From eaca9430b3b5698da7defaae542c5a0417d26dd6 Mon Sep 17 00:00:00 2001
From: Junjie <junjie.zhang@eonerc.rwth-aachen.de>
Date: Fri, 30 Aug 2019 09:36:34 +0200
Subject: [PATCH] update plottools and timeseries
---
villas/dataprocessing/plottools.py | 21 +++++++++++-------
villas/dataprocessing/timeseries.py | 34 ++++++++++++++++++++++++-----
2 files changed, 42 insertions(+), 13 deletions(-)
diff --git a/villas/dataprocessing/plottools.py b/villas/dataprocessing/plottools.py
index 768b709..06d46fe 100644
--- a/villas/dataprocessing/plottools.py
+++ b/villas/dataprocessing/plottools.py
@@ -1,9 +1,10 @@
import matplotlib.pyplot as plt
import numpy as np
from .timeseries import *
+import scipy.signal as signal
-def plot_timeseries(figure_id, timeseries, plt_linestyle='-', plt_linewidth=2, plt_color=None, plt_legend_loc='lower right'):
+def plot_timeseries(figure_id, timeseries, marker='None', plt_linestyle='-', plt_linewidth=2, plt_color=None, plt_legend_loc='lower right', plt_legend=True, inlineLabel = False):
"""
This function plots either a single timeseries or several timeseries in the figure defined by figure_id.
Several timeseries (handed over in a list) are plotted in several subplots.
@@ -12,20 +13,24 @@ def plot_timeseries(figure_id, timeseries, plt_linestyle='-', plt_linewidth=2, p
plt.figure(figure_id)
if not isinstance(timeseries, list):
if plt_color:
- plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth, color=plt_color)
+ plt.plot(timeseries.time, timeseries.values, marker=marker, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth, color=plt_color)
else:
- plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth)
+ plt.plot(timeseries.time, timeseries.values, marker=marker, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
- plt.legend(loc=plt_legend_loc)
+ if(plt_legend):
+ plt.legend(loc=plt_legend_loc)
else:
for ts in timeseries:
plt.subplot(len(timeseries), 1, timeseries.index(ts) + 1)
if plt_color:
- plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth, color=plt_color)
+ plt.plot(ts.time, ts.values, marker=marker, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth, color=plt_color)
else:
- plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth)
+ plt.plot(ts.time, ts.values, marker=marker, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
- plt.legend()
+ if(plt_legend):
+ plt.legend()
+ if(inlineLabel):
+ plt.text(timeseries.time[-1], timeseries.values[-1], timeseries.label)
def set_timeseries_labels(timeseries, timeseries_labels):
@@ -37,4 +42,4 @@ def set_timeseries_labels(timeseries, timeseries_labels):
timeseries.label = timeseries_labels
else:
for ts in timeseries:
- ts.label = timeseries_labels[timeseries.index(ts)]
+ ts.label = timeseries_labels[timeseries.index(ts)]
\ No newline at end of file
diff --git a/villas/dataprocessing/timeseries.py b/villas/dataprocessing/timeseries.py
index 91583d1..3f992dc 100644
--- a/villas/dataprocessing/timeseries.py
+++ b/villas/dataprocessing/timeseries.py
@@ -1,6 +1,6 @@
import numpy as np
import cmath
-from scipy.signal import hilbert, chirp
+from scipy.signal import hilbert, find_peaks
class TimeSeries:
"""Stores data from different simulation sources.
@@ -10,7 +10,10 @@ class TimeSeries:
self.time = np.array(time)
self.values = np.array(values)
self.name = name
- self.label = name
+ if not label:
+ self.label = name
+ else:
+ self.label = label
def scale(self, factor):
"""Returns scaled timeseries.
@@ -18,13 +21,22 @@ class TimeSeries:
ts_scaled = TimeSeries(self.name+'_scl', self.time, self.values * factor)
return ts_scaled
+ def slice_ts(self, start_time, end_time):
+ time_step=self.time[1]-self.time[0]
+ start_index=int(start_time/time_step)
+ end_index=int(end_time/time_step)
+ slice_time=self.time[start_index:end_index]
+ slice_values=self.values[start_index:end_index]
+ ts_slice=TimeSeries(self.name+'_slice', slice_time, slice_values)
+ return ts_slice
+
def abs(self):
""" Calculate absolute value of complex time series.
"""
abs_values = []
for value in self.values:
abs_values.append(np.abs(value))
- ts_abs = TimeSeries(self.name+'_abs', self.time, abs_values)
+ ts_abs = TimeSeries(self.name+'_abs', self.time, abs_values, self.label+'_abs')
return ts_abs
def phase(self):
@@ -33,7 +45,7 @@ class TimeSeries:
phase_values = []
for value in self.values:
phase_values.append(np.angle(value, deg=True))
- ts_phase = TimeSeries(self.name+'_phase', self.time, phase_values)
+ ts_phase = TimeSeries(self.name+'_phase', self.time, phase_values, self.label+'_phase')
return ts_phase
def real(self):
@@ -239,8 +251,20 @@ class TimeSeries:
duration = ts.time.max()
samples = ts.time.size
sampling_freq = samples / duration
- analytic_signal = hilbert(ts.abs().values)
+ analytic_signal = hilbert(ts.values)
instaneous_phase=np.unwrap(np.angle(analytic_signal))
instantaneous_frequency = (np.diff(instaneous_phase) / (2.0 * np.pi) * sampling_freq)
ts_instFreq=TimeSeries(str(ts.name + "instFreq"), ts.time[:-1], instantaneous_frequency)
return ts_instFreq
+
+ @staticmethod
+ def getPeaks(ts, nom, tolerance):
+ peaks_, properties_ = find_peaks(ts.values, height=nom+tolerance)
+ ts_peaks=TimeSeries(str(ts.name+".peaks"), ts.time[peaks_], ts.values[peaks_])
+ return ts_peaks
+
+ @staticmethod
+ def getDeviation(ts, nom, tolerance):
+ dev_=np.where(np.abs(ts.abs().values-nom)>tolerance)
+ ts_dev=TimeSeries(str(ts.name+".peaks"), ts.time[dev_], ts.values[dev_])
+ return ts_dev
\ No newline at end of file
--
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