feat: added visualizer to verifier

bio_check/processing_tools.py

@@ -0,0 +1,16 @@
+import numpy as np
+
+
+def generate_color_gradient(self, simulator_names) -> list[str]:
+ """Generate a gradient of colors from red to green to blue for a list of simulator names."""
+ num_simulators = len(simulator_names)
+
+ red_to_green = np.linspace([1, 0, 0], [0, 1, 0], num=int(np.ceil(num_simulators / 2)), endpoint=False)
+ green_to_blue = np.linspace([0, 1, 0], [0, 0, 1], num=int(np.ceil(num_simulators / 2 + 1)))
+
+ full_gradient = np.vstack([red_to_green, green_to_blue])[1:num_simulators + 1]
+
+ hex_colors = ['#{:02x}{:02x}{:02x}'.format(int(r * 255), int(g * 255), int(b * 255)) for r, g, b in full_gradient]
+
+ return hex_colors
+

bio_check/verifier.py

@@ -1,15 +1,20 @@
 import os
 from time import sleep
 
+import numpy as np
 import requests
 from uuid import uuid4
 
+import seaborn as sns
+from matplotlib import pyplot as plt
 from requests import Response
 from requests.exceptions import RequestException
 from requests_toolbelt.multipart.encoder import MultipartEncoder
 from typing import *
 from dataclasses import dataclass, asdict
 
+from processing_tools import generate_color_gradient
+
 
 @dataclass
 class RequestError:
@@ -173,9 +178,65 @@ def get_verify_output(self, job_id: str) -> Union[Dict[str, Union[str, Dict]], R
  except Exception as e:
  return RequestError(error=str(e))
 
- def visualize(self):
- # TODO: get results and viz here
- pass
+ def visualize(
+ self,
+ data: dict,
+ simulators: list[str],
+ output_start: int,
+ output_end: int,
+ num_points: int,
+ hue: str = 'simulators',
+ use_grid=False
+ ) -> None:
+ """Visualize simulation output data, not comparison data, with subplots for each species.
+
+ Args:
+ data (dict): simulation output data
+ simulators (list[str]): list of simulators
+ output_start (int): start time of simulation output recording.
+ output_end (int): end time of simulation output recording.
+ num_points (int): number of points in simulation output time series.
+ hue (str): hue upon which the linplot colors are based. Options are: `'simulators'` or `'species'`. Defaults to 'simulators'.
+ use_grid (bool): whether to use a grid for each subplot. Defaults to False.
+
+ """
+ # grid plot params
+ species_data_content = data['content']['results']['results']
+ species_names = list(species_data_content.keys())
+ num_species = len(species_names)
+
+ # plot data params
+ t = np.linspace(output_start, output_end, num_points) # TODO: extract this dynamically.
+ simulator_colors = generate_color_gradient(simulators)
+
+ # TODO: extract simulator names dynamically as well.
+
+ fig, axes = plt.subplots(nrows=num_species, ncols=3, figsize=(20, 6 * num_species))
+
+ if num_species == 1:
+ axes = [axes]
+
+ # iterate over grid rows
+ for i, species_name in enumerate(species_names):
+ # iterate over grid cols
+ for j, simulator_name in enumerate(simulators):
+ ax = axes[i][j]
+ species_data = data['content']['results']['results'][species_name]
+ output_data = species_data.get('output_data')
+
+ if output_data:
+ # create one plot in each column mapped to each individual simulator output (for clarity :) )
+ simulator_output = output_data[simulator_name]
+ sns.lineplot(ax=ax, color=simulator_colors[j], data=simulator_output, label=f"{simulator_name}")
+
+ # set row title
+ ax.set_title(f"{species_name} simulation outputs for {simulator_name}")
+ ax.legend()
+ ax.grid(use_grid)
+
+ # TODO: adjust this
+ plt.tight_layout()
+ plt.show()
 
  def export_csv(self):
  pass