Homework_3

Parcial 1/Homework3/Archivo_Lookup_Table.py

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+
+"""Untitled3.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-ykZUAdzB6m7y0UvnUpPthAxPl4htzqB
+"""
+
+!pip -q install numpy
+import numpy as np
+from google.colab import files
+
+def voltage_from_percent(p):
+    x = p
+    # f(x) = 4.60897e-09*x^5 - 1.13065e-06*x^4 + 9.13377e-05*x^3 - 0.00277445*x^2 + 0.0639597*x + 0.154238
+    return (((((4.60897e-09*x - 1.13065e-06)*x + 9.13377e-05)*x
+              - 0.00277445)*x + 0.0639597)*x + 0.154238)
+
+# --- Parámetros LUT ---
+MV_FS = 3300           # índice 0..3300 mV
+USE_UINT8 = True       # True: % entero (0..100); False: uint16 con x10 (0..1000)
+
+ mV/1000 ---
+def percent_from_mV(mv):
+    target = mv / 1000.0
+    # Rango físico de tu poli (aprox): V(0)~0.154V, V(100)~3.168V
+    v0 = voltage_from_percent(0.0)
+    v1 = voltage_from_percent(100.0)
+    if target <= v0:  return 0.0
+    if target >= v1:  return 100.0
+    lo, hi = 0.0, 100.0
+    for _ in range(40):  # precisión alta
+        mid = 0.5*(lo+hi)
+        vm  = voltage_from_percent(mid)
+        if vm < target: lo = mid
+        else:           hi = mid
+    return 0.5*(lo+hi)
+
+percent = np.array([percent_from_mV(mv) for mv in range(MV_FS+1)])
+
+if USE_UINT8:
+    lut_vals = np.rint(np.clip(percent, 0, 100)).astype(np.uint8)  # 0..100
+    c_type   = "uint8_t"; LUT_SCALE = 1
+else:
+    lut_vals = np.rint(np.clip(percent*10.0, 0, 1000)).astype(np.uint16)  # 0..1000
+    c_type   = "uint16_t"; LUT_SCALE = 10
+
+lines = []
+lines.append("#ifndef LOOKUPTABLE_H")
+lines.append("#define LOOKUPTABLE_H")
+lines.append("")
+lines.append("#include <stdint.h>")
+lines.append(f"#define LUT_SIZE {MV_FS+1}")
+lines.append(f"#define LUT_SCALE {LUT_SCALE}   // 1:% entero; 10: décimas de %")
+lines.append("#define LUT_INDEX_IS_MV 1      // índice = mV (0..3300)")
+lines.append("")
+lines.append(f"static const {c_type} lookup_table[LUT_SIZE] = {{")
+
+row=[]
+for i,v in enumerate(lut_vals):
+    row.append(str(int(v)))
+    if (i+1)%16==0:
+        lines.append("  " + ", ".join(row) + ",")
+        row=[]
+if row:
+    lines.append("  " + ", ".join(row) + ",")
+
+lines.append("};")
+lines.append("")
+lines.append("#endif // LOOKUPTABLE_H")
+
+with open("lookuptable.h","w") as f:
+    f.write("\n".join(lines))
+
+files.download("lookuptable.h")
+print("Generado lookuptable.h (mV -> %) ✅")

Parcial 1/Homework3/CMakeLists.txt

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+idf_component_register(
+    SRCS "oneshot_read_main.c"
+    INCLUDE_DIRS "."
+    PRIV_REQUIRES esp_timer esp_adc
+)
+

Parcial 1/Homework3/Generador de Polinomio.py

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+# -*- coding: utf-8 -*-
+"""Untitled2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/19jSibHAWRwWB1Jo3CqqgVLrz-AeG2Fxe
+"""
+
+!pip -q install openpyxl ipywidgets
+
+import io, numpy as np, pandas as pd, matplotlib.pyplot as plt
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+from google.colab import files
+from google.colab import output as colab_output
+colab_output.enable_custom_widget_manager()
+
+# ---------- Subir archivo ----------
+print("Sube tu archivo Excel (.xlsx) 👇")
+uploaded = files.upload()
+fname = next(iter(uploaded))
+
+# ---------- Utilidades ----------
+def guess_col(cols, df, keywords):
+    for c in cols:
+        lc = str(c).lower()
+        if any(k in lc for k in keywords):
+            return c
+    for c in cols:  # fallback: primera numérica
+        if pd.api.types.is_numeric_dtype(df[c]): return c
+    return cols[0]
+
+def format_poly_equation(coef):
+    # coef en orden descendente: a_n, a_{n-1}, ..., a0
+    n = len(coef) - 1
+    terms = []
+    for i, a in enumerate(coef):
+        p = n - i
+        if abs(a) < 1e-12:
+            continue
+        a_str = f"{a:.6g}"
+        if p == 0:
+            terms.append(f"{a_str}")
+        elif p == 1:
+            terms.append(f"{a_str}*x")
+        else:
+            terms.append(f"{a_str}*x^{p}")
+    eq = " + ".join(terms)
+    eq = eq.replace("+ -", "- ")
+    return f"f(x) = {eq}" if eq else "f(x) = 0"
+
+def r2_score(y_true, y_pred):
+    ss_res = np.sum((y_true - y_pred)**2)
+    ss_tot = np.sum((y_true - np.mean(y_true))**2)
+    return 1 - ss_res/ss_tot if ss_tot > 0 else np.nan
+
+# ---------- UI: elegir hoja ----------
+xls = pd.ExcelFile(io.BytesIO(uploaded[fname]))
+sheet_dd = widgets.Dropdown(options=xls.sheet_names, value=xls.sheet_names[0], description='Hoja:')
+load_btn = widgets.Button(description='Cargar hoja', button_style='primary')
+box_out = widgets.Output()
+
+def load_sheet(_):
+    with box_out:
+        clear_output()
+        df = pd.read_excel(io.BytesIO(uploaded[fname]), sheet_name=sheet_dd.value)
+        cols = list(df.columns)
+
+        x_dd = widgets.Dropdown(options=cols, value=guess_col(cols, df, ['agua','water','%']), description='X (% agua):')
+        y_dd = widgets.Dropdown(options=cols, value=guess_col(cols, df, ['volt','voltage','tensión','tension']), description='Y (Voltaje):')
+
+        model_dd = widgets.Dropdown(options=['Lineal','Polinomial','Exponencial'], value='Polinomial', description='Modelo:')
+        deg = widgets.IntSlider(value=2, min=1, max=5, step=1, description='Grado (poly)')
+        fit_btn = widgets.Button(description='Graficar y ajustar', button_style='success')
+        out_area = widgets.Output()
+
+        def do_fit(_):
+            with out_area:
+                clear_output()
+
+                # --- preparar datos ---
+                x = pd.to_numeric(df[x_dd.value], errors='coerce')
+                y = pd.to_numeric(df[y_dd.value], errors='coerce')
+                data = pd.DataFrame({'x': x, 'y': y}).dropna().sort_values('x')
+                x = data['x'].values.astype(float)
+                y = data['y'].values.astype(float)
+
+                if len(x) < 2:
+                    print("Necesitas al menos 2 puntos.")
+                    return
+
+                # --- ajuste según modelo ---
+                model = model_dd.value
+                eq_text, coef, yhat = "", None, None
+
+                if model == 'Lineal':
+                    coef = np.polyfit(x, y, 1)       # [m, b]
+                    yhat = np.polyval(coef, x)
+                    eq_text = format_poly_equation(coef)
+
+                elif model == 'Polinomial':
+                    d = int(deg.value)
+                    d = min(d, len(x)-1)             # evitar sobreajuste sin puntos suficientes
+                    coef = np.polyfit(x, y, d)       # [a_d, ..., a0]
+                    yhat = np.polyval(coef, x)
+                    eq_text = format_poly_equation(coef)
+
+                else:  # Exponencial: y = a * exp(b x) => ln y = ln a + b x  (requiere y>0)
+                    pos = y > 0
+                    if pos.sum() < 2:
+                        print("Para exponencial se requieren valores Y positivos.")
+                        return
+                    cx, cy = x[pos], np.log(y[pos])
+                    b, ln_a = np.polyfit(cx, cy, 1)  # cy ≈ ln_a + b*cx
+                    a = np.exp(ln_a)
+                    yhat = a * np.exp(b*x)
+                    coef = (a, b)                    # guardar como (a, b)
+                    eq_text = f"f(x) = {a:.6g} * exp({b:.6g}*x)"
+
+                # --- métricas ---
+                r2 = r2_score(y, yhat)
+
+                # --- gráfica ---
+                plt.figure(figsize=(7,4.5))
+                plt.scatter(x, y, label='Medido')
+                xf = np.linspace(x.min(), x.max(), 200)
+                if model == 'Exponencial':
+                    yf = coef[0] * np.exp(coef[1] * xf)
+                else:
+                    yf = np.polyval(coef, xf)
+                plt.plot(xf, yf, label='Ajuste')
+                plt.xlabel('Porcentaje de agua (%)')
+                plt.ylabel('Voltaje (V)')
+                plt.title('Calibración y función f(x)')
+                plt.grid(True)
+                plt.legend()
+                plt.show()
+
+                # --- resultados ---
+                print("Ecuación:")
+                print(eq_text)
+                print(f"R² = {r2:.6f}")
+
+                # código de la función en Python listo para copiar
+                if model == 'Exponencial':
+                    a, b = coef
+                    print("\nFunción f(x) en Python:")
+                    print(f"def f(x):\n    return {a:.12g} * np.exp({b:.12g} * x)")
+                else:
+                    # coef es [a_n, ..., a0]
+                    terms = []
+                    n = len(coef)-1
+                    for i, a in enumerate(coef):
+                        p = n - i
+                        if p == 0:
+                            terms.append(f"{a:.12g}")
+                        elif p == 1:
+                            terms.append(f"{a:.12g}*x")
+                        else:
+                            terms.append(f"{a:.12g}*x**{p}")
+                    body = " + ".join(terms).replace("+ -", "- ")
+                    print("\nFunción f(x) en Python:")
+                    print("def f(x):")
+                    print(f"    return {body}")
+
+        fit_btn.on_click(do_fit)
+        display(widgets.VBox([
+            widgets.HBox([x_dd, y_dd]),
+            widgets.HBox([model_dd, deg, fit_btn]),
+            out_area
+        ]))
+
+display(widgets.VBox([widgets.HBox([sheet_dd, load_btn]), box_out]))
+load_btn.on_click(load_sheet)

Parcial 1/Homework3/Readme.md

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+Homework 3 Juan Pablo Larios Franco 0244215