chore: resolve warnings

dynamo/configuration.py

@@ -352,7 +352,7 @@ def use_default_var_if_none(val: Any, key: str, replace_val: Optional[Any] = Non
         Returns:
             `val` or config value set in DynamoAdataConfig according to the method description above.
         """
-        if not key in DynamoAdataConfig.config_key_to_values:
+        if key not in DynamoAdataConfig.config_key_to_values:
             assert KeyError("Config %s not exist in DynamoAdataConfig." % (key))
         if val == replace_val:
             config_val = DynamoAdataConfig.config_key_to_values[key]

dynamo/data_io.py

@@ -138,7 +138,7 @@ def load_NASC_seq(
         tot_RNA = None
         cells_raw, cells = None, None
 
-        for f in tqdm(files, desc=f"reading rmse output files:"):
+        for f in tqdm(files, desc="reading rmse output files:"):
             tmp = pd.read_csv(f, index_col=0, sep="\t")
 
             if tot_RNA is None:
@@ -251,7 +251,7 @@ def aggregate_adata(file_list: list) -> AnnData:
 
     if len(valid_cells) == 0 or len(valid_genes) == 0:
         raise Exception(
-            f"we don't find any gene or cell names shared across different adata objects." f"Please check your data. "
+            "we don't find any gene or cell names shared across different adata objects."+"Please check your data. "
         )
 
     layer_dict = {}

dynamo/estimation/csc/utils_velocity.py

@@ -302,7 +302,7 @@ def fit_linreg_robust(
                 f"estimation method {est_method} is not implemented. "
                 f"Currently supported linear regression methods include `rlm` and `ransac`."
             )
-    except:
+    except Exception as e:
         if intercept:
             ym = np.mean(yy)
             xm = np.mean(xx)

dynamo/estimation/csc/velocity.py

@@ -2,6 +2,7 @@
 from multiprocessing.dummy import Pool as ThreadPool
 from warnings import warn
 
+import numpy as np
 from scipy.sparse import csr_matrix
 from tqdm import tqdm
 
@@ -909,7 +910,7 @@ def fit(
                     for i in tqdm(range(n_genes), desc="estimating gamma"):
                         try:
                             gamma[i], u0[i] = fit_first_order_deg_lsq(t_uniq, uu_m[i])
-                        except:
+                        except Exception as e:
                             gamma[i], u0[i] = 0, 0
                     self.parameters["gamma"], self.aux_param["uu0"] = gamma, u0
                     alpha = np.zeros(n_genes)

dynamo/estimation/fit_jacobian.py

@@ -171,7 +171,7 @@ def fit_hill_grad(
                 msd_min = msd
                 p_opt_min = [A, K, n, g]
 
-        except:
+        except Exception as e: #TODO: not a good practice
             pass
 
     if p_opt_min is None:
@@ -236,7 +236,7 @@ def fit_hill_inh_grad(
                 msd_min = msd
                 p_opt_min = p_opt
 
-        except:
+        except Exception as e: #TODO: not a good practice 
             pass
 
     return {"A": p_opt_min[0], "K": p_opt_min[1], "n": np.exp(p_opt_min[2]), "g": p_opt_min[3]}, msd_min
@@ -296,7 +296,7 @@ def fit_hill_act_grad(
                 msd_min = msd
                 p_opt_min = p_opt
 
-        except:
+        except Exception as e: # not a good practice
             pass
 
     return {"A": p_opt_min[0], "K": p_opt_min[1], "n": np.exp(p_opt_min[2]), "g": p_opt_min[3]}, msd_min

dynamo/plot/dynamics.py

@@ -7,6 +7,7 @@
 except ImportError:
     from typing_extensions import Literal
 
+import numpy as np
 import pandas as pd
 from anndata import AnnData
 from matplotlib.figure import Figure