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Merge pull request #31 from gymrek-lab/mgymrek-plotting
Updating documentation of plotting
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,234 @@ | ||
| """Plot simulation as directed graph.""" | ||
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| import pydot | ||
| from PIL import Image, ImageDraw | ||
|
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| from pheno_sim.pheno_simulation import PhenoSimulation | ||
| from pheno_sim.base_nodes import AbstractBaseCombineFunctionNode | ||
| from .utils import MSG | ||
|
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| def visualize(input_spec: dict, filename: str, | ||
| img_format: str, verbose: bool=False) -> int: | ||
| """ | ||
| Visualize a phenotype model | ||
| Parameters | ||
| ---------- | ||
| input_spec : dict | ||
| Model configuration | ||
| filename : str | ||
| Prefix of output filename | ||
| img_format : str | ||
| Format of output file (jpg, png, or svg}) | ||
| verbose : bool | ||
| If true, print extra output to terminal | ||
| Returns | ||
| ------- | ||
| retcode : int | ||
| Return code (0 for success) | ||
| """ | ||
|
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| # Generate sim object | ||
| sim = PhenoSimulation(input_spec) | ||
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| # Add input nodes | ||
| sim_nodes = dict() | ||
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| for input_source in sim.input_runner.input_sources: | ||
| for input_node in input_source.input_nodes: | ||
| sim_nodes[input_node.alias] = CITRUSNode( | ||
| alias=input_node.alias, | ||
| node_type='input', | ||
| class_name=type(input_node).__name__ | ||
| ) | ||
|
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| # Add operator nodes | ||
| for sim_step in sim.simulation_steps: | ||
| step_alias = sim_step.alias | ||
| step_inputs = sim_step.inputs | ||
|
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| if isinstance(step_inputs, str): | ||
| step_inputs = [step_inputs] | ||
| elif isinstance(step_inputs, dict): | ||
| step_inputs = list(step_inputs.values()) | ||
|
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| if isinstance(sim_step, AbstractBaseCombineFunctionNode): | ||
| step_type = 'combine' | ||
| else: | ||
| step_type = 'trans' | ||
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| sim_nodes[step_alias] = CITRUSNode( | ||
| alias=step_alias, | ||
| inputs=step_inputs, | ||
| node_type=step_type, | ||
| class_name=type(sim_step).__name__ | ||
| ) | ||
|
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| # Identify nodes that are of type 'combine'. | ||
| combine_nodes = [node for node in sim_nodes.values() if node.node_type == 'combine'] | ||
|
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| # For each combine node, identify its ancestors. | ||
| all_ancestor_nodes = set() | ||
| for combine_node in combine_nodes: | ||
| all_ancestor_nodes.update(get_ancestor_nodes(combine_node, sim_nodes)) | ||
|
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| # Change the node_type of ancestor nodes which are of type 'trans' to 'cis'. | ||
| for ancestor_node_alias in all_ancestor_nodes: | ||
| if sim_nodes[ancestor_node_alias].node_type == 'trans': | ||
| sim_nodes[ancestor_node_alias].node_type = 'cis' | ||
|
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| # Print nodes | ||
| if verbose: | ||
| for k,v in sim_nodes.items(): | ||
| MSG(f"{k}:\n\t{str(v)}") | ||
|
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| # Plot the graph | ||
| plot_graph_with_legend(sim_nodes, filename, img_format) | ||
| MSG(f"Plot output to {filename}.{img_format}") | ||
|
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| # Return | ||
| return 0 | ||
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| class CITRUSNode: | ||
| """ | ||
| Helper class for plotting. | ||
| Represents a single CITRUS model node | ||
| Attributes | ||
| ---------- | ||
| alias : str | ||
| Node alias | ||
| inputs : list | ||
| Simulation steps input to this node | ||
| node_type : str | ||
| One of: input, cis, trans, combine | ||
| class_name : str | ||
| Name of the class of the node | ||
| """ | ||
| def __init__( | ||
| self, | ||
| alias, | ||
| inputs=[], | ||
| node_type=None, | ||
| class_name=None | ||
| ): | ||
| self.alias = alias | ||
| self.inputs = inputs | ||
| self.node_type = node_type | ||
| self.class_name = class_name | ||
|
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| def __str__(self): | ||
| return f"alias: {self.alias}\tnode_type: {self.node_type}\tclass_name: {self.class_name}\tinputs: {self.inputs}" | ||
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| def get_ancestor_nodes(node: CITRUSNode, | ||
| sim_nodes: dict[str, CITRUSNode]) -> list[CITRUSNode]: | ||
| """ | ||
| Iteratively get ancestor nodes of a given node. | ||
| Parameters | ||
| ---------- | ||
| node : CITRUSNode | ||
| node to get ancestors of | ||
| sim_nodes : dict[str]->CITRUSNode | ||
| Dictionary of all nodes | ||
| Returns | ||
| ------- | ||
| ancestors : list of CITRUSNode | ||
| List of nodes that are ancestors of node | ||
| """ | ||
| ancestors = set() | ||
| to_visit = [node] | ||
|
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| while to_visit: | ||
| current_node = to_visit.pop() | ||
|
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| # Add the direct parents to the ancestors set | ||
| for input_node_alias in current_node.inputs: | ||
| # Check if the ancestor is already in the set to avoid cycles | ||
| if input_node_alias not in ancestors: | ||
| ancestors.add(input_node_alias) | ||
| to_visit.append(sim_nodes[input_node_alias]) | ||
|
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| return list(ancestors) | ||
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| def plot_graph_with_legend(sim_nodes: dict[str, CITRUSNode], | ||
| filename: str, img_format: str): | ||
| """ | ||
| Make the plot from a list of nodes | ||
| Parameters | ||
| ---------- | ||
| sim_nodes : dict[str]->CITRUSNode | ||
| Dictionary of all nodes | ||
| filename : str | ||
| Prefix of output filename | ||
| img_format : str | ||
| Format of output file (jpg, png, or svg}) | ||
| """ | ||
|
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| # Define a dictionary for colors based on node_type | ||
| node_colors = { | ||
| 'input': '#FF8C00', # dark orange | ||
| 'cis': '#90EE90', # light green | ||
| 'trans': '#DEB887', # burly wood (light brown) | ||
| 'combine': '#FFF44F' # lemon yellow | ||
| } | ||
|
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| # Create a new graph | ||
| graph = pydot.Dot(graph_type='digraph', rankdir='UD', ranksep='0.5') | ||
|
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| # Add nodes with their respective colors | ||
| for node in sim_nodes.values(): | ||
| label = f"{node.alias}\n<{node.class_name}>" | ||
| graph.add_node(pydot.Node(node.alias, label=label, style="filled", fillcolor=node_colors[node.node_type])) | ||
|
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| # Add edges based on the inputs of each node | ||
| for node in sim_nodes.values(): | ||
| for input_node in node.inputs: | ||
| graph.add_edge(pydot.Edge(input_node, node.alias)) | ||
|
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| # Save graph to file | ||
| graph.write(filename + '.' + img_format, format=img_format) | ||
|
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| # Adding a legend using PIL | ||
| img = Image.open(filename + '.' + img_format) | ||
| legend = Image.new('RGB', (250, 100), (255, 255, 255)) | ||
|
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| # Draw the legend | ||
| for index, (label, color) in enumerate(node_colors.items()): | ||
| d = ImageDraw.Draw(legend) | ||
| d.rectangle([10, 10 + index * 25, 30, 30 + index * 25], fill=color) | ||
| d.text((40, 10 + index * 25), label, fill=(0, 0, 0)) | ||
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| # Scale up the legend by any desired factor | ||
| scale_factor = 2.0 | ||
| scaled_width = int(legend.width * scale_factor) | ||
| scaled_height = int(legend.height * scale_factor) | ||
| legend = legend.resize( | ||
| (scaled_width, scaled_height), | ||
| resample=Image.BICUBIC | ||
| ) | ||
|
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| # Trim whitespace: Here we'll crop out 40% from the right, adjust as needed | ||
| crop_percentage = 0.5 | ||
| cropped_width = int(scaled_width * (1 - crop_percentage)) | ||
| legend = legend.crop((0, 0, cropped_width, scaled_height)) | ||
|
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| # Combine original image and legend | ||
| total_width = img.width + legend.width | ||
| max_height = max(img.height, legend.height) | ||
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| combined = Image.new('RGB', (total_width, max_height), 'white') | ||
| combined.paste(img, (0, 0)) | ||
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| # Center the legend vertically | ||
| y_offset = (img.height - legend.height) // 2 | ||
| combined.paste(legend, (img.width, y_offset)) | ||
|
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| combined.save(filename + '.' + img_format) |
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