update

lactate-eval.py

@@ -1,6 +1,8 @@
 import numpy as np
 from scipy import optimize
 import matplotlib.pyplot as plt
+import yaml
+
 
 x0 = np.array(
     [
@@ -59,19 +61,51 @@ y1 = np.array(
     ]
 )
 
+x2 = np.array(
+    [
+        154,
+        172,
+        191,
+        209,
+        228,
+        246,
+        265,
+        283,
+        302,
+    ]
+)
+
+y2 = np.array(
+    [
+        1.3,
+        1.4,
+        1.4,
+        1.8,
+        2,
+        2.6,
+        3.5,
+        4.1,
+        6.6,
+    ]
+)
+
+
 def get_poly_fit(x, y, remove_first_n=1, remove_last_n=1, n_points=100):
     coeffs = np.polyfit(x, y, 3)
     poly = np.poly1d(coeffs)
     x_start = x[remove_first_n]
-    x_end = x[-1-remove_last_n]
+    x_end = x[-1 - remove_last_n]
     x_poly = np.linspace(x_start, x_end, n_points)
     y_poly = poly(x_poly)
     return x_poly, y_poly
 
+
 def get_log_log_threshold(x, y):
     x_log = np.log(x)
     y_log = np.log(y)
-    p, e = optimize.curve_fit(piecewise_linear, x_log, y_log, p0=[np.mean(x_log), np.mean(y_log), 0.5, 4.0])
+    p, e = optimize.curve_fit(
+        piecewise_linear, x_log, y_log, p0=[np.mean(x_log), np.mean(y_log), 0.5, 4.0]
+    )
     return np.exp(p[0])
 
 
@@ -80,25 +114,32 @@ def piecewise_linear(x, x0, y0, k1, k2):
         x, [x < x0], [lambda x: k1 * x + y0 - k1 * x0, lambda x: k2 * x + y0 - k2 * x0]
     )
 
+
 def main():
+    with open('/home/lennartalff/Nextcloud/Private/HRV4Training/lactate/ramp.yaml') as f:
+        data = yaml.safe_load(f)
     first_x, first_y = get_poly_fit(x0, y0)
     second_x, second_y = get_poly_fit(x1, y1)
     first_lt1 = get_log_log_threshold(first_x, first_y)
     second_lt1 = get_log_log_threshold(second_x, second_y)
-    plt.plot(first_x, first_y, color='C0')
+    third_x, third_y = get_poly_fit(x2, y2)
-    plt.scatter(x0, y0, color='C0')
+    third_lt1 = get_log_log_threshold(third_x, third_y)
-    plt.plot(second_x, second_y, color='C1')
+    plt.plot(first_x, first_y, color="C0")
-    plt.scatter(x1, y1, color='C1')
+    plt.scatter(x0, y0, color="C0")
-    plt.axvline(first_lt1, color='C0', linestyle='dashed')
+    plt.plot(second_x, second_y, color="C1")
-    plt.axvline(second_lt1, color='C1', linestyle='dashed')
+    plt.scatter(x1, y1, color="C1")
-    plt.grid(True, which='major')
+    plt.plot(third_x, third_y, color="C2")
+    plt.scatter(x2, y2, color="C2")
+    plt.axvline(first_lt1, color="C0", linestyle="dashed")
+    plt.axvline(second_lt1, color="C1", linestyle="dashed")
+    plt.axvline(third_lt1, color="C2", linestyle="dashed")
+    plt.grid(True, which="major")
     major_ticks = np.arange(0, 8, 1)
     minor_ticks = np.arange(0, 8, 0.1)
     plt.yticks(major_ticks)
     plt.yticks(minor_ticks, minor=True)
     plt.show()
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()
-
-

requirements.txt

@@ -3,3 +3,4 @@ pandas
 scipy
 matplotlib
 pwlf
+pyyaml