Numpy2 updates

.gitignore

@@ -0,0 +1,224 @@
+# Auto-generated code
+dcca.c
+dfa.c
+ht.c
+mfdcca.c
+mfdfa.c
+
+# generic Python gitignore
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer,
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml

examples/ht_example.py

@@ -3,7 +3,7 @@
 import fathon
 from fathon import fathonUtils as fu
 
-print('This is fathon v{}'.format(fathon.__version__))
+print("This is fathon v{}".format(fathon.__version__))
 
 a = np.random.randn(10000)
 b = np.random.randn(10000)
@@ -17,18 +17,22 @@
 mfdfaPolOrd = 1
 polOrd = 1
 
-ht = pyht.computeHt(scales, mfdfaPolOrd=mfdfaPolOrd, polOrd=polOrd)
+ht = pyht.computeHt(scales, mfdfaPolOrd=mfdfaPolOrd, polOrd=polOrd, verbose=True)
 
-plt.rc('font', size=14)
+plt.rc("font", size=14)
 plt.figure(figsize=(10, 6))
-w = 3 if len(scales) >= 3 else len(scales)
-h = np.ceil(len(scales)/3)
+w = int(3 if len(scales) >= 3 else len(scales))
+h = int(np.ceil(len(scales) / 3))
 for i, scale in enumerate(scales):
-    plt.subplot(h, w, i+1)
-    plt.plot(np.arange(1, len(ht[i, 0:len(a)-scale+1])+1), ht[i, 0:len(a)-scale+1],
-             'r-', label='scale = {}'.format(scale))
-    plt.xlabel('window number', fontsize=14)
-    plt.ylabel('$h_t$', fontsize=14)
+    plt.subplot(h, w, i + 1)
+    plt.plot(
+        np.arange(1, len(ht[i, 0 : len(a) - scale + 1]) + 1),
+        ht[i, 0 : len(a) - scale + 1],
+        "r-",
+        label="scale = {}".format(scale),
+    )
+    plt.xlabel("window number", fontsize=14)
+    plt.ylabel("$h_t$", fontsize=14)
     plt.legend(loc=0, fontsize=14)
 plt.subplots_adjust(hspace=0.6, wspace=0.3)
 plt.show()

fathon/dcca.pyx

@@ -16,6 +16,8 @@
 
 #cython: language_level=3
 
+from libc.stdint cimport int64_t
+
 import numpy as np
 cimport numpy as np
 cimport cython
@@ -98,10 +100,10 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cdef cy_flucCompute(self, np.ndarray[np.float64_t, ndim=1, mode='c'] vects1, np.ndarray[np.float64_t, ndim=1, mode='c'] vects2, np.ndarray[int, ndim=1, mode='c'] vecn, np.ndarray[np.float64_t, ndim=1, mode='c'] vecf, int polOrd, bint absVals, bint overlap, bint revSeg):
+    cdef cy_flucCompute(self, np.ndarray[double, ndim=1, mode='c'] vects1, np.ndarray[double, ndim=1, mode='c'] vects2, np.ndarray[int, ndim=1, mode='c'] vecn, np.ndarray[double, ndim=1, mode='c'] vecf, int polOrd, bint absVals, bint overlap, bint revSeg):
         cdef int nLen, tsLen
         cdef Py_ssize_t i, j
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] t
+        cdef np.ndarray[double, ndim=1, mode='c'] t
 
         nLen = len(vecn)
         tsLen = len(vects1)
@@ -131,7 +133,7 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cpdef computeFlucVec(self, np.ndarray[np.int64_t, ndim=1, mode='c'] winSizes, int polOrd=1, bint absVals=True, bint overlap=False, bint revSeg=False):
+    cpdef computeFlucVec(self, np.ndarray[int64_t, ndim=1, mode='c'] winSizes, int polOrd=1, bint absVals=True, bint overlap=False, bint revSeg=False):
         """Computation of the fluctuations in each window.
 
         Parameters
@@ -179,12 +181,12 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cdef computeFlucVecSameTs(self, np.ndarray[np.float64_t, ndim=1, mode='c'] vec, np.ndarray[int, ndim=1, mode='c'] wins, int polOrd, bint overlap, bint revSeg):
+    cdef computeFlucVecSameTs(self, np.ndarray[double, ndim=1, mode='c'] vec, np.ndarray[int, ndim=1, mode='c'] wins, int polOrd, bint overlap, bint revSeg):
         cdef int nLen, tsLen = len(vec)
         cdef Py_ssize_t i, j
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] F_same
+        cdef np.ndarray[double, ndim=1, mode='c'] F_same
         cdef np.ndarray[int, ndim=1, mode='c'] vecn
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] t
+        cdef np.ndarray[double, ndim=1, mode='c'] t
 
         vecn = np.array(wins, dtype=ctypes.c_int)
         nLen = len(vecn)
@@ -229,7 +231,7 @@ cdef class DCCA:
             Intercept of the fit.
         """
         cdef int start, end
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] log_fit
+        cdef np.ndarray[double, ndim=1, mode='c'] log_fit
 
         if len(self.n) > 1:
             if self.isComputed:
@@ -261,7 +263,7 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cpdef multiFitFlucVec(self, np.ndarray[np.int_t, ndim=2, mode='c'] limitsList, float logBase=np.e, bint verbose=False):
+    cpdef multiFitFlucVec(self, np.ndarray[long, ndim=2, mode='c'] limitsList, float logBase=np.e, bint verbose=False):
         """Fit of the fluctuations values in different intervals at the same time.
 
         Parameters
@@ -282,7 +284,7 @@ cdef class DCCA:
         """
         cdef Py_ssize_t i
         cdef int limLen = len(limitsList)
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] list_H_intercept, list_H
+        cdef np.ndarray[double, ndim=1, mode='c'] list_H_intercept, list_H
 
         if self.isComputed:
             list_H = np.zeros((limLen, ), dtype=float)
@@ -304,7 +306,7 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cpdef computeRho(self, np.ndarray[np.int64_t, ndim=1, mode='c'] winSizes, int polOrd=1, bint verbose=False, bint overlap=False, bint revSeg=False):
+    cpdef computeRho(self, np.ndarray[int64_t, ndim=1, mode='c'] winSizes, int polOrd=1, bint verbose=False, bint overlap=False, bint revSeg=False):
         """Computation of the cross-correlation index in each window.
 
         Parameters
@@ -330,7 +332,7 @@ cdef class DCCA:
         """
         cdef Py_ssize_t i
         cdef int nLen, tsLen = len(self.tsVec1)
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] Fxy, Fxx, Fyy
+        cdef np.ndarray[double, ndim=1, mode='c'] Fxy, Fxx, Fyy
 
         if polOrd < 1:
             raise ValueError('Error: Polynomial order must be greater than 0.')
@@ -368,7 +370,7 @@ cdef class DCCA:
     @cython.boundscheck(False)
     @cython.wraparound(False)
     @cython.nonecheck(False)
-    cpdef rhoThresholds(self, int L, np.ndarray[np.int64_t, ndim=1, mode='c'] winSizes, int nSim, double confLvl, int polOrd=1, bint verbose=False):
+    cpdef rhoThresholds(self, int L, np.ndarray[int64_t, ndim=1, mode='c'] winSizes, int nSim, double confLvl, int polOrd=1, bint verbose=False):
         """Computation of the cross-correlation index's confidence levels in each window.
 
         Parameters
@@ -396,8 +398,8 @@ cdef class DCCA:
         numpy ndarray
             Array containing the second confidence interval.
         """
-        cdef np.ndarray[np.float64_t, ndim=2, mode='c'] rho_all
-        cdef np.ndarray[np.float64_t, ndim=1, mode='c'] ran1, ran2, vecfx, vecfy, vecfxy
+        cdef np.ndarray[double, ndim=2, mode='c'] rho_all
+        cdef np.ndarray[double, ndim=1, mode='c'] ran1, ran2, vecfx, vecfy, vecfxy
         cdef int nLen
 
         if polOrd < 1: