serapieum-of-alex / statista Goto Github PK

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4. See error

Expected behavior
after installing the package, not able to import it

Screenshots
___________________ ERROR collecting tests/test_plot_map.py ____________________ ImportError while importing test module '/home/runner/work/cleopatra/cleopatra/tests/test_plot_map.py'. Hint: make sure your test modules/packages have valid Python names. Traceback: /usr/share/miniconda3/lib/python3.9/importlib/__init__.py:1[27](https://github.com/MAfarrag/cleopatra/runs/6661943937?check_suite_focus=true#step:6:28): in import_module return _bootstrap._gcd_import(name[level:], package, level) tests/test_plot_map.py:9: in <module> from cleopatra.map import Map cleopatra/__init__.py:33: in <module> import cleopatra.map cleopatra/map.py:20: in <module> from statista.tools import Tools as ST E ModuleNotFoundError: No module named 'statista'

Additional context
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move this part to the constructor method for the PlottingPosition class and convert the weibul method normal method instead of static method

[statista] statista/distributions.py (Lines 73-74)

data = np.array(data)
        data.sort()

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[statista] statista/distributions.py (Lines 32-33)

 def __init__(self):
        pass

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the probability_plot function differs from the GEV and the Gumbel distributions (especially in the method, n_samples, and func parameters) and needs to be unified

[statista] statista/distributions.py (Lines 1382-1395)

def probability_plot(
        self,
        parameters: Dict[str, Union[float, Any]],
        cdf: Union[np.ndarray, list],
        alpha: Number = 0.1,
        func: Callable = None,
        method: str = "lmoments",
        n_samples=100,
        fig1_size=(10, 5),
        fig2_size=(6, 6),
        xlabel="Actual data",
        ylabel="cdf",
        fontsize=15,
    ):

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This plotting part is repeated in all distribution but is not yet passed upstream to the abstract class method because of the

[statista] statista/distributions.py (Lines 977-994)

if plot_figure:
            Qx = np.linspace(
                float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000
            )
            pdf_fitted = self.pdf(parameters, actualdata=Qx)

            fig, ax = Plot.pdf(
                Qx,
                pdf_fitted,
                self.data_sorted,
                figsize=figsize,
                xlabel=xlabel,
                ylabel=ylabel,
                fontsize=fontsize,
            )
            return pdf, fig, ax
        else:
            return pdf

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replace the individual keyword arguments to one single dictionary positional argument.
parameters = {"loc", val, "scale": val}

[statista] statista/distributions.py (Lines 626-639)

def get_rp(self, loc, scale, data):
        """getRP.

            getRP calculates the return period for a list/array of values or a single value.

        Parameters
        ----------
        data:[list/array/float]
            value you want the coresponding return value for
        loc: [float]
            location parameter
        scale: [float]
            scale parameter

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move this part to the constructor method for the PlottingPosition class and convert the weibul method normal method instead of static method

[statista] statista/distributions.py (Lines 73-74)

data = np.array(data)
        data.sort()

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[statista] statista/distributions.py (Lines 32-33)

 def __init__(self):
        pass

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create github action to check and enforce naming of pull requests
create a github action to check the name of a pull request and make sure it follows a certain pattern

the naming convention should follow
<module-name>/<class-name>/<method-name>/<task-tybe>

task-type: feature|bugfix|hotfix|release|docs

workflow Link

the probability_plot function differs from the GEV and the Gumbel distributions (especially in the method, n_samples, and func parameters) and needs to be unified

[statista] statista/distributions.py (Lines 1382-1395)

def probability_plot(
        self,
        parameters: Dict[str, Union[float, Any]],
        cdf: Union[np.ndarray, list],
        alpha: Number = 0.1,
        func: Callable = None,
        method: str = "lmoments",
        n_samples=100,
        fig1_size=(10, 5),
        fig2_size=(6, 6),
        xlabel="Actual data",
        ylabel="cdf",
        fontsize=15,
    ):

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^{Created from JetBrains using CodeStream}