further cleaning, move imp to helper to avoid circ imp

Author: pregHosh

navicat_mikimo/helper.py

@@ -6,6 +6,30 @@
 
 import autograd.numpy as np
 import pandas as pd
+from sklearn.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
+
+
+def call_imputter(imp_alg):
+    """
+    Create an instance of the specified imputer type.
+
+    Parameters:
+        imputer_type: Type of imputer. Options: "knn", "iterative", "simple".
+
+    Returns:
+        An instance of the specified imputer type.
+    """
+    if imp_alg == "knn":
+        imputer = KNNImputer(n_neighbors=5, weights="uniform")
+    elif imp_alg == "iterative":
+        imputer = IterativeImputer(max_iter=10, random_state=0)
+    elif imp_alg == "simple":
+        imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
+    else:
+        print("Invalid imputer type, use KNN imputer instead.")
+        imputer = KNNImputer(n_neighbors=5, weights="uniform")
+    return imputer
 
 
 def yesno(question):

navicat_mikimo/km_k_volcanic.py

@@ -4,37 +4,32 @@
 import os
 import shutil
 import sys
-from typing import List, Optional, Tuple, Union
 import warnings
+from typing import List, Optional, Tuple, Union
 
 import h5py
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import sklearn as sk
 from joblib import Parallel, delayed
-from navicat_volcanic.dv1 import curate_d
 from navicat_volcanic.dv2 import find_2_dv
 from navicat_volcanic.helpers import (
     bround,
     group_data_points,
     user_choose_1_dv,
     user_choose_2_dv,
 )
-from navicat_volcanic.plotting2d import calc_ci, plot_2d, plot_2d_lsfer, get_reg_targets
+from navicat_volcanic.plotting2d import calc_ci, get_reg_targets, plot_2d, plot_2d_lsfer
 from navicat_volcanic.plotting3d import (
     get_bases,
     plot_3d_contour,
     plot_3d_contour_regions,
-    plot_3d_lsfer,
-    plot_3d_scatter,
 )
 from scipy.interpolate import interp1d
-from sklearn.experimental import enable_iterative_imputer
-from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
 from tqdm import tqdm
 
-from .helper import check_km_inp, preprocess_data_mkm, process_data_mkm, yesno
+from .helper import process_data_mkm, yesno
 from .kinetic_solver import calc_km
 from .plot_function import (
     plot_2d_combo,
@@ -43,7 +38,6 @@
     plot_3d_np,
     plot_evo,
 )
-from .km_volcanic import call_imputter
 
 warnings.filterwarnings("ignore")
 

navicat_mikimo/km_volcanic.py

@@ -25,16 +25,12 @@
     get_bases,
     plot_3d_contour,
     plot_3d_contour_regions,
-    plot_3d_lsfer,
-    plot_3d_scatter,
 )
 from scipy.interpolate import interp1d
-from sklearn.experimental import enable_iterative_imputer
-from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
 from tqdm import tqdm
 
 from . import km_k_volcanic
-from .helper import check_km_inp, preprocess_data_mkm, process_data_mkm, yesno
+from .helper import call_imputter, preprocess_data_mkm, process_data_mkm, yesno
 from .kinetic_solver import calc_km
 from .plot_function import (
     plot_2d_combo,
@@ -45,28 +41,6 @@
 )
 
 
-def call_imputter(imp_alg):
-    """
-    Create an instance of the specified imputer type.
-
-    Parameters:
-        imputer_type: Type of imputer. Options: "knn", "iterative", "simple".
-
-    Returns:
-        An instance of the specified imputer type.
-    """
-    if imp_alg == "knn":
-        imputer = KNNImputer(n_neighbors=5, weights="uniform")
-    elif imp_alg == "iterative":
-        imputer = IterativeImputer(max_iter=10, random_state=0)
-    elif imp_alg == "simple":
-        imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
-    else:
-        print("Invalid imputer type, use KNN imputer instead.")
-        imputer = KNNImputer(n_neighbors=5, weights="uniform")
-    return imputer
-
-
 def process_n_calc_2d(
     profile: List[float],
     sigma_p: float,

navicat_mikimo/plot_function.py

@@ -624,7 +624,7 @@ def plot_3d_contour_regions_np(
     plt.ylim(np.min(yticks), np.max(yticks))
     plt.xticks(np.arange(x1min, x1max + 0.1, x1base))
     plt.yticks(np.arange(x2min, x2max + 0.1, x2base))
-    ax.contour(xint, yint, grid, cszet.levels, colors="black", linewidths=0.1)
+    ax.contour(xint, yint, grid, cset.levels, colors="black", linewidths=0.1)
 
     def fmt(x, pos):
         return "%.0f" % x