[Enh] Generate code with __all__ and no private import

Author: johmedr

.mpython/templates/docstring.py

@@ -4,4 +4,4 @@
 [Matlab code]( https://github.com/spm/spm/blob/main/<rfilepath> )
 
 Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
-"""
+"""

examples/DCM_ERP.py

@@ -6,42 +6,43 @@
 you have to change 'Pbase' to your own analysis-directory, and choose a name ('DCM.xY.Dfile')
 of an existing SPM for M/EEG-file with at least two evoked responses.
 """
+
 import os
 import spm
 
 num = spm.Array.from_any
 
 # Please replace filenames etc. by your own.
 # ----------------------------------------------------------------------
-spm.spm('defaults', 'EEG')
+spm.spm("defaults", "EEG")
 
 # Data and analysis directories
 # ----------------------------------------------------------------------
 
-Pbase     = os.path.dirname(__file__)    # directory with your data,
+Pbase = os.path.dirname(__file__)  # directory with your data,
 
-Pdata     = os.path.join(Pbase, '.')     # data directory in Pbase
-Panalysis = os.path.join(Pbase, '.')     # analysis directory in Pbase
+Pdata = os.path.join(Pbase, ".")  # data directory in Pbase
+Panalysis = os.path.join(Pbase, ".")  # analysis directory in Pbase
 
 os.chdir(Pbase)
 
 # Data filename
 # ----------------------------------------------------------------------
 DCM = spm.Struct()
-DCM.xY.Dfile = 'maeMdfspm8_subject1'
+DCM.xY.Dfile = "maeMdfspm8_subject1"
 
 # Parameters and options used for setting up model
 # ----------------------------------------------------------------------
-DCM.options.analysis = 'ERP'        # analyze evoked responses
-DCM.options.model    = 'ERP'        # ERP model
-DCM.options.spatial  = 'IMG'        # spatial model
-DCM.options.trials   = num([1, 2])  # index of ERPs within ERP/ERF file
-DCM.options.Tdcm[0]  = 0            # start of peri-stimulus time to be modelled
-DCM.options.Tdcm[1]  = 200          # end of peri-stimulus time to be modelled
-DCM.options.Nmodes   = 8            # nr of modes for data selection
-DCM.options.h        = 1            # nr of DCT components
-DCM.options.onset    = 60           # selection of onset (prior mean)
-DCM.options.D        = 1            # downsampling
+DCM.options.analysis = "ERP"  # analyze evoked responses
+DCM.options.model = "ERP"  # ERP model
+DCM.options.spatial = "IMG"  # spatial model
+DCM.options.trials = num([1, 2])  # index of ERPs within ERP/ERF file
+DCM.options.Tdcm[0] = 0  # start of peri-stimulus time to be modelled
+DCM.options.Tdcm[1] = 200  # end of peri-stimulus time to be modelled
+DCM.options.Nmodes = 8  # nr of modes for data selection
+DCM.options.h = 1  # nr of DCT components
+DCM.options.onset = 60  # selection of onset (prior mean)
+DCM.options.D = 1  # downsampling
 
 # ----------------------------------------------------------------------
 # Data and spatial model
@@ -51,14 +52,10 @@
 # ----------------------------------------------------------------------
 # Location priors for dipoles
 # ----------------------------------------------------------------------
-DCM.Lpos = num([
-    [-42, -22, 7],
-    [46, -14, 8],
-    [-61, -32, 8],
-    [59, -25, 8],
-    [46, 20, 8]
-]).T
-DCM.Sname = ['left AI', 'right A1', 'left STG', 'right STG', 'right IFG']
+DCM.Lpos = num(
+    [[-42, -22, 7], [46, -14, 8], [-61, -32, 8], [59, -25, 8], [46, 20, 8]]
+).T
+DCM.Sname = ["left AI", "right A1", "left STG", "right STG", "right IFG"]
 Nareas = DCM.Lpos.shape[1]
 
 # ----------------------------------------------------------------------
@@ -98,11 +95,11 @@
 # Between trial effects
 # ----------------------------------------------------------------------
 DCM.xU.X = num([[0, 1]]).T
-DCM.xU.name = ['rare']
+DCM.xU.name = ["rare"]
 
 # ----------------------------------------------------------------------
 # Invert
 # ----------------------------------------------------------------------
-DCM.name = 'DCMexample'
+DCM.name = "DCMexample"
 
 DCM = spm.spm_dcm_erp(DCM)

examples/DCM_fMRI_Attention.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from spm import * \n",
+    "from spm import *\n",
     "\n",
     "import numpy as np\n",
     "import os.path as op"
@@ -38,15 +38,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_path = op.join('data', 'attention')\n",
-    "zip_path  = op.join('data', 'attention.zip')\n",
+    "data_path = op.join(\"data\", \"attention\")\n",
+    "zip_path = op.join(\"data\", \"attention.zip\")\n",
     "if not op.isdir(data_path):\n",
     "    if not op.isfile(zip_path):\n",
     "        import wget\n",
-    "        wget.download('https://www.fil.ion.ucl.ac.uk/spm/download/data/attention/attention.zip', 'data')\n",
+    "\n",
+    "        wget.download(\n",
+    "            \"https://www.fil.ion.ucl.ac.uk/spm/download/data/attention/attention.zip\",\n",
+    "            \"data\",\n",
+    "        )\n",
     "\n",
     "    import shutil\n",
-    "    shutil.unpack_archive(zip_path, 'data', 'zip')"
+    "\n",
+    "    shutil.unpack_archive(zip_path, \"data\", \"zip\")"
    ]
   },
   {
@@ -77,8 +82,8 @@
     }
    ],
    "source": [
-    "spm('Defaults','fMRI')\n",
-    "spm_jobman('initcfg', nargout=0)"
+    "spm(\"Defaults\", \"fMRI\")\n",
+    "spm_jobman(\"initcfg\", nargout=0)"
    ]
   },
   {
@@ -106,7 +111,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "SPM = Runtime.call('load', op.join(data_path, 'GLM', 'SPM.mat'))['SPM']"
+    "SPM = Runtime.call(\"load\", op.join(data_path, \"GLM\", \"SPM.mat\"))[\"SPM\"]"
    ]
   },
   {
@@ -116,11 +121,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DCM = Struct() \n",
+    "DCM = Struct()\n",
     "\n",
-    "xY1 = Runtime.call('load', op.join(data_path,'GLM','VOI_V1_1.mat'))['xY']\n",
-    "xY2 = Runtime.call('load', op.join(data_path,'GLM','VOI_V5_1.mat'))['xY']\n",
-    "xY3 = Runtime.call('load', op.join(data_path,'GLM','VOI_SPC_1.mat'))['xY']\n",
+    "xY1 = Runtime.call(\"load\", op.join(data_path, \"GLM\", \"VOI_V1_1.mat\"))[\"xY\"]\n",
+    "xY2 = Runtime.call(\"load\", op.join(data_path, \"GLM\", \"VOI_V5_1.mat\"))[\"xY\"]\n",
+    "xY3 = Runtime.call(\"load\", op.join(data_path, \"GLM\", \"VOI_SPC_1.mat\"))[\"xY\"]\n",
     "\n",
     "DCM.xY = StructArray(xY1, xY2, xY3)"
    ]
@@ -132,8 +137,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DCM.n = 3     \n",
-    "DCM.v = xY1['u'].shape[0]; "
+    "DCM.n = 3\n",
+    "DCM.v = xY1[\"u\"].shape[0]"
    ]
   },
   {
@@ -163,13 +168,13 @@
    "outputs": [],
    "source": [
     "DCM.Y = Struct()\n",
-    "DCM.Y.dt  = SPM.xY.RT\n",
-    "DCM.Y.X0  = xY1.X0\n",
+    "DCM.Y.dt = SPM.xY.RT\n",
+    "DCM.Y.X0 = xY1.X0\n",
     "\n",
     "DCM.Y.y = np.concatenate([xY.u for xY in (xY1, xY2, xY3)], axis=1)\n",
-    "DCM.Y.name = [xY.name for xY in  (xY1, xY2, xY3)]\n",
+    "DCM.Y.name = [xY.name for xY in (xY1, xY2, xY3)]\n",
     "\n",
-    "DCM.Y.Q    = spm_Ce(np.ones((1,DCM.n))*DCM.v);"
+    "DCM.Y.Q = spm_Ce(np.ones((1, DCM.n)) * DCM.v)"
    ]
   },
   {
@@ -196,11 +201,9 @@
    "outputs": [],
    "source": [
     "DCM.U = Struct()\n",
-    "DCM.U.dt   =  SPM.Sess.U[0].dt\n",
+    "DCM.U.dt = SPM.Sess.U[0].dt\n",
     "DCM.U.name = [u.name for u in SPM.Sess.U]\n",
-    "DCM.U.u    = np.concatenate([\n",
-    "        u.u[32:] for u in SPM.Sess.U\n",
-    "    ], axis=1);"
+    "DCM.U.u = np.concatenate([u.u[32:] for u in SPM.Sess.U], axis=1)"
    ]
   },
   {
@@ -228,14 +231,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DCM.delays = np.repeat([[SPM['xY']['RT']/2]], DCM.n, 1)\n",
-    "DCM.TE     = 0.04\n",
+    "DCM.delays = np.repeat([[SPM[\"xY\"][\"RT\"] / 2]], DCM.n, 1)\n",
+    "DCM.TE = 0.04\n",
     "\n",
     "DCM.options = Struct()\n",
-    "DCM.options.nonlinear  = 0\n",
-    "DCM.options.two_state  = 0\n",
+    "DCM.options.nonlinear = 0\n",
+    "DCM.options.two_state = 0\n",
     "DCM.options.stochastic = 0\n",
-    "DCM.options.nograph    = 1;"
+    "DCM.options.nograph = 1"
    ]
   },
   {
@@ -263,11 +266,11 @@
    "outputs": [],
    "source": [
     "DCM.a = np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]])\n",
-    "DCM.b = np.zeros((3,3,3))  \n",
-    "DCM.b[1,0,1] = 1  \n",
-    "DCM.b[1,2,2] = 1\n",
+    "DCM.b = np.zeros((3, 3, 3))\n",
+    "DCM.b[1, 0, 1] = 1\n",
+    "DCM.b[1, 2, 2] = 1\n",
     "DCM.c = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 0]])\n",
-    "DCM.d = np.zeros((3,3,0))\n",
+    "DCM.d = np.zeros((3, 3, 0))\n",
     "\n",
     "DCMbwd = spm_dcm_estimate(DCM)"
    ]
@@ -293,9 +296,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DCM.b = np.zeros((3,3,3))  \n",
-    "DCM.b[1,0,1] = 1  \n",
-    "DCM.b[1,0,2] = 1\n",
+    "DCM.b = np.zeros((3, 3, 3))\n",
+    "DCM.b[1, 0, 1] = 1\n",
+    "DCM.b[1, 0, 2] = 1\n",
     "\n",
     "DCMfwd = spm_dcm_estimate(DCM)"
    ]