remove wildcard imports from old tutorials

doc/tutorial/networktypes.rst

@@ -8,13 +8,13 @@ Start by importing the library:
 
 .. code-block:: python
 
-    >>> from pymnet import *
+    >>> import pymnet
 
 :code:`MultilayerNetwork` is the basic network class in the library – all other types of networks are special cases of it. In order to get a monoplex network object, you can simply construct a :code:`MultilayerNetwork` with 0 aspects.
 
 .. code-block:: python
 
-    >>> net = MultilayerNetwork(aspects=0)
+    >>> net = pymnet.MultilayerNetwork(aspects=0)
 
 You can now start adding nodes to the network with the :code:`add_node` method:
 
@@ -105,7 +105,7 @@ By default, all network objects are undirected. Directed network objects can be
 
 .. code-block:: python
 
-    >>> dirnet = MultilayerNetwork(aspects=0, directed=True)
+    >>> dirnet = pymnet.MultilayerNetwork(aspects=0, directed=True)
     >>> dirnet[1, 2] = 1
     >>> dirnet[1, 2]
     1
@@ -122,7 +122,7 @@ We are now ready to move to more general multilayer networks with an  arbitrary
 
 .. code-block:: python
 
-    >>> mnet = MultilayerNetwork(aspects=1)
+    >>> mnet = pymnet.MultilayerNetwork(aspects=1)
 
 Networks of this type are similar to the monoplex ones, but now you have layers in addition to nodes. You can add new layers with the :code:`add_layer` method:
 
@@ -163,7 +163,7 @@ You can create networks with arbitrary number of aspects. The syntax for this ty
 
 .. code-block:: python
 
-    >>> mnet2 = MultilayerNetwork(aspects=2)
+    >>> mnet2 = pymnet.MultilayerNetwork(aspects=2)
     >>> mnet2[1, 2, "a" ,"b", "x" ,"y"] = 1
     >>> mnet2[1, "a", "x"][2, "b", "y"]
     1
@@ -191,7 +191,7 @@ The simplest multiplex network is the one with no coupling edges. You would crea
 
 :code:`MultiplexNetwork`
 
->>> mplex = MultiplexNetwork(couplings="none")
+>>> mplex = pymnet.MultiplexNetwork(couplings="none")
 
 The nodes and edges can be accessed and added as usual:
 
@@ -212,7 +212,7 @@ In categorical networks, all the diagonal inter-layer edges are present.
 
 .. code-block:: python
 
-    >>> cnet = MultiplexNetwork(couplings="categorical")
+    >>> cnet = pymnet.MultiplexNetwork(couplings="categorical")
     >>> cnet.add_node(1)
     >>> cnet.add_layer("a")
     >>> cnet.add_layer("b")
@@ -223,7 +223,7 @@ In ordinal networks, only adjacent layers are connected to each other. In a :cod
 
 .. code-block:: python
 
-    >>> onet = MultiplexNetwork(couplings="ordinal")
+    >>> onet = pymnet.MultiplexNetwork(couplings="ordinal")
     >>> onet.add_node("node")
     >>> onet.add_layer(1)
     >>> onet.add_layer(2)
@@ -237,7 +237,7 @@ You can also give the coupling strength, i.e. the weight of the inter-layer edge
 
 .. code-block:: python
 
-    >>> cnet = MultiplexNetwork(couplings=("categorical", 10))
+    >>> cnet = pymnet.MultiplexNetwork(couplings=("categorical", 10))
     >>> cnet.add_node(1)
     >>> cnet.add_layer("a")
     >>> cnet.add_layer("b")
@@ -249,7 +249,7 @@ the following code constructs a multiplex network where the first aspect is cate
 
 .. code-block:: python
 
-    >>> conet = MultiplexNetwork(couplings=["categorical", "ordinal"])
+    >>> conet = pymnet.MultiplexNetwork(couplings=["categorical", "ordinal"])
     >>> conet.add_node("node")
     >>> conet.add_layer("a", 1)
     >>> conet.add_layer("b", 1)

doc/tutorial/nx.rst

@@ -5,7 +5,7 @@ Using NetworkX functions
 
 Start by importing the library:
 
->>> from pymnet import nx, models
+>>> from pymnet import nx
 
 You can then run any NetworkX function from the pymnet.nx module. For example, you can produce the Karate Club network with the following command.
 
@@ -25,5 +25,6 @@ For the sake of reproducability in the next example, let's explicitly seed the r
 
 You can also pass Pymnet objects as arguments to NetworkX functions in a similar way. This is handy, for example, when analyzing monoplex structures of intra-layer networks of multiplex networks. For example, producing a multiplex network with three Erdos-Renyi intra-layer networks using Pymnet and calculating the number of connected components in each layer can be done with the following command:
 
->>> {name: nx.number_connected_components(layer) for name, layer in models.er(1000, 3*[0.005]).A.items()}
+>>> import pymnet
+>>> {name: nx.number_connected_components(layer) for name, layer in pymnet.er(1000, 3*[0.005]).A.items()}
 {0: 10, 1: 9, 2: 5}

doc/tutorial/visualizing.rst

@@ -9,9 +9,9 @@ Drawing networks with the library is easy and is usually done with the :code:`dr
 
 .. code-block:: python
 
-    >>> from pymnet import *
-    >>> net = models.er_multilayer(5, 2, 0.2)
-    >>> fig = draw(net)
+    >>> import pymnet
+    >>> net = pymnet.er_multilayer(5, 2, 0.2)
+    >>> fig = pymnet.draw(net)
 
 The first line of this code imports :code:`pymnet`. The second line creates a random node-aligned multilayer network with 5 nodes and 2 layers, where two node-layers are connected to each other with a probability of 0.2. The third line then creates a picture of that network.
 
@@ -25,7 +25,7 @@ Alternatively, you can view the network straight away by telling the draw method
 
 .. code-block:: python
 
-    >>> fig = draw(net, show=True)
+    >>> fig = pymnet.draw(net, show=True)
 
 The figure produced looks like this:
 
@@ -36,7 +36,7 @@ Multiplex-network figures can also be produced. For example, running the followi
 
 .. code-block:: python
 
-    >>> fig = draw(er(10, 3*[0.4]), layout="spring")
+    >>> fig = pymnet.draw(er(10, 3*[0.4]), layout="spring")
 
 produces the following picture of a multiplex Erdos-Renyi network:
 
@@ -47,7 +47,7 @@ There are multiple ways of customizing the figures. For documentation, look at t
 
 .. code-block:: python
 
-    >>> fig = draw(er(10, 3*[0.3]),
+    >>> fig = pymnet.draw(er(10, 3*[0.3]),
                  layout="circular",
                  layershape="circle",
                  nodeColorDict={(0,0):"r", (1,0):"r", (0,1):"r"},
@@ -65,9 +65,9 @@ If the network is large, then it is often desirable not to plot the coupling edg
 
 .. code-block:: python
 
-    >>> net = read_ucinet("bkfrat.dat", couplings="none")
-    >>> net = transforms.threshold(net, 4)
-    >>> fig = draw(net,
+    >>> net = pymnet.read_ucinet("bkfrat.dat", couplings="none")
+    >>> net = pymnet.transforms.threshold(net, 4)
+    >>> fig = pymnet.draw(net,
                  show=True,
                  layout="spring",
                  layerColorRule={},