Feature/history datetime (#1420)

* added history_date_tine and earliest/latest_date_time to node * fmt * Added functions onto nodes and edges * Added third node in the wrong spot * Added small fixes to docs and tidied up warnings * fmt * Fix for Lucas Review * fmt
Pometry · Dec 20, 2023 · c679a92 · c679a92
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commit c679a92
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diff --git a/docs/source/reference/core/edge.rst b/docs/source/reference/core/edge.rst
@@ -9,6 +9,14 @@ Edge
    :inherited-members:
 
 .. autoclass:: raphtory.Edges
+   :autosummary:
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :inherited-members:
+
+.. autoclass:: raphtory.MutableEdge
    :autosummary:
    :members:
    :undoc-members:

diff --git a/docs/source/reference/core/node.rst b/docs/source/reference/core/node.rst
@@ -0,0 +1,25 @@
+Node
+*****************
+.. autoclass:: raphtory.Node
+   :autosummary:
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :inherited-members:
+
+.. autoclass:: raphtory.Nodes
+   :autosummary:
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :inherited-members:
+
+.. autoclass:: raphtory.MutableNode
+   :autosummary:
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :private-members:
+   :inherited-members:
diff --git a/python/python/raphtory/plottingutils.py b/python/python/raphtory/plottingutils.py
@@ -7,42 +7,63 @@
 import matplotlib.pyplot as plt
 from matplotlib.offsetbox import OffsetImage, AnnotationBbox
 
-motif_im_dir = Path(__file__).parents[1].absolute().as_posix()+"/motif-images/"
+motif_im_dir = Path(__file__).parents[1].absolute().as_posix() + "/motif-images/"
 
-def get_motif(xory:str,y:int):
-    path = motif_im_dir+xory+str(y)+".png"
+
+def get_motif(xory: str, y: int):
+    path = motif_im_dir + xory + str(y) + ".png"
     return plt.imread(path)
 
+
 def get_motif_labels(motif_map):
     return np.vectorize(human_format)(motif_map)
 
+
 def offset_image(xory, coord, name, ax):
     img = get_motif(xory, name)
-    im = OffsetImage(img,zoom=0.04)
+    im = OffsetImage(img, zoom=0.04)
     im.image.axes = ax
 
-    if(xory=="x"):
-        ab = AnnotationBbox(im, (coord+0.5, 5.5),  xybox=(0., -40.), frameon=False,
-                            xycoords='data',  boxcoords="offset points", box_alignment=(0.5,0.5), pad=0)
+    if xory == "x":
+        ab = AnnotationBbox(
+            im,
+            (coord + 0.5, 5.5),
+            xybox=(0.0, -40.0),
+            frameon=False,
+            xycoords="data",
+            boxcoords="offset points",
+            box_alignment=(0.5, 0.5),
+            pad=0,
+        )
 
     else:
-        ab = AnnotationBbox(im, (0, coord),  xybox=(0., -40.), frameon=False,
-                            xycoords='data',  boxcoords="offset points", box_alignment=(1.0,0.0), pad=0)
+        ab = AnnotationBbox(
+            im,
+            (0, coord),
+            xybox=(0.0, -40.0),
+            frameon=False,
+            xycoords="data",
+            boxcoords="offset points",
+            box_alignment=(1.0, 0.0),
+            pad=0,
+        )
+
+    ax.add_artist(ab)
 
-    ax.add_artist(ab) 
 
 def add_motifs_to_ax(ax):
     for i in range(6):
-        offset_image("x",i,i,ax)
-        offset_image("y",i,i,ax)
+        offset_image("x", i, i, ax)
+        offset_image("y", i, i, ax)
+
 
 def global_motif_heatplot(motifs, cmap="YlGnBu", **kwargs):
     """
     Out-of-the-box plotting of global motif counts corresponding to the layout in Motifs in Temporal Networks (Paranjape et al)
 
     Args:
         motifs(list/np.array): 1 dimensional length-40 array of motifs, which should be the list of motifs returned from the `global_temporal_three_node_motifs` function in Raphtory.
-        **kwargs: arguments to 
+        **kwargs: arguments to
 
     Returns:
         matplotlib.axes: ax item containing the heatmap with motif labels on the axes.
@@ -53,71 +74,85 @@ def global_motif_heatplot(motifs, cmap="YlGnBu", **kwargs):
     motif_matrix = to_motif_matrix(motifs)
     labels = get_motif_labels(motif_matrix)
 
-    ax = sns.heatmap(motif_matrix,square=True, cbar=True, cmap=cmap, annot=labels, annot_kws={"size":13}, fmt='', cbar_kws={"shrink": 1.0}, **kwargs)
+    ax = sns.heatmap(
+        motif_matrix,
+        square=True,
+        cbar=True,
+        cmap=cmap,
+        annot=labels,
+        annot_kws={"size": 13},
+        fmt="",
+        cbar_kws={"shrink": 1.0},
+        **kwargs,
+    )
     add_motifs_to_ax(ax)
-    ax.tick_params(axis='x', which='major', pad=50)
-    ax.tick_params(axis='y', which='major', pad=50)
+    ax.tick_params(axis="x", which="major", pad=50)
+    ax.tick_params(axis="y", which="major", pad=50)
     plt.setp(ax.get_xticklabels(), visible=False)
     plt.setp(ax.get_yticklabels(), visible=False)
     plt.tight_layout()
     return ax
 
+
 def to_motif_matrix(motifs, data_type=int):
     """
     Converts a 40d vector of global motifs to a 2d grid of motifs corresponding to the layout in Motifs in Temporal Networks (Paranjape et al)
 
     Args:
-        motifs(list/np.array): 1 dimensional length-40 array of motifs. 
+        motifs(list/np.array): 1 dimensional length-40 array of motifs.
 
     Returns:
         numpy.array: 6x6 array of motifs whose ijth element is M_ij in Motifs in Temporal Networks (Paranjape et al).
     """
-    mapper = {0:(5,5),
-    1:(5,4),
-    2:(4,5),
-    3:(4,4),
-    4:(4,3),
-    5:(4,2),
-    6:(5,3),
-    7:(5,2),
-    8:(0,0),
-    9:(0,1),
-    10:(1,0),
-    11:(1,1),
-    12:(2,1),
-    13:(2,0),
-    14:(3,1),
-    15:(3,0),
-    16:(0,5),
-    17:(0,4),
-    18:(1,5),
-    19:(1,4),
-    20:(2,3),
-    21:(2,2),
-    22:(3,3),
-    23:(3,2),
-    24:(5,0),
-    25:(5,1),
-    26:(4,0),
-    27:(4,1),
-    28:(4,1),
-    29:(4,0),
-    30:(5,1),
-    31:(5,0),
-    32:(0,2),
-    33:(2,4),
-    34:(1,2),
-    35:(3,4),
-    36:(0,3),
-    37:(2,5),
-    38:(1,3),
-    39:(3,5)}
-
-    motif_2d = np.zeros((6,6),dtype=data_type)
+    mapper = {
+        0: (5, 5),
+        1: (5, 4),
+        2: (4, 5),
+        3: (4, 4),
+        4: (4, 3),
+        5: (4, 2),
+        6: (5, 3),
+        7: (5, 2),
+        8: (0, 0),
+        9: (0, 1),
+        10: (1, 0),
+        11: (1, 1),
+        12: (2, 1),
+        13: (2, 0),
+        14: (3, 1),
+        15: (3, 0),
+        16: (0, 5),
+        17: (0, 4),
+        18: (1, 5),
+        19: (1, 4),
+        20: (2, 3),
+        21: (2, 2),
+        22: (3, 3),
+        23: (3, 2),
+        24: (5, 0),
+        25: (5, 1),
+        26: (4, 0),
+        27: (4, 1),
+        28: (4, 1),
+        29: (4, 0),
+        30: (5, 1),
+        31: (5, 0),
+        32: (0, 2),
+        33: (2, 4),
+        34: (1, 2),
+        35: (3, 4),
+        36: (0, 3),
+        37: (2, 5),
+        38: (1, 3),
+        39: (3, 5),
+    }
+
+    motif_2d = np.zeros((6, 6), dtype=data_type)
     for i in range(40):
-        motif_2d[mapper[i]]=motifs[i]
+        motif_2d[mapper[i]] = motifs[i]
     return motif_2d
 
+
 def human_format(num):
     """
     Converts a number over 1000 to a string with 1 d.p and the corresponding letter. e.g. with input 24134, 24.1k as a string would be returned. This is used in the motif plots to make annotated heatmap cells more concise.
@@ -133,13 +168,15 @@ def human_format(num):
         magnitude += 1
         num /= 1000.0
     # add more suffixes if you need them
-    return '%.1f%s' % (num, ['', 'K', 'M', 'B', 'T', 'P'][magnitude])
+    return "%.1f%s" % (num, ["", "K", "M", "B", "T", "P"][magnitude])
+
 
 # Relating to distributions
 
+
 def cdf(observations, normalised=True):
     """
-    Returns x coordinates and y coordinates for a cdf (cumulative density function) from a list of observations. 
+    Returns x coordinates and y coordinates for a cdf (cumulative density function) from a list of observations.
 
     Args:
         observations(list): list of observations, should be numeric
@@ -149,19 +186,20 @@ def cdf(observations, normalised=True):
         list(float): x coordinates for the cdf
         list(float): y coordinates for the cdf
     """
-    data = np.array(listlike,dtype=object)
+    data = np.array(listlike, dtype=object)
     N = len(listlike)
 
     x = np.sort(data)
-    if (normalised):
-        y = np.arange(N)/float(N-1)
+    if normalised:
+        y = np.arange(N) / float(N - 1)
     else:
         y = np.arange(N)
     return x, y
 
+
 def ccdf(observations, normalised=True):
     """
-    Returns x coordinates and y coordinates for a ccdf (complementary cumulative density function) from a list of observations. 
+    Returns x coordinates and y coordinates for a ccdf (complementary cumulative density function) from a list of observations.
 
     Args:
         observations(list): list of observations, should be numeric
@@ -171,16 +209,16 @@ def ccdf(observations, normalised=True):
         list(float): x coordinates for the cdf
         list(float): y coordinates for the cdf
     """
-    x, y = cdf(listlike,normalised)
+    x, y = cdf(listlike, normalised)
     if normalised:
-        return x, 1.0-y
+        return x, 1.0 - y
     else:
-        return x, len(listlike)-y
+        return x, len(listlike) - y
 
 
-def lorenz(observations): 
+def lorenz(observations):
     """
-    Returns x coordinates and y coordinates for a Lorenz Curve from a list of observations. 
+    Returns x coordinates and y coordinates for a Lorenz Curve from a list of observations.
 
     Args:
         observations(list): list of observations, should be numeric
@@ -191,9 +229,10 @@ def lorenz(observations):
     """
     tmp_arr = np.array(sorted(listlike))
     # print(tmp_arr[0])
-    x= np.arange(listlike.size)/(listlike.size -1)
+    x = np.arange(listlike.size) / (listlike.size - 1)
     y = tmp_arr.cumsum() / tmp_arr.sum()
-    return x,y
+    return x, y
+
 
 def ordinal_number(number):
     """
@@ -206,9 +245,9 @@ def ordinal_number(number):
         str: ordinal for that number as string
     """
     if 10 <= number % 100 <= 20:
-        suffix = 'th'
+        suffix = "th"
     else:
-        suffixes = {1: 'st', 2: 'nd', 3: 'rd'}
-        suffix = suffixes.get(number % 10, 'th')
-    
-    return str(number) + suffix
+        suffixes = {1: "st", 2: "nd", 3: "rd"}
+        suffix = suffixes.get(number % 10, "th")
+
+    return str(number) + suffix
diff --git a/python/src/lib.rs b/python/src/lib.rs
@@ -6,7 +6,7 @@ use pyo3::prelude::*;
 use raphtory_core::python::{
     graph::{
         algorithm_result::AlgorithmResult,
-        edge::{PyDirection, PyEdge, PyEdges},
+        edge::{PyDirection, PyEdge, PyEdges, PyMutableEdge},
         graph::PyGraph,
         graph_with_deletions::PyGraphWithDeletions,
         index::GraphIndex,
@@ -50,6 +50,7 @@ fn raphtory(py: Python<'_>, m: &PyModule) -> PyResult<()> {
         PyMutableNode,
         PyEdge,
         PyEdges,
+        PyMutableEdge,
         PyProperties,
         PyConstProperties,
         PyTemporalProperties,

diff --git a/python/tests/test_algorithms.py b/python/tests/test_algorithms.py
@@ -473,8 +473,9 @@ def test_temporal_SEIR():
     seeded = [v for v in res.get_all_values() if v.infected == 0]
     assert len(seeded) == 2
 
-    res = algorithms.temporal_SEIR(g, [1], 1.0, 0, rng_seed=1).sort_by_value(reverse=False)
+    res = algorithms.temporal_SEIR(g, [1], 1.0, 0, rng_seed=1).sort_by_value(
+        reverse=False
+    )
     for i, (n, v) in enumerate(res):
-        assert n == g.node(i+1)
+        assert n == g.node(i + 1)
         assert v.infected == i
-