Fix RMAP and stats dependencies

README.rst

@@ -1,6 +1,8 @@
 py_neuromodulation
 ==================
 
+Documentation: https://neuromodulation.github.io/py_neuromodulation/
+
 Analyzing neural data can be a troublesome, trial and error prone,
 and beginner unfriendly process. *py_neuromodulation* allows using a simple
 interface for extraction of established neurophysiological features and includes commonly applied pre -and postprocessing methods.

docs/source/contributing.rst

@@ -7,30 +7,36 @@ In general we recommend placing questions and issues in the `GitHub issue tracke
 
 For code formatting we use `ruff <https://docs.astral.sh/ruff/formatter/>`_.
 
-For code development, we recommend using the package manager `rye <https://rye.astral.sh/>`_.
+For code development, we recommend using the package manager `uv <https://docs.astral.sh/uv/getting-started/installation/>`_.
 
 To setup the python environment, type
 
 ::
 
-    rye pin 3.12
+    uv python install 3.11
+    uv venv
 
-then sync the environment with
+
+Depending on your operating system, activate the rye virtual environment: 
 
 ::
 
-    rye sync
+    . .venv/bin/activate
 
-Depending on your operating system, activate the rye virtual environment: 
+And install the pyproject.toml dependencies:
+
+:: 
+
+    then sync the environment with
 
 ::
 
-    . .venv/bin/activate
+    uv sync
 
 To install the documentation dependencies use pip:
 
 ::
 
-    python -m pip install .[docs]
+    uv pip install -e .[docs]
 
 

py_neuromodulation/analysis/RMAP.py

@@ -8,8 +8,8 @@
 import nibabel as nib
 from matplotlib import pyplot as plt
 
-from py_neuromodulation.plots import reg_plot
-from py_neuromodulation.types import _PathLike
+from py_neuromodulation.analysis import reg_plot
+from py_neuromodulation.utils.types import _PathLike
 from py_neuromodulation import PYNM_DIR
 
 LIST_STRUC_UNCONNECTED_GRIDPOINTS_HULL = [256, 385, 417, 447, 819, 914]
@@ -335,7 +335,7 @@ def save_Nii(
         reshape: bool = True,
     ):
         if reshape:
-            fp = np.reshape(fp, (91, 109, 91), order="F")
+            fp = np.reshape(fp, (91, 109, 91), order="C")
 
         img = nib.nifti1.Nifti1Image(fp, affine=affine)
 

py_neuromodulation/analysis/stats.py

@@ -6,7 +6,7 @@
 # from numba import njit
 import numpy as np
 import pandas as pd
-import scipy.stats as stats
+import scipy.stats as scipy_stats
 
 
 def fitlm_kfold(x, y, kfold_splits=5):
@@ -51,7 +51,7 @@ def permutationTestSpearmansRho(x, y, plot_distr=True, x_unit=None, p=5000):
     """
 
     # compute ground truth difference
-    gT = stats.spearmanr(x, y)[0]
+    gT = scipy_stats.spearmanr(x, y)[0]
     #
     pV = np.array((x, y))
     # Initialize permutation:
@@ -65,7 +65,7 @@ def permutationTestSpearmansRho(x, y, plot_distr=True, x_unit=None, p=5000):
         random.shuffle(args_order_2)
         # Compute permuted absolute difference of your two sampled
         # distributions and store it in pD:
-        pD.append(stats.spearmanr(pV[0, args_order], pV[1, args_order_2])[0])
+        pD.append(scipy_stats.spearmanr(pV[0, args_order], pV[1, args_order_2])[0])
 
     # calculate p value
     if gT < 0:

pyproject.toml

@@ -51,6 +51,7 @@ dependencies = [
   "seaborn >= 0.11",
   "llvmlite>=0.43.0",
   "numba>=0.60.0",
+  "nibabel>=5.3.2",
 ]
 
 [project.optional-dependencies]