diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/dfatool.py | 61 | ||||
| -rwxr-xr-x | lib/plotter.py | 74 |
2 files changed, 133 insertions, 2 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index a03abc4..a591be3 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -50,6 +50,45 @@ def vprint(verbose, string): if verbose: print(string) + def _gplearn_add_(x, y): + return x + y + + def _gplearn_sub_(x, y): + return x - y + + def _gplearn_mul_(x, y): + return x * y + + def _gplearn_div_(x, y): + if np.abs(y) > 0.001: + return x / y + return 1. + +def gplearn_to_function(function_str): + + eval_globals = { + 'add' : lambda x, y : x + y, + 'sub' : lambda x, y : x - y, + 'mul' : lambda x, y : x * y, + 'div' : lambda x, y : np.divide(x, y) if np.abs(y) > 0.001 else 1., + 'sqrt': lambda x : np.sqrt(np.abs(x)), + 'log' : lambda x : np.log(np.abs(x)) if np.abs(x) > 0.001 else 0., + 'inv' : lambda x : 1. / x if np.abs(x) > 0.001 else 0., + } + + last_arg_index = 0 + for i in range(0, 100): + if function_str.find('X{:d}'.format(i)) >= 0: + last_arg_index = i + + arg_list = [] + for i in range(0, last_arg_index+1): + arg_list.append('X{:d}'.format(i)) + + eval_str = 'lambda {}, *whatever: {}'.format(','.join(arg_list), function_str) + print(eval_str) + return eval(eval_str, eval_globals) + def _elem_param_and_arg_list(elem): param_dict = elem['parameter'] paramkeys = sorted(param_dict.keys()) @@ -446,7 +485,7 @@ class AnalyticFunction: self._function = eval('lambda reg_param, model_param: ' + rawfunction); self._regression_args = list(np.ones((num_vars))) - def _get_fit_data(self, by_param, state_or_tran, model_attribute): + def get_fit_data(self, by_param, state_or_tran, model_attribute): dimension = len(self._parameter_names) + self._num_args X = [[] for i in range(dimension)] Y = [] @@ -477,7 +516,7 @@ class AnalyticFunction: return X, Y, num_valid, num_total def fit(self, by_param, state_or_tran, model_attribute): - X, Y, num_valid, num_total = self._get_fit_data(by_param, state_or_tran, model_attribute) + X, Y, num_valid, num_total = self.get_fit_data(by_param, state_or_tran, model_attribute) if num_valid > 2: error_function = lambda P, X, y: self._function(P, X) - y try: @@ -750,6 +789,7 @@ class EnergyModel: self.by_param = {} self.by_trace = {} self.stats = {} + self.cache = {} np.seterr('raise') self._parameter_names = sorted(self.traces[0]['trace'][0]['parameter'].keys()) self._num_args = {} @@ -768,6 +808,11 @@ class EnergyModel: self._aggregate_to_ndarray(self.by_name) self._compute_all_param_statistics() + def distinct_param_values(self, state_or_tran, param_index = None, arg_index = None): + if param_index != None: + param_values = map(lambda x: x[param_index], self.by_name[state_or_tran]['param']) + return sorted(set(param_values)) + def _compute_outlier_stats(self, ignore_trace_indexes, threshold): tmp_by_param = {} self.median_by_param = {} @@ -934,7 +979,16 @@ class EnergyModel: def get_param_analytic(self): static_model = self._get_model_from_dict(self.by_name, np.median) + def param_index(self, param_name): + if param_name in self._parameter_names: + return self._parameter_names.index(param_name) + return len(self._parameter_names) + int(param_name) + def get_fitted(self): + + if 'fitted_model_getter' in self.cache and 'fitted_info_getter' in self.cache: + return self.cache['fitted_model_getter'], self.cache['fitted_info_getter'] + static_model = self._get_model_from_dict(self.by_name, np.median) param_model = dict([[state_or_tran, {}] for state_or_tran in self.by_name.keys()]) fit_queue = [] @@ -1018,6 +1072,9 @@ class EnergyModel: return param_model[name][key] return None + self.cache['fitted_model_getter'] = model_getter + self.cache['fitted_info_getter'] = info_getter + return model_getter, info_getter diff --git a/lib/plotter.py b/lib/plotter.py index 784ba56..6ee2691 100755 --- a/lib/plotter.py +++ b/lib/plotter.py @@ -5,6 +5,14 @@ import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Polygon +def float_or_nan(n): + if n == None: + return np.nan + try: + return float(n) + except ValueError: + return np.nan + def flatten(somelist): return [item for sublist in somelist for item in sublist] @@ -71,6 +79,72 @@ def plot_substate_thresholds_p(model, aggregate): data = [aggregate[key]['sub_thresholds'] for key in keys] boxplot(keys, None, None, data, 'Zustand', '% Clipping') +def plot_y(Y, ylabel = None, title = None): + plot_xy(np.arange(len(Y)), Y, ylabel = ylabel, title = title) + +def plot_xy(X, Y, xlabel = None, ylabel = None, title = None): + fig, ax1 = plt.subplots(figsize=(10,6)) + if title != None: + fig.canvas.set_window_title(title) + if xlabel != None: + ax1.set_xlabel(xlabel) + if ylabel != None: + ax1.set_ylabel(ylabel) + plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.99, top = 0.99) + plt.plot(X, Y, "rx") + plt.show() + +def _param_slice_eq(a, b, index): + return (*a[1][:index], *a[1][index+1:]) == (*b[1][:index], *b[1][index+1:]) and a[0] == b[0] + +def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabel = None, title = None, extra_functions = []): + fig, ax1 = plt.subplots(figsize=(10,6)) + if title != None: + fig.canvas.set_window_title(title) + if xlabel != None: + ax1.set_xlabel(xlabel) + if ylabel != None: + ax1.set_ylabel(ylabel) + plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.99, top = 0.99) + + param_model, param_info = model.get_fitted() + + by_other_param = {} + + for k, v in model.by_param.items(): + if k[0] == state_or_trans: + other_param_key = (*k[1][:param_idx], *k[1][param_idx+1:]) + if not other_param_key in by_other_param: + by_other_param[other_param_key] = {'X': [], 'Y': []} + by_other_param[other_param_key]['X'].extend([float(k[1][param_idx])] * len(v[attribute])) + by_other_param[other_param_key]['Y'].extend(v[attribute]) + + cm = plt.get_cmap('brg', len(by_other_param)) + for i, k in enumerate(by_other_param): + v = by_other_param[k] + v['X'] = np.array(v['X']) + v['Y'] = np.array(v['Y']) + plt.plot(v['X'], v['Y'], "rx", color=cm(i)) + x_range = int((v['X'].max() - v['X'].min()) * 2) + xsp = np.linspace(v['X'].min(), v['X'].max(), x_range) + if param_model: + ysp = [] + for x in xsp: + xarg = [*k[:param_idx], x, *k[param_idx:]] + ysp.append(param_model(state_or_trans, attribute, param = xarg)) + plt.plot(xsp, ysp, "r-", color=cm(i), linewidth=0.5) + if len(extra_functions) != 0: + for f in extra_functions: + ysp = [] + with np.errstate(divide='ignore', invalid='ignore'): + for x in xsp: + xarg = [*k[:param_idx], x, *k[param_idx:]] + ysp.append(f(*xarg)) + plt.plot(xsp, ysp, "r--", color=cm(i), linewidth=1, dashes=(3, 3)) + + plt.show() + + def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X, Y, xaxis=None, yaxis=None): fig, ax1 = plt.subplots(figsize=(10,6)) fig.canvas.set_window_title("fit %s" % (function)) |