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gem.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
def collect_data(path: Path, cohort):
csv_path = path / "all_runs_summary_combined_unaveraged.csv"
# df = pd.read_csv(csv_path)
# df = pd.read_csv(Path(r"C:\Users\Richard\_Vork\MVT\Nulliparous\all_runs_summary_combined_unaveraged.csv"))
# PLot void
# pivot_df = df.pivot(index="run", columns="Mouse", values="void")
# pivot_df = df.pivot(index="run", columns="Mouse", values="leak")
pivot_df = df.pivot(index="run", columns="Mouse", values="Avg Void Vol (ul)")
print(pivot_df.head())
mouse_to_cohort = df.set_index("Mouse")["cohort"].to_dict()
fig, ax = plt.subplots(figsize=(8,6))
x_labels = ["Run1", "Run2"]
x_indices = [1,2]
colors = plt.cm.tab10(np.linspace(0,1, len(pivot_df.columns)))
line_styles = ['-']
# line_styles = ['-', '--', '-.', ':']
jitter_amount = 0.1
for i, ((mouse_id, column_data), color) in enumerate(zip(pivot_df.items(), colors)):
# Look up cohort for this mouse
cohort_name = mouse_to_cohort.get(mouse_id, "Unknown")
label_text = f"{mouse_id} ({cohort_name})"
# Line style
line_style = line_styles[i % len(line_styles)]
# Jitter
shift = (i - len(pivot_df.columns)/2) * 0.01
jittered_x = [x + shift for x in x_indices]
ax.plot(jittered_x, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.8, label=label_text)
# ax.plot(x_indices, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.7, label=label_text)
mean_values = pivot_df.mean(axis=1)
ax.bar(x_indices, mean_values, color="lightgray", alpha=0.5, edgecolor="gray")
ax.set_xticks(x_indices)
ax.set_xticklabels(x_labels),
# ax.set_ylabel("Void Count")
# ax.set_title(f"Voids by Run: \n{cohort}")
# ax.set_ylabel("Leak Count")
# ax.set_title(f"Leaks by Run: \n{cohort}")
ax.set_ylabel("Avg Void Vol (ul)")
ax.set_title(f"AVV by Run: \n{cohort}")
# ax.set_ylim(0,10)
# ax.set_ylim(0,20)
ax.set_ylim(0, 750)
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", title="Subject ID")
plt.tight_layout()
plt.show()
def plot_void_count(path: Path, cohort):
csv_path = path / "all_runs_summary_combined_unaveraged.csv"
df = pd.read_csv(csv_path)
# df = pd.read_csv(Path(r"C:\Users\Richard\_Vork\MVT\Nulliparous\all_runs_summary_combined_unaveraged.csv"))
# PLot void
pivot_df = df.pivot(index="run", columns="Mouse", values="void")
print(pivot_df.head())
mouse_to_cohort = df.set_index("Mouse")["cohort"].to_dict()
fig, ax = plt.subplots(figsize=(8,6))
x_labels = ["Run1", "Run2"]
x_indices = [1,2]
colors = plt.cm.tab10(np.linspace(0,1, len(pivot_df.columns)))
line_styles = ['-']
# line_styles = ['-', '--', '-.', ':']
jitter_amount = 0.1
for i, ((mouse_id, column_data), color) in enumerate(zip(pivot_df.items(), colors)):
# Look up cohort for this mouse
cohort_name = mouse_to_cohort.get(mouse_id, "Unknown")
label_text = f"{mouse_id} ({cohort_name})"
# Line style
line_style = line_styles[i % len(line_styles)]
# Jitter
shift = (i - len(pivot_df.columns)/2) * 0.01
jittered_x = [x + shift for x in x_indices]
ax.plot(jittered_x, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.8, label=label_text)
# ax.plot(x_indices, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.7, label=label_text)
mean_values = pivot_df.mean(axis=1)
ax.bar(x_indices, mean_values, color="lightgray", alpha=0.5, edgecolor="gray")
ax.set_xticks(x_indices)
ax.set_xticklabels(x_labels),
ax.set_ylabel("Void Count")
ax.set_title(f"Voids by Run: \n{cohort}")
ax.set_ylim(0,10)
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", title="Subject ID")
plt.tight_layout()
plt.show()
def plot_leak_count(path: Path, cohort):
csv_path = path / "all_runs_summary_combined_unaveraged.csv"
df = pd.read_csv(csv_path)
# df = pd.read_csv(Path(r"C:\Users\Richard\_Vork\MVT\Nulliparous\all_runs_summary_combined_unaveraged.csv"))
# PLot leak
pivot_df = df.pivot(index="run", columns="Mouse", values="leak")
print(pivot_df.head())
mouse_to_cohort = df.set_index("Mouse")["cohort"].to_dict()
fig, ax = plt.subplots(figsize=(8,6))
x_labels = ["Run1", "Run2"]
x_indices = [1,2]
colors = plt.cm.tab10(np.linspace(0,1, len(pivot_df.columns)))
line_styles = ['-']
# line_styles = ['-', '--', '-.', ':']
jitter_amount = 0.1
for i, ((mouse_id, column_data), color) in enumerate(zip(pivot_df.items(), colors)):
# Look up cohort for this mouse
cohort_name = mouse_to_cohort.get(mouse_id, "Unknown")
label_text = f"{mouse_id} ({cohort_name})"
# Line style
line_style = line_styles[i % len(line_styles)]
# Jitter
shift = (i - len(pivot_df.columns)/2) * 0.01
jittered_x = [x + shift for x in x_indices]
ax.plot(jittered_x, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.8, label=label_text)
# ax.plot(x_indices, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.7, label=label_text)
mean_values = pivot_df.mean(axis=1)
ax.bar(x_indices, mean_values, color="lightgray", alpha=0.5, edgecolor="gray")
ax.set_xticks(x_indices)
ax.set_xticklabels(x_labels),
ax.set_ylabel("Leak Count")
ax.set_title(f"Leaks by Run: \n{cohort}")
ax.set_ylim(0,20)
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", title="Subject ID")
plt.tight_layout()
plt.show()
def plot_AVV(path: Path, cohort):
csv_path = path / "all_runs_summary_combined_unaveraged.csv"
df = pd.read_csv(csv_path)
# df = pd.read_csv(Path(r"C:\Users\Richard\_Vork\MVT\Nulliparous\all_runs_summary_combined_unaveraged.csv"))
# PLot AVV
pivot_df = df.pivot(index="run", columns="Mouse", values="Avg Void Vol (ul)")
print(pivot_df.head())
mouse_to_cohort = df.set_index("Mouse")["cohort"].to_dict()
fig, ax = plt.subplots(figsize=(8,6))
x_labels = ["Run1", "Run2"]
x_indices = [1,2]
colors = plt.cm.tab10(np.linspace(0,1, len(pivot_df.columns)))
line_styles = ['-']
# line_styles = ['-', '--', '-.', ':']
jitter_amount = 0.1
for i, ((mouse_id, column_data), color) in enumerate(zip(pivot_df.items(), colors)):
# Look up cohort for this mouse
cohort_name = mouse_to_cohort.get(mouse_id, "Unknown")
label_text = f"{mouse_id} ({cohort_name})"
# Line style
line_style = line_styles[i % len(line_styles)]
# Jitter
shift = (i - len(pivot_df.columns)/2) * 0.01
jittered_x = [x + shift for x in x_indices]
ax.plot(jittered_x, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.8, label=label_text)
# ax.plot(x_indices, column_data, marker="o", color=color, linewidth=2, linestyle=line_style, alpha=0.7, label=label_text)
mean_values = pivot_df.mean(axis=1)
ax.bar(x_indices, mean_values, color="lightgray", alpha=0.5, edgecolor="gray")
ax.set_xticks(x_indices)
ax.set_xticklabels(x_labels),
ax.set_ylabel("Avg Void Vol (ul)")
ax.set_title(f"AVV by Run: \n{cohort}")
ax.set_ylim(0, 750)
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", title="Subject ID")
plt.tight_layout()
plt.show()
# Execution
# collect_data(Path(r"C:\Users\Richard\_Work\MVT\Nulliparous"), "Nulliparous")
def plot_graphs(path, cohort):
plot_void_count(path, cohort)
plot_leak_count(path, cohort)
plot_AVV(path, cohort)
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\Nulliparous"), "Nulliparous")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\Parous"), "Parous")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\VCD\runs"), "VCD")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\OldMales"), "OldMales")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\GSAdultM\runs"), "AdultMales")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\AdultF_combined"), "AdultFemales")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\GSTeenM/runs"), "TeenMales")
# plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\TeenF_combined"), "TeenFemales")
plot_graphs(Path(r"C:\Users\Richard\_Vork\MVT\Machado Old Females\runs"), "MOF")