From 2384a4e12cb029cbd6c8595fa9f3c5a666a391da Mon Sep 17 00:00:00 2001
From: Trygve Laugstøl <trygvis@inamo.no>
Date: Wed, 2 Aug 2017 22:28:17 +0200
Subject: o Importing formatting tools.

---
 src/ee/__init__.py               | 145 +++++++++++++++++++++++++++++++++++++++
 src/ee/formatting/__init__.py    |  85 +++++++++++++++++++++++
 src/ee/tools/__init__.py         |   0
 src/ee/tools/read_ltspice_raw.py |  31 +++++++++
 4 files changed, 261 insertions(+)
 create mode 100644 src/ee/__init__.py
 create mode 100644 src/ee/formatting/__init__.py
 create mode 100644 src/ee/tools/__init__.py
 create mode 100644 src/ee/tools/read_ltspice_raw.py

(limited to 'src/ee')

diff --git a/src/ee/__init__.py b/src/ee/__init__.py
new file mode 100644
index 0000000..a22ecc6
--- /dev/null
+++ b/src/ee/__init__.py
@@ -0,0 +1,145 @@
+import numpy as np
+
+__all__ = [
+  'EeError',
+  'read_ltspice_raw'
+]
+
+class EeError(Exception):
+    pass
+
+class LtSpiceRaw(object):
+  def __init__(self, variables, values_first, values_rest):
+    self.variables = variables
+    self.values_first = values_first
+    self.values_rest = values_rest
+
+  def get_values(self, variable):
+    return self.values[variable.idx]
+
+  def get_variable(self, idx=None, expression=None):
+    if idx is not None:
+      return self.variables[idx]
+    if expression is not None:
+      v = [v for v in self.variables if v.expression == expression]
+      if len(v) != 1:
+          raise Exception('Unknown variable: ' + str(variable))
+      return v[0]
+    raise Exception('idx or expression must be given')
+
+  def to_pandas(self):
+    import pandas
+    data = []
+    for (i, v) in enumerate(self.values_first):
+      data.append(pandas.Series(v, dtype='float64'))
+
+    data = {}
+    for (i, v) in enumerate(self.values_first):
+      data[self.variables[i].expression] = v
+
+    return pandas.DataFrame(data = data)
+
+class LtSpiceVariable(object):
+  def __init__(self, idx, expression, kind):
+    self.idx = idx
+    self.expression = expression
+    self.kind = kind
+
+class LtSpiceReader(object):
+
+  def __init__(self, f):
+    self.f = f
+    self.mode = 'header'
+    self.headers = {}
+    self.variables = []
+    self.values = []
+
+  def read_header(self, line):
+    sep = line.find(':')
+    key = line[0:sep]
+    value = line[sep + 1:].strip()
+
+#    print("key='{}', value='{}'".format(key, str(value)))
+
+    if key == 'Binary':
+      self.set_binary_mode()
+    elif key == 'Variables':
+      self.no_variables = int(self.headers['No. Variables'])
+      self.mode = 'variables'
+    else:
+      self.headers[key] = value
+
+  def read_variable(self, line):
+    parts = line.split('\t')
+    idx = int(parts[1])
+    expression = parts[2]
+    kind = parts[3]
+    self.variables.append(LtSpiceVariable(idx, expression, kind))
+
+    if len(self.variables) == self.no_variables:
+      self.mode = 'header'
+
+  def set_binary_mode(self):
+    self.mode = 'binary'
+
+  def read_binary(self):
+    pos = self.f.tell()
+    no_points = int(self.headers['No. Points'])
+    no_variables = int(self.headers['No. Variables'])
+
+#    print("no_points={}, no_variables={}".format(no_points, no_variables))
+
+    if True:
+      self.values = np.zeros((no_variables, no_points), dtype='float32')
+
+      for p in range(no_points):
+        self.values[0][p] = np.fromfile(self.f, count=1, dtype='float64')
+        row = np.fromfile(self.f, count=no_variables - 1, dtype='float32');
+        for v in range(0, len(row)):
+            self.values[v + 1][p] = row[v]
+    else:
+      self.values = np.zeros((no_points, no_variables), dtype='float64')
+
+      for p in range(no_points):
+        self.values[p][0] = np.fromfile(self.f, count=1, dtype='float64')
+        row = np.fromfile(self.f, count=no_variables - 1, dtype='float32');
+        for v in range(0, len(row)):
+            self.values[p][v + 1] = row[v]
+
+  def read(self):
+    while True:
+      line = self.f.readline();
+
+      if len(line) == 0:
+        break
+
+      self.f.read(1) # The data is utf16 encoded so read the extra null byte
+
+      # if this wasn't the last line .readline() includes the newline
+      if line[-1] == '\n' or line[-1] == 0x0a:
+        line = line[:-1]
+
+#      print("len(line)={}, line={}".format(len(line), str(line)))
+      line = line.decode(encoding="utf-16")
+
+      if self.mode == 'header':
+        self.read_header(line)
+      elif self.mode == 'variables':
+        self.read_variable(line)
+
+      # The binary data can't be read with readline()
+      if self.mode == 'binary':
+#        self.f.read()
+        self.read_binary()
+        break
+
+    if len(self.variables) == 0:
+      raise Exception("Something didn't quite work when parsing file, no variables found")
+
+    return LtSpiceRaw(self.variables, self.values, [])
+
+def read_ltspice_raw(filename):
+
+  with open(filename, mode="rb") as f:
+    r = LtSpiceReader(f)
+    return r.read()
diff --git a/src/ee/formatting/__init__.py b/src/ee/formatting/__init__.py
new file mode 100644
index 0000000..3c592ef
--- /dev/null
+++ b/src/ee/formatting/__init__.py
@@ -0,0 +1,85 @@
+#import sympy as sp
+import math
+from ee import EeError
+
+__all__ = [
+    'e6', 'e12', 'e24', 'e48', 'e96', 'e192',
+    'eng_str',
+]
+
+#class Symbols(object):
+#    def __init__(self):
+#        self.tau = sp.symbols('tau'),
+#        self.r = sp.symbols('r'),
+#        self.c = sp.symbols('c')
+#
+#_s = Symbols()
+
+class ESeries(object):
+    def __init__(self, series):
+        self.series = series
+
+    def closest(self, value):
+        e = math.floor(math.log10(value))
+        value = float(value/(10**e))
+
+        return min(self.series, key=lambda v: abs(v - value)) * 10**e
+
+# https://en.wikipedia.org/wiki/E-series_of_preferred_numbers
+_e_series_24 = [1.0, 1.1, 1.2, 1.3, 1.5, 1.6, 1.8, 2.0, 2.2, 2.4, 2.7, 3.0, 3.3, 3.6, 3.9, 4.3, 4.7, 5.1, 5.6, 6.2, 6.8, 7.5, 8.2, 9.1]
+_e_series_12 = [v for i, v in enumerate(_e_series_24) if i % 2 == 0]
+_e_series_6 = [v for i, v in enumerate(_e_series_24) if i % 4 == 0]
+
+_e_series_192 = [
+  1.00, 1.01, 1.02, 1.04, 1.05, 1.06, 1.07, 1.09, 1.10, 1.11,
+  1.13, 1.14, 1.15, 1.17, 1.18, 1.20, 1.21, 1.23, 1.24, 1.26,
+  1.27, 1.29, 1.30, 1.32, 1.33, 1.35, 1.37, 1.38, 1.40, 1.42,
+  1.43, 1.45, 1.47, 1.49, 1.50, 1.52, 1.54, 1.56, 1.58, 1.60,
+  1.62, 1.64, 1.65, 1.67, 1.69, 1.72, 1.74, 1.76, 1.78, 1.80,
+  1.82, 1.84, 1.87, 1.89, 1.91, 1.93, 1.96, 1.98, 2.00, 2.03,
+  2.05, 2.08, 2.10, 2.13, 2.15, 2.18, 2.21, 2.23, 2.26, 2.29,
+  2.32, 2.34, 2.37, 2.40, 2.43, 2.46, 2.49, 2.52, 2.55, 2.58,
+  2.61, 2.64, 2.67, 2.71, 2.74, 2.77, 2.80, 2.84, 2.87, 2.91,
+  2.94, 2.98, 3.01, 3.05, 3.09, 3.12, 3.16, 3.20, 3.24, 3.28,
+  3.32, 3.36, 3.40, 3.44, 3.48, 3.52, 3.57, 3.61, 3.65, 3.70,
+  3.74, 3.79, 3.83, 3.88, 3.92, 3.97, 4.02, 4.07, 4.12, 4.17,
+  4.22, 4.27, 4.32, 4.37, 4.42, 4.48, 4.53, 4.59, 4.64, 4.70,
+  4.75, 4.81, 4.87, 4.93, 4.99, 5.05, 5.11, 5.17, 5.23, 5.30,
+  5.36, 5.42, 5.49, 5.56, 5.62, 5.69, 5.76, 5.83, 5.90, 5.97,
+  6.04, 6.12, 6.19, 6.26, 6.34, 6.42, 6.49, 6.57, 6.65, 6.73,
+  6.81, 6.90, 6.98, 7.06, 7.15, 7.23, 7.32, 7.41, 7.50, 7.59,
+  7.68, 7.77, 7.87, 7.96, 8.06, 8.16, 8.25, 8.35, 8.45, 8.56,
+  8.66, 8.76, 8.87, 8.98, 9.09, 9.20, 9.31, 9.42, 9.53, 9.65,
+  9.76, 9.88,
+]
+_e_series_96 = [v for i, v in enumerate(_e_series_192) if i % 2 == 0]
+_e_series_48 = [v for i, v in enumerate(_e_series_192) if i % 4 == 0]
+
+e6 = ESeries(_e_series_6)
+e12 = ESeries(_e_series_12)
+e24 = ESeries(_e_series_24)
+e48 = ESeries(_e_series_48)
+e96 = ESeries(_e_series_96)
+e192 = ESeries(_e_series_192)
+
+def e_series_find_closest(value):
+    e = math.floor(math.log10(value))
+    value = float(value/(10**e))
+
+    return min(series, key=lambda v: abs(v - value)) * 10**e
+
+def eng_str(value):
+    big = ['', ' k', ' M', ' G', ' T']
+    e_floor = math.floor(math.log10(value))
+
+    div3 = int(math.floor(e_floor/3))
+    suffix = big[div3]
+    scale = 10**(div3*3)
+    scaled = value/scale
+
+    if scaled == int(scaled):
+        return "{:1.0f}{}".format(scaled, suffix)
+    else:
+        return "{:1.1f}{}".format(scaled, suffix)
+
+#    return "scaled={:>9}, div3={}, floor(e)={}, scale={}, suffix={}".format(str(scaled), div3, e_floor, scale, suffix)
diff --git a/src/ee/tools/__init__.py b/src/ee/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/ee/tools/read_ltspice_raw.py b/src/ee/tools/read_ltspice_raw.py
new file mode 100644
index 0000000..279fe73
--- /dev/null
+++ b/src/ee/tools/read_ltspice_raw.py
@@ -0,0 +1,31 @@
+import ee
+import sys
+
+raw = ee.read_ltspice_raw(sys.argv[1])
+
+#print("Variables:")
+#for i, v in enumerate(raw.variables):
+#  print("{:2}: kind: {:20} expression: {}".format(i, v.kind, v.expression))
+
+#for i, v in enumerate(raw.variables):
+#  print("{:2}: kind: {:20} expression: {}".format(i, v.kind, v.expression))
+#  for p in raw.values[i]:
+#    print("  {}".format(p))
+
+x = raw.get_variable(idx = 0)
+y = raw.get_variable(expression = 'V(load)')
+xs = raw.get_values(y)
+
+import matplotlib
+matplotlib.use('Agg') 
+
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FuncFormatter, MaxNLocator
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(xs)
+ax.set_xlabel(x.expression)
+ax.set_ylabel(y.expression)
+
+with open("ltspice.png", "wb") as f:
+  plt.savefig(f, format="png")
-- 
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