Relative imports for submodules

kymata/datasets/sample.py

@@ -4,9 +4,9 @@
 from typing import Optional
 from urllib import request
 
-from kymata.entities.expression import HexelExpressionSet, SensorExpressionSet
-from kymata.io.file import path_type
-from kymata.io.nkg import load_expression_set
+from ..entities.expression import HexelExpressionSet, SensorExpressionSet
+from ..io.file import path_type
+from ..io.nkg import load_expression_set
 
 _DATA_PATH_ENVIRONMENT_VAR_NAME = "KYMATA_DATA_ROOT"
 _DATA_DIR_NAME = "kymata-toolbox-data/tutorial_nkg_data"

kymata/entities/expression.py

@@ -13,10 +13,10 @@
 from sparse import SparseArray, COO
 from xarray import DataArray, Dataset, concat
 
-from kymata.entities.datatypes import HexelDType, SensorDType, LatencyDType, FunctionNameDType, Hexel, Sensor, \
+from .datatypes import HexelDType, SensorDType, LatencyDType, FunctionNameDType, Hexel, Sensor, \
     Latency
-from kymata.entities.iterables import all_equal
-from kymata.entities.sparse_data import expand_dims, densify_dataset, sparsify_log_pmatrix
+from .iterables import all_equal
+from .sparse_data import expand_dims, densify_dataset, sparsify_log_pmatrix
 
 _InputDataArray = Union[ndarray, SparseArray]  # Type alias for data which can be accepted
 

kymata/gridsearch/plain.py

@@ -1,13 +1,13 @@
 import numpy as np
 from numpy.typing import NDArray
 from scipy import stats
-
-from kymata.entities.functions import Function
-from kymata.math.combinatorics import generate_derangement
-from kymata.math.vector import normalize, get_stds
-#from kymata.entities.expression import SensorExpressionSet, p_to_logp
 import matplotlib.pyplot as plt
 
+from ..entities.functions import Function
+from ..math.combinatorics import generate_derangement
+from ..math.vector import normalize, get_stds
+#from ..entities.expression import SensorExpressionSet, p_to_logp
+
 
 def do_gridsearch(
         emeg_values: NDArray,  # chan x time

kymata/io/functions.py

@@ -1,12 +1,11 @@
 from pathlib import Path
 
-from numpy import array, float16, convolve, mean
 import numpy as np
 from numpy.typing import NDArray
 from scipy.io import loadmat
 
-from kymata.entities.functions import Function
-from kymata.io.file import path_type
+from ..entities.functions import Function
+from .file import path_type
 
 
 def load_function(function_path_without_suffix: path_type, func_name: str, n_derivatives: int = 0, bruce_neurons: tuple = (0, 10)) -> Function:

kymata/io/matlab.py

@@ -7,8 +7,8 @@
 from scipy.io import loadmat as loadmat_pre_73
 from mat73 import loadmat as loadmat_post_73
 
-from kymata.entities.expression import HexelExpressionSet, p_to_logp
-from kymata.entities.iterables import all_equal
+from ..entities.expression import HexelExpressionSet, p_to_logp
+from ..entities.iterables import all_equal
 
 
 def load_mat(path):

kymata/io/mne.py

@@ -1,13 +1,8 @@
-from pathlib import Path
-
 from mne import read_evokeds, minimum_norm, set_eeg_reference
 import numpy as np
 from numpy.typing import NDArray
 from os.path import isfile
 
-from kymata.io.file import path_type
-
-
 
 def load_single_emeg(emeg_path, need_names=False, inverse_operator=None, snr=4):
     emeg_path_npy = f"{emeg_path}.npy"

kymata/io/nkg.py

@@ -10,11 +10,11 @@
 from numpy import ndarray, frombuffer
 from sparse import COO
 
-from kymata.entities.datatypes import HexelDType, LatencyDType, FunctionNameDType, SensorDType
-from kymata.entities.expression import ExpressionSet, LAYER_LEFT, LAYER_RIGHT, LAYER_SCALP, HexelExpressionSet, \
+from ..entities.datatypes import HexelDType, LatencyDType, FunctionNameDType, SensorDType
+from ..entities.expression import ExpressionSet, LAYER_LEFT, LAYER_RIGHT, LAYER_SCALP, HexelExpressionSet, \
     SensorExpressionSet, p_to_logp
-from kymata.entities.sparse_data import expand_dims
-from kymata.io.file import path_type, file_type, open_or_use
+from ..entities.sparse_data import expand_dims
+from .file import path_type, file_type, open_or_use
 
 
 class _Keys(StrEnum):

kymata/io/nkg_compatibility.py

@@ -6,8 +6,8 @@
 from numpy import frombuffer
 from numpy.typing import NDArray
 
-from kymata.entities.datatypes import LatencyDType, FunctionNameDType, HexelDType
-from kymata.io.file import path_type, file_type, open_or_use
+from ..entities.datatypes import LatencyDType, FunctionNameDType, HexelDType
+from .file import path_type, file_type, open_or_use
 
 
 # Version 0.3: moved to log p-values.

kymata/ippm/data_tools.py

@@ -1,20 +1,21 @@
 import json
+import math
 from itertools import cycle
 from statistics import NormalDist
 from typing import Tuple, Dict, List
 
-import matplotlib.colors
 import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+import matplotlib.colors
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import normalize
 from sklearn.metrics.pairwise import euclidean_distances
-from copy import deepcopy
 import requests
 import seaborn as sns
-from matplotlib.lines import Line2D
-import math
-from kymata.entities.expression import HexelExpressionSet, DIM_FUNCTION, DIM_LATENCY
+
+from ..entities.expression import HexelExpressionSet, DIM_FUNCTION, DIM_LATENCY
+
 
 class IPPMHexel(object):
     """

kymata/ippm/denoiser.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from sklearn.cluster import DBSCAN as DBSCAN_, MeanShift as MeanShift_
 from sklearn.mixture import GaussianMixture
-from sklearn.preprocessing import StandardScaler, normalize
+from sklearn.preprocessing import normalize
 
 from .data_tools import IPPMHexel
 

kymata/ippm/experiments.ipynb

@@ -8,17 +8,19 @@
    "outputs": [],
    "source": [
     "from copy import deepcopy\n",
-    "from typing import Dict, List, Tuple\n",
+    "from typing import Dict\n",
+    "\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "from kymata.ippm.data_tools import IPPMHexel, stem_plot, build_hexel_dict_from_expression_set, convert_to_power10, remove_excess_funcs, plot_4_dist, plot_k_dist_1D, copy_hemisphere, plot_denoised_vs_noisy\n",
-    "from kymata.ippm.denoiser import DenoisingStrategy, AdaptiveMaxPooler, MaxPooler\n",
     "\n",
     "from kymata.datasets.sample import KymataMirror2023Q3Dataset\n",
     "from kymata.entities.expression import HexelExpressionSet\n",
     "from kymata.ippm.builder import IPPMBuilder\n",
+    "from kymata.ippm.data_tools import IPPMHexel, stem_plot, build_hexel_dict_from_expression_set, convert_to_power10, \\\n",
+    "    remove_excess_funcs, plot_k_dist_1D, plot_denoised_vs_noisy\n",
+    "from kymata.ippm.denoiser import DenoisingStrategy, MaxPooler\n",
     "from kymata.ippm.plotter import IPPMPlotter\n",
     "\n",
+    "\n",
     "class AdaptiveMaxPooler(DenoisingStrategy):\n",
     "    def cluster(self, hexels: Dict[str, IPPMHexel], hemi: str, normalise: bool = False) -> Dict[str, IPPMHexel]:\n",
     "        hexels = deepcopy(hexels)\n",
@@ -480,7 +482,7 @@
     }
    ],
    "source": [
-    "from kymata.ippm.denoiser import MaxPooler\n",
+    "\n",
     "\n",
     "clusterer = DBSCANv2(eps=5, min_samples=2)\n",
     "denoised_hexels = clusterer.cluster(hexels, 'leftHemisphere')\n",

kymata/plot/plot.py

@@ -9,9 +9,9 @@
 from pandas import DataFrame
 from seaborn import color_palette
 
-from kymata.entities.expression import HexelExpressionSet, ExpressionSet, SensorExpressionSet, DIM_SENSOR, DIM_FUNCTION, \
+from ..entities.expression import HexelExpressionSet, ExpressionSet, SensorExpressionSet, DIM_SENSOR, DIM_FUNCTION, \
     p_to_logp
-from kymata.plot.layouts import get_meg_sensor_xy, eeg_sensors
+from .layouts import get_meg_sensor_xy, eeg_sensors
 
 # log scale: 10 ** -this will be the ytick interval and also the resolution to which the ylims will be rounded
 _MAJOR_TICK_SIZE = 50

kymata/preproc/data_cleansing.py

@@ -7,8 +7,8 @@
 from pathlib import Path
 from matplotlib import pyplot as plt
 
-from kymata.io.cli import print_with_color, input_with_color
-from kymata.io.yaml import load_config, modify_param_config
+from ..io.cli import print_with_color, input_with_color
+from ..io.yaml import load_config, modify_param_config
 
 
 def run_first_pass_cleansing_and_maxwell_filtering(list_of_participants: list[str],

submit_gridsearch.sh

@@ -20,7 +20,7 @@ conda activate mne_venv
 args=(5) # 2 3 4 5 6 7 8 9 10)
 ARG=${args[$SLURM_ARRAY_TASK_ID - 1]}
 
-python invokers/run_gridsearch.py
+python -m invokers.run_gridsearch
     # --snr $ARG # >> result3.txt
 
 conda deactivate