From 3a4720c657be15523ec45dca0f2e5d581cbe9e4f Mon Sep 17 00:00:00 2001
From: Niclas Esser <nesser@mpifr-bonn.mpg.de>
Date: Fri, 2 Aug 2024 11:52:46 +0200
Subject: [PATCH] Pylint changes
---
pafsim/chain.py | 41 +++++++++++-----------
pafsim/connector.py | 23 ++++++-------
pafsim/executor.py | 37 +++++++++++++-------
pafsim/processor/__init__.py | 61 ++++++++++++++++++++++++++-------
pafsim/processor/_processor.py | 13 ++++---
pafsim/processor/beamformer.py | 10 +++---
pafsim/processor/calibrator.py | 15 ++++----
pafsim/processor/channelizer.py | 15 ++++----
pafsim/processor/correlator.py | 12 +++----
pafsim/processor/generator.py | 24 ++++++-------
pafsim/processor/waveform.py | 16 ++++-----
pafsim/vector.py | 8 +++--
12 files changed, 163 insertions(+), 112 deletions(-)
diff --git a/pafsim/chain.py b/pafsim/chain.py
index 576ef48..18eb7f9 100644
--- a/pafsim/chain.py
+++ b/pafsim/chain.py
@@ -1,6 +1,6 @@
import os
import shutil
-from typing import List, Dict
+from typing import Dict
import networkx as nx
import matplotlib.pyplot as plt
@@ -20,11 +20,12 @@ class ProcessingChain():
name (str, optional): When creating multiple ProcessingChain objects with dependencies,
a name can be provided to refer to dependend ProcessingChains. Defaults to "".
"""
- self.name: str=name
- self.conf: dict=conf
- self.setup_flag: bool=False
- self.processed: bool=False
- self.dag: nx.DiGraph=nx.DiGraph()
+ self.name: str = name
+ self.conf: dict = conf
+ self.setup_flag: bool = False
+ self.processed: bool = False
+ self.dag: nx.DiGraph = nx.DiGraph()
+ self.dag_list: list = []
self.processors: Dict[str,processing.Processor] = {}
def setup(self):
@@ -81,11 +82,11 @@ class ProcessingChain():
return proc
return None
- def plot(self, dir: str="", figsize: tuple=(8,4)):
+ def plot(self, path: str="", figsize: tuple=(8,4)):
"""Plots the DAG
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
fig = plt.figure(num = 1, figsize=figsize)
@@ -98,8 +99,8 @@ class ProcessingChain():
linewidths=2,
font_size=9)
fig.suptitle("Directed acyclic graph (DAG)")
- if dir:
- fig.savefig(dir + 'dag.png')
+ if path:
+ fig.savefig(path + 'dag.png')
plt.close()
else:
plt.show()
@@ -117,11 +118,11 @@ class ProcessingChain():
self.processors[pname].process()
self.processed = True
- def plotAll(self, dir: str="", figsize: tuple=(8,4)):
+ def plotAll(self, path: str="", figsize: tuple=(8,4)):
"""Calls all plot methods of the processors
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
Raises:
@@ -131,7 +132,7 @@ class ProcessingChain():
if self.processed is False:
raise Exception("Chain not processed, use process() before plot()")
for pname in self.dag_list:
- self.processors[pname].plot(dir, figsize)
+ self.processors[pname].plot(path, figsize)
def save(self, plot: bool=True, overwrite:bool=True):
"""Stores the configurations and marked vectors of the procesing chains in dedicated folders
@@ -145,18 +146,18 @@ class ProcessingChain():
return
directory = os.path.abspath(self.conf["location"])
- if overwrite == True and os.path.exists(directory):
+ if overwrite is True and os.path.exists(directory):
shutil.rmtree(directory)
if not os.path.exists(directory):
os.makedirs(directory)
- if directory[-1]!="/": directory+="/"
- with open(directory + "/conf.json", "w") as o:
+
+ with open(os.path.join(directory, "conf.json"), "w", encoding="utf-8") as o:
o.write(str(self.conf))
- os.mkdir(directory + '/data/')
+ os.mkdir(os.path.join(directory, 'data/'))
if plot:
- os.mkdir(directory + '/plot/')
- self.plotAll(directory + '/plot/')
+ os.mkdir(os.path.join(directory, 'plot/'))
+ self.plotAll(os.path.join(directory, 'plot/'))
for tv_name in self.conf["store"]:
tv = Vector(self.getProcessor(tv_name))
- tv.store(directory + '/data/')
\ No newline at end of file
+ tv.store(os.path.join(directory, 'data/'))
diff --git a/pafsim/connector.py b/pafsim/connector.py
index 8240f9a..89fb4dc 100644
--- a/pafsim/connector.py
+++ b/pafsim/connector.py
@@ -1,10 +1,9 @@
# import numpy as np
from pafsim.processor import Processor
-class ConnectionError(Exception):
+class EdgeConnectionError(Exception):
"""Exception class used for invalid connections
"""
- pass
class Connector:
"""The Connector class is used as edges in the ProcessingChain. It transposes output
@@ -20,31 +19,31 @@ class Connector:
"""Connects to Processors and checks if the connection is valid
Raises:
- ConnectionError: If a connection is invalid
+ EdgeConnectionError: If a connection is invalid
"""
if self.a_node.O_FORMAT in self.b_node.I_FORMAT:
print("Connection without transpose")
- self._assign()
+ self.assign()
elif set(self.a_node.O_FORMAT) in [set(x) for x in self.b_node.I_FORMAT]:
print("Connection needs transpose")
- self._transpose()
- self._assign()
+ self.transpose()
+ self.assign()
else:
- raise ConnectionError("Output format of node A does not match input formats of node B")
+ raise EdgeConnectionError("Output format of node A does not match input formats of node B")
- def _assign(self):
+ def assign(self):
"""Assigns the Processors
Raises:
- ConnectionError: Mismatch of inputs
+ EdgeConnectionError: Mismatch of inputs
"""
if self.b_node.N_INPUTS > len(self.b_node.pp):
- raise ConnectionError(f"Can not assign more than {self.b_node.N_INPUTS} connections")
+ raise EdgeConnectionError(f"Can not assign more than {self.b_node.N_INPUTS} connections")
self.b_node.pp.append(self.a_node)
- def _transpose(self):
+ def transpose(self):
"""Transpose the output of the pre processor to the expected input of the post processor
ToDo: Not implemented yet
"""
- pass
\ No newline at end of file
+
diff --git a/pafsim/executor.py b/pafsim/executor.py
index e7f0fa0..687755d 100644
--- a/pafsim/executor.py
+++ b/pafsim/executor.py
@@ -1,6 +1,8 @@
-import networkx as nx
+import typing as ty
from collections import OrderedDict
+import networkx as nx
from pafsim.chain import ProcessingChain
+from pafsim.vector import Vector
class Executor():
"""The Executor enables the simulation of comprehensive simulation configuration with nested
@@ -12,10 +14,12 @@ class Executor():
Args:
conf (dict, optional): The discrition of the simulation. Defaults to None.
"""
- self.test_vectors = []
- self.chains = []
- self.dag = nx.DiGraph()
- self.conf = conf
+ self.test_vectors: ty.List[Vector] = []
+ self.chains: ty.List[ProcessingChain] = []
+ self.dag: nx.DiGraph = nx.DiGraph()
+ self.conf: dict = conf
+ self.input_vectors: ty.List[Vector] = []
+ self.output_vectors: ty.List[Vector] = []
def update(self, conf: dict):
"""Updates the current configuration
@@ -29,7 +33,7 @@ class Executor():
try:
self.conf.update(conf)
except Exception as e:
- raise Exception("Failed to update configuration file with {}".format(e))
+ raise Exception(f"Failed to update configuration file with {e}")
def _sort_chains(self):
"""Used to sort the processing chains according to the dependencies speciefied in
@@ -74,27 +78,34 @@ class Executor():
self.update(chain.conf)
self.chains.append(chain)
- def getVectorById(self, id: str):
+ def getVectorById(self, name: str):
"""Get a test vector by its ID
Args:
- id (str): The ID of a test vector
+ name (str): The ID of a test vector
Returns:
Vector: The Vector
"""
for i in self.input_vectors:
- if id == i.id:
+ if name == i.name:
return i
for i in self.output_vectors:
- if id == i.id:
+ if name == i.name:
return i
- print("No Vector with id={} loaded".format(id))
+ print(f"No Vector with name '{name}' found")
return None
- def getChain(self, name):
+ def getChain(self, name: str) -> ProcessingChain:
+ """Get a registered ProcessingChain object by its name
+
+ Args:
+ name (str): The name of the ProcessingChain
+
+ Returns:
+ ProcessingChain: The ProcessingChain object if it exists, otherwise None
+ """
for chain in self.chains:
if chain.name == name:
return chain
return None
-
diff --git a/pafsim/processor/__init__.py b/pafsim/processor/__init__.py
index af1a20e..7fbdc0c 100644
--- a/pafsim/processor/__init__.py
+++ b/pafsim/processor/__init__.py
@@ -1,31 +1,68 @@
import numpy as np
+from pafsim.processor._processor import Processor
+from pafsim.processor.beamformer import Beamformer
+from pafsim.processor.calibrator import WeightGenerator
+from pafsim.processor.channelizer import Channelizer
+from pafsim.processor.correlator import Correlator
+from pafsim.processor.waveform import WaveformGenerator
+from pafsim.processor.generator import Generator
+
+def createProcessor(class_name: str, name: str, kwargs: dict) -> Processor:
+ """Factory function to create a Processor by and name and configuration options
+
+ Args:
+ class_name (str): The class name of the Processor (e.g. Channelizer)
+ name (str): The unique name of the processor
+ kwargs (dict): The configuration options of the processor
+
+ Returns:
+ Processor: The created processor
+ """
+ return globals()[class_name](name, **kwargs)
+
+def scale(data: np.ndarray, bits: int) -> np.ndarray:
+ """Scales a numpy array to passed bit size
-def createProcessor(class_name, id, kwargs):
- return globals()[class_name](id, **kwargs)
+ Args:
+ data (np.ndarray): The array to scale
+ bits (int): The number of bits
-def scale(data, bits):
+ Returns:
+ np.ndarray: The rescaled array
+ """
return (data / np.max(np.abs(data)) * 2**(bits - 1))
-def float2int(data, dtype):
+def rescalefloat2int(data: np.ndarray, dtype: np.dtype) -> np.ndarray:
+ """Rescales a float array to an int array
+
+ Args:
+ data (np.ndarray): The float array to rescale
+ dtype (np.dtype): The desired integer data type (e.g. np.int32)
+
+ Returns:
+ np.ndarray: The converted integer array
+ """
bits = np.dtype(dtype).itemsize * 8
if np.max(data) > 2**(bits-1):
data = scale(data, bits)
return data.astype(dtype)
-def complex2int2(data, dtype):
+def complex2int2(data: np.ndarray, dtype: np.dtype) -> np.ndarray:
+ """Rescales a float array to an int array
+
+ Args:
+ data (np.ndarray): The float array to rescale
+ dtype (np.dtype): The desired integer data type (e.g. np.int32)
+
+ Returns:
+ np.ndarray: The converted integer array
+ """
bits = np.dtype(dtype).itemsize * 8
dtype = np.dtype([('real', dtype), ('imag', dtype)])
if np.max(data) > 2**(bits-1):
data = scale(data, bits)
return data.astype(dtype)
-from pafsim.processor._processor import Processor
-from pafsim.processor.beamformer import Beamformer
-from pafsim.processor.calibrator import WeightGenerator
-from pafsim.processor.channelizer import Channelizer
-from pafsim.processor.correlator import Correlator
-from pafsim.processor.waveform import WaveformGenerator
-from pafsim.processor.generator import Generator
__all__ = [
"Processor",
diff --git a/pafsim/processor/_processor.py b/pafsim/processor/_processor.py
index f3c86e3..1b90762 100644
--- a/pafsim/processor/_processor.py
+++ b/pafsim/processor/_processor.py
@@ -1,7 +1,7 @@
+from abc import ABC, abstractmethod
+import os
import pickle
import numpy as np
-import os
-from abc import ABC, abstractmethod
class Processor(ABC):
@@ -16,7 +16,7 @@ class Processor(ABC):
"""Base constructor of the Processor. Can not be initiatied as this class is abstract
Args:
- name (str): The ID of the Processor
+ name (str): The unique name of theProcessor
"""
self.pp: list[Processor] = [None for __ in range(self.N_INPUT)]
self.name: str = name
@@ -66,15 +66,14 @@ class Processor(ABC):
pass
@abstractmethod
- def plot(self, dir: str="", figsize: tuple=(8,4)):
+ def plot(self, path: str="", figsize: tuple=(8,4)):
"""Abstract method to plot the output array.
Needs to be implemented by inherited classes
Args:
- dir (str, optional): If set stores the plot in the directory. Defaults to "".
+ path (str, optional): If set stores the plot in the directory. Defaults to "".
figsize (tuple, optional): Te size of the figure to plot. Defaults to (8,4).
"""
- pass
def dim(self, label: str) -> int:
"""Returns the size of the requested dimension
@@ -154,4 +153,4 @@ def getDim(label: str, processor: Processor) -> int:
idx = processor.O_FORMAT.index(label)
except:
raise ValueError(f"Label {label} does not exists in {processor.O_FORMAT}")
- return processor.shape[idx]
\ No newline at end of file
+ return processor.shape[idx]
diff --git a/pafsim/processor/beamformer.py b/pafsim/processor/beamformer.py
index 4b1d641..6d83270 100644
--- a/pafsim/processor/beamformer.py
+++ b/pafsim/processor/beamformer.py
@@ -21,7 +21,7 @@ class Beamformer(Processor):
"""Construct a Beamformer object
Args:
- name (str): The ID of the Beamformer
+ name (str): The unique name of the Beamformer
"""
super().__init__(name, **kwargs)
@@ -76,11 +76,11 @@ class Beamformer(Processor):
return np.matmul(i, w.conj()).transpose(0,3,1,2)
return np.dot(self.pp[0].output, self.pp[1].output.T)
- def plot(self, dir="", figsize=(8,4)):
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the beamformed time series
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
sub = []
@@ -93,8 +93,8 @@ class Beamformer(Processor):
sub[b*self.dim('P')+p].set_ylabel("Channel")
plt.imshow(np.log(np.abs(self.output[:, b, p, :])), interpolation='nearest', aspect='auto')
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
plt.show()
diff --git a/pafsim/processor/calibrator.py b/pafsim/processor/calibrator.py
index d832d5b..8a1914a 100644
--- a/pafsim/processor/calibrator.py
+++ b/pafsim/processor/calibrator.py
@@ -16,10 +16,11 @@ class WeightGenerator(Processor):
"""Construct a WeightGenerator object
Args:
- name (str): The ID of the WeightGenerator
+ name (str): The unique name of theWeightGenerator
kwargs:
- beams: The number of beams to produce. This parameter is implicitly determined
+ beams (int): The number of beams to produce. This parameter is implicitly determined
when using the maxsnr processing
+ mode (str): maxsnr -> maximum signal-to-noise algorithm (default)
"""
super().__init__(name, **kwargs)
self.beam = kwargs.get('beams', 2)
@@ -103,11 +104,11 @@ class WeightGenerator(Processor):
return data
- def plot(self, dir="", figsize=(8,4)):
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the amplitude of the beamweight
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
sub = []
@@ -120,8 +121,8 @@ class WeightGenerator(Processor):
sub[b*self.dim('P')+p].set_ylabel("Channel")
sub[b*self.dim('P')+p].imshow(np.log(np.abs(self.output[b, :, p])),interpolation='nearest', aspect='auto')
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
- plt.show()
\ No newline at end of file
+ plt.show()
diff --git a/pafsim/processor/channelizer.py b/pafsim/processor/channelizer.py
index fef9c37..4af9e00 100644
--- a/pafsim/processor/channelizer.py
+++ b/pafsim/processor/channelizer.py
@@ -17,7 +17,7 @@ class Channelizer(Processor):
"""Construct a Generator object
Args:
- name (str): The ID of the Generator
+ name (str): The unique name of theGenerator
kwargs:
taps (int): Number of taps used for the filter. Defaults to 2
channels (int): Number of channels to produce. Defaults to 32
@@ -35,6 +35,7 @@ class Channelizer(Processor):
self.pd = kwargs.get('pd', 1)
# self.stop_min = kwargs.get('stop_min', 0.00001)
+ self.coeff = np.array()
self.os_factor = self.pn / self.pd
self.tap_length = int(self.channels * 2)
self.channel_bw = 1/self.channels
@@ -97,8 +98,8 @@ class Channelizer(Processor):
if end > x.size:
break
x_p = x[start:end].reshape(self.taps, self.tap_length).T
- sum = (x_p * self.coeff).sum(axis=1)
- y.append(sum)
+ summed = (x_p * self.coeff).sum(axis=1)
+ y.append(summed)
offset+=1
return np.asarray(y)
@@ -138,11 +139,11 @@ class Channelizer(Processor):
reshaped = self.pp[0].output.reshape(self.dim('A'), self.dim('P'), -1, self.channels*2)
return np.fft.rfft(reshaped * self.coeff, axis=-1).transpose(3,0,1,2)[1:]
- def plot(self, dir="", figsize=(8,4)):
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the channeles vs time
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
sub = []
@@ -163,8 +164,8 @@ class Channelizer(Processor):
sub[self.dim('A') * self.dim('P')].plot(np.arange(0,len(self.coeff)),
self.coeff.flatten())
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
plt.show()
diff --git a/pafsim/processor/correlator.py b/pafsim/processor/correlator.py
index 34f8a10..5f4b4de 100644
--- a/pafsim/processor/correlator.py
+++ b/pafsim/processor/correlator.py
@@ -16,7 +16,7 @@ class Correlator(Processor):
"""Construct a Correlator object
Args:
- name (str): The ID of the Correlator
+ name (str): The unique name of theCorrelator
kwargs:
acc (int): Number of ACMs to create.
mode (str): 'acm' -> correlate and build covariance matrices (default)
@@ -58,12 +58,12 @@ class Correlator(Processor):
o[n] = np.matmul(x[..., n*nacc:(n+1)*nacc], np.conj(x[..., n*nacc:(n+1)*nacc]).transpose(0,1,3,2))
return o
- def plot(self, dir="", figsize=(8,4)):
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the amplitude of covariance matrices. Just a sub-set is
randomly chosen
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory.
+ path (str, optional): If not set to "" it stores the plot in the given directory.
Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
@@ -75,8 +75,8 @@ class Correlator(Processor):
sub[i].set_title("Channel " + str(f))
sub[i].imshow(np.log(np.abs(self.output[0, f, 0])),interpolation='nearest', aspect='auto')
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
- plt.show()
\ No newline at end of file
+ plt.show()
diff --git a/pafsim/processor/generator.py b/pafsim/processor/generator.py
index b86bf61..819ef0c 100644
--- a/pafsim/processor/generator.py
+++ b/pafsim/processor/generator.py
@@ -1,11 +1,10 @@
-import numpy as np
-from matplotlib import pyplot as plt
-from pafsim.processor._processor import Processor
import os
+import typing as ty
+from matplotlib import pyplot as plt
import h5py as h5
import numpy as np
-import typing as ty
+from pafsim.processor._processor import Processor
class FrontendSimFile(h5.File):
"""
The FrontendSimFile is class to read ACMs and properties from an HDF5 file produced
@@ -95,9 +94,8 @@ class FrontendSimFile(h5.File):
"""
if self.exists(path):
return self[path]
- else:
- print(f"Path {path} does not exists in dataset")
- return None
+ print(f"Path {path} does not exists in dataset")
+ return None
def datasets(self, base_path: str, names: set, suffix: str="acm") -> ty.List[np.ndarray]:
"""Returns multiple datasets with matching names and suffix within the base path.
@@ -170,7 +168,7 @@ class Generator(Processor):
"""Construct a Generator object
Args:
- name (str): The ID of the Generator
+ name (str): The unique name of theGenerator
kwargs:
dataset (str): Path to an HDF5 file containing the output of the PAF Frontend simulator
noise (str): Names of the noise datasets to use for the time series generation
@@ -185,6 +183,7 @@ class Generator(Processor):
self.rfi = self.conf.get("rfi", [0])
self.width = self.conf.get("width", 10000)
self.fs = 0
+ self.duration = 0
self.reader = FrontendSimFile(self.dataset)
self.antennas = self.reader.nelements
self.pol = self.reader.npol
@@ -242,11 +241,12 @@ class Generator(Processor):
out[:, 0, t*nchan:(t+1)*nchan] = np.fft.ifft(channelized_array[:,:,t], axis=0).T
return out
- def plot(self, dir="", figsize=(8,4)):
+#pylint: disable=R0801
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the time series
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
sub = []
@@ -260,8 +260,8 @@ class Generator(Processor):
sub[a*self.pol+p].set_xlabel("Time [s]")
sub[a*self.pol+p].plot(taxis, self.output[a])
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
plt.show()
diff --git a/pafsim/processor/waveform.py b/pafsim/processor/waveform.py
index 45cbbfa..9578394 100644
--- a/pafsim/processor/waveform.py
+++ b/pafsim/processor/waveform.py
@@ -1,5 +1,5 @@
+from scipy import signal
import numpy as np
-import scipy.signal as signal
import matplotlib.pyplot as plt
@@ -15,7 +15,7 @@ class WaveformGenerator(Processor):
def __init__(self, name:str, **kwargs):
"""Construct a WaveformGenerator object
Args:
- name (str): The ID of the Generator
+ name (str): The unique name of theGenerator
kwargs:
fs (float): The sampling rate at which to generate the time series [Hz]. Defaults to 1000 Hz
duration (float): The duration of the signal [seconds]. Defaults to 1s
@@ -83,7 +83,7 @@ class WaveformGenerator(Processor):
o = np.zeros(self.shape)
for a in range(self.antennas):
for p in range(self.pol):
- if type(self.freq) == list:
+ if isinstance(self.freq, list):
for f in self.freq:
o[a,p] += self.gain * np.sin(2 * np.pi * f * self.t + self.phase) + self.offset
else:
@@ -125,11 +125,11 @@ class WaveformGenerator(Processor):
# def sawtooth(self):
# return self.gain * signal.sawtooth(2 * np.pi * self.period * self.t)
- def plot(self, dir="", figsize=(8,4)):
+ def plot(self, path="", figsize=(8,4)):
"""Plotting function to plot the time series
Args:
- dir (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
+ path (str, optional): If not set to "" it stores the plot in the given directory. Defaults to "".
figsize (tuple, optional): Size of the plotted figure. Defaults to (8,4).
"""
sub = []
@@ -142,8 +142,8 @@ class WaveformGenerator(Processor):
sub[a*self.pol+p].set_xlabel("Time [s]")
sub[a*self.pol+p].plot(self.t, self.output[a,p])
fig.tight_layout()
- if dir:
- fig.savefig(dir + self.name + '.png')
+ if path:
+ fig.savefig(path + self.name + '.png')
plt.close()
else:
- plt.show()
\ No newline at end of file
+ plt.show()
diff --git a/pafsim/vector.py b/pafsim/vector.py
index 326b941..ce308d6 100644
--- a/pafsim/vector.py
+++ b/pafsim/vector.py
@@ -45,8 +45,8 @@ class Vector():
Args:
path (str): The location where to store the Vector
"""
- self.path = path + self.name + '.pk1'
- with open(self.path, 'wb') as f:
+ path = path + self.name + '.pk1'
+ with open(path, 'wb') as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
def load(self, path: str):
@@ -56,4 +56,6 @@ class Vector():
path (str): The location where the vector was stored
"""
with open(path, 'rb') as f:
- self = pickle.load(f)
\ No newline at end of file
+ loaded = pickle.load(f)
+ self.__dict__.update(loaded.__dict__)
+
--
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