Update

.gitlab-ci.yml

@@ -1,9 +0,0 @@
-image: alpine:latest
-
-pages:
-  stage: deploy
-  script:
-  - echo 'Nothing to do...'
-  artifacts:
-    paths:
-    - public

drcstools/_modidx.py

@@ -95,6 +95,9 @@ d = { 'settings': { 'branch': 'master',
                                  'drcstools.stats.normalize_0_to_1': ('stats.html#normalize_0_to_1', 'drcstools/stats.py'),
                                  'drcstools.stats.normalize_from_minus_plus_one': ( 'stats.html#normalize_from_minus_plus_one',
                                                                                     'drcstools/stats.py'),
-                                 'drcstools.stats.regression': ('stats.html#regression', 'drcstools/stats.py')},
+                                 'drcstools.stats.regression': ('stats.html#regression', 'drcstools/stats.py'),
+                                 'drcstools.stats.segments_fit': ('stats.html#segments_fit', 'drcstools/stats.py')},
             'drcstools.storm': {},
-            'drcstools.tef': {}}}
+            'drcstools.tef': {},
+            'drcstools.wavelet': { 'drcstools.wavelet.wavelet_object': ('wavelet.html#wavelet_object', 'drcstools/wavelet.py'),
+                                   'drcstools.wavelet.wavelet_object.__init__': ('wavelet.html#__init__', 'drcstools/wavelet.py')}}}

drcstools/stats.py

@@ -2,12 +2,13 @@
 
 # %% auto 0
 __all__ = ['normalize_from_minus_plus_one', 'normalize_0_to_1', 'regression', 'mean2', 'corr2', 'linear_regression_lag',
-           'linear_trend', 'detrend_dim', 'detrend']
+           'segments_fit', 'linear_trend', 'detrend_dim', 'detrend']
 
 # %% ../nbs/02_stats.ipynb 3
 import xarray as xr
 import numpy as np
 from scipy import stats
+from scipy import optimize
 import matplotlib.pyplot as plt
 
 # %% ../nbs/02_stats.ipynb 4
@@ -108,7 +109,31 @@ def linear_regression_lag(x, y, shiftx=0, shifty=0, dim = "time"):
         }
     )
 
-# %% ../nbs/02_stats.ipynb 21
+# %% ../nbs/02_stats.ipynb 18
+def segments_fit(X, Y, count):
+    xmin = X.min()
+    xmax = X.max()
+
+    seg = np.full(count - 1, (xmax - xmin) / count)
+
+    px_init = np.r_[np.r_[xmin, seg].cumsum(), xmax]
+    py_init = np.array([Y[np.abs(X - x) < (xmax - xmin) * 0.01].mean() for x in px_init])
+
+    def func(p):
+        seg = p[:count - 1]
+        py = p[count - 1:]
+        px = np.r_[np.r_[xmin, seg].cumsum(), xmax]
+        return px, py
+
+    def err(p):
+        px, py = func(p)
+        Y2 = np.interp(X, px, py)
+        return np.mean((Y - Y2)**2)
+
+    r = optimize.minimize(err, x0=np.r_[seg, py_init], method='Nelder-Mead')
+    return func(r.x)
+
+# %% ../nbs/02_stats.ipynb 22
 def linear_trend(obj, dim):
     """Convenience wrapper for 'lag_linregress_3D'. Calculates the trend per
     given timestep. E.g. if the data is passed as yearly values, the
@@ -120,14 +145,21 @@ def linear_trend(obj, dim):
     trend = linear_regression_lag(x, obj, dim=dim)
     return trend
 
-# %% ../nbs/02_stats.ipynb 25
+# %% ../nbs/02_stats.ipynb 26
+def detrend_dim(da, dim, deg=1):
+    # detrend along a single dimension
+    p = da.polyfit(dim=dim, deg=deg)
+    fit = xr.polyval(da[dim], p.polyfit_coefficients)
+    return da - fit
+
+# %% ../nbs/02_stats.ipynb 30
 def detrend_dim(da, dim, deg=1):
     # detrend along a single dimension
     p = da.polyfit(dim=dim, deg=deg)
     fit = xr.polyval(da[dim], p.polyfit_coefficients)
     return da - fit
 
-# %% ../nbs/02_stats.ipynb 26
+# %% ../nbs/02_stats.ipynb 31
 def detrend(da, dims, deg=1):
     """Linear along possible multiple dimensions
 

drcstools/wavelet.py

@@ -0,0 +1,25 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/11_wavelet.ipynb.
+
+# %% auto 0
+__all__ = ['wavelet_object']
+
+# %% ../nbs/11_wavelet.ipynb 4
+class wavelet_object:
+    
+    def __init__(self, data, varname, timename="time", remove_trend = False):
+        self.N    = data[varname].size
+        self.dt   = 1
+        self.t    = np.arange(0, self.N) * self.dt
+        self.data = data[varname].values
+        self.time = data[timename]
+        if remove_trend == True:
+            p = np.polyfit(self.t, self.data, 1)
+            self.data = self.data - np.polyval(p, self.t)
+        self.std  = self.data.std()
+        self.var  = self.std ** 2
+        self.data = (self.data - self.data.mean()) / self.std
+        self.mother = wavelet.Morlet(6)
+        self.s0     = 2 * self.dt
+        self.dj     = 1 / 24
+        self.J      = 7 / self.dj
+        self.alpha, _, _ = wavelet.ar1(self.data)

nbs/11_wavelet.ipynb

@@ -70,20 +70,6 @@
     "        self.J      = 7 / self.dj\n",
     "        self.alpha, _, _ = wavelet.ar1(self.data)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| export\n",
-    "def cwt(wavelet_object):\n",
-    "\n",
-    "     self.wave, self.scales, self.freqs, self.coi, self.fft, self.fftfreqs = wavelet.cwt(wavelet_object.data, self.dt, self.dj,\n",
-    "                                                                                            self.s0, self.J, self.mother)\n",
-    "        self.iwave = wavelet.icwt(self.wave, self.scales, self.dt, self.dj, self.mother)"
-   ]
   }
  ],
  "metadata": {
@@ -102,7 +88,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:41:22) [Clang 13.0.1 ]"
   },
   "vscode": {
    "interpreter": {