Remove most unused perl scripts and modules

2 files changed, 0 insertions, 1077 deletions

diff --git a/bin/merge.py b/bin/merge.py
deleted file mode 100755
index 551bc9e..0000000
--- a/bin/merge.py
+++ /dev/null
@@ -1,1053 +0,0 @@
-#!/usr/bin/env python3
-
-import getopt
-import json
-import numpy as np
-import os
-import re
-import sys
-import plotter
-from copy import deepcopy
-from dfatool import aggregate_measures, regression_measures, is_numeric, powerset
-from dfatool import append_if_set, mean_or_none, float_or_nan
-from matplotlib.patches import Polygon
-from scipy import optimize, stats
-#import pickle
-
-opts = {}
-
-def load_json(filename):
-    with open(filename, "r") as f:
-        return json.load(f)
-
-def save_json(data, filename):
-    with open(filename, "w") as f:
-        return json.dump(data, f)
-
-def print_data(aggregate):
-    for key in sorted(aggregate.keys()):
-        data = aggregate[key]
-        name, params = key
-        print("%s @ %s : ~ = %.f (%.f, %.f)  µ_σ_outer = %.f  n = %d" %
-            (name, params, np.median(data['means']), np.percentile(data['means'], 25),
-                np.percentile(data['means'], 75), np.mean(data['stds']), len(data['means'])))
-
-def flatten(somelist):
-    return [item for sublist in somelist for item in sublist]
-
-def mimosa_data(elem):
-    means = [x['uW_mean'] for x in elem['offline']]
-    durations = [x['us'] - 20 for x in elem['offline']]
-    stds = [x['uW_std'] for x in elem['offline']]
-    energies = [x['uW_mean'] * (x['us'] - 20) for x in elem['offline']]
-    clips = [x['clip_rate'] for x in elem['offline']]
-    substate_thresholds = []
-    substate_data = []
-    timeouts = []
-    rel_energies_prev = []
-    rel_energies_next = []
-    if 'timeout' in elem['offline'][0]:
-        timeouts = [x['timeout'] for x in elem['offline']]
-    if 'uW_mean_delta_prev' in elem['offline'][0]:
-        rel_energies_prev = [x['uW_mean_delta_prev'] * (x['us'] - 20) for x in elem['offline']]
-    if 'uW_mean_delta_next' in elem['offline'][0]:
-        rel_energies_next = [x['uW_mean_delta_next'] * (x['us'] - 20) for x in elem['offline']]
-    for x in elem['offline']:
-        if 'substates' in x:
-            substate_thresholds.append(x['substates']['threshold'])
-            substate_data.append(x['substates']['states'])
-
-    return (means, stds, durations, energies, rel_energies_prev,
-        rel_energies_next, clips, timeouts, substate_thresholds)
-
-def online_data(elem):
-    means = [int(x['power']) for x in elem['online']]
-    durations = [int(x['time']) for x in elem['online']]
-
-    return means, durations
-
-# parameters = statistic variables such as txpower, bitrate etc.
-# variables = function variables/parameters set by linear regression
-def str_to_param_function(function_string, parameters, variables):
-    rawfunction = function_string
-    dependson = [False] * len(parameters)
-
-    for i in range(len(parameters)):
-        if rawfunction.find("global(%s)" % (parameters[i])) >= 0:
-            dependson[i] = True
-            rawfunction = rawfunction.replace("global(%s)" % (parameters[i]), "arg[%d]" % (i))
-        if rawfunction.find("local(%s)" % (parameters[i])) >= 0:
-            dependson[i] = True
-            rawfunction = rawfunction.replace("local(%s)" % (parameters[i]), "arg[%d]" % (i))
-    for i in range(len(variables)):
-        rawfunction = rawfunction.replace("param(%d)" % (i), "param[%d]" % (i))
-    fitfunc = eval("lambda param, arg: " + rawfunction);
-
-    return fitfunc, dependson
-
-def mk_function_data(name, paramdata, parameters, dependson, datatype):
-    X = [[] for i in range(len(parameters))]
-    Xm = [[] for i in range(len(parameters))]
-    Y = []
-    Ym = []
-
-    num_valid = 0
-    num_total = 0
-
-    for key, val in paramdata.items():
-        if key[0] == name and len(key[1]) == len(parameters):
-            valid = True
-            num_total += 1
-            for i in range(len(parameters)):
-                if dependson[i] and not is_numeric(key[1][i]):
-                    valid = False
-            if valid:
-                num_valid += 1
-                Y.extend(val[datatype])
-                Ym.append(np.median(val[datatype]))
-                for i in range(len(parameters)):
-                    if dependson[i] or is_numeric(key[1][i]):
-                        X[i].extend([float(key[1][i])] * len(val[datatype]))
-                        Xm[i].append(float(key[1][i]))
-                    else:
-                        X[i].extend([0] * len(val[datatype]))
-                        Xm[i].append(0)
-
-    for i in range(len(parameters)):
-        X[i] = np.array(X[i])
-        Xm[i] = np.array(Xm[i])
-    X = tuple(X)
-    Xm = tuple(Xm)
-    Y = np.array(Y)
-    Ym = np.array(Ym)
-
-    return X, Y, Xm, Ym, num_valid, num_total
-
-def raw_num0_8(num):
-    return (8 - num1(num))
-
-def raw_num0_16(num):
-    return (16 - num1(num))
-
-def raw_num1(num):
-    return bin(int(num)).count("1")
-
-num0_8 = np.vectorize(raw_num0_8)
-num0_16 = np.vectorize(raw_num0_16)
-num1 = np.vectorize(raw_num1)
-
-def try_fits(name, datatype, paramidx, paramdata):
-    functions = {
-        'linear' : lambda param, arg: param[0] + param[1] * arg,
-        'logarithmic' : lambda param, arg: param[0] + param[1] * np.log(arg),
-        'logarithmic1' : lambda param, arg: param[0] + param[1] * np.log(arg + 1),
-        'exponential' : lambda param, arg: param[0] + param[1] * np.exp(arg),
-        #'polynomial' : lambda param, arg: param[0] + param[1] * arg + param[2] * arg ** 2,
-        'square' : lambda param, arg: param[0] + param[1] * arg ** 2,
-        'fractional' : lambda param, arg: param[0] + param[1] / arg,
-        'sqrt' : lambda param, arg: param[0] + param[1] * np.sqrt(arg),
-        'num0_8' : lambda param, arg: param[0] + param[1] * num0_8(arg),
-        'num0_16' : lambda param, arg: param[0] + param[1] * num0_16(arg),
-        'num1' : lambda param, arg: param[0] + param[1] * num1(arg),
-    }
-    results = dict([[key, []] for key in functions.keys()])
-    errors = {}
-
-    allvalues = [(*key[1][:paramidx], *key[1][paramidx+1:]) for key in paramdata.keys() if key[0] == name]
-    allvalues = list(set(allvalues))
-
-    for value in allvalues:
-        X = []
-        Xm = []
-        Y = []
-        Ym = []
-        num_valid = 0
-        num_total = 0
-        for key, val in paramdata.items():
-            if key[0] == name and len(key[1]) > paramidx and (*key[1][:paramidx], *key[1][paramidx+1:]) == value:
-                num_total += 1
-                if is_numeric(key[1][paramidx]):
-                    num_valid += 1
-                    Y.extend(val[datatype])
-                    Ym.append(np.median(val[datatype]))
-                    X.extend([float(key[1][paramidx])] * len(val[datatype]))
-                    Xm.append(float(key[1][paramidx]))
-                    if float(key[1][paramidx]) == 0:
-                        functions.pop('fractional', None)
-                    if float(key[1][paramidx]) <= 0:
-                        functions.pop('logarithmic', None)
-                    if float(key[1][paramidx]) < 0:
-                        functions.pop('logarithmic1', None)
-                        functions.pop('sqrt', None)
-                    if float(key[1][paramidx]) > 64:
-                        functions.pop('exponential', None)
-
-        # there should be -at least- two values when fitting
-        if num_valid > 1:
-            Y = np.array(Y)
-            Ym = np.array(Ym)
-            X = np.array(X)
-            Xm = np.array(Xm)
-            for kind, function in functions.items():
-                results[kind] = {}
-                errfunc = lambda P, X, y: function(P, X) - y
-                try:
-                    res = optimize.least_squares(errfunc, [0, 1], args=(X, Y), xtol=2e-15)
-                    measures = regression_measures(function(res.x, X), Y)
-                    for k, v in measures.items():
-                        if not k in results[kind]:
-                            results[kind][k] = []
-                        results[kind][k].append(v)
-                except:
-                    pass
-
-    for function_name, result in results.items():
-        if len(result) > 0 and function_name in functions:
-            errors[function_name] = {}
-            for measure in result.keys():
-                errors[function_name][measure] = np.mean(result[measure])
-
-    return errors
-
-def fit_function(function, name, datatype, parameters, paramdata, xaxis=None, yaxis=None):
-
-    variables = list(map(lambda x: float(x), function['params']))
-    fitfunc, dependson = str_to_param_function(function['raw'], parameters, variables)
-
-    X, Y, Xm, Ym, num_valid, num_total = mk_function_data(name, paramdata, parameters, dependson, datatype)
-
-    if num_valid > 0:
-
-        if num_valid != num_total:
-            num_invalid = num_total - num_valid
-            print("Warning: fit(%s): %d of %d states had incomplete parameter hashes" % (name, num_invalid, len(paramdata)))
-
-        errfunc = lambda P, X, y: fitfunc(P, X) - y
-        try:
-            res = optimize.least_squares(errfunc, variables, args=(X, Y), xtol=2e-15) # loss='cauchy'
-        except ValueError as err:
-            function['error'] = str(err)
-            return
-        #x1 = optimize.curve_fit(lambda param, *arg: fitfunc(param, arg), X, Y, functionparams)
-        measures = regression_measures(fitfunc(res.x, X), Y)
-
-        if res.status <= 0:
-            function['error'] = res.message
-            return
-
-        if 'fit' in opts:
-            for i in range(len(parameters)):
-                plotter.plot_param_fit(function['raw'], name, fitfunc, res.x, parameters, datatype, i, X, Y, xaxis, yaxis)
-
-        function['params'] = list(res.x)
-        function['fit'] = measures
-
-    else:
-        function['error'] = 'log contained no numeric parameters'
-
-def assess_function(function, name, datatype, parameters, paramdata):
-
-    variables = list(map(lambda x: float(x), function['params']))
-    fitfunc, dependson = str_to_param_function(function['raw'], parameters, variables)
-    X, Y, Xm, Ym, num_valid, num_total = mk_function_data(name, paramdata, parameters, dependson, datatype)
-
-    if num_valid > 0:
-        return regression_measures(fitfunc(variables, X), Y)
-    else:
-        return None
-
-def xv_assess_function(name, funbase, what, validation, mae, smape):
-    goodness = assess_function(funbase, name, what, parameters, validation)
-    if goodness != None:
-        if not name in mae:
-            mae[name] = []
-        if not name in smape:
-            smape[name] = []
-        append_if_set(mae, goodness, 'mae')
-        append_if_set(smape, goodness, 'smape')
-
-def xv2_assess_function(name, funbase, what, validation, mae, smape, rmsd):
-    goodness = assess_function(funbase, name, what, parameters, validation)
-    if goodness != None:
-        if goodness['mae'] < 10e9:
-            mae.append(goodness['mae'])
-            rmsd.append(goodness['rmsd'])
-            smape.append(goodness['smape'])
-        else:
-            print('[!] Ignoring MAE of %d (SMAPE %.f)' % (goodness['mae'], goodness['smape']))
-
-# Returns the values used for each parameter in the measurement, e.g.
-# { 'txpower' : [1, 2, 4, 8], 'length' : [16] }
-# non-numeric values such as '' are skipped
-def param_values(parameters, by_param):
-    paramvalues = dict([[param, set()] for param in parameters])
-
-    for _, paramvalue in by_param.keys():
-        for i, param in enumerate(parameters):
-            if is_numeric(paramvalue[i]):
-                paramvalues[param].add(paramvalue[i])
-
-    return paramvalues
-
-def param_hash(values):
-    ret = {}
-
-    for i, param in enumerate(parameters):
-        ret[param] = values[i]
-
-    return ret
-
-# Returns the values used for each function argument in the measurement, e.g.
-# { 'data': [], 'length' : [16, 31, 32] }
-# non-numeric values such as '' or 'long_test_string' are skipped
-def arg_values(name, by_arg):
-    TODO
-    argvalues = dict([[arg, set()] for arg in parameters])
-
-    for _, paramvalue in by_param.keys():
-        for i, param in enumerate(parameters):
-            if is_numeric(paramvalue[i]):
-                paramvalues[param].add(paramvalue[i])
-
-    return paramvalues
-
-def mk_param_key(elem):
-    name = elem['name']
-    paramtuple = ()
-
-    if 'parameter' in elem:
-        paramkeys = sorted(elem['parameter'].keys())
-        paramtuple = tuple([elem['parameter'][x] for x in paramkeys])
-
-    return (name, paramtuple)
-
-def mk_arg_key(elem):
-    name = elem['name']
-    argtuple = ()
-
-    if 'args' in elem:
-        argtuple = tuple(elem['args'])
-
-    return (name, argtuple)
-
-def add_data_to_aggregate(aggregate, key, isa, data):
-    if not key in aggregate:
-        aggregate[key] = {
-            'isa' : isa,
-        }
-        for datakey in data.keys():
-            aggregate[key][datakey] = []
-    for datakey, dataval in data.items():
-        aggregate[key][datakey].extend(dataval)
-
-def fake_add_data_to_aggregate(aggregate, key, isa, database, idx):
-    timeout_val = []
-    if len(database['timeouts']):
-        timeout_val = [database['timeouts'][idx]]
-    rel_energy_p_val = []
-    if len(database['rel_energies_prev']):
-        rel_energy_p_val = [database['rel_energies_prev'][idx]]
-    rel_energy_n_val = []
-    if len(database['rel_energies_next']):
-        rel_energy_n_val = [database['rel_energies_next'][idx]]
-    add_data_to_aggregate(aggregate, key, isa, {
-        'means' : [database['means'][idx]],
-        'stds' : [database['stds'][idx]],
-        'durations' : [database['durations'][idx]],
-        'energies' : [database['energies'][idx]],
-        'rel_energies_prev' : rel_energy_p_val,
-        'rel_energies_next' : rel_energy_n_val,
-        'clip_rate' : [database['clip_rate'][idx]],
-        'timeouts' : timeout_val,
-    })
-
-def weight_by_name(aggdata):
-    total = {}
-    count = {}
-    weight = {}
-    for key in aggdata.keys():
-        if not key[0] in total:
-            total[key[0]] = 0
-        total[key[0]] += len(aggdata[key]['means'])
-        count[key] = len(aggdata[key]['means'])
-    for key in aggdata.keys():
-        weight[key] = float(count[key]) / total[key[0]]
-    return weight
-
-# returns the mean standard deviation of all measurements of 'what'
-# (e.g. power consumption or timeout) for state/transition 'name' where
-# parameter 'index' is dynamic and all other parameters are fixed.
-# I.e., if parameters are a, b, c ∈ {1,2,3} and 'index' corresponds to b', then
-# this function returns the mean of the standard deviations of (a=1, b=*, c=1),
-# (a=1, b=*, c=2), and so on
-def mean_std_by_param(data, keys, name, what, index):
-    partitions = []
-    for key in keys:
-        partition = []
-        for k, v in data.items():
-            if (*k[1][:index], *k[1][index+1:]) == key and k[0] == name:
-                partition.extend(v[what])
-        partitions.append(partition)
-    return np.mean([np.std(partition) for partition in partitions])
-
-def spearmanr_by_param(name, what, index):
-    sr = stats.spearmanr(by_name[name][what], list(map(lambda x : float_or_nan(x[index]), by_name[name]['param'])))[0]
-    if sr == np.nan:
-        return None
-    return sr
-
-# returns the mean standard deviation of all measurements of 'what'
-# (e.g. energy or duration) for transition 'name' where
-# the 'index'th argumetn is dynamic and all other arguments are fixed.
-# I.e., if arguments are a, b, c ∈ {1,2,3} and 'index' is 1, then
-# this function returns the mean of the standard deviations of (a=1, b=*, c=1),
-# (a=1, b=*, c=2), and so on
-def mean_std_by_arg(data, keys, name, what, index):
-    return mean_std_by_param(data, keys, name, what, index)
-
-# returns the mean standard deviation of all measurements of 'what'
-# (e.g. power consumption or timeout) for state/transition 'name' where the
-# trace of previous transitions is fixed except for a single transition,
-# whose occurence or absence is silently ignored.
-# this is done separately for each transition (-> returns a dictionary)
-def mean_std_by_trace_part(data, transitions, name, what):
-    ret = {}
-    for transition in transitions:
-        keys = set(map(lambda x : (x[0], x[1], tuple([y for y in x[2] if y != transition])), data.keys()))
-        ret[transition] = {}
-        partitions = []
-        for key in keys:
-            partition = []
-            for k, v in data.items():
-                key_without_transition = (k[0], k[1], tuple([y for y in k[2] if y != transition]))
-                if key[0] == name and key == key_without_transition:
-                    partition.extend(v[what])
-            if len(partition):
-                partitions.append(partition)
-        ret[transition] = np.mean([np.std(partition) for partition in partitions])
-    return ret
-
-
-def load_run_elem(index, element, trace, by_name, by_arg, by_param, by_trace):
-    means, stds, durations, energies, rel_energies_prev, rel_energies_next, clips, timeouts, sub_thresholds = mimosa_data(element)
-
-    online_means = []
-    online_durations = []
-    if element['isa'] == 'state':
-        online_means, online_durations = online_data(element)
-
-    if 'voltage' in opts:
-        element['parameter']['voltage'] = opts['voltage']
-
-    arg_key   = mk_arg_key(element)
-    param_key = mk_param_key(element)
-    pre_trace = tuple(map(lambda x : x['name'], trace[1:index:2]))
-    trace_key = (*param_key, pre_trace)
-    name = element['name']
-
-    elem_data = {
-        'means' : means,
-        'stds' : stds,
-        'durations' : durations,
-        'energies' : energies,
-        'rel_energies_prev' : rel_energies_prev,
-        'rel_energies_next' : rel_energies_next,
-        'clip_rate' : clips,
-        'timeouts' : timeouts,
-        'sub_thresholds' : sub_thresholds,
-        'param' : [param_key[1]] * len(means),
-        'online_means' : online_means,
-        'online_durations' : online_durations,
-    }
-    add_data_to_aggregate(by_name, name, element['isa'], elem_data)
-    add_data_to_aggregate(by_arg, arg_key, element['isa'], elem_data)
-    add_data_to_aggregate(by_param, param_key, element['isa'], elem_data)
-    add_data_to_aggregate(by_trace, trace_key, element['isa'], elem_data)
-
-def fmap(reftype, name, funtype):
-    if funtype == 'linear':
-        return "%s(%s)" % (reftype, name)
-    if funtype == 'logarithmic':
-        return "np.log(%s(%s))" % (reftype, name)
-    if funtype == 'logarithmic1':
-        return "np.log(%s(%s) + 1)" % (reftype, name)
-    if funtype == 'exponential':
-        return "np.exp(%s(%s))" % (reftype, name)
-    if funtype == 'square':
-        return "%s(%s)**2" % (reftype, name)
-    if funtype == 'fractional':
-        return "1 / %s(%s)" % (reftype, name)
-    if funtype == 'sqrt':
-        return "np.sqrt(%s(%s))" % (reftype, name)
-    if funtype == 'num0_8':
-        return "num0_8(%s(%s))" % (reftype, name)
-    if funtype == 'num0_16':
-        return "num0_16(%s(%s))" % (reftype, name)
-    if funtype == 'num1':
-        return "num1(%s(%s))" % (reftype, name)
-    return "ERROR"
-
-def fguess_to_function(name, datatype, aggdata, parameters, paramdata, yaxis):
-    best_fit = {}
-    fitguess = aggdata['fit_guess']
-    params = list(filter(lambda x : x in fitguess, parameters))
-    if len(params) > 0:
-        for param in params:
-            best_fit_val = np.inf
-            for func_name, fit_val in fitguess[param].items():
-                if fit_val['rmsd'] < best_fit_val:
-                    best_fit_val = fit_val['rmsd']
-                    best_fit[param] = func_name
-        buf = '0'
-        pidx = 0
-        for elem in powerset(best_fit.items()):
-            buf += " + param(%d)" % pidx
-            pidx += 1
-            for fun in elem:
-                buf += " * %s" % fmap('global', *fun)
-        aggdata['function']['estimate'] = {
-            'raw' : buf,
-            'params' : list(np.ones((pidx))),
-            'base' : [best_fit[param] for param in params]
-        }
-        fit_function(
-            aggdata['function']['estimate'], name, datatype, parameters,
-            paramdata, yaxis=yaxis)
-
-def arg_fguess_to_function(name, datatype, aggdata, arguments, argdata, yaxis):
-    best_fit = {}
-    fitguess = aggdata['arg_fit_guess']
-    args = list(filter(lambda x : x in fitguess, arguments))
-    if len(args) > 0:
-        for arg in args:
-            best_fit_val = np.inf
-            for func_name, fit_val in fitguess[arg].items():
-                if fit_val['rmsd'] < best_fit_val:
-                    best_fit_val = fit_val['rmsd']
-                    best_fit[arg] = func_name
-        buf = '0'
-        pidx = 0
-        for elem in powerset(best_fit.items()):
-            buf += " + param(%d)" % pidx
-            pidx += 1
-            for fun in elem:
-                buf += " * %s" % fmap('local', *fun)
-        aggdata['function']['estimate_arg'] = {
-            'raw' : buf,
-            'params' : list(np.ones((pidx))),
-            'base' : [best_fit[arg] for arg in args]
-        }
-        fit_function(
-            aggdata['function']['estimate_arg'], name, datatype, arguments,
-            argdata, yaxis=yaxis)
-
-def param_measures(name, paramdata, key, fun):
-    mae = []
-    smape = []
-    rmsd = []
-    for pkey, pval in paramdata.items():
-        if pkey[0] == name:
-            # Median ist besseres Maß für MAE / SMAPE,
-            # Mean ist besseres für SSR. Da least_squares SSR optimiert
-            # nutzen wir hier auch Mean.
-            goodness = aggregate_measures(fun(pval[key]), pval[key])
-            append_if_set(mae, goodness, 'mae')
-            append_if_set(rmsd, goodness, 'rmsd')
-            append_if_set(smape, goodness, 'smape')
-    ret = {
-        'mae' : mean_or_none(mae),
-        'rmsd' : mean_or_none(rmsd),
-        'smape' : mean_or_none(smape)
-    }
-
-    return ret
-
-def arg_measures(name, argdata, key, fun):
-    return param_measures(name, argdata, key, fun)
-
-def lookup_table(name, paramdata, key, fun, keyfun):
-    lut = []
-
-    for pkey, pval in paramdata.items():
-        if pkey[0] == name:
-            lut.append({
-                'key': keyfun(pkey[1]),
-                'value': fun(pval[key]),
-            })
-
-    return lut
-
-def keydata(name, val, argdata, paramdata, tracedata, key):
-    ret = {
-        'count' : len(val[key]),
-        'median' : np.median(val[key]),
-        'mean'   : np.mean(val[key]),
-        'median_by_param' : lookup_table(name, paramdata, key, np.median, param_hash),
-        'mean_goodness' : aggregate_measures(np.mean(val[key]), val[key]),
-        'median_goodness' : aggregate_measures(np.median(val[key]), val[key]),
-        'param_mean_goodness' : param_measures(name, paramdata, key, np.mean),
-        'param_median_goodness' : param_measures(name, paramdata, key, np.median),
-        'std_inner' : np.std(val[key]),
-        'std_param' : np.mean([np.std(paramdata[x][key]) for x in paramdata.keys() if x[0] == name]),
-        'std_trace' : np.mean([np.std(tracedata[x][key]) for x in tracedata.keys() if x[0] == name]),
-        'std_by_param' : {},
-        'spearmanr_by_param' : {},
-        'fit_guess' : {},
-        'function' : {},
-    }
-
-    if val['isa'] == 'transition':
-        ret['arg_mean_goodness'] = arg_measures(name, argdata, key, np.mean)
-        ret['arg_median_goodness'] = arg_measures(name, argdata, key, np.median)
-        ret['median_by_arg'] = lookup_table(name, argdata, key, np.median, list)
-        ret['std_arg'] = np.mean([np.std(argdata[x][key]) for x in argdata.keys() if x[0] == name])
-        ret['std_by_arg'] = {}
-        ret['arg_fit_guess'] = {}
-
-    return ret
-
-def splitidx_kfold(length, num_slices):
-    pairs = []
-    indexes = np.arange(length)
-    for i in range(0, num_slices):
-        training = np.delete(indexes, slice(i, None, num_slices))
-        validation = indexes[i::num_slices]
-        pairs.append((training, validation))
-    return pairs
-
-def splitidx_srs(length, num_slices):
-    pairs = []
-    for i in range(0, num_slices):
-        shuffled = np.random.permutation(np.arange(length))
-        border = int(length * float(2) / 3)
-        training = shuffled[:border]
-        validation = shuffled[border:]
-        pairs.append((training, validation))
-    return pairs
-
-def val_run(aggdata, split_fun, count):
-    mae = []
-    smape = []
-    rmsd = []
-    pairs = split_fun(len(aggdata), count)
-    for i in range(0, count):
-        training = aggdata[pairs[i][0]]
-        validation = aggdata[pairs[i][1]]
-        median = np.median(training)
-        goodness = aggregate_measures(median, validation)
-        append_if_set(mae, goodness, 'mae')
-        append_if_set(rmsd, goodness, 'rmsd')
-        append_if_set(smape, goodness, 'smape')
-
-    mae_mean = np.mean(mae)
-    rmsd_mean = np.mean(rmsd)
-    if len(smape):
-        smape_mean = np.mean(smape)
-    else:
-        smape_mean = -1
-
-    return mae_mean, smape_mean, rmsd_mean
-
-# by_trace is not part of the cross-validation process
-def val_run_fun(aggdata, by_trace, name, key, funtype1, funtype2, splitfun, count):
-    aggdata = aggdata[name]
-    isa = aggdata['isa']
-    mae = []
-    smape = []
-    rmsd = []
-    estimates = []
-    pairs = splitfun(len(aggdata[key]), count)
-    for i in range(0, count):
-        bpa_training = {}
-        bpa_validation = {}
-
-        for idx in pairs[i][0]:
-            bpa_key = (name, aggdata['param'][idx])
-            fake_add_data_to_aggregate(bpa_training, bpa_key, isa, aggdata, idx)
-        for idx in pairs[i][1]:
-            bpa_key = (name, aggdata['param'][idx])
-            fake_add_data_to_aggregate(bpa_validation, bpa_key, isa, aggdata, idx)
-
-        fake_by_name = { name : aggdata }
-        ares = analyze(fake_by_name, {}, bpa_training, by_trace, parameters)
-        if name in ares[isa] and funtype2 in ares[isa][name][funtype1]['function']:
-            xv2_assess_function(name, ares[isa][name][funtype1]['function'][funtype2], key, bpa_validation, mae, smape, rmsd)
-            if funtype2 == 'estimate':
-                if 'base' in ares[isa][name][funtype1]['function'][funtype2]:
-                    estimates.append(tuple(ares[isa][name][funtype1]['function'][funtype2]['base']))
-                else:
-                    estimates.append(None)
-    return mae, smape, rmsd, estimates
-
-# by_trace is not part of the cross-validation process
-def val_run_fun_p(aggdata, by_trace, name, key, funtype1, funtype2, splitfun, count):
-    aggdata = dict([[x, aggdata[x]] for x in aggdata if x[0] == name])
-    isa = aggdata[list(aggdata.keys())[0]]['isa']
-    mae = []
-    smape = []
-    rmsd = []
-    estimates = []
-    pairs = splitfun(len(aggdata.keys()), count) # pairs are by_param index arrays
-    keys = sorted(aggdata.keys())
-    for i in range(0, count):
-        bpa_training = dict([[keys[x], aggdata[keys[x]]] for x in pairs[i][0]])
-        bpa_validation = dict([[keys[x], aggdata[keys[x]]] for x in pairs[i][1]])
-        bna_training = {}
-        for val in bpa_training.values():
-            for idx in range(0, len(val[key])):
-                fake_add_data_to_aggregate(bna_training, name, isa, val, idx)
-
-        ares = analyze(bna_training, {}, bpa_training, by_trace, parameters)
-        if name in ares[isa] and funtype2 in ares[isa][name][funtype1]['function']:
-            xv2_assess_function(name, ares[isa][name][funtype1]['function'][funtype2], key, bpa_validation, mae, smape, rmsd)
-            if funtype2 == 'estimate':
-                if 'base' in ares[isa][name][funtype1]['function'][funtype2]:
-                    estimates.append(tuple(ares[isa][name][funtype1]['function'][funtype2]['base']))
-                else:
-                    estimates.append(None)
-    return mae, smape, rmsd, estimates
-
-def crossvalidate(by_name, by_param, by_trace, model, parameters):
-    param_mc_count = 200
-    paramv = param_values(parameters, by_param)
-    for name in sorted(by_name.keys()):
-        isa = by_name[name]['isa']
-        by_name[name]['means'] = np.array(by_name[name]['means'])
-        by_name[name]['energies'] = np.array(by_name[name]['energies'])
-        by_name[name]['rel_energies_prev'] = np.array(by_name[name]['rel_energies_prev'])
-        by_name[name]['rel_energies_next'] = np.array(by_name[name]['rel_energies_next'])
-        by_name[name]['durations'] = np.array(by_name[name]['durations'])
-
-        if isa == 'state':
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['means'], splitidx_srs, 200)
-            print('%16s,   static        power,             Monte Carlo: MAE %8.f µW,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['means'], splitidx_kfold, 10)
-            print('%16s,   static        power,             10-fold sys: MAE %8.f µW,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-        else:
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['energies'], splitidx_srs, 200)
-            print('%16s,   static       energy,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['energies'], splitidx_kfold, 10)
-            print('%16s,   static       energy,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_srs, 200)
-            print('%16s,   static rel_energy_p,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_kfold, 10)
-            print('%16s,   static rel_energy_p,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_srs, 200)
-            print('%16s,   static rel_energy_n,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_kfold, 10)
-            print('%16s,   static rel_energy_n,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_srs, 200)
-            print('%16s,   static     duration,             Monte Carlo: MAE %8.f µs,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_kfold, 10)
-            print('%16s,   static     duration,             10-fold sys: MAE %8.f µs,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-
-        def print_estimates(estimates, total):
-            histogram = {}
-            buf = '    '
-            for estimate in estimates:
-                if not estimate in histogram:
-                    histogram[estimate] = 1
-                else:
-                    histogram[estimate] += 1
-            for estimate, count in sorted(histogram.items(), key=lambda kv: kv[1], reverse=True):
-                buf += '  %.f%% %s' % (count * 100 / total, estimate)
-            if len(estimates):
-                print(buf)
-
-        def val_run_funs(by_name, by_trace, name, key1, key2, key3, unit):
-            mae, smape, rmsd, estimates = val_run_fun(by_name, by_trace, name, key1, key2, key3, splitidx_srs, param_mc_count)
-            print('%16s, %8s %12s,             Monte Carlo: MAE %8.f %s,  SMAPE %6.2f%%,  RMS %d' % (
-                name, key3, key2, np.mean(mae), unit, np.mean(smape), np.mean(rmsd)))
-            print_estimates(estimates, param_mc_count)
-            mae, smape, rmsd, estimates = val_run_fun(by_name, by_trace, name, key1, key2, key3, splitidx_kfold, 10)
-            print('%16s, %8s %12s,             10-fold sys: MAE %8.f %s,  SMAPE %6.2f%%,  RMS %d' % (
-                name, key3, key2, np.mean(mae), unit, np.mean(smape), np.mean(rmsd)))
-            print_estimates(estimates, 10)
-            mae, smape, rmsd, estimates = val_run_fun_p(by_param, by_trace, name, key1, key2, key3, splitidx_srs, param_mc_count)
-            print('%16s, %8s %12s, param-aware Monte Carlo: MAE %8.f %s,  SMAPE %6.2f%%,  RMS %d' % (
-                name, key3, key2, np.mean(mae), unit, np.mean(smape), np.mean(rmsd)))
-            print_estimates(estimates, param_mc_count)
-            mae, smape, rmsd, estimates = val_run_fun_p(by_param, by_trace, name, key1, key2, key3, splitidx_kfold, 10)
-            print('%16s, %8s %12s, param-aware 10-fold sys: MAE %8.f %s,  SMAPE %6.2f%%,  RMS %d' % (
-                name, key3, key2, np.mean(mae), unit, np.mean(smape), np.mean(rmsd)))
-            print_estimates(estimates, 10)
-
-        if 'power' in model[isa][name] and 'function' in model[isa][name]['power']:
-            if 'user' in model[isa][name]['power']['function']:
-                val_run_funs(by_name, by_trace, name, 'means', 'power', 'user', 'µW')
-            if 'estimate' in model[isa][name]['power']['function']:
-                val_run_funs(by_name, by_trace, name, 'means', 'power', 'estimate', 'µW')
-        if 'timeout' in model[isa][name] and 'function' in model[isa][name]['timeout']:
-            if 'user' in model[isa][name]['timeout']['function']:
-                val_run_funs(by_name, by_trace, name, 'timeouts', 'timeout', 'user', 'µs')
-            if 'estimate' in model[isa][name]['timeout']['function']:
-                val_run_funs(by_name, by_trace, name, 'timeouts', 'timeout', 'estimate', 'µs')
-        if 'duration' in model[isa][name] and 'function' in model[isa][name]['duration']:
-            if 'user' in model[isa][name]['duration']['function']:
-                val_run_funs(by_name, by_trace, name, 'durations', 'duration', 'user', 'µs')
-            if 'estimate' in model[isa][name]['duration']['function']:
-                val_run_funs(by_name, by_trace, name, 'durations', 'duration', 'estimate', 'µs')
-        if 'energy' in model[isa][name] and 'function' in model[isa][name]['energy']:
-            if 'user' in model[isa][name]['energy']['function']:
-                val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'user', 'pJ')
-            if 'estimate' in model[isa][name]['energy']['function']:
-                val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'estimate', 'pJ')
-        if 'rel_energy_prev' in model[isa][name] and 'function' in model[isa][name]['rel_energy_prev']:
-            if 'user' in model[isa][name]['rel_energy_prev']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'user', 'pJ')
-            if 'estimate' in model[isa][name]['rel_energy_prev']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'estimate', 'pJ')
-        if 'rel_energy_next' in model[isa][name] and 'function' in model[isa][name]['rel_energy_next']:
-            if 'user' in model[isa][name]['rel_energy_next']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'user', 'pJ')
-            if 'estimate' in model[isa][name]['rel_energy_next']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'estimate', 'pJ')
-
-    return
-    for i, param in enumerate(parameters):
-        user_mae = {}
-        user_smape = {}
-        estimate_mae = {}
-        estimate_smape = {}
-        for val in paramv[param]:
-            bpa_training = dict([[x, by_param[x]] for x in by_param if x[1][i] != val])
-            bpa_validation = dict([[x, by_param[x]] for x in by_param if x[1][i] == val])
-            to_pop = []
-            for name in by_name.keys():
-                if not any(map(lambda x : x[0] == name, bpa_training.keys())):
-                    to_pop.append(name)
-            for name in to_pop:
-                by_name.pop(name, None)
-            ares = analyze(by_name, {}, bpa_training, by_trace, parameters)
-            for name in sorted(ares['state'].keys()):
-                state = ares['state'][name]
-                if 'function' in state['power']:
-                    if 'user' in state['power']['function']:
-                        xv_assess_function(name, state['power']['function']['user'], 'means', bpa_validation, user_mae, user_smape)
-                    if 'estimate' in state['power']['function']:
-                        xv_assess_function(name, state['power']['function']['estimate'], 'means', bpa_validation, estimate_mae, estimate_smape)
-            for name in sorted(ares['transition'].keys()):
-                trans = ares['transition'][name]
-                if 'timeout' in trans and 'function' in trans['timeout']:
-                    if 'user' in trans['timeout']['function']:
-                        xv_assess_function(name, trans['timeout']['function']['user'], 'timeouts', bpa_validation, user_mae, user_smape)
-                    if 'estimate' in trans['timeout']['function']:
-                        xv_assess_function(name, trans['timeout']['function']['estimate'], 'timeouts', bpa_validation, estimate_mae, estimate_smape)
-
-        for name in sorted(user_mae.keys()):
-            if by_name[name]['isa'] == 'state':
-                print('user function %s power by %s: MAE %.f µW,  SMAPE %.2f%%' % (
-                    name, param, np.mean(user_mae[name]), np.mean(user_smape[name])))
-            else:
-                print('user function %s timeout by %s: MAE %.f µs,  SMAPE %.2f%%' % (
-                    name, param, np.mean(user_mae[name]), np.mean(user_smape[name])))
-        for name in sorted(estimate_mae.keys()):
-            if by_name[name]['isa'] == 'state':
-                print('estimate function %s power by %s: MAE %.f µW,  SMAPE %.2f%%' % (
-                    name, param, np.mean(estimate_mae[name]), np.mean(estimate_smape[name])))
-            else:
-                print('estimate function %s timeout by %s: MAE %.f µs,  SMAPE %.2f%%' % (
-                    name, param, np.mean(estimate_mae[name]), np.mean(estimate_smape[name])))
-
-def analyze_by_param(aggval, by_param, allvalues, name, key1, key2, param, param_idx):
-    aggval[key1]['std_by_param'][param] = mean_std_by_param(
-        by_param, allvalues, name, key2, param_idx)
-    aggval[key1]['spearmanr_by_param'][param] = spearmanr_by_param(name, key2, param_idx)
-    if aggval[key1]['std_by_param'][param] > 0 and aggval[key1]['std_param'] / aggval[key1]['std_by_param'][param] < 0.6:
-        aggval[key1]['fit_guess'][param] = try_fits(name, key2, param_idx, by_param)
-
-def analyze_by_arg(aggval, by_arg, allvalues, name, key1, key2, arg_name, arg_idx):
-    aggval[key1]['std_by_arg'][arg_name] = mean_std_by_arg(
-        by_arg, allvalues, name, key2, arg_idx)
-    if aggval[key1]['std_by_arg'][arg_name] > 0 and aggval[key1]['std_arg'] / aggval[key1]['std_by_arg'][arg_name] < 0.6:
-        aggval[key1]['arg_fit_guess'][arg_name] = try_fits(name, key2, arg_idx, by_arg)
-
-def maybe_fit_function(aggval, model, by_param, parameters, name, key1, key2, unit):
-    if 'function' in model[key1] and 'user' in model[key1]['function']:
-        aggval[key1]['function']['user'] = {
-            'raw' : model[key1]['function']['user']['raw'],
-            'params' : model[key1]['function']['user']['params'],
-        }
-        fit_function(
-            aggval[key1]['function']['user'], name, key2, parameters, by_param,
-            yaxis='%s %s by param [%s]' % (name, key1, unit))
-
-def analyze(by_name, by_arg, by_param, by_trace, parameters):
-    aggdata = {
-        'state' : {},
-        'transition' : {},
-        'min_voltage' : min_voltage,
-        'max_voltage' : max_voltage,
-    }
-    transition_names = list(map(lambda x: x[0], filter(lambda x: x[1]['isa'] == 'transition', by_name.items())))
-    for name, val in by_name.items():
-        isa = val['isa']
-        model = data['model'][isa][name]
-
-        aggdata[isa][name] = {
-            'power' : keydata(name, val, by_arg, by_param, by_trace, 'means'),
-            'duration' : keydata(name, val, by_arg, by_param, by_trace, 'durations'),
-            'energy' : keydata(name, val, by_arg, by_param, by_trace, 'energies'),
-            'clip' : {
-                'mean' : np.mean(val['clip_rate']),
-                'max'  : max(val['clip_rate']),
-            },
-            'timeout' : {},
-        }
-
-        aggval = aggdata[isa][name]
-        aggval['power']['std_outer'] = np.mean(val['stds'])
-
-        if isa == 'transition':
-            aggval['rel_energy_prev'] = keydata(name, val, by_arg, by_param, by_trace, 'rel_energies_prev')
-            aggval['rel_energy_next'] = keydata(name, val, by_arg, by_param, by_trace, 'rel_energies_next')
-            aggval['timeout'] = keydata(name, val, by_arg, by_param, by_trace, 'timeouts')
-
-        for i, param in enumerate(parameters):
-            values = list(set([key[1][i] for key in by_param.keys() if key[0] == name and key[1][i] != '']))
-            allvalues = [(*key[1][:i], *key[1][i+1:]) for key in by_param.keys() if key[0] == name]
-            #allvalues = list(set(allvalues))
-            if len(values) > 1:
-                if isa == 'state':
-                    analyze_by_param(aggval, by_param, allvalues, name, 'power', 'means', param, i)
-                else:
-                    analyze_by_param(aggval, by_param, allvalues, name, 'duration', 'durations', param, i)
-                    analyze_by_param(aggval, by_param, allvalues, name, 'energy', 'energies', param, i)
-                    analyze_by_param(aggval, by_param, allvalues, name, 'rel_energy_prev', 'rel_energies_prev', param, i)
-                    analyze_by_param(aggval, by_param, allvalues, name, 'rel_energy_next', 'rel_energies_next', param, i)
-                    analyze_by_param(aggval, by_param, allvalues, name, 'timeout', 'timeouts', param, i)
-
-        if isa == 'state':
-            fguess_to_function(name, 'means', aggval['power'], parameters, by_param,
-                'estimated %s power by param [µW]' % name)
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'power', 'means', 'µW')
-            if aggval['power']['std_param'] > 0 and aggval['power']['std_trace'] / aggval['power']['std_param'] < 0.5:
-                aggval['power']['std_by_trace'] = mean_std_by_trace_part(by_trace, transition_names, name, 'means')
-        else:
-            fguess_to_function(name, 'durations', aggval['duration'], parameters, by_param,
-                'estimated %s duration by param [µs]' % name)
-            fguess_to_function(name, 'energies', aggval['energy'], parameters, by_param,
-                'estimated %s energy by param [pJ]' % name)
-            fguess_to_function(name, 'rel_energies_prev', aggval['rel_energy_prev'], parameters, by_param,
-                'estimated relative_prev %s energy by param [pJ]' % name)
-            fguess_to_function(name, 'rel_energies_next', aggval['rel_energy_next'], parameters, by_param,
-                'estimated relative_next %s energy by param [pJ]' % name)
-            fguess_to_function(name, 'timeouts', aggval['timeout'], parameters, by_param,
-                'estimated %s timeout by param [µs]' % name)
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'duration', 'durations', 'µs')
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'energy', 'energies', 'pJ')
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'rel_energy_prev', 'rel_energies_prev', 'pJ')
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'rel_energy_next', 'rel_energies_next', 'pJ')
-            maybe_fit_function(aggval, model, by_param, parameters, name, 'timeout', 'timeouts', 'µs')
-
-            for i, arg in enumerate(model['parameters']):
-                values = list(set([key[1][i] for key in by_arg.keys() if key[0] == name and is_numeric(key[1][i])]))
-                allvalues = [(*key[1][:i], *key[1][i+1:]) for key in by_arg.keys() if key[0] == name]
-                analyze_by_arg(aggval, by_arg, allvalues, name, 'duration', 'durations', arg['name'], i)
-                analyze_by_arg(aggval, by_arg, allvalues, name, 'energy', 'energies', arg['name'], i)
-                analyze_by_arg(aggval, by_arg, allvalues, name, 'rel_energy_prev', 'rel_energies_prev', arg['name'], i)
-                analyze_by_arg(aggval, by_arg, allvalues, name, 'rel_energy_next', 'rel_energies_next', arg['name'], i)
-                analyze_by_arg(aggval, by_arg, allvalues, name, 'timeout', 'timeouts', arg['name'], i)
-
-            arguments = list(map(lambda x: x['name'], model['parameters']))
-            arg_fguess_to_function(name, 'durations', aggval['duration'], arguments, by_arg,
-                'estimated %s duration by arg [µs]' % name)
-            arg_fguess_to_function(name, 'energies', aggval['energy'], arguments, by_arg,
-                'estimated %s energy by arg [pJ]' % name)
-            arg_fguess_to_function(name, 'rel_energies_prev', aggval['rel_energy_prev'], arguments, by_arg,
-                'estimated relative_prev %s energy by arg [pJ]' % name)
-            arg_fguess_to_function(name, 'rel_energies_next', aggval['rel_energy_next'], arguments, by_arg,
-                'estimated relative_next %s energy by arg [pJ]' % name)
-            arg_fguess_to_function(name, 'timeouts', aggval['timeout'], arguments, by_arg,
-                'estimated %s timeout by arg [µs]' % name)
-
-    return aggdata
-
-try:
-    raw_opts, args = getopt.getopt(sys.argv[1:], "", [
-        "fit", "states", "transitions", "params", "clipping", "timing",
-        "histogram", "substates", "validate", "crossvalidate", "ignore-trace-idx=", "voltage"])
-    for option, parameter in raw_opts:
-        optname = re.sub(r'^--', '', option)
-        opts[optname] = parameter
-    if 'ignore-trace-idx' in opts:
-        opts['ignore-trace-idx'] = int(opts['ignore-trace-idx'])
-except getopt.GetoptError as err:
-    print(err)
-    sys.exit(2)
-
-data = load_json(args[0])
-by_name = {}
-by_arg = {}
-by_param = {}
-by_trace = {}
-
-if 'voltage' in opts:
-    data['model']['parameter']['voltage'] = {
-        'default' : float(data['setup']['mimosa_voltage']),
-        'function' : None,
-        'arg_name' : None,
-    }
-
-min_voltage = float(data['setup']['mimosa_voltage'])
-max_voltage = float(data['setup']['mimosa_voltage'])
-
-parameters = sorted(data['model']['parameter'].keys())
-
-for arg in args:
-    mdata = load_json(arg)
-    this_voltage = float(mdata['setup']['mimosa_voltage'])
-    if this_voltage > max_voltage:
-        max_voltage = this_voltage
-    if this_voltage < min_voltage:
-        min_voltage = this_voltage
-    if 'voltage' in opts:
-        opts['voltage'] = this_voltage
-    for runidx, run in enumerate(mdata['traces']):
-        if 'ignore-trace-idx' not in opts or opts['ignore-trace-idx'] != runidx:
-            for i, elem in enumerate(run['trace']):
-                if elem['name'] != 'UNINITIALIZED':
-                    load_run_elem(i, elem, run['trace'], by_name, by_arg, by_param, by_trace)
-
-#with open('/tmp/by_name.pickle', 'wb') as f:
-#    pickle.dump(by_name, f, pickle.HIGHEST_PROTOCOL)
-#with open('/tmp/by_arg.pickle', 'wb') as f:
-#    pickle.dump(by_arg, f, pickle.HIGHEST_PROTOCOL)
-#with open('/tmp/by_param.pickle', 'wb') as f:
-#    pickle.dump(by_param, f, pickle.HIGHEST_PROTOCOL)
-
-if 'states' in opts:
-    if 'params' in opts:
-        plotter.plot_states_param(data['model'], by_param)
-    else:
-        plotter.plot_states(data['model'], by_name)
-    if 'timing' in opts:
-        plotter.plot_states_duration(data['model'], by_name)
-        plotter.plot_states_duration(data['model'], by_param)
-    if 'clipping' in opts:
-        plotter.plot_states_clips(data['model'], by_name)
-if 'transitions' in opts:
-    plotter.plot_transitions(data['model'], by_name)
-    if 'timing' in opts:
-        plotter.plot_transitions_duration(data['model'], by_name)
-        plotter.plot_transitions_timeout(data['model'], by_param)
-    if 'clipping' in opts:
-        plotter.plot_transitions_clips(data['model'], by_name)
-if 'histogram' in opts:
-    for key in sorted(by_name.keys()):
-        plotter.plot_histogram(by_name[key]['means'])
-if 'substates' in opts:
-    if 'params' in opts:
-        plotter.plot_substate_thresholds_p(data['model'], by_param)
-    else:
-        plotter.plot_substate_thresholds(data['model'], by_name)
-
-if 'crossvalidate' in opts:
-    crossvalidate(by_name, by_param, by_trace, data['model'], parameters)
-else:
-    data['aggregate'] = analyze(by_name, by_arg, by_param, by_trace, parameters)
-
-# TODO optionally also plot data points for states/transitions which do not have
-# a function, but may depend on a parameter (visualization is always good!)
-
-save_json(data, args[0])
diff --git a/bin/mimosawatch b/bin/mimosawatch
deleted file mode 100755
index 88d34a3..0000000
--- a/bin/mimosawatch
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env perl
-
-use strict;
-use warnings;
-use 5.020;
-
-use File::Slurp qw(slurp);
-use IO::Handle;
-
-our $VERSION = '0.00';
-
-STDOUT->autoflush(1);
-
-while (sleep(1)) {
-	if ( -e '/tmp/mimosa/mimosa_scale_100000.tmp' ) {
-		my $raw_data = slurp('/tmp/mimosa/mimosa_scale_100000.tmp');
-		my @data = map { $_ >> 8 } unpack('L*', $raw_data);
-		my @buzzer = map { $_ & 0x0f } unpack('L*', $raw_data);
-
-		if (@data > 8) {
-			printf("\r\e[2K" . ('%8d ' x 8), @data[-8 .. -1]);
-		}
-	}
-}