ENH: FIX: Bug fixes and first notebook

CHANGES.txt

@@ -7,6 +7,7 @@ v.1.0.0
 + Unit tests are significantly improved(>80%)
 + Lots and lots of bug fixes.
 + Change the documentation theme to guzzle
++ Added first order hold discretization method
 - Removed FAQ from docs
 
 v.0.1.1rc1

docs/index.rst

@@ -19,6 +19,7 @@ Contents:
    tut_model_props
    tut_polyfuncs
    tut_freq_domain
+   tut_time_domain
 
 
 Additional Information

docs/tut_discrete.rst

@@ -45,6 +45,8 @@ Method                  Aliases
 ``lft``
 ----------------------- ------------------------
 ``zoh``
+----------------------- ------------------------
+``foh``
 ======================= ========================
 
 Hence, if a model with ``DiscretizedWith`` property set to 

docs/tut_time_domain.rst

@@ -0,0 +1,11 @@
+Time Domain Computations and Plots
+==================================
+
+.. py:currentmodule:: harold    
+.. autofunction:: simulate_linear_system
+.. autofunction:: simulate_step_response
+.. autofunction:: simulate_impulse_response
+.. autofunction:: step_response_plot
+.. autofunction:: impulse_response_plot
+
+

harold/_frequency_domain_plots.py

@@ -49,9 +49,10 @@ def bode_plot(G, w=None, use_db=False, use_hz=True, use_degree=True):
         frequencies are in Hz.
     use_degree : bool, optional
         The phase angle is shown in degrees or in radians.
+
     Returns
     -------
-    plot : matplotlib.figure.Figure
+    plot : matplotlib.axes._subplots.AxesSubplot
 
     """
     _check_for_state_or_transfer(G)
@@ -114,7 +115,8 @@ def bode_plot(G, w=None, use_db=False, use_hz=True, use_degree=True):
                 axs[2*row, col].set_ylabel(r'Magnitude {}'.format('(dB)'
                                            if use_db else
                                            r'($\mathregular{10^x}$)'))
-                axs[2*row+1, col].set_ylabel(r'Phase (deg)')
+                axs[2*row+1, col].set_ylabel(r'Phase ({})'.format(
+                                             'deg' if use_degree else 'rad'))
             if row == p - 1:
                 axs[2*row+1, col].set_xlabel(r'Frequency ({})'.format(f_unit))
 
@@ -136,7 +138,7 @@ def nyquist_plot(G, w=None):
 
     Returns
     -------
-    plot : matplotlib.figure.Figure
+    plot : matplotlib.axes._subplots.AxesSubplot
 
     """
     _check_for_state_or_transfer(G)

harold/_system_funcs.py

@@ -536,7 +536,7 @@ def _minimal_realization_state(A, B, C, tol=1e-6):
                 bs = int(sum(blocks_c))
                 A, B, C = Ac[:bs, :bs], Bc[:bs, :], Cc[:, :bs]
             else:
-                bs = n - int(sum(blocks_o))
+                bs = int(sum(blocks_o))
                 A, B, C = Ao[bs:, bs:], Bo[bs:, :], Co[:, bs:]
 
     return A, B, C

harold/_time_domain.py

@@ -194,6 +194,17 @@ def simulate_step_response(sys, t=None):
         discrete time models increments different than the sampling period also
         raises an error. On the other hand for discrete models this can be
         omitted and a time sequence will be generated automatically.
+
+    Returns
+    -------
+    yout : ndarray
+        The resulting response array. The array is 1D if sys is SISO and
+        has p columns if sys has p outputs. If there are also m inputs the
+        array is 3D array with the shape (<num of samples>, p, m)
+    tout : ndarray
+        The time sequence used in the simulation. If the parameter t is not
+        None then a copy of t is given.
+
     """
     _check_for_state_or_transfer(sys)
     # Always works with State Models
@@ -215,7 +226,7 @@ def simulate_step_response(sys, t=None):
 def simulate_impulse_response(sys, t=None):
     """
     Compute the linear model response to an Dirac delta pulse (or all-zeros
-    array except the first sample being 1. at each channel) sampled at given
+    array except the first sample being 1/dt at each channel) sampled at given
     time instances.
 
     If the time array is omitted then a time sequence is generated based on
@@ -231,6 +242,17 @@ def simulate_impulse_response(sys, t=None):
         discrete time models increments different than the sampling period also
         raises an error. On the other hand for discrete models this can be
         omitted and a time sequence will be generated automatically.
+
+    Returns
+    -------
+    yout : ndarray
+        The resulting response array. The array is 1D if sys is SISO and
+        has p columns if sys has p outputs. If there are also m inputs the
+        array is 3D array with the shape (<num of samples>, p, m)
+    tout : ndarray
+        The time sequence used in the simulation. If the parameter t is not
+        None then a copy of t is given.
+
     """
     _check_for_state_or_transfer(sys)
     # Always works with State Models
@@ -245,7 +267,7 @@ def simulate_impulse_response(sys, t=None):
 
     m = sys.shape[1]
     u = np.zeros([len(t), m], dtype=float)
-    u[0] = 1.
+    u[0] = 1./ts
 
     return simulate_linear_system(sys, u=u, t=t, per_channel=1)
 

harold/_time_domain_plots.py

@@ -44,7 +44,7 @@ def step_response_plot(sys, t=None):
 
     Returns
     -------
-    fig : matplotlib.figure.Figure
+    fig : matplotlib.axes._subplots.AxesSubplot
         Returns the figure handle of the step response
 
     """
@@ -95,7 +95,7 @@ def impulse_response_plot(sys, t=None):
 
     Returns
     -------
-    fig : matplotlib.figure.Figure
+    fig : matplotlib.axes._subplots.AxesSubplot
         Returns the figure handle of the impulse response
 
     """