resolve some issues

pastas/model.py

@@ -960,7 +960,7 @@ def get_tmin(self, tmin: Optional[pstTm] = None, use_oseries: Optional[bool] = T
 
         return tmin
 
-    def get_tmax(self, tmin: Optional[pstTm] = None, use_oseries: Optional[bool] = True, use_stresses: Optional[bool] = False) -> Type[Timestamp]:
+    def get_tmax(self, tmax: Optional[pstTm] = None, use_oseries: Optional[bool] = True, use_stresses: Optional[bool] = False) -> Type[Timestamp]:
         """Method that checks and returns valid values for tmax.
 
         Parameters

pastas/modelplots.py

@@ -8,7 +8,7 @@
 import numpy as np
 from matplotlib.backends.backend_pdf import PdfPages
 from matplotlib.ticker import MultipleLocator, LogFormatter
-from pandas import concat, Timestamp, Series
+from pandas import concat, Series
 
 from .decorators import model_tmin_tmax
 from .plots import series, diagnostics, cum_frequency, \
@@ -687,7 +687,7 @@ def contributions_pie(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm]
 
     @model_tmin_tmax
     def stacked_results(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, figsize: Optional[tuple] = (10, 8),
-                        stacklegend: Optional[bool] = False, **kwargs) -> pstAx:
+                        stacklegend: Optional[bool] = False, stacklegend_kws: Optional[dict] = None, **kwargs) -> pstAx:
         """Create a results plot, similar to `ml.plots.results()`, in which the
         individual contributions of stresses (in stressmodels with multiple
         stresses) are stacked.
@@ -802,7 +802,7 @@ def series(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, spl
 
     @model_tmin_tmax
     def summary_pdf(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, fname: Optional[str] = None, dpi: Optional[int] = 150,
-                    results_kwargs: Optional[dict] = {}, diagnostics_kwargs: Optional[dict] = {}):
+                    results_kwargs: Optional[dict] = None, diagnostics_kwargs: Optional[dict] = None):
         """Create a PDF file (A4) with the results and diagnostics plot.
 
         Parameters
@@ -825,6 +825,13 @@ def summary_pdf(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None
         """
         if fname is None:
             fname = "{}.pdf".format(self.ml.name)
+
+        if results_kwargs is None:
+            results_kwargs = {}
+
+        if diagnostics_kwargs is None:
+            diagnostics_kwargs = {}
+
         pdf = PdfPages(fname)
 
         fig = plt.figure(figsize=(8.27, 11.69), dpi=50)

pastas/modelstats.py

@@ -27,7 +27,7 @@
 
 from .decorators import model_tmin_tmax
 from .stats import diagnostics, metrics
-from .typeh import Type, Union, Optional, pstMl, pstTm
+from .typeh import Type, Optional, pstMl, pstTm
 
 
 class Statistics:

pastas/noisemodels.py

@@ -179,7 +179,7 @@ def weights(self, res: Type[Series], p: pstAL) -> Type[Series]:
         .. math:: w = 1 / sqrt((1 - exp(-2 \\Delta t / \\alpha)))
 
         which are then normalized so that sum(w) = len(res).
-Ò
+
         """
         alpha = p[0]
         # large for first measurement

pastas/plots.py

@@ -12,16 +12,15 @@
 from pastas.stats.core import acf as get_acf
 from pastas.stats.metrics import rmse, evp
 from pastas.typeh import Type, Optional, pstAx, pstFi, pstTm, pstMl, pstAL
-
+from pastas.modelcompare import CompareModels
 
 logger = logging.getLogger(__name__)
 
 __all__ = ["compare", "series", "acf", "diagnostics", "cum_frequency",
            "TrackSolve"]
 
 
-def compare(models: list[pstMl], tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, block_or_step: Optional[str] = 'step',
-            adjust_height: Optional[bool] = True, **kwargs) -> pstAx:
+def compare(models: list[pstMl], adjust_height: Optional[bool] = True, **kwargs) -> pstAx:
     """Plot multiple Pastas models in one figure to visually compare models.
 
     Note
@@ -195,8 +194,7 @@ def series(head: Optional[Type[Series]] = None, stresses: Optional[list[Type[Ser
 
 
 def acf(series: Type[Series], alpha: Optional[float] = 0.05, lags: Optional[int] = 365, acf_options: Optional[dict] = None, smooth_conf: Optional[bool] = True,
-        ax: Optional[pstAx] = None, figsize: Optional[tuple] = (5, 2)):
-
+        color: Optional[str] = "k", ax: Optional[pstAx] = None, figsize: Optional[tuple] = (5, 2)):
     """Plot of the autocorrelation function of a time series.
 
     Parameters

pastas/rfunc.py

@@ -6,7 +6,7 @@
 import numpy as np
 from pandas import DataFrame
 from scipy.integrate import quad
-from scipy.special import (erfc, erfcinv, exp1, gamma, gammainc, gammaincinv, 
+from scipy.special import (erfc, erfcinv, exp1, gamma, gammainc, gammaincinv,
                            k0, k1, lambertw)
 from scipy.interpolate import interp1d
 
@@ -117,7 +117,7 @@ def block(self, p: pstAL, dt: Optional[float] = 1.0, cutoff: Optional[float] = N
         """
         s = self.step(p, dt, cutoff, maxtmax)
         return np.append(s[0], np.subtract(s[1:], s[:-1]))
-    
+
     def impulse(self, t, p):
         """Method to return the impulse response function.
 
@@ -137,7 +137,7 @@ def impulse(self, t, p):
         -------
         s: numpy.array
             Array with the impulse response.
-            
+
         Note
         ----
         Only used for internal consistency checks
@@ -237,7 +237,7 @@ def step(self, p: pstAL, dt: Optional[float] = 1.0, cutoff: Optional[float] = No
         t = self.get_t(p, dt, cutoff, maxtmax)
         s = p[0] * gammainc(p[1], t / p[2])
         return s
-    
+
     def impulse(self, t, p):
         A, n, a = p
         ir = A * t ** (n - 1) * np.exp(-t / a) / (a ** n * gamma(n))
@@ -268,7 +268,6 @@ class Exponential(RfuncBase):
     """
     _name = "Exponential"
 
-
     def __init__(self):
         RfuncBase.__init__(self)
         self.nparam = 2
@@ -302,7 +301,7 @@ def step(self, p: pstAL, dt: Optional[float] = 1.0, cutoff: Optional[float] = No
         t = self.get_t(p, dt, cutoff, maxtmax)
         s = p[0] * (1.0 - np.exp(-t / p[1]))
         return s
-    
+
     def impulse(self, t, p):
         A, a = p
         ir = A / a * np.exp(-t / a)
@@ -341,7 +340,6 @@ class HantushWellModel(RfuncBase):
     """
     _name = "HantushWellModel"
 
-
     def __init__(self):
         RfuncBase.__init__(self)
         self.distances = None
@@ -398,7 +396,7 @@ def get_tmax(self, p: pstAL, cutoff: Optional[float] = None) -> float:
         else:
             return lambertw(1 / ((1 - cutoff) * k0rho)).real * cS
 
-    def gain(self, p: pstAL: r: Optional[float] = None) -> float:
+    def gain(self, p: pstAL, r: Optional[float] = None) -> float:
         if r is None:
             r = self._get_distance_from_params(p)
         rho = 2 * r * np.sqrt(p[2])
@@ -464,11 +462,11 @@ def variance_gain(A: float, b: float, var_A: float, var_b: float, cov_Ab: float,
         ps.WellModel.variance_gain
         """
         var_gain = (
-                (k0(2 * np.sqrt(r ** 2 * b))) ** 2 * var_A +
-                (-A * r * k1(2 * np.sqrt(r ** 2 * b)) / np.sqrt(
-                    b)) ** 2 * var_b -
-                2 * A * r * k0(2 * np.sqrt(r ** 2 * b)) *
-                k1(2 * np.sqrt(r ** 2 * b)) / np.sqrt(b) * cov_Ab
+            (k0(2 * np.sqrt(r ** 2 * b))) ** 2 * var_A +
+            (-A * r * k1(2 * np.sqrt(r ** 2 * b)) / np.sqrt(
+                b)) ** 2 * var_b -
+            2 * A * r * k0(2 * np.sqrt(r ** 2 * b)) *
+            k1(2 * np.sqrt(r ** 2 * b)) / np.sqrt(b) * cov_Ab
         )
         return var_gain
 
@@ -506,7 +504,6 @@ class Hantush(RfuncBase):
     """
     _name = "Hantush"
 
-
     def __init__(self):
         RfuncBase.__init__(self)
         self.nparam = 3
@@ -555,7 +552,7 @@ def step(self, p: pstAL, dt: float = 1.0, cutoff: Optional[float] = None, maxtma
         F[tau >= rho / 2] = 2 * k0rho - w * exp1(tau2) + (w - 1) * exp1(
             tau2 + rho ** 2 / (4 * tau2))
         return p[0] * F / (2 * k0rho)
-    
+
     def impulse(self, t, p):
         A, a, b = p
         ir = A / (2 * t * k0(2 * np.sqrt(b))) * np.exp(-t / a - a * b / t)
@@ -620,10 +617,10 @@ def step(self, p: pstAL, dt: Optional[float] = 1.0, cutoff: Optional[float] = No
         if not self.up:
             s = -s
         return s
-    
+
     def impulse(self, t, p):
         A, a, b = p
-        ir = A * t ** (-1.5) * np.exp(-t / a - b / t) 
+        ir = A * t ** (-1.5) * np.exp(-t / a - b / t)
         return ir
 
     @staticmethod
@@ -649,7 +646,6 @@ class One(RfuncBase):
     """
     _name = "One"
 
-
     def __init__(self):
         RfuncBase.__init__(self)
         self.nparam = 1
@@ -710,7 +706,6 @@ class FourParam(RfuncBase):
     """
     _name = "FourParam"
 
-
     def __init__(self, quad=False):
         RfuncBase.__init__(self, quad=quad)
         self.nparam = 4
@@ -859,7 +854,6 @@ class DoubleExponential(RfuncBase):
     """
     _name = "DoubleExponential"
 
-
     def __init__(self):
         RfuncBase.__init__(self)
         self.nparam = 4
@@ -993,7 +987,6 @@ class Kraijenhoff(RfuncBase):
     """
     _name = "Kraijenhoff"
 
-
     def __init__(self, n_terms=10):
         RfuncBase.__init__(self, n_terms=n_terms)
         self.nparam = 3
@@ -1031,8 +1024,8 @@ def step(self, p: pstAL, dt: Optional[float] = 1.0, cutoff: Optional[float] = No
         h = 0
         for n in range(self.n_terms):
             h += (-1) ** n / (2 * n + 1) ** 3 * \
-                 np.cos((2 * n + 1) * np.pi * p[2]) * \
-                 np.exp(-(2 * n + 1) ** 2 * t / p[1])
+                np.cos((2 * n + 1) * np.pi * p[2]) * \
+                np.exp(-(2 * n + 1) ** 2 * t / p[1])
         s = p[0] * (1 - (8 / (np.pi ** 3 * (1 / 4 - p[2] ** 2)) * h))
         return s
 
@@ -1069,7 +1062,6 @@ class Spline(RfuncBase):
     """
     _name = "Spline"
 
-
     def __init__(self, kind: Optional[str] = 'quadratic',
                  t: Optional[list] = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]):
         RfuncBase.__init__(self, kind=kind, t=t)

pastas/solver.py

@@ -17,7 +17,7 @@
 from scipy.linalg import svd
 from scipy.optimize import least_squares
 
-from pastas.typeh import Type, Optional, Tuple, pstAL, pstMl, pstCB, pstFu
+from pastas.typeh import Type, Optional, Tuple, Union, pstAL, pstMl, pstCB, pstFu
 
 logger = getLogger(__name__)
 

pastas/stats/dutch.py

@@ -355,8 +355,8 @@ def mean_all(s, min_n_meas):
 
 # Helper functions
 
-def _mean_spring(series: Type[Series], min_n_meas: int) -> float:
-    """Internal method to determine mean of timeseries values in spring.
+def _get_spring(series: Type[Series], min_n_meas: int) -> float:
+    """Internal method to get values of timeseries values in spring.
 
     Part of year aggregator function for gvg method.
 

pastas/stats/signatures.py

@@ -1,7 +1,7 @@
 """This module contains methods to compute the groundwater signatures."""
 import pandas as pd
-from pandas import NA, Timedelta, DatetimeIndex, cut, Series
-from numpy import diff, sqrt, log, arange
+from pandas import Timedelta, DatetimeIndex, cut, Series
+from numpy import diff, sqrt, log, arange, nan
 
 import pastas as ps
 from scipy.stats import linregress

pastas/stressmodels.py

@@ -49,7 +49,7 @@ class StressModelBase:
     """
     _name = "StressModelBase"
 
-    def __init__(self, name: str, tmin: pstTm, tmax: pstTm, rfunc: Optional[pstRF] = None: up: Optional[bool] = True, meanstress: Optional[float] = 1.0, cutoff: Optional[float] = 0.999):
+    def __init__(self, name: str, tmin: pstTm, tmax: pstTm, rfunc: Optional[pstRF] = None, up: Optional[bool] = True, meanstress: Optional[float] = 1.0, cutoff: Optional[float] = 0.999):
         self.name = validate_name(name)
         self.tmin = tmin
         self.tmax = tmax

pastas/typeh.py

@@ -34,4 +34,4 @@
 pstCB = TypeVar("pstCB", bound=Union[SolveTimer, TrackSolve])  # Callback
 pstFu = TypeVar("pstFu")  # Function (e.g. Objective Function)
 pstRF = TypeVar("pstRF", bound=RfuncBase)  # rFunc Base
-pstAL = TypeVar("pstAL", bound=ArrayLike)  # Array Like (NumPy based)
+pstAL = TypeVar("pstAL", bound=Type[ArrayLike])  # Array Like (NumPy based)