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Replace asserts with errors, resolves #242 (#276)
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* loads/extreme convert asserts to errors

* loads/general convert asserts to errors

* loads/graphics convert asserts to errors

* power module convert asserts to errors

* loads module, convert try-except validation to errors

* utils module, convert asserts to errors

* dolfyn module, convert asserts to errors

* tidal module, convert asserts to errors

* river module, convert asserts to errors

* wave hindcast, replace asserts with errors

* wave/io module, convert asserts to errors

* wave module, convert asserts to errors

* rename reserved variable min in plot_directional_spectrum

* fix miscellaneous typos

* catch new error type

* fix test logic and parameter name

* minor review comments, fix f strings, fix messages, etc

* list correct types in error messages, f strings, etc

* standardize error messages for optional parameters

* Apply suggestions from ssolson's 2nd review
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@akeeste
akeeste committed Nov 16, 2023
1 parent f5f25a2 commit 66a9d9b
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Showing 23 changed files with 927 additions and 586 deletions.
9 changes: 4 additions & 5 deletions examples/directional_waves.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1165,7 +1165,7 @@
}
],
"source": [
"wave.graphics.plot_directional_spectrum(spectrum, min=0.3)"
"wave.graphics.plot_directional_spectrum(spectrum, color_level_min=0.3)"
]
},
{
Expand Down Expand Up @@ -1195,7 +1195,7 @@
}
],
"source": [
"wave.graphics.plot_directional_spectrum(spectrum, min=0.3, fill=False, nlevels=4)"
"wave.graphics.plot_directional_spectrum(spectrum, color_level_min=0.3, fill=False, nlevels=4)"
]
},
{
Expand Down Expand Up @@ -1318,7 +1318,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 ('.venv': venv)",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -1332,9 +1332,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
"version": "3.9.17"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "15fd306e44580d8cf431083454c399b84f9cc4f7f2c761501397671836835f49"
Expand Down
5 changes: 3 additions & 2 deletions mhkit/dolfyn/velocity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -861,8 +861,9 @@ def power_spectral_density(self, veldat,

# Spectra, if input is full velocity or a single array
if len(vel.shape) == 2:
assert vel.shape[0] == 3, "Function can only handle 1D or 3D arrays." \
" If ADCP data, please select a specific depth bin."
if not vel.shape[0] == 3:
raise Exception("Function can only handle 1D or 3D arrays." \
" If ADCP data, please select a specific depth bin.")
if (noise is not None) and (np.shape(noise)[0] != 3):
raise Exception(
'Noise should have same first dimension as velocity')
Expand Down
158 changes: 103 additions & 55 deletions mhkit/loads/extreme.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -59,8 +59,10 @@ def global_peaks(t, data):
peaks: np.array
Peak values of the response time-series
"""
assert isinstance(t, np.ndarray), 't must be of type np.ndarray'
assert isinstance(data, np.ndarray), 'data must be of type np.ndarray'
if not isinstance(t, np.ndarray):
raise TypeError(f't must be of type np.ndarray. Got: {type(t)}')
if not isinstance(data, np.ndarray):
raise TypeError(f'data must be of type np.ndarray. Got: {type(data)}')

# Find zero up-crossings
inds = upcrossing(t, data)
Expand Down Expand Up @@ -99,9 +101,12 @@ def number_of_short_term_peaks(n, t, t_st):
n_st : float
Number of peaks in short term period.
"""
assert isinstance(n, int), 'n must be of type int'
assert isinstance(t, float), 't must be of type float'
assert isinstance(t_st, float), 't_st must be of type float'
if not isinstance(n, int):
raise TypeError(f'n must be of type int. Got: {type(n)}')
if not isinstance(t, float):
raise TypeError(f't must be of type float. Got: {type(t)}')
if not isinstance(t_st, float):
raise TypeError(f't_st must be of type float. Got: {type(t_st)}')

return n * t_st / t

Expand All @@ -124,7 +129,8 @@ def peaks_distribution_weibull(x):
peaks: scipy.stats.rv_frozen
Probability distribution of the peaks.
"""
assert isinstance(x, np.ndarray), 'x must be of type np.ndarray'
if not isinstance(x, np.ndarray):
raise TypeError(f'x must be of type np.ndarray. Got: {type(x)}')

# peaks distribution
peaks_params = stats.exponweib.fit(x, f0=1, floc=0)
Expand Down Expand Up @@ -154,7 +160,8 @@ def peaks_distribution_weibull_tail_fit(x):
peaks: scipy.stats.rv_frozen
Probability distribution of the peaks.
"""
assert isinstance(x, np.ndarray), 'x must be of type np.ndarray'
if not isinstance(x, np.ndarray):
raise TypeError(f'x must be of type np.ndarray. Got: {type(x)}')

# Initial guess for Weibull parameters
p0 = stats.exponweib.fit(x, f0=1, floc=0)
Expand Down Expand Up @@ -231,16 +238,17 @@ def automatic_hs_threshold(
Threshold that results in the best correlation.
"""
if not isinstance(sampling_rate, (float, int)):
raise TypeError('sampling_rate must be of type float or int')

raise TypeError(
f'sampling_rate must be of type float or int. Got: {type(sampling_rate)}')
if not isinstance(peaks, np.ndarray):
raise TypeError('peaks must be of type np.ndarray')

raise TypeError(
f'peaks must be of type np.ndarray. Got: {type(peaks)}')
if not len(initial_threshold_range) == 3:
raise ValueError('initial_threshold_range must be length 3')

raise ValueError(
f'initial_threshold_range must be length 3. Got: {len(initial_threshold_range)}')
if not isinstance(max_refinement, int):
raise TypeError('max_refinement must be of type int')
raise TypeError(
f'max_refinement must be of type int. Got: {type(max_refinement)}')

range_min, range_max, range_step = initial_threshold_range
best_threshold = -1
Expand Down Expand Up @@ -309,11 +317,13 @@ def peaks_distribution_peaks_over_threshold(x, threshold=None):
peaks: scipy.stats.rv_frozen
Probability distribution of the peaks.
"""
assert isinstance(x, np.ndarray), 'x must be of type np.ndarray'
if not isinstance(x, np.ndarray):
raise TypeError(f'x must be of type np.ndarray. Got: {type(x)}')
if threshold is None:
threshold = np.mean(x) + 1.4 * np.std(x)
assert isinstance(threshold, float
), 'threshold must be of type float'
if not isinstance(threshold, float):
raise TypeError(
f'If specified, threshold must be of type float. Got: {type(threshold)}')

# peaks over threshold
x = np.sort(x)
Expand Down Expand Up @@ -370,9 +380,10 @@ def ste_peaks(peaks_distribution, npeaks):
ste: scipy.stats.rv_frozen
Short-term extreme distribution.
"""
assert callable(peaks_distribution.cdf
), 'peaks_distribution must be a scipy.stat distribution.'
assert isinstance(npeaks, float), 'npeaks must be of type float'
if not callable(peaks_distribution.cdf):
raise TypeError('peaks_distribution must be a scipy.stat distribution.')
if not isinstance(npeaks, float):
raise TypeError(f'npeaks must be of type float. Got: {type(npeaks)}')

class _ShortTermExtreme(stats.rv_continuous):

Expand Down Expand Up @@ -415,9 +426,12 @@ def block_maxima(t, x, t_st):
block_maxima: np.array
Block maxima (i.e. largest peak in each block).
"""
assert isinstance(t, np.ndarray), 't must be of type np.ndarray'
assert isinstance(x, np.ndarray), 'x must be of type np.ndarray'
assert isinstance(t_st, float), 't_st must be of type float'
if not isinstance(t, np.ndarray):
raise TypeError(f't must be of type np.ndarray. Got: {type(t)}')
if not isinstance(x, np.ndarray):
raise TypeError(f'x must be of type np.ndarray. Got: {type(x)}')
if not isinstance(t_st, float):
raise TypeError(f't_st must be of type float. Got: {type(t_st)}')

nblock = int(t[-1] / t_st)
block_maxima = np.zeros(int(nblock))
Expand All @@ -442,8 +456,9 @@ def ste_block_maxima_gev(block_maxima):
ste: scipy.stats.rv_frozen
Short-term extreme distribution.
"""
assert isinstance(
block_maxima, np.ndarray), 'block_maxima must be of type np.ndarray'
if not isinstance(block_maxima, np.ndarray):
raise TypeError(
f'block_maxima must be of type np.ndarray. Got: {type(block_maxima)}')

ste_params = stats.genextreme.fit(block_maxima)
param_names = ['c', 'loc', 'scale']
Expand All @@ -468,8 +483,9 @@ def ste_block_maxima_gumbel(block_maxima):
ste: scipy.stats.rv_frozen
Short-term extreme distribution.
"""
assert isinstance(
block_maxima, np.ndarray), 'block_maxima must be of type np.ndarray'
if not isinstance(block_maxima, np.ndarray):
raise TypeError(
f'block_maxima must be of type np.ndarray. Got: {type(block_maxima)}')

ste_params = stats.gumbel_r.fit(block_maxima)
param_names = ['loc', 'scale']
Expand Down Expand Up @@ -514,10 +530,14 @@ def short_term_extreme(t, data, t_st, method):
ste: scipy.stats.rv_frozen
Short-term extreme distribution.
"""
assert isinstance(t, np.ndarray), 't must be of type np.ndarray'
assert isinstance(data, np.ndarray), 'x must be of type np.ndarray'
assert isinstance(t_st, float), 't_st must be of type float'
assert isinstance(method, str), 'method must be of type string'
if not isinstance(t, np.ndarray):
raise TypeError(f't must be of type np.ndarray. Got: {type(t)}')
if not isinstance(data, np.ndarray):
raise TypeError(f'data must be of type np.ndarray. Got: {type(data)}')
if not isinstance(t_st, float):
raise TypeError(f't_st must be of type float. Got: {type(t_st)}')
if not isinstance(method, str):
raise TypeError(f'method must be of type string. Got: {type(method)}')

peaks_methods = {
'peaks_weibull': peaks_distribution_weibull,
Expand Down Expand Up @@ -555,18 +575,20 @@ def full_seastate_long_term_extreme(ste, weights):
ste: list[scipy.stats.rv_frozen]
Short-term extreme distribution of the quantity of interest for
each sample sea state.
weights: list[floats]
weights: list, np.ndarray
The weights from the full sea state sampling
Returns
-------
ste: scipy.stats.rv_frozen
Short-term extreme distribution.
"""
assert isinstance(
ste, list), 'ste must be of type list[scipy.stats.rv_frozen]'
assert isinstance(weights, (list, np.ndarray)
), 'weights must be of type list[floats]'
if not isinstance(ste, list):
raise TypeError(
f'ste must be of type list[scipy.stats.rv_frozen]. Got: {type(ste)}')
if not isinstance(weights, (list, np.ndarray)):
raise TypeError(
f'weights must be of type list or np.ndarray. Got: {type(weights)}')

class _LongTermExtreme(stats.rv_continuous):

Expand Down Expand Up @@ -612,11 +634,16 @@ def mler_coefficients(rao, wave_spectrum, response_desired):
rao = np.array(rao)
except:
pass
assert isinstance(rao, np.ndarray), 'rao must be of type np.ndarray'
assert isinstance(wave_spectrum, pd.DataFrame
), 'wave_spectrum must be of type pd.DataFrame'
assert isinstance(response_desired, (int, float)
), 'response_desired must be of type int or float'

if not isinstance(rao, np.ndarray):
raise TypeError(
f'rao must be of type np.ndarray. Got: {type(rao)}')
if not isinstance(wave_spectrum, pd.DataFrame):
raise TypeError(
f'wave_spectrum must be of type pd.DataFrame. Got: {type(wave_spectrum)}')
if not isinstance(response_desired, (int, float)):
raise TypeError(
f'response_desired must be of type int or float. Got: {type(response_desired)}')

freq_hz = wave_spectrum.index.values
# convert from Hz to rad/s
Expand Down Expand Up @@ -707,8 +734,9 @@ def mler_simulation(parameters=None):
Simulation parameters including spatial and time calculated
arrays.
"""
if not parameters == None:
assert isinstance(parameters, dict), 'parameters must be of type dict'
if not isinstance(parameters, (type(None), dict)):
raise TypeError(
f'If specified, parameters must be of type dict. Got: {type(parameters)}')

sim = {}

Expand Down Expand Up @@ -762,11 +790,18 @@ def mler_wave_amp_normalize(wave_amp, mler, sim, k):
k = np.array(k)
except:
pass
assert isinstance(mler, pd.DataFrame), 'mler must be of type pd.DataFrame'
assert isinstance(wave_amp, (int, float)
), 'wave_amp must be of type int or float'
assert isinstance(sim, dict), 'sim must be of type dict'
assert isinstance(k, np.ndarray), 'k must be of type ndarray'
if not isinstance(mler, pd.DataFrame):
raise TypeError(
f'mler must be of type pd.DataFrame. Got: {type(mler)}')
if not isinstance(wave_amp, (int, float)):
raise TypeError(
f'wave_amp must be of type int or float. Got: {type(wave_amp)}')
if not isinstance(sim, dict):
raise TypeError(
f'sim must be of type dict. Got: {type(sim)}')
if not isinstance(k, np.ndarray):
raise TypeError(
f'k must be of type ndarray. Got: {type(k)}')

freq = mler.index.values * 2*np.pi
dw = (max(freq) - min(freq)) / (len(freq)-1) # get delta
Expand Down Expand Up @@ -826,10 +861,18 @@ def mler_export_time_series(rao, mler, sim, k):
k = np.array(k)
except:
pass
assert isinstance(rao, np.ndarray), 'rao must be of type ndarray'
assert isinstance(mler, pd.DataFrame), 'mler must be of type pd.DataFrame'
assert isinstance(sim, dict), 'sim must be of type dict'
assert isinstance(k, np.ndarray), 'k must be of type ndarray'
if not isinstance(rao, np.ndarray):
raise TypeError(
f'rao must be of type ndarray. Got: {type(rao)}')
if not isinstance(mler, pd.DataFrame):
raise TypeError(
f'mler must be of type pd.DataFrame. Got: {type(mler)}')
if not isinstance(sim, dict):
raise TypeError(
f'sim must be of type dict. Got: {type(sim)}')
if not isinstance(k, np.ndarray):
raise TypeError(
f'k must be of type ndarray. Got: {type(k)}')

freq = mler.index.values * 2*np.pi # convert Hz to rad/s
dw = (max(freq) - min(freq)) / (len(freq)-1) # get delta
Expand Down Expand Up @@ -874,9 +917,14 @@ def return_year_value(ppf, return_year, short_term_period_hr):
value: float
The value corresponding to the return period from the distribution.
"""
assert callable(ppf)
assert isinstance(return_year, (float, int))
assert isinstance(short_term_period_hr, (float, int))
if not callable(ppf):
raise TypeError('ppf must be a callable Percentage Point Function')
if not isinstance(return_year, (float, int)):
raise TypeError(
f'return_year must be of type float or int. Got: {type(return_year)}')
if not isinstance(short_term_period_hr, (float, int)):
raise TypeError(
f'short_term_period_hr must be of type float or int. Got: {type(short_term_period_hr)}')

p = 1 / (return_year * 365.25 * 24 / short_term_period_hr)

Expand Down
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