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#import os
#!pip install gradio seaborn scipy scikit-learn openpyxl pydantic==1.10.0 -q
from pydantic import BaseModel, ConfigDict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.integrate import odeint
from scipy.optimize import curve_fit
from sklearn.metrics import mean_squared_error
import gradio as gr
import io
from PIL import Image
import tempfile
class YourModel(BaseModel):
class Config:
arbitrary_types_allowed = True
class BioprocessModel:
def __init__(self, model_type='logistic', maxfev=50000):
self.params = {}
self.r2 = {}
self.rmse = {}
self.datax = []
self.datas = []
self.datap = []
self.dataxp = []
self.datasp = []
self.datapp = []
self.datax_std = []
self.datas_std = []
self.datap_std = []
self.biomass_model = None
self.biomass_diff = None
self.model_type = model_type
self.maxfev = maxfev
@staticmethod
def logistic(time, xo, xm, um):
return (xo * np.exp(um * time)) / (1 - (xo / xm) * (1 - np.exp(um * time)))
@staticmethod
def gompertz(time, xm, um, lag):
return xm * np.exp(-np.exp((um * np.e / xm) * (lag - time) + 1))
@staticmethod
def moser(time, Xm, um, Ks):
return Xm * (1 - np.exp(-um * (time - Ks)))
@staticmethod
def logistic_diff(X, t, params):
xo, xm, um = params
return um * X * (1 - X / xm)
@staticmethod
def gompertz_diff(X, t, params):
xm, um, lag = params
return X * (um * np.e / xm) * np.exp((um * np.e / xm) * (lag - t) + 1)
@staticmethod
def moser_diff(X, t, params):
Xm, um, Ks = params
return um * (Xm - X)
def substrate(self, time, so, p, q, biomass_params):
X_t = self.biomass_model(time, *biomass_params)
dXdt = np.gradient(X_t, time)
integral_X = np.cumsum(X_t) * np.gradient(time)
return so - p * (X_t - biomass_params[0]) - q * integral_X
def product(self, time, po, alpha, beta, biomass_params):
X_t = self.biomass_model(time, *biomass_params)
dXdt = np.gradient(X_t, time)
integral_X = np.cumsum(X_t) * np.gradient(time)
return po + alpha * (X_t - biomass_params[0]) + beta * integral_X
def process_data(self, df):
biomass_cols = [col for col in df.columns if col[1] == 'Biomasa']
substrate_cols = [col for col in df.columns if col[1] == 'Sustrato']
product_cols = [col for col in df.columns if col[1] == 'Producto']
time_col = [col for col in df.columns if col[1] == 'Tiempo'][0]
time = df[time_col].values
data_biomass = [df[col].values for col in biomass_cols]
data_biomass = np.array(data_biomass)
self.datax.append(data_biomass)
self.dataxp.append(np.mean(data_biomass, axis=0))
self.datax_std.append(np.std(data_biomass, axis=0, ddof=1))
data_substrate = [df[col].values for col in substrate_cols]
data_substrate = np.array(data_substrate)
self.datas.append(data_substrate)
self.datasp.append(np.mean(data_substrate, axis=0))
self.datas_std.append(np.std(data_substrate, axis=0, ddof=1))
data_product = [df[col].values for col in product_cols]
data_product = np.array(data_product)
self.datap.append(data_product)
self.datapp.append(np.mean(data_product, axis=0))
self.datap_std.append(np.std(data_product, axis=0, ddof=1))
self.time = time
def fit_model(self):
if self.model_type == 'logistic':
self.biomass_model = self.logistic
self.biomass_diff = self.logistic_diff
elif self.model_type == 'gompertz':
self.biomass_model = self.gompertz
self.biomass_diff = self.gompertz_diff
elif self.model_type == 'moser':
self.biomass_model = self.moser
self.biomass_diff = self.moser_diff
def fit_biomass(self, time, biomass):
try:
if self.model_type == 'logistic':
p0 = [min(biomass), max(biomass)*1.5 if max(biomass)>0 else 1.0, 0.1]
popt, _ = curve_fit(self.logistic, time, biomass, p0=p0, maxfev=self.maxfev)
self.params['biomass'] = {'xo': popt[0], 'xm': popt[1], 'um': popt[2]}
y_pred = self.logistic(time, *popt)
elif self.model_type == 'gompertz':
p0 = [max(biomass) if max(biomass)>0 else 1.0, 0.1, time[np.argmax(np.gradient(biomass))]]
popt, _ = curve_fit(self.gompertz, time, biomass, p0=p0, maxfev=self.maxfev)
self.params['biomass'] = {'xm': popt[0], 'um': popt[1], 'lag': popt[2]}
y_pred = self.gompertz(time, *popt)
elif self.model_type == 'moser':
p0 = [max(biomass) if max(biomass)>0 else 1.0, 0.1, min(time)]
popt, _ = curve_fit(self.moser, time, biomass, p0=p0, maxfev=self.maxfev)
self.params['biomass'] = {'Xm': popt[0], 'um': popt[1], 'Ks': popt[2]}
y_pred = self.moser(time, *popt)
self.r2['biomass'] = 1 - (np.sum((biomass - y_pred) ** 2) / np.sum((biomass - np.mean(biomass)) ** 2))
self.rmse['biomass'] = np.sqrt(mean_squared_error(biomass, y_pred))
return y_pred
except Exception as e:
print(f"Error en fit_biomass_{self.model_type}: {e}")
return None
def fit_substrate(self, time, substrate, biomass_params):
try:
if self.model_type == 'logistic':
p0 = [min(substrate), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, so, p, q: self.substrate(t, so, p, q, [biomass_params['xo'], biomass_params['xm'], biomass_params['um']]),
time, substrate, p0=p0, maxfev=self.maxfev
)
self.params['substrate'] = {'so': popt[0], 'p': popt[1], 'q': popt[2]}
y_pred = self.substrate(time, *popt, [biomass_params['xo'], biomass_params['xm'], biomass_params['um']])
elif self.model_type == 'gompertz':
p0 = [min(substrate), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, so, p, q: self.substrate(t, so, p, q, [biomass_params['xm'], biomass_params['um'], biomass_params['lag']]),
time, substrate, p0=p0, maxfev=self.maxfev
)
self.params['substrate'] = {'so': popt[0], 'p': popt[1], 'q': popt[2]}
y_pred = self.substrate(time, *popt, [biomass_params['xm'], biomass_params['um'], biomass_params['lag']])
elif self.model_type == 'moser':
p0 = [min(substrate), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, so, p, q: self.substrate(t, so, p, q, [biomass_params['Xm'], biomass_params['um'], biomass_params['Ks']]),
time, substrate, p0=p0, maxfev=self.maxfev
)
self.params['substrate'] = {'so': popt[0], 'p': popt[1], 'q': popt[2]}
y_pred = self.substrate(time, *popt, [biomass_params['Xm'], biomass_params['um'], biomass_params['Ks']])
self.r2['substrate'] = 1 - (np.sum((substrate - y_pred) ** 2) / np.sum((substrate - np.mean(substrate)) ** 2))
self.rmse['substrate'] = np.sqrt(mean_squared_error(substrate, y_pred))
return y_pred
except Exception as e:
print(f"Error en fit_substrate_{self.model_type}: {e}")
return None
def fit_product(self, time, product, biomass_params):
try:
if self.model_type == 'logistic':
p0 = [min(product), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, po, alpha, beta: self.product(t, po, alpha, beta, [biomass_params['xo'], biomass_params['xm'], biomass_params['um']]),
time, product, p0=p0, maxfev=self.maxfev
)
self.params['product'] = {'po': popt[0], 'alpha': popt[1], 'beta': popt[2]}
y_pred = self.product(time, *popt, [biomass_params['xo'], biomass_params['xm'], biomass_params['um']])
elif self.model_type == 'gompertz':
p0 = [min(product), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, po, alpha, beta: self.product(t, po, alpha, beta, [biomass_params['xm'], biomass_params['um'], biomass_params['lag']]),
time, product, p0=p0, maxfev=self.maxfev
)
self.params['product'] = {'po': popt[0], 'alpha': popt[1], 'beta': popt[2]}
y_pred = self.product(time, *popt, [biomass_params['xm'], biomass_params['um'], biomass_params['lag']])
elif self.model_type == 'moser':
p0 = [min(product), 0.01, 0.01]
popt, _ = curve_fit(
lambda t, po, alpha, beta: self.product(t, po, alpha, beta, [biomass_params['Xm'], biomass_params['um'], biomass_params['Ks']]),
time, product, p0=p0, maxfev=self.maxfev
)
self.params['product'] = {'po': popt[0], 'alpha': popt[1], 'beta': popt[2]}
y_pred = self.product(time, *popt, [biomass_params['Xm'], biomass_params['um'], biomass_params['Ks']])
self.r2['product'] = 1 - (np.sum((product - y_pred) ** 2) / np.sum((product - np.mean(product)) ** 2))
self.rmse['product'] = np.sqrt(mean_squared_error(product, y_pred))
return y_pred
except Exception as e:
print(f"Error en fit_product_{self.model_type}: {e}")
return None
def generate_fine_time_grid(self, time):
time_fine = np.linspace(time.min(), time.max(), 500)
return time_fine
def system(self, y, t, biomass_params, substrate_params, product_params, model_type):
X, S, P = y
if model_type == 'logistic':
dXdt = self.logistic_diff(X, t, biomass_params)
elif model_type == 'gompertz':
dXdt = self.gompertz_diff(X, t, biomass_params)
elif model_type == 'moser':
dXdt = self.moser_diff(X, t, biomass_params)
else:
dXdt = 0.0
so, p, q = substrate_params
po, alpha, beta = product_params
dSdt = -p * dXdt - q * X
dPdt = alpha * dXdt + beta * X
return [dXdt, dSdt, dPdt]
def get_initial_conditions(self, time, biomass, substrate, product):
if 'biomass' in self.params:
if self.model_type == 'logistic':
xo = self.params['biomass']['xo']
X0 = xo
elif self.model_type == 'gompertz':
xm = self.params['biomass']['xm']
um = self.params['biomass']['um']
lag = self.params['biomass']['lag']
X0 = xm * np.exp(-np.exp((um * np.e / xm)*(lag - 0)+1))
elif self.model_type == 'moser':
Xm = self.params['biomass']['Xm']
um = self.params['biomass']['um']
Ks = self.params['biomass']['Ks']
X0 = Xm*(1 - np.exp(-um*(0 - Ks)))
else:
X0 = biomass[0]
if 'substrate' in self.params:
so = self.params['substrate']['so']
S0 = so
else:
S0 = substrate[0]
if 'product' in self.params:
po = self.params['product']['po']
P0 = po
else:
P0 = product[0]
return [X0, S0, P0]
def solve_differential_equations(self, time, biomass, substrate, product):
if 'biomass' not in self.params or not self.params['biomass']:
print("No hay parámetros de biomasa, no se pueden resolver las EDO.")
return None, None, None, time
if self.model_type == 'logistic':
biomass_params = [self.params['biomass']['xo'], self.params['biomass']['xm'], self.params['biomass']['um']]
elif self.model_type == 'gompertz':
biomass_params = [self.params['biomass']['xm'], self.params['biomass']['um'], self.params['biomass']['lag']]
elif self.model_type == 'moser':
biomass_params = [self.params['biomass']['Xm'], self.params['biomass']['um'], self.params['biomass']['Ks']]
else:
biomass_params = [0,0,0]
if 'substrate' in self.params:
substrate_params = [self.params['substrate']['so'], self.params['substrate']['p'], self.params['substrate']['q']]
else:
substrate_params = [0,0,0]
if 'product' in self.params:
product_params = [self.params['product']['po'], self.params['product']['alpha'], self.params['product']['beta']]
else:
product_params = [0,0,0]
initial_conditions = self.get_initial_conditions(time, biomass, substrate, product)
time_fine = self.generate_fine_time_grid(time)
sol = odeint(self.system, initial_conditions, time_fine,
args=(biomass_params, substrate_params, product_params, self.model_type))
X = sol[:, 0]
S = sol[:, 1]
P = sol[:, 2]
return X, S, P, time_fine
def plot_results(self, time, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std=None, substrate_std=None, product_std=None,
experiment_name='', legend_position='best', params_position='upper right',
show_legend=True, show_params=True,
style='whitegrid',
line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
use_differential=False):
if y_pred_biomass is None:
print(f"No se pudo ajustar biomasa para {experiment_name} con {self.model_type}. Omitiendo figura.")
return None
sns.set_style(style)
if use_differential and 'biomass' in self.params and self.params['biomass']:
X, S, P, time_to_plot = self.solve_differential_equations(time, biomass, substrate, product)
if X is not None:
y_pred_biomass, y_pred_substrate, y_pred_product = X, S, P
else:
time_to_plot = time
else:
time_to_plot = time
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 15))
fig.suptitle(f'{experiment_name}', fontsize=16)
plots = [
(ax1, biomass, y_pred_biomass, biomass_std, 'Biomasa', 'Modelo', self.params.get('biomass', {}),
self.r2.get('biomass', np.nan), self.rmse.get('biomass', np.nan)),
(ax2, substrate, y_pred_substrate, substrate_std, 'Sustrato', 'Modelo', self.params.get('substrate', {}),
self.r2.get('substrate', np.nan), self.rmse.get('substrate', np.nan)),
(ax3, product, y_pred_product, product_std, 'Producto', 'Modelo', self.params.get('product', {}),
self.r2.get('product', np.nan), self.rmse.get('product', np.nan))
]
for idx, (ax, data, y_pred, data_std, ylabel, model_name, params, r2, rmse) in enumerate(plots):
if data_std is not None:
ax.errorbar(time, data, yerr=data_std, fmt=marker_style, color=point_color,
label='Datos experimentales', capsize=5)
else:
ax.plot(time, data, marker=marker_style, linestyle='', color=point_color,
label='Datos experimentales')
if y_pred is not None:
ax.plot(time_to_plot, y_pred, linestyle=line_style, color=line_color, label=model_name)
ax.set_xlabel('Tiempo')
ax.set_ylabel(ylabel)
if show_legend:
ax.legend(loc=legend_position)
ax.set_title(f'{ylabel}')
if show_params and params and all(np.isfinite(list(map(float, params.values())))):
param_text = '\n'.join([f"{k} = {v:.3f}" for k, v in params.items()])
text = f"{param_text}\nR² = {r2:.3f}\nRMSE = {rmse:.3f}"
if params_position == 'outside right':
bbox_props = dict(boxstyle='round', facecolor='white', alpha=0.5)
ax.annotate(text, xy=(1.05, 0.5), xycoords='axes fraction',
verticalalignment='center', bbox=bbox_props)
else:
if params_position in ['upper right', 'lower right']:
text_x = 0.95
ha = 'right'
else:
text_x = 0.05
ha = 'left'
if params_position in ['upper right', 'upper left']:
text_y = 0.95
va = 'top'
else:
text_y = 0.05
va = 'bottom'
ax.text(text_x, text_y, text, transform=ax.transAxes,
verticalalignment=va, horizontalalignment=ha,
bbox={'boxstyle': 'round', 'facecolor':'white', 'alpha':0.5})
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
image = Image.open(buf).convert("RGB")
plt.close(fig)
return image
def plot_combined_results(self, time, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std=None, substrate_std=None, product_std=None,
experiment_name='', legend_position='best', params_position='upper right',
show_legend=True, show_params=True,
style='whitegrid',
line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
use_differential=False):
if y_pred_biomass is None:
print(f"No se pudo ajustar biomasa para {experiment_name} con {self.model_type}. Omitiendo figura.")
return None
sns.set_style(style)
if use_differential and 'biomass' in self.params and self.params['biomass']:
X, S, P, time_to_plot = self.solve_differential_equations(time, biomass, substrate, product)
if X is not None:
y_pred_biomass, y_pred_substrate, y_pred_product = X, S, P
else:
time_to_plot = time
else:
time_to_plot = time
fig, ax1 = plt.subplots(figsize=(10, 7))
fig.suptitle(f'{experiment_name}', fontsize=16)
colors = {'Biomasa': 'blue', 'Sustrato': 'green', 'Producto': 'red'}
ax1.set_xlabel('Tiempo')
ax1.set_ylabel('Biomasa', color=colors['Biomasa'])
if biomass_std is not None:
ax1.errorbar(time, biomass, yerr=biomass_std, fmt=marker_style, color=colors['Biomasa'],
label='Biomasa (Datos)', capsize=5)
else:
ax1.plot(time, biomass, marker=marker_style, linestyle='', color=colors['Biomasa'],
label='Biomasa (Datos)')
ax1.plot(time_to_plot, y_pred_biomass, linestyle=line_style, color=colors['Biomasa'],
label='Biomasa (Modelo)')
ax1.tick_params(axis='y', labelcolor=colors['Biomasa'])
ax2 = ax1.twinx()
ax2.set_ylabel('Sustrato', color=colors['Sustrato'])
if substrate_std is not None:
ax2.errorbar(time, substrate, yerr=substrate_std, fmt=marker_style, color=colors['Sustrato'],
label='Sustrato (Datos)', capsize=5)
else:
ax2.plot(time, substrate, marker=marker_style, linestyle='', color=colors['Sustrato'],
label='Sustrato (Datos)')
if y_pred_substrate is not None:
ax2.plot(time_to_plot, y_pred_substrate, linestyle=line_style, color=colors['Sustrato'],
label='Sustrato (Modelo)')
ax2.tick_params(axis='y', labelcolor=colors['Sustrato'])
ax3 = ax1.twinx()
ax3.spines["right"].set_position(("axes", 1.2))
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
for sp in ax3.spines.values():
sp.set_visible(True)
ax3.set_ylabel('Producto', color=colors['Producto'])
if product_std is not None:
ax3.errorbar(time, product, yerr=product_std, fmt=marker_style, color=colors['Producto'],
label='Producto (Datos)', capsize=5)
else:
ax3.plot(time, product, marker=marker_style, linestyle='', color=colors['Producto'],
label='Producto (Datos)')
if y_pred_product is not None:
ax3.plot(time_to_plot, y_pred_product, linestyle=line_style, color=colors['Producto'],
label='Producto (Modelo)')
ax3.tick_params(axis='y', labelcolor=colors['Producto'])
lines_labels = [ax.get_legend_handles_labels() for ax in [ax1, ax2, ax3]]
lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
if show_legend:
ax1.legend(lines, labels, loc=legend_position)
if show_params:
param_text_biomass = ''
if 'biomass' in self.params:
param_text_biomass = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['biomass'].items()])
text_biomass = f"Biomasa:\n{param_text_biomass}\nR² = {self.r2.get('biomass', np.nan):.3f}\nRMSE = {self.rmse.get('biomass', np.nan):.3f}"
param_text_substrate = ''
if 'substrate' in self.params:
param_text_substrate = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['substrate'].items()])
text_substrate = f"Sustrato:\n{param_text_substrate}\nR² = {self.r2.get('substrate', np.nan):.3f}\nRMSE = {self.rmse.get('substrate', np.nan):.3f}"
param_text_product = ''
if 'product' in self.params:
param_text_product = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['product'].items()])
text_product = f"Producto:\n{param_text_product}\nR² = {self.r2.get('product', np.nan):.3f}\nRMSE = {self.rmse.get('product', np.nan):.3f}"
total_text = f"{text_biomass}\n\n{text_substrate}\n\n{text_product}"
if params_position == 'outside right':
bbox_props = dict(boxstyle='round', facecolor='white', alpha=0.5)
ax3.annotate(total_text, xy=(1.2, 0.5), xycoords='axes fraction',
verticalalignment='center', bbox=bbox_props)
else:
if params_position in ['upper right', 'lower right']:
text_x = 0.95
ha = 'right'
else:
text_x = 0.05
ha = 'left'
if params_position in ['upper right', 'upper left']:
text_y = 0.95
va = 'top'
else:
text_y = 0.05
va = 'bottom'
ax1.text(text_x, text_y, total_text, transform=ax1.transAxes,
verticalalignment=va, horizontalalignment=ha,
bbox={'boxstyle':'round', 'facecolor':'white', 'alpha':0.5})
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
image = Image.open(buf).convert("RGB")
plt.close(fig)
return image
def process_all_data(file, legend_position, params_position, model_types, experiment_names, lower_bounds, upper_bounds,
mode='independent', style='whitegrid', line_color='#0000FF', point_color='#000000',
line_style='-', marker_style='o', show_legend=True, show_params=True, use_differential=False, maxfev_val=50000):
try:
xls = pd.ExcelFile(file.name)
except Exception as e:
print(f"Error al leer el archivo Excel: {e}")
return [], pd.DataFrame()
sheet_names = xls.sheet_names
figures = []
comparison_data = []
experiment_counter = 0
for sheet_name in sheet_names:
try:
df = pd.read_excel(file.name, sheet_name=sheet_name, header=[0, 1])
except Exception as e:
print(f"Error al leer la hoja '{sheet_name}': {e}")
continue
model_dummy = BioprocessModel()
model_dummy.process_data(df)
time = model_dummy.time
if mode == 'independent':
num_experiments = len(df.columns.levels[0])
for idx in range(num_experiments):
col = df.columns.levels[0][idx]
try:
time_exp = df[(col, 'Tiempo')].dropna().values
biomass = df[(col, 'Biomasa')].dropna().values
substrate = df[(col, 'Sustrato')].dropna().values
product = df[(col, 'Producto')].dropna().values
except KeyError as e:
print(f"Error al procesar el experimento '{col}': {e}")
continue
biomass_std = None
substrate_std = None
product_std = None
if biomass.ndim > 1:
biomass_std = np.std(biomass, axis=0, ddof=1)
biomass = np.mean(biomass, axis=0)
if substrate.ndim > 1:
substrate_std = np.std(substrate, axis=0, ddof=1)
substrate = np.mean(substrate, axis=0)
if product.ndim > 1:
product_std = np.std(product, axis=0, ddof=1)
product = np.mean(product, axis=0)
experiment_name = (experiment_names[experiment_counter] if experiment_counter < len(experiment_names)
else f"Tratamiento {experiment_counter + 1}")
for model_type in model_types:
model = BioprocessModel(model_type=model_type, maxfev=maxfev_val)
model.fit_model()
y_pred_biomass = model.fit_biomass(time_exp, biomass)
if y_pred_biomass is None:
comparison_data.append({
'Experimento': experiment_name,
'Modelo': model_type.capitalize(),
'R² Biomasa': np.nan,
'RMSE Biomasa': np.nan,
'R² Sustrato': np.nan,
'RMSE Sustrato': np.nan,
'R² Producto': np.nan,
'RMSE Producto': np.nan
})
continue
else:
if 'biomass' in model.params and model.params['biomass']:
y_pred_substrate = model.fit_substrate(time_exp, substrate, model.params['biomass'])
y_pred_product = model.fit_product(time_exp, product, model.params['biomass'])
else:
y_pred_substrate = None
y_pred_product = None
comparison_data.append({
'Experimento': experiment_name,
'Modelo': model_type.capitalize(),
'R² Biomasa': model.r2.get('biomass', np.nan),
'RMSE Biomasa': model.rmse.get('biomass', np.nan),
'R² Sustrato': model.r2.get('substrate', np.nan),
'RMSE Sustrato': model.rmse.get('substrate', np.nan),
'R² Producto': model.r2.get('product', np.nan),
'RMSE Producto': model.rmse.get('product', np.nan)
})
if mode == 'combinado':
fig = model.plot_combined_results(time_exp, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std, substrate_std, product_std,
experiment_name,
legend_position, params_position,
show_legend, show_params,
style,
line_color, point_color, line_style, marker_style,
use_differential)
else:
fig = model.plot_results(time_exp, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std, substrate_std, product_std,
experiment_name,
legend_position, params_position,
show_legend, show_params,
style,
line_color, point_color, line_style, marker_style,
use_differential)
if fig is not None:
figures.append(fig)
experiment_counter += 1
elif mode in ['average', 'combinado']:
try:
time_exp = df[(df.columns.levels[0][0], 'Tiempo')].dropna().values
biomass = model_dummy.dataxp[-1]
substrate = model_dummy.datasp[-1]
product = model_dummy.datapp[-1]
except IndexError as e:
print(f"Error al obtener los datos promedio de la hoja '{sheet_name}': {e}")
continue
biomass_std = model_dummy.datax_std[-1]
substrate_std = model_dummy.datas_std[-1]
product_std = model_dummy.datap_std[-1]
experiment_name = (experiment_names[experiment_counter] if experiment_counter < len(experiment_names)
else f"Tratamiento {experiment_counter + 1}")
for model_type in model_types:
model = BioprocessModel(model_type=model_type, maxfev=maxfev_val)
model.fit_model()
y_pred_biomass = model.fit_biomass(time_exp, biomass)
if y_pred_biomass is None:
comparison_data.append({
'Experimento': experiment_name,
'Modelo': model_type.capitalize(),
'R² Biomasa': np.nan,
'RMSE Biomasa': np.nan,
'R² Sustrato': np.nan,
'RMSE Sustrato': np.nan,
'R² Producto': np.nan,
'RMSE Producto': np.nan
})
continue
else:
if 'biomass' in model.params and model.params['biomass']:
y_pred_substrate = model.fit_substrate(time_exp, substrate, model.params['biomass'])
y_pred_product = model.fit_product(time_exp, product, model.params['biomass'])
else:
y_pred_substrate = None
y_pred_product = None
comparison_data.append({
'Experimento': experiment_name,
'Modelo': model_type.capitalize(),
'R² Biomasa': model.r2.get('biomass', np.nan),
'RMSE Biomasa': model.rmse.get('biomass', np.nan),
'R² Sustrato': model.r2.get('substrate', np.nan),
'RMSE Sustrato': model.rmse.get('substrate', np.nan),
'R² Producto': model.r2.get('product', np.nan),
'RMSE Producto': model.rmse.get('product', np.nan)
})
if mode == 'combinado':
fig = model.plot_combined_results(time_exp, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std, substrate_std, product_std,
experiment_name,
legend_position, params_position,
show_legend, show_params,
style,
line_color, point_color, line_style, marker_style,
use_differential)
else:
fig = model.plot_results(time_exp, biomass, substrate, product,
y_pred_biomass, y_pred_substrate, y_pred_product,
biomass_std, substrate_std, product_std,
experiment_name,
legend_position, params_position,
show_legend, show_params,
style,
line_color, point_color, line_style, marker_style,
use_differential)
if fig is not None:
figures.append(fig)
experiment_counter += 1
comparison_df = pd.DataFrame(comparison_data)
if not comparison_df.empty:
comparison_df_sorted = comparison_df.sort_values(
by=['R² Biomasa', 'R² Sustrato', 'R² Producto', 'RMSE Biomasa', 'RMSE Sustrato', 'RMSE Producto'],
ascending=[False, False, False, True, True, True]
).reset_index(drop=True)
else:
comparison_df_sorted = comparison_df
return figures, comparison_df_sorted
def create_interface():
with gr.Blocks() as demo:
gr.Markdown("# Modelos de Bioproceso: Logístico, Gompertz, Moser y Luedeking-Piret")
gr.Markdown(r"""
## Ecuaciones Diferenciales Utilizadas
**Biomasa:**
- Logístico:
$$
\frac{dX}{dt} = \mu_m X\left(1 - \frac{X}{X_m}\right)
$$
- Gompertz:
$$
X(t) = X_m \exp\left(-\exp\left(\left(\frac{\mu_m e}{X_m}\right)(\text{lag}-t)+1\right)\right)
$$
Ecuación diferencial:
$$
\frac{dX}{dt} = X(t)\left(\frac{\mu_m e}{X_m}\right)\exp\left(\left(\frac{\mu_m e}{X_m}\right)(\text{lag}-t)+1\right)
$$
- Moser (simplificado):
$$
X(t)=X_m(1-e^{-\mu_m(t-K_s)})
$$
$$
\frac{dX}{dt}=\mu_m(X_m - X)
$$
**Sustrato y Producto (Luedeking-Piret):**
$$
\frac{dS}{dt} = -p \frac{dX}{dt} - q X
$$
$$
\frac{dP}{dt} = \alpha \frac{dX}{dt} + \beta X
$$
""")
file_input = gr.File(label="Subir archivo Excel")
with gr.Row():
with gr.Column():
legend_position = gr.Radio(
choices=["upper left", "upper right", "lower left", "lower right", "best"],
label="Posición de la leyenda",
value="best"
)
show_legend = gr.Checkbox(label="Mostrar Leyenda", value=True)
with gr.Column():
params_positions = ["upper left", "upper right", "lower left", "lower right", "outside right"]
params_position = gr.Radio(
choices=params_positions,
label="Posición de los parámetros",
value="upper right"
)
show_params = gr.Checkbox(label="Mostrar Parámetros", value=True)
model_types = gr.CheckboxGroup(
choices=["logistic", "gompertz", "moser"],
label="Tipo(s) de Modelo",
value=["logistic"]
)
mode = gr.Radio(["independent", "average", "combinado"], label="Modo de Análisis", value="independent")
use_differential = gr.Checkbox(label="Usar ecuaciones diferenciales para graficar", value=False)
experiment_names = gr.Textbox(
label="Nombres de los experimentos (uno por línea)",
placeholder="Experimento 1\nExperimento 2\n...",
lines=5
)
with gr.Row():
with gr.Column():
lower_bounds = gr.Textbox(
label="Lower Bounds (uno por línea, formato: param1,param2,param3)",
placeholder="0,0,0\n0,0,0\n...",
lines=5
)
with gr.Column():
upper_bounds = gr.Textbox(
label="Upper Bounds (uno por línea, formato: param1,param2,param3)",
placeholder="inf,inf,inf\ninf,inf,inf\n...",
lines=5
)
styles = ['white', 'dark', 'whitegrid', 'darkgrid', 'ticks']
style_dropdown = gr.Dropdown(choices=styles, label="Selecciona el estilo de gráfico", value='whitegrid')
line_color_picker = gr.ColorPicker(label="Color de la línea", value='#0000FF')
point_color_picker = gr.ColorPicker(label="Color de los puntos", value='#000000')
line_style_options = ['-', '--', '-.', ':']
line_style_dropdown = gr.Dropdown(choices=line_style_options, label="Estilo de línea", value='-')
marker_style_options = ['o', 's', '^', 'v', 'D', 'x', '+', '*']
marker_style_dropdown = gr.Dropdown(choices=marker_style_options, label="Estilo de punto", value='o')
maxfev_input = gr.Number(label="maxfev (Máx. evaluaciones para el ajuste)", value=50000)
simulate_btn = gr.Button("Simular")
output_gallery = gr.Gallery(label="Resultados", columns=2, height='auto')
output_table = gr.Dataframe(
label="Tabla Comparativa de Modelos",
headers=["Experimento", "Modelo", "R² Biomasa", "RMSE Biomasa",
"R² Sustrato", "RMSE Sustrato", "R² Producto", "RMSE Producto"],
interactive=False
)
state_df = gr.State()
def process_and_plot(file, legend_position, params_position, model_types, mode, experiment_names,
lower_bounds, upper_bounds, style,
line_color, point_color, line_style, marker_style,
show_legend, show_params, use_differential, maxfev_input):
experiment_names_list = experiment_names.strip().split('\n') if experiment_names.strip() else []
lower_bounds_list = []
if lower_bounds.strip():
for lb in lower_bounds.strip().split('\n'):
lb_values = []
for val in lb.split(','):
val = val.strip().lower()
if val in ['inf', 'infty', 'infinity']:
lb_values.append(-np.inf)
else:
try:
lb_values.append(float(val))
except ValueError:
lb_values.append(0.0)
lower_bounds_list.append(tuple(lb_values))
upper_bounds_list = []
if upper_bounds.strip():
for ub in upper_bounds.strip().split('\n'):
ub_values = []
for val in ub.split(','):
val = val.strip().lower()
if val in ['inf', 'infty', 'infinity']:
ub_values.append(np.inf)
else:
try:
ub_values.append(float(val))
except ValueError:
ub_values.append(np.inf)
upper_bounds_list.append(tuple(ub_values))
figures, comparison_df = process_all_data(file, legend_position, params_position, model_types, experiment_names_list,
lower_bounds_list, upper_bounds_list, mode, style,
line_color, point_color, line_style, marker_style,
show_legend, show_params, use_differential, maxfev_val=int(maxfev_input))
return figures, comparison_df, comparison_df
simulate_output = simulate_btn.click(
fn=process_and_plot,
inputs=[file_input,
legend_position,
params_position,
model_types,
mode,
experiment_names,
lower_bounds,
upper_bounds,
style_dropdown,
line_color_picker,
point_color_picker,
line_style_dropdown,
marker_style_dropdown,
show_legend,
show_params,
use_differential,
maxfev_input],
outputs=[output_gallery, output_table, state_df]
)
def export_excel(df):
if df.empty:
return None
with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as tmp:
df.to_excel(tmp.name, index=False)
return tmp.name
export_btn = gr.Button("Exportar Tabla a Excel")
file_output = gr.File()
export_btn.click(
fn=export_excel,
inputs=state_df,
outputs=file_output
)
return demo
demo = create_interface()
demo.launch(share=True)
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