Update setup configurations

.gitignore

@@ -93,4 +93,10 @@ ENV/
 ./stmetrics/spatial.py
 
 # Vs Code
-.vscode/
+.vscode/
+
+# Poetry
+poetry.lock
+
+# others
+TODO

pyproject.toml

@@ -0,0 +1,37 @@
+[tool.poetry]
+name = "stmetrics"
+version = "0.1.8"
+description = "A package to compute features from Satellite Image Time Series (SITS)."
+authors = ["Brazil Data Cube Team <[email protected]>"]
+readme = "README.rst"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+dtaidistance = "==2.3.11"
+pandas = "==2.1.4"
+matplotlib = "==3.8.2"
+numba = "==0.58.1"
+numpy = "==1.26.2"
+xarray = "==2023.12.0"
+rasterio = "==1.3.9"
+shapely = "==2.0.2"
+affine = "==2.4.0"
+descartes = "==1.1.0"
+connected-components-3d = "==3.12.4"
+rasterstats = "==0.19.0"
+pointpats = "==2.4.0"
+geopandas = "==0.14.1"
+nolds = "==0.5.2"
+urllib3 = "~1"
+fastremap = "==1.14.0"
+scikit-learn = "==1.3.2"
+
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "==7.4.3"
+flake8 = "==6.1.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

stmetrics/basics.py

@@ -1,9 +1,11 @@
 import numpy
-from .utils import fixseries, truncate
+from stmetrics.utils import fixseries, truncate
 
 
 def ts_basics(timeseries, funcs=["all"], nodata=-9999):
-    """This function compute all basic metrics in a single call, returning a dictionary:
+    """This function compute all basic metrics in a single call, returning a
+    dictionary:
+
         - "Max" - Maximum value of the time series.
 
         - "Min" - Minimum value of the time series.
@@ -31,7 +33,7 @@ def ts_basics(timeseries, funcs=["all"], nodata=-9999):
         - "IQR" - Interquaritle range (IQR) of the time series.
 
         - "FQR" - First quartile of the time series.
-        
+
         - "SQR" - Second quartile of the time series.
 
         - "TQR" - Third quaritle of the time series.
@@ -47,7 +49,7 @@ def ts_basics(timeseries, funcs=["all"], nodata=-9999):
 
     out_metrics = dict()
 
-    metrics_count = 15
+    # metrics_count = 15
 
     if "all" in funcs:
         funcs = [
@@ -78,8 +80,8 @@ def ts_basics(timeseries, funcs=["all"], nodata=-9999):
 
 
 def mean_ts(timeseries, nodata=-9999):
-    """Average value (mean) of the time series, considering only valid \
-    values. When nodata is found, it is not included in N value (for all functions). 
+    """Average value (mean) of the time series, considering only valid values.
+    When nodata is found, it is not included in N value (for all functions).
 
     :param timeseries: Time series.
     :type timeseries: numpy.ndarray

stmetrics/fractal.py

@@ -1,16 +1,16 @@
 import numpy
-from .utils import fixseries, truncate
+from stmetrics.utils import fixseries, truncate
 
 
 def ts_fractal(timeseries, funcs=['all'], nodata=-9999):
     """This function computes 4 fractal dimensions and the hurst exponential.
 
-        - DFA: measures the Hurst parameter H, which is similar to the \
+        - DFA: measures the Hurst parameter H, which is similar to the
         Hurst exponent.
 
-        - HE: self-similarity measure that assess long-range dependence in a \
+        - HE: self-similarity measure that assess long-range dependence in a
         time series.
-        
+
         - KFD: This algorirhm computes the FD using Katz algorithm.
 
     :param timeseries: Time series.
@@ -65,11 +65,11 @@ def dfa_fd(timeseries, nvals=None,  overlap=True, order=1, nodata=-9999):
 
     .. Note::
 
-        This function uses the Detrended Fluctuation Analysis (DFA) \
-        implementation from the Nolds package. Due to time series \
-        characteristcs we use by default the 'RANSAC' \
+        This function uses the Detrended Fluctuation Analysis (DFA)
+        implementation from the Nolds package. Due to time series
+        characteristcs we use by default the 'RANSAC'
         fitting method as it is more robust to outliers.
-        For more details regarding the hurst implementation, check Nolds \
+        For more details regarding the hurst implementation, check Nolds
         documentation page.
 
     """
@@ -90,10 +90,10 @@ def hurst_exp(timeseries, nvals=None, nodata=-9999):
 
     :param timeseries: Time series.
     :type timeseries: numpy.ndarray
-    
+
     :param nvals: Sizes of subseries to use.
     :type nvals: int
-    
+
     :param nodata: nodata of the time series. Default is -9999.
     :type nodata: int
 
@@ -125,7 +125,7 @@ def katz_fd(timeseries, nodata=-9999):
 
     :param timeseries: Time series.
     :type timeseries: numpy.ndarray
-    
+
     :param nodata: nodata of the time series. Default is -9999.
     :type nodata: int
 

stmetrics/polar.py

@@ -32,7 +32,7 @@ def ts_polar(timeseries, funcs=["all"], nodata=-9999, show=False):
 
     - CSI - This is a dimensionless quantitative measure of morphology, \
     that characterize the standard deviation of an object from a circle.
-    
+
     To visualize the time series on polar space use: ts_polar(timeseries,\
      show=True)
 
@@ -56,7 +56,7 @@ def ts_polar(timeseries, funcs=["all"], nodata=-9999, show=False):
     """
     out_metrics = dict()
 
-    metrics_count = 9
+    # metrics_count = 9
 
     if "all" in funcs:
         funcs = [
@@ -161,7 +161,7 @@ def polar_plot(timeseries, nodata=-9999):
     """
     import matplotlib.pyplot as plt
     from descartes import PolygonPatch
-    from matplotlib.ticker import FormatStrFormatter
+    # from matplotlib.ticker import FormatStrFormatter
 
     # filter time series
     ts = fixseries(timeseries, nodata)
@@ -175,7 +175,7 @@ def polar_plot(timeseries, nodata=-9999):
     maxY = numpy.max(numpy.abs(y))
 
     # get season rings
-    ringTopLeft, ringTopRight,\
+    ringTopLeft, ringTopRight, \
         ringBottomLeft, ringBottomRight = get_seasons(x, y)
 
     # setup plot