Rename the package to metadpy

Author: LegrandNico

.github/workflows/linting.yml

@@ -29,7 +29,7 @@ jobs:
         pip install types-setuptools
     - name: Run tests and coverage
       run: |
-        mypy ./metadPy/ --ignore-missing-imports
-        flake8 ./metadPy/
-        black ./metadPy/
-        isort ./metadPy/
+        mypy ./metadpy/ --ignore-missing-imports
+        flake8 ./metadpy/
+        black ./metadpy/
+        isort ./metadpy/

.pre-commit-config.yaml

@@ -20,5 +20,5 @@ repos:
     rev: 'v0.982'  # Use the sha / tag you want to point at
     hooks:
     -   id: mypy
-        files: ^metadPy/
+        files: ^metadpy/
         args: [--ignore-missing-imports]

MANIFEST.in

@@ -1,4 +1,4 @@
 # Add models files
-include metadPy/models/group_level_numpyro.py
-include metadPy/models/subject_level_numpyro.py
-include metadPy/models/subject_level_pymc.py
+include metadpy/models/group_level_numpyro.py
+include metadpy/models/subject_level_numpyro.py
+include metadpy/models/subject_level_pymc.py

README.md

@@ -1,2 +1,2 @@
 ignore:
-  - "./metadPy/tests/*"
+  - "./metadpy/tests/*"

codecov.yml

@@ -1,6 +1,6 @@
 .. _api_ref:
 
-.. currentmodule:: metadPy
+.. currentmodule:: metadpy
 
 
 .. contents:: Table of Contents
@@ -12,7 +12,7 @@ API
 Bayesian
 --------
 
-.. currentmodule:: metadPy.bayesian
+.. currentmodule:: metadpy.bayesian
 
 .. autosummary::
    :toctree: generated/bayesian
@@ -25,7 +25,7 @@ Bayesian
 MLE
 ---
 
-.. currentmodule:: metadPy.mle
+.. currentmodule:: metadpy.mle
 
 .. autosummary::
    :toctree: generated/mle
@@ -37,7 +37,7 @@ MLE
 Plotting
 --------
 
-.. currentmodule:: metadPy.plotting
+.. currentmodule:: metadpy.plotting
 
 .. autosummary::
    :toctree: generated/plotting
@@ -48,7 +48,7 @@ Plotting
 SDT
 ---
 
-.. currentmodule:: metadPy.sdt
+.. currentmodule:: metadpy.sdt
 
 .. autosummary::
    :toctree: generated/sdt
@@ -62,7 +62,7 @@ SDT
 Utils
 -----
 
-.. currentmodule:: metadPy.utils
+.. currentmodule:: metadpy.utils
 
 .. autosummary::
    :toctree: generated/utils

docs/source/api.rst

@@ -12,16 +12,15 @@
 #
 import time
 
+import metadpy
 import sphinx_bootstrap_theme
 
-import metadPy
-
 # -- Project information -----------------------------------------------------
 
-project = "metadPy"
+project = "metadpy"
 copyright = "2022-{}, Nicolas Legrand".format(time.strftime("%Y"))
 author = "Nicolas Legrand"
-release = metadPy.__version__
+release = metadpy.__version__
 
 
 image_scrapers = ("matplotlib",)
@@ -86,7 +85,7 @@
     "icon_links": [
         dict(
             name="GitHub",
-            url="https://github.com/embodied-computation-group/metadPy",
+            url="https://github.com/embodied-computation-group/metadpy",
             icon="fab fa-github-square",
         ),
         dict(
@@ -101,7 +100,7 @@
         ),
     ],
     "logo": {
-        "text": "metadPy",
+        "text": "metadpy",
     },
 }
 

docs/source/conf.py

@@ -17,8 +17,8 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
-    "from metadPy.plotting import plot_confidence\n",
-    "from metadPy.utils import type2_SDT_simuation\n",
+    "from metadpy.plotting import plot_confidence\n",
+    "from metadpy.utils import type2_SDT_simuation\n",
     "\n",
     "sns.set_context(\"talk\")"
    ]

docs/source/examples/1-What metacognition looks like.ipynb

@@ -19,8 +19,8 @@ Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/me
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
 import seaborn as sns
-from metadPy.plotting import plot_confidence
-from metadPy.utils import type2_SDT_simuation
+from metadpy.plotting import plot_confidence
+from metadpy.utils import type2_SDT_simuation
 
 sns.set_context("talk")
 ```

docs/source/examples/1-What metacognition looks like.md

@@ -19,10 +19,10 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from metadPy.mle import fit_metad, metad\n",
-    "from metadPy.plotting import plot_confidence, plot_roc\n",
-    "from metadPy.sdt import roc_auc\n",
-    "from metadPy.utils import (responseSimulation, trials2counts,\n",
+    "from metadpy.mle import fit_metad, metad\n",
+    "from metadpy.plotting import plot_confidence, plot_roc\n",
+    "from metadpy.sdt import roc_auc\n",
+    "from metadpy.utils import (responseSimulation, trials2counts,\n",
     "                           type2_SDT_simuation)\n",
     "\n",
     "sns.set_context(\"talk\")"
@@ -204,8 +204,8 @@
     "```\n",
     "\n",
     "```{hint}\n",
-    "* You can plot the ROC curve using `metadPy.plotting.plot_roc()`\n",
-    "* You can calculate the area using `metadPy.std.roc_auc()`\n",
+    "* You can plot the ROC curve using `metadpy.plotting.plot_roc()`\n",
+    "* You can calculate the area using `metadpy.std.roc_auc()`\n",
     "```"
    ]
   },
@@ -278,7 +278,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**NOTE:** nR_S1 and nR_S2 are the counts of the responses to stimulus 1 (nR_S1) and stimulus 2 (nR_S2) for each confidence level. We can compute this using the function py:func:`metadPy.utils.trials2counts()`."
+    "**NOTE:** nR_S1 and nR_S2 are the counts of the responses to stimulus 1 (nR_S1) and stimulus 2 (nR_S2) for each confidence level. We can compute this using the function py:func:`metadpy.utils.trials2counts()`."
    ]
   },
   {

docs/source/examples/2-Fitting the model-MLE.ipynb

@@ -21,10 +21,10 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from metadPy.mle import fit_metad, metad
-from metadPy.plotting import plot_confidence, plot_roc
-from metadPy.sdt import roc_auc
-from metadPy.utils import (responseSimulation, trials2counts,
+from metadpy.mle import fit_metad, metad
+from metadpy.plotting import plot_confidence, plot_roc
+from metadpy.sdt import roc_auc
+from metadpy.utils import (responseSimulation, trials2counts,
                            type2_SDT_simuation)
 
 sns.set_context("talk")
@@ -109,8 +109,8 @@ Now you should be able to see that the area under the type2ROC curve with lower
 ```
 
 ```{hint}
-* You can plot the ROC curve using `metadPy.plotting.plot_roc()`
-* You can calculate the area using `metadPy.std.roc_auc()`
+* You can plot the ROC curve using `metadpy.plotting.plot_roc()`
+* You can calculate the area using `metadpy.std.roc_auc()`
 ```
 
 +++
@@ -151,7 +151,7 @@ high_noise_df = responseSimulation(
 
 +++
 
-**NOTE:** nR_S1 and nR_S2 are the counts of the responses to stimulus 1 (nR_S1) and stimulus 2 (nR_S2) for each confidence level. We can compute this using the function py:func:`metadPy.utils.trials2counts()`.
+**NOTE:** nR_S1 and nR_S2 are the counts of the responses to stimulus 1 (nR_S1) and stimulus 2 (nR_S2) for each confidence level. We can compute this using the function py:func:`metadpy.utils.trials2counts()`.
 
 ```{code-cell} ipython3
 low_noise_nR_S1, low_noise_nR_S2 = trials2counts(data=low_noise_df, nRatings=nRatings)

docs/source/examples/2-Fitting the model-MLE.md

@@ -24,7 +24,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from metadPy.bayesian import hmetad\n",
+    "from metadpy.bayesian import hmetad\n",
     "\n",
     "sns.set_context(\"talk\")"
    ]
@@ -57,7 +57,7 @@
     "id": "ZOop3bjebxbZ"
    },
    "source": [
-    "## Using metadPy"
+    "## Using metadpy"
    ]
   },
   {
@@ -203,7 +203,7 @@
     "az.plot_posterior(traces, var_names=[\"meta_d\"], ax=axs[1])\n",
     "\n",
     "axs[0].set_ylabel(\"HMeta-d\")\n",
-    "axs[1].set_ylabel(\"metadPy\")\n",
+    "axs[1].set_ylabel(\"metadpy\")\n",
     "\n",
     "plt.tight_layout()"
    ]

docs/source/examples/3-Comparison with the hmeta-d toolbox.ipynb

@@ -26,7 +26,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from metadPy.bayesian import hmetad
+from metadpy.bayesian import hmetad
 
 sns.set_context("talk")
 ```
@@ -45,7 +45,7 @@ nR_S2 = np.array([2, 5, 15, 22, 33, 38, 40, 45])
 
 +++ {"id": "ZOop3bjebxbZ"}
 
-## Using metadPy
+## Using metadpy
 
 ```{code-cell} ipython3
 ---
@@ -93,7 +93,7 @@ az.plot_posterior(hmetad_df.meta_d.to_numpy(), ax=axs[0])
 az.plot_posterior(traces, var_names=["meta_d"], ax=axs[1])
 
 axs[0].set_ylabel("HMeta-d")
-axs[1].set_ylabel("metadPy")
+axs[1].set_ylabel("metadpy")
 
 plt.tight_layout()
 ```

docs/source/examples/3-Comparison with the hmeta-d toolbox.md

@@ -23,9 +23,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "from metadPy import load_dataset\n",
-    "from metadPy.mle import metad\n",
-    "from metadPy.plotting import plot_confidence, plot_roc\n",
+    "from metadpy import load_dataset\n",
+    "from metadpy.mle import metad\n",
+    "from metadpy.plotting import plot_confidence, plot_roc\n",
     "\n",
     "sns.set_context(\"talk\")"
    ]
@@ -37,9 +37,9 @@
     "id": "2oE_wkIxVPbe"
    },
    "source": [
-    "In this notebook, we are going to estimate meta-*d'* using Maximum Likelihood Estimation ([MLE](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)) {cite:p}`fleming:2014,maniscalo:2014,maniscalo:2012` using the function implemented in [metadPy](https://github.com/LegrandNico/metadPy). This function is directly adapted from the transcription of the Matlab `fit_meta_d_MLE.m` by Alan Lee that can be retrieved [here](http://www.columbia.edu/~bsm2105/type2sdt/).\n",
+    "In this notebook, we are going to estimate meta-*d'* using Maximum Likelihood Estimation ([MLE](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)) {cite:p}`fleming:2014,maniscalo:2014,maniscalo:2012` using the function implemented in [metadpy](https://github.com/LegrandNico/metadpy). This function is directly adapted from the transcription of the Matlab `fit_meta_d_MLE.m` by Alan Lee that can be retrieved [here](http://www.columbia.edu/~bsm2105/type2sdt/).\n",
     "\n",
-    "We are going to see, however, that [metadPy](https://github.com/LegrandNico/metadPy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance."
+    "We are going to see, however, that [metadpy](https://github.com/LegrandNico/metadpy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance."
    ]
   },
   {
@@ -114,7 +114,7 @@
     "id": "GJFs74YdcqxR"
    },
    "source": [
-    "The model is fitted using the `metadPy.mle.metad()` function. This function accepts response-signal arrays as input if the data comes from a single subject."
+    "The model is fitted using the `metadpy.mle.metad()` function. This function accepts response-signal arrays as input if the data comes from a single subject."
    ]
   },
   {
@@ -651,7 +651,7 @@
       "Python version       : 3.8.8\n",
       "IPython version      : 8.4.0\n",
       "\n",
-      "metadPy: 0.0.1\n",
+      "metadpy: 0.0.1\n",
       "jax    : 0.3.5\n",
       "numpyro: not installed\n",
       "pymc   : 4.3.0\n",
@@ -667,7 +667,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p metadPy,jax,numpyro,pymc"
+    "%watermark -n -u -v -iv -w -p metadpy,jax,numpyro,pymc"
    ]
   },
   {

docs/source/examples/Example 1 - Fitting MLE - Subject and group level.ipynb

@@ -23,18 +23,18 @@ Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-from metadPy import load_dataset
-from metadPy.mle import metad
-from metadPy.plotting import plot_confidence, plot_roc
+from metadpy import load_dataset
+from metadpy.mle import metad
+from metadpy.plotting import plot_confidence, plot_roc
 
 sns.set_context("talk")
 ```
 
 +++ {"id": "2oE_wkIxVPbe"}
 
-In this notebook, we are going to estimate meta-*d'* using Maximum Likelihood Estimation ([MLE](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)) {cite:p}`fleming:2014,maniscalo:2014,maniscalo:2012` using the function implemented in [metadPy](https://github.com/LegrandNico/metadPy). This function is directly adapted from the transcription of the Matlab `fit_meta_d_MLE.m` by Alan Lee that can be retrieved [here](http://www.columbia.edu/~bsm2105/type2sdt/).
+In this notebook, we are going to estimate meta-*d'* using Maximum Likelihood Estimation ([MLE](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)) {cite:p}`fleming:2014,maniscalo:2014,maniscalo:2012` using the function implemented in [metadpy](https://github.com/LegrandNico/metadpy). This function is directly adapted from the transcription of the Matlab `fit_meta_d_MLE.m` by Alan Lee that can be retrieved [here](http://www.columbia.edu/~bsm2105/type2sdt/).
 
-We are going to see, however, that [metadPy](https://github.com/LegrandNico/metadPy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance.
+We are going to see, however, that [metadpy](https://github.com/LegrandNico/metadpy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance.
 
 +++ {"id": "unable-tattoo"}
 
@@ -68,7 +68,7 @@ sns.despine()
 
 +++ {"id": "GJFs74YdcqxR"}
 
-The model is fitted using the `metadPy.mle.metad()` function. This function accepts response-signal arrays as input if the data comes from a single subject.
+The model is fitted using the `metadpy.mle.metad()` function. This function accepts response-signal arrays as input if the data comes from a single subject.
 
 ```{code-cell} ipython3
 ---
@@ -211,7 +211,7 @@ sns.despine()
 
 ```{code-cell} ipython3
 %load_ext watermark
-%watermark -n -u -v -iv -w -p metadPy,jax,numpyro,pymc
+%watermark -n -u -v -iv -w -p metadpy,jax,numpyro,pymc
 ```
 
 ```{code-cell} ipython3

docs/source/examples/Example 1 - Fitting MLE - Subject and group level.md

@@ -22,7 +22,7 @@
    "source": [
     "import arviz as az\n",
     "import numpy as np\n",
-    "from metadPy.bayesian import hmetad"
+    "from metadpy.bayesian import hmetad"
    ]
   },
   {
@@ -379,7 +379,7 @@
       "Python version       : 3.8.8\n",
       "IPython version      : 8.3.0\n",
       "\n",
-      "metadPy: 0.0.1\n",
+      "metadpy: 0.0.1\n",
       "pymc   : 4.2.2\n",
       "\n",
       "numpy: 1.20.1\n",
@@ -392,7 +392,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p metadPy,pymc"
+    "%watermark -n -u -v -iv -w -p metadpy,pymc"
    ]
   },
   {