Plots for poster

experiment_loader.py

@@ -0,0 +1,28 @@
+import csv
+def load_2d_experiment(filename: str):
+    with open(filename) as csvfile:
+        spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
+        rows = list(spamreader)
+        x = [int(row[0]) for row in rows]
+        y = [float(row[1]) for row in rows]
+    return (x, y)
+
+def load_3d_experiment(filename: str):
+    with open(filename) as csvfile:
+        offset = 1 if csv.Sniffer().has_header(csvfile.read(1024)) else 0
+        csvfile.seek(0)
+        spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
+        rows = list(spamreader)[offset:]
+        x = []
+        y = []
+        z = []
+        # for row in rows[offset]:
+        #     print(row)
+        #     x.append(int(row[0]))
+        #     y.append(int(row[1]))
+        #     z.append(float(row[2]))
+        
+        x = [int(row[0]) for row in rows]
+        y = [int(row[1]) for row in rows]
+        z = [float(row[2]) for row in rows]
+    return (x, y, z)

flake.nix

@@ -30,6 +30,7 @@
             pandas
             plotly
             scipy
+            kaleido
           ]))
         ];
         packages = [
@@ -37,8 +38,7 @@
         ];
         shellHook = ''
           python --version
-          exec fish
+          jupyter-lab --no-browser
-          . ./.venv/bin/activate.fish
         '';
       };
     };

modeling.py

@@ -0,0 +1,31 @@
+import numpy as np
+def calc_upper(c: int, m: float, b: float) -> float:
+    return np.floor(c / 32) * 32 * m + b
+
+def calc_lower(c: int, m: float, b: float) -> float:
+    return np.ceil(c / 32) * 32 * m + b
+
+def calc_mean(c:int , m_u: float, b_u: float, m_l: float, b_l: float) -> float:
+    return (calc_upper(c, m_u, b_u) + calc_lower(c, m_l, b_l)) / 2
+
+def calc_rect(c:int , m_u: float, b_u: float, m_l: float, b_l: float) -> float:
+    if ((c - 1) % 8) < 4:
+        return calc_upper(c, m_u, b_u)
+    else:
+        return calc_lower(c, m_l, b_l)
+
+def lin_interpol(x0:float, x1:float, y0:float, y1:float) -> (float, float):
+    m = (y1 - y0) / (x1 - x0)
+    b = y1 - m * x1
+    return m, b
+
+def calculate_deltas(sweep:list[float]):
+    deltas = []
+    lv = sweep[0]
+    for meas in list(sweep)[1:]:
+        deltas.append((meas / lv))
+        lv = meas
+    return deltas
+
+def compute_absolute_percentage_errors(measurements: list[float], guesses: list[float]):
+    return [np.abs((guess - meas) / meas) * 100 for guess, meas in zip(guesses, measurements)]