def create_network_graph(p1, p2):
files = glob.glob('static/images/*_network_graph.png')
for f in files:
os.remove(f)
p_graph = nx.read_gpickle(f'../data/combined_graphs/{p1}/{p2}.gpickle')
chapter_names = pickle.load(open('../data/chapter_names.p', 'rb'))
spa_dictionary = Dictionary.load('../data/spa.dict')
syn_dictionary = Dictionary.load('../data/syn.dict')
for node, data in p_graph.nodes(data=True):
# it's a synonym node
if data['type'] == 'SYN':
data['document'] = 'Synonym'
data['term'] = syn_dictionary[data['term_id']]
else:
data['document'] = chapter_names[data['doc_id']]
data['term'] = spa_dictionary[data['term_id']]
p_graph = nx.relabel_nodes(
p_graph,
{node: f"{node}: {data['term']}" for node, data in p_graph.nodes(data=True)}
)
ap = nv.CircosPlot(
p_graph,
node_color='doc_id',
node_grouping='document',
group_label_position='middle',
group_label_color=True,
node_labels=True,
node_label_layout="numbers",
figsize=(10, 8)
)
ap.draw_group_labels()
ap.draw()
plt.savefig(f'./static/images/{p1}_{p2}_network_graph.png')
G.node[n]['degree'] = nx.degree(G)[n]
import nxviz as nv
import matplotlib.pyplot as plt
# Create the ArcPlot object: a
a = nv.ArcPlot(G, node_order='degree', node_labels=True)
a.draw()
ap = nv.ArcPlot(G, node_order='date', node_color='date', node_labels=True, node_size='date')
ap.draw()
h = nv.MatrixPlot(G)#, node_grouping='grouping')
h.draw()
c = nv.CircosPlot(G, node_order='degree', node_grouping = 'date', node_color='date')
c.draw()
###IDENTIFYING IMPORTANT NODES
list(G.neighbors(1))
nx.degree_centrality(G)
nx.betweenness_centrality(G)
# Define path_exists()
def path_exists(G, node1, node2):
"""
This function checks whether a path exists between two nodes (node1, node2) in graph G.
"""
visited_nodes = set()
queue = [node1]
def circos(df=DataFrame([]),
label=True,
node_color='None',
column=1,
inter=25,
size=5,
fontsize=10):
df1 = df[['GO', 'Entry', 'Term',
'Short_Term']].merge(df, on='Entry',
how='left').drop_duplicates()
df2 = DataFrame(df[['GO', 'Term', 'Short_Term',
'Entry']].drop_duplicates().groupby(
['GO', 'Term',
'Short_Term']).Entry.count()).reset_index()
#### >>>>>>>>>>>>>>>>
#### A partir de una matriz de datos extrae valores no redundantes
matrix = df1.pivot_table(values='Entry',
index=['GO_x', 'Term_x', 'Short_Term_x'],
aggfunc=len,
columns=['GO_y', 'Term_y', 'Short_Term_y'])
###
df_mat = []
n = -1
for i in list(matrix.columns.values):
n += 1
new = DataFrame(matrix.iloc[n:len(matrix)][i])
nn = -1
for index, row in new.iterrows():
nn += 1
df_mat.append([index, i, new.iloc[nn][i]])
nn = 0
###
df_mat = DataFrame(df_mat, columns=['go0', 'go1', 'val']).dropna()
###
nodos = []
for index, row in df_mat.iterrows():
if row.go0 == row.go1:
#print(row.go0, row.go1)
continue
else:
#print(row.go0, row.go1)
nodos.append([row.go0, row.go1, row.val])
nodos = DataFrame(nodos)
columnas = {0: 'GO', 1: 'Term', 2: 'Short_Term'}
nodos = DataFrame([[i[column] for i in nodos[0]],
[i[column] for i in nodos[1]], nodos[2] / 2]).T
#### >>>>>>>>>>>>>>>>
# si interacciona con mas uno, eliminar la redundancia, y si no interacciona con ninguno, dejar el nodo
# y su valor, este se verá en la red como un nodo aislado
aislado = [i for i in matrix.columns if len(matrix[[i]].dropna()) == 1]
aislado = [df_mat[df_mat.go0 == i] for i in aislado]
if len(aislado) > 0:
aislado = pd.concat(aislado)
aislado.columns = [0, 1, 2]
aislado = DataFrame([[i[column] for i in aislado[0]],
[i[column] for i in aislado[1]],
aislado[2] / 2]).T
nodos = pd.concat([nodos, aislado])
else:
pass
nodos.columns = ['Source', 'Target', 'Weight']
edges = nodos
order = []
for index, row in nodos.iterrows():
order.append(row[0])
order.append(row[1])
orden3 = DataFrame(order).drop_duplicates(keep='first').reset_index(
drop=True)
orden3.columns = [columnas[column]]
nodes = pd.merge(orden3, df2, on=[columnas[column]], how='left')
def make_graph(nodes, edges):
g = nx.Graph()
for i, row in nodes.iterrows():
keys = row.index.tolist()
values = row.values
# The dict contains all attributes
g.add_node(row[nodes.columns[0]], **dict(zip(keys, values)))
for i, row in edges.iterrows():
keys = row.index.tolist()
values = row.values
g.add_edge(row['Source'], row['Target'], **dict(zip(keys, values)))
return g
g = make_graph(nodes, edges)
for i, row in nodes.iterrows():
if row['Entry'] >= inter:
g.add_node(row[nodes.columns[0]], umbral='up')
if row['Entry'] < inter:
g.add_node(row[nodes.columns[0]], umbral='down')
color_nodo = {
'Uniques': nodes.columns[0],
'Umbral': 'umbral',
'None': False
}
c = nxv.CircosPlot(
g,
node_color=color_nodo[node_color], # nodes.columns[0],
node_grouping=color_nodo[node_color],
node_labels=label,
node_label_layout='rotation',
edge_width='Weight',
#edge_color = 'umbral',
figsize=(size, size),
fontsize=fontsize)
return c.draw()
# - Directed : Twitter # 3. networkx : API for analysis of Graph # 4. nxviz : API for creating beautiful and rational graph viz import networkx as nx G.nodes() G.edges() len(G.nodes()) len(G.edges()) type(G) #Check type of Graph import nxviz as nv import matplotlib.pyplot as plt c = nv.CircosPlot(G) c.draw() plt.show() ############################# Task 1 (Exploratory data analysis) type(G) len(G.nodes()) len(G.edges()) ############################ Task 2 (Plotting using nxviz) # Create the CircosPlot object: c c = CircosPlot(G,node_color='bipartite',node_grouping='bipartite',node_order='centrality') # Draw c to the screen c.draw()
def nxvizPlot(
adata,
cluster,
cluster_name,
edges_list,
plot="arcplot",
network=None,
weight_scale=5e3,
weight_threshold=1e-4,
figsize=(6, 6),
save_show_or_return="show",
save_kwargs={},
**kwargs,
):
"""Arc or circos plot of gene regulatory network for a particular cell cluster.
Parameters
----------
adata: :class:`~anndata.AnnData`.
AnnData object.
cluster: `str`
The group key that points to the columns of `adata.obs`.
cluster_name: `str` (default: `None`)
The group whose network and arcplot will be constructed and created.
edges_list: `dict` of `pandas.DataFrame`
A dictionary of dataframe of interactions between input genes for each group of cells based on ranking
information of Jacobian analysis. Each composite dataframe has `regulator`, `target` and `weight` three
columns.
plot: `str` (default: `arcplot`)
Which nxviz plot to use, one of {'arcplot', 'circosplot'}.
network: class:`~networkx.classes.digraph.DiGraph`
A direct network for this cluster constructed based on Jacobian analysis.
weight_scale: `float` (default: `1e3`)
Because values in Jacobian matrix is often small, the value will be multiplied by the weight_scale so that
the edge will have proper width in display.
weight_threshold: `float` (default: `weight_threshold`)
The threshold of weight that will be used to trim the edges for network reconstruction.
figsize: `None` or `[float, float]` (default: (6, 6)
The width and height of each panel in the figure.
save_show_or_return: `str` {'save', 'show', 'return'} (default: `show`)
Whether to save, show or return the figure.
save_kwargs: `dict` (default: `{}`)
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the
save_fig function will use the {"path": None, "prefix": 'arcplot', "dpi": None, "ext": 'pdf', "transparent":
True, "close": True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that
properly modify those keys according to your needs.
**kwargs:
Additional parameters that will pass to ArcPlot or CircosPlot
Returns
-------
Nothing but plot an ArcPlot of the input direct network.
"""
_, has_labeling = (
adata.uns["pp"].get("has_splicing"),
adata.uns["pp"].get("has_labeling"),
)
layer = "M_s" if not has_labeling else "M_t"
import matplotlib.pyplot as plt
try:
import networkx as nx
import nxviz as nv
except ImportError:
raise ImportError(
"You need to install the packages `networkx, nxviz`."
"install networkx via `pip install networkx`."
"install nxviz via `pip install nxviz`."
)
if edges_list is not None:
network = nx.from_pandas_edgelist(
edges_list[cluster_name].query("weight > @weight_threshold"),
"regulator",
"target",
edge_attr="weight",
create_using=nx.DiGraph(),
)
if len(network.node) == 0:
raise ValueError(
f"weight_threshold is too high, no edge has weight than {weight_threshold} " f"for cluster {cluster}."
)
# Iterate over all the nodes in G, including the metadata
if type(cluster_name) is str:
cluster_names = [cluster_name]
for n, d in network.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
network.nodes[n]["degree"] = nx.degree(network, n)
# data has to be float
if cluster is not None:
network.nodes[n]["size"] = (
adata[adata.obs[cluster].isin(cluster_names), n].layers[layer].A.mean().astype(float)
)
else:
network.nodes[n]["size"] = adata[:, n].layers[layer].A.mean().astype(float)
network.nodes[n]["label"] = n
for e in network.edges():
network.edges[e]["weight"] *= weight_scale
if plot.lower() == "arcplot":
prefix = "arcPlot"
# Create the customized ArcPlot object: a2
nv_ax = nv.ArcPlot(
network,
node_order=kwargs.pop("node_order", "degree"),
node_size=kwargs.pop("node_size", None),
node_grouping=kwargs.pop("node_grouping", None),
group_order=kwargs.pop("group_order", "alphabetically"),
node_color=kwargs.pop("node_color", "size"),
node_labels=kwargs.pop("node_labels", True),
edge_width=kwargs.pop("edge_width", "weight"),
edge_color=kwargs.pop("edge_color", None),
data_types=kwargs.pop("data_types", None),
nodeprops=kwargs.pop(
"nodeprops",
{
"facecolor": "None",
"alpha": 0.2,
"cmap": "viridis",
"label": "label",
},
),
edgeprops=kwargs.pop("edgeprops", {"facecolor": "None", "alpha": 0.2}),
node_label_color=kwargs.pop("node_label_color", False),
group_label_position=kwargs.pop("group_label_position", None),
group_label_color=kwargs.pop("group_label_color", False),
fontsize=kwargs.pop("fontsize", 10),
fontfamily=kwargs.pop("fontfamily", "serif"),
figsize=figsize,
)
elif plot.lower() == "circosplot":
prefix = "circosPlot"
# Create the customized CircosPlot object: a2
nv_ax = nv.CircosPlot(
network,
node_order=kwargs.pop("node_order", "degree"),
node_size=kwargs.pop("node_size", None),
node_grouping=kwargs.pop("node_grouping", None),
group_order=kwargs.pop("group_order", "alphabetically"),
node_color=kwargs.pop("node_color", "size"),
node_labels=kwargs.pop("node_labels", True),
edge_width=kwargs.pop("edge_width", "weight"),
edge_color=kwargs.pop("edge_color", None),
data_types=kwargs.pop("data_types", None),
nodeprops=kwargs.pop("nodeprops", None),
node_label_layout="rotation",
edgeprops=kwargs.pop("edgeprops", {"facecolor": "None", "alpha": 0.2}),
node_label_color=kwargs.pop("node_label_color", False),
group_label_position=kwargs.pop("group_label_position", None),
group_label_color=kwargs.pop("group_label_color", False),
fontsize=kwargs.pop("fontsize", 10),
fontfamily=kwargs.pop("fontfamily", "serif"),
figsize=figsize,
)
# recover network edge weights
for e in network.edges():
network.edges[e]["weight"] /= weight_scale
if save_show_or_return == "save":
# Draw a to the screen
nv_ax.draw()
plt.autoscale()
s_kwargs = {
"path": None,
"prefix": prefix,
"dpi": None,
"ext": "pdf",
"transparent": True,
"close": True,
"verbose": True,
}
s_kwargs = update_dict(s_kwargs, save_kwargs)
save_fig(**s_kwargs)
elif save_show_or_return == "show":
# Draw a to the screen
nv_ax.draw()
plt.autoscale()
# Display the plot
plt.show()
# plt.savefig('./unknown_arcplot.pdf', dpi=300)
elif save_show_or_return == "return":
return nv_ax
plt.clf()
nx.draw_spectral(G, with_labels=True)
plt.pause(2)
plt.close()
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
circ = nv.CircosPlot(G, node_order='degree',
node_grouping='grouping',
node_color='gender',
node_labels=True,
# ["beginning", "middle", "end"]
# group_label_position="beginning",
group_label_color=True
)
circ.draw()
plt.show()
import os
import networkx as nx
import nxviz as nv
import pickle as plk
def clone_overlap(self: Union[AnnData, Dandelion],
groupby: str,
colorby: str,
min_clone_size: Union[None, int] = None,
clone_key: Union[None, str] = None,
color_mapping: Union[None, Sequence, Dict] = None,
node_labels: bool = True,
node_label_layout: Literal[None, 'rotation',
'numbers'] = 'rotation',
group_label_position: Literal['beginning', 'middle',
'end'] = 'middle',
group_label_offset: int = 8,
figsize: Tuple[Union[int, float], Union[int,
float]] = (8, 8),
return_graph: bool = False,
save: Union[None, str] = None,
**kwargs):
"""
A plot function to visualise clonal overlap as a circos-style plot. Requires nxviz.
Parameters
----------
self : Dandelion, AnnData
`Dandelion` or `AnnData` object.
groupby : str
column name in obs/metadata for collapsing to nodes in circos plot.
colorby : str
column name in obs/metadata for grouping and color of nodes in circos plot.
min_clone_size : int, optional
minimum size of clone for plotting connections. Defaults to 2 if left as None.
clone_key : str, optional
column name for clones. None defaults to 'clone_id'.
color_maopping : Dict, Sequence, optional
custom color mapping provided as a sequence (correpsonding to order of categories or alpha-numeric order if dtype is not category), or dictionary containing custom {category:color} mapping.
node_labels : bool, optional
whether to use node objects as labels or not
node_label_layout : bool, optional
which/whether (a) node layout is used. One of 'rotation', 'numbers' or None.
group_label_position : str
The position of the group label. One of 'beginning', 'middle' or 'end'.
group_label_offset : int, float
how much to offset the group labels, so that they are not overlapping with node labels.
figsize : Tuple[Union[int,float], Union[int,float]]
figure size. Default is (8, 8).
return_graph : bool
whether or not to return the graph for fine tuning. Default is False.
**kwargs
passed to `matplotlib.pyplot.savefig`.
Returns
-------
a `nxviz.CircosPlot`.
"""
import networkx as nx
try:
import nxviz as nxv
except:
raise (ImportError(
"Unable to import module `nxviz`. Have you done install nxviz? Try pip install git+https://github.com/zktuong/nxviz.git"
))
if min_clone_size is None:
min_size = 2
else:
min_size = int(min_clone_size)
if clone_key is None:
clone_ = 'clone_id'
else:
clone_ = clone_key
if self.__class__ == AnnData:
data = self.obs.copy()
# get rid of problematic rows that appear because of category conversion?
data = data[~(data[clone_].isin(
[np.nan, 'nan', 'NaN', 'No_contig', 'unassigned', None]))]
if 'clone_overlap' in self.uns:
overlap = self.uns['clone_overlap'].copy()
else:
# prepare a summary table
datc_ = data[clone_].str.split('|', expand=True).stack()
datc_ = pd.DataFrame(datc_)
datc_.reset_index(drop=False, inplace=True)
datc_.columns = ['cell_id', 'tmp', clone_]
datc_.drop('tmp', inplace=True, axis=1)
datc_ = datc_[~(datc_[clone_].isin(
['', np.nan, 'nan', 'NaN', 'No_contig', 'unassigned', None]))]
dictg_ = dict(data[groupby])
datc_[groupby] = [dictg_[l] for l in datc_['cell_id']]
overlap = pd.crosstab(data[clone_], data[groupby])
if min_size == 0:
raise ValueError('min_size must be greater than 0.')
elif min_size > 2:
overlap[overlap < min_size] = 0
overlap[overlap >= min_size] = 1
elif min_size == 2:
overlap[overlap >= min_size] = 1
overlap.index.name = None
overlap.columns.name = None
elif self.__class__ == Dandelion:
data = self.metadata.copy()
# get rid of problematic rows that appear because of category conversion?
data = data[~(data[clone_].isin(
[np.nan, 'nan', 'NaN', 'No_contig', 'unassigned', None]))]
# prepare a summary table
datc_ = data[clone_].str.split('|', expand=True).stack()
datc_ = pd.DataFrame(datc_)
datc_.reset_index(drop=False, inplace=True)
datc_.columns = ['cell_id', 'tmp', clone_]
datc_.drop('tmp', inplace=True, axis=1)
dictg_ = dict(data[groupby])
datc_[groupby] = [dictg_[l] for l in datc_['cell_id']]
overlap = pd.crosstab(data[clone_], data[groupby])
if min_size == 0:
raise ValueError('min_size must be greater than 0.')
elif min_size > 2:
overlap[overlap < min_size] = 0
overlap[overlap >= min_size] = 1
elif min_size == 2:
overlap[overlap >= min_size] = 1
overlap.index.name = None
overlap.columns.name = None
edges = {}
for x in overlap.index:
if overlap.loc[x].sum() > 1:
edges[x] = [
y + ({
str(clone_): x
}, ) for y in list(
combinations(
[i for i in overlap.loc[x][overlap.loc[x] == 1].index],
2))
]
# create graph
G = nx.Graph()
# add in the nodes
G.add_nodes_from([(p, {
str(colorby): d
}) for p, d in zip(data[groupby], data[colorby])])
# unpack the edgelist and add to the graph
for edge in edges:
G.add_edges_from(edges[edge])
groupby_dict = dict(zip(data[groupby], data[colorby]))
if color_mapping is None:
if self.__class__ == AnnData:
if pd.api.types.is_categorical_dtype(self.obs[groupby]):
try:
colorby_dict = dict(
zip(list(self.obs[str(colorby)].cat.categories),
self.uns[str(colorby) + '_colors']))
except:
pass
else:
if type(color_mapping) is dict:
colorby_dict = color_mapping
else:
if pd.api.types.is_categorical_dtype(data[groupby]):
colorby_dict = dict(
zip(list(data[str(colorby)].cat.categories),
color_mapping))
else:
colorby_dict = dict(
zip(sorted(list(set(data[str(colorby)]))), color_mapping))
df = data[[groupby, colorby]]
if groupby == colorby:
df = data[[groupby]]
df = df.sort_values(groupby).drop_duplicates(
subset=groupby, keep="first").reset_index(drop=True)
else:
df = df.sort_values(colorby).drop_duplicates(
subset=groupby, keep="first").reset_index(drop=True)
c = nxv.CircosPlot(G,
node_color=colorby,
node_grouping=colorby,
node_labels=node_labels,
node_label_layout=node_label_layout,
group_label_position=group_label_position,
group_label_offset=group_label_offset,
figsize=figsize)
c.nodes = list(df[groupby])
if 'colorby_dict' in locals():
c.node_colors = [colorby_dict[groupby_dict[c]] for c in c.nodes]
c.compute_group_label_positions()
c.compute_group_colors()
c.draw()
if save is not None:
plt.savefig(save, bbox_inches='tight', **kwargs)
if return_graph:
return (c)
'recipient1',
edge_attr=['date', 'subject'])
plot = nv.ArcPlot(G)
plot.draw()
plt.show()
# We can see our 500 nodes (employees) at the bottom and the dominance of the one node on the right side. Please bear in mind, of course, that I am taking the first 500 rows of a 517,000 row dataset here, so we are only looking at emails from one of the 150 Enron executives contained in the full dataset.
#
# Next we'll bend the ends of this plot and join them together to form a circle, otherwise known as a
#
# CircosPlot
# ----------
# In[ ]:
plot = nv.CircosPlot(G)
plot.draw()
plt.show()
# Same deal here, although in my opinion a lot clearer and easier to comprehend.
#
# Still, this leaves a lot to be desired. It just doesn't *look* like what most folks expect from a network graph. So next we will use nxviz to
#
# Draw the Network
# ----------------
# In[ ]:
plt.figure(figsize=(20, 20))
pos = nx.spring_layout(G, k=.1)
nx.draw_networkx(G,
plt.subplot(223)
nx.draw_circular(Gd,
with_labels=True,
node_size=nodesizeHub,
node_color=nodecolorHub,
cmap="coolwarm")
plt.title('Showing the HITS Hub scores')
# plt.show()
plt.subplot(224)
nx.draw_spectral(Gd,
with_labels=True,
node_size=nodesizeHub,
node_color=nodecolorHub,
cmap="coolwarm")
plt.title('Showing the HITS Hub scores')
circ = nv.CircosPlot(Gd,
node_labels=True,
node_order="Auth",
node_color="Auth",
node_size='nodesizeAuth')
circ.draw()
plt.title('Showing the HITS Authority scores')
plt.show()
print(Gd.nodes['A'])
print(nodesizeAuth)
# Creating the correlation DataFrame
cor = pd.DataFrame.from_records(measures)
cor_T = cor.transpose()
# Calculating the correlation
cor_T.corr()
# Finding the most important character in the fifth book,
# according to degree centrality, betweenness centrality and pagerank.
p_rank, b_cent, d_cent = cor.idxmax(axis=1)
# Printing out the top character accoding to the three measures
print(p_rank, b_cent, d_cent)
#bk = books
#bk = nx.barbell_graph(m1=5,m2=1)
#nx.find_cliques(bk) ; plt.show()
G = nx.erdos_renyi_graph(n=20, p=0.2)
len(G)
len(G.edges())
len(G.nodes())
circ = nv.CircosPlot(G, node_color='key', node_group='key')
circ.draw()
#bk.nodes()
#nodes = bk.neighbors(8)
#G_eight = bk.subgraph(nodes)
#nx.draw(G_eight, with_labels=True)
#plt.savefig("GOT_Network")
#plt.show()
# Network visualization ------------------------------------------------------------------------------------------------
# Matrix plot
m = nv.MatrixPlot(T)
m.draw()
plt.show()
# Convert T to a matrix format: A
A = nx.to_numpy_matrix(T)
# Convert A back to the NetworkX form as a directed graph: T_conv
T_conv = nx.from_numpy_matrix(A, create_using=nx.DiGraph())
# Check that the `category` metadata field is lost from each node
for n, d in T_conv.nodes(data=True):
assert 'category' not in d.keys()
# Circos plot
c = nv.CircosPlot(T)
c.draw()
plt.show()
# Arc plot
a = ArcPlot(T, node_order='category', node_color='category')
a.draw()
plt.show()
# Important nodes ------------------------------------------------------------------------------------------------------
# The number of neighbors that a node has is called its "degree".
# Degree centrality = (no. neighbours) / (no. all possible neighbours) -> N if self-loops allowed, N-1 otherwise
# Get number of neighbours of node 1
T.neighbors(1)
# plt.show()
nodes = pd.read_csv('got-s7-nodes.csv', index_col=1)
edges = pd.read_csv('got-s7-edges.csv')
g = make_graph(nodes, edges)
nx.draw(g, with_labels=True, edge_color='black')
print(g.nodes)
print(g.edges)
"""Node link diagram :
The nodes are shown as a jumbled mess with too much overlap.
"""
c = nxv.CircosPlot(g,
edge_width=(edges['Weight'] / edges['Weight'].quantile(0.97)).tolist(),
node_labels=True, node_label_layout='rotation',
)
c.draw()
"""Circos plot which is much easier to read."""
deg_cen = nx.degree_centrality(g)
nodes['deg_cen'] = nodes['Id'].apply(lambda x: deg_cen[x])
g = make_graph(nodes, edges)
fig, ax = plt.subplots(figsize=(20,8))
sns.barplot(data=nodes.nlargest(50, 'deg_cen'),
x='Id', y='deg_cen',
ax=ax);
ax.set_xticklabels(ax.get_xticklabels(), rotation=90);
print(counts.groupby("type").sum())
pl = (ggplot(counts, aes(x="knocked", y="counts", fill="type")) + geom_col() +
labs(x="", y="strong interactions") + coord_flip())
pl.save("figures/knockout_counts.svg", width=4, height=12)
graph = nx.from_pandas_edgelist(pos[pos.change.abs() > 1e-2], "knocked",
"genus", "change")
for idx, _ in graph.nodes(data=True):
graph.node[idx]["degree"] = float(graph.degree(idx))
circos = nxviz.CircosPlot(
graph,
node_labels=True,
rotate_labels=True,
edge_cmap="bwr",
edge_color="change",
edge_limits=(-pos.change.abs().max(), pos.change.abs().max()),
node_color="degree",
figsize=(9, 7.5),
)
# circos.figure.set_dpi(200)
circos.draw()
plt.tight_layout(rect=[0.05, 0.15, 0.7, 0.8])
plt.savefig("figures/circos.svg")
plt.close()
ns = (ko[(ko.change.abs() > 1e-2) & (ko.knocked != ko.genus)].groupby(
["genus", "knocked"]).sample.count().reset_index())
pl = (ggplot(ns, aes(x="sample")) +
geom_histogram(bins=32, fill="lightgray", color="black") +
# https://stackoverflow.com/questions/53366634/how-to-set-edge-color-in-nxviz-arcplot D = nx.DiGraph() D.add_nodes_from([1, 2, 3]) D.add_edge(1, 2) D.add_edge(2, 3) m = nz.MatrixPlot(D) m.draw() plt.show() a = nz.ArcPlot(D) a.draw( ) # here you can provide node_order and node_color which is provided in nodes metadata plt.show() c = nz.CircosPlot(D) c.draw() plt.show() # %% # to get the neighbours of the node D = nx.DiGraph() D.add_nodes_from([1, 2, 3]) D.add_edge(1, 2) D.add_edge(2, 3) list(D.neighbors(2)) # this is the directed graph G = nx.Graph() G.add_nodes_from([1, 2, 3]) G.add_edge(1, 2)
import nxviz as nv import networkx as nx import matplotlib.pyplot as plt G = nx.Graph() G.add_nodes_from([1, 2, 3]) G.add_edge(1, 2) G.add_edge(1, 3) G.add_edges_from([(2, 3)]) nx.draw(G) m = nv.MatrixPlot(G) m.draw() c = nv.CircosPlot(G, node_size=1, edge_width=10, node_labels=True) c.draw() a = nv.ArcPlot(G) a.draw() plt.show()