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496 lines
19 KiB
Python
496 lines
19 KiB
Python
import importlib
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import inspect
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import json
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from typing import Union
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import numpy as np
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import networkx as nx
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try:
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from pylabrobot.resources.resource import Resource as ResourcePLR
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except ImportError:
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pass
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from typing import Union, get_origin, get_args
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physical_setup_graph: nx.Graph = None
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def canonicalize_nodes_data(data: dict, parent_relation: dict = {}) -> dict:
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for node in data.get("nodes", []):
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if node.get("label") is not None:
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id = node.pop("label")
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node["id"] = node["name"] = id
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if "id" not in node:
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node["id"] = node.get("name", "NaN")
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if "name" not in node:
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node["name"] = node["id"]
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if node.get("position") is None:
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node["position"] = {
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"x": node.pop("x", 0.0),
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"y": node.pop("y", 0.0),
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"z": node.pop("z", 0.0),
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}
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if node.get("config") is None:
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node["config"] = {}
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node["data"] = {}
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for k in list(node.keys()):
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if k not in [
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"id",
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"name",
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"class",
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"type",
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"position",
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"children",
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"parent",
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"config",
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"data",
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]:
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if k in ["chemical", "current_volume"]:
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if node["data"].get("liquids") is None:
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node["data"]["liquids"] = [{}]
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if k == "chemical":
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node["data"]["liquids"][0]["liquid_name"] = node.pop(k)
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elif k == "current_volume":
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node["data"]["liquids"][0]["liquid_volume"] = node.pop(k)
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elif k == "max_volume":
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node["data"]["max_volume"] = node.pop(k)
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elif k == "url":
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node.pop(k)
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else:
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node["config"][k] = node.pop(k)
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if "class" not in node:
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node["class"] = None
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if "type" not in node:
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node["type"] = (
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"container"
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if node["class"] is None
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else "device" if node["class"] not in ["container", "plate"] else node["class"]
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)
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if "children" not in node:
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node["children"] = []
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id2idx = {node_data["id"]: idx for idx, node_data in enumerate(data["nodes"])}
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for parent, children in parent_relation.items():
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data["nodes"][id2idx[parent]]["children"] = children
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for child in children:
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data["nodes"][id2idx[child]]["parent"] = parent
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return data
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def canonicalize_links_ports(data: dict) -> dict:
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# 第一遍处理：将字符串类型的port转换为字典格式
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for link in data.get("links", []):
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port = link.get("port")
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if isinstance(port, int):
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port = str(port)
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if isinstance(port, str):
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port_str = port.strip()
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if port_str.startswith("(") and port_str.endswith(")"):
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# 处理格式为 "(A,B)" 的情况
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content = port_str[1:-1].strip()
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parts = [p.strip() for p in content.split(",", 1)]
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source_port = parts[0]
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dest_port = parts[1] if len(parts) > 1 else None
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else:
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# 处理格式为 "A" 的情况
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source_port = port_str
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dest_port = None
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link["port"] = {link["source"]: source_port, link["target"]: dest_port}
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elif not isinstance(port, dict):
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# 若port既非字符串也非字典，初始化为空结构
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link["port"] = {link["source"]: None, link["target"]: None}
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# 构建边字典，键为(source节点, target节点)，值为对应的port信息
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edges = {(link["source"], link["target"]): link["port"] for link in data.get("links", [])}
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# 第二遍处理：填充反向边的dest信息
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delete_reverses = []
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for i, link in enumerate(data.get("links", [])):
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s, t = link["source"], link["target"]
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current_port = link["port"]
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if current_port.get(t) is None:
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reverse_key = (t, s)
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reverse_port = edges.get(reverse_key)
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if reverse_port:
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reverse_source = reverse_port.get(s)
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if reverse_source is not None:
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# 设置当前边的dest为反向边的source
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current_port[t] = reverse_source
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delete_reverses.append(i)
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else:
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# 若不存在反向边，初始化为空结构
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current_port[t] = current_port[s]
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# 删除已被使用反向端口信息的反向边
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data["links"] = [link for i, link in enumerate(data.get("links", [])) if i not in delete_reverses]
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return data
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def handle_communications(G: nx.Graph):
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available_communication_types = ["serial", "io_device", "plc", "io"]
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for e, edata in G.edges.items():
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if edata.get("type", "physical") != "communication":
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continue
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if G.nodes[e[0]].get("class") in available_communication_types:
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device_comm, device = e[0], e[1]
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elif G.nodes[e[1]].get("class") in available_communication_types:
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device_comm, device = e[1], e[0]
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else:
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continue
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if G.nodes[device_comm].get("class") == "serial":
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G.nodes[device]["config"]["port"] = device_comm
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elif G.nodes[device_comm].get("class") == "io_device":
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print(f'!!! Modify {device}\'s io_device_port to {edata["port"][device_comm]}')
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G.nodes[device]["config"]["io_device_port"] = int(edata["port"][device_comm])
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def read_node_link_json(json_file):
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global physical_setup_graph
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data = json.load(open(json_file, encoding="utf-8"))
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data = canonicalize_nodes_data(data)
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data = canonicalize_links_ports(data)
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physical_setup_graph = nx.node_link_graph(data, multigraph=False) # edges="links" 3.6 warning
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handle_communications(physical_setup_graph)
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return physical_setup_graph
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def read_graphml(graphml_file):
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global physical_setup_graph
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G = nx.read_graphml(graphml_file)
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mapping = {}
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parent_relation = {}
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for node in G.nodes():
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label = G.nodes[node].pop("label", G.nodes[node].get("id", G.nodes[node].get("name", "NaN")))
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mapping[node] = label
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if "::" in node:
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parent = mapping[node.split("::")[0]]
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if parent not in parent_relation:
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parent_relation[parent] = []
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parent_relation[parent].append(label)
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G2 = nx.relabel_nodes(G, mapping)
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data = nx.node_link_data(G2)
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data = canonicalize_nodes_data(data, parent_relation=parent_relation)
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data = canonicalize_links_ports(data)
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physical_setup_graph = nx.node_link_graph(data, edges="links", multigraph=False) # edges="links" 3.6 warning
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handle_communications(physical_setup_graph)
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return physical_setup_graph
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def dict_from_graph(graph: nx.Graph) -> dict:
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nodes_copy = {node_id: {"id": node_id, **node} for node_id, node in graph.nodes(data=True)}
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return nodes_copy
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def dict_to_tree(nodes: dict, devices_only: bool = False) -> list[dict]:
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# 将节点转换为字典，以便通过 ID 快速查找
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nodes_list = [node for node in nodes.values() if node.get("type") == "device" or not devices_only]
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# 初始化每个节点的 children 为包含节点字典的列表
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for node in nodes_list:
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node["children"] = [nodes[child_id] for child_id in node.get("children", [])]
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# 找到根节点并返回
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root_nodes = [
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node for node in nodes_list if len(nodes_list) == 1 or node.get("parent", node.get("parent_name")) in [None, "", "None", np.nan]
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]
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# 如果存在多个根节点，返回所有根节点
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return root_nodes
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def dict_to_nested_dict(nodes: dict, devices_only: bool = False) -> dict:
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# 将节点转换为字典，以便通过 ID 快速查找
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nodes_list = [node for node in nodes.values() if node.get("type") == "device" or not devices_only]
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# 初始化每个节点的 children 为包含节点字典的列表
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for node in nodes_list:
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node["children"] = {
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child_id: nodes[child_id]
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for child_id in node.get("children", [])
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if nodes[child_id].get("type") == "device" or not devices_only
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}
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if len(node["children"]) > 0 and node["type"].lower() == "device" and devices_only:
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node["config"]["children"] = node["children"]
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# 找到根节点并返回
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root_nodes = {
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node["id"]: node
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for node in nodes_list
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if node.get("parent", node.get("parent_name")) in [None, "", "None", np.nan]
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}
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# 如果存在多个根节点，返回所有根节点
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return root_nodes
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def list_to_nested_dict(nodes: list[dict]) -> dict:
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nodes_dict = {node["id"]: node for node in nodes}
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return dict_to_nested_dict(nodes_dict)
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def tree_to_list(tree: list[dict]) -> list[dict]:
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def _tree_to_list(tree: list[dict], result: list[dict]):
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for node_ in tree:
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node = node_.copy()
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result.append(node)
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if node.get("children"):
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_tree_to_list(node["children"], result)
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node["children"] = [n["id"] for n in node["children"]]
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result = []
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_tree_to_list(tree, result)
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return result
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def nested_dict_to_list(nested_dict: dict) -> list[dict]: # FIXME 是tree？
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"""
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将嵌套字典转换为扁平列表
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嵌套字典的层次结构将通过children属性表示
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Args:
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nested_dict: 嵌套的字典结构
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Returns:
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扁平化的字典列表
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"""
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result = []
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# 如果输入本身是一个节点，先添加它
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if "id" in nested_dict:
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node = nested_dict.copy()
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# 暂存子节点
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children_dict = node.get("children", {})
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# 如果children是字典，将其转换为键列表
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if isinstance(children_dict, dict):
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node["children"] = list(children_dict.keys())
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elif not isinstance(children_dict, list):
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node["children"] = []
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result.append(node)
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# 处理子节点字典
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if isinstance(children_dict, dict):
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for child_id, child_data in children_dict.items():
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if isinstance(child_data, dict):
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# 为子节点添加ID（如果不存在）
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if "id" not in child_data:
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child_data["id"] = child_id
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# 递归处理子节点
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result.extend(nested_dict_to_list(child_data))
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# 处理children字段
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elif "children" in nested_dict:
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children_dict = nested_dict.get("children", {})
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if isinstance(children_dict, dict):
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for child_id, child_data in children_dict.items():
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if isinstance(child_data, dict):
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# 为子节点添加ID（如果不存在）
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if "id" not in child_data:
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child_data["id"] = child_id
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# 递归处理子节点
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result.extend(nested_dict_to_list(child_data))
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return result
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def convert_resources_to_type(
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resources_list: list[dict], resource_type: type, *, plr_model: bool = False
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) -> Union[list[dict], dict, None, "ResourcePLR"]:
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"""
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Convert resources to a given type (PyLabRobot or NestedDict) from flattened list of dictionaries.
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Args:
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resources: List of resources in the flattened dictionary format.
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resource_type: Type of the resources to convert to.
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plr_model: 是否有plr_model类型
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Returns:
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List of resources in the given type.
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"""
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if resource_type == dict:
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return list_to_nested_dict(resources_list)
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elif isinstance(resource_type, type) and issubclass(resource_type, ResourcePLR):
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if isinstance(resources_list, dict):
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return resource_ulab_to_plr(resources_list, plr_model)
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resources_tree = dict_to_tree({r["id"]: r for r in resources_list})
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return resource_ulab_to_plr(resources_tree[0], plr_model)
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elif isinstance(resource_type, list) :
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if all((get_origin(t) is Union) for t in resource_type):
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resources_tree = dict_to_tree({r["id"]: r for r in resources_list})
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return [resource_ulab_to_plr(r, plr_model) for r in resources_tree]
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elif all(issubclass(t, ResourcePLR) for t in resource_type):
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resources_tree = dict_to_tree({r["id"]: r for r in resources_list})
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return [resource_ulab_to_plr(r, plr_model) for r in resources_tree]
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else:
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return None
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def convert_resources_from_type(resources_list, resource_type: type) -> Union[list[dict], dict, None, "ResourcePLR"]:
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"""
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Convert resources from a given type (PyLabRobot or NestedDict) to flattened list of dictionaries.
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Args:
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resources_list: List of resources in the given type.
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resource_type: Type of the resources to convert from.
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Returns:
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List of resources in the flattened dictionary format.
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"""
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if resource_type == dict:
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return nested_dict_to_list(resources_list)
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elif isinstance(resource_type, type) and issubclass(resource_type, ResourcePLR):
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resources_tree = [resource_plr_to_ulab(resources_list)]
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return tree_to_list(resources_tree)
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elif isinstance(resource_type, list) :
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if all((get_origin(t) is Union) for t in resource_type):
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resources_tree = [resource_plr_to_ulab(r) for r in resources_list]
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return tree_to_list(resources_tree)
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elif all(issubclass(t, ResourcePLR) for t in resource_type):
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resources_tree = [resource_plr_to_ulab(r) for r in resources_list]
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return tree_to_list(resources_tree)
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else:
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return None
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def resource_ulab_to_plr(resource: dict, plr_model=False) -> "ResourcePLR":
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"""
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Resource有model字段，但是Deck下没有，这个plr由外面判断传入
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"""
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if ResourcePLR is None:
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raise ImportError("pylabrobot not found")
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all_states = {resource["id"]: resource["data"]}
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def resource_ulab_to_plr_inner(resource: dict):
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all_states[resource["name"]] = resource["data"]
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d = {
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"name": resource["name"],
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"type": resource["type"],
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"size_x": resource["config"].get("size_x", 0),
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"size_y": resource["config"].get("size_y", 0),
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"size_z": resource["config"].get("size_z", 0),
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"location": {**resource["position"], "type": "Coordinate"},
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"rotation": {"x": 0, "y": 0, "z": 0, "type": "Rotation"}, # Resource如果没有rotation，是plr版本太低
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"category": resource["type"],
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"model": resource["config"].get("model", None), # resource中deck没有model
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"children": (
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[resource_ulab_to_plr_inner(child) for child in resource["children"]]
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if isinstance(resource["children"], list)
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else [resource_ulab_to_plr_inner(child) for child_id, child in resource["children"].items()]
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),
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"parent_name": resource["parent"] if resource["parent"] is not None else None,
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**resource["config"],
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}
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if not plr_model:
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d.pop("model")
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return d
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d = resource_ulab_to_plr_inner(resource)
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"""无法通过Resource进行反序列化，例如TipSpot必须内部序列化好，直接用TipSpot序列化会多参数，导致出错"""
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from pylabrobot.utils.object_parsing import find_subclass
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sub_cls = find_subclass(d["type"], ResourcePLR)
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spect = inspect.signature(sub_cls)
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if "category" not in spect.parameters:
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d.pop("category")
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resource_plr = sub_cls.deserialize(d, allow_marshal=True)
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resource_plr.load_all_state(all_states)
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return resource_plr
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def resource_plr_to_ulab(resource_plr: "ResourcePLR", parent_name: str = None):
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def resource_plr_to_ulab_inner(d: dict, all_states: dict) -> dict:
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r = {
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"id": d["name"],
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"name": d["name"],
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"sample_id": None,
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"children": [resource_plr_to_ulab_inner(child, all_states) for child in d["children"]],
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"parent": d["parent_name"] if d["parent_name"] else parent_name if parent_name else None,
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"type": "device", # FIXME plr自带的type是python class name
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"class": d.get("class", ""),
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"position": (
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{"x": d["location"]["x"], "y": d["location"]["y"], "z": d["location"]["z"]}
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if d["location"]
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else {"x": 0, "y": 0, "z": 0}
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),
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"config": {k: v for k, v in d.items() if k not in ["name", "children", "parent_name", "location"]},
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"data": all_states[d["name"]],
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}
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return r
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d = resource_plr.serialize()
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all_states = resource_plr.serialize_all_state()
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r = resource_plr_to_ulab_inner(d, all_states)
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return r
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def initialize_resource(resource_config: dict, lab_registry: dict) -> list[dict]:
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"""Initializes a resource based on its configuration.
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If the config is detailed, then do nothing;
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If it is a string, then import the appropriate class and create an instance of it.
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Args:
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resource_config (dict): The configuration dictionary for the resource, which includes the class type and other parameters.
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Returns:
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None
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"""
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resource_class_config = resource_config.get("class", None)
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if resource_class_config is None:
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return [resource_config]
|
||
elif type(resource_class_config) == str:
|
||
# Allow special resource class names to be used
|
||
if resource_class_config not in lab_registry.resource_type_registry:
|
||
return [resource_config]
|
||
# If the resource class is a string, look up the class in the
|
||
# resource_type_registry and import it
|
||
resource_class_config = resource_config["class"] = lab_registry.resource_type_registry[resource_class_config][
|
||
"class"
|
||
]
|
||
if type(resource_class_config) == dict:
|
||
module = importlib.import_module(resource_class_config["module"].split(":")[0])
|
||
mclass = resource_class_config["module"].split(":")[1]
|
||
RESOURCE = getattr(module, mclass)
|
||
|
||
if resource_class_config["type"] == "pylabrobot":
|
||
resource_plr = RESOURCE(name=resource_config["name"])
|
||
r = resource_plr_to_ulab(resource_plr=resource_plr, parent_name=resource_config.get("parent", None))
|
||
# r = resource_plr_to_ulab(resource_plr=resource_plr)
|
||
if resource_config.get("position") is not None:
|
||
r["position"] = resource_config["position"]
|
||
r = tree_to_list([r])
|
||
elif isinstance(RESOURCE, dict):
|
||
r = [RESOURCE.copy()]
|
||
|
||
return r
|
||
|
||
|
||
def initialize_resources(resources_config) -> list[dict]:
|
||
"""Initializes a list of resources based on their configuration.
|
||
|
||
If the config is detailed, then do nothing;
|
||
If it is a string, then import the appropriate class and create an instance of it.
|
||
|
||
Args:
|
||
resources_config (list[dict]): The configuration dictionary for the resources, which includes the class type and other parameters.
|
||
|
||
Returns:
|
||
None
|
||
"""
|
||
|
||
from unilabos.registry.registry import lab_registry
|
||
resources = []
|
||
for resource_config in resources_config:
|
||
if resource_config["parent"] == "tip_rack" or resource_config["parent"] == "plate_well":
|
||
continue
|
||
resources.extend(initialize_resource(resource_config, lab_registry))
|
||
|
||
return resources
|