Merge 2c20a47 into f4e4b91

.travis.yml

@@ -22,7 +22,7 @@ before_install:
   - hash -r
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
-  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION pip atlas numpy matplotlib
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION pip atlas numpy matplotlib gmpy2
   - source activate test-environment
   - pip install pyflakes coverage coveralls sympy
 install:

qctoolkit/_program/_loop.py

@@ -3,7 +3,6 @@
 from collections import defaultdict, deque
 from copy import deepcopy
 from enum import Enum
-from fractions import Fraction
 import warnings
 
 import numpy as np
@@ -122,14 +121,12 @@ def repetition_count(self, val) -> None:
     def unroll(self) -> None:
         if self.is_leaf():
             raise RuntimeError('Leaves cannot be unrolled')
-        for i, e in enumerate(self.parent):
-            if id(e) == id(self):
-                self.parent[i:i+1] = (child.copy_tree_structure(new_parent=self.parent)
-                                      for _ in range(self.repetition_count)
-                                      for child in self)
-                self.parent.assert_tree_integrity()
-                return
-        raise Exception('self not found in parent')
+
+        i = self.parent_index
+        self.parent[i:i+1] = (child.copy_tree_structure(new_parent=self.parent)
+                              for _ in range(self.repetition_count)
+                              for child in self)
+        self.parent.assert_tree_integrity()
 
     def __setitem__(self, idx, value):
         super().__setitem__(idx, value)
@@ -522,7 +519,7 @@ def _is_compatible(program: Loop, min_len: int, quantum: int, sample_rate: TimeT
             return _CompatibilityLevel.action_required
 
 
-def _make_compatible(program: Loop, min_len: int, quantum: int, sample_rate: Fraction) -> None:
+def _make_compatible(program: Loop, min_len: int, quantum: int, sample_rate: TimeType) -> None:
 
     if program.is_leaf():
         program.waveform = to_waveform(program.copy_tree_structure())
@@ -549,7 +546,7 @@ def _make_compatible(program: Loop, min_len: int, quantum: int, sample_rate: Fra
                     _make_compatible(sub_program, min_len, quantum, sample_rate)
 
 
-def make_compatible(program: Loop, minimal_waveform_length: int, waveform_quantum: int, sample_rate: Fraction):
+def make_compatible(program: Loop, minimal_waveform_length: int, waveform_quantum: int, sample_rate: TimeType):
     comp_level = _is_compatible(program,
                                 min_len=minimal_waveform_length,
                                 quantum=waveform_quantum,

qctoolkit/_program/waveforms.py

@@ -10,7 +10,6 @@
 from typing import Union, Set, Sequence, NamedTuple, Tuple, Any, Iterable, FrozenSet, Optional
 
 import numpy as np
-import pandas as pd
 
 from qctoolkit import ChannelID
 from qctoolkit.utils import checked_int_cast

qctoolkit/pulses/plotting.py

@@ -128,7 +128,7 @@ def _render_instruction_block(sequence: AbstractInstructionBlock,
     sample_count = total_time * sample_rate + 1
     if not float(sample_count).is_integer():
         warnings.warn('Sample count not whole number. Casted to integer.')
-    times = np.linspace(0, total_time, num=int(sample_count), dtype=float)
+    times = np.linspace(0, float(total_time), num=int(sample_count), dtype=float)
     # move the last sample inside the waveform
     times[-1] = np.nextafter(times[-1], times[-2])
 

qctoolkit/utils/tree.py

@@ -124,6 +124,10 @@ def children(self: _NodeType) -> List[_NodeType]:
     def parent(self: _NodeType) -> Union[None, _NodeType]:
         return self.__parent()
 
+    @property
+    def parent_index(self) -> int:
+        return self.__parent_index
+
     def get_root(self: _NodeType) -> _NodeType:
         if self.parent:
             return self.parent.get_root()

qctoolkit/utils/types.py

@@ -2,7 +2,7 @@
 import abc
 import inspect
 import numbers
-import fractions
+import gmpy2
 
 import numpy
 
@@ -11,11 +11,12 @@
 
 MeasurementWindow = typing.Tuple[str, numbers.Real, numbers.Real]
 ChannelID = typing.Union[str, int]
-TimeType = fractions.Fraction
+TimeType = gmpy2.mpq
 
 
 def time_from_float(time: float, absolute_error: float=1e-12) -> TimeType:
-    return fractions.Fraction(time).limit_denominator(int(1/absolute_error))
+    # gmpy2 is at least an order of magnitude faster than fractions.Fraction
+    return gmpy2.mpq(gmpy2.f2q(time, absolute_error))
 
 
 class DocStringABCMeta(abc.ABCMeta):

setup.py

@@ -21,7 +21,7 @@
       package_dir={'qctoolkit': 'qctoolkit'},
       packages=packages,
       tests_require=['pytest'],
-      install_requires=['sympy>=1.1.1', 'numpy', 'pandas', 'cached_property'] + requires_typing,
+      install_requires=['sympy>=1.1.1', 'numpy', 'cached_property', 'gmpy2'] + requires_typing,
       extras_require={
           'testing': ['pytest'],
           'plotting': ['matplotlib'],

tests/_program/transformation_tests.py

@@ -1,7 +1,6 @@
 import unittest
 from unittest import mock
 
-import pandas as pd
 import numpy as np
 
 from qctoolkit._program.transformation import LinearTransformation, Transformation, IdentityTransformation,\
@@ -146,9 +145,7 @@ def test_singleton(self):
 
     def test_call(self):
         time = np.arange(12)
-        data = (np.arange(12.) + 1).reshape((3, 4))
-        data = pd.DataFrame(data, index=list('abc'))
-
+        data = dict(zip('abc',(np.arange(12.) + 1).reshape((3, 4))))
         self.assertIs(IdentityTransformation()(time, data), data)
 
     def test_output_channels(self):
@@ -210,10 +207,10 @@ def test_call(self):
         time = np.arange(12)
         data = (np.arange(12.) + 1).reshape((3, 4))
 
-        data_in = pd.DataFrame(data, index=list('abc'))
-        data_0 = data_in + 42
-        data_1 = data_0 + 42
-        data_2 = data_1 + 42
+        data_in = dict(zip('abc', data))
+        data_0 = dict(zip('abc', data + 42))
+        data_1 = dict(zip('abc', data + 2*42))
+        data_2 = dict(zip('abc', data + 3*42))
         with mock.patch('tests._program.transformation_tests.TransformationStub.__call__',
                         side_effect=[data_0, data_1, data_2]) as call:
             outs = chained(time, data_in)