From a5b0d6c7b57a7068ef4cf654c2c190026290f538 Mon Sep 17 00:00:00 2001
From: Jean-Matthieu Etancelin <jean-matthieu.etancelin@univ-pau.fr>
Date: Wed, 24 Mar 2021 13:01:27 +0100
Subject: [PATCH] fixup
---
hysop/core/graph/computational_graph.py | 78 ++++++++++++-------------
1 file changed, 39 insertions(+), 39 deletions(-)
diff --git a/hysop/core/graph/computational_graph.py b/hysop/core/graph/computational_graph.py
index e20bc9402..e2c942aeb 100644
--- a/hysop/core/graph/computational_graph.py
+++ b/hysop/core/graph/computational_graph.py
@@ -29,10 +29,10 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
@debug
def __new__(cls, candidate_input_tensors=None,
- candidate_output_tensors=None,
- **kwds):
+ candidate_output_tensors=None,
+ **kwds):
return super(ComputationalGraph, cls).__new__(cls,
- input_fields=None, output_fields=None, **kwds)
+ input_fields=None, output_fields=None, **kwds)
@debug
def __init__(self, candidate_input_tensors=None,
@@ -183,19 +183,19 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
titles = [[('OPERATOR', 'FIELD', 'DISCRETIZATION', 'GHOSTS',
'MEMORY ORDER', 'CAN_SPLIT', 'TSTATES')]]
vals = tuple(sinputs.values()) + tuple(soutputs.values()) + tuple(titles)
- name_size = max(len(s[0]) for ss in vals for s in ss)
- field_size = max(len(s[1]) for ss in vals for s in ss)
- discr_size = max(len(s[2]) for ss in vals for s in ss)
- ghosts_size = max(len(s[3]) for ss in vals for s in ss)
- order_size = max(len(s[4]) for ss in vals for s in ss)
+ name_size = max(len(s[0]) for ss in vals for s in ss)
+ field_size = max(len(s[1]) for ss in vals for s in ss)
+ discr_size = max(len(s[2]) for ss in vals for s in ss)
+ ghosts_size = max(len(s[3]) for ss in vals for s in ss)
+ order_size = max(len(s[4]) for ss in vals for s in ss)
cansplit_size = max(len(s[5]) for ss in vals for s in ss)
- tstates_size = max(len(s[6]) for ss in vals for s in ss)
+ tstates_size = max(len(s[6]) for ss in vals for s in ss)
template = '\n {:<{name_size}} {:^{field_size}} {:^{discr_size}} {:^{ghosts_size}} {:^{order_size}} {:^{cansplit_size}} {:^{tstates_size}}'
ss = '>INPUTS:'
if sinputs:
- for (td, sreqs) in sorted(sinputs.items()):
+ for (td, sreqs) in sorted(sinputs.items(), key=lambda _: _[1][0]):
if isinstance(td, Topology):
ss += '\n {}'.format(td.short_description())
else:
@@ -216,7 +216,7 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
ss += ' None'
ss += '\n>OUTPUTS:'
if soutputs:
- for (td, sreqs) in sorted(soutputs.items()):
+ for (td, sreqs) in sorted(soutputs.items(), key=lambda _: _[1][0]):
if isinstance(td, Topology):
ss += '\n {}'.format(td.short_description())
else:
@@ -254,13 +254,13 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
continue
for op in sorted(operators, key=lambda x: x.pretty_name):
finputs = ','.join(sorted([f.pretty_name
- for f in op.iter_input_fields() if f.domain is domain]))
+ for f in op.iter_input_fields() if f.domain is domain]))
foutputs = ','.join(sorted([f.pretty_name
- for f in op.iter_output_fields() if f.domain is domain]))
+ for f in op.iter_output_fields() if f.domain is domain]))
pinputs = ','.join(sorted([p.pretty_name
- for p in op.input_params.values()]))
+ for p in op.input_params.values()]))
poutputs = ','.join(sorted([p.pretty_name
- for p in op.output_params.values()]))
+ for p in op.output_params.values()]))
infields = '[{}]'.format(finputs) if finputs else ''
outfields = '[{}]'.format(foutputs) if foutputs else ''
inparams = '[{}]'.format(pinputs) if pinputs else ''
@@ -287,9 +287,9 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
operators = domains[None]
for op in sorted(operators, key=lambda x: x.pretty_name):
pinputs = ','.join(sorted([p.pretty_name
- for p in op.input_params.values()]))
+ for p in op.input_params.values()]))
poutputs = ','.join(sorted([p.pretty_name
- for p in op.output_params.values()]))
+ for p in op.output_params.values()]))
inparams = '[{}]'.format(pinputs) if pinputs else ''
outparams = '[{}]'.format(poutputs) if poutputs else ''
@@ -306,11 +306,11 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
op_data = ops.setdefault(None, [])
op_data += multiline_split(strdata, maxlen, split_sep, replace, newline_prefix)
- name_size = max(strlen(s[0]) for ss in ops.values() for s in ss)
- in_size = max(strlen(s[1]) for ss in ops.values() for s in ss)
+ name_size = max(strlen(s[0]) for ss in ops.values() for s in ss)
+ in_size = max(strlen(s[1]) for ss in ops.values() for s in ss)
arrow_size = max(strlen(s[2]) for ss in ops.values() for s in ss)
- out_size = max(strlen(s[3]) for ss in ops.values() for s in ss)
- type_size = max(strlen(s[4]) for ss in ops.values() for s in ss)
+ out_size = max(strlen(s[3]) for ss in ops.values() for s in ss)
+ type_size = max(strlen(s[4]) for ss in ops.values() for s in ss)
ss = ''
for (domain, dops) in ops.items():
@@ -350,7 +350,7 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
for (backend, topologies) in self.get_topologies().items():
ss += '\n {}:'.format(backend.short_description())
ss += '\n *'+'\n *'.join(t.short_description()
- for t in sorted(topologies, key=lambda x: x.id))
+ for t in sorted(topologies, key=lambda x: x.id))
title = 'ComputationalGraph {} topology report '.format(self.pretty_name)
return '\n{}\n'.format(framed_str(title=title, msg=ss[1:]))
@@ -394,7 +394,7 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
titles = [[('BACKEND', 'TOPOLOGY', 'OPERATORS')]]
vals = tuple(topologies.values()) + tuple(titles)
backend_size = max(len(s[0]) for ss in vals for s in ss)
- topo_size = max(len(s[1]) for ss in vals for s in ss)
+ topo_size = max(len(s[1]) for ss in vals for s in ss)
template = '\n {:<{backend_size}} {:<{topo_size}} {}'
sizes = {'backend_size': backend_size,
'topo_size': topo_size}
@@ -428,30 +428,30 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
t0 = node.input_fields[f0]
if all((node.input_fields[fi] is t0) for fi in f.fields):
finputs.append('{}.{}'.format(f.pretty_name,
- t0.pretty_tag))
+ t0.pretty_tag))
handled_inputs += f.fields
for f in node.output_tensor_fields:
f0 = f.fields[0]
t0 = node.output_fields[f0]
if all((node.output_fields[fi] is t0) for fi in f.fields):
foutputs.append('{}.{}'.format(f.pretty_name,
- t0.pretty_tag))
+ t0.pretty_tag))
handled_outputs += f.fields
finputs += ['{}.{}'.format(f.pretty_name,
- t.pretty_tag)
+ t.pretty_tag)
for (f, t) in node.input_fields.items()
if f not in handled_inputs]
foutputs += ['{}.{}'.format(f.pretty_name,
- t.pretty_tag)
+ t.pretty_tag)
for (f, t) in node.output_fields.items()
if f not in handled_outputs]
finputs = ','.join(sorted(finputs))
foutputs = ','.join(sorted(foutputs))
pinputs = ','.join(sorted([p.pretty_name
- for p in node.input_params.values()]))
+ for p in node.input_params.values()]))
poutputs = ','.join(sorted([p.pretty_name
- for p in node.output_params.values()]))
+ for p in node.output_params.values()]))
infields = '[{}]'.format(finputs) if finputs else ''
outfields = '[{}]'.format(foutputs) if foutputs else ''
@@ -738,13 +738,14 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
Convert the graph to a pyvis network for vizualization.
"""
try:
- import pyvis, matplotlib
+ import pyvis
+ import matplotlib
except ImportError:
- msg='\nGraph vizualization requires pyvis and matplotlib, which are not present on your system.\n'
+ msg = '\nGraph vizualization requires pyvis and matplotlib, which are not present on your system.\n'
print(msg)
return
- width = first_not_None(width, 1920)
+ width = first_not_None(width, 1920)
height = first_not_None(height, 1080)
graph = self.reduced_graph
@@ -755,16 +756,16 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
node_id = int(node)
if node_id not in known_nodes:
network.add_node(node_id, label=node.label,
- title=node.title, color=node.color,
- shape=node.shape(with_custom_nodes))
+ title=node.title, color=node.color,
+ shape=node.shape(with_custom_nodes))
known_nodes.add(node_id)
def add_edge(from_node, to_node):
from_node_id = int(from_node)
- to_node_id = int(to_node)
+ to_node_id = int(to_node)
edge = graph[from_node][to_node]
network.add_edge(from_node_id, to_node_id,
- title=str(edge.get('data', 'no edge data')))
+ title=str(edge.get('data', 'no edge data')))
for node in graph:
add_node(node)
@@ -774,7 +775,6 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
return network
-
@debug
@graph_built
def discretize(self):
@@ -820,10 +820,10 @@ class ComputationalGraph(ComputationalGraphNode, metaclass=ABCMeta):
output_discrete_tensor_fields[tfield] = tdfield
discrete_fields = tuple(set(input_discrete_fields.values()).union(
- output_discrete_fields.values()))
+ output_discrete_fields.values()))
discrete_tensor_fields = tuple(set(input_discrete_tensor_fields.values()).union(
- output_discrete_tensor_fields.values()))
+ output_discrete_tensor_fields.values()))
else:
input_discrete_fields = None
--
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