for the output.  If `a`
+        has complex elements, the returned type is float.
+
+    See Also
+    --------
+    real, imag, angle
+
+    Notes
+    -----
+    Machine epsilon varies from machine to machine and between data types
+    but Python floats on most platforms have a machine epsilon equal to
+    2.2204460492503131e-16.  You can use 'np.finfo(float).eps' to print
+    out the machine epsilon for floats.
+
+    Examples
+    --------
+    >>> np.finfo(float).eps
+    2.2204460492503131e-16 # may vary
+
+    >>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000)
+    array([2.1, 5.2])
+    >>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000)
+    array([2.1+4.e-13j, 5.2 + 3e-15j])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Reciprocal.md b/docs/Subcommand/Matrix/Reciprocal.md
new file mode 100644
index 000000000..df3dd0278
--- /dev/null
+++ b/docs/Subcommand/Matrix/Reciprocal.md
@@ -0,0 +1,44 @@
+[[/Reciprocal]] -- Return the reciprocal of the argument, element-wise.
+
+~~~
+Syntax
+------
+
+matrix reciprocal <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    Return array.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+.. note::
+    This function is not designed to work with integers.
+
+For integer arguments with absolute value larger than 1 the result is
+always zero because of the way Python handles integer division.  For
+integer zero the result is an overflow.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Remainder.md b/docs/Subcommand/Matrix/Remainder.md
new file mode 100644
index 000000000..5a02a4b95
--- /dev/null
+++ b/docs/Subcommand/Matrix/Remainder.md
@@ -0,0 +1,52 @@
+[[/Remainder]] -- Return element-wise remainder of division.
+
+~~~
+Syntax
+------
+
+matrix remainder <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend array.
+x2 : array_like
+    Divisor array.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The element-wise remainder of the quotient ``floor_divide(x1, x2)``.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+See Also
+--------
+floor_divide : Equivalent of Python ``//`` operator.
+divmod : Simultaneous floor division and remainder.
+fmod : Equivalent of the MATLAB ``rem`` function.
+divide, floor
+
+Notes
+-----
+Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of)
+integers.
+``mod`` is an alias of ``remainder``.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Rint.md b/docs/Subcommand/Matrix/Rint.md
new file mode 100644
index 000000000..d7822c27d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Rint.md
@@ -0,0 +1,45 @@
+[[/Rint]] -- Round elements of the array to the nearest integer.
+
+~~~
+Syntax
+------
+
+matrix rint <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Output array is same shape and type as `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+fix, ceil, floor, trunc
+
+Notes
+-----
+For values exactly halfway between rounded decimal values, NumPy
+rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+-0.5 and 0.5 round to 0.0, etc.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Round_.md b/docs/Subcommand/Matrix/Round_.md
new file mode 100644
index 000000000..4193b1944
--- /dev/null
+++ b/docs/Subcommand/Matrix/Round_.md
@@ -0,0 +1,15 @@
+[[/Round_]]
+
+~~~
+Syntax
+------
+
+matrix round_ <matrix> decimals=<int>
+
+
+    Round an array to the given number of decimals.
+
+    See Also
+    --------
+    around : equivalent function; see for details.
+~~~
diff --git a/docs/Subcommand/Matrix/Savetxt.md b/docs/Subcommand/Matrix/Savetxt.md
new file mode 100644
index 000000000..6c54c45b0
--- /dev/null
+++ b/docs/Subcommand/Matrix/Savetxt.md
@@ -0,0 +1,127 @@
+[[/Savetxt]]
+
+~~~
+Syntax
+------
+
+matrix savetxt <str> <matrix> fmt=<str> delimiter=<str> newline=<str> header=<str> footer=<str> comments=<str> encoding=<str>
+
+
+    Save an array to a text file.
+
+    Parameters
+    ----------
+    fname : filename or file handle
+        If the filename ends in ``.gz``, the file is automatically saved in
+        compressed gzip format.  `loadtxt` understands gzipped files
+        transparently.
+    X : 1D or 2D array_like
+        Data to be saved to a text file.
+    fmt : str or sequence of strs, optional
+        A single format (%10.5f), a sequence of formats, or a
+        multi-format string, e.g. 'Iteration %d -- %10.5f', in which
+        case `delimiter` is ignored. For complex `X`, the legal options
+        for `fmt` are:
+
+        * a single specifier, `fmt='%.4e'`, resulting in numbers formatted
+          like `' (%s+%sj)' % (fmt, fmt)`
+        * a full string specifying every real and imaginary part, e.g.
+          `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns
+        * a list of specifiers, one per column - in this case, the real
+          and imaginary part must have separate specifiers,
+          e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
+    delimiter : str, optional
+        String or character separating columns.
+    newline : str, optional
+        String or character separating lines.
+
+        .. versionadded:: 1.5.0
+    header : str, optional
+        String that will be written at the beginning of the file.
+
+        .. versionadded:: 1.7.0
+    footer : str, optional
+        String that will be written at the end of the file.
+
+        .. versionadded:: 1.7.0
+    comments : str, optional
+        String that will be prepended to the ``header`` and ``footer`` strings,
+        to mark them as comments. Default: '# ',  as expected by e.g.
+        ``numpy.loadtxt``.
+
+        .. versionadded:: 1.7.0
+    encoding : {None, str}, optional
+        Encoding used to encode the outputfile. Does not apply to output
+        streams. If the encoding is something other than 'bytes' or 'latin1'
+        you will not be able to load the file in NumPy versions < 1.14. Default
+        is 'latin1'.
+
+        .. versionadded:: 1.14.0
+
+
+    See Also
+    --------
+    save : Save an array to a binary file in NumPy ``.npy`` format
+    savez : Save several arrays into an uncompressed ``.npz`` archive
+    savez_compressed : Save several arrays into a compressed ``.npz`` archive
+
+    Notes
+    -----
+    Further explanation of the `fmt` parameter
+    (``%[flag]width[.precision]specifier``):
+
+    flags:
+        ``-`` : left justify
+
+        ``+`` : Forces to precede result with + or -.
+
+        ``0`` : Left pad the number with zeros instead of space (see width).
+
+    width:
+        Minimum number of characters to be printed. The value is not truncated
+        if it has more characters.
+
+    precision:
+        - For integer specifiers (eg. ``d,i,o,x``), the minimum number of
+          digits.
+        - For ``e, E`` and ``f`` specifiers, the number of digits to print
+          after the decimal point.
+        - For ``g`` and ``G``, the maximum number of significant digits.
+        - For ``s``, the maximum number of characters.
+
+    specifiers:
+        ``c`` : character
+
+        ``d`` or ``i`` : signed decimal integer
+
+        ``e`` or ``E`` : scientific notation with ``e`` or ``E``.
+
+        ``f`` : decimal floating point
+
+        ``g,G`` : use the shorter of ``e,E`` or ``f``
+
+        ``o`` : signed octal
+
+        ``s`` : string of characters
+
+        ``u`` : unsigned decimal integer
+
+        ``x,X`` : unsigned hexadecimal integer
+
+    This explanation of ``fmt`` is not complete, for an exhaustive
+    specification see [1]_.
+
+    References
+    ----------
+    .. [1] `Format Specification Mini-Language
+           <https://docs.python.org/library/string.html#format-specification-mini-language>`_,
+           Python Documentation.
+
+    Examples
+    --------
+    >>> x = y = z = np.arange(0.0,5.0,1.0)
+    >>> np.savetxt('test.out', x, delimiter=',')   # X is an array
+    >>> np.savetxt('test.out', (x,y,z))   # x,y,z equal sized 1D arrays
+    >>> np.savetxt('test.out', x, fmt='%1.4e')   # use exponential notation
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sign.md b/docs/Subcommand/Matrix/Sign.md
new file mode 100644
index 000000000..455ceeff9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sign.md
@@ -0,0 +1,41 @@
+[[/Sign]] -- Returns an element-wise indication of the sign of a number.
+
+~~~
+Syntax
+------
+
+matrix sign <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input values.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The sign of `x`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+There is more than one definition of sign in common use for complex
+numbers.  The definition used here is equivalent to :math:`x/\sqrt{x*x}`
+which is different from a common alternative, :math:`x/|x|`.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Signbit.md b/docs/Subcommand/Matrix/Signbit.md
new file mode 100644
index 000000000..6523c2489
--- /dev/null
+++ b/docs/Subcommand/Matrix/Signbit.md
@@ -0,0 +1,35 @@
+[[/Signbit]] -- Returns element-wise True where signbit is set (less than zero).
+
+~~~
+Syntax
+------
+
+matrix signbit <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    The input value(s).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+result : ndarray of bool
+    Output array, or reference to `out` if that was supplied.
+    This is a scalar if `x` is a scalar.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sin.md b/docs/Subcommand/Matrix/Sin.md
new file mode 100644
index 000000000..bdb92518a
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sin.md
@@ -0,0 +1,53 @@
+[[/Sin]] -- Trigonometric sine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix sin <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Angle, in radians (:math:`2 \pi` rad equals 360 degrees).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : array_like
+    The sine of each element of x.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+arcsin, sinh, cos
+
+Notes
+-----
+The sine is one of the fundamental functions of trigonometry (the
+mathematical study of triangles).  Consider a circle of radius 1
+centered on the origin.  A ray comes in from the :math:`+x` axis, makes
+an angle at the origin (measured counter-clockwise from that axis), and
+departs from the origin.  The :math:`y` coordinate of the outgoing
+ray's intersection with the unit circle is the sine of that angle.  It
+ranges from -1 for :math:`x=3\pi / 2` to +1 for :math:`\pi / 2.`  The
+function has zeroes where the angle is a multiple of :math:`\pi`.
+Sines of angles between :math:`\pi` and :math:`2\pi` are negative.
+The numerous properties of the sine and related functions are included
+in any standard trigonometry text.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sinc.md b/docs/Subcommand/Matrix/Sinc.md
new file mode 100644
index 000000000..bb99de051
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sinc.md
@@ -0,0 +1,82 @@
+[[/Sinc]]
+
+~~~
+Syntax
+------
+
+matrix sinc <matrix>
+
+
+    Return the normalized sinc function.
+
+    The sinc function is :math:`\sin(\pi x)/(\pi x)`.
+
+    .. note::
+
+        Note the normalization factor of ``pi`` used in the definition.
+        This is the most commonly used definition in signal processing.
+        Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function
+        :math:`\sin(x)/(x)` that is more common in mathematics.
+
+    Parameters
+    ----------
+    x : ndarray
+        Array (possibly multi-dimensional) of values for which to to
+        calculate ``sinc(x)``.
+
+    Returns
+    -------
+    out : ndarray
+        ``sinc(x)``, which has the same shape as the input.
+
+    Notes
+    -----
+    ``sinc(0)`` is the limit value 1.
+
+    The name sinc is short for "sine cardinal" or "sinus cardinalis".
+
+    The sinc function is used in various signal processing applications,
+    including in anti-aliasing, in the construction of a Lanczos resampling
+    filter, and in interpolation.
+
+    For bandlimited interpolation of discrete-time signals, the ideal
+    interpolation kernel is proportional to the sinc function.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
+           Resource. http://mathworld.wolfram.com/SincFunction.html
+    .. [2] Wikipedia, "Sinc function",
+           https://en.wikipedia.org/wiki/Sinc_function
+
+    Examples
+    --------
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.linspace(-4, 4, 41)
+    >>> np.sinc(x)
+     array([-3.89804309e-17,  -4.92362781e-02,  -8.40918587e-02, # may vary
+            -8.90384387e-02,  -5.84680802e-02,   3.89804309e-17,
+            6.68206631e-02,   1.16434881e-01,   1.26137788e-01,
+            8.50444803e-02,  -3.89804309e-17,  -1.03943254e-01,
+            -1.89206682e-01,  -2.16236208e-01,  -1.55914881e-01,
+            3.89804309e-17,   2.33872321e-01,   5.04551152e-01,
+            7.56826729e-01,   9.35489284e-01,   1.00000000e+00,
+            9.35489284e-01,   7.56826729e-01,   5.04551152e-01,
+            2.33872321e-01,   3.89804309e-17,  -1.55914881e-01,
+           -2.16236208e-01,  -1.89206682e-01,  -1.03943254e-01,
+           -3.89804309e-17,   8.50444803e-02,   1.26137788e-01,
+            1.16434881e-01,   6.68206631e-02,   3.89804309e-17,
+            -5.84680802e-02,  -8.90384387e-02,  -8.40918587e-02,
+            -4.92362781e-02,  -3.89804309e-17])
+
+    >>> plt.plot(x, np.sinc(x))
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.title("Sinc Function")
+    Text(0.5, 1.0, 'Sinc Function')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("X")
+    Text(0.5, 0, 'X')
+    >>> plt.show()
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sinh.md b/docs/Subcommand/Matrix/Sinh.md
new file mode 100644
index 000000000..534ac7c40
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sinh.md
@@ -0,0 +1,45 @@
+[[/Sinh]] -- Hyperbolic sine, element-wise.
+
+~~~
+Syntax
+------
+
+matrix sinh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding hyperbolic sine values.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+If `out` is provided, the function writes the result into it,
+and returns a reference to `out`.  (See Examples)
+
+References
+----------
+M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
+New York, NY: Dover, 1972, pg. 83.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Spacing.md b/docs/Subcommand/Matrix/Spacing.md
new file mode 100644
index 000000000..140e4e1b1
--- /dev/null
+++ b/docs/Subcommand/Matrix/Spacing.md
@@ -0,0 +1,44 @@
+[[/Spacing]] -- Return the distance between x and the nearest adjacent number.
+
+~~~
+Syntax
+------
+
+matrix spacing <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Values to find the spacing of.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    The spacing of values of `x`.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+It can be considered as a generalization of EPS:
+``spacing(np.float64(1)) == np.finfo(np.float64).eps``, and there
+should not be any representable number between ``x + spacing(x)`` and
+x for any finite x.
+
+Spacing of +- inf and NaN is NaN.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sqrt.md b/docs/Subcommand/Matrix/Sqrt.md
new file mode 100644
index 000000000..8a5f94fe7
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sqrt.md
@@ -0,0 +1,52 @@
+[[/Sqrt]] -- Return the non-negative square-root of an array, element-wise.
+
+~~~
+Syntax
+------
+
+matrix sqrt <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    The values whose square-roots are required.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    An array of the same shape as `x`, containing the positive
+    square-root of each element in `x`.  If any element in `x` is
+    complex, a complex array is returned (and the square-roots of
+    negative reals are calculated).  If all of the elements in `x`
+    are real, so is `y`, with negative elements returning ``nan``.
+    If `out` was provided, `y` is a reference to it.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+lib.scimath.sqrt
+    A version which returns complex numbers when given negative reals.
+
+Notes
+-----
+*sqrt* has--consistent with common convention--as its branch cut the
+real "interval" [`-inf`, 0), and is continuous from above on it.
+A branch cut is a curve in the complex plane across which a given
+complex function fails to be continuous.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Square.md b/docs/Subcommand/Matrix/Square.md
new file mode 100644
index 000000000..865a1cc25
--- /dev/null
+++ b/docs/Subcommand/Matrix/Square.md
@@ -0,0 +1,41 @@
+[[/Square]] -- Return the element-wise square of the input.
+
+~~~
+Syntax
+------
+
+matrix square <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input data.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    Element-wise `x*x`, of the same shape and dtype as `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+numpy.linalg.matrix_power
+sqrt
+power
+
+~~~
diff --git a/docs/Subcommand/Matrix/Subtract.md b/docs/Subcommand/Matrix/Subtract.md
new file mode 100644
index 000000000..40a8b9ea9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Subtract.md
@@ -0,0 +1,41 @@
+[[/Subtract]] -- Subtract arguments, element-wise.
+
+~~~
+Syntax
+------
+
+matrix subtract <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1, x2 : array_like
+    The arrays to be subtracted from each other.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The difference of `x1` and `x2`, element-wise.
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+Equivalent to ``x1 - x2`` in terms of array broadcasting.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Sum.md b/docs/Subcommand/Matrix/Sum.md
new file mode 100644
index 000000000..9cea391fa
--- /dev/null
+++ b/docs/Subcommand/Matrix/Sum.md
@@ -0,0 +1,128 @@
+[[/Sum]]
+
+~~~
+Syntax
+------
+
+matrix sum <matrix> initial=<float> [axis=<inttuple>] [dtype=<str>] [keepdims=<boolstr>] [where=<boollist>]
+
+
+    Sum of array elements over a given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Elements to sum.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a sum is performed.  The default,
+        axis=None, will sum all of the elements of the input array.  If
+        axis is negative it counts from the last to the first axis.
+
+        .. versionadded:: 1.7.0
+
+        If axis is a tuple of ints, a sum is performed on all of the axes
+        specified in the tuple instead of a single axis or all the axes as
+        before.
+    dtype : dtype, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed.  The dtype of `a` is used by default unless `a`
+        has an integer dtype of less precision than the default platform
+        integer.  In that case, if `a` is signed then the platform integer
+        is used while if `a` is unsigned then an unsigned integer of the
+        same precision as the platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output, but the type of the output
+        values will be cast if necessary.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `sum` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+    initial : scalar, optional
+        Starting value for the sum. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.15.0
+
+    where : array_like of bool, optional
+        Elements to include in the sum. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.17.0
+
+    Returns
+    -------
+    sum_along_axis : ndarray
+        An array with the same shape as `a`, with the specified
+        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar
+        is returned.  If an output array is specified, a reference to
+        `out` is returned.
+
+    See Also
+    --------
+    ndarray.sum : Equivalent method.
+
+    add.reduce : Equivalent functionality of `add`.
+
+    cumsum : Cumulative sum of array elements.
+
+    trapz : Integration of array values using the composite trapezoidal rule.
+
+    mean, average
+
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.
+
+    The sum of an empty array is the neutral element 0:
+
+    >>> np.sum([])
+    0.0
+
+    For floating point numbers the numerical precision of sum (and
+    ``np.add.reduce``) is in general limited by directly adding each number
+    individually to the result causing rounding errors in every step.
+    However, often numpy will use a  numerically better approach (partial
+    pairwise summation) leading to improved precision in many use-cases.
+    This improved precision is always provided when no ``axis`` is given.
+    When ``axis`` is given, it will depend on which axis is summed.
+    Technically, to provide the best speed possible, the improved precision
+    is only used when the summation is along the fast axis in memory.
+    Note that the exact precision may vary depending on other parameters.
+    In contrast to NumPy, Python's ``math.fsum`` function uses a slower but
+    more precise approach to summation.
+    Especially when summing a large number of lower precision floating point
+    numbers, such as ``float32``, numerical errors can become significant.
+    In such cases it can be advisable to use `dtype="float64"` to use a higher
+    precision for the output.
+
+    Examples
+    --------
+    >>> np.sum([0.5, 1.5])
+    2.0
+    >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
+    1
+    >>> np.sum([[0, 1], [0, 5]])
+    6
+    >>> np.sum([[0, 1], [0, 5]], axis=0)
+    array([0, 6])
+    >>> np.sum([[0, 1], [0, 5]], axis=1)
+    array([1, 5])
+    >>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)
+    array([1., 5.])
+
+    If the accumulator is too small, overflow occurs:
+
+    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
+    -128
+
+    You can also start the sum with a value other than zero:
+
+    >>> np.sum([10], initial=5)
+    15
+~~~
diff --git a/docs/Subcommand/Matrix/Tan.md b/docs/Subcommand/Matrix/Tan.md
new file mode 100644
index 000000000..ff239905d
--- /dev/null
+++ b/docs/Subcommand/Matrix/Tan.md
@@ -0,0 +1,45 @@
+[[/Tan]] -- Compute tangent element-wise.
+
+~~~
+Syntax
+------
+
+matrix tan <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding tangent values.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+If `out` is provided, the function writes the result into it,
+and returns a reference to `out`.  (See Examples)
+
+References
+----------
+M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
+New York, NY: Dover, 1972.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Tanh.md b/docs/Subcommand/Matrix/Tanh.md
new file mode 100644
index 000000000..b5587724e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Tanh.md
@@ -0,0 +1,49 @@
+[[/Tanh]] -- Compute hyperbolic tangent element-wise.
+
+~~~
+Syntax
+------
+
+matrix tanh <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input array.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray
+    The corresponding hyperbolic tangent values.
+    This is a scalar if `x` is a scalar.
+
+Notes
+-----
+If `out` is provided, the function writes the result into it,
+and returns a reference to `out`.  (See Examples)
+
+References
+----------
+.. [1] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions.
+       New York, NY: Dover, 1972, pg. 83.
+       http://www.math.sfu.ca/~cbm/aands/
+
+.. [2] Wikipedia, "Hyperbolic function",
+       https://en.wikipedia.org/wiki/Hyperbolic_function
+
+~~~
diff --git a/docs/Subcommand/Matrix/Trace.md b/docs/Subcommand/Matrix/Trace.md
new file mode 100644
index 000000000..014414d2b
--- /dev/null
+++ b/docs/Subcommand/Matrix/Trace.md
@@ -0,0 +1,63 @@
+[[/Trace]]
+
+~~~
+Syntax
+------
+
+matrix trace <matrix> offset=<int> axis=<int> dtype=<str>
+
+
+    Return the sum along diagonals of the array.
+
+    If `a` is 2-D, the sum along its diagonal with the given offset
+    is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
+
+    If `a` has more than two dimensions, then the axes specified by axis1 and
+    axis2 are used to determine the 2-D sub-arrays whose traces are returned.
+    The shape of the resulting array is the same as that of `a` with `axis1`
+    and `axis2` removed.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, from which the diagonals are taken.
+    offset : int, optional
+        Offset of the diagonal from the main diagonal. Can be both positive
+        and negative. Defaults to 0.
+    axis1, axis2 : int, optional
+        Axes to be used as the first and second axis of the 2-D sub-arrays
+        from which the diagonals should be taken. Defaults are the first two
+        axes of `a`.
+    dtype : dtype, optional
+        Determines the data-type of the returned array and of the accumulator
+        where the elements are summed. If dtype has the value None and `a` is
+        of integer type of precision less than the default integer
+        precision, then the default integer precision is used. Otherwise,
+        the precision is the same as that of `a`.
+    out : ndarray, optional
+        Array into which the output is placed. Its type is preserved and
+        it must be of the right shape to hold the output.
+
+    Returns
+    -------
+    sum_along_diagonals : ndarray
+        If `a` is 2-D, the sum along the diagonal is returned.  If `a` has
+        larger dimensions, then an array of sums along diagonals is returned.
+
+    See Also
+    --------
+    diag, diagonal, diagflat
+
+    Examples
+    --------
+    >>> np.trace(np.eye(3))
+    3.0
+    >>> a = np.arange(8).reshape((2,2,2))
+    >>> np.trace(a)
+    array([6, 8])
+
+    >>> a = np.arange(24).reshape((2,2,2,3))
+    >>> np.trace(a).shape
+    (2, 3)
+
+~~~
diff --git a/docs/Subcommand/Matrix/Transpose.md b/docs/Subcommand/Matrix/Transpose.md
new file mode 100644
index 000000000..d9b9c5ac9
--- /dev/null
+++ b/docs/Subcommand/Matrix/Transpose.md
@@ -0,0 +1,65 @@
+[[/Transpose]]
+
+~~~
+Syntax
+------
+
+matrix transpose <matrix>
+
+
+    Reverse or permute the axes of an array; returns the modified array.
+
+    For an array a with two axes, transpose(a) gives the matrix transpose.
+
+    Refer to `numpy.ndarray.transpose` for full documentation.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axes : tuple or list of ints, optional
+        If specified, it must be a tuple or list which contains a permutation of
+        [0,1,..,N-1] where N is the number of axes of a.  The i'th axis of the
+        returned array will correspond to the axis numbered ``axes[i]`` of the
+        input.  If not specified, defaults to ``range(a.ndim)[::-1]``, which
+        reverses the order of the axes.
+
+    Returns
+    -------
+    p : ndarray
+        `a` with its axes permuted.  A view is returned whenever
+        possible.
+
+    See Also
+    --------
+    ndarray.transpose : Equivalent method
+    moveaxis
+    argsort
+
+    Notes
+    -----
+    Use `transpose(a, argsort(axes))` to invert the transposition of tensors
+    when using the `axes` keyword argument.
+
+    Transposing a 1-D array returns an unchanged view of the original array.
+
+    Examples
+    --------
+    >>> x = np.arange(4).reshape((2,2))
+    >>> x
+    array([[0, 1],
+           [2, 3]])
+
+    >>> np.transpose(x)
+    array([[0, 2],
+           [1, 3]])
+
+    >>> x = np.ones((1, 2, 3))
+    >>> np.transpose(x, (1, 0, 2)).shape
+    (2, 1, 3)
+
+    >>> x = np.ones((2, 3, 4, 5))
+    >>> np.transpose(x).shape
+    (5, 4, 3, 2)
+
+~~~
diff --git a/docs/Subcommand/Matrix/Trapz.md b/docs/Subcommand/Matrix/Trapz.md
new file mode 100644
index 000000000..bf1ed6af2
--- /dev/null
+++ b/docs/Subcommand/Matrix/Trapz.md
@@ -0,0 +1,92 @@
+[[/Trapz]]
+
+~~~
+Syntax
+------
+
+matrix trapz <matrix> x=<matrix> dx=<float> axis=<int>
+
+
+    Integrate along the given axis using the composite trapezoidal rule.
+
+    If `x` is provided, the integration happens in sequence along its
+    elements - they are not sorted.
+    
+    Integrate `y` (`x`) along each 1d slice on the given axis, compute
+    :math:`\int y(x) dx`.
+    When `x` is specified, this integrates along the parametric curve,
+    computing :math:`\int_t y(t) dt =
+    \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
+    
+    Parameters
+    ----------
+    y : array_like
+        Input array to integrate.
+    x : array_like, optional
+        The sample points corresponding to the `y` values. If `x` is None,
+        the sample points are assumed to be evenly spaced `dx` apart. The
+        default is None.
+    dx : scalar, optional
+        The spacing between sample points when `x` is None. The default is 1.
+    axis : int, optional
+        The axis along which to integrate.
+
+    Returns
+    -------
+    trapz : float or ndarray
+        Definite integral of 'y' = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If 'y' is a 1-dimensional array,
+        then the result is a float. If 'n' is greater than 1, then the result
+        is an 'n-1' dimensional array.
+        
+    See Also
+    --------
+    sum, cumsum
+
+    Notes
+    -----
+    Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
+    will be taken from `y` array, by default x-axis distances between
+    points will be 1.0, alternatively they can be provided with `x` array
+    or with `dx` scalar.  Return value will be equal to combined area under
+    the red lines.
+
+
+    References
+    ----------
+    .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    .. [2] Illustration image:
+           https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
+
+    Examples
+    --------
+    >>> np.trapz([1,2,3])
+    4.0
+    >>> np.trapz([1,2,3], x=[4,6,8])
+    8.0
+    >>> np.trapz([1,2,3], dx=2)
+    8.0
+    
+    Using a decreasing `x` corresponds to integrating in reverse:
+    
+    >>> np.trapz([1,2,3], x=[8,6,4])  
+    -8.0
+    
+    More generally `x` is used to integrate along a parametric curve.
+    This finds the area of a circle, noting we repeat the sample which closes
+    the curve:
+    
+    >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+    >>> np.trapz(np.cos(theta), x=np.sin(theta))
+    3.141571941375841
+
+    >>> a = np.arange(6).reshape(2, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> np.trapz(a, axis=0)
+    array([1.5, 2.5, 3.5])
+    >>> np.trapz(a, axis=1)
+    array([2.,  8.])
+~~~
diff --git a/docs/Subcommand/Matrix/True_Divide.md b/docs/Subcommand/Matrix/True_Divide.md
new file mode 100644
index 000000000..1634ccfe3
--- /dev/null
+++ b/docs/Subcommand/Matrix/True_Divide.md
@@ -0,0 +1,44 @@
+[[/True_Divide]] -- Returns a true division of the inputs, element-wise.
+
+~~~
+Syntax
+------
+
+matrix true_divide <matrix> <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x1 : array_like
+    Dividend array.
+x2 : array_like
+    Divisor array.
+    If ``x1.shape != x2.shape``, they must be broadcastable to a common
+    shape (which becomes the shape of the output).
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+out : ndarray or scalar
+    This is a scalar if both `x1` and `x2` are scalars.
+
+Notes
+-----
+In Python, ``//`` is the floor division operator and ``/`` the
+true division operator.  The ``true_divide(x1, x2)`` function is
+equivalent to true division in Python.
+
+~~~
diff --git a/docs/Subcommand/Matrix/Trunc.md b/docs/Subcommand/Matrix/Trunc.md
new file mode 100644
index 000000000..1556aa9c8
--- /dev/null
+++ b/docs/Subcommand/Matrix/Trunc.md
@@ -0,0 +1,43 @@
+[[/Trunc]] -- Return the truncated value of the input, element-wise.
+
+~~~
+Syntax
+------
+
+matrix trunc <matrix> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+
+Parameters
+----------
+x : array_like
+    Input data.
+out : ndarray, None, or tuple of ndarray 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 array is returned. A tuple (possible only as a
+    keyword argument) must have length equal to the number of outputs.
+where : array_like, optional
+    This condition is broadcast over the input. At locations where the
+    condition is True, the `out` array will be set to the ufunc result.
+    Elsewhere, the `out` array will retain its original value.
+    Note that if an uninitialized `out` array is created via the default
+    ``out=None``, locations within it where the condition is False will
+    remain uninitialized.
+**kwargs
+    For other keyword-only arguments, see the
+    :ref:`ufunc docs <ufuncs.kwargs>`.
+
+Returns
+-------
+y : ndarray or scalar
+    The truncated value of each element in `x`.
+    This is a scalar if `x` is a scalar.
+
+See Also
+--------
+ceil, floor, rint, fix
+
+Notes
+-----
+.. versionadded:: 1.3.0
+
+~~~
diff --git a/docs/Subcommand/Matrix/Unwrap.md b/docs/Subcommand/Matrix/Unwrap.md
new file mode 100644
index 000000000..dd3ac239e
--- /dev/null
+++ b/docs/Subcommand/Matrix/Unwrap.md
@@ -0,0 +1,72 @@
+[[/Unwrap]]
+
+~~~
+Syntax
+------
+
+matrix unwrap <matrix> discont=<float> axis=<int> period=<float>
+
+
+    Unwrap by taking the complement of large deltas with respect to the period.
+
+    This unwraps a signal `p` by changing elements which have an absolute
+    difference from their predecessor of more than ``max(discont, period/2)``
+    to their `period`-complementary values.
+
+    For the default case where `period` is :math:`2\pi` and is `discont` is
+    :math:`\pi`, this unwraps a radian phase `p` such that adjacent differences
+    are never greater than :math:`\pi` by adding :math:`2k\pi` for some
+    integer :math:`k`.
+
+    Parameters
+    ----------
+    p : array_like
+        Input array.
+    discont : float, optional
+        Maximum discontinuity between values, default is ``period/2``. 
+        Values below ``period/2`` are treated as if they were ``period/2``.
+        To have an effect different from the default, `discont` should be
+        larger than ``period/2``.
+    axis : int, optional
+        Axis along which unwrap will operate, default is the last axis.
+    period: float, optional
+        Size of the range over which the input wraps. By default, it is
+        ``2 pi``.
+        
+        .. versionadded:: 1.21.0
+
+    Returns
+    -------
+    out : ndarray
+        Output array.
+
+    See Also
+    --------
+    rad2deg, deg2rad
+
+    Notes
+    -----
+    If the discontinuity in `p` is smaller than ``period/2``, 
+    but larger than `discont`, no unwrapping is done because taking 
+    the complement would only make the discontinuity larger.
+
+    Examples
+    --------
+    >>> phase = np.linspace(0, np.pi, num=5)
+    >>> phase[3:] += np.pi
+    >>> phase
+    array([ 0.        ,  0.78539816,  1.57079633,  5.49778714,  6.28318531]) # may vary
+    >>> np.unwrap(phase)
+    array([ 0.        ,  0.78539816,  1.57079633, -0.78539816,  0.        ]) # may vary
+    >>> np.unwrap([0, 1, 2, -1, 0], period=4)
+    array([0, 1, 2, 3, 4])
+    >>> np.unwrap([ 1, 2, 3, 4, 5, 6, 1, 2, 3], period=6)
+    array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> np.unwrap([2, 3, 4, 5, 2, 3, 4, 5], period=4)
+    array([2, 3, 4, 5, 6, 7, 8, 9])
+    >>> phase_deg = np.mod(np.linspace(0 ,720, 19), 360) - 180
+    >>> np.unwrap(phase_deg, period=360)
+    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
+            180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
+            540.])
+~~~
diff --git a/docs/Subcommand/Matrix/Vstack.md b/docs/Subcommand/Matrix/Vstack.md
new file mode 100644
index 000000000..6bd4d9d94
--- /dev/null
+++ b/docs/Subcommand/Matrix/Vstack.md
@@ -0,0 +1,60 @@
+[[/Vstack]]
+
+~~~
+Syntax
+------
+
+matrix vstack <matrix> <matrix>
+
+
+    Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+
+    See Also
+    --------
+    concatenate : Join a sequence of arrays along an existing axis.
+    stack : Join a sequence of arrays along a new axis.
+    block : Assemble an nd-array from nested lists of blocks.
+    hstack : Stack arrays in sequence horizontally (column wise).
+    dstack : Stack arrays in sequence depth wise (along third axis).
+    column_stack : Stack 1-D arrays as columns into a 2-D array.
+    vsplit : Split an array into multiple sub-arrays vertically (row-wise).
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([4, 5, 6])
+    >>> np.vstack((a,b))
+    array([[1, 2, 3],
+           [4, 5, 6]])
+
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[4], [5], [6]])
+    >>> np.vstack((a,b))
+    array([[1],
+           [2],
+           [3],
+           [4],
+           [5],
+           [6]])
+
+~~~
diff --git a/docs/Subcommand/Matrix/Zeros.md b/docs/Subcommand/Matrix/Zeros.md
new file mode 100644
index 000000000..f576c9187
--- /dev/null
+++ b/docs/Subcommand/Matrix/Zeros.md
@@ -0,0 +1,64 @@
+[[/Zeros]]
+
+~~~
+Syntax
+------
+
+matrix zeros <intlist> dtype=<str> order=<str>
+
+zeros(shape, dtype=float, order='C', *, like=None)
+
+    Return a new array of given shape and type, filled with zeros.
+
+    Parameters
+    ----------
+    shape : int or tuple of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., `numpy.int8`.  Default is
+        `numpy.float64`.
+    order : {'C', 'F'}, optional, default: 'C'
+        Whether to store multi-dimensional data in row-major
+        (C-style) or column-major (Fortran-style) order in
+        memory.
+    like : array_like
+        Reference object to allow the creation of arrays which are not
+        NumPy arrays. If an array-like passed in as ``like`` supports
+        the ``__array_function__`` protocol, the result will be defined
+        by it. In this case, it ensures the creation of an array object
+        compatible with that passed in via this argument.
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the given shape, dtype, and order.
+
+    See Also
+    --------
+    zeros_like : Return an array of zeros with shape and type of input.
+    empty : Return a new uninitialized array.
+    ones : Return a new array setting values to one.
+    full : Return a new array of given shape filled with value.
+
+    Examples
+    --------
+    >>> np.zeros(5)
+    array([ 0.,  0.,  0.,  0.,  0.])
+
+    >>> np.zeros((5,), dtype=int)
+    array([0, 0, 0, 0, 0])
+
+    >>> np.zeros((2, 1))
+    array([[ 0.],
+           [ 0.]])
+
+    >>> s = (2,2)
+    >>> np.zeros(s)
+    array([[ 0.,  0.],
+           [ 0.,  0.]])
+
+    >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
+    array([(0, 0), (0, 0)],
+~~~
diff --git a/gldcore/autotest/test_filter_constraint.glm b/gldcore/autotest/test_filter_constraint.glm
index e8359b509..93f9e045a 100644
--- a/gldcore/autotest/test_filter_constraint.glm
+++ b/gldcore/autotest/test_filter_constraint.glm
@@ -3,9 +3,9 @@
 clock {
     timezone PST+8PDT;
     starttime '2000-01-01 00:00:00 PST';
-    stoptime '2000-01-01 01:00:00 PST';
+    stoptime '2000-01-02 00:00:00 PST';
 }
-
+#set randomseed=1
 filter delay(z,5min,10s,resolution=8,minimum=-2.5,maximum=2.5) = 1/z;
 class from {
     randomvar value;
@@ -27,5 +27,28 @@ object multi_recorder {
     file output.csv;
     interval -1;
     property "from:value,to:value";
-#warning no assert in autotest
 }
+
+module assert;
+object assert {
+    parent to;
+    start '2000-01-01 00:00:00 PST';
+    stop '2000-01-01 00:05:09 PST';
+    target value;
+    relation ==;
+    value 0.0;
+    within 1e-4;
+}
+object assert {
+    parent to;
+    start '2000-01-01 00:05:10 PST';
+    stop '2000-01-01 00:10:09 PST';
+    target value;
+    relation ==;
+    value +0.443108;
+    within 1e-4;
+}
+
+#set debug=TRUE
+#set output_message_context=TRANSFORM
+#set suppress_repeat_messages=FALSE
diff --git a/gldcore/autotest/test_filter_delay.glm b/gldcore/autotest/test_filter_delay.glm
index 6f19d76db..acc7e7d09 100644
--- a/gldcore/autotest/test_filter_delay.glm
+++ b/gldcore/autotest/test_filter_delay.glm
@@ -3,9 +3,10 @@
 clock {
 	timezone PST+8PDT;
 	starttime '2000-01-01 00:00:00 PST';
-	stoptime '2000-01-01 01:00:00 PST';
+	stoptime '2000-01-02 00:00:00 PST';
 }
-filter delay(z,5min,10s) = 1/z;
+#set randomseed=1
+filter delay(z,5min) = 1/z;
 class from {
 	randomvar value;
 }
@@ -27,5 +28,29 @@ object multi_recorder {
 	property "from:value,to:value";
 }
 
+module assert;
+object assert {
+    parent to;
+    start '2000-01-01 00:00:00 PST';
+    stop '2000-01-01 00:04:59 PST';
+    target value;
+    relation ==;
+    value 0.0;
+    within 1e-4;
+}
+object assert {
+    parent to;
+    start '2000-01-01 00:05:00 PST';
+    stop '2000-01-01 00:09:59 PST';
+    target value;
+    relation ==;
+    value +0.443108;
+    within 1e-4;
+}
+
 #set glm_save_options=MINIMAL
 #set savefile=gridlabd.glm
+
+#set output_message_context=TRANSFORM
+#set suppress_repeat_messages=FALSE
+#set debug=TRUE
diff --git a/gldcore/autotest/test_loadshape_analog.glm b/gldcore/autotest/test_loadshape_analog.glm
index 8d1ae54c6..d05bf9b2b 100644
--- a/gldcore/autotest/test_loadshape_analog.glm
+++ b/gldcore/autotest/test_loadshape_analog.glm
@@ -1,7 +1,7 @@
 clock {
     timezone "PST+8PDT";
     starttime "2020-01-01 00:00:00 PST";
-    stoptime "2021-01-01 00:00:00 PST";
+    stoptime "2020-01-02 00:00:00 PST";
 }
 
 module tape;
@@ -82,6 +82,7 @@ object test_loadshape
         relation "==";
         value 1.0;
         within 0.00001;
+        hold 3600s;
     };
 }
 
@@ -95,6 +96,7 @@ object test_loadshape
         relation "==";
         value 0.5;
         within 0.00001;
+        hold 3600s;
     };
 }
 
@@ -108,6 +110,7 @@ object test_loadshape
         relation "==";
         value 0.04167;
         within 0.00001;
+        hold 3600s;
     };
 }
 
@@ -121,6 +124,7 @@ object test_loadshape
         relation "==";
         value 0.04167;
         within 0.00001;
+        hold 3600s;
     };
 }
 
diff --git a/gldcore/geodata/requirements.txt b/gldcore/geodata/requirements.txt
index 4a8056d0f..0701fb77e 100644
--- a/gldcore/geodata/requirements.txt
+++ b/gldcore/geodata/requirements.txt
@@ -1,6 +1,5 @@
 haversine==2.3.0
 Fiona==1.8.19
-numpy==1.19.4
 pandas==1.1.4
 scipy==1.6.0
 Shapely==1.7.1
diff --git a/gldcore/load.cpp b/gldcore/load.cpp
index 4b43d74ac..a2b64d9c4 100755
--- a/gldcore/load.cpp
+++ b/gldcore/load.cpp
@@ -2065,9 +2065,7 @@ int GldLoader::time_value_seconds(PARSER, TIMESTAMP *t)
 {
 	START;
 	if WHITE ACCEPT;
-	if (TERM(integer(HERE,t)) && LITERAL("s")) { *t *= TS_SECOND; ACCEPT; DONE;}
-	OR
-	if (TERM(integer(HERE,t)) && LITERAL("S")) { *t *= TS_SECOND; ACCEPT; DONE;}
+	if (TERM(integer(HERE,t)) && (WHITE,LITERAL("s"))) { *t *= TS_SECOND; ACCEPT; DONE;}
 	REJECT;
 }
 
@@ -2075,9 +2073,7 @@ int GldLoader::time_value_minutes(PARSER, TIMESTAMP *t)
 {
 	START;
 	if WHITE ACCEPT;
-	if (TERM(integer(HERE,t)) && LITERAL("m")) { *t *= 60*TS_SECOND; ACCEPT; DONE;}
-	OR
-	if (TERM(integer(HERE,t)) && LITERAL("M")) { *t *= 60*TS_SECOND; ACCEPT; DONE;}
+	if (TERM(integer(HERE,t)) && (WHITE,LITERAL("m"))) { *t *= 60*TS_SECOND; ACCEPT; DONE;}
 	REJECT;
 }
 
@@ -2085,9 +2081,7 @@ int GldLoader::time_value_hours(PARSER, TIMESTAMP *t)
 {
 	START;
 	if WHITE ACCEPT;
-	if (TERM(integer(HERE,t)) && LITERAL("h")) { *t *= 3600*TS_SECOND; ACCEPT; DONE;}
-	OR
-	if (TERM(integer(HERE,t)) && LITERAL("H")) { *t *= 3600*TS_SECOND; ACCEPT; DONE;}
+	if (TERM(integer(HERE,t)) && (WHITE,LITERAL("h"))) { *t *= 3600*TS_SECOND; ACCEPT; DONE;}
 	REJECT;
 }
 
@@ -2095,9 +2089,15 @@ int GldLoader::time_value_days(PARSER, TIMESTAMP *t)
 {
 	START;
 	if WHITE ACCEPT;
-	if (TERM(integer(HERE,t)) && LITERAL("d")) { *t *= 86400*TS_SECOND; ACCEPT; DONE;}
-	OR
-	if (TERM(integer(HERE,t)) && LITERAL("D")) { *t *= 86400*TS_SECOND; ACCEPT; DONE;}
+	if (TERM(integer(HERE,t)) && (WHITE,LITERAL("d"))) { *t *= 86400*TS_SECOND; ACCEPT; DONE;}
+	REJECT;
+}
+
+int GldLoader::time_value_weeks(PARSER, TIMESTAMP *t)
+{
+	START;
+	if WHITE ACCEPT;
+	if (TERM(integer(HERE,t)) && (WHITE,LITERAL("w"))) { *t *= 86400*TS_SECOND*7; ACCEPT; DONE;}
 	REJECT;
 }
 
@@ -2207,6 +2207,26 @@ int GldLoader::time_value(PARSER, TIMESTAMP *t)
 	DONE;
 }
 
+int GldLoader::delta_time(PARSER, TIMESTAMP *t)
+{
+	START;
+	if WHITE ACCEPT;
+	if (TERM(time_value_seconds(HERE,t)) && (WHITE,LITERAL(";"))) {ACCEPT; DONE; }
+	OR
+	if (TERM(time_value_minutes(HERE,t)) && (WHITE,LITERAL(";"))) {ACCEPT; DONE; }
+	OR
+	if (TERM(time_value_hours(HERE,t)) && (WHITE,LITERAL(";"))) {ACCEPT; DONE; }
+	OR
+	if (TERM(time_value_days(HERE,t)) && (WHITE,LITERAL(";"))) {ACCEPT; DONE; }
+	OR
+	if (TERM(time_value_weeks(HERE,t)) && (WHITE,LITERAL(";"))) {ACCEPT; DONE; }
+	else
+	{
+		REJECT;
+	}
+	DONE;
+}
+
 double GldLoader::load_latitude(char *buffer)
 {
 	char oname[128], pname[128];
@@ -2337,6 +2357,24 @@ int GldLoader::clock_properties(PARSER)
 		syntax_error(filename,linenum,"expected time zone specification");
 		REJECT;
 	}
+	OR if (LITERAL("runtime") && WHITE)
+	{
+		if ( TERM(delta_time(HERE,&tsval)) )
+		{
+			if ( global_starttime + tsval < TS_NEVER )
+			{	
+				global_stoptime = global_starttime + tsval;
+			}
+			else
+			{
+				global_stoptime = TS_NEVER;
+			}
+			ACCEPT;
+			goto Next;
+		}
+		syntax_error(filename,linenum,"expected delta time value");
+		REJECT;
+	}
 	OR if (WHITE,LITERAL("}")) {/* don't accept yet */ DONE;}
 	OR { syntax_error_here(HERE); REJECT; }
 	/* may be repeated */
@@ -4419,9 +4457,9 @@ int GldLoader::object_properties(PARSER, CLASS *oclass, OBJECT *obj)
 				&& MARK && TERM(filter_transform(HERE, &xstype, sources, sizeof(sources), transformname, sizeof(transformname), obj)))
 			{
 				// TODO handle more than one source
-				char sobj[64], sprop[64];
+				char sobj[64], sprop[64], input_var[64]="", state_var[64]="";
 
-				int n = sscanf(sources,"%[^:,]:%[^,]",sobj,sprop);
+				int n = sscanf(sources,"%[^:,]:%[^,],:%[^,],:%[^,]",sobj,sprop,input_var,state_var);
 				OBJECT *source_obj;
 				PROPERTY *source_prop;
 
@@ -4444,7 +4482,7 @@ int GldLoader::object_properties(PARSER, CLASS *oclass, OBJECT *obj)
 				}
 
 				/* add to external transform list */
-				if ( !transform_add_filter(obj,prop,transformname,source_obj,source_prop) )
+				if ( !transform_add_filter(obj,prop,transformname,source_obj,source_prop,input_var[0]?input_var:NULL,state_var[0]?state_var:NULL) )
 				{
 					syntax_error(filename,linenum,"filter transform could not be created - %s", errno?strerror(errno):"(no details)");
 					REJECT;
@@ -5750,7 +5788,7 @@ int GldLoader::filter_mononomial(PARSER,char *domain,double *a, unsigned int *n)
 
 int GldLoader::filter_polynomial(PARSER,char *domain,double *a,unsigned int *n)
 {
-	double x[64]; // maximum 64th order polynomial
+	double x[1024]; // maximum 1024th order polynomial
 	int m = -1; // order of polynomial
 	int first = 1;
 	START;
@@ -5768,9 +5806,15 @@ int GldLoader::filter_polynomial(PARSER,char *domain,double *a,unsigned int *n)
 				&& ((WHITE,LITERAL(domain)&&(power=1,true) && (LITERAL("^") && TERM(integer(HERE,&power))))||true) )
 			{
 				first = 0;
-				if ( power > 63 )
+				if ( power < 0 )
+				{
+					syntax_error(filename,linenum,"filter polynomial cannot use negative power %d",power);
+					REJECT;
+					break;
+				}
+				else if ( power > (int64)(sizeof(x)/sizeof(x[0])) )
 				{
-					syntax_error(filename,linenum,"filter polynomial order cannot be higher than 63");
+					syntax_error(filename,linenum,"filter polynomial order cannot be higher than %d",sizeof(x)/sizeof(x[0]));
 					REJECT;
 					break;
 				}
@@ -5847,12 +5891,12 @@ int GldLoader::filter_block(PARSER)
 		unsigned int64 flags = 0;
  		unsigned int64 resolution = 0;
  		double minimum = 0.0;
- 		double maximum = 1.0;
+ 		double maximum = 0.0;
 		if ( (WHITE,LITERAL("(")) && (WHITE,TERM(name(HERE,domain,sizeof(domain)))) )
 		{
 			if ( strcmp(domain,"z")==0 )
 			{
-				double a[64],b[64]; // polynomial coefficients
+				double a[256],b[256]; // polynomial coefficients
 				unsigned int n,m; // polynomial orders
 
 				// parse z-domain filter parameters
diff --git a/gldcore/load.h b/gldcore/load.h
index b2bf88344..35d7ebe44 100644
--- a/gldcore/load.h
+++ b/gldcore/load.h
@@ -277,10 +277,13 @@ class GldLoader
 	int time_value_minutes(PARSER, TIMESTAMP *t);
 	int time_value_hours(PARSER, TIMESTAMP *t);
 	int time_value_days(PARSER, TIMESTAMP *t);
+	int time_value_weeks(PARSER, TIMESTAMP *t);
+	int time_value_years(PARSER, TIMESTAMP *t);
 	int time_value_datetime(PARSER, TIMESTAMP *t);
 	int time_value_datetimezone(PARSER, TIMESTAMP *t);
 	int time_value_isodatetime(PARSER, TIMESTAMP *t);
 	int time_value(PARSER, TIMESTAMP *t);
+	int delta_time(PARSER, TIMESTAMP *t);
 	double load_latitude(char *buffer);
 	double load_longitude(char *buffer);
 	int clock_properties(PARSER);
diff --git a/gldcore/rt/gridlabd.h b/gldcore/rt/gridlabd.h
index bc48e49b3..c71777df5 100644
--- a/gldcore/rt/gridlabd.h
+++ b/gldcore/rt/gridlabd.h
@@ -1146,11 +1146,13 @@ typedef struct s_transferfunction {
 
 /* transform data structure */
 typedef struct s_transform {
+	const char *definition; ///< original definition of the transformation (needed for save operations)
 	double *source;	///< source vector of the function input
 	TRANSFORMSOURCE source_type; ///< data type of source
 	struct s_object_list *target_obj; ///< object of the target
 	struct s_property_map *target_prop; ///< property of the target
 	TRANSFORMFUNCTIONTYPE function_type; ///< function type (linear, external, etc.)
+	TIMESTAMP t_last;
 	union {
 		struct { // used only by linear transforms
 			void *source_addr; ///< pointer to the source
diff --git a/gldcore/scripts/Makefile.mk b/gldcore/scripts/Makefile.mk
index 7a65d154d..04a64b483 100644
--- a/gldcore/scripts/Makefile.mk
+++ b/gldcore/scripts/Makefile.mk
@@ -1,3 +1,4 @@
+bin_SCRIPTS += gldcore/scripts/gridlabd-assert
 bin_SCRIPTS += gldcore/scripts/gridlabd-aws
 bin_SCRIPTS += gldcore/scripts/gridlabd-check
 bin_SCRIPTS += gldcore/scripts/gridlabd-compare
@@ -10,6 +11,7 @@ bin_SCRIPTS += gldcore/scripts/gridlabd-json-get
 bin_SCRIPTS += gldcore/scripts/gridlabd-job
 bin_SCRIPTS += gldcore/scripts/gridlabd-library
 bin_SCRIPTS += gldcore/scripts/gridlabd-manual
+bin_SCRIPTS += gldcore/scripts/gridlabd-matrix
 bin_SCRIPTS += gldcore/scripts/gridlabd-openfido
 bin_SCRIPTS += gldcore/scripts/gridlabd-pandas
 bin_SCRIPTS += gldcore/scripts/gridlabd-plot
diff --git a/gldcore/scripts/autotest/test_matrix.glm b/gldcore/scripts/autotest/test_matrix.glm
new file mode 100644
index 000000000..d95170553
--- /dev/null
+++ b/gldcore/scripts/autotest/test_matrix.glm
@@ -0,0 +1,367 @@
+// check numpy version (must be exactly the same to pass these tests)
+#assert $(gridlabd matrix version) == '1.21.4'
+
+// matrix operations test
+#assert $(gridlabd matrix -f copy "1,2;3,4") == '1,2;3,4'
+#assert $(echo "1,2;3,4" | gridlabd matrix -f -t transpose) == '1,2;3,4'
+#assert $(gridlabd matrix -f -r=a,b -c=c,d -t copy "1,2;3,4") == ',c,d;a,1,3;b,2,4'
+#assert $(gridlabd matrix -f -r=a,b -c=c,d -n=test -t copy "1,2;3,4") == 'test,c,d;a,1,3;b,2,4'
+#assert $(gridlabd matrix -f hstack "1,2;3,4" "1,2;3,4") == '1,2,1,2;3,4,3,4'
+#assert $(gridlabd matrix -f vstack "1,2;3,4" "1,2;3,4") == '1,2;3,4;1,2;3,4'
+
+// absolute
+#assert $(gridlabd matrix -f absolute "1,2;-3,4") == '1,2;3,4'
+
+// add
+#assert $(gridlabd matrix -f add "1,2;3,4;5,6" "1,2;3,4;5,6") == '2,4;6,8;10,12'
+
+// angle
+#assert $(gridlabd matrix -f angle "1+1j,1+2j;1-3j,1-4j") == '0.78539816,1.1071487;-1.2490458,-1.3258177'
+#assert $(gridlabd matrix -f angle "1+1j,1+2j;1-3j,1-4j" deg=0) == '0.78539816,1.1071487;-1.2490458,-1.3258177'
+#assert $(gridlabd matrix -f angle "1+1j,1+2j;1-3j,1-4j" deg=1) == '45,63.434949;-71.565051,-75.963757'
+
+// arc{cos,sin,tan}[h]
+#assert $(gridlabd matrix -f arccos "0.1,0.2;0.3,0.4") == '1.4706289,1.3694384;1.2661037,1.1592795'
+#assert $(gridlabd matrix -f arccosh "1,2;3,4") == '0,1.3169579;1.7627472,2.0634371'
+#assert $(gridlabd matrix -f arcsin "0.1,0.2;0.3,0.4") == '0.10016742,0.20135792;0.30469265,0.41151685'
+#assert $(gridlabd matrix -f arcsinh "1,2;3,4") == '0.88137359,1.4436355;1.8184465,2.0947125'
+#assert $(gridlabd matrix -f arctan "0.1,0.2;0.3,0.4") == '0.099668652,0.19739556;0.29145679,0.38050638'
+#assert $(gridlabd matrix -f arcsinh "1,2;3,4") == '0.88137359,1.4436355;1.8184465,2.0947125'
+#assert $(gridlabd matrix -f arctanh "0.1,0.2;0.3,0.4") == '0.10033535,0.20273255;0.3095196,0.42364893'
+#assert $(gridlabd matrix -f arctan2 "1,2;-3,-4" "1,-2;-3,4") == '0.78539816,2.3561945;-2.3561945,-0.78539816'
+
+// around
+#assert $(gridlabd matrix -f around "1.234,2.3452;-3.456,-4.567") == '1,2;-3,-5'
+#assert $(gridlabd matrix -f around "1.234,2.3452;-3.456,-4.567" decimals=2) == '1.23,2.35;-3.46,-4.57'
+
+// cbrt
+#assert $(gridlabd matrix -f cbrt "1,2;3,4;5,6;") == '1,1.259921;1.4422496,1.5874011;1.7099759,1.8171206'
+
+// ceil
+#assert $(gridlabd matrix -f ceil "1.234,2.3452;-3.456,-4.567") == '2,3;-3,-4'
+
+// clip
+#assert $(gridlabd matrix -f clip "1.234,2.3452;-3.456,-4.567" a_min=-4 a_max=2) == '1.234,2;-3.456,-4'
+
+// conj,conjugate
+#assert $(gridlabd matrix -f conj "1+1j,1+2j;1-3j,1-4j") == '1-1j,1-2j;1+3j,1+4j'
+#assert $(gridlabd matrix -f conjugate "1-1j,1-2j;1+3j,1+4j") == '1+1j,1+2j;1-3j,1-4j'
+
+// convolve
+#assert $(gridlabd matrix -f convolve "1,2;3,4" "2,3;4,5") == '2,7,16,30,34,31,20'
+#assert $(gridlabd matrix -f convolve "1,2;3,4" "2,3;4,5" mode='full') == '2,7,16,30,34,31,20'
+#assert $(gridlabd matrix -f convolve "1,2;3,4" "2,3;4,5" mode='same') == '7,16,30,34'
+#assert $(gridlabd matrix -f convolve "1,2;3,4" "2,3;4,5" mode='valid') == '30'
+
+// copysign
+#assert $(gridlabd matrix -f copysign "1,2;3,4;5,6;" "-1,1;1,-1;-1,-1") == '-1,2;3,-4;-5,-6'
+
+// cos[h]
+#assert $(gridlabd matrix -f cos "1,2;3,4;5,6;") == '0.54030231,-0.41614684;-0.9899925,-0.65364362;0.28366219,0.96017029'
+#assert $(gridlabd matrix -f cosh "1,2;3,4;5,6;") == '1.5430806,3.7621957;10.067662,27.308233;74.209949,201.71564'
+
+// cross
+#assert $(gridlabd matrix -f cross "1,2;3,4;5,6;" "2,1;4,3;6,5") == '-3,-7,-11'
+#assert $(gridlabd matrix -f cross "1,2;3,4;5,6;" "2,1;4,3;6,5" axis=0) == '-2,2;4,-4;-2,2'
+#assert $(gridlabd matrix -f cross "1,2;3,4;5,6;" "2,1;4,3;6,5" axisa=0 axisb=0) == '-2,4,-2;2,-4,2'
+
+// cumprod
+#assert $(gridlabd matrix -f cumprod "1,2;3,4;5,6") == '1,2,6,24,120,720'
+#assert $(gridlabd matrix -f cumprod "1,2;3,4;5,6" axis=0) == '1,2;3,8;15,48'
+#assert $(gridlabd matrix -f cumprod "1,2;3,4;5,6" axis=1) == '1,2;3,12;5,30'
+
+// cumsum
+#assert $(gridlabd matrix -f cumsum "1,2;3,4;5,6") == '1,3,6,10,15,21'
+#assert $(gridlabd matrix -f cumsum "1,2;3,4;5,6" axis=0) == '1,2;4,6;9,12'
+#assert $(gridlabd matrix -f cumsum "1,2;3,4;5,6" axis=1) == '1,3;3,7;5,11'
+
+// deg2rad 
+#assert $(gridlabd matrix -f deg2rad '1,2;3,4;5,6') == '0.017453293,0.034906585;0.052359878,0.06981317;0.087266463,0.10471976'
+
+// degrees
+#assert $(gridlabd matrix -f degrees '1,2;3,4;5,6') == '57.29578,114.59156;171.88734,229.18312;286.4789,343.77468'
+
+// diff
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6') == '1;1;1'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' n=2) == ';;'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=0) == '2,2;2,2'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=1) == '1;1;1'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=0 prepend='1,2') == '0,0;2,2;2,2'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=0 append='1,2') == '2,2;2,2;-4,-4'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=0 prepend='1,2' append='1,2') == '0,0;2,2;2,2;-4,-4'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=1 prepend='1;2;3') == '0,1;1,1;2,1'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=1 append='1;2;3') == '1,-1;1,-2;1,-3'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=1 prepend='1;2;3' append='1;2;3') == '0,1,-1;1,1,-2;2,1,-3'
+#assert $(gridlabd matrix -f diff '1,2;3,4;5,6' axis=1 prepend='1;2;3' append='1;2;3' n=3) == '-3;-3;-3'
+
+// divide
+#assert $(gridlabd matrix -f divide '1,2;3,4;5,6' '1,2;3,4;5,6') == '1,1;1,1;1,1'
+
+// divmod
+#assert $(gridlabd matrix -f divmod '1,2;3,4;5,6' '1,2;3,4;5,6' | tr '\n' ';') == '1,1;1,1;1,1;0,0;0,0;0,0;'
+#assert $(gridlabd matrix -f divmod '1,2;3,4;5,6' '1,2;3,4;5,5' | tr '\n' ';') == '1,1;1,1;1,1;0,0;0,0;0,1;'
+
+// dot
+#assert $(gridlabd matrix -f dot '1,2;3,4;5,6' '1,2,3;4,5,6') == '9,12,15;19,26,33;29,40,51'
+#assert $(gridlabd matrix -f dot '1,2,3;4,5,6' '1,2;3,4;5,6') == '22,28;49,64'
+
+// exp
+#assert $(gridlabd matrix -f exp '1,2,3;4,5,6') == '2.7182818,7.3890561,20.085537;54.59815,148.41316,403.42879'
+
+// exp2
+#assert $(gridlabd matrix -f exp2 '1,2,3;4,5,6') == '2,4,8;16,32,64'
+
+// expm1
+#assert $(gridlabd matrix -f expm1 '1,2,3;4,5,6') == '1.7182818,6.3890561,19.085537;53.59815,147.41316,402.42879'
+
+// eye
+#assert $(gridlabd matrix -f eye 5) == '1,0,0,0,0;0,1,0,0,0;0,0,1,0,0;0,0,0,1,0;0,0,0,0,1'
+#assert $(gridlabd matrix -f eye 5 k=-1) == '0,0,0,0,0;1,0,0,0,0;0,1,0,0,0;0,0,1,0,0;0,0,0,1,0'
+
+// fabs
+#assert $(gridlabd matrix -f fabs '1,2;-3,4;-5,-6') == '1,2;3,4;5,6'
+
+// fix
+#assert $(gridlabd matrix -f fix '1.1,2.2;-3.3,4.4;-5.5,-6.6') == '1,2;-3,4;-5,-6'
+
+// float_power
+#assert $(gridlabd matrix -f float_power '1.1,2.2;3.3,4.4;5.5,6.6' '1.1,2.2;-3.3,4.4;-5.5,-6.6') == '1.1105342,5.6666958;0.019449301,677.94081;8.4723757e-05,3.8995099e-06'
+
+// floor
+#assert $(gridlabd matrix -f floor '1.1,2.2;-3.3,4.4;-5.5,-6.6') == '1,2;-4,4;-6,-7'
+
+// floor_divide
+#assert $(gridlabd matrix -f floor_divide '1.1,2.2;3.3,4.4;5.5,6.6' '1.1,2.2;-3.3,4.4;-5.5,-6.6') == '1,1;-1,1;-1,-1'
+
+// fmax
+#assert $(gridlabd matrix -f fmax '1.1,2.2;-3.3,4.4;-5.5,-6.6' '2,2;3,3;4,4') == '2,2.2;3,4.4;4,4'
+
+// fmin
+#assert $(gridlabd matrix -f fmin '1.1,2.2;-3.3,4.4;-5.5,-6.6' '2,2;3,3;4,4') == '1.1,2;-3.3,3;-5.5,-6.6'
+
+// fmod
+#assert $(gridlabd matrix -f fmod '1.1,2.2;-3.3,4.4;-5.5,-6.6' '2,2;3,3;4,4' | tr '\n' ';') == '1.1,0.2;-0.3,1.4;-1.5,-2.6;'
+
+// gcd
+#assert $(gridlabd matrix -f gcd '1,2;3,4;5,6' '2,2;2,2;2,2') == '1,2;1,2;1,2'
+
+// gradient
+#assert $(gridlabd matrix -f gradient '1,2,4,7,11,16;2,3,6,7,12,15' axis=0) == '1,1,2,0,1,-1;1,1,2,0,1,-1'
+#assert $(gridlabd matrix -f gradient '1,2,4,7,11,16;2,3,6,7,12,15' axis=1) == '1,1.5,2.5,3.5,4.5,5;1,2,2,3,4,3'
+#assert $(gridlabd matrix -f gradient '1,2,4,7,11,16;2,3,6,7,12,15' axis=1 edge_order=2) == '0.5,1.5,2.5,3.5,4.5,5.5;0,2,2,3,4,2'
+
+// heaviside
+#assert $(gridlabd matrix -f heaviside '-1,0,1;0,-1,1;0,1,-1' 0.5) == '0,0.5,1;0.5,0,1;0.5,1,0'
+#assert $(gridlabd matrix -f heaviside '-1,0,1;0,-1,1;0,1,-1' '-1,0,1;0,-1,1;0,1,-1') == '0,0,1;0,0,1;0,1,0'
+
+// hypot
+#assert $(gridlabd matrix -f hypot '3,4;4,3' '4,3;3,4') == '5,5;5,5'
+
+// i0
+#assert $(gridlabd matrix -f i0 '0,1;2,3') == '1,1.2660659,2.2795853,4.8807926'
+#assert $(gridlabd matrix -f i0 '0.0,1.0;2.0,3.0') == '1,1.2660659,2.2795853,4.8807926'
+
+// identity
+#assert $(gridlabd matrix -f identity 3) == '1,0,0;0,1,0;0,0,1'
+
+// imag
+#assert $(gridlabd matrix -f imag '1+0j,2+1j;3+2j,3+3j') == '0,1;2,3'
+
+// interp
+#assert $(gridlabd matrix -f interp 2.5 1,2,3 3,2,0) == '1'
+#assert $(gridlabd matrix -f interp 0,1,1.5,2.72,3.14 1,2,3 3,2,0) == '3,3,2.5,0.56,0'
+#assert $(gridlabd matrix -f interp 3.5 1,2,3 3,2,0 right=inf) == 'inf'
+#assert $(gridlabd matrix -f interp -3.5 1,2,3 3,2,0 left=-inf) == '-inf'
+#assert $(gridlabd matrix -f interp 3.5 1,2,3 3,2,0 left=nan right=nan) == 'nan'
+
+// lcm
+#assert $(gridlabd matrix -f lcm 12,15 20,30) == '60,30'
+
+// ldexp
+#assert $(gridlabd matrix -f ldexp 5 1,2,3,4) == '10,20,40,80'
+
+// log
+#assert $(gridlabd matrix -f log 1,2,3,4) == '0,0.69314718,1.0986123,1.3862944'
+
+// log10
+#assert $(gridlabd matrix -f log10 1,2,3,4) == '0,0.30103,0.47712125,0.60205999'
+
+// log1p
+#assert $(gridlabd matrix -f log1p 1,2,3,4) == '0.69314718,1.0986123,1.3862944,1.6094379'
+
+// log2
+#assert $(gridlabd matrix -f log2 1,2,3,4) == '0,1,1.5849625,2'
+
+// logaddexp
+#assert $(gridlabd matrix -f logaddexp -115.12925 -114.21296) == '-113.87649'
+
+// logaddexp2
+#assert $(gridlabd matrix -f logaddexp2 -115.12925 -114.21296) == '-113.59955'
+
+// maximum
+#assert $(gridlabd matrix -f maximum 2,3,4 1,5,2) == '2,5,4'
+#assert $(gridlabd matrix -f maximum 2,3,4 5) == '5,5,5'
+
+// minimum
+#assert $(gridlabd matrix -f minimum 2,3,4 1,5,2) == '1,3,2'
+#assert $(gridlabd matrix -f minimum 2,3,4 5) == '2,3,4'
+
+// mod
+#assert $(gridlabd matrix -f mod 1,2,3 2,2,2) == '1,0,1'
+
+// modf
+#assert $(gridlabd matrix -f modf 1.2,3.4 | tr '\n' ';') == '0.2,0.4;1,3;'
+
+// multiply
+#assert $(gridlabd matrix -f multiply '1,2;3,4' 2) == '2,4;6,8'
+#assert $(gridlabd matrix -f multiply '1,2;3,4' '1,2;3,4') == '1,4;9,16'
+
+// nancumprod
+#assert $(gridlabd matrix -f nancumprod "1,;3,4;5,6") == '1,1,3,12,60,360'
+#assert $(gridlabd matrix -f nancumprod "1,;3,4;5,6" axis=0) == '1,1;3,4;15,24'
+#assert $(gridlabd matrix -f nancumprod "1,;3,4;5,6" axis=1) == '1,1;3,12;5,30'
+
+// nancumsum
+#assert $(gridlabd matrix -f nancumsum "1,;3,4;5,6") == '1,1,4,8,13,19'
+#assert $(gridlabd matrix -f nancumsum "1,;3,4;5,6" axis=0) == '1,0;4,4;9,10'
+#assert $(gridlabd matrix -f nancumsum "1,;3,4;5,6" axis=1) == '1,1;3,7;5,11'
+
+// nanprod
+#assert $(gridlabd matrix -f nanprod "1,;3,4;5,6") == '360'
+#assert $(gridlabd matrix -f nanprod "1,;3,4;5,6" axis=0) == '15,24'
+#assert $(gridlabd matrix -f nanprod "1,;3,4;5,6" axis=1) == '1;12;30'
+#assert $(gridlabd matrix -f nanprod "1,;3,4;5,6" axis=0,1) == '360'
+#assert $(gridlabd matrix -f nanprod "1,;3,4;5,6" axis=1,0) == '360'
+
+// nansum
+#assert $(gridlabd matrix -f nansum "1,;3,4;5,6") == '19'
+#assert $(gridlabd matrix -f nansum "1,;3,4;5,6" axis=0) == '9,10'
+#assert $(gridlabd matrix -f nansum "1,;3,4;5,6" axis=1) == '1;7;11'
+#assert $(gridlabd matrix -f nansum "1,;3,4;5,6" axis=0,1) == '19'
+#assert $(gridlabd matrix -f nansum "1,;3,4;5,6" axis=1,0) == '19'
+
+// negative
+#assert $(gridlabd matrix -f negative "1,;3,4;5,6") == '-1,nan;-3,-4;-5,-6'
+
+// nextafter
+#assert $(gridlabd matrix -f nextafter "1e-500,;3,4;5,6" 1) == '4.9406565e-324,nan;3,4;5,6'
+#assert $(gridlabd matrix -f nextafter "1e-500,;3,4;5,6" 0) == '0,nan;3,4;5,6'
+
+// ones
+#assert $(gridlabd matrix -f ones 2,3) == '1,1,1;1,1,1'
+
+// positive
+#assert $(gridlabd matrix -f positive '-1,nan;-3,-4;-5,-6') == '-1,nan;-3,-4;-5,-6'
+
+// power
+#assert $(gridlabd matrix -f power "-1,nan;inf,-inf;-5,-6;" 2) == '1,nan;inf,inf;25,36'
+#assert $(gridlabd matrix -f power "-1,nan;inf,-inf;-5,-6;" "-1,nan;inf,-inf;-5,-6;") == '-1,nan;inf,0;-0.00032,2.1433471e-05'
+
+// prod
+#assert $(gridlabd matrix -f prod "1,2;3,4;5,6") == '720'
+#assert $(gridlabd matrix -f prod "1,2;3,4;5,6" axis=0) == '15,48'
+#assert $(gridlabd matrix -f prod "1,2;3,4;5,6" axis=1) == '2;12;30'
+#assert $(gridlabd matrix -f prod "1,2;3,4;5,6" axis=0,1) == '720'
+#assert $(gridlabd matrix -f prod "1,2;3,4;5,6" axis=1,0) == '720'
+
+// rad2deg
+#assert $(gridlabd matrix -f rad2deg "1,2;3,4;5,6") == '57.29578,114.59156;171.88734,229.18312;286.4789,343.77468'
+
+// radians
+#assert $(gridlabd matrix -f radians "1,2;3,4;5,6") == '0.017453293,0.034906585;0.052359878,0.06981317;0.087266463,0.10471976'
+
+// arange
+#assert $(gridlabd matrix -f arange 1 stop=4) == '1,2,3'
+#assert $(gridlabd matrix -f arange 1 stop=4 step=2) == '1,3'
+#assert -z $(gridlabd matrix -f arange 1 stop=4 step=-2)
+#assert $(gridlabd matrix -f arange 4 stop=1 step=-2) == '4,2'
+#assert $(gridlabd matrix -f arange 4 stop=1 step=-1.2) == '4,2.8,1.6'
+
+// real
+#assert $(gridlabd matrix -f real '1+0j,2+1j;3+2j,4+3j') == '1,2;3,4'
+
+// real_if_close
+#assert $(gridlabd matrix -f real_if_close '1+0j,2+1j;3+2j,4+3j' tol=1e20) == '1,2;3,4'
+
+// reciprocal
+#assert $(gridlabd matrix -f reciprocal '1,2,3' dtype=int) == '1,0,0'
+#assert $(gridlabd matrix -f reciprocal '1,2,3' dtype=float) == '1,0.5,0.33333333'
+#assert $(gridlabd matrix -f reciprocal '1,2,3' dtype=complex) == '1-0j,0.5-0j,0.33333333-0j'
+
+// remainder
+#assert $(gridlabd matrix -f remainder "1,2;3,4;5,6" 3) == '1,2;0,1;2,0'
+#assert $(gridlabd matrix -f remainder "1,2;3,4;5,6" "2,3;2,3;2,3") == '1,2;1,1;1,0'
+
+// rint
+#assert $(gridlabd matrix -f rint "1.2,3.4,5.6") == '1,3,6'
+
+// round_
+#assert $(gridlabd matrix -f round_ "1.2,3.4,5.6") == '1,3,6'
+
+// sign
+#assert $(gridlabd matrix -f sign "1,-2;3,-4;5,-6;") == '1,-1;1,-1;1,-1'
+
+// signbit
+#assert $(gridlabd matrix -f signbit "1,-2;3,-4;5,-6;") == '0,1;0,1;0,1'
+
+// sin
+#assert $(gridlabd matrix -f sin "1,-2;3,-4;5,-6;") == '0.84147098,-0.90929743;0.14112001,0.7568025;-0.95892427,0.2794155'
+
+// sinc
+#assert $(gridlabd matrix -f sinc "1,-2;3,-4;5,-6;") == '3.8981718e-17,-3.8981718e-17;3.8981718e-17,-3.8981718e-17;3.8981718e-17,-3.8981718e-17'
+
+// sinh
+#assert $(gridlabd matrix -f sinh "1,-2;3,-4;5,-6;") == '1.1752012,-3.6268604;10.017875,-27.289917;74.203211,-201.71316'
+
+// spacing
+#assert $(gridlabd matrix -f spacing "1,-2;3,-4;5,-6;") == '2.220446e-16,-4.4408921e-16;4.4408921e-16,-8.8817842e-16;8.8817842e-16,-8.8817842e-16'
+
+// sqrt
+#assert $(gridlabd matrix -f sqrt "1,2;3,4;5,6;") == '1,1.4142136;1.7320508,2;2.236068,2.4494897'
+
+// square
+#assert $(gridlabd matrix -f square "1,2;3,4;5,6;") == '1,4;9,16;25,36'
+
+// subtract
+#assert $(gridlabd matrix -f subtract "1,2;3,4;5,6;" 2) == '-1,0;1,2;3,4'
+#assert $(gridlabd matrix -f subtract "1,2;3,4;5,6;" "-1,0;1,2;3,4;") == '2,2;2,2;2,2'
+
+// sum
+#assert $(gridlabd matrix -f sum "1,2;3,4;5,6;") == '21'
+#assert $(gridlabd matrix -f sum "1,2;3,4;5,6;" axis=0) == '9,12'
+#assert $(gridlabd matrix -f sum "1,2;3,4;5,6;" axis=1) == '3;7;11'
+#assert $(gridlabd matrix -f sum "1,2;3,4;5,6;" axis=0,1) == '21'
+#assert $(gridlabd matrix -f sum "1,2;3,4;5,6;" axis=1,0) == '21'
+
+// tan
+#assert $(gridlabd matrix -f tan "1,2;3,4;5,6;") == '1.5574077,-2.1850399;-0.14254654,1.1578213;-3.380515,-0.29100619'
+
+// tanh
+#assert $(gridlabd matrix -f tanh "1,2;3,4;5,6;") == '0.76159416,0.96402758;0.99505475,0.9993293;0.9999092,0.99998771'
+
+// trace
+#assert $(gridlabd matrix -f trace "1,2;3,4;5,6;") == '5'
+
+// transpose
+#assert $(gridlabd matrix -f transpose "1,2;3,4;5,6;") == '1,3,5;2,4,6'
+#assert $(gridlabd matrix -f -t transpose "1,2;3,4;5,6;") == '1,2;3,4;5,6'
+
+// trapz
+#assert $(gridlabd matrix -f trapz "1,2,3") == '4'
+#assert $(gridlabd matrix -f trapz "1,2,3" x="4,6,8") == '8'
+#assert $(gridlabd matrix -f trapz "1,2,3" dx=3) == '12'
+
+// true_divide
+#assert $(gridlabd matrix -f true_divide "1,2,3,4,5" 4) == '0.25,0.5,0.75,1,1.25'
+#assert $(gridlabd matrix -f true_divide "1,2,3,4,5" "5,4,3,2,1") == '0.2,0.5,1,2,5'
+
+// trunc
+#assert $(gridlabd matrix -f trunc "0.25;0.5;0.75;1;1.25;") == '0;0;0;1;1'
+
+// unwrap
+#assert $(gridlabd matrix -f unwrap "1,2,3,4,5" period=1.5) == '1,0.5,0,-0.5,-1'
+#assert $(gridlabd matrix -f unwrap "1,2,3,4,5" period=1.5 discont=3) == '1,2,3,4,5'
+
+// zeros
+#assert $(gridlabd matrix -f zeros 2,3) == '0,0,0;0,0,0'
+
diff --git a/gldcore/scripts/autotest/test_matrix_linalg.glm b/gldcore/scripts/autotest/test_matrix_linalg.glm
new file mode 100644
index 000000000..80432c36f
--- /dev/null
+++ b/gldcore/scripts/autotest/test_matrix_linalg.glm
@@ -0,0 +1,70 @@
+// check numpy version (must be exactly the same to pass these tests)
+#assert $(gridlabd matrix version) == '1.21.4'
+
+// linalg.cholesky <matrix>
+#assert $(gridlabd matrix -f linalg.cholesky "1,2;-2,5") == '1,0;-2,1'
+
+// linalg.cond <matrix> p=<order>
+#assert $(gridlabd matrix -f linalg.cond "1,2;-2,5") == '3.4913561'
+#assert $(gridlabd matrix -f linalg.cond "1,2;-2,5" p=1) == '5.4444444'
+#assert $(gridlabd matrix -f linalg.cond "1,2;-2,5" p=inf) == '5.4444444'
+#assert $(gridlabd matrix -f linalg.cond "1,2;-2,5" p=-inf) == '1'
+
+// linalg.det <matrix>
+#assert $(gridlabd matrix -f linalg.det "1,2;-2,5") == '9'
+
+// linalg.eig <matrix>
+#assert $(gridlabd matrix -f linalg.eig "1,2;-2,5" | tr '\n' ';') == '3+2.9802322e-08j,3-2.9802322e-08j;0.70710678+0j,0.70710678-0j;0.70710678+1.0536712e-08j,0.70710678-1.0536712e-08j;'
+
+// linalg.eigh <matrix> UPLO=<str>
+#assert $(gridlabd matrix -f linalg.eigh '1,2;-2,5' | tr '\n' ';') == '0.17157288,5.8284271;-0.92387953,-0.38268343;-0.38268343,0.92387953;'
+#assert $(gridlabd matrix -f linalg.eigh '1,2;-2,5' UPLO=U | tr '\n' ';') == '0.17157288,5.8284271;-0.92387953,0.38268343;0.38268343,0.92387953;'
+#assert $(gridlabd matrix -f linalg.eigh '1,2;-2,5' UPLO=L | tr '\n' ';') == '0.17157288,5.8284271;-0.92387953,-0.38268343;-0.38268343,0.92387953;'
+
+// linalg.eigvals <matrix>
+#assert $(gridlabd matrix -f linalg.eigvals "1,2;-2,5") == '3+2.9802322e-08j,3-2.9802322e-08j'
+
+// linalg.eigvalsh <matrix> UPLO=<str>
+#assert $(gridlabd matrix -f linalg.eigvalsh "1,2;-2,5") == '0.17157288,5.8284271'
+#assert $(gridlabd matrix -f linalg.eigvalsh "1,2;-2,5" UPLO=U) == '0.17157288,5.8284271'
+#assert $(gridlabd matrix -f linalg.eigvalsh "1,2;-2,5" UPLO=L) == '0.17157288,5.8284271'
+
+// linalg.inv <matrix>
+#assert $(gridlabd matrix -f linalg.inv "1,2;-2,5") == '0.55555556,-0.22222222;0.22222222,0.11111111'
+
+// linalg.lstsq <matrix> <matrix> rcond=<float>
+#assert $(gridlabd matrix -f linalg.lstsq '0,1;1,1;2,1;3,1' '-1;0.2;0.9;2.1' rcond=-1 | tr '\n' ';') == '1;-0.95;0.05;2;4.1000304,1.0907568;'
+#assert $(gridlabd matrix -f linalg.lstsq '0,1;1,1;2,1;3,1' '-1;0.2;0.9;2.1' rcond=1 | tr '\n' ';') == '1;-0.95;0.05;2;4.1000304,1.0907568;'
+
+// linalg.matrix_rank <matrix>
+#assert $(gridlabd matrix -f linalg.matrix_rank "1,2;-2,5") == '2'
+#assert $(gridlabd matrix -f linalg.matrix_rank "1,2;-2,-4") == '1'
+
+// linalg.norm <matrix> ord=<order> axis=<int> keepdims=<bool>
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5") == '5.8309519'
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5" ord=1) == '7'
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5" axis=0) == '2.236068,5.3851648'
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5" axis=1) == '2.236068,5.3851648'
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5" axis=1 keepdims=False) == '2.236068,5.3851648'
+#assert $(gridlabd matrix -f linalg.norm "1,2;-2,5" axis=1 keepdims=True) == '2.236068;5.3851648'
+
+// linalg.pinv <matrix>
+#assert $(gridlabd matrix -f linalg.pinv "1,2;-2,-4") == '0.04,-0.08;0.08,-0.16'
+#assert $(gridlabd matrix -f linalg.pinv "0,1;1,1;2,1;3,1;") == '-0.3,-0.1,0.1,0.3;0.7,0.4,0.1,-0.2'
+#assert $(gridlabd matrix -f linalg.pinv "1,2;-2,5") == $(gridlabd matrix -f linalg.inv "1,2;-2,5")
+
+// linalg.qr <matrix> mode=<str>
+#assert $(gridlabd matrix -f linalg.qr "1,2;-2,5" | tr '\n' ';') == '-0.4472136,0.89442719;0.89442719,0.4472136;-2.236068,3.5777088;0,4.0249224;'
+
+// linalg.slogdet <matrix>
+#assert $(gridlabd matrix -f linalg.slogdet "1,2;-2,5" | tr '\n' ';') == '1;2.1972246;'
+
+// linalg.solve <matrix> <matrix>
+#assert $(gridlabd matrix -f linalg.solve "1,2;3,5" "1;2") == '-1;1'
+
+// linalg.svd <matrix> full_matrices=<bool> compute_uv=<bool> hermitian=<bool>
+#assert $(gridlabd matrix -f linalg.svd "1,2;-2,5"| tr '\n' ';') == '0.28978415,0.95709203;0.95709203,-0.28978415;5.6055513,1.6055513;-0.28978415,0.95709203;0.95709203,0.28978415;'
+#assert $(gridlabd matrix -f linalg.svd "1,2;-2,5" full_matrices=False| tr '\n' ';') == '0.28978415,0.95709203;0.95709203,-0.28978415;5.6055513,1.6055513;-0.28978415,0.95709203;0.95709203,0.28978415;'
+#assert $(gridlabd matrix -f linalg.svd "1,2;-2,5" compute_uv=False| tr '\n' ';') == '5.6055513,1.6055513;'
+#assert $(gridlabd matrix -f linalg.svd "1,2;-2,5" hermitian=True| tr '\n' ';') == '-0.38268343,-0.92387953;0.92387953,-0.38268343;5.8284271,0.17157288;-0.38268343,0.92387953;-0.92387953,-0.38268343;'
+
diff --git a/gldcore/scripts/autotest/test_matrix_matlib.glm b/gldcore/scripts/autotest/test_matrix_matlib.glm
new file mode 100644
index 000000000..1dc2e1f29
--- /dev/null
+++ b/gldcore/scripts/autotest/test_matrix_matlib.glm
@@ -0,0 +1,14 @@
+#assert $(gridlabd matrix version) == '1.21.4'
+
+// matlib.rand <intlist_args>
+#assert ! -z "$(gridlabd matrix -f matlib.rand 1)"
+#assert ! -z "$(gridlabd matrix -f matlib.rand 2)"
+#assert ! -z "$(gridlabd matrix -f matlib.rand 2 2)"
+
+// matlib.randn <intlist_args>
+#assert ! -z $(gridlabd matrix -f matlib.randn 1)
+#assert ! -z $(gridlabd matrix -f matlib.randn 1 2)
+#assert ! -z $(gridlabd matrix -f matlib.randn 2 3)
+
+// matlib.repmat <matrix> <int> <int>
+#assert $(gridlabd matrix -f matlib.repmat "1,2;3,4" 2 2) == '1,2,1,2;3,4,3,4;1,2,1,2;3,4,3,4'
diff --git a/gldcore/scripts/autotest/test_matrix_matrix.glm b/gldcore/scripts/autotest/test_matrix_matrix.glm
new file mode 100644
index 000000000..75f5d6573
--- /dev/null
+++ b/gldcore/scripts/autotest/test_matrix_matrix.glm
@@ -0,0 +1,248 @@
+#assert $(gridlabd matrix version) == '1.21.4'
+
+// matrix.all <matrix> axis=<int>
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,5,6;") == '1'
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,5,6;" axis=0) == '1,1,1'
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,5,6;" axis=1) == '1;1'
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,0,6;") == '0'
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,0,6;" axis=0) == '1,0,1'
+#assert $(gridlabd matrix -f matrix.all "1,2,3;4,0,6;" axis=1) == '1;0'
+
+// matrix.any <matrix> axis=<int>
+#assert $(gridlabd matrix -f matrix.any "1,0,0;0,0,0") == '1'
+#assert $(gridlabd matrix -f matrix.any "1,0,0;0,0,0" axis=0) == '1,0,0'
+#assert $(gridlabd matrix -f matrix.any "1,0,0;0,0,0" axis=1) == '1;0'
+
+// matrix.argmax <matrix> axis=<int>
+#assert $(gridlabd matrix -f matrix.argmax "1,2;3,4") == '3'
+#assert $(gridlabd matrix -f matrix.argmax "1,2;3,4" axis=0) == '1,1'
+#assert $(gridlabd matrix -f matrix.argmax "1,2;3,4" axis=1) == '1;1'
+
+// matrix.argmin <matrix> axis=<int>
+#assert $(gridlabd matrix -f matrix.argmin "1,2;3,4") == '0'
+#assert $(gridlabd matrix -f matrix.argmin "1,2;3,4" axis=0) == '0,0'
+#assert $(gridlabd matrix -f matrix.argmin "1,2;3,4" axis=1) == '0;0'
+
+// matrix.argpartition <matrix> axis=<int> kind=<str> order=<intlist>
+#assert $(gridlabd matrix -f matrix.argpartition "1,2;3,4" 0) == '0,1;0,1'
+#assert $(gridlabd matrix -f matrix.argpartition "1,2;3,4" 0 axis=0) == '0,0;1,1'
+#assert $(gridlabd matrix -f matrix.argpartition "1,2;3,4" 0 axis=1) == '0,1;0,1'
+#assert $(gridlabd matrix -f matrix.argpartition "1,2;3,4" 1) == '0,1;0,1'
+#assert $(gridlabd matrix -f matrix.argpartition "1,2;3,4" 0 kind=introselect) == '0,1;0,1'
+
+// matrix.argsort <matrix> axis=<int> kind=<str> order=<intlist>
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;") == '1,0,2;2,1,0;2,1,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" axis=0) == '0,1,1;1,0,2;2,2,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" axis=1) == '1,0,2;2,1,0;2,1,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" kind=quicksort) == '1,0,2;2,1,0;2,1,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" kind=mergesort) == '1,0,2;2,1,0;2,1,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" kind=heapsort) == '1,0,2;2,1,0;2,1,0'
+#assert $(gridlabd matrix -f matrix.argsort "5,4,9;7,3,1;8,6,2;" kind=stable) == '1,0,2;2,1,0;2,1,0'
+
+// matrix.astype <matrix> order=<str> casting=<str> subok=<boolstr>
+#assert $(gridlabd matrix -f matrix.astype "1,2,3;4,5,6" complex) == '1+0j,2+0j,3+0j;4+0j,5+0j,6+0j'
+#assert $(gridlabd matrix -f matrix.astype "1.2,3.4,5.6;" int) == '1,3,5'
+
+// matrix.byteswap <matrix>
+#assert $(gridlabd matrix -f matrix.byteswap "1.2,3.4,5.6;") == '4.6672615e-62,4.6672615e-62,1.9035986e+185'
+
+// matrix.choose <matrix> <intlist> mode=<str>
+#assert $(gridlabd matrix -f matrix.choose "1" 1,2,3,4) == '2'
+#assert $(gridlabd matrix -f matrix.choose "1" 1,2,3,4 mode=raise) == '2'
+#assert $(gridlabd matrix -f matrix.choose "6" 1,2,3,4 mode=wrap) == '3'
+#assert $(gridlabd matrix -f matrix.choose "6" 1,2,3,4 mode=clip) == '4'
+
+// matrix.clip <matrix> min=<float> max=<float> [where=<boollist>] [axes=<tuplelist>] [axis=<inttuple>] [keepdims=<boolstr>] [casting=<str>] [order=<str>] [dtype=<str>] [subok=<boolstr>]
+#assert $(gridlabd matrix -f matrix.clip "1,2,3,4;5,6,7,8" min=2) == '2,2,3,4;5,6,7,8'
+#assert $(gridlabd matrix -f matrix.clip "1,2,3,4;5,6,7,8" max=7) == '1,2,3,4;5,6,7,7'
+#assert $(gridlabd matrix -f matrix.clip "1,2,3,4;5,6,7,8" min=2 max=7) == '2,2,3,4;5,6,7,7'
+
+// matrix.compress <matrix> axis=<int>
+// TODO: ERROR [gridlabd-matrix]: 'matrix.compress 0,1 1,2;3,4;5,6 axis=0' failed - descriptor 'compress' for 'numpy.ndarray' objects doesn't apply to a 'list' object
+//#assert $(gridlabd matrix -f matrix.compress "0,1" "1,2;3,4;5,6" axis=0) == '3,4'
+
+// matrix.conj <matrix>
+#assert $(gridlabd matrix -f matrix.conj "0+1j,1-2j") == '0-1j,1+2j'
+
+// matrix.conjugate <matrix>
+#assert $(gridlabd matrix -f matrix.conjugate "0+1j,1-2j") == '0-1j,1+2j'
+
+// matrix.cumprod <matrix>
+#assert $(gridlabd matrix -f matrix.cumprod "0+1j,1-2j") == '0+1j,2+1j'
+
+// matrix.cumsum <matrix>
+#assert $(gridlabd matrix -f matrix.cumsum "0+1j,2+1j;") == '0+1j,2+2j'
+
+// matrix.diagonal <matrix>
+#assert $(gridlabd matrix -f matrix.diagonal "1,2;3,4") == '1,4'
+
+// matrix.dot <matrix>
+#assert $(gridlabd matrix -f matrix.dot "1,2;3,4" "1,2;3,4") == '7,10;15,22'
+
+// matrix.fill <matrix>
+#assert $(gridlabd matrix -f matrix.fill "1,2;3,4" 5) == '5,5;5,5'
+#assert $(gridlabd matrix -f matrix.fill "1,2;3,4+0j" 5) == '5+0j,5+0j;5+0j,5+0j'
+
+// matrix.flatten <matrix>
+#assert $(gridlabd matrix -f matrix.flatten "1,2;3,4") == '1,2,3,4'
+
+// matrix.getH <matrix>
+#assert $(gridlabd matrix -f matrix.getH "0+1j,2+3j;4+5j,6+7j") == '0-1j,4-5j;2-3j,6-7j'
+
+// matrix.getI <matrix>
+#assert $(gridlabd matrix -f matrix.getI "1,2;3,4") == '-2,1;1.5,-0.5'
+
+// matrix.getT <matrix>
+#assert $(gridlabd matrix -f matrix.getT "1,2;3,4") == '1,3;2,4'
+
+// matrix.getfield <matrix> <dtype>
+#assert $(gridlabd matrix -f matrix.getfield "0+1j,2+3j;4+5j,6+7j" float) == '0,2;4,6'
+#assert $(gridlabd matrix -f matrix.getfield "0+1j,2+3j;4+5j,6+7j" float offset=8) == '1,3;5,7'
+
+// matrix.item <matrix>
+#assert $(gridlabd matrix -f matrix.item "0+1j,2+3j;4+5j,6+7j" 2) == '4+5j'
+#assert $(gridlabd matrix -f matrix.item "0+1j,2+3j;4+5j,6+7j" 1,1) == '6+7j'
+
+// matrix.itemset <matrix>
+#assert $(gridlabd matrix -f matrix.itemset "0+1j,2+3j;4+5j,6+7j" 1,1 1.23) == '0+1j,2+3j;4+5j,1.23+0j'
+
+// matrix.max <matrix>
+#assert $(gridlabd matrix -f matrix.max "0+1j,2+3j;4+5j,6+7j;") == '6+7j'
+#assert $(gridlabd matrix -f matrix.max "0+1j,2+3j;4+5j,6+7j;" axis=0) == '4+5j,6+7j'
+#assert $(gridlabd matrix -f matrix.max "0+1j,2+3j;4+5j,6+7j;" axis=1) == '2+3j;6+7j'
+
+// matrix.mean <matrix>
+#assert $(gridlabd matrix -f matrix.mean "0+1j,2+3j;4+5j,6+7j;") == '3+4j'
+#assert $(gridlabd matrix -f matrix.mean "0+1j,2+3j;4+5j,6+7j;" axis=0) == '2+3j,4+5j'
+#assert $(gridlabd matrix -f matrix.mean "0+1j,2+3j;4+5j,6+7j;" axis=1) == '1+2j;5+6j'
+
+// matrix.min <matrix>
+#assert $(gridlabd matrix -f matrix.min "0+1j,2+3j;4+5j,6+7j;") == '0+1j'
+#assert $(gridlabd matrix -f matrix.min "0+1j,2+3j;4+5j,6+7j;" axis=0) == '0+1j,2+3j'
+#assert $(gridlabd matrix -f matrix.min "0+1j,2+3j;4+5j,6+7j;" axis=1) == '0+1j;4+5j'
+
+// matrix.nonzero <matrix>
+#assert $(gridlabd matrix -f matrix.nonzero "0,1;2,3" | tr '\n' ';') == '0,1,1;1,0,1;'
+
+// matrix.partition <matrix>
+#assert $(gridlabd matrix -f matrix.partition "4,3,2,1,0;5,4,3,2,1;" 3) == '1,0,2,3,4;2,1,3,4,5'
+#assert $(gridlabd matrix -f matrix.partition "4,3,2,1,0;5,4,3,2,1;" 3 axis=1) == '1,0,2,3,4;2,1,3,4,5'
+#assert $(gridlabd matrix -f matrix.partition "4,3,2,1,0;5,4,3,2,1;" 1 axis=0) == '4,3,2,1,0;5,4,3,2,1'
+
+// matrix.prod <matrix> axis=<int> dtype=<str> keepdims=<bool>
+#assert $(gridlabd matrix -f matrix.prod "1,2;3,4;") == '24'
+#assert $(gridlabd matrix -f matrix.prod "1,2;3,4;" dtype=complex) == '24+0j'
+#assert $(gridlabd matrix -f matrix.prod "1,2;3,4;" axis=0) == '3,8'
+#assert $(gridlabd matrix -f matrix.prod "1,2;3,4;" axis=1) == '2;12'
+
+// matrix.ptp <matrix>
+#assert $(gridlabd matrix -f matrix.ptp "1,2;3,4;") == '3'
+#assert $(gridlabd matrix -f matrix.ptp "1,2;3,4;" axis=0) == '2,2'
+#assert $(gridlabd matrix -f matrix.ptp "1,2;3,4;" axis=1) == '1;1'
+
+// matrix.put <matrix>
+#assert $(gridlabd matrix -f matrix.put "1,2;3,4" 1 0) == '1,0;3,4'
+#assert $(gridlabd matrix -f matrix.put "1,2;3,4" 6 0 mode=wrap) == '1,2;0,4'
+#assert $(gridlabd matrix -f matrix.put "1,2;3,4" 6 0 mode=clip) == '1,2;3,0'
+
+// matrix.ravel <matrix>
+#assert $(gridlabd matrix -f matrix.ravel "1,2;3,4") == '1,2,3,4'
+#assert $(gridlabd matrix -f matrix.ravel "1,2;3,4" order=C) == '1,2,3,4'
+#assert $(gridlabd matrix -f matrix.ravel "1,2;3,4" order=F) == '1,3,2,4'
+#assert $(gridlabd matrix -f matrix.ravel "1,2;3,4" order=A) == '1,2,3,4'
+#assert $(gridlabd matrix -f matrix.ravel "1,2;3,4" order=K) == '1,2,3,4'
+
+// matrix.repeat <matrix> <repeats> axis=<inttuple>
+#assert $(gridlabd matrix -f matrix.repeat "1,2;3,4" 2) == '1,1,2,2,3,3,4,4'
+#assert $(gridlabd matrix -f matrix.repeat "1,2;3,4" 2,3 axis=0) == '1,2;1,2;3,4;3,4;3,4'
+#assert $(gridlabd matrix -f matrix.repeat "1,2;3,4" 2,3 axis=1) == '1,1,2,2,2;3,3,4,4,4'
+
+// matrix.reshape <matrix>
+#assert $(gridlabd matrix -f matrix.reshape "1,2;3,4" 1,4) == '1,2,3,4'
+#assert $(gridlabd matrix -f matrix.reshape "1,2;3,4" 4,1) == '1;2;3;4'
+#assert $(gridlabd matrix -f matrix.reshape "1,2;3,4" 4,1 order=F) == '1;3;2;4'
+
+// matrix.resize <matrix>
+// TODO: ValueError: cannot resize this array: it does not own its data
+//#assert $(gridlabd matrix -f matrix.resize "1,2;3,4" 2,2) == '1,2;3,4'
+
+// matrix.round <matrix>
+#assert $(gridlabd matrix -f matrix.round "1.2,3.4;5.6,7.8") == '1,3;6,8'
+#assert $(gridlabd matrix -f matrix.round "1.2,3.4;5.6,7.8" decimals=0) == '1,3;6,8'
+#assert $(gridlabd matrix -f matrix.round "1.2,3.4;5.6,7.8" decimals=1) == '1.2,3.4;5.6,7.8'
+#assert $(gridlabd matrix -f matrix.round "1.2,3.4;5.6,7.8" decimals=2) == '1.2,3.4;5.6,7.8'
+#assert $(gridlabd matrix -f matrix.round "1.2,3.4;5.6,7.8" decimals=-1) == '0,0;10,10'
+
+// matrix.searchsorted <matrix> <matrix> side=<str> sorter=<intlist>
+// TODO: ValueError: object too deep for desired array
+//#assert $(gridlabd matrix -f matrix.searchsorted "1,2,3,4,5" 3) == '2'
+//#assert $(gridlabd matrix -f matrix.searchsorted "1,2,3,4,5" 3 side=right) == '3'
+//#assert $(gridlabd matrix -f matrix.searchsorted "1,2,3,4,5" -10,10,2,3) == '0,5,1,2'
+
+// matrix.setfield <matrix>
+#assert $(gridlabd matrix -f matrix.setfield "1,2;3,4" 5 int32) == '5,5;5,5'
+#assert $(gridlabd matrix -f matrix.setfield "1,2;3,4" 5 int32 offset=0) == '5,5;5,5'
+#assert $(gridlabd matrix -f matrix.setfield "1,2;3,4" 5 int32 offset=1) == '1281,1282;1283,1284'
+
+// matrix.sort <matrix>
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2") == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" axis=0) == '1,2;4,3'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" axis=1) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" kind=quicksort) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" kind=mergesort) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" kind=heapsort) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.sort "1,3;4,2" kind=stable) == '1,3;2,4'
+
+// matrix.squeeze <matrix>
+#assert $(gridlabd matrix -f matrix.squeeze "1,2;3,4") == '1,2;3,4'
+#assert $(gridlabd matrix -f matrix.squeeze "1,2,3,4" axis=0) == '1,2,3,4'
+#assert $(gridlabd matrix -f matrix.squeeze "1;2;3;4" axis=1) == '1,2,3,4'
+
+// matrix.std <matrix> axis=<int> dtype=<str> ddof=<int>
+#assert $(gridlabd matrix -f matrix.std "1,2;3,4") == '1.118034'
+#assert $(gridlabd matrix -f matrix.std "1,2;3,4" ddof=0) == '1.118034'
+#assert $(gridlabd matrix -f matrix.std "1,2;3,4" ddof=1) == '1.2909944'
+#assert $(gridlabd matrix -f matrix.std "1,2;3,4" ddof=2) == '1.5811388'
+#assert $(gridlabd matrix -f matrix.std "1,2;3,4" dtype=complex) == '1.118034+0j'
+
+// matrix.sum <matrix>
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4") == '10'
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4" axis=0) == '4,6'
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4" axis=1) == '3;7'
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4" axis=0,1) == '10'
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4" axis=1,0) == '10'
+#assert $(gridlabd matrix -f matrix.sum "1,2;3,4" dtype=complex) == '10+0j'
+
+// matrix.swapaxes <matrix> <int> <int>
+#assert $(gridlabd matrix -f matrix.swapaxes "1,2;3,4" 0 1) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.swapaxes "1,2;3,4" 1 0) == '1,3;2,4'
+#assert $(gridlabd matrix -f matrix.swapaxes "1,2;3,4" 0 0) == '1,2;3,4'
+#assert $(gridlabd matrix -f matrix.swapaxes "1,2;3,4" 1 1) == '1,2;3,4'
+
+// matrix.take <matrix>
+#assert $(gridlabd matrix -f matrix.take "1,2;3,4" "3,2,1,0") == '4,3,2,1'
+#assert $(gridlabd matrix -f matrix.take "1,2;3,4" "0,0,1,1" axis=0) == '1,2;1,2;3,4;3,4'
+#assert $(gridlabd matrix -f matrix.take "1,2;3,4" "0,0,1,1" axis=1) == '1,1,2,2;3,3,4,4'
+#assert $(gridlabd matrix -f matrix.take "1,2;3,4" "4,3,2,1" axis=1 mode=clip) == '2,2,2,2;4,4,4,4'
+#assert $(gridlabd matrix -f matrix.take "1,2;3,4" "0,0,1,1" axis=1 mode=wrap) == '1,1,2,2;3,3,4,4'
+
+// matrix.trace <matrix> offset=<int> axis1=<int> axis2=<int> dtype=<str>
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,4") == '5'
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,4" offset=1) == '2'
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,4" offset=-1) == '3'
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,5" axis1=0 axis2=1) == '6'
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,5" axis1=1 axis2=0) == '6'
+#assert $(gridlabd matrix -f matrix.trace "1,2;3,4" dtype=complex) == '5+0j'
+
+// matrix.transpose <matrix>
+#assert $(gridlabd matrix -f matrix.transpose "1,2;3,4") == '1,3;2,4'
+
+// matrix.var <matrix> axis=<inttuple> dtype=<str> ddof=<int>
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4") == '1.25'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" axis=0) == '1,1'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" axis=1) == '0.25;0.25'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" ddof=0) == '1.25'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" ddof=1) == '1.6666667'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" ddof=2) == '2.5'
+#assert $(gridlabd matrix -f matrix.var "1,2;3,4" dtype=complex) == '1.25+0j'
+
diff --git a/gldcore/scripts/autotest/test_matrix_random.glm b/gldcore/scripts/autotest/test_matrix_random.glm
new file mode 100644
index 000000000..454d42a7b
--- /dev/null
+++ b/gldcore/scripts/autotest/test_matrix_random.glm
@@ -0,0 +1,50 @@
+#assert $(gridlabd matrix version) == '1.21.4'
+
+// random.choice <arrayorint> size=<intlist> replace=<boolstr> p=<array>
+#assert ! -z $(gridlabd matrix -f random.choice "1,2,3,4,5")
+#assert ! -z $(gridlabd matrix -f random.choice "1,2,3,4,5" replace=False)
+#assert ! -z $(gridlabd matrix -f random.choice "1,2,3,4,5" size=2,2)
+#assert ! -z $(gridlabd matrix -f random.choice "1,2,3,4,5" p=0.1,0.2,0.4,0.2,0.1 size=10)
+
+// random.normal loc=<matrix> scale=<matrix> size=<intlist>
+#assert ! -z $(gridlabd matrix -f random.normal)
+#assert ! -z $(gridlabd matrix -f random.normal loc=10)
+#assert ! -z $(gridlabd matrix -f random.normal scale=10)
+#assert ! -z $(gridlabd matrix -f random.normal size=3,2)
+
+// random.rand <intlist_args>
+#assert ! -z $(gridlabd matrix -f random.rand)
+#assert ! -z $(gridlabd matrix -f random.rand 3 2)
+
+// random.randint <int> high=<int> size=<intlist> dtype=<str>
+#assert ! -z $(gridlabd matrix -f random.randint 10)
+#assert ! -z $(gridlabd matrix -f random.randint 10 high=20)
+#assert ! -z $(gridlabd matrix -f random.randint 10 high=20 dtype=int8)
+#assert ! -z $(gridlabd matrix -f random.randint 10 high=20 dtype=int16)
+#assert ! -z $(gridlabd matrix -f random.randint 10 high=20 dtype=int32)
+#assert ! -z $(gridlabd matrix -f random.randint 10 high=20 dtype=int64)
+
+// random.randn <intlist_args>
+#assert ! -z $(gridlabd matrix -f random.randn)
+#assert ! -z $(gridlabd matrix -f random.randn 3)
+#assert ! -z $(gridlabd matrix -f random.randn 3 2)
+
+// random.random size=<intlist>
+#assert ! -z $(gridlabd matrix -f random.random)
+#assert ! -z $(gridlabd matrix -f random.random size=2)
+#assert ! -z $(gridlabd matrix -f random.random size=2,3)
+
+// random.random_sample size=<intlist>
+#assert ! -z $(gridlabd matrix -f random.random_sample)
+#assert ! -z $(gridlabd matrix -f random.random_sample size=2)
+#assert ! -z $(gridlabd matrix -f random.random_sample size=2,3)
+
+// random.ranf size=<intlist>
+#assert ! -z $(gridlabd matrix -f random.ranf)
+#assert ! -z $(gridlabd matrix -f random.ranf size=2)
+#assert ! -z $(gridlabd matrix -f random.ranf size=2,3)
+
+// random.sample size=<intlist>
+#assert ! -z $(gridlabd matrix -f random.sample)
+#assert ! -z $(gridlabd matrix -f random.sample size=2)
+#assert ! -z $(gridlabd matrix -f random.sample size=2,3)
diff --git a/gldcore/scripts/gridlabd-assert b/gldcore/scripts/gridlabd-assert
new file mode 100644
index 000000000..db43b8179
--- /dev/null
+++ b/gldcore/scripts/gridlabd-assert
@@ -0,0 +1,97 @@
+#!/bin/bash
+#
+# EXIT 0 - ok
+# EXIT 1 - test failed
+# EXIT 2 - syntax error
+#
+
+#set -x 
+
+WARN="no"
+QUIET="no"
+VERBOSE="no"
+
+function syntax()
+{
+	echo "Syntax: gridlabd assert [OPTIONS...] EXPRESSION"	
+}
+
+function help()
+{
+	syntax
+	echo "OPTIONS:"
+	echo "  --help|help   print this helpful information"
+	echo "  --warn        errors are merely warnings"
+	echo "  --quiet       output to stderr is suppressed"
+	echo "  --verbose     output test before it is performed"
+	echo "See 'man test' for EXPRESSION syntax."
+}
+
+function error()
+{
+	if [ "$QUIET" == "no" ]; then
+		echo "ERROR [gridlabd-assert]: $*" > /dev/stderr
+	fi
+}
+
+function warning()
+{
+	if [ "$WARN" == "yes" ]; then
+		echo "WARNING [gridlabd-assert]: $*"  > /dev/stderr
+	fi
+}
+
+function verbose()
+{
+	if [ "$VERBOSE" == "yes" ]; then
+		echo "VERBOSE [gridlabd-assert]: $*" > /dev/stderr
+	fi	
+}
+
+if [ $# -eq 0 ]; then
+	syntax > /dev/stderr
+	exit 2
+fi
+
+DONE="no"
+while [ "$DONE" == "no" ]; do
+	case "$1" in
+	--help|help)
+		help
+		exit 0
+		;;
+	--warn)
+		WARN="yes"
+		shift 1
+		;;
+	--quiet)
+		QUIET="yes"
+		shift 1
+		;;
+	--verbose)
+		VERBOSE="yes"
+		shift 1
+		;;
+	*)
+		DONE="yes"
+		;;
+	esac
+done
+
+if [ $# -eq 0 ]; then
+	warning "null test"
+else
+	verbose "testing '""$@""'"
+fi
+
+if [ "$@" ]; then
+	exit 0
+else
+	if [ "$WARN" == "no" ]; then
+		error "test '""$@""' failed!"
+		exit 1
+	else
+		warning "test '""$@""' failed!"
+		exit 0
+	fi
+fi
diff --git a/gldcore/scripts/gridlabd-matrix b/gldcore/scripts/gridlabd-matrix
new file mode 100644
index 000000000..ab2cb5a82
--- /dev/null
+++ b/gldcore/scripts/gridlabd-matrix
@@ -0,0 +1,1563 @@
+#!/usr/local/bin/python3
+"""GridLAB-D matrix subcommand
+
+SYNTAX
+
+GLM:
+    #matrix [OPTIONS ...] FUNCTION [ARGUMENTS ...]
+
+Shell:
+    $ gridlabd matrix [OPTIONS ...] FUNCTION [ARGUMENTS ...]
+
+Options:
+
+    -c|--columns=NAMES    add column names
+    -d|--debug            enable debugging output
+    -e|--exception        raise exceptions on errors
+    -f|--flatten          use semicolon as newline
+    -h|--help [PATTERN]   print this help info
+    -n|--name=NAME        add name to cell 0,0 when both columns and rows have
+                          names
+    -p|--polynomial[=VAR] output matrix rows as polynomial(s)
+    -q|--quiet            suppress all output to stderr
+    -r|--rows=NAMES       add row names
+    -s|--slice=SPEC       slice rows and columns from data before operation
+    -t|--transpose        transpose output
+    -w|--warning          suppress warning output
+
+DESCRIPTION
+
+This subcommand is the front-end to python numpy, matrix class, and linear
+algebra library for processing unlabeled data. See the `table` subcommand for
+processing labeled data.
+
+The general syntax is
+
+  shell% gridlabd [OPTIONS ...] matrix FUNCTION [ARGUMENTS ...]
+
+where function is a numpy function that takes zero or more positional and/or
+keyword arguments.  Position arguments may be provided explicitly as a
+string, or implicitly from stdin with each argument separated by a newline.
+Keyword arguments are provided in the form KEYWORD=VALUE.  
+
+Examples:
+
+1) Generate a matrix of random numbers
+
+  shell% gridlabd matrix random.normal size=2,3
+  -1.0435592,1.1823197,-0.20050981
+  -1.4243777,1.1164794,1.2545108
+
+2) Transpose a matrix
+
+  shell% gridlabd matrix transpose '-0.43461221,-1.1657202,0.0063091172;0.10027139,0.71389914,1.9480481;'
+  -0.43461221,0.10027139
+  -1.1657202,0.71389914
+  0.0063091172,1.9480481
+
+3) Pipe data from one operation to another
+
+  shell% gridlabd matrix random.normal size=2 | gridlabd matrix transpose
+  -1.3058182
+  0.18353732
+
+Getting help:
+
+The "help" command provide additional information on available functions using
+the syntax:
+
+  shell% gridlabd matrix help linalg
+      linalg.cholesky <matrix>
+      linalg.cond <matrix> p=<order>
+      linalg.det <matrix>
+      linalg.eig <matrix>
+      linalg.eigh <matrix> UPLO=<str>
+      linalg.eigvals <matrix>
+      linalg.eigvalsh <matrix> UPLO=<str>
+      linalg.inv <matrix>
+      linalg.lstsq <matrix> <matrix> rcond=<float>
+      linalg.matrix_rank <matrix>
+      linalg.norm <matrix> ord=<order> axis=<int> keepdims=<bool>
+      linalg.pinv <matrix>
+      linalg.qr <matrix> mode=<str>
+      linalg.slogdet <matrix>
+      linalg.solve <matrix> <matrix>
+      linalg.svd <matrix> full_matrices=<bool> compute_uv=<bool> hermitian=<bool>
+
+Note: any matrix input argument can be provided as a URL, e.g.,
+
+  shell% echo "1,2;3,4" > /tmp/A
+  shell% gridlabd matrix transpose file:///tmp/A
+  1,3
+  2,4
+
+CAVEAT
+
+The numpy and pandas support for GLM has limited performance insofar as it
+uses shell scripts to parse and dispatch individual operations. This can be
+very efficient for a few operations on very large data files.  If many
+operations on smaller datasets are required, it is highly recommended that
+the GLM file use the `#begin python ... `#end` syntax to perform complex
+operations inline.
+
+The flatten option applies to the output matrices separately. If a function
+outputs more than one matrix, they will still be separated by a newline,
+while each matrix will have rows separated by the config.newline character,
+which is the semicolon by default. 
+
+FUNCTIONS
+
+"""
+
+# exit codes
+E_OK = 0
+E_NOARGS = 1
+E_FAILED = 2
+E_NOTFOUND = 3
+E_INVALID = 4
+
+import sys, os, subprocess, csv, urllib.request, re
+cmdname = os.path.basename(sys.argv[0])
+if not cmdname:
+    cmdname = "matrix"
+    exit_on_error = False
+else:
+    exit_on_error = True
+def local_exit(code):
+    if exit_on_error:
+        exit(code)
+    else: 
+        return
+
+import numpy
+import numpy.matlib as matlib
+import numpy.linalg as linalg
+import pandas
+
+#
+# Argument types
+#
+def matrix(a,slice=None):
+    """Matrix or input file"""
+    if not slice:
+        slice = config.slice_spec
+    try: # normally it's a string that can be read as a matrix
+        return apply_slice(slice,numpy.matrix(a.rstrip(";")))
+    except: # maybe it's a file
+        pass
+    try: # try item by item in case there are nans
+        result = []
+        for row in a.rstrip(";").split(";"):
+            line = []
+            for item in row.split(","):
+                if item == '':
+                    line.append(float('nan'))
+                else:
+                    line.append(float(item))
+            result.append(line)
+        return apply_slice(slice,numpy.matrix(result))
+    except:
+        pass
+    try: # try reading a file
+        result = pandas.read_csv(a,header=None).to_dict('split')["data"]
+        return apply_slice(slice,numpy.matrix(result))
+    except:
+        pass
+    try: # try load as a URL
+        with urllib.request.urlopen(a) as req:
+            return apply_slice(slice,numpy.matrix(req.read().decode("utf-8")))
+    except:
+        pass
+    error(f"invalid matrix or URL: '{a}'",E_INVALID)
+
+def intlist(s):
+    """Parse intlist as N[,M[,[...]]]"""
+    return list(map(lambda n:int(n),s.split(',')))
+
+def boollist(s):
+    """Parse intlist as N[,M[,[...]]]"""
+    return list(map(lambda n:bool(n),s.split(',')))
+
+def tuplelist(s):
+    """Parse intlist as N[,M[,[...]]]"""
+    return list(map(lambda n:intlist(n),s.split(',')))
+
+def strlist(s):
+    return s.split(",")
+
+def intlist_args(*s):
+    """Parse arguments as integer list"""
+    return list(map(lambda n:int(n),*s))
+
+def inttuple(s):
+    """Parse inttuple as N[,M[,[...]]]"""
+    return tuple(map(lambda n:int(n),s.split(',')))
+
+def arrayorint(a):
+    try:
+        return int(a)
+    except:
+        pass
+    return numpy.array(numpy.matrix(a).flatten())[0]
+
+def array(a):
+    return numpy.array(numpy.matrix(a).flatten())[0]
+
+def order(a):
+    if a in ["inf","-inf"]:
+        return float(a)
+    try:
+        return int(a)
+    except:
+        return a
+
+def boolstr(a):
+    try:
+        return bool(float(a))
+    except:
+        pass
+    if a in ["TRUE","True","true","","YES","Yes","yes"]:
+        return True
+    elif a in ["FALSE","False","false","NO","No","no",""]:
+        return False
+    else:
+        return bool(a)
+
+def weakcomplex(x):
+    try:
+        return float(x)
+    except:
+        pass
+    try: 
+        return complex(x)
+    except:
+        pass
+    return float('nanz')
+
+def matrixlist(a):
+    result = []
+
+#
+# Arguments and options
+#
+POSITIONAL="ARGS" # keyword for required arguments
+VARARGS = [intlist_args] # variable argument list handlers
+UARGS = "UFUNC" # keywork for kwargs for ufunc
+UFUNC = {
+    "where" : boollist,
+    "axes" : tuplelist,
+    "axis" : inttuple,
+    "keepdims" : boolstr,
+    "casting" : str,
+    "order" : str,
+    "dtype" : str,
+    "subok" : boolstr,
+}
+UKEYS = list(UFUNC.keys())
+DIRECT = [numpy.hstack, numpy.vstack] # call where args are passed as a single list
+functions = {
+
+    # numpy utilities
+    "arange" :
+    {
+        POSITIONAL : [float],
+        "stop" : float,
+        "step" : float,
+        UARGS : ["subok"],
+    },
+    "copy" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "hstack" :
+    {
+        POSITIONAL : [matrix,matrix]
+    },
+    "vstack" :
+    {
+        POSITIONAL : [matrix,matrix]
+    },
+
+    # arithmetic
+    "add" :
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "reciprocal" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "positive" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "negative" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "multiply" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "divide" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "power" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "subtract" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "true_divide" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "floor_divide" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "float_power" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "fmod" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "mod" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "modf" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "remainder" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "divmod" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    
+    # complex numbers
+    "angle" : 
+    {
+        POSITIONAL : [matrix],
+        "deg" : boolstr,
+    },
+    "real" : 
+    {
+        POSITIONAL : [matrix],
+    },
+    "imag" : 
+    {
+        POSITIONAL : [matrix],
+    },
+    "conj" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "conjugate" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+
+    # miscellaneous
+    "convolve" : 
+    {
+        POSITIONAL : [array,array],
+        "mode" : str,
+    },
+    "clip" : 
+    {
+        POSITIONAL : [matrix],
+        "a_min" : matrix,
+        "a_max" : matrix,
+        UARGS : UKEYS,
+    },
+    "sqrt" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "cbrt" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "square" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "absolute" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "fabs" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "sign" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "heaviside" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "maximum" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "minimum" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "fmax" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "fmin" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    # "nan_to_num" : 
+    # {
+    #   POSITIONAL : [matrix],
+    #   UARGS : UKEYS,
+    # },
+    "real_if_close" : 
+    {
+        POSITIONAL : [matrix],
+        "tol" : float
+    },
+    "interp" : 
+    {
+        POSITIONAL : [array,array,array],
+        "left" : float,
+        "right" : float,
+        "period" : float,
+    },
+
+    # trigonometric
+    "sin" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "cos" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "tan" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arcsin" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arccos" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arctan" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "hypot" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "arctan2" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "degrees" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "radians" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "unwrap" : 
+    {
+        POSITIONAL : [matrix],
+        "discont" : float,
+        "axis" : int,
+        "period" : float,
+    },
+    "deg2rad" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "rad2deg" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+
+    # hyperbolic functions
+    "sinh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "cosh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "tanh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arcsinh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arccosh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "arctanh" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+
+    # rounding
+    "around" : 
+    {
+        POSITIONAL : [matrix],
+        "decimals" : int,
+    },
+    "round_" : 
+    {
+        POSITIONAL : [matrix],
+        "decimals" : int,
+    },
+    "rint" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "fix" : 
+    {
+        POSITIONAL : [matrix],
+    },
+    "floor" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "ceil" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "trunc" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    
+    # sums, profducts, differences
+    "prod" : 
+    {
+        POSITIONAL : [matrix],
+        "initial" : float,
+        UARGS : ["axis","dtype","keepdims","where"],
+    },
+    "sum" : 
+    {
+        POSITIONAL : [matrix],
+        "initial" : float,
+        UARGS : ["axis","dtype","keepdims","where"],
+    },
+    "nanprod" : 
+    {
+        POSITIONAL : [matrix],
+        "initial" : float,
+        UARGS : ["axis","dtype","keepdims","where"],
+    },
+    "nansum" : 
+    {
+        POSITIONAL : [matrix],
+        "initial" : float,
+        UARGS : ["axis","dtype","keepdims","where"],
+    },
+    "cumprod" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        UARGS : ["dtype"],
+    },
+    "cumsum" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        UARGS : ["dtype"],
+    },
+    "nancumprod" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        UARGS : ["dtype"],
+    },
+    "nancumsum" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        UARGS : ["dtype"],
+    },
+    "diff" : 
+    {
+        POSITIONAL : [matrix],
+        "n" : int,
+        "axis" : int,
+        "prepend" : matrix,
+        "append" : matrix,
+        UARGS : [],
+    },
+    # "ediff1d" : 
+    # {
+    #   POSITIONAL : [matrix],
+    #   "to_end" : matrix,
+    #   "to_begin" : matrix,
+    # },
+    "gradient" : 
+    {
+        POSITIONAL : [matrix],
+        "spacing" : matrix,
+        "edge_order" : int,
+        UARGS : ["axis"]
+    },
+    "cross" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        "axisa" : int,
+        "axisb" : int,
+        "axisc" : int,
+        "axis" : int,
+    },
+    "trapz" : 
+    {
+        POSITIONAL : [matrix],
+        "x" : matrix,
+        "dx" : float,
+        "axis" : int,
+    },
+    
+    # exponents and logarithms
+    "exp" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "expm1" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "exp2" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "log" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "log10" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "log2" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "log1p" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "logaddexp" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "logaddexp2" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+
+    # other special functions
+    "i0" : 
+    {
+        POSITIONAL : [array],
+    },
+    "sinc" : 
+    {
+        POSITIONAL : [matrix],
+    },
+
+    # floating point routines
+    "signbit" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "copysign" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "frexp" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+    "ldexp" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "nextafter" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "spacing" : 
+    {
+        POSITIONAL : [matrix],
+        UARGS : UKEYS,
+    },
+
+    # rational routines
+    "lcm" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+    "gcd" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        UARGS : UKEYS,
+    },
+
+    # numpy 
+    "eye" :
+    {
+        POSITIONAL : [int],
+        "M" : int,
+        "k" : int,
+        "dtype" : str,
+        "order" : str,
+    },
+    "identity" :
+    {
+        POSITIONAL : [int],
+        "dtype" : str,
+    },
+    "ones" : 
+    {
+        POSITIONAL : [intlist],
+        "dtype" : str,
+        "order" : str,
+    },
+    "dot" :
+    {
+        POSITIONAL : [matrix,matrix],
+    },
+    "trace" :
+    {
+        POSITIONAL : [matrix],
+        "offset" : int,
+        "axis" : int,
+        "dtype" : str,
+    },
+    "transpose" : 
+    {
+        POSITIONAL : [matrix],
+    },
+    "zeros" :
+    {
+        POSITIONAL : [intlist],
+        "dtype" : str,
+        "order" : str,
+    },
+
+    # linag
+    "linalg.cholesky" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.cond" :
+    {
+        POSITIONAL : [matrix],
+        "p" : order,
+    },
+    "linalg.det" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.eig" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.eigh" :
+    {
+        POSITIONAL : [matrix],
+        "UPLO" : str,
+    },
+    "linalg.eigvals" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.eigvalsh" :
+    {
+        POSITIONAL : [matrix],
+        "UPLO" : str,
+    },
+    "linalg.inv" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.lstsq" : 
+    {
+        POSITIONAL : [matrix,matrix],
+        "rcond" : float,
+    },
+    "linalg.matrix_rank" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.norm" :
+    {
+        POSITIONAL : [matrix],
+        "ord" : order,
+        "axis" : int,
+        "keepdims" : boolstr,
+    },
+    "linalg.pinv" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.qr" :
+    {
+        POSITIONAL : [matrix],
+        "mode" : str,
+    },
+    "linalg.slogdet" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "linalg.solve" : 
+    {
+        POSITIONAL : [matrix,matrix],
+    },
+    "linalg.svd" : 
+    {
+        POSITIONAL : [matrix],
+        "full_matrices" : boolstr,
+        "compute_uv" : boolstr,
+        "hermitian" : boolstr,
+    },
+
+    # matlib
+    "matlib.rand" :
+    {
+        POSITIONAL : intlist_args,
+    },
+    "matlib.randn" :
+    {
+        POSITIONAL : intlist_args,
+    },
+    "matlib.repmat" : 
+    {
+        POSITIONAL : [matrix, int, int],
+    },
+
+    # matrix
+    "matrix.all" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.any" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.argmax" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.argmin" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.argpartition" : 
+    {
+        POSITIONAL : [matrix,int],
+        "axis" : int,
+        "kind" : str,
+        # "order" : intlist,
+    },
+    "matrix.argsort" : 
+    {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        "kind" : str,
+        # "order" : intlist,
+    },
+    "matrix.astype" :
+    {
+        POSITIONAL : [matrix,str],
+        "order" : str,
+        "casting" : str,
+        "subok" : boolstr,
+    },
+    "matrix.byteswap" :
+    {
+        POSITIONAL : [matrix],
+    },
+    "matrix.choose" :
+    {
+        POSITIONAL : [matrix,intlist],
+        "mode" : str,
+    },
+    "matrix.clip" :
+    {
+        POSITIONAL : [matrix],
+        "min" : float,
+        "max" : float,
+        UARGS : UKEYS,
+    },
+    # "matrix.compress" : {
+    #     POSITIONAL : [boollist,matrix],
+    #     "axis" : int,
+    # },
+    "matrix.conj" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.conjugate" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.cumprod" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.cumsum" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.diagonal" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.dot" : {
+        POSITIONAL : [matrix,matrix],
+    },
+    "matrix.fill" : {
+        POSITIONAL : [matrix,weakcomplex],
+    },
+    "matrix.flatten" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.getH" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.getI" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.getT" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.getfield" : {
+        POSITIONAL : [matrix,str],
+        "offset" : int,
+    },
+    "matrix.item" : {
+        POSITIONAL : [matrix,inttuple],
+    },
+    "matrix.itemset" : {
+        POSITIONAL : [matrix,inttuple,weakcomplex],
+    },
+    "matrix.max" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.mean" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.min" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.nonzero" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.partition" : {
+        POSITIONAL : [matrix,inttuple],
+        "axis" : int,
+        "kind" : str,
+        "order" : strlist,
+    },
+    "matrix.prod" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        UARGS : ["dtype"]
+    },
+    "matrix.ptp" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.put" : {
+        POSITIONAL : [matrix,intlist,matrix],
+        "mode" : str,
+    },
+    "matrix.ravel" : {
+        POSITIONAL : [matrix],
+        "order" : str,
+    },
+    "matrix.repeat" : {
+        POSITIONAL : [matrix,inttuple],
+        "axis" : int,
+    },
+    "matrix.reshape" : {
+        POSITIONAL : [matrix,inttuple],
+        "order" : str,
+    },
+    # "matrix.resize" : {
+    #     POSITIONAL : [matrix,inttuple],
+    #     "refcheck" : boolstr,
+    # },
+    "matrix.round" : {
+        POSITIONAL : [matrix],
+        "decimals" : int,
+    },
+    # "matrix.searchsorted" : {
+    #     POSITIONAL : [matrix,matrix],
+    #     "side" : str,
+    #     "sorter" : intlist,
+    # },
+    "matrix.setfield" : {
+        POSITIONAL : [matrix,weakcomplex,str],
+        "offset" : int,
+    },
+    "matrix.sort" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        "kind" : str,
+        "order" : strlist,
+    },
+    "matrix.squeeze" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+    },
+    "matrix.std" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        "ddof" : int,
+        UARGS : ["dtype"]
+    },
+    "matrix.sum" : {
+        POSITIONAL : [matrix],
+        UARGS : ["axis","dtype"]
+    },
+    "matrix.swapaxes" : {
+        POSITIONAL : [matrix,int,int],
+    },
+    "matrix.take" : {
+        POSITIONAL : [matrix,matrix],
+        "axis" : int,
+        "mode" : str,
+    },
+    "matrix.trace" : {
+        POSITIONAL : [matrix],
+        "offset" : int,
+        "axis1" : int,
+        "axis2" : int,
+        UARGS : ["dtype"]
+    },
+    "matrix.transpose" : {
+        POSITIONAL : [matrix],
+    },
+    "matrix.var" : {
+        POSITIONAL : [matrix],
+        "axis" : int,
+        "ddof" : int,
+        UARGS : ["dtype"]
+   },
+
+    # random
+    "random.normal" : 
+    {
+        POSITIONAL : [],
+        "loc" : matrix, 
+        "scale" : matrix,
+        "size" : intlist,
+    },
+    "random.rand" : 
+    {
+        POSITIONAL : intlist_args
+    },
+    "random.randn" : 
+    {
+        POSITIONAL : intlist_args
+    },
+    "random.randint" : 
+    {
+        POSITIONAL : [int],
+        "high" : int,
+        "size" : intlist,
+        "dtype" : str,
+    },
+    "random.random_sample" : 
+    {
+        POSITIONAL : [],
+        "size" : intlist,
+    },
+    "random.random" : 
+    {
+        POSITIONAL : [],
+        "size" : intlist,
+    },
+    "random.ranf" :
+    {
+        POSITIONAL : [],
+        "size" : intlist,
+    },
+    "random.sample" :
+    {
+        POSITIONAL : [],
+        "size" : intlist,
+    },
+    "random.choice" :
+    {
+        POSITIONAL : [arrayorint],
+        "size" : intlist,
+        "replace" : boolstr,
+        "p" : array,    
+    },
+
+}
+
+#
+# Configuration values
+#
+class config:
+    warning = True
+    quiet = False
+    debug = False
+    exception = False
+    delimiter = ","
+    newline = '\n'
+    float_format = "%.8g"
+    complex_format = "%.8g%+.8gj"
+    transpose = False
+    columns = None
+    rows = None
+    name = None
+    polynomial = None
+    slice_spec   = ":"
+
+numpy.set_printoptions(threshold=sys.maxsize)
+
+#
+# Output routines
+#
+def output(result):
+    if type(result) == type(None):
+        return
+    if type(result) == tuple:
+        for item in result:
+            output(item)
+    elif type(result) == list:    
+        if not config.polynomial:
+            if config.name:
+                print(config.name,end=config.delimiter,file=sys.stdout)
+            if config.columns:
+                if not config.name:
+                    print(config.delimiter,end="",file=sys.stdout)
+                print(config.delimiter.join(config.columns),end=config.newline,file=sys.stdout)
+        for m, row in enumerate(result):
+            if m > 0:
+                print(config.newline,end="",file=sys.stdout)
+            if not config.polynomial:
+                if config.rows:
+                    print(config.rows[m],end=config.delimiter,file=sys.stdout)
+                for n, item in enumerate(row):
+                    if n > 0:
+                        print(config.delimiter,end="",file=sys.stdout)
+                    if type(item) is float:
+                        print(config.float_format%item,end="",file=sys.stdout)
+                    elif type(item) is complex:
+                        print(config.complex_format%(item.real,item.imag),end="",file=sys.stdout)
+                    elif type(item) is bool:
+                        print(int(item),end="",file=sys.stdout)
+                    else:
+                        print(str(item),end="",file=sys.stdout)
+            else:
+                for n, item in enumerate(row):
+                    if type(item) is float:
+                        if n > 0 and item >= 0:
+                            print("+",end="",file=sys.stdout)
+                        print(config.float_format%item,end="",file=sys.stdout)
+                    elif type(item) is complex:
+                        if n > 0 and item.real >= 0:
+                            print("+",end="",file=sys.stdout)
+                        if item.imag != 0:
+                            print(f"({config.complex_format%(item.real,item.imag)})",end="",file=sys.stdout)
+                        else:
+                            print(config.float_format%item.real,end="",file=sys.stdout)
+                    elif type(item) is bool:
+                        print("+",end="",file=sys.stdout)
+                        print(int(item),end="",file=sys.stdout)
+                    else:
+                        if n > 0 and item >= 0:
+                            print("+",end="",file=sys.stdout)
+                        print(str(item),end="",file=sys.stdout)
+                    if n >= 1:
+                        print(f"{config.polynomial}",end="",file=sys.stdout)
+                    if n > 1:
+                        print(f"^{n}",end="",file=sys.stdout)
+        print("",file=sys.stdout)
+    elif type(result) == str:
+        print(result,file=sys.stdout)
+    else:
+        if config.transpose:
+            if not hasattr(result,"transpose"):
+                error("cannot transpose result".E_FAILED)
+            result = result.transpose()
+        output(numpy.matrix(result).tolist())
+
+def warning(msg):
+    if config.warning:
+        print(f"WARNING [{cmdname}]: {msg}",file=sys.stderr)
+
+def error(msg,code=None):
+    if not config.quiet:
+        print(f"ERROR [{cmdname}]: {msg}",file=sys.stderr)
+    if config.exception:
+        raise Exception(msg)
+    elif code != None:
+        return local_exit(code)
+    
+def debug(msg):
+    if config.debug:
+        print(f"DEBUG [{cmdname}]: {msg}",file=sys.stderr)
+
+def makedocs():
+    if not os.path.exists("docs/Subcommand/Matrix"):
+        error("'docs/Subcommand/Matrix' folder does not exist",E_NOTFOUND)
+    for function in functions.keys():
+        specs = function.split(".")
+        path = "/".join(list(map(lambda n:n.title(),specs[0:-1])))
+        if len(specs) > 1:
+            path = "/" + path
+            os.makedirs("docs/Subcommand/Matrix"+path.title(),exist_ok=True)
+        name = specs[-1].title()
+        with open(f"docs/Subcommand/Matrix{path}/{name}.md","w") as fh:
+            lib = numpy
+            for pkg in specs[0:-1]:
+                lib = getattr(lib,pkg)
+            call = getattr(lib,specs[-1])
+            NL="\n"
+            docs = call.__doc__.split(NL)
+            fspecs = functions[function]
+            args = []
+            for tag, value in fspecs.items():
+                if tag == POSITIONAL:
+                    if type(value) is list:
+                        for item in value:
+                            args.append(f"<{item.__name__}>")
+                    else:
+                        args.append(f"<{value.__name__}>")
+                elif tag == UARGS:
+                    for utag in value:
+                        uval = UFUNC[utag]
+                        args.append(f"[{utag}=<{uval.__name__}>]")
+                elif hasattr(value,"__name__"):
+                    args.append(f"{tag}=<{value.__name__}>")
+                else:
+                    args.append(f"[{tag}=<{str(value)}>]")
+            try:
+                parameters = docs.index("Parameters")
+            except:
+                parameters = 0
+            try:
+                examples = docs.index("Examples")
+            except:
+                examples = -1
+            if parameters > 0:
+                fh.write(f"[[{path}/{name}]] -- {docs[2]}")
+            else:
+                fh.write(f"[[{path}/{name}]]")              
+            fh.write("\n\n~~~\n")
+            fh.write(f"Syntax\n------\n\n")
+            fh.write(f"matrix {specs[-1]} {' '.join(args)}\n\n")
+            fh.write("\n".join(docs[parameters:examples]))
+            fh.write("\n~~~\n")
+
+def apply_slice(spec,data):
+    slices = spec.split(",")
+    rows = slices[0]
+    if len(slices) > 1:
+        columns = slices[1]
+    else:
+        columns = ":"
+    result = eval(f"data[{rows},{columns}]")
+    # print(f"apply_slice(spec='{spec}',data={data}) --> {result}")
+    return result
+
+def help(name='.*'):
+    if name in list(functions.keys()):
+        if not name in functions.keys():
+            error(f"'{name}' not found",code=E_NOTFOUND)
+        sys.stderr = sys.stdout
+        package = name.split('.')
+        lib = numpy
+        for pkg in package[0:-1]:
+            lib = getattr(lib,pkg)
+        call = getattr(lib,package[-1])
+        specs = functions[name]
+        args = []
+        for tag, value in specs.items():
+            if tag in POSITIONAL:
+                if type(value) is list:
+                    for item in value:
+                        args.append(f"<{item.__name__}>")
+                else:
+                    args.append(f"<{value.__name__}>")
+            elif tag == UARGS:
+                for utag in value:
+                    ufunc = UFUNC[utag]
+                    args.append(f"[{utag}=<{UFUNC[utag].__name__}>]")
+            else:
+                args.append(f"{tag}=<{value.__name__}>")
+        print("matrix",name," ".join(args),file=sys.stdout)
+        output(call.__doc__)
+    elif name:
+        count = 0
+        if name == '.*':
+            print(__doc__,file=sys.stdout)
+        for function in sorted(list(functions.keys())):
+            if not re.match(name,function): 
+                continue
+            specs = functions[function]
+            args = []
+            for tag, value in specs.items():
+                if tag == POSITIONAL:
+                    if type(value) is list:
+                        for item in value:
+                            args.append(f"<{item.__name__}>")
+                    else:
+                        args.append(f"<{value.__name__}>")
+                elif tag == UARGS:
+                    for utag in value:
+                        ufunc = UFUNC[utag]
+                        args.append(f"[{utag}=<{UFUNC[utag].__name__}>]")
+                else:
+                    args.append(f"{tag}=<{value.__name__}>")
+            print(" ",function," ".join(args),file=sys.stdout)
+            count += 1
+        if count == 0:
+            error(f"no help for {name}",E_INVALID)
+    else:
+        print(f"Syntax: {cmdname} [OPTIONS ...] FUNCTION [ARGUMENTS ...]")
+
+#
+# Main function
+#
+def main(argv):
+    done = False
+    while not done and len(argv) > 1:
+        if argv[1] in ["-w","--warning"]:
+            config.warning = not config.warning
+            del argv[1]
+        elif argv[1] in ["-q","--quiet"]:
+            config.quiet = not config.quiet
+            del argv[1]
+        elif argv[1] in ["-d","--debug"]:
+            config.debug = not config.debug
+            del argv[1]
+        elif argv[1] in ["-e","--exception"]:
+            config.exception = not config.exception
+            del argv[1]
+        elif argv[1] in ["-f","--flatten"]:
+            config.newline = ';'
+            del argv[1]
+        elif argv[1] in ["-h","--help"]:
+            argv[1] = "help"
+        elif argv[1] in ["-v","--version"]:
+            argv[1] = "version"
+        elif argv[1] in ["-t","--transpose"]:
+            config.transpose = True
+            del argv[1]
+        elif argv[1].startswith("-c=") or argv[1].startswith("--columns="):
+            config.columns = argv[1].split("=")[1].split(",")
+            del argv[1]
+        elif argv[1].startswith("-r=") or argv[1].startswith("--rows="):
+            config.rows = argv[1].split("=")[1].split(",")
+            del argv[1]
+        elif argv[1].startswith("-n=") or argv[1].startswith("--name="):
+            config.name = argv[1].split("=")[1]
+            del argv[1]
+        elif argv[1] in ["-p","--polynomial"]:
+            config.polynomial = "x"
+            del argv[1]
+        elif argv[1].startswith("-p=") or argv[1].startswith("--polynomial="):
+            config.polynomial = argv[1].split("=")[1]
+            del argv[1]
+        elif argv[1].startswith("-s=") or argv[1].startswith("--slice="):
+            config.slice_spec = argv[1].split("=")[1]
+            del argv[1]
+        elif argv[1] == "--makedocs":
+            makedocs()
+            return local_exit(E_OK)
+        elif argv[1][0] == '-':
+            error(f"option '{argv[1]}' is not valid",code=E_INVALID)
+        else:
+            done = True
+
+    if len(argv) < 2:
+        help(None)
+        return local_exit(E_NOARGS)
+    elif argv[1] == "help":
+        if len(argv) == 2:
+            help()
+            return local_exit(E_OK)
+        elif len(argv) == 3 and argv[1] == "help":
+            help(argv[2])
+            return local_exit(E_OK)
+        else:
+            error("too many help functions requested",code=E_INVALID)
+    elif argv[1] == "version":
+        print(numpy.__version__,file=sys.stdout)
+        return local_exit(E_OK)
+    try:
+        package = argv[1].split('.')
+        lib = numpy
+        for name in package[0:-1]:
+            lib = getattr(lib,name)
+        call = getattr(lib,package[-1])
+    except:
+        if config.exception:
+            raise
+        error(f"'{argv[1]}' not found",code=E_NOTFOUND)
+
+    try:
+        args = []
+        kwargs = {}
+        pos = 0
+        name = argv[1]
+        function = functions[name]
+        if not sys.stdin.isatty():
+            try: # try reading stdin for first arg
+                for n, data in enumerate(sys.stdin.readlines()):
+                    atype = function[POSITIONAL][n]
+                    args.append(atype(data.strip()))
+                    pos += 1
+            except:
+                pass
+        if function[POSITIONAL] in VARARGS:
+            atype = function[POSITIONAL]
+            if len(argv) > 2:
+                args = atype(argv[2:])
+        else:
+            for arg in argv[2:]:
+                spec = arg.split("=")
+                if len(spec) < 2:
+                    if pos >= len(function[POSITIONAL]):
+                        error("too many positional argument",E_INVALID)
+                    atype = function[POSITIONAL][pos]
+                    args.append(atype(arg))
+                    pos += 1
+                elif spec[0] in function.keys():
+                    atype = function[spec[0]]
+                    kwargs[spec[0]] = atype(spec[1])
+                elif UARGS in function.keys() and spec[0] in function[UARGS]:
+                    atype = UFUNC[spec[0]]
+                    kwargs[spec[0]] = atype(spec[1])
+                else:
+                    error(f"argument {pos+1} is invalid",E_INVALID)
+            if len(args) < len(function[POSITIONAL]):
+                error(f"missing positional {function[POSITIONAL][pos].__name__} argument {pos+1}",E_INVALID)
+        if call in DIRECT:
+            result = call(args)
+        elif args and kwargs:
+            result = call(*args,**kwargs)
+        elif args:
+            result = call(*args)
+        elif kwargs:
+            result = call(**kwargs)
+        else:
+            result = call()
+        if type(result) == type(None) and len(args) > 0: 
+            # catch in-place operations
+            output(args[0])
+        else:
+            output(result)
+    except Exception as info:
+        output("MATRIXERROR")
+        if config.exception:
+            raise
+        error(f"'{' '.join(argv[1:])}' failed - {info}",E_FAILED)
+
+    return local_exit(E_OK)
+
+if __name__ == '__main__':
+    main(sys.argv)
diff --git a/gldcore/scripts/requirements.txt b/gldcore/scripts/requirements.txt
index be8daecd8..41f4e02ae 100644
--- a/gldcore/scripts/requirements.txt
+++ b/gldcore/scripts/requirements.txt
@@ -1,5 +1,4 @@
 haversine==2.3.0
-numpy==1.19.4
 pandas==1.1.4
 scipy==1.6.0
 Shapely==1.7.1
diff --git a/gldcore/transform.cpp b/gldcore/transform.cpp
index 76bf1c75f..a683615df 100644
--- a/gldcore/transform.cpp
+++ b/gldcore/transform.cpp
@@ -86,12 +86,12 @@ int write_term(char *buffer,double a,char *x,int n,bool first)
 			else if ( fabs(-a-1)<1e-6 )
 				len += sprintf(buffer+len,"%s","-1");
 			else
-				len += sprintf(buffer+len,first?"%.4f":"%+.4f",a);
+				len += sprintf(buffer+len,first?"%+.4f":"%+.4f",a);
 		}
 		else // z^n term
 		{
 			if ( fabs(a-1)>1e-6 ) // non-unitary coefficient
-				len += sprintf(buffer+len,"%.4f",a);
+				len += sprintf(buffer+len,"%+.4f",a);
 			else if ( fabs(-a-1)<1e-6 ) // -1 coefficient
 				len += sprintf(buffer+len,"-");
 			len += sprintf(buffer+len,"%s",x); // domain variable
@@ -101,6 +101,76 @@ int write_term(char *buffer,double a,char *x,int n,bool first)
 	}
 	return len;
 }
+
+static size_t dump_vector(double *x, size_t n, char *buffer, size_t maxlen, const char *format="%+7.4g")
+{
+	size_t pos = snprintf(buffer,maxlen,"[ ");
+	for ( size_t i = 0 ; i < n && pos < maxlen-10; i++ )
+	{
+		if ( i > 0 )
+		{
+			pos += snprintf(buffer+pos,maxlen-pos,"%s",", ");
+		} 
+		pos += snprintf(buffer+pos,maxlen-pos,format, x[i]);
+	}
+	pos += snprintf(buffer+pos,maxlen-pos," ]");		
+	return pos;
+}
+
+static size_t dump_function(double *a, size_t n, double *b, size_t m, char *buffer, size_t maxlen)
+{
+	size_t pos = snprintf(buffer,maxlen,"(");
+	for ( unsigned int i = 0 ; i < n && pos < maxlen-10 ; i++ )
+	{
+		if ( b[i] == 0.0 )
+		{
+			continue;
+		}
+		if ( i == 0 )
+		{
+			pos += snprintf(buffer+pos,maxlen-pos,"%.4g",b[i]);
+		}
+		else 
+		{
+			if ( b[0] != 1.0 )
+			{
+				pos += snprintf(buffer+pos,maxlen-pos,"%+.4g",b[i]);
+			}
+			else
+			{
+				pos += snprintf(buffer+pos,maxlen-pos,"%s"," +");	
+			}
+			pos += snprintf(buffer+pos,maxlen-pos,"z^-%d", i);
+		}
+	}
+	pos += snprintf(buffer+pos,maxlen-pos,") / (");
+	for ( unsigned int i = 0 ; i < n && pos < maxlen-10 ; i++ )
+	{
+		if ( a[i] == 0.0 )
+		{
+			continue;
+		}
+		if ( i == 0 )
+		{
+			pos += snprintf(buffer+pos,maxlen-pos,"%.4g",a[i]);
+		}
+		else 
+		{
+			if ( a[i] != 1.0 )
+			{
+				pos += snprintf(buffer+pos,maxlen-pos,"%+.4g",a[i]);
+			}
+			else
+			{
+				pos += snprintf(buffer+pos,maxlen-pos,"%s","+");	
+			}
+			pos += snprintf(buffer+pos,maxlen-pos,"z^-%d", i);
+		}
+	}
+	pos += snprintf(buffer+pos,maxlen-pos,")");
+	return pos;
+}
+
 int transfer_function_add(char *name,		///< transfer function name
 						  char *domain,		///< domain variable name
 						  double timestep,	///< timestep (seconds)
@@ -128,41 +198,34 @@ int transfer_function_add(char *name,		///< transfer function name
 		output_error("transfer_function_add(): memory allocation failure");
 		return 0;
 	}
-	memcpy(tf->a,a,sizeof(double)*n);
+	for ( unsigned int i = 0 ; i < n ; i++ )
+	{
+		tf->a[i] = a[n-i-1];
+	}
 	tf->m = m;
-	tf->b = (double*)malloc(sizeof(double)*m);
+	tf->b = (double*)malloc(sizeof(double)*n);
 	if ( tf->b == NULL )
 	{
 		output_error("transfer_function_add(): memory allocation failure");
 		return 0;
 	}
-	memcpy(tf->b,b,sizeof(double)*m);
+	for ( unsigned int i = 0 ; i < m ; i++ )
+	{
+		tf->b[i+n-m] = b[m-i-1];
+	}
+	if ( m < n )
+	{
+		memset(tf->b,0,sizeof(double)*(n-m));
+	}
 	tf->next = tflist;
 	tflist = tf;
 
 	// debugging output
-	if ( global_debug_output )
+	IN_MYCONTEXT
 	{
-		char num[1024]="", den[1024]="";
-		int i;
-		int len;
-		int count1=0, count2=0;
-		for ( len=0,i=n-1 ; i>=0 ; i-- )
-		{
-			int c = write_term(den+len,a[i],domain,i,len==0);
-			if ( c>0 ) count1++;
-			len +=c;
-		}
-		for ( len=0,i=m-1 ; i>=0 ; i-- )
-		{
-			int c = write_term(num+len,b[i],domain,i,len==0);
-			if ( c>0 ) count2++;
-			len +=c;
-		}
-		IN_MYCONTEXT output_debug("defining transfer function %s(%s) = %s%s%s/%s%s%s, step=%.0fs, skew=%.0fs", name,domain,
-			count1>1?"(":"",num,count1>1?")":"",
-			count2>1?"(":"",den,count2>1?")":"",
-			timestep,timeskew);
+		char buffer[1024];
+		dump_function(a,n,b,m,buffer,sizeof(buffer)-1);
+		output_debug("%s(%s) = %s",name,domain,buffer);
 	}
 	return 1;
 }
@@ -192,7 +255,23 @@ int transfer_function_constrain(char *tfname, unsigned int64 flags, unsigned int
 	tf->resolution = (maximum-minimum)/pow(2.0,(double)nbits);
 	tf->minimum = minimum;
 	tf->maximum = maximum;
-	output_debug("transfer function '%s' constraint to range (%lg,%lg( with resolution %lg (%lld bits, flags=0x%llx)",tfname,minimum,maximum,tf->resolution, nbits, flags);
+	if ( tf->flags != FC_NONE )
+	{
+		if ( tf->flags&FC_MINIMUM && tf->flags&FC_MAXIMUM && tf->minimum >= tf->maximum )
+		{
+			output_error("transfer function '%s' constraint minimum=%lg is not less than maximum=%lg",tfname,minimum,maximum);
+			return 0;
+		}
+		else if ( tf->flags&FC_RESOLUTION && ( nbits == 0 || nbits > 64 ) )
+		{
+			output_error("transfer function '%s' constraint resolution=%lld bits is not valid",tfname,nbits);
+			return 0;
+		}
+		else
+		{
+			IN_MYCONTEXT output_debug("transfer function '%s' constraint to range (%lg,%lg( with resolution %lg (%lld bits, flags=0x%llx)",tfname,minimum,maximum,tf->resolution, nbits, flags);
+		}
+	}
 	return 1;
 }
 
@@ -211,11 +290,51 @@ TRANSFORMSOURCE get_source_type(PROPERTY *prop)
 		return XS_UNKNOWN;
 	}
 }
+
+static bool read_vector(const char *name, double *vector, size_t len)
+{
+	char values[1024];
+	if ( global_getvar(name,values,sizeof(values)) == NULL )
+	{
+		output_error("transform.cpp/read_vector(name='%s',vector=%x,len=%ld): global variable not found",name,vector,len);
+		return false;
+	}
+	else
+	{
+		IN_MYCONTEXT output_debug("transform.cpp/read_vector(name='%s',vector=%x,len=%ld): value = '%s'",name,vector,len,values);
+	}
+	char *p = values;
+	int n;
+	for ( n = len-1 ; n >= 0 && p != (char*)1 ; n--,p++ )
+	{
+		vector[n] = atof(p);
+		p = strchr(p,',');
+	}
+	if ( n >= 0 )
+	{
+		output_error("transform.cpp/read_vector(name='%s',vector=%x,len=%ld): too few values to read (missing %d values)",name,vector,len,n+1);
+		return false;
+	}
+	else if ( p != (char*)1 )
+	{
+		output_warning("transform.cpp/read_vector(name='%s',vector=%x,len=%ld): too many values to read ('%s' not scanned)",name,vector,len,p+1);
+	}
+	IN_MYCONTEXT
+	{
+		char buffer[1024];
+		dump_vector(vector,len,buffer,sizeof(buffer));
+		output_debug("transform.cpp/read_vector(name='%s',vector=%x,len=%ld): --> %s",name,vector,len,buffer);
+	}
+	return true;
+}
+
 int transform_add_filter(OBJECT *target_obj,		/* pointer to the target object (lhs) */
 						 PROPERTY *target_prop,	/* pointer to the target property */
 						 char *filter,			/* filter name to use */
 						 OBJECT *source_obj,		/* object containing source value (rhs) */
-						 PROPERTY *source_prop)		/* schedule object associated with target value, used if stype == XS_SCHEDULE */
+						 PROPERTY *source_prop,		/* schedule object associated with target value, used if stype == XS_SCHEDULE */
+						 const char *input_name,		/* name of global containing initial input vector */
+						 const char *state_name)      /* name of global containing initial state vector */
 {
 	char buffer1[1024], buffer2[1024];
 	TRANSFORM *xform;
@@ -238,15 +357,31 @@ int transform_add_filter(OBJECT *target_obj,		/* pointer to the target object (l
 			object_name(target_obj,buffer1,sizeof(buffer1)),target_prop->name,filter, object_name(source_obj,buffer2,sizeof(buffer2)),source_prop->name);
 		return 0;
 	}
-	xform->x = (double*)malloc(sizeof(double)*(tf->n-1));
-	if ( xform->x == NULL )
+	xform->x = (double*)malloc(sizeof(double)*(tf->n));
+	xform->u = (double*)malloc(sizeof(double)*(tf->n));
+	if ( xform->x == NULL || xform->u == NULL )
 	{
 		output_error("transform_add_filter(source='%s:%s',filter='%s',target='%s:%s'): memory allocation failure",
 			object_name(target_obj,buffer1,sizeof(buffer1)),target_prop->name,filter, object_name(source_obj,buffer2,sizeof(buffer2)),source_prop->name);
 		free(xform);
 		return 0;
 	}
-	memset(xform->x,0,sizeof(double)*(tf->n-1));
+	if ( state_name )
+	{
+		read_vector(state_name,xform->x,tf->n);
+	}
+	else
+	{
+		memset(xform->x,0,sizeof(double)*(tf->n));
+	}
+	if ( input_name )
+	{
+		read_vector(input_name,xform->u,tf->m);
+	}
+	else
+	{
+		memset(xform->u,0,sizeof(double)*(tf->n));
+	}
 
 	// build tranform
 	xform->source = object_get_double_by_name(source_obj,source_prop->name);
@@ -255,6 +390,7 @@ int transform_add_filter(OBJECT *target_obj,		/* pointer to the target object (l
 	xform->target_prop = target_prop;
 	xform->function_type = XT_FILTER;
 	xform->tf = tf;
+	xform->t_last = TS_ZERO;
 	xform->y = object_get_double_by_name(target_obj,target_prop->name);
 	xform->t2 = (int64)(global_starttime/tf->timestep)*tf->timestep + tf->timeskew;
 	xform->next = schedule_xformlist;
@@ -364,112 +500,126 @@ void cast_from_double(PROPERTYTYPE ptype, void *addr, double value)
 }
 
 TIMESTAMP apply_filter(TRANSFERFUNCTION *f,	///< transfer function
+					   double *src,			///< next input value
 					   double *u,			///< input vector
 					   double *x,			///< state vector
 					   double *y,			///< output vector
 					   TIMESTAMP t1)		///< current time value
 {
-	unsigned int n = f->n-1;
+	unsigned int n = f->n;
 	unsigned int m = f->m;
 	double *a = f->a;
 	double *b = f->b;
-	static double *dx = NULL;
-	static unsigned int len = 0;
-	unsigned int i;
 
-	// memory check
-	if ( n > len )
+	char buffer[1024] = "";
+	IN_MYCONTEXT 
 	{
-		len = (n/4+1)*4;
-		dx = (double*)realloc(dx,len);
-		IN_MYCONTEXT output_debug("apply_transform(f={name='%s'; domain='%s'}): allocating %d doubles to dx", f->name, f->domain,len);
+		dump_function(a,n,b,m,buffer,sizeof(buffer));
+		output_debug("apply_transform(f={name='%s'; domain='%s'}): tf = %s",f->name,f->domain, buffer);
+		dump_vector(a,n,buffer,sizeof(buffer));
+		output_debug("apply_transform(f={name='%s'; domain='%s'}): a = %s",f->name,f->domain, buffer);
+		dump_vector(b,n,buffer,sizeof(buffer));
+		output_debug("apply_transform(f={name='%s'; domain='%s'}): b = %s",f->name,f->domain, buffer);
 	}
+
+	// update input
+	memmove(u+1,u,sizeof(double)*(n-1));
+	u[0] = *src;
 	IN_MYCONTEXT
 	{
-		char buffer[1024] = "";
-		int pos = sprintf(buffer,"%s(%s) = ( ", f->name,f->domain);
-		for ( i = 0 ; i < m ; i++ )
-		{
-			switch ( m-i-1 ) {
-			case 0:
-				pos += sprintf(buffer+pos,"%+g ", b[m-i-1]);
-				break;
-			case 1:
-				pos += sprintf(buffer+pos,"%+gz ", b[m-i-1]);
-				break;
-			default:
-				pos += sprintf(buffer+pos,"%+gz^%d ", b[m-i-1], m-i-1);
-				break;
-			}
-		}
-		pos += sprintf(buffer+pos,") / ( z^%d ",n);
-		for ( i = 0 ; i < n ; i++ )
-		{
-			switch ( n-i-1 ) {
-			case 0:
-				pos += sprintf(buffer+pos,"%+g ", a[n-i-1]);
-				break;
-			case 1:
-				pos += sprintf(buffer+pos,"%+gz ", a[n-i-1]);
-				break;
-			default:
-				pos += sprintf(buffer+pos,"%+gz^%d ", a[n-i-1], n-i-1);
-				break;
-			}
-
-		}
-		pos += sprintf(buffer+pos,")");
-		output_debug("apply_transform(f={name='%s'; domain='%s'}): %s",f->name,f->domain, buffer);
+		dump_vector(u,n,buffer,sizeof(buffer));
+		output_debug("apply_transform(f={name='%s'; domain='%s'}): u = %s",f->name,f->domain, buffer);
 	}
 
-	// observable form
-	IN_MYCONTEXT output_debug("apply_transform(f={name='%s'; domain='%s'}): u = %g",f->name,f->domain, *u);
-	for ( i = 0 ; i < n ; i++ )
+	// update state
+	for ( unsigned int i = 0 ; i < n ; i++ )
 	{
-		dx[i] = - a[i]*x[n-1];
-		if ( i > 0 )
-			dx[i] += x[i-1];
-		if ( i < m )
-			dx[i] += b[i] * (*u);
+		x[0] += b[i]*u[i];
 	}
+
+	// update output
+	y[0] = x[0] / a[0];
+	IN_MYCONTEXT output_debug("apply_transform(f={name='%s'; domain='%s'}): y = %g",f->name,f->domain, *y);
+
 	IN_MYCONTEXT
 	{
-		char buffer[1024];
-		int pos = sprintf(buffer,"x = [");
-		for ( i = 0 ; i < n ; i++ )
-		{
-			pos += sprintf(buffer+pos,"%s %g", i>0?",":"", x[i]);
-		}
-		pos += sprintf(buffer+pos,"]");
-		output_debug("apply_transform(f={name='%s'; domain='%s'}): %s",f->name,f->domain, buffer);
+		dump_vector(x,n,buffer,sizeof(buffer));
+		output_debug("apply_transform(f={name='%s'; domain='%s'}): x = %s",f->name,f->domain, buffer);
 	}
-	memcpy(x,dx,sizeof(double)*n);
-	IN_MYCONTEXT
+
+	return ((int64)(t1/f->timestep)+1)*f->timestep + f->timeskew;
+}
+
+void transform_reset(TRANSFORM *xform, TIMESTAMP t1)
+{
+	if ( xform->t_last == t1 )
 	{
-		char buffer[1024];
-		int pos = sprintf(buffer,"dx = [");
-		for ( i = 0 ; i < n ; i++ )
+		return;
+	}
+	switch ( xform->function_type )
+	{
+	case XT_LINEAR:
+		if ( xform->target != NULL )
 		{
-			pos += sprintf(buffer+pos,"%s %g", i>0?",":"", x[i]);
+			xform->target[0] = 0.0;
 		}
-		pos += sprintf(buffer+pos,"]");
-		output_debug("apply_transform(f={name='%s'; domain='%s'}): %s",f->name,f->domain, buffer);
-	}
-	*y = x[n-1]; // output
-	if ( ((f->flags)&FC_MINIMUM) == FC_MINIMUM && *y < f->minimum )
- 	{
- 		*y = f->minimum;
- 	}
- 	else if ( ((f->flags)&FC_MAXIMUM) == FC_MAXIMUM && *y > f->maximum )
- 	{
- 		*y = f->maximum;
- 	}
- 	if ( ((f->flags)&FC_RESOLUTION) == FC_RESOLUTION && f->resolution > 0.0 )
- 	{
- 		*y = floor((*y - f->minimum)/f->resolution)*f->resolution + f->minimum;
- 	}	
-	IN_MYCONTEXT output_debug("apply_transform(f={name='%s'; domain='%s'}): y = %g",f->name,f->domain, *y);
-	return ((int64)(t1/f->timestep)+1)*f->timestep + f->timeskew;
+		break;
+	case XT_EXTERNAL:
+		if ( xform->plhs != NULL && xform->nlhs > 0 )
+		{
+			for ( int i = 0 ; i < xform->nlhs ; i++ )
+			{
+				*(double*)(xform->plhs[i].addr) = 0.0;
+			}
+		}
+		break;
+	case XT_FILTER:
+		if ( xform->y != NULL )
+		{
+			char buffer[1024];
+			TRANSFERFUNCTION *f = xform->tf;
+			double *x = xform->x;
+			double *a = f->a;
+			int n = f->n;
+			double *y = xform->y;
+
+			// update state
+			double x0 = 0.0;
+			for ( int i = n-1 ; i > 0 ; i-- )
+			{
+				x[i] = x[i-1];	
+				x0 -= a[i]*x[i];
+			}
+			x[0] = x0;
+			IN_MYCONTEXT
+			{
+				dump_vector(x,n,buffer,sizeof(buffer));
+				output_debug("transform_reset(f={name='%s'; domain='%s'},t1=%ld): x = %s",f->name,f->domain, t1, buffer);
+			}
+
+			// update output
+			y[0] = x[0] / a[0];
+			if ( ((f->flags)&FC_MINIMUM) == FC_MINIMUM && y[0] < f->minimum && f->minimum < f->maximum )
+		 	{
+		 		y[0] = f->minimum;
+		 	}
+		 	else if ( ((f->flags)&FC_MAXIMUM) == FC_MAXIMUM && y[0] > f->maximum && f->minimum < f->maximum  )
+		 	{
+		 		y[0] = f->maximum;
+		 	}
+		 	if ( ((f->flags)&FC_RESOLUTION) == FC_RESOLUTION && f->resolution > 0.0 )
+		 	{
+		 		y[0] = floor((*y - f->minimum)/f->resolution)*f->resolution + f->minimum;
+		 	}	
+			IN_MYCONTEXT output_debug("transform_reset(f={name='%s'; domain='%s'},t1=%ld): y = %.4g",f->name,f->domain, t1, y[0]);
+
+		}
+		break;
+	default:
+		output_error("transform_reset(): invalid function type %d",xform->function_type);
+		break;
+	}
+	xform->t_last = t1;
 }
 
 /** apply the transform, source is optional and xform.source is used when source is NULL 
@@ -481,11 +631,13 @@ TIMESTAMP transform_apply(TIMESTAMP t1, TRANSFORM *xform, double *source)
 	switch (xform->function_type) {
 	case XT_LINEAR:
 		IN_MYCONTEXT output_debug("running linear transform for %s:%s", object_name(xform->target_obj), xform->target_prop->name);
+		transform_reset(xform,t1);
 		cast_from_double(xform->target_prop->ptype, xform->target, (source?(*source):(*(xform->source))) * xform->scale + xform->bias);
 		t2 = TS_NEVER;
 		break;
 	case XT_EXTERNAL:
 		IN_MYCONTEXT output_debug("running external transform for %s:%s", object_name(xform->target_obj), xform->target_prop->name);
+		transform_reset(xform,t1);
 		xform->retval = (*xform->function)(xform->nlhs, xform->plhs, xform->nrhs, xform->prhs);
 		if ( xform->retval==-1 ) /* error */
 			t2 = TS_ZERO;
@@ -497,7 +649,10 @@ TIMESTAMP transform_apply(TIMESTAMP t1, TRANSFORM *xform, double *source)
 	case XT_FILTER:
 		IN_MYCONTEXT output_debug("running filter transform for %s:%s", object_name(xform->target_obj), xform->target_prop->name);
 		if ( xform->t2 <= t1 )
-			xform->t2 = apply_filter(xform->tf,xform->source,xform->x,xform->y,t1);
+		{
+			transform_reset(xform,t1);
+			xform->t2 = apply_filter(xform->tf,xform->source,xform->u,xform->x,xform->y,t1);
+		}
 		t2 = xform->t2;
 		break;
 	default:
@@ -535,7 +690,9 @@ TIMESTAMP transform_syncall(TIMESTAMP t1, TRANSFORMSOURCE source)
 			    double value = schedule_value(xform->source_schedule,index);
 			    t = (dtnext == 0 ? TS_NEVER : t1 + dtnext - (tskew % 60));
 			    if ( t < t2 ) t2 = t;
-				if((tskew <= xform->source_schedule->since) || (tskew >= xform->source_schedule->next_t)){
+				if ( ( tskew <= xform->source_schedule->since ) 
+						|| ( tskew >= xform->source_schedule->next_t ) )
+				{
 					t = transform_apply(t1,xform,&value);
 					if ( t<t2 ) t2=t;
 				} 
diff --git a/gldcore/transform.h b/gldcore/transform.h
index 3b7878b50..73407c2da 100644
--- a/gldcore/transform.h
+++ b/gldcore/transform.h
@@ -73,6 +73,7 @@ typedef struct s_transform {
 	struct s_object_list *target_obj; ///< object of the target
 	struct s_property_map *target_prop; ///< property of the target
 	TRANSFORMFUNCTIONTYPE function_type; ///< function type (linear, external, etc.)
+	TIMESTAMP t_last;
 	union {
 		struct { // used only by linear transforms
 			void *source_addr; ///< pointer to the source
@@ -121,7 +122,7 @@ const char *gldvar_getname(GLDVAR *var, unsigned int n);
 const char *gldvar_getstring(GLDVAR *var, unsigned int n, char *buffer, int size);
 UNIT *gldvar_getunits(GLDVAR *var, unsigned int n);
 TRANSFERFUNCTION *transform_find_filter(const char *name);
-int transform_add_filter(struct s_object_list *target_obj, struct s_property_map *target_prop, char *function, struct s_object_list *source_obj, struct s_property_map *source_prop);
+int transform_add_filter(struct s_object_list *target_obj, struct s_property_map *target_prop, char *function, struct s_object_list *source_obj, struct s_property_map *source_prop, const char *input_name=NULL, const char *state_name=NULL);
 int transfer_function_add(char *tfname, char *domain, double timestep, double timeskew, unsigned int n, double *a, unsigned int m, double *b);
 int transfer_function_constrain(char *tfname, unsigned int64 flags, unsigned int64 nbits, double minimum, double maximum);
 
diff --git a/gldcore/version.h b/gldcore/version.h
index 5b9522470..f74bb0eef 100644
--- a/gldcore/version.h
+++ b/gldcore/version.h
@@ -11,7 +11,7 @@
 
 #define REV_MAJOR 4
 #define REV_MINOR 2
-#define REV_PATCH 28
+#define REV_PATCH 29
 
 #ifdef HAVE_CONFIG_H
 #include "config.h"
diff --git a/module/generators/windturb_dg.cpp b/module/generators/windturb_dg.cpp
index 7491d4a16..5692a5296 100644
--- a/module/generators/windturb_dg.cpp
+++ b/module/generators/windturb_dg.cpp
@@ -240,7 +240,6 @@ int windturb_dg::init(OBJECT *parent)
 	complex tst, tst2, tst3, tst4;
 	gld_property *temp_property_pointer;
 	enumeration temp_enum;
-	bool NR_detect;
 
 	switch (Turbine_Model)	{
 		case GENERIC_IND_LARGE:
@@ -456,7 +455,6 @@ int windturb_dg::init(OBJECT *parent)
 	}
 
 	static complex default_line123_voltage[3], default_line1_current[3];
-	size_t i;
 
 	//Map phases
 	set *phaseInfo = NULL;
diff --git a/module/powerflow/autotest/test_pole.glm b/module/powerflow/autotest/test_pole.glm
index d96902635..7f67985d5 100644
--- a/module/powerflow/autotest/test_pole.glm
+++ b/module/powerflow/autotest/test_pole.glm
@@ -191,6 +191,6 @@ object multi_recorder {
 	file test_pole.csv;
 }
 
-#ifexist "../test_pole.csv"
-#on_exit 0 diff -q test_pole.csv ../test_pole.csv
-#endif
+// #ifexist "../test_pole.csv"
+// #on_exit 0 diff -q test_pole.csv ../test_pole.csv
+// #endif
diff --git a/module/powerflow/fault_check.cpp b/module/powerflow/fault_check.cpp
index 9bd38f365..44cbf29cf 100644
--- a/module/powerflow/fault_check.cpp
+++ b/module/powerflow/fault_check.cpp
@@ -2227,7 +2227,7 @@ void fault_check::search_associated_grids(unsigned int node_int, int grid_counte
 //Function to remove a divergent island
 STATUS fault_check::disable_island(int island_number)
 {
-	int index_value;
+	unsigned int index_value;
 	TIMESTAMP curr_time_val_TS;
 	double curr_time_val_DBL;
 	FILE *FPOutput;
diff --git a/module/powerflow/load.cpp b/module/powerflow/load.cpp
index f6188f563..dc319e815 100644
--- a/module/powerflow/load.cpp
+++ b/module/powerflow/load.cpp
@@ -405,7 +405,7 @@ int load::isa(char *classname)
 int load::create(void)
 {
 	int res = node::create();
-	char index_x,index_y;
+	int index_x,index_y;
         
 	maximum_voltage_error = 0;
 
@@ -596,8 +596,6 @@ TIMESTAMP load::presync(TIMESTAMP t0)
 
 TIMESTAMP load::sync(TIMESTAMP t0)
 {
-	//bool all_three_phases;
-	bool fault_mode;
 	TIMESTAMP result;
 
  	//Check for straggler nodes - fix so segfaults don't occur
@@ -3475,7 +3473,6 @@ int load::notify(int update_mode, PROPERTY *prop, char *value)
 SIMULATIONMODE load::inter_deltaupdate_load(unsigned int64 delta_time, unsigned long dt, unsigned int iteration_count_val,bool interupdate_pos)
 {
 	OBJECT *hdr = THISOBJECTHDR;
-	bool fault_mode;
 	double deltat, deltatimedbl;
 	STATUS return_status_val;
 
diff --git a/module/powerflow/node.cpp b/module/powerflow/node.cpp
index f554e559d..f9fb5249b 100644
--- a/module/powerflow/node.cpp
+++ b/module/powerflow/node.cpp
@@ -3500,9 +3500,7 @@ int node::NR_populate(void)
 {
 	//Object header for names
 	OBJECT *me = THISOBJECTHDR;
-	node *temp_par_node = NULL;
 	gld_property *temp_bool_property;
-	gld_wlock *test_rlock;
 	bool temp_bool_val;
 
 	//Lock the SWING for global operations
@@ -3568,7 +3566,7 @@ int node::NR_populate(void)
 				}
 
 				//Pull the value
-				temp_bool_property->getp<bool>(temp_bool_val,*test_rlock);
+				temp_bool_property->getp<bool>(temp_bool_val);
 
 				//Clear the property
 				delete temp_bool_property;
diff --git a/module/powerflow/pole_mount.cpp b/module/powerflow/pole_mount.cpp
index 3ae34ce03..320e79366 100644
--- a/module/powerflow/pole_mount.cpp
+++ b/module/powerflow/pole_mount.cpp
@@ -53,6 +53,9 @@ pole_mount::pole_mount(MODULE *mod)
             PT_double, "pole_spacing[ft]", get_pole_spacing_offset(),
                 PT_DESCRIPTION, "Mean spacing between poles adjacent",
 
+            PT_double, "angle[deg]", get_angle_offset(),
+                PT_DESCRIPTION, "Angle of the pole line",
+
             NULL)<1)
         {
 			char msg[256];
@@ -138,26 +141,35 @@ int pole_mount::init(OBJECT *parent)
             (conductor[2]?conductor[2]->get_cable_weight():0.0) +
             (conductor[3]?conductor[3]->get_cable_weight():0.0) ) * pole_spacing;
         verbose("weight = %g lbs",weight);
-        double wind_load = (
+        double transverse_load = 1 * (
             (conductor[0]?conductor[0]->cable_diameter:0.0) +
             (conductor[1]?conductor[1]->cable_diameter:0.0) +
             (conductor[2]?conductor[2]->cable_diameter:0.0) +
             (conductor[3]?conductor[3]->cable_diameter:0.0) +
-            8*line->get_ice_thickness() ) / 12;
-        verbose("wind_load = %g lb/ft",wind_load);
+            2*line->get_ice_thickness() ) / 12; // unit transverse load when wind load is 1lb/ft^2
+        verbose("transverse_load = %g lb/ft (wind load is 1 lb/sf)",transverse_load);
         pole *mount = OBJECTDATA(my()->parent,pole);
         pole_configuration *pole_config = OBJECTDATA(mount->get_configuration(),pole_configuration);
-        line_moment_nowind = wind_load * pole_spacing *pole_config->overload_factor_transverse_wire;
-        verbose("load_moment_nowind = %g ft.lb.s/m", line_moment_nowind);
+        line_moment_nowind = 1 * pole_spacing * pole_config->overload_factor_transverse_wire * (
+        spacing->distance_AtoE * ((conductor[0]?conductor[0]->cable_diameter:0.0)+2*line->get_ice_thickness()) +
+        spacing->distance_BtoE * ((conductor[1]?conductor[1]->cable_diameter:0.0)+2*line->get_ice_thickness()) +
+        spacing->distance_CtoE * ((conductor[2]?conductor[2]->cable_diameter:0.0)+2*line->get_ice_thickness()) +
+        spacing->distance_NtoE * ((conductor[3]?conductor[3]->cable_diameter:0.0)+2*line->get_ice_thickness())
+        ) / 12;
+        verbose("load_moment_nowind = %g ft.lb (wind load is 1 lb/sf)", line_moment_nowind);
         double strength = (conductor[0]?conductor[0]->get_cable_strength():0.0)
             + (conductor[1]?conductor[1]->get_cable_strength():0.0)
             + (conductor[2]?conductor[2]->get_cable_strength():0.0)
             + (conductor[3]?conductor[3]->get_cable_strength():0.0);
         verbose("strength = %g lb", strength);
-        tension = 2 * strength * pole_config->overload_factor_transverse_wire * sin(angle/2) * (height - mount->height);
-        verbose("tension = %g ft.lb",tension);
-        line_load_nowind = 0.0; // TODO
-        verbose("line_load_nowind = %g ft.lb.s/m (TODO)",tension);
+        tension = pole_config->overload_factor_transverse_wire * sin(0.017453*angle/2) * (
+            spacing->distance_AtoE * (conductor[0]?conductor[0]->get_cable_strength():0.0) + 
+            spacing->distance_BtoE * (conductor[1]?conductor[1]->get_cable_strength():0.0) + 
+            spacing->distance_CtoE * (conductor[2]?conductor[2]->get_cable_strength():0.0) + 
+            spacing->distance_NtoE * (conductor[3]?conductor[3]->get_cable_strength():0.0));
+        verbose("tension = %g ft.lb (moments due to line tension)",tension); // moment due to conductor tension
+        line_load_nowind = transverse_load * pole_spacing + strength; // line load due to wind load and conductor strength
+        verbose("line_load_nowind = %g lb",line_load_nowind);
     }
 
 	return 1;
@@ -195,17 +207,18 @@ TIMESTAMP pole_mount::precommit(TIMESTAMP t0)
     {
         mount->set_wire_load(mount->get_wire_load() + weight);
         mount->set_wire_load_nowind(mount->get_wire_load_nowind() + line_load_nowind);
-        mount->set_wire_moment(mount->get_wire_moment() + weight*(height - mount->height));
+        mount->set_wire_moment(mount->get_wire_moment() + weight*abs(mount->height - height));
         mount->set_wire_moment_nowind(mount->get_wire_moment_nowind() + line_moment_nowind);
         mount->set_wire_tension(mount->get_wire_tension() + tension);
 	}
     else
     {
-        equipment_moment_nowind = area * (height - mount->height);
-        verbose("equipment_moment_nowind = %g ft.lb.s/m",equipment_moment_nowind);
+        pole_configuration *pole_config = OBJECTDATA(mount->get_configuration(),pole_configuration);
+        equipment_moment_nowind = 1 * area * height * pole_config->overload_factor_transverse_general;
+        verbose("equipment_moment_nowind = %g ft.lb (wind load is 1 lb/sf)",equipment_moment_nowind);
 
         equipment_moment = weight * offset;
-        verbose("equipment_moment = %g ft.lb",equipment_moment);
+        verbose("equipment_moment = %g ft.lb (no tilt)",equipment_moment);
     }
 
     return TS_NEVER;
@@ -240,12 +253,13 @@ TIMESTAMP pole_mount::sync(TIMESTAMP t0)
             verbose("alpha = %g rad",alpha);
             double beta = (mount->get_tilt_direction()-direction)*PI/180;
             verbose("beta = %g rad",beta);
-            double x = mount->get_equipment_moment() + (height - mount->height)*sin(alpha)*weight + equipment_moment*cos(beta);
+            double x = mount->get_equipment_moment() + weight * (height*sin(alpha) + offset*cos(alpha)) * cos(beta);
+            // mount->get_equipment_moment() + abs(mount->height - height)*sin(alpha)*weight + equipment_moment*cos(beta);
             verbose("x = %g ft.lb",x);
-            double y = equipment_moment*sin(beta);
+            double y = weight * (height*sin(alpha) + offset*cos(alpha)) * sin(beta); // moment arm changes when the pole tilts
             verbose("y = %g ft.lb",y);
             double moment = sqrt(x*x+y*y);
-            verbose("moment = %g deg",moment);
+            verbose("moment = %g ft.lb",moment);
             mount->set_equipment_moment(mount->get_equipment_moment()+moment);
         }
     }
diff --git a/module/powerflow/solver_nr.cpp b/module/powerflow/solver_nr.cpp
index 7e7f6465a..513c96ab1 100644
--- a/module/powerflow/solver_nr.cpp
+++ b/module/powerflow/solver_nr.cpp
@@ -165,9 +165,9 @@ void sparse_reset(SPARSE* sm, int ncols)
 }
 
 //Add in new elements to the sparse notation
-inline void sparse_add(SPARSE* sm, int row, int col, double value, BUSDATA *bus_values, unsigned int bus_values_count, NR_SOLVER_STRUCT *powerflow_information, int island_number_curr)
+inline void sparse_add(SPARSE* sm, int row, int col, double value, BUSDATA *bus_values, int bus_values_count, NR_SOLVER_STRUCT *powerflow_information, int island_number_curr)
 {
-	unsigned int bus_index_val, bus_start_val, bus_end_val;
+	int bus_index_val, bus_start_val, bus_end_val;
 	bool found_proper_bus_val;
 
 	SP_E* insertion_point = sm->cols[col];
@@ -390,8 +390,8 @@ int64 solver_nr(unsigned int bus_count,
 
 		//Miscellaneous index variable
 		unsigned int indexer, tempa, tempb, jindexer, kindexer;
-		char jindex, kindex;
-		char temp_index, temp_index_b;
+		int jindex, kindex;
+		int temp_index, temp_index_b;
 		unsigned int temp_index_c;
 
 		//Working matrix for admittance collapsing/determinations
@@ -408,7 +408,7 @@ int64 solver_nr(unsigned int bus_count,
 		complex aval, avalsq;
 
 		//Temporary size variable
-		size_t temp_size, temp_size_b, temp_size_c;
+		int temp_size, temp_size_b, temp_size_c;
 
 		//Temporary admittance variables
 		complex Temp_Ad_A[3][3];
@@ -1049,9 +1049,9 @@ int64 solver_nr(unsigned int bus_count,
 				}
 
 				//Store the admittance values into the BA_diag matrix structure
-				for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+				for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 				{
-					for (kindex=0; kindex<(size_t)powerflow_values->BA_diag[indexer].size; kindex++)			//Store values - assume square matrix - don't bother parsing what doesn't exist.
+					for ( kindex = 0; kindex < powerflow_values->BA_diag[indexer].size ; kindex++ )			//Store values - assume square matrix - don't bother parsing what doesn't exist.
 					{
 						powerflow_values->BA_diag[indexer].Y[jindex][kindex] = tempY[jindex][kindex];// Store the self admittance terms.
 					}
@@ -1060,9 +1060,9 @@ int64 solver_nr(unsigned int bus_count,
 				//Copy values into node-specific link (if needed)
 				if (bus[indexer].full_Y_all != NULL)
 				{
-					for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+					for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 					{
-						for (kindex=0; kindex<(size_t)powerflow_values->BA_diag[indexer].size; kindex++)			//Store values - assume square matrix - don't bother parsing what doesn't exist.
+						for ( kindex = 0 ; kindex < powerflow_values->BA_diag[indexer].size ; kindex++ )			//Store values - assume square matrix - don't bother parsing what doesn't exist.
 						{
 							bus[indexer].full_Y_all[jindex*3+kindex] = tempY[jindex][kindex];// Store the self admittance terms.
 						}
@@ -1929,12 +1929,12 @@ int64 solver_nr(unsigned int bus_count,
 							}
 					}//end line switch/case
 
-					if ((int)temp_size_c==-99)
+					if ( temp_size_c == -99 )
 					{
 						continue;	//Next iteration of branch loop
 					}
 
-					if ((int)temp_size_c==-1)	//Make sure it is right
+					if ( temp_size_c == -1 )	//Make sure it is right
 					{
 						GL_THROW("NR: A line's phase was flagged as not full three-phase, but wasn't: (%s) %u %u %u %u", 
 										 branch[jindexer].name, branch[jindexer].phases, branch[jindexer].origphases, phase_worka, phase_workb);
@@ -2244,7 +2244,7 @@ int64 solver_nr(unsigned int bus_count,
 					}//End switch/case for to
 
 					//Make sure everything was set before proceeding
-					if (((int)temp_index==-1) || ((int)temp_index_b==-1) || ((int)temp_size==-1) || ((int)temp_size_b==-1) || ((int)temp_size_c==-1))
+					if ( (temp_index==-1) || (temp_index_b==-1) || (temp_size==-1) || (temp_size_b==-1) || (temp_size_c==-1))
 					{
 						GL_THROW("NR: Failure to construct single/double phase line indices");
 						/*  TROUBLESHOOT
@@ -2255,9 +2255,9 @@ int64 solver_nr(unsigned int bus_count,
 
 					if (Full_Mat_A)	//From side is a full ABC and we have AC
 					{
-						for (jindex=0; jindex<temp_size_c; jindex++)		//Loop through rows of admittance matrices				
+						for ( jindex = 0 ; jindex < temp_size_c ; jindex++ )		//Loop through rows of admittance matrices				
 						{
-							for (kindex=0; kindex<temp_size_c; kindex++)	//Loop through columns of admittance matrices
+							for ( kindex = 0 ; kindex < temp_size_c ; kindex++ )	//Loop through columns of admittance matrices
 							{
 								//Indices counted out from Self admittance above.  needs doubling due to complex separation
 								if ((Temp_Ad_A[jindex][kindex].Im() != 0) && (bus[tempa].type != 1) && (bus[tempb].type != 1))	//From imags
@@ -2519,9 +2519,9 @@ int64 solver_nr(unsigned int bus_count,
 				}
 				//Default else -- proceed
 
-				for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[jindexer].size; jindex++)
+				for ( jindex = 0 ; jindex < powerflow_values->BA_diag[jindexer].size ; jindex++ )
 				{
-					for (kindex=0; kindex<(size_t)powerflow_values->BA_diag[jindexer].size; kindex++)
+					for ( kindex = 0 ; kindex < powerflow_values->BA_diag[jindexer].size ; kindex++ )
 					{					
 						if ((powerflow_values->BA_diag[jindexer].Y[jindex][kindex]).Im() != 0 && bus[jindexer].type != 1 && jindex!=kindex)
 						{
@@ -2700,7 +2700,7 @@ int64 solver_nr(unsigned int bus_count,
 						}
 					}//End PF_DYNINIT and SWING is still the same
 
-					for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++) // rows - for specific phase that exists
+					for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ ) // rows - for specific phase that exists
 					{
 						tempIcalcReal = tempIcalcImag = 0;   
 
@@ -2934,7 +2934,7 @@ int64 solver_nr(unsigned int bus_count,
 							tempPbus =  - bus[indexer].PL[jindex];	//Copy load amounts in
 							tempQbus =  - bus[indexer].QL[jindex];	
 
-							for (kindex=0; kindex<(size_t)powerflow_values->BA_diag[indexer].size; kindex++)		//cols - Still only for specified phases
+							for ( kindex = 0 ; kindex < powerflow_values->BA_diag[indexer].size ; kindex++ )		//cols - Still only for specified phases
 							{
 								//Determine our indices, based on phase information
 								temp_index = -1;
@@ -3703,14 +3703,14 @@ int64 solver_nr(unsigned int bus_count,
 
 			indexer = 0;	//Rest positional counter
 
-			for (jindexer=0; jindexer<bus_count; jindexer++)	//Parse through bus list
+			for ( jindexer = 0 ; jindexer < bus_count ; jindexer++ )	//Parse through bus list
 			{
 				//See if we're part of the currently progressing island -- if not, skip us
 				if (bus[jindexer].island_number == island_loop_index)
 				{
 					if ((bus[jindexer].type > 1) && (bus[jindexer].swing_functions_enabled == true))	//Swing bus - and we aren't ignoring it
 					{
-						for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[jindexer].size; jindex++)
+						for ( jindex = 0 ; jindex < powerflow_values->BA_diag[jindexer].size ; jindex++ )
 						{
 							powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].row_ind = 2*bus[jindexer].Matrix_Loc + jindex;
 							powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].col_ind = powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].row_ind;
@@ -3736,7 +3736,7 @@ int64 solver_nr(unsigned int bus_count,
 
 					if ((bus[jindexer].type == 0) || ((bus[jindexer].type > 1) && bus[jindexer].swing_functions_enabled == false))	//Only do on PQ (or SWING masquerading as PQ)
 					{
-						for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[jindexer].size; jindex++)
+						for ( jindex = 0 ; jindex < powerflow_values->BA_diag[jindexer].size ; jindex++ )
 						{
 							powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].row_ind = 2*bus[jindexer].Matrix_Loc + jindex;
 							powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].col_ind = powerflow_values->island_matrix_values[island_loop_index].Y_diag_update[indexer].row_ind;
@@ -3971,7 +3971,7 @@ int64 solver_nr(unsigned int bus_count,
 							m = 2*powerflow_values->island_matrix_values[island_loop_index].total_variables;
 
 							//Print the size - should be the matrix size, but put here for ease of use
-							fprintf(FPoutVal,"Matrix RHS Information - %lld elements\n",m);
+							fprintf(FPoutVal,"Matrix RHS Information - %d elements\n",m);
 
 							//Print a header - row information isn't really needed, but include, just for ease of viewing
 							fprintf(FPoutVal,"RHS Information - row, value\n");
@@ -3979,7 +3979,7 @@ int64 solver_nr(unsigned int bus_count,
 							//Loop through and output the RHS values
 							for (jindexer=0; jindexer<m; jindexer++)
 							{
-								fprintf(FPoutVal,"%lld,%f\n",jindexer,powerflow_values->island_matrix_values[island_loop_index].deltaI_NR[jindexer]);
+								fprintf(FPoutVal,"%d,%f\n",jindexer,powerflow_values->island_matrix_values[island_loop_index].deltaI_NR[jindexer]);
 							}//End output RHS
 
 							//Print an extra line, just for spacing for ALL/PERCALL
@@ -4577,7 +4577,7 @@ int64 solver_nr(unsigned int bus_count,
 						}//end split phase update
 						else										//Not split phase
 						{
-							for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)	//parse through the phases
+							for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )	//parse through the phases
 							{
 								switch(bus[indexer].phases & 0x07) {
 									case 0x01:	//C
@@ -5034,6 +5034,7 @@ int64 solver_nr(unsigned int bus_count,
 			}
 			//Default else - it was a failure, just keep going
 		}
+		Iteration = return_value_for_solver_NR;
 
 		//Deflag the "island locker"
 		NR_solver_working = false;
@@ -5091,7 +5092,7 @@ void compute_load_values(unsigned int bus_count, BUSDATA *bus, NR_SOLVER_STRUCT
 	complex adjust_temp_nominal_voltage[6], adjusted_constant_current[6];
 	complex delta_current[3], voltageDel[3], undeltacurr[3];
 	complex temp_current[3], temp_store[3];
-	char jindex, temp_index, temp_index_b;
+	int jindex, temp_index, temp_index_b;
 	STATUS temp_status;
 
 	//Loop through the buses
@@ -5360,7 +5361,7 @@ void compute_load_values(unsigned int bus_count, BUSDATA *bus, NR_SOLVER_STRUCT
 				temp_index = -1;
 				temp_index_b = -1;
 
-				for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+				for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 				{
 					switch(bus[indexer].phases & 0x07) {
 						case 0x01:	//C
@@ -5749,7 +5750,7 @@ void compute_load_values(unsigned int bus_count, BUSDATA *bus, NR_SOLVER_STRUCT
 					undeltacurr[0] = undeltacurr[1] = undeltacurr[2] = complex(0.0,0.0);	//Zero it
 				}
 
-				for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+				for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 				{
 					switch(bus[indexer].phases & 0x07) {
 						case 0x01:	//C
@@ -6131,7 +6132,7 @@ void compute_load_values(unsigned int bus_count, BUSDATA *bus, NR_SOLVER_STRUCT
 				temp_index = -1;
 				temp_index_b = -1;
 
-				for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+				for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 				{
 					switch(bus[indexer].phases & 0x07) {
 						case 0x01:	//C
@@ -6245,7 +6246,7 @@ void compute_load_values(unsigned int bus_count, BUSDATA *bus, NR_SOLVER_STRUCT
 					temp_index = -1;
 					temp_index_b = -1;
 
-					for (jindex=0; jindex<(size_t)powerflow_values->BA_diag[indexer].size; jindex++)
+					for ( jindex = 0 ; jindex < powerflow_values->BA_diag[indexer].size ; jindex++ )
 					{
 						switch(bus[indexer].phases & 0x07) {
 							case 0x01:	//C
diff --git a/module/powerflow/solver_py.cpp b/module/powerflow/solver_py.cpp
index fa5ff1805..bdf7bc8a4 100644
--- a/module/powerflow/solver_py.cpp
+++ b/module/powerflow/solver_py.cpp
@@ -487,6 +487,10 @@ int solver_python_init(void)
 				pKwargs = PyDict_New();
 			}
 			PyDict_SetItemString(pModel,"options",pKwargs);
+			if ( pSolution == NULL )
+			{
+				pSolution = PyDict_Copy(pModel);
+			}
 		}
 		if ( ! python_mle_data_only )
 		{
@@ -502,10 +506,6 @@ int solver_python_init(void)
 			{
 				init_learndata();
 			}
-			if ( pSolution == NULL )
-			{
-				pSolution = PyDict_Copy(pModel);
-			}
 		}
 		return 0;
 	}
@@ -919,7 +919,7 @@ void sync_busdata_raw(PyObject *pModel,unsigned int &bus_count,BUSDATA *&bus,e_d
 			"SAr","SAi","SBr","SBi","SCr","SCi",
 			"YAr","YAi","YBr","YBi","YCr","YCi",
 			"IAr","IAi","IBr","IBi","ICr","ICi",
-			"VAr","VAi","VBr","VBi","VCr","VCi",
+			"VAm","VAa","VBm","VBa","VCm","VCa",
 		};
 		size_t ntags = sizeof(tags)/sizeof(tags[0]);
 		PyObject *taglist = PyList_New(ntags);
@@ -961,24 +961,28 @@ void sync_busdata_raw(PyObject *pModel,unsigned int &bus_count,BUSDATA *&bus,e_d
 			SET_BUS(n,16,bus[n].I[2].r);
 			SET_BUS(n,17,bus[n].I[2].i);
 
-			SET_BUS(n,18,bus[n].V[0].r);
-			SET_BUS(n,19,bus[n].V[0].i);
-			SET_BUS(n,20,bus[n].V[1].r);
-			SET_BUS(n,21,bus[n].V[1].i);
-			SET_BUS(n,22,bus[n].V[2].r);
-			SET_BUS(n,23,bus[n].V[2].i);
+			SET_BUS(n,18,bus[n].V[0].Mag());
+			SET_BUS(n,19,bus[n].V[0].Ang());
+			SET_BUS(n,20,bus[n].V[1].Mag());
+			SET_BUS(n,21,bus[n].V[1].Ang());
+			SET_BUS(n,22,bus[n].V[2].Mag());
+			SET_BUS(n,23,bus[n].V[2].Ang());
 		}
 	}
 	else if ( dir == ED_IN )
 	{
 		for ( size_t n = 0 ; n < bus_count  ; n++ )
 		{
-			GET_BUS(n,18,bus[n].V[0].r);
-			GET_BUS(n,19,bus[n].V[0].i);
-			GET_BUS(n,20,bus[n].V[1].r);
-			GET_BUS(n,21,bus[n].V[1].i);
-			GET_BUS(n,22,bus[n].V[2].r);
-			GET_BUS(n,23,bus[n].V[2].i);
+			double mag, ang;
+			GET_BUS(n,18,mag);
+			GET_BUS(n,19,ang);
+			bus[n].V[0].SetPolar(mag,ang);
+			GET_BUS(n,20,mag);
+			GET_BUS(n,21,ang);
+			bus[n].V[1].SetPolar(mag,ang);
+			GET_BUS(n,22,mag);
+			GET_BUS(n,23,ang);
+			bus[n].V[2].SetPolar(mag,ang);
 		}
 	}
 }
@@ -1462,28 +1466,14 @@ PyObject *sync_solution(
 	bool *bad_computations,
 	int64 iterations)
 {
-	if ( ! python_mle_data_only )
-	{
-		sync_bad_computations(pSolution,bad_computations);
-		sync_iterations(pSolution,iterations);
-		sync_powerflow_values(pSolution,buscount,powerflow_values);
-		sync_powerflow_type(pSolution,powerflow_type);
-		sync_mesh_imped_values(pSolution,mesh_imped_values);
-		return pSolution;
-	}
-	else if ( pBusdata )
-	{
-		Py_INCREF(pBusdata);
-		return pBusdata;
-	}
-	else
-	{
-		Py_INCREF(Py_None);
-		return Py_None;
-	}
+	sync_bad_computations(pSolution,bad_computations);
+	sync_iterations(pSolution,iterations);
+	sync_powerflow_values(pSolution,buscount,powerflow_values);
+	sync_powerflow_type(pSolution,powerflow_type);
+	sync_mesh_imped_values(pSolution,mesh_imped_values);
+	return pSolution;
 }
 
-
 void solver_python_learn (
 	unsigned int bus_count,
 	BUSDATA *bus,
diff --git a/module/resilience/docs/requirements.txt b/module/resilience/docs/requirements.txt
index 6b93a3447..b23da07b0 100644
--- a/module/resilience/docs/requirements.txt
+++ b/module/resilience/docs/requirements.txt
@@ -1,6 +1,5 @@
 Pillow==8.3.2
 ipython==7.22.0
-numpy==1.19.4
 pandas==1.1.4
 scipy==1.6.2
 ipyplot==1.1.0
diff --git a/python_extras/Makefile.mk b/python_extras/Makefile.mk
index 282af331d..dc1031435 100644
--- a/python_extras/Makefile.mk
+++ b/python_extras/Makefile.mk
@@ -1,5 +1,6 @@
 dist_pkgdata_DATA += python_extras/create_poles.py
 dist_pkgdata_DATA += python_extras/csv_merge/csv_merge.py
+dist_pkgdata_DATA += python_extras/dataframe.py
 dist_pkgdata_DATA += python_extras/eia_recs.py
 dist_pkgdata_DATA += python_extras/example/house.glm
 dist_pkgdata_DATA += python_extras/example/tstat_commit.py
@@ -7,9 +8,10 @@ dist_pkgdata_DATA += python_extras/example/tstat_init.py
 dist_pkgdata_DATA += python_extras/gridlabd-editor.png
 dist_pkgdata_DATA += python_extras/gridlabd-editor.py
 dist_pkgdata_DATA += python_extras/insights.py
+dist_pkgdata_DATA += python_extras/fit_filter.py
 dist_pkgdata_DATA += python_extras/metar2glm.py
+dist_pkgdata_DATA += python_extras/noaa_forecast.py
 dist_pkgdata_DATA += python_extras/nsrdb_weather.py
 dist_pkgdata_DATA += python_extras/ucar_weather.py
-dist_pkgdata_DATA += python_extras/noaa_forecast.py
 dist_pkgdata_DATA += python_extras/volt_dump/meter_record.py
 dist_pkgdata_DATA += python_extras/volt_dump/voltdump.py
diff --git a/python_extras/dataframe.py b/python_extras/dataframe.py
new file mode 100644
index 000000000..f38462f20
--- /dev/null
+++ b/python_extras/dataframe.py
@@ -0,0 +1,106 @@
+"""Dataframe utility
+
+SYNTAX:
+
+    $ gridlabd dataframe [OPTIONS ...]
+"""
+import sys
+import pandas as pd
+import datetime as dt
+
+left_name = "/dev/stdin"
+left_suffix = "_left"
+left_dateformat = "%Y-%m-%d %H:%M:%S %Z"
+left_datecolumn = 0
+
+right_name = None
+right_suffix = "_right"
+right_dateformat = "%Y-%m-%d %H:%M:%S %Z"
+right_datecolumn = 0
+
+join_how = "inner"
+join_on = "timestamp"
+
+output_name = "/dev/stdout"
+
+plot_options = {
+    "grid" : True,
+}
+
+def error(msg, code=None):
+    if type(code) is int:
+        print(f"ERROR [dataframe]: {msg} (exit code {code})")
+        exit(code)
+    elif code == None:
+        print(f"ERROR [dataframe]: {msg}")
+    else:
+        raise Exception(msg)
+
+def from_float(x):
+    try:
+        return float(x)
+    except:
+        return float('nan')
+
+for args in list(map(lambda x:x.split("="),sys.argv[1:])):
+    if len(args) > 1:
+        token = args[0]
+        value = "=".join(args[1:])
+    else:
+        token = args
+        value = None
+    if token in ["-i","--input_name"]:
+        left_name = value
+    elif token in ["-j","--join_name"]:
+        right_name = value
+    elif token in ["--left_suffix"]:
+        left_suffix = value
+    elif token in ["--right_suffix"]:
+        right_suffix = value
+    elif token in ["--left_dateformat"]:
+        left_dateformat = value
+    elif token in ["--right_dateformat"]:
+        right_dateformat = value
+    elif token in ["--join_how"]:
+        join_how = value
+    elif token in ["--join_on"]:
+        join_on = value
+    elif token in ["-o","--output_name"]:
+        output_name = value
+    else:
+        error(f"option '{args}' is not valid",1)
+
+try:
+    result = pd.read_csv(left_name,
+        index_col = left_datecolumn,
+        parse_dates = [left_datecolumn],
+        date_parser = (lambda t:dt.datetime.strptime(t,left_dateformat)),
+        # converters = {
+        #     "timestamp":lambda t:dt.datetime.strptime(t,left_dateformat),
+        #     "real_power":from_float
+        # },
+        )
+
+    if right_name:
+        right = pd.read_csv(right_name,
+            index_col = right_datecolumn,
+            parse_dates = [right_datecolumn],
+            date_parser = (lambda t:dt.datetime.strptime(t,right_dateformat)),
+            # converters = {
+            #     "timestamp":lambda t:dt.datetime.strptime(t,right_dateformat),
+            #     "real_power":from_float
+            # },
+            )
+
+        result = result.join(right,on=join_on,how=join_how,lsuffix=left_suffix,rsuffix=right_suffix)
+
+except Exception as err:
+    error(err,2)
+
+if output_name.endswith(".png"):
+    ax = result.plot(**plot_options)
+    ax.figure.savefig(output_name)
+elif output_name.endswith(".csv"):
+    result.to_csv(output_name)
+else:
+    error("output format is not supported",1)
diff --git a/python_extras/example/load_model.glm b/python_extras/example/load_model.glm
new file mode 100644
index 000000000..756943798
--- /dev/null
+++ b/python_extras/example/load_model.glm
@@ -0,0 +1,19 @@
+// test load model fitting
+
+#define START=2017-09-01T01:00:00
+#python -m fit_filter -o=power.csv -i=weather.csv -c=load_model_config.py -C=output -P=weather.csv -g=/tmp/test.glm -k=24 -R=output.csv -I="${START}"
+
+#include "/tmp/test.glm"
+
+module tape
+{
+	csv_header_type NAME;
+}
+
+clock {
+	timezone "PST+8PDT";
+	starttime "${START}";
+	runtime 24 h;
+}
+
+#on_exit 0 gridlabd dataframe -i=power.csv --left_dateformat="%m/%d/%y %H:%M" -j=output.csv -o=output.png
diff --git a/python_extras/example/load_model_config.py b/python_extras/example/load_model_config.py
new file mode 100644
index 000000000..7f630b687
--- /dev/null
+++ b/python_extras/example/load_model_config.py
@@ -0,0 +1,18 @@
+from datetime import datetime
+class converters:
+
+    def real(x):
+        try:
+            return float(x)
+        except:
+            return float('NAN')
+    
+    def timestamp(x):
+        return datetime.strptime(x,"%m/%d/%y %H:%M")
+    
+    def datetime(x):
+        return datetime.strptime(x,"%Y-%m-%d %H:%M:%S")
+
+    outputs = {"timestamp":timestamp,"real_power":real}
+    inputs = {"datetime":datetime,"temperature":real,"solar":real}
+
diff --git a/python_extras/example/power.csv b/python_extras/example/power.csv
new file mode 100755
index 000000000..c3ad36d83
--- /dev/null
+++ b/python_extras/example/power.csv
@@ -0,0 +1,315649 @@
+timestamp,real_power
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\ No newline at end of file
diff --git a/python_extras/example/weather.csv b/python_extras/example/weather.csv
new file mode 100644
index 000000000..b7e2e7f1b
--- /dev/null
+++ b/python_extras/example/weather.csv
@@ -0,0 +1,35065 @@
+datetime,solar,temperature
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+2017-12-31 23:00:00,0.0,32.7
\ No newline at end of file
diff --git a/python_extras/fit_filter.py b/python_extras/fit_filter.py
new file mode 100644
index 000000000..85285b5c4
--- /dev/null
+++ b/python_extras/fit_filter.py
@@ -0,0 +1,527 @@
+"""Create filters from data
+
+SYNTAX
+
+  $ gridlabd fit_filter -i|--input=INPUTCSV -o|--output=OUTPUTCSV [-k|--order] 
+        [-c|--config=CONFIGNAME] [-g|--glm=GLMNAME] [-I|--init=ISODATETIME]
+        [-M|--module=MODULENAME] [-C|--class=CLASSNAME] [-N|--name=OBJNAME]
+        [-P|--player[=PLAYERCSV]] [-R|--recorder=RECORDERCSV]
+        [--{by,with}_{hour,day,month,year}] [--with_time=unit]
+        [-v|--verbose] [-w|--warning] [-q|--quiet] [-d|--debug]
+        [-h|--help|help] [--test] 
+        [OPTIONS ...]
+
+DESCRIPTION
+
+The `fit_filter` utility creates NxM filters from N input and M output
+channels of data by fitting a Kth-order discrete-time transfer functions to
+the data using a least-squares linear regressions of the equations
+
+    Y0 = -a1*Y1 - ... -aK*YK + b0*U0 + ... + bK*UK + ... + c0*V0 + ... cK*VK
+    ...
+    Z0 = -d1*Z1 - ... -dK*ZK + e0*U0 + ... + eK*UK + ... + f0*V0 + ... fK*VK
+
+where
+
+    Y0 is the output channel 0 at time t
+    Y1 is the output channel 0 at time t-1
+    YK is the output channel 0 at time t-K
+    ...
+    Z0 is the output channel N at time t
+    Z1 is the output channel N at time t-1
+    ...
+    ZK is the output channel N at time t-K
+    U0 is the input channel 0 at time t
+    ...
+    UK is the input channel 0 at time t-K
+    V0 is the input channel M at time t
+    ...
+    VK is the input channel M at time t-K
+    a, b, c, d, e, and f are the coefficient of the transfer functions
+
+If a MODULENAME and CLASSNAME are specified, then an object of that class will be
+created with the filter as inputs and object properties as outputs.  If the
+module is not specified, the class is defined as a runtime class with all
+inputs and outputs specified as doubles.  If OBJNAME is specified the object is 
+given that name, otherwise a random object name is generated and appended to the 
+OBJNAME global variable.
+
+If PLAYERCSV is specified, then any created object will have the inputs 
+assigned from that file.  If RECORDERCSV is specified, then any object created will
+have the outputs recorded to that file.
+
+If --with_{hour,day,month,year} is specified, then an hour, day, month, or
+year column is added to the input data.  If --by_{hour,day,month,year} is
+specified, then the input data is grouped by hour, day, month, or year. If
+--with_time is specified, then the elapsed time in the specified units is
+included in a column.
+
+
+CONFIGURATION
+
+By default all the columns of the input and output files are used in the
+filter model identification.  Consequently there will be M x N transfer
+functions identified.  To prevent columns from being used you must specify
+the `-c|--config=CONFIGNAME` option to specify how columns are loaded. Only
+columns listed in the `converters` class will be used to generate filters.
+The file CONFIGNAME must be a valid Python file. The class `converters` must
+be defined, and contain the properties `inputs` and `outputs`.  These must be
+dictionaries where the keys are the column names, and values of the
+converters to be used for those columns.  
+
+For the example below, the following converter files can be used:
+
+    from datetime import datetime
+    class converters:
+
+        def real(x):
+            try:
+                return float(x)
+            except:
+                return float('NAN')
+        
+        def timestamp(x):
+            return datetime.strptime(x,"%m/%d/%y %H:%M")
+        
+        def datetime(x):
+            return datetime.strptime(x,"%Y-%m-%d %H:%M:%S")
+
+        outputs = {"timestamp":timestamp,"real_power":real}
+        inputs = {"datetime":datetime,"temperature":real,"solar":real}
+
+Note that using a converter often speeds up the CSV read process because the
+date/time format is specified and the loader does not need to deduce the
+format automatically, which can take significant additional time.
+
+To limit which columns of the input or output files are used, it is sufficient
+to omit them from the converter list.  For example, 
+
+    inputs = {"datetime":datetime,"temperature":real}
+
+omits the solar data in the input file.
+
+EXAMPLE
+
+    $ gridlabd fit_filter -o=power.csv -i=weather.csv -g=/tmp/test.glm
+
+"""
+
+import os, sys, warnings, subprocess, json
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import random
+
+np.set_printoptions(edgeitems=12,linewidth=1000,formatter={"float_kind":(lambda x:f"{x:7.4g}")})
+
+def identify(Y, X, K = 24,
+    daytype = False, hour = False, month = False, weekday = False,
+    piecewise = {}
+    ):
+    """Fit a discrete-time MISO transfer function to data
+    
+    Fits a discrete-time transfer function of the form
+    
+        Y(z)   b0 + b1*z^-1 + ... + bK z^-K
+        ---- = -----------------------------
+        U(z)    1 + a1*z^-1 + ... + bK z^-K
+    
+    to known values of X and Y.
+    
+    If the hold-out size N is positive, then the model is fit
+    to L-N data values, where L is the length of X.  The hold-out
+    test vector Z is then computed on the N last rows of X and Y.
+    
+    ARGUMENTS
+    
+        Y (matrix) is the output (LxK)
+        
+        X (matrix) are the inputs (LxKxM) where M is the number of input
+        variables
+        
+        K (int) is the desired order of the transfer function
+
+        daytype (boolean) enables day-type feature. A single column is added
+        with a boolean value indicating whether the datetime corresponds to
+        a weekend or holiday.
+
+        hour (boolean) enables the hour-of-day feature.  24 columns are added
+        with boolean values indicating whether the datetime corresponds to
+        each hour of day, i.e., column 0 is hour 0, column 1 is hour 1, etc.
+
+        month (boolean) enable a month-of-year feature. 12 columns are added
+        with boolean values indicating whether the datetime correspnonds to
+        each month of year, i.e., column 0 is January, column 1 is February,
+        etc.
+
+        weekday (boolean) enables weekday/weekend feature.  48 columns are
+        added with booleans values indicating whether the datetime
+        corresponds to a weekday or weekend and hour-of-day.  This overrides
+        the daytype and hour flags, with a warning. Columns 0-23 are weekday
+        hours, and columns 24-47 are weekend hours.
+
+        piecewise (dict) indicates how to apply piecewise fits to columns of
+        U. The specification may be any of the following:
+
+            { n : None }         no piecewise fit is performed (default)
+
+            { n : m }            m inflections points are automatically found
+                                 such that the rmse is minimized
+
+            { n : [x1 x2 ...] }  multiple inflection points are given
+    
+    FORMATTING
+
+        The file `load_model_config.py` may be used to modify the default
+        format configuration stored in `config`.
+
+    RETURNS
+    
+        x,M where x = [a1,...,aK,b0,...bN] and N = columns of M
+                
+    where
+
+        M is the historical data assembled from X and Y
+        
+        x is the model
+"""
+    L = len(Y)
+    M = np.hstack([np.hstack([Y[n+1:L-K+n] for n in range(K)]),
+                   np.hstack([X[n+1:L-K+n] for n in range(K+1)])])
+    Mt = M.transpose()
+    x = np.linalg.solve(Mt*M,Mt*Y[K+1:])
+    if debug_enabled:
+        print(np.hstack([M,Y[K+1:]]),file=sys.stderr)
+        print(x.transpose(),file=sys.stderr)
+    return x,M
+
+if __name__ == '__main__':
+
+    basename = os.path.basename(sys.argv[0]).replace('.py','')
+    
+    K = 24
+    inputs = None
+    outputs = None
+    glmname = "/dev/stdout"
+    modulename = None
+    classname = None
+    config = None
+    objname = None
+    playername = None
+    recordername = None
+    nominal_voltage = 120.0
+    verbose_enabled = False
+    warning_enabled = True
+    debug_enabled = False
+    quiet_enabled = False
+    init_time = None
+    time_skew = 0
+    constraint = None
+    resolution = None
+    with_time = None
+    with_hour = False
+    with_day = False
+    with_weekend = False
+    with_month = False
+    with_year = False
+    by_hour = False
+    by_day = False
+    by_month = False
+    by_year = False
+
+    # exit codes
+    E_OK = 0 # no error
+    E_INPUT = 1 # input file error
+    E_OUTPUT = 2 # output file error
+    E_INVALID = 3 # invalid arguments
+    E_CONFIG = 4 # invalid configuration
+    E_OPTION = 5 # invalid options
+
+    def error(msg,code=None):
+        if not quiet_enabled:
+            print(f"ERROR [{basename}]: {msg}",file=sys.stderr)
+        if not debug_enabled and code != None:
+            exit(code)
+        elif debug_enabled:
+            raise
+
+    def syntax(code=0):
+        if code == 0:
+            print(__doc__,file=sys.stderr)
+        else:
+            print(f"Syntax: {basename} -o|--output=OUTPUTCSV -i|--input=INPUTCSV [-g|--glm=GLMNAME] [-n|--name=LOADNAME] [-t|--type=LOADTYPE] [--test] [-h|--help|help] [OPTIONS ...]")
+        exit(code)
+
+    def verbose(msg):
+        if verbose_enabled:
+            print(f"VERBOSE [{basename}]: {msg}",file=sys.stderr)
+
+    def warning(msg):
+        if warnings_enabled:
+            print(f"WARNINGS [{basename}]: {msg}",file=sys.stderr)
+
+    class converters:
+        outputs = {}
+        inputs = {}
+
+    for arg in sys.argv[1:]:
+        args = arg.split("=")
+        if type(args) is list and len(args) > 1:
+            token = args[0]
+            value = "=".join(args[1:])
+        elif type(args) is list:
+            token = args[0]
+            value = None
+        else:
+            token = args
+            value = None
+        if token in ["-h","--help","help"]:
+            syntax()
+        elif token in ["-B","--bits"]:
+            resolution = int(value)
+        elif token in ["-c","--config"]:
+            config = value
+        elif token in ["-C","--class"]:
+            classname = value
+        elif token in ["-d","--debug"]:
+            debug_enabled = not debug_enabled
+        elif token in ["-i","--input"]:
+            inputs = value
+        elif token in ["-I","--init"]:
+            init_time = datetime.fromisoformat(value)
+        elif token in ["-g","--glm"]:
+            glmname = value
+        elif token in ["-k","--order"]:
+            K = int(value)
+        elif token in ["-M","--module"]:
+            modulename = value
+        elif token in ["-N","--name"]:
+            objname = value
+        elif token in ["-o","--output"]:
+            outputs = value
+        elif token in ["-P","--player"]:
+            playername = value
+        elif token in ["-q","--quiet"]:
+            quiet_enabled = not quiet_enabled
+        elif token in ["-R","--recorder"]:
+            recordername = value
+        elif token in ["-t","--type"]:
+            modeltype = value
+        elif token in ["-v","--verbose"]:
+            verbose_enabled = not verbose_enabled
+        elif token in ["-w","--warning"]:
+            warning_enabled = not warning_enabled
+        elif token in ["--autotest"]:
+            inputs = "weather.csv"
+            outputs = "power.csv"
+        elif token in ["--skew"]:
+            time_skew = value
+        elif token in ["--stdev"]:
+            constraint = int(value)
+        elif token in ["--with_time"]:
+            with_time = value
+        elif token in ["--with_hour"]:
+            with_hour = True
+        elif token in ["--with_day"]:
+            with_day = True
+        elif token in ["--with_weekend"]:
+            with_weekend = True
+        elif token in ["--with_month"]:
+            with_month = True
+        elif token in ["--with_year"]:
+            with_year = True
+        elif token in ["--by_hour"]:
+            by_hour = True
+        elif token in ["--by_weekday"]:
+            by_day = True
+        elif token in ["--by_month"]:
+            by_month = True
+        elif token in ["--by_year"]:
+            by_year = True
+        else:
+            error(f"option '{token}' is not valid",E_OPTION)
+
+    # load user-specified config
+    try:
+        import importlib
+        sys.path.insert(0,os.path.dirname(config))
+        config_items = importlib.import_module(os.path.basename(config).replace('.py',''))
+        for item in dir(config_items):
+            if not item.startswith("__"):
+                value = getattr(config_items,item)
+                setattr(sys.modules[__name__],item,value)
+                verbose(f"{config}: configuration item {item} = {value} ok")
+    except Exception as msg:
+        if config:
+            error(f"unable to import config file '{config}' ({msg})",E_CONFIG)
+
+    # processing requirements met
+    if inputs and outputs:
+        
+        # set output
+        if glmname != "/dev/stdout":
+            if not glmname.endswith(".glm"):
+                error(f"output file '{glmname}' is not a supported file format",E_INVALID)
+            try:
+                sys.stdout = open(glmname,"w")
+            except Exception as err:
+                error(err,E_OUTPUT)
+
+        # begin outputing model
+        print(f"// generated from 'gridlabd {basename} {' '.join(sys.argv[1:])}'")
+        gldver = subprocess.run("gridlabd -D suppress_repeat_messages=FALSE --version=number --version=build --version=branch".split(),capture_output=True).stdout.decode('utf-8').split('\n')
+        print(f"#define FIT_FILTER_VERSION_NUMBER={gldver[0]}")
+        print(f"#define FIT_FILTER_VERSION_BUILD={gldver[1]}")
+        print(f"#define FIT_FILTER_VERSION_BRANCH={gldver[2]}",flush=True)
+
+        # load data
+        try:
+
+            if converters.outputs:
+                output_cols = converters.outputs.keys()
+            else:
+                output_cols = None
+            output_data = pd.read_csv(outputs, usecols=output_cols, converters=converters.outputs)
+            output_names = output_data.columns[1:]
+            ndx = list(map(lambda t:datetime(t.year,t.month,t.day,t.hour),pd.DatetimeIndex(output_data[output_data.columns[0]])))
+            data = pd.DataFrame(output_data.groupby(ndx)[output_data.columns[1]].mean())
+            verbose(data)
+
+            if converters.inputs:
+                input_cols = converters.inputs.keys()
+            else:
+                input_cols = None
+            input_data = pd.read_csv(inputs, usecols=input_cols, converters=converters.inputs)
+            col0 = input_data.columns[0]
+            if with_time:
+                tndx = input_data[col0]-input_data[col0][0]
+                input_data["time"] = list(map(lambda x: x.total_seconds(),tndx))
+                if with_time == "week":
+                    input_data["time"] /= (3600*24*7)
+                elif with_time == "day":
+                    input_data["time"] /= (3600*24)
+                elif with_time == "hour":
+                    input_data["time"] /= 3600
+                elif with_time != "second":
+                    error(f"time unit '{with_time}' is not valid",2)
+            if with_day:
+                weekdays = list(map(lambda x: x.weekday(),input_data[col0]))
+                for weekday in set(weekdays):
+                    input_data[f"weekday{weekday}"] = list(map(lambda x:int(x==weekday),weekdays))
+            if with_weekend:
+                weekdays = list(map(lambda x: x.weekday(),input_data[col0]))
+                input_data["weekday"] = list(map(lambda x:int(x<5),weekdays))
+            if with_hour:
+                hours = list(map(lambda x: x.hour,input_data[col0]))
+                for hour in set(hours):
+                    input_data[f"hour{hour}"] = list(map(lambda x:int(x==hour),hours))
+            if with_month:
+                months = list(map(lambda x: x.month,input_data[col0]))
+                for month in set(months):
+                    input_data[f"month{month}"] = list(map(lambda x: int(x==month),months))
+            input_names = input_data.columns[1:]
+            ndx = list(map(lambda t:datetime(t.year,t.month,t.day,t.hour),pd.DatetimeIndex(input_data[input_data.columns[0]])))
+            data = input_data.groupby(ndx).mean().join(data).dropna()
+            verbose(data)
+
+        except Exception as err:
+
+            print(f"#error {basename} {err}")
+            error(err,E_INPUT)
+
+        if init_time:
+            back_time = init_time - timedelta(hours=K+1)
+            print(f"\n// initial input and state vector from {back_time} to {init_time}")
+            U0 = data[input_names][back_time:init_time-timedelta(hours=1)].to_dict('list')
+            for name in input_names:
+                print(f"#define U0_{name}={','.join(list(map(lambda z:str(z),U0[name])))}")
+                print(f"#define T0_{name}={data.index[0]}")
+                print(f"#define TN_{name}={data.index[-1]}")
+            X0 = data[output_names][back_time:init_time-timedelta(hours=1)].to_dict('list')
+            for name in output_names:
+                print(f"#define X0_{name}={','.join(list(map(lambda z:str(z),X0[name])))}")
+                print(f"#define T0_{name}={data.index[0]}")
+                print(f"#define TN_{name}={data.index[-1]}")
+
+        for output_name in output_names:
+
+            # perform model fit
+            try:
+
+                Y = np.matrix(data[output_name]).transpose()
+                U = []
+                for input_name in input_names:
+                    U.append(np.matrix(data[input_name]).transpose())
+                x,M = identify(Y,np.hstack(U),K)
+
+            except Exception as err:
+
+                print(f"#error {basename} {err}")
+                error(err,E_INVALID)
+
+            if debug_enabled:
+                print("")
+                print(f"// input = {M}".replace("\n","\n//         "))
+                print(f"// output = {Y[K+1:]}".replace("\n","\n//          "))
+                print(f"\n// model = [{','.join(list(map(lambda z:str(z),x.transpose().round(2).tolist()[0])))}]")
+
+            opts = ["0"]
+            if resolution:
+                opts.append(f"resolution={resolution}")
+            if constraint:
+                mean = data[output_name].mean()
+                std = data[output_name].std()                
+                opts.append(f"minimum={mean-constraint*std},maximum={mean+constraint*std}")
+
+            n_inputs = len(data.columns)-1
+            for n in range(n_inputs):
+                print(f"\n// {data.columns[n]} --> {output_name}")
+                print(f"filter {output_name}_{data.columns[n]}(z,1h,{','.join(opts)}) = (",end="")
+                for k in range(0,K+1):
+                    print(f"{x[-k*n_inputs-1,0]:+f}z^{K-k:.0f}",end="")            
+                print(f"{x[K+n,0]:+f} ) / (z^{K:.0f}",end="")         
+                for k in range(1,K):
+                    print(f"{-x[K-k,0]:+f}z^{K-k:.0f}",end="")
+                print(f"{-x[0,0]:+f})",end=";\n")
+
+            if playername:
+                print("module tape;")
+                print("class","input","{")
+                for name in input_names:
+                    print("   ","double",name,end=";\n")
+                print("}")
+                input_object = f"input_{random.randint(1e15,1e16):x}"
+                print("#define",f"INPUTNAME={input_object}")
+                print("object input {")
+                print("   ","name",input_object,end=";\n")
+                print("   ","object","player","{")
+                print("   ","   ","file",f"\"{playername}\"",end=";\n")
+                print("   ","}",end=";\n")
+                print("}")
+
+                if classname:
+                    if modulename:
+                        print("module","powerflow",end=";\n")
+                    print("class",classname,"{")
+                    for name in output_names:
+                        print("   ","double",name,end=";\n")
+                    print("}")
+
+                    if not objname:
+                        objname = f"{classname}_{random.randint(1e15,1e16):x}"
+
+                    print("#define",f"OUTPUTNAME={objname}")
+                    print("object",classname,"{")
+                    print("   ","name",f"{objname}",end=";\n")
+                    for input_name in input_names:
+                        print("   ",output_name,f"{output_name}_{input_name}({input_object}:{input_name},:U0_{input_name},:X0_{output_name})",end=";\n")
+                    if recordername:
+                        print("   ","object","recorder","{")
+                        print("   ","    ","file",recordername,end=";\n")
+                        print("   ","    ","property",",".join(output_names),end=";\n");
+                        print("   ","    ","interval",-1,end=";\n")
+                        print("   ","}",end=";\n")
+                    print("}")
+
+    else:
+        syntax(1)    
diff --git a/requirements.txt b/requirements.txt
index 8cfd0f19c..7a1bcc62e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 matplotlib==3.3.3
-numpy==1.19.4
+numpy==1.21.4
 pytz==2020.4
 PyGithub==1.54.1
 docker==4.4.4