From 263149799d722023b2f7ab1fe653fbdebb2fe894 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Fri, 13 May 2022 17:34:10 +0200 Subject: [PATCH 01/18] Bump to v0.6.0.dev --- VERSION.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION.txt b/VERSION.txt index a918a2aa1..b8aacd3fb 100644 --- a/VERSION.txt +++ b/VERSION.txt @@ -1 +1 @@ -0.6.0 +0.6.0.dev From 1739bfd7427703c71ca78f1229597fcccdd27935 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 6 Jun 2022 09:55:06 +0200 Subject: [PATCH 02/18] Small improvements to the docs (#375) * Small improvements to the docs * Renaming the first tutorial --- docs/source/installation.rst | 3 +- docs/source/intro_rydberg_blockade.ipynb | 130 +++++++---------------- 2 files changed, 40 insertions(+), 93 deletions(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index ff19d6e67..b28a29554 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -37,6 +37,7 @@ Bear in mind that your installation will track the contents of your local Pulser repository folder, so if you checkout a different branch (e.g. ``master``), your installation will change accordingly. -If you want to install the development requirements, follow up by running: :: +If you want to install the development requirements, stay inside the same ``Pulser`` +directory and follow up by running: :: pip install -r requirements.txt diff --git a/docs/source/intro_rydberg_blockade.ipynb b/docs/source/intro_rydberg_blockade.ipynb index 38f3fb929..c823ee46c 100644 --- a/docs/source/intro_rydberg_blockade.ipynb +++ b/docs/source/intro_rydberg_blockade.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# A First Look: The Rydberg Blockade" + "# Quickstart: The Rydberg Blockade" ] }, { @@ -27,64 +27,54 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With pulser, it is easy to define a **Register** consisting of any arrangement of atoms in a quantum processor:" + "With ``pulser``, it is easy to define a ``Register`` consisting of any arrangement of atoms in a quantum processor. For example, we can generate a register with hexagonal shape:" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "from pulser import Register\n", "from pulser.devices import Chadoq2\n", - "import numpy as np\n", "\n", - "qubits = np.loadtxt(\"files/ml_coords\")\n", - "ml_reg = Register.from_coordinates(qubits)\n", - "ml_reg.rotate(90)\n", - "ml_reg.draw(with_labels=False)" + "layers = 3\n", + "reg = Register.hexagon(layers)\n", + "reg.draw(with_labels=False)" ] }, { + "attachments": { + "download%20%282%29.png": { + "image/png": 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" + } + }, "cell_type": "markdown", "metadata": {}, "source": [ - "It is also simple to create and design **Pulses** that will act on the atom array:" + "In fact, we can place the atoms in arbitrary positions by specifying the positions of each one. As an exotic example, here is a picture of the Gioconda as a register of neutral atoms made using Pulser:\n", + "\n", + "![download%20%282%29.png](attachment:download%20%282%29.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also simple to create and design a ``Pulse`` that will act on the atom array:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "import numpy as np\n", + "\n", "from pulser import Pulse\n", "from pulser.waveforms import RampWaveform, BlackmanWaveform\n", "\n", @@ -99,37 +89,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Each pulse acts on a set of atoms at a certain moment of time. The entire **Sequence** is stored by Pulser and can then be either simulated or sent to a real device. Below is the example of a sequence sending the same $\\pi$-pulse to two atoms, sequentially, using the same channel." + "Each pulse acts on a set of atoms at a certain moment of time. The entire ``Sequence`` is stored by Pulser and can then be either simulated or sent to a real device. Below is the example of a sequence sending the same $\\pi$-pulse to two atoms, sequentially, using the same channel." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pulser import Sequence\n", "\n", @@ -160,7 +127,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Rydberg atoms are a prominent architecture for exploring condensed matter physics and quantum information processing. For example, one can use Pulser to write a sequence of succesive $\\pi$-pulses on a two atom system, each one coupling the atom to its excited Rydberg state. This will allow us to study the *Rydberg Blockade* effect, using Pulser's **Simulation** library:\n", + "Rydberg atoms are a prominent architecture for exploring condensed matter physics and quantum information processing. For example, one can use Pulser to write a sequence of succesive $\\pi$-pulses on a two atom system, each one coupling the atom to its excited Rydberg state. This will allow us to study the *Rydberg Blockade* effect, using the dedicated ``pulser_simulation`` library:\n", "\n", "The presence of the van der Waals interaction when both atoms are in the Rydberg state, prevents the collective ground state $|gg\\rangle$ to couple to $|rr\\rangle$, which is shifted out of resonance. " ] @@ -183,17 +150,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "...Simulation Complete!\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from pulser_simulation import Simulation\n", @@ -234,22 +193,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i, R in enumerate(distances):\n", " plt.plot(data[i], label=f\"R={R}\")\n", @@ -286,7 +232,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.3" } }, "nbformat": 4, From b551e406a0369daceb2bb2d082977d6b6eab36cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 6 Jun 2022 17:58:44 +0200 Subject: [PATCH 03/18] Updating the installation page (#377) * Updating the installation page * Adding warning box --- docs/source/installation.rst | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index b28a29554..53274c563 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -1,13 +1,15 @@ Installation ============== -**Note**: Pulser v0.6 introduced a split of the ``pulser`` package that prevents -it from being correctly upgraded. If you have an older version of ``pulser`` installed -and wish to upgrade, make sure to uninstall it first by running ``pip uninstall pulser`` -before proceeding to any of the steps below. +.. warning:: + Pulser v0.6 introduced a split of the ``pulser`` package that prevents + it from being correctly upgraded. If you have an older version of ``pulser`` installed + and wish to upgrade, make sure to uninstall it first by running: :: + + pip uninstall pulser + + before proceeding to any of the steps below. -Stable version ------------------ To install the latest release of ``pulser``, have Python 3.7.0 or higher installed, then use ``pip``: :: @@ -22,12 +24,12 @@ If you wish to install only the core ``pulser`` features, you can instead run: : pip install pulser-core -Latest version ---------------- -For the latest version of Pulser, you can install Pulser from source by +Development version +-------------------- +For the development version of Pulser, you can install Pulser from source by cloning the `Pulser Github repository `_, and entering your freshly created ``Pulser`` directory. There, you'll checkout -the ``develop`` branch - which holds the latest (unstable) version of Pulser - +the ``develop`` branch - which holds the development (unstable) version of Pulser - and install from source by running: :: git checkout develop @@ -37,7 +39,7 @@ Bear in mind that your installation will track the contents of your local Pulser repository folder, so if you checkout a different branch (e.g. ``master``), your installation will change accordingly. -If you want to install the development requirements, stay inside the same ``Pulser`` +If you want to install the development requirements, stay inside the same ``Pulser`` directory and follow up by running: :: pip install -r requirements.txt From 2b49cf4b6156ed3724777fcc56b08bf07108edfe Mon Sep 17 00:00:00 2001 From: WingCode Date: Fri, 17 Jun 2022 14:35:14 +0530 Subject: [PATCH 04/18] Add sphinx typehints auto generation [unitaryhack] (#376) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Added sphinx dependency for typehints auto generation * Removed type from docstring since it will be inferred from typehint * Fix missing automated typehints by standardizing keyword arg to * Fill docstrings line. * Fix overflowing docstring line, missing type annotations available from __future__ by bumping docstring build to python 3.9 * Fix python version bump. * Remove return type from docstring since it will be autogenerated now onwards. * Remove unnecessary typestrings from docstrings, fix docstring not generating by removing `*`. * Fix typehints_defaults key name * Fix black error, move config key to autodocs config position in the file * Remove return type from docstring since it will be autogenerated now onwards. Co-authored-by: Henrique Silvério --- .readthedocs.yml | 8 +- docs/requirements.txt | 1 + docs/source/conf.py | 3 +- pulser-core/pulser/_seq_drawer.py | 20 +-- pulser-core/pulser/channels.py | 34 ++-- pulser-core/pulser/devices/_device_datacls.py | 18 +-- pulser-core/pulser/json/utils.py | 2 +- pulser-core/pulser/parametrized/paramobj.py | 2 +- pulser-core/pulser/parametrized/variable.py | 6 +- pulser-core/pulser/pulse.py | 34 ++-- pulser-core/pulser/register/_patterns.py | 16 +- pulser-core/pulser/register/_reg_drawer.py | 8 +- pulser-core/pulser/register/base_register.py | 22 +-- pulser-core/pulser/register/mappable_reg.py | 14 +- pulser-core/pulser/register/register.py | 74 ++++----- pulser-core/pulser/register/register3d.py | 48 +++--- .../pulser/register/register_layout.py | 28 ++-- .../pulser/register/special_layouts.py | 38 ++--- pulser-core/pulser/sampler/noise_model.py | 4 +- pulser-core/pulser/sampler/sampler.py | 10 +- pulser-core/pulser/sequence.py | 149 ++++++++---------- pulser-core/pulser/waveforms.py | 94 +++++------ pulser-simulation/pulser_simulation/noises.py | 14 +- .../pulser_simulation/simconfig.py | 18 +-- .../pulser_simulation/simresults.py | 114 +++++++------- .../pulser_simulation/simulation.py | 44 +++--- 26 files changed, 402 insertions(+), 421 deletions(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 69ee988bc..716d9c9b9 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -5,13 +5,17 @@ # Required version: 2 +build: + os: ubuntu-22.04 + tools: + python: "3.9" + # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py -# Optionally set the version of Python and requirements required to build your docs +# Optionally declare the Python requirements required to build your docs python: - version: 3.8 install: - requirements: docs/requirements.txt - requirements: requirements.txt diff --git a/docs/requirements.txt b/docs/requirements.txt index 9186a06fc..5271ea8f1 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,6 +1,7 @@ # For generating documentation. Sphinx sphinx-rtd-theme # documentation theme +sphinx_autodoc_typehints nbsphinx nbsphinx-link diff --git a/docs/source/conf.py b/docs/source/conf.py index 56bc9ae21..86bba145c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -39,6 +39,7 @@ "sphinx.ext.autodoc", "sphinx.ext.mathjax", "sphinx.ext.napoleon", + "sphinx_autodoc_typehints", ] # Add any paths that contain templates here, relative to this directory. @@ -56,7 +57,7 @@ autosummary_generate = True autodoc_member_order = "bysource" autodoc_typehints = "none" - +typehints_defaults = "comma" # -- Options for HTML output ------------------------------------------------- diff --git a/pulser-core/pulser/_seq_drawer.py b/pulser-core/pulser/_seq_drawer.py index b87d26eaf..ac675c0bf 100644 --- a/pulser-core/pulser/_seq_drawer.py +++ b/pulser-core/pulser/_seq_drawer.py @@ -33,10 +33,10 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: """Collects the whole sequence data for plotting. Args: - seq (pulser.Sequence): The input sequence of operations on a device. + seq: The input sequence of operations on a device. Returns: - dict: The data to plot. + The data to plot. """ # The minimum time axis length is 100 ns total_duration = max(seq.get_duration(), 100) @@ -117,23 +117,23 @@ def draw_sequence( """Draws the entire sequence. Args: - seq (pulser.Sequence): The input sequence of operations on a device. - sampling_rate (float): Sampling rate of the effective pulse used by + seq: The input sequence of operations on a device. + sampling_rate: Sampling rate of the effective pulse used by the solver. If present, plots the effective pulse alongside the input pulse. - draw_phase_area (bool): Whether phase and area values need to be shown + draw_phase_area: Whether phase and area values need to be shown as text on the plot, defaults to False. - draw_interp_pts (bool): When the sequence has pulses with waveforms of + draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation on top of the respective waveforms (defaults to True). - draw_phase_shifts (bool): Whether phase shift and reference information + draw_phase_shifts: Whether phase shift and reference information should be added to the plot, defaults to False. - draw_register (bool): Whether to draw the register before the pulse + draw_register: Whether to draw the register before the pulse sequence, with a visual indication (square halo) around the qubits masked by the SLM, defaults to False. - draw_input(bool): Draws the programmed pulses on the channels, defaults + draw_input: Draws the programmed pulses on the channels, defaults to True. - draw_modulation(bool): Draws the expected channel output, defaults to + draw_modulation: Draws the expected channel output, defaults to False. If the channel does not have a defined 'mod_bandwidth', this is skipped unless 'draw_input=False'. """ diff --git a/pulser-core/pulser/channels.py b/pulser-core/pulser/channels.py index 1dd230b62..782434fe3 100644 --- a/pulser-core/pulser/channels.py +++ b/pulser-core/pulser/channels.py @@ -103,15 +103,15 @@ def Local( """Initializes the channel with local addressing. Args: - max_abs_detuning (float): Maximum possible detuning (in rad/µs), in + max_abs_detuning: Maximum possible detuning (in rad/µs), in absolute value. - max_amp(float): Maximum pulse amplitude (in rad/µs). - phase_jump_time (int): Time taken to change the phase between + max_amp: Maximum pulse amplitude (in rad/µs). + phase_jump_time: Time taken to change the phase between consecutive pulses (in ns). min_retarget_interval (int): Minimum time required between two target instructions (in ns). - fixed_retarget_t (int): Time taken to change the target (in ns). - max_targets (int): Maximum number of atoms the channel can target + fixed_retarget_t: Time taken to change the target (in ns). + max_targets: Maximum number of atoms the channel can target simultaneously. """ return cls( @@ -136,10 +136,10 @@ def Global( """Initializes the channel with global addressing. Args: - max_abs_detuning (float): Maximum possible detuning (in rad/µs), in + max_abs_detuning: Maximum possible detuning (in rad/µs), in absolute value. - max_amp(float): Maximum pulse amplitude (in rad/µs). - phase_jump_time (int): Time taken to change the phase between + max_amp: Maximum pulse amplitude (in rad/µs). + phase_jump_time: Time taken to change the phase between consecutive pulses (in ns). """ return cls( @@ -150,10 +150,10 @@ def validate_duration(self, duration: int) -> int: """Validates and adapts the duration of an instruction on this channel. Args: - duration (int): The duration to validate. + duration: The duration to validate. Returns: - int: The duration, potentially adapted to the channels specs. + The duration, potentially adapted to the channels specs. """ try: _duration = int(duration) @@ -189,12 +189,12 @@ def modulate( """Modulates the input according to the channel's modulation bandwidth. Args: - input_samples (np.ndarray): The samples to modulate. - keep_ends (bool): Assume the end values of the samples were kept + input_samples: The samples to modulate. + keep_ends: Assume the end values of the samples were kept constant (i.e. there is no ramp from zero on the ends). Returns: - np.ndarray: The modulated output signal. + The modulated output signal. """ if not self.mod_bandwidth: warnings.warn( @@ -230,14 +230,14 @@ def calc_modulation_buffer( """Calculates the minimal buffers needed around a modulated waveform. Args: - input_samples (ArrayLike): The input samples. - mod_samples (ArrayLike): The modulated samples. Must be of size + input_samples: The input samples. + mod_samples: The modulated samples. Must be of size ``len(input_samples) + 2 * self.rise_time``. - max_allowed_diff (float): The maximum allowed difference between + max_allowed_diff: The maximum allowed difference between the input and modulated samples at the end points. Returns: - tuple[int, int]: The minimum buffer times at the start and end of + The minimum buffer times at the start and end of the samples, in ns. """ if not self.mod_bandwidth: diff --git a/pulser-core/pulser/devices/_device_datacls.py b/pulser-core/pulser/devices/_device_datacls.py index 68a0ef7f3..34a21ff77 100644 --- a/pulser-core/pulser/devices/_device_datacls.py +++ b/pulser-core/pulser/devices/_device_datacls.py @@ -100,7 +100,7 @@ def change_rydberg_level(self, ryd_lvl: int) -> None: """Changes the Rydberg level used in the Device. Args: - ryd_lvl(int): the Rydberg level to use (between 50 and 100). + ryd_lvl: the Rydberg level to use (between 50 and 100). Note: Modifications to the `rydberg_level` attribute only affect the @@ -117,10 +117,10 @@ def rydberg_blockade_radius(self, rabi_frequency: float) -> float: """Calculates the Rydberg blockade radius for a given Rabi frequency. Args: - rabi_frequency(float): The Rabi frequency, in rad/µs. + rabi_frequency: The Rabi frequency, in rad/µs. Returns: - float: The rydberg blockade radius, in μm. + The rydberg blockade radius, in μm. """ return (self.interaction_coeff / rabi_frequency) ** (1 / 6) @@ -128,10 +128,10 @@ def rabi_from_blockade(self, blockade_radius: float) -> float: """The maximum Rabi frequency value to enforce a given blockade radius. Args: - blockade_radius(float): The Rydberg blockade radius, in µm. + blockade_radius: The Rydberg blockade radius, in µm. Returns: - float: The maximum rabi frequency value, in rad/µs. + The maximum rabi frequency value, in rad/µs. """ return self.interaction_coeff / blockade_radius**6 @@ -139,7 +139,7 @@ def validate_register(self, register: BaseRegister) -> None: """Checks if 'register' is compatible with this device. Args: - register(BaseRegister): The Register to validate. + register: The Register to validate. """ if not isinstance(register, BaseRegister): raise TypeError( @@ -167,7 +167,7 @@ def validate_layout(self, layout: RegisterLayout) -> None: """Checks if a register layout is compatible with this device. Args: - layout(RegisterLayout): The RegisterLayout to validate. + layout: The RegisterLayout to validate. """ if not isinstance(layout, RegisterLayout): raise TypeError("'layout' must be a RegisterLayout instance.") @@ -184,8 +184,8 @@ def validate_pulse(self, pulse: Pulse, channel_id: str) -> None: """Checks if a pulse can be executed on a specific device channel. Args: - pulse (Pulse): The pulse to validate. - channel_id (str): The channel ID used to index the chosen channel + pulse: The pulse to validate. + channel_id: The channel ID used to index the chosen channel on this device. """ ch = self.channels[channel_id] diff --git a/pulser-core/pulser/json/utils.py b/pulser-core/pulser/json/utils.py index dfd4e395a..c3e59d92e 100644 --- a/pulser-core/pulser/json/utils.py +++ b/pulser-core/pulser/json/utils.py @@ -45,7 +45,7 @@ def obj_to_dict( creation. Returns: - dict: The dictionary encoding the object. + The dictionary encoding the object. """ d = { "_build": _build, diff --git a/pulser-core/pulser/parametrized/paramobj.py b/pulser-core/pulser/parametrized/paramobj.py index 867565e8c..0d9335eb6 100644 --- a/pulser-core/pulser/parametrized/paramobj.py +++ b/pulser-core/pulser/parametrized/paramobj.py @@ -133,7 +133,7 @@ class ParamObj(Parametrized, OpSupport): When called, a ParamObj instance returns `cls(*args, **kwargs)`. Args: - cls (callable): The object to call. Usually it's a class that's + cls: The object to call. Usually it's a class that's instantiated when called. args: The args for calling `cls`. kwargs: The kwargs for calling `cls`. diff --git a/pulser-core/pulser/parametrized/variable.py b/pulser-core/pulser/parametrized/variable.py index 9dfa3702e..773cf9702 100644 --- a/pulser-core/pulser/parametrized/variable.py +++ b/pulser-core/pulser/parametrized/variable.py @@ -32,10 +32,10 @@ class Variable(Parametrized, OpSupport): """A variable for parametrized sequence building. Args: - name (str): Unique name for the variable. - dtype (type): Type of the variable's content. Supports `float` and + name: Unique name for the variable. + dtype: Type of the variable's content. Supports `float` and `int`. - size (int=1): The number of values stored. Defaults to a single value. + size: The number of values stored. Defaults to a single value. """ name: str diff --git a/pulser-core/pulser/pulse.py b/pulser-core/pulser/pulse.py index e800bc7a5..01f55a546 100644 --- a/pulser-core/pulser/pulse.py +++ b/pulser-core/pulser/pulse.py @@ -49,10 +49,10 @@ class Pulse: :math:`\delta`, also in rad/µs. Args: - amplitude (Waveform): The pulse amplitude waveform. - detuning (Waveform): The pulse detuning waveform. - phase (float): The pulse phase (in radians). - post_phase_shift (float, default=0.): Optionally lets you add a phase + amplitude: The pulse amplitude waveform. + detuning: The pulse detuning waveform. + phase: The pulse phase (in radians). + post_phase_shift: Optionally lets you add a phase shift(in rads) immediately after the end of the pulse. This allows for enconding of arbitrary single-qubit gates into a single pulse (see ``Sequence.phase_shift()`` for more information). @@ -119,10 +119,10 @@ def ConstantDetuning( """Creates a Pulse with an amplitude waveform and a constant detuning. Args: - amplitude (Waveform): The pulse amplitude waveform. - detuning (float): The detuning value (in rad/µs). - phase (float): The pulse phase (in radians). - post_phase_shift (float, default=0.): Optionally lets you add a + amplitude: The pulse amplitude waveform. + detuning: The detuning value (in rad/µs). + phase: The pulse phase (in radians). + post_phase_shift: Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ detuning_wf = ConstantWaveform( @@ -142,10 +142,10 @@ def ConstantAmplitude( """Pulse with a constant amplitude and a detuning waveform. Args: - amplitude (float): The pulse amplitude value (in rad/µs). - detuning (Waveform): The pulse detuning waveform. - phase (float): The pulse phase (in radians). - post_phase_shift (float, default=0.): Optionally lets you add a + amplitude: The pulse amplitude value (in rad/µs). + detuning: The pulse detuning waveform. + phase: The pulse phase (in radians). + post_phase_shift: Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ amplitude_wf = ConstantWaveform( @@ -166,11 +166,11 @@ def ConstantPulse( """Pulse with a constant amplitude and a constant detuning. Args: - duration (int): The pulse duration (in multiples of 4 ns). - amplitude (float): The pulse amplitude value (in rad/µs). - detuning (float): The detuning value (in rad/µs). - phase (float): The pulse phase (in radians). - post_phase_shift (float, default=0.): Optionally lets you add a + duration: The pulse duration (in multiples of 4 ns). + amplitude: The pulse amplitude value (in rad/µs). + detuning: The detuning value (in rad/µs). + phase: The pulse phase (in radians). + post_phase_shift: Optionally lets you add a phase shift (in rads) immediately after the end of the pulse. """ amplitude_wf = ConstantWaveform(duration, amplitude) diff --git a/pulser-core/pulser/register/_patterns.py b/pulser-core/pulser/register/_patterns.py index afb45025f..6b622e783 100644 --- a/pulser-core/pulser/register/_patterns.py +++ b/pulser-core/pulser/register/_patterns.py @@ -20,11 +20,11 @@ def square_rect(rows: int, columns: int) -> np.ndarray: """A square lattice pattern in a rectangular shape. Args: - rows(int): Number of rows. - columns(int): Number of columns. + rows: Number of rows. + columns: Number of columns. Returns: - np.ndarray: The coordinates of the points in the pattern. + The coordinates of the points in the pattern. """ points = np.mgrid[:columns, :rows].transpose().reshape(-1, 2) # Centering @@ -36,11 +36,11 @@ def triangular_rect(rows: int, columns: int) -> np.ndarray: """A triangular lattice pattern in a rectangular shape. Args: - rows(int): Number of rows. - columns(int): Number of columns. + rows: Number of rows. + columns: Number of columns. Returns: - np.ndarray: The coordinates of the points in the pattern. + The coordinates of the points in the pattern. """ points = square_rect(rows, columns) points[:, 0] += 0.5 * np.mod(points[:, 1], 2) @@ -52,11 +52,11 @@ def triangular_hex(n_points: int) -> np.ndarray: """A triangular lattice pattern in an hexagonal shape. Args: - n_points(int): The number of points in the pattern. + n_points: The number of points in the pattern. Returns: - np.ndarray: The coordinates of the points in the pattern. + The coordinates of the points in the pattern. """ # y coordinates of the top vertex of a triangle crest_y = np.sqrt(3) / 2.0 diff --git a/pulser-core/pulser/register/_reg_drawer.py b/pulser-core/pulser/register/_reg_drawer.py index e8aeaa67c..d4b4d714d 100644 --- a/pulser-core/pulser/register/_reg_drawer.py +++ b/pulser-core/pulser/register/_reg_drawer.py @@ -361,13 +361,13 @@ def _draw_checks( """Checks common in all register drawings. Args: - n_atoms(int): Number of atoms in the register. - blockade_radius(float, default=None): The distance (in μm) between + n_atoms: Number of atoms in the register. + blockade_radius: The distance (in μm) between atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the + draw_half_radius: Whether or not to draw the half the blockade radius surrounding each atoms. If `True`, requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the + draw_graph: Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. """ diff --git a/pulser-core/pulser/register/base_register.py b/pulser-core/pulser/register/base_register.py index f3a28b07d..4a3420083 100644 --- a/pulser-core/pulser/register/base_register.py +++ b/pulser-core/pulser/register/base_register.py @@ -113,11 +113,11 @@ def find_indices(self, id_list: abcSequence[QubitId]) -> list[int]: and ``phase_shift_index``. Args: - id_list (typing::Sequence[QubitId]): IDs of the qubits to find. + id_list: IDs of the qubits to find. Returns: - list[int]: Indices of the qubits to denote, only valid for the - given mapping. + Indices of the qubits to denote, only valid for the + given mapping. """ if not set(id_list) <= set(self.qubit_ids): raise ValueError( @@ -137,20 +137,20 @@ def from_coordinates( """Creates the register from an array of coordinates. Args: - coords (ndarray): The coordinates of each qubit to include in the + coords: The coordinates of each qubit to include in the register. - Keyword args: - center(defaut=True): Whether or not to center the entire array - around the origin. - prefix (str): The prefix for the qubit ids. If defined, each qubit + Args: + center: Whether or not to center the entire array around the + origin. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). - labels (ArrayLike): The list of qubit ids. If defined, each qubit - id will be set to the corresponding value. + labels: The list of qubit ids. If defined, each qubit id will be + set to the corresponding value. Returns: - Register: A register with qubits placed on the given coordinates. + A register with qubits placed on the given coordinates. """ if center: coords = coords - np.mean(coords, axis=0) # Centers the array diff --git a/pulser-core/pulser/register/mappable_reg.py b/pulser-core/pulser/register/mappable_reg.py index 486d8bae2..2554fe5e0 100644 --- a/pulser-core/pulser/register/mappable_reg.py +++ b/pulser-core/pulser/register/mappable_reg.py @@ -29,9 +29,9 @@ class MappableRegister: """A register with the traps of each qubit still to be defined. Args: - register_layout (RegisterLayout): The register layout on which this + register_layout: The register layout on which this register will be defined. - qubit_ids (QubitId): The Ids for the qubits to pre-declare on this + qubit_ids: The Ids for the qubits to pre-declare on this register. """ @@ -60,13 +60,13 @@ def build_register(self, qubits: Mapping[QubitId, int]) -> BaseRegister: """Builds an actual register. Args: - qubits (Mapping[QubitId, int]): A map between the qubit IDs to use + qubits: A map between the qubit IDs to use and the layout traps where the qubits will be placed. Qubit IDs declared in the MappableRegister but not defined here will simply be left out of the final register. Returns: - BaseRegister: The resulting register. + The resulting register. """ chosen_ids = tuple(qubits.keys()) if not set(chosen_ids) <= set(self._qubit_ids): @@ -110,12 +110,12 @@ def find_indices( to tell how to instantiate the register from the mappable register. Args: - chosen_ids (set[QubitId]): IDs of the qubits that are chosen to + chosen_ids: IDs of the qubits that are chosen to map the MappableRegister - id_list (typing::Sequence[QubitId]): IDs of the qubits to denote. + id_list: IDs of the qubits to denote. Returns: - list[int]: Indices of the qubits to denote, only valid for the + Indices of the qubits to denote, only valid for the given mapping. """ if not chosen_ids <= set(self._qubit_ids): diff --git a/pulser-core/pulser/register/register.py b/pulser-core/pulser/register/register.py index 21f4471d9..159ecb43b 100644 --- a/pulser-core/pulser/register/register.py +++ b/pulser-core/pulser/register/register.py @@ -32,7 +32,7 @@ class Register(BaseRegister, RegDrawer): """A 2D quantum register containing a set of qubits. Args: - qubits (dict): Dictionary with the qubit names as keys and their + qubits: Dictionary with the qubit names as keys and their position coordinates (in μm) as values (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). """ @@ -54,16 +54,14 @@ def square( """Initializes the register with the qubits in a square array. Args: - side (int): Side of the square in number of qubits. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + side: Side of the square in number of qubits. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: A register with qubits placed in a square array. + A register with qubits placed in a square array. """ # Check side if side < 1: @@ -85,17 +83,15 @@ def rectangle( """Initializes the register with the qubits in a rectangular array. Args: - rows (int): Number of rows. - columns (int): Number of columns. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + rows: Number of rows. + columns: Number of columns. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) Returns: - Register: A register with qubits placed in a rectangular array. + A register with qubits placed in a rectangular array. """ # Check rows if rows < 1: @@ -137,17 +133,15 @@ def triangular_lattice( triangles are pointing up and down. Args: - rows (int): Number of rows. - atoms_per_row (int): Number of atoms per row. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + rows: Number of rows. + atoms_per_row: Number of atoms per row. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: A register with qubits placed in a triangular lattice. + A register with qubits placed in a triangular lattice. """ # Check rows if rows < 1: @@ -182,16 +176,14 @@ def hexagon( """Initializes the register with the qubits in a hexagonal layout. Args: - layers (int): Number of layers around a central atom. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + layers: Number of layers around a central atom. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: A register with qubits placed in a hexagonal layout. + A register with qubits placed in a hexagonal layout. """ # Check layers if layers < 1: @@ -228,18 +220,16 @@ def max_connectivity( symmetries are enforced as often as possible. Args: - n_qubits (int): Number of qubits. - device (Device): The device whose constraints must be obeyed. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. + n_qubits: Number of qubits. + device: The device whose constraints must be obeyed. + spacing: The distance between neighbouring qubits in μm. If omitted, the minimal distance for the device is used. - prefix (str): The prefix for the qubit ids. If defined, each qubit + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: A register with qubits placed for maximum connectivity. + A register with qubits placed for maximum connectivity. """ # Check device if not isinstance(device, pulser.devices._device_datacls.Device): @@ -283,7 +273,7 @@ def rotate(self, degrees: float) -> None: """Rotates the array around the origin by the given angle. Args: - degrees (float): The angle of rotation in degrees. + degrees: The angle of rotation in degrees. """ if self.layout is not None: raise TypeError( @@ -306,20 +296,20 @@ def draw( ) -> None: """Draws the entire register. - Keyword Args: - with_labels(bool, default=True): If True, writes the qubit ID's + Args: + with_labels: If True, writes the qubit ID's next to each qubit. - blockade_radius(float, default=None): The distance (in μm) between + blockade_radius: The distance (in μm) between atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the + draw_half_radius: Whether or not to draw the half the blockade radius surrounding each atoms. If `True`, requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the + draw_graph: Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. - fig_name(str, default=None): The name on which to save the figure. + fig_name: The name on which to save the figure. If None the figure will not be saved. - kwargs_savefig(dict, default={}): Keywords arguments for + kwargs_savefig: Keywords arguments for ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` is ``None``. diff --git a/pulser-core/pulser/register/register3d.py b/pulser-core/pulser/register/register3d.py index 746cb3b41..3488a2ede 100644 --- a/pulser-core/pulser/register/register3d.py +++ b/pulser-core/pulser/register/register3d.py @@ -31,7 +31,7 @@ class Register3D(BaseRegister, RegDrawer): """A 3D quantum register containing a set of qubits. Args: - qubits (dict): Dictionary with the qubit names as keys and their + qubits: Dictionary with the qubit names as keys and their position coordinates (in μm) as values (e.g. {'q0':(2, -1, 0), 'q1':(-5, 10, 0), ...}). """ @@ -53,16 +53,14 @@ def cubic( """Initializes the register with the qubits in a cubic array. Args: - side (int): Side of the cube in number of qubits. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + side: Side of the cube in number of qubits. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register3D : A 3D register with qubits placed in a cubic array. + A 3D register with qubits placed in a cubic array. """ # Check side if side < 1: @@ -85,18 +83,16 @@ def cuboid( """Initializes the register with the qubits in a cuboid array. Args: - rows (int): Number of rows. - columns (int): Number of columns. - layers (int): Number of layers. - - Keyword args: - spacing(float): The distance between neighbouring qubits in μm. - prefix (str): The prefix for the qubit ids. If defined, each qubit + rows: Number of rows. + columns: Number of columns. + layers: Number of layers. + spacing: The distance between neighbouring qubits in μm. + prefix: The prefix for the qubit ids. If defined, each qubit id starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...) Returns: - Register3D : A 3D register with qubits placed in a cuboid array. + A 3D register with qubits placed in a cuboid array. """ # Check rows if rows < 1: @@ -145,12 +141,12 @@ def to_2D(self, tol_width: float = 0.0) -> Register: """Converts a Register3D into a Register (if possible). Args: - tol_width (float): The allowed transverse width of + tol_width: The allowed transverse width of the register to be projected. Returns: - Register: Returns a 2D register with the coordinates of the atoms - in a plane, if they are coplanar. + Returns a 2D register with the coordinates of the atoms + in a plane, if they are coplanar. Raises: ValueError: If the atoms are not coplanar. @@ -188,22 +184,22 @@ def draw( ) -> None: """Draws the entire register. - Keyword Args: - with_labels(bool, default=True): If True, writes the qubit ID's + Args: + with_labels: If True, writes the qubit ID's next to each qubit. - blockade_radius(float, default=None): The distance (in μm) between + blockade_radius: The distance (in μm) between atoms below the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw the + draw_half_radius: Whether or not to draw the half the blockade radius surrounding each atoms. If `True`, requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the + draw_graph: Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. - projection(bool, default=False): Whether to draw a 2D projection + projection: Whether to draw a 2D projection instead of a perspective view. - fig_name(str, default=None): The name on which to save the figure. + fig_name: The name on which to save the figure. If None the figure will not be saved. - kwargs_savefig(dict, default={}): Keywords arguments for + kwargs_savefig: Keywords arguments for ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` is ``None``. diff --git a/pulser-core/pulser/register/register_layout.py b/pulser-core/pulser/register/register_layout.py index 0a74273d9..744d79a81 100644 --- a/pulser-core/pulser/register/register_layout.py +++ b/pulser-core/pulser/register/register_layout.py @@ -56,7 +56,7 @@ class RegisterLayout(RegDrawer): the traps are then numbered starting from 0. Args: - trap_coordinates(ArrayLike): The trap coordinates defining the layout. + trap_coordinates: The trap coordinates defining the layout. """ trap_coordinates: ArrayLike @@ -116,10 +116,10 @@ def get_traps_from_coordinates(self, *coordinates: ArrayLike) -> list[int]: """Finds the trap ID for a given set of trap coordinates. Args: - *coordinates (ArrayLike): The coordinates to return the trap IDs. + coordinates: The coordinates to return the trap IDs. Returns: - list[int]: The list of trap IDs corresponding to the coordinates. + The list of trap IDs corresponding to the coordinates. """ traps = [] rounded_coords = np.round( @@ -141,13 +141,13 @@ def define_register( """Defines a register from selected traps. Args: - *trap_ids (int): The trap IDs selected to form the Register. - qubit_ids (Optional[abcSequence[QubitId]] = None): A sequence of + trap_ids: The trap IDs selected to form the Register. + qubit_ids: A sequence of unique qubit IDs to associated to the selected traps. Must be of the same length as the selected traps. Returns: - BaseRegister: The respective register instance. + The respective register instance. """ trap_ids_set = set(trap_ids) @@ -195,16 +195,16 @@ def draw( ) -> None: """Draws the entire register layout. - Keyword Args: - blockade_radius(float, default=None): The distance (in μm) between + Args: + blockade_radius: The distance (in μm) between atoms below which the Rydberg blockade effect occurs. - draw_half_radius(bool, default=False): Whether or not to draw + draw_half_radius: Whether or not to draw half the blockade radius surrounding each trap. If `True`, requires `blockade_radius` to be defined. - draw_graph(bool, default=True): Whether or not to draw the + draw_graph: Whether or not to draw the interaction between atoms as edges in a graph. Will only draw if the `blockade_radius` is defined. - projection(bool, default=True): If the layout is in 3D, draws it + projection: If the layout is in 3D, draws it as projections on different planes. Note: @@ -263,14 +263,14 @@ def make_mappable_register( as many qubits as you need for your largest register. Args: - n_qubits(int): The number of qubits to reserve in the mappable + n_qubits: The number of qubits to reserve in the mappable register. - prefix (str): The prefix for the qubit ids. Each qubit ID starts + prefix: The prefix for the qubit ids. Each qubit ID starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - MappableRegister: A substitute for a regular register that can be + A substitute for a regular register that can be used to initialize a Sequence. """ qubit_ids = [f"{prefix}{i}" for i in range(n_qubits)] diff --git a/pulser-core/pulser/register/special_layouts.py b/pulser-core/pulser/register/special_layouts.py index 27f60ee45..4b2d02441 100644 --- a/pulser-core/pulser/register/special_layouts.py +++ b/pulser-core/pulser/register/special_layouts.py @@ -27,9 +27,9 @@ class SquareLatticeLayout(RegisterLayout): """A RegisterLayout with a square lattice pattern in a rectangular shape. Args: - rows (int): The number of rows of traps. - columns (int): The number of columns of traps. - spacing (int): The distance between neighbouring traps (in µm). + rows: The number of rows of traps. + columns: The number of columns of traps. + spacing: The distance between neighbouring traps (in µm). """ def __init__(self, rows: int, columns: int, spacing: int): @@ -45,13 +45,13 @@ def square_register(self, side: int, prefix: str = "q") -> Register: """Defines a register with a square shape. Args: - side (int): The length of the square's side, in number of atoms. - prefix (str): The prefix for the qubit ids. Each qubit ID starts + side: The length of the square's side, in number of atoms. + prefix: The prefix for the qubit ids. Each qubit ID starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: The register instance created from this layout. + The register instance created from this layout. """ return self.rectangular_register(side, side, prefix=prefix) @@ -64,14 +64,14 @@ def rectangular_register( """Defines a register with a rectangular shape. Args: - rows (int): The number of rows in the register. - columns (int): The number of columns in the register. - prefix (str): The prefix for the qubit ids. Each qubit ID starts + rows: The number of rows in the register. + columns: The number of columns in the register. + prefix: The prefix for the qubit ids. Each qubit ID starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: The register instance created from this layout. + The register instance created from this layout. """ if rows * columns > self.max_atom_num: raise ValueError( @@ -104,8 +104,8 @@ class TriangularLatticeLayout(RegisterLayout): """A RegisterLayout with a triangular lattice pattern in an hexagonal shape. Args: - n_traps (int): The number of traps in the layout. - spacing (int): The distance between neighbouring traps (in µm). + n_traps: The number of traps in the layout. + spacing: The distance between neighbouring traps (in µm). """ def __init__(self, n_traps: int, spacing: int): @@ -117,13 +117,13 @@ def hexagonal_register(self, n_atoms: int, prefix: str = "q") -> Register: """Defines a register with an hexagonal shape. Args: - n_atoms (int): The number of atoms in the register. - prefix (str): The prefix for the qubit ids. Each qubit ID starts + n_atoms: The number of atoms in the register. + prefix: The prefix for the qubit ids. Each qubit ID starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: The register instance created from this layout. + The register instance created from this layout. """ if n_atoms > self.max_atom_num: raise ValueError( @@ -143,14 +143,14 @@ def rectangular_register( """Defines a register with a rectangular shape. Args: - rows (int): The number of rows in the register. - atoms_per_row (int): The number of atoms in each row. - prefix (str): The prefix for the qubit ids. Each qubit ID starts + rows: The number of rows in the register. + atoms_per_row: The number of atoms in each row. + prefix: The prefix for the qubit ids. Each qubit ID starts with the prefix, followed by an int from 0 to N-1 (e.g. prefix='q' -> IDs: 'q0', 'q1', 'q2', ...). Returns: - Register: The register instance created from this layout. + The register instance created from this layout. """ if rows * atoms_per_row > self.max_atom_num: raise ValueError( diff --git a/pulser-core/pulser/sampler/noise_model.py b/pulser-core/pulser/sampler/noise_model.py index 18297b75d..d7b661496 100644 --- a/pulser-core/pulser/sampler/noise_model.py +++ b/pulser-core/pulser/sampler/noise_model.py @@ -53,8 +53,8 @@ def apply_noises( the last element is applied first. Args: - samples (list[QubitSamples]): A list of QubitSamples. - noises (list[NoiseModel]): A list of NoiseModel. + samples: A list of QubitSamples. + noises: A list of NoiseModel. Return: A list of QubitSamples on which each element of noises has been diff --git a/pulser-core/pulser/sampler/sampler.py b/pulser-core/pulser/sampler/sampler.py index 0029288cd..04ebab7a3 100644 --- a/pulser-core/pulser/sampler/sampler.py +++ b/pulser-core/pulser/sampler/sampler.py @@ -41,12 +41,12 @@ def sample( It is intended to be used like the json.dumps() function. Args: - seq (Sequence): A pulser.Sequence instance. - modulation (bool): Flag to account for the modulation of AOM/EOM + seq: A pulser.Sequence instance. + modulation: Flag to account for the modulation of AOM/EOM before sampling. - common_noises (Optional[list[LocalNoise]]): A list of the noise sources + common_noises: A list of the noise sources for all channels. - global_noises (Optional[list[LocalNoise]]): A list of the noise sources + global_noises: A list of the noise sources for global channels. Returns: @@ -253,7 +253,7 @@ def _group_between_retargets( [[A, B], [C, D, E], [F]] Args: - ts (list[_TimeSlot]): A list of TimeSlot from a Sequence schedule. + ts: A list of TimeSlot from a Sequence schedule. Returns: A list of list of _TimeSlot. _TimeSlot instances are successive and diff --git a/pulser-core/pulser/sequence.py b/pulser-core/pulser/sequence.py index cab6fb89d..b9c8fe62a 100644 --- a/pulser-core/pulser/sequence.py +++ b/pulser-core/pulser/sequence.py @@ -176,11 +176,10 @@ class Sequence: generated from a single "parametrized" ``Sequence``. Args: - register(Union[BaseRegister, MappableRegister]): The atom register on - which to apply the pulses. If given as a MappableRegister - instance, the traps corrresponding to each qubit ID must be given - when building the sequence. - device(Device): A valid device in which to execute the Sequence (import + register: The atom register on which to apply the pulses. If given as + a MappableRegister instance, the traps corrresponding to each + qubit ID must be given when building the sequence. + device: A valid device in which to execute the Sequence (import it from ``pulser.devices``). Note: @@ -321,7 +320,7 @@ def is_parametrized(self) -> bool: are given a value (when ``Sequence.build()`` is called). Returns: - bool: Whether the sequence is parametrized. + Whether the sequence is parametrized. """ return not self._building @@ -333,7 +332,7 @@ def is_register_mappable(self) -> bool: `Sequence.build()` call. Returns: - bool: Whether the register is a MappableRegister. + Whether the register is a MappableRegister. """ return isinstance(self._register, MappableRegister) @@ -343,16 +342,15 @@ def get_duration( ) -> int: """Returns the current duration of a channel or the whole sequence. - Keyword Args: - channel (Optional[str]): A specific channel to return the duration - of. If left as None, it will return the duration of the whole - sequence. - include_fall_time (bool): Whether to include in the duration the + Args: + channel: A specific channel to return the duration of. If left as + None, it will return the duration of the whole sequence. + include_fall_time: Whether to include in the duration the extra time needed by the last pulse to finish, if there is modulation. Returns: - int: The duration of the channel or sequence, in ns. + The duration of the channel or sequence, in ns. """ if channel is None: channels = tuple(self._channels.keys()) @@ -390,14 +388,13 @@ def current_phase_ref( """Current phase reference of a specific qubit for a given basis. Args: - qubit (Union[int, str]): The id of the qubit whose phase shift is - desired. - basis (str): The basis (i.e. electronic transition) the phase + qubit: The id of the qubit whose phase shift is desired. + basis: The basis (i.e. electronic transition) the phase reference is associated with. Must correspond to the basis of a declared channel. Returns: - float: Current phase reference of 'qubit' in 'basis'. + Current phase reference of 'qubit' in 'basis'. """ if qubit not in self._qids: raise ValueError( @@ -424,10 +421,10 @@ def set_magnetic_field( defined through the declaration of a Microwave channel, calling this function will enable the "XY Mode". - Keyword Args: - bx (float): The magnetic field in the x direction (in Gauss). - by (float): The magnetic field in the y direction (in Gauss). - bz (float): The magnetic field in the z direction (in Gauss). + Args: + bx: The magnetic field in the x direction (in Gauss). + by: The magnetic field in the y direction (in Gauss). + bz: The magnetic field in the z direction (in Gauss). """ if not self._in_xy: if self._channels: @@ -457,8 +454,8 @@ def config_slm_mask(self, qubits: Iterable[QubitId]) -> None: """Setup an SLM mask by specifying the qubits it targets. Args: - qubits (Iterable[QubitId]): Iterable of qubit ID's to mask during - the first global pulse of the sequence. + qubits: Iterable of qubit ID's to mask during the first global + pulse of the sequence. """ try: targets = set(qubits) @@ -514,15 +511,15 @@ def declare_channel( ``MockDevice`` channels can be repeatedly declared if needed. Args: - name (str): Unique name for the channel in the sequence. - channel_id (str): How the channel is identified in the device. + name: Unique name for the channel in the sequence. + channel_id: How the channel is identified in the device. Consult ``Sequence.available_channels`` to see which channel ID's are still available and the associated channel's description. - initial_target (Optional[Union[int, str, Iterable]]): For 'Local' - addressing channels only. Declares the initial target of the - channel. If left as None, the initial target will have to be - set manually as the first addition to this channel. + initial_target: For 'Local' addressing channels only. Declares the + initial target of the channel. If left as None, the initial + target will have to be set manually as the first addition + to this channel. """ if name in self._channels: raise ValueError("The given name is already in use.") @@ -627,18 +624,18 @@ def declare_variable( are dependent on the involved variables. Args: - name (str): The name for the variable. Must be unique within a + name: The name for the variable. Must be unique within a Sequence. - Keyword Args: - size (Optional[int]=None): The number of entries stored in the - variable. If defined, returns an array of variables with the - given size. If left as ``None``, returns a single variable. - dtype (default=float): The type of the data that will be assigned + Args: + size: The number of entries stored in the variable. If defined, + returns an array of variables with the given size. If left + as ``None``, returns a single variable. + dtype: The type of the data that will be assigned to the variable. Must be ``float`` or ``int``. Returns: - Variable: The declared Variable instance. + The declared Variable instance. Note: To avoid confusion, it is recommended to store the returned @@ -672,9 +669,9 @@ def add( """Adds a pulse to a channel. Args: - pulse (pulser.Pulse): The pulse object to add to the channel. - channel (str): The channel's name provided when declared. - protocol (str, default='min-delay'): Stipulates how to deal with + pulse: The pulse object to add to the channel. + channel: The channel's name provided when declared. + protocol: Stipulates how to deal with eventual conflicts with other channels, specifically in terms of having multiple channels act on the same target simultaneously. @@ -835,10 +832,10 @@ def target( """Changes the target qubit of a 'Local' channel. Args: - qubits (Union[int, str, Iterable]): The new target for this - channel. Must correspond to a qubit ID in device or an iterable - of qubit IDs, when multi-qubit addressing is possible. - channel (str): The channel's name provided when declared. Must be + qubits: The new target for this channel. Must correspond to a + qubit ID in device or an iterable of qubit IDs, when + multi-qubit addressing is possible. + channel: The channel's name provided when declared. Must be a channel with 'Local' addressing. """ self._target(qubits, channel) @@ -852,14 +849,14 @@ def target_index( """Changes the target qubit of a 'Local' channel. Args: - qubits (Union[int, Iterable, Parametrized]): The new target for - this channel. Must correspond to a qubit index or an iterable - of qubit indices, when multi-qubit addressing is possible. + qubits: The new target for this channel. Must correspond to a + qubit index or an iterable of qubit indices, when multi-qubit + addressing is possible. A qubit index is a number between 0 and the number of qubits. It is then converted to a Qubit ID using the order in which they were declared when instantiating the ``Register`` or ``MappableRegister``. - channel (str): The channel's name provided when declared. Must be + channel: The channel's name provided when declared. Must be a channel with 'Local' addressing. Note: @@ -887,9 +884,8 @@ def delay( """Idles a given channel for a specific duration. Args: - duration (Union[int, Parametrized]): Time to delay (in multiples - of 4 ns). - channel (str): The channel's name provided when declared. + duration: Time to delay (in multiples of 4 ns). + channel: The channel's name provided when declared. """ self._delay(duration, channel) @@ -905,7 +901,7 @@ def measure(self, basis: str = "ground-rydberg") -> None: possible to measure in the 'XY' basis outside of XY mode. Args: - basis (str): Valid basis for measurement (consult the + basis: Valid basis for measurement (consult the ``supported_bases`` attribute of the selected device for the available options). """ @@ -943,11 +939,9 @@ def phase_shift( Bloch sphere). Args: - phi (Union[float, Parametrized]): The intended phase shift (in - rads). - targets (Union[int, str]): The ids of the qubits to apply the phase - shift to. - basis (str): The basis (i.e. electronic transition) to associate + phi: The intended phase shift (in rads). + targets: The ids of the qubits to apply the phase shift to. + basis: The basis (i.e. electronic transition) to associate the phase shift to. Must correspond to the basis of a declared channel. """ @@ -967,15 +961,13 @@ def phase_shift_index( Bloch sphere). Args: - phi (Union[float, Parametrized]): The intended phase shift (in - rads). - targets (Union[int, Parametrized]): The indices of the qubits to - apply the phase shift to. + phi: The intended phase shift (in rads). + targets: The indices of the qubits to apply the phase shift to. A qubit index is a number between 0 and the number of qubits. It is then converted to a Qubit ID using the order in which they were declared when instantiating the ``Register`` or ``MappableRegister``. - basis (str): The basis (i.e. electronic transition) to associate + basis: The basis (i.e. electronic transition) to associate the phase shift to. Must correspond to the basis of a declared channel. @@ -995,7 +987,7 @@ def align(self, *channels: str) -> None: will start right after the latest channel has finished. Args: - channels (str): The names of the channels to align, as given upon + channels: The names of the channels to align, as given upon declaration. """ ch_set = set(channels) @@ -1038,17 +1030,16 @@ def build( ) -> Sequence: """Builds a sequence from the programmed instructions. - Keyword Args: - qubits (Optional[Mapping[QubitId, int]]): A mapping between qubit - IDs and trap IDs used to define the register. Must only be - provided when the sequence is initialized with a - MappableRegister. + Args: + qubits: A mapping between qubit IDs and trap IDs used to define + the register. Must only be provided when the sequence is + initialized with a MappableRegister. vars: The values for all the variables declared in this Sequence instance, indexed by the name given upon declaration. Check ``Sequence.declared_variables`` to see all the variables. Returns: - Sequence: The Sequence built with the given variable values. + The Sequence built with the given variable values. Example: :: @@ -1133,7 +1124,7 @@ def serialize(self, **kwargs: Any) -> str: ``cls``. Returns: - str: The sequence encoded in a JSON formatted string. + The sequence encoded in a JSON formatted string. See Also: ``json.dumps``: Built-in function for serialization to a JSON @@ -1146,7 +1137,7 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: """Deserializes a JSON formatted string. Args: - obj (str): The JSON formatted string to deserialize, coming from + obj: The JSON formatted string to deserialize, coming from the serialization of a ``Sequence`` through ``Sequence.serialize()``. @@ -1155,7 +1146,7 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: ``cls`` and ``object_hook``. Returns: - Sequence: The deserialized Sequence object. + The deserialized Sequence object. See Also: ``json.loads``: Built-in function for deserialization from a JSON @@ -1181,32 +1172,32 @@ def draw( ) -> None: """Draws the sequence in its current state. - Keyword Args: - mode (str, default="input+output"): The curves to draw. 'input' + Args: + mode: The curves to draw. 'input' draws only the programmed curves, 'output' the excepted curves after modulation. 'input+output' will draw both curves except for channels without a defined modulation bandwidth, in which case only the input is drawn. - draw_phase_area (bool): Whether phase and area values need to be + draw_phase_area: Whether phase and area values need to be shown as text on the plot, defaults to False. Doesn't work in 'output' mode. - draw_interp_pts (bool): When the sequence has pulses with waveforms + draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation on top of the respective input waveforms (defaults to True). Doesn't work in 'output' mode. - draw_phase_shifts (bool): Whether phase shift and reference + draw_phase_shifts: Whether phase shift and reference information should be added to the plot, defaults to False. - draw_register (bool): Whether to draw the register before the pulse + draw_register: Whether to draw the register before the pulse sequence, with a visual indication (square halo) around the qubits masked by the SLM, defaults to False. Can't be set to True if the sequence is defined with a mappable register. - fig_name(str, default=None): The name on which to save the + fig_name: The name on which to save the figure. If `draw_register` is True, both pulses and register will be saved as figures, with a suffix ``_pulses`` and ``_register`` in the file name. If `draw_register` is False, only the pulses are saved, with no suffix. If `fig_name` is None, no figure is saved. - kwargs_savefig(dict, default={}): Keywords arguments for + kwargs_savefig: Keywords arguments for ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` is ``None``. diff --git a/pulser-core/pulser/waveforms.py b/pulser-core/pulser/waveforms.py index d82c2b751..cba1c4fe3 100644 --- a/pulser-core/pulser/waveforms.py +++ b/pulser-core/pulser/waveforms.py @@ -64,7 +64,7 @@ def __init__(self, duration: Union[int, Parametrized]): """Initializes a waveform with a given duration. Args: - duration (int): The waveforms duration (in ns). + duration: The waveforms duration (in ns). """ duration = cast(int, duration) try: @@ -105,7 +105,7 @@ def samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - np.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return self._samples.copy() @@ -152,7 +152,7 @@ def change_duration(self, new_duration: int) -> Waveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. """ raise NotImplementedError( f"{self.__class__.__name__} does not support" @@ -165,10 +165,10 @@ def modulated_samples(self, channel: Channel) -> np.ndarray: This duration is adjusted according to the minimal buffer times. Args: - channel (Channel): The channel modulating the waveform. + channel: The channel modulating the waveform. Returns: - numpy.ndarray: The array of samples after modulation. + The array of samples after modulation. """ start, end = self.modulation_buffers(channel) mod_samples = self._modulated_samples(channel) @@ -181,10 +181,10 @@ def modulation_buffers(self, channel: Channel) -> tuple[int, int]: """The minimal buffers needed around a modulated waveform. Args: - channel (Channel): The channel modulating the waveform. + channel: The channel modulating the waveform. Returns: - tuple[int, int]: The minimum buffer times at the start and end of + The minimum buffer times at the start and end of the samples, in ns. """ if not channel.mod_bandwidth: @@ -202,10 +202,10 @@ def _modulated_samples(self, channel: Channel) -> np.ndarray: ``Waveform.modulated_samples()`` to get the output already truncated. Args: - channel (Channel): The channel modulating the waveform. + channel: The channel modulating the waveform. Returns: - numpy.ndarray: The array of samples after modulation. + The array of samples after modulation. """ return channel.modulate(self._samples) @@ -339,7 +339,7 @@ class CompositeWaveform(Waveform): """A waveform combining multiple smaller waveforms. Args: - waveforms(Waveform): Two or more waveforms to combine. + waveforms: Two or more waveforms to combine. """ def __init__(self, *waveforms: Union[Parametrized, Waveform]): @@ -367,7 +367,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return cast( np.ndarray, np.concatenate([wf.samples for wf in self._waveforms]) @@ -405,7 +405,7 @@ class CustomWaveform(Waveform): """A custom waveform. Args: - samples (array_like): The modulation values at each time step + samples: The modulation values at each time step (in rad/µs). The number of samples dictates the duration, in ns. """ @@ -425,7 +425,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ # self._samples is already cached when initialized in __init__ pass @@ -447,8 +447,8 @@ class ConstantWaveform(Waveform): """A waveform of constant value. Args: - duration (int): The waveform duration (in ns). - value (float): The modulation value (in rad/µs). + duration: The waveform duration (in ns). + value: The modulation value (in rad/µs). """ def __init__( @@ -471,7 +471,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return np.full(self.duration, self._value) @@ -479,10 +479,10 @@ def change_duration(self, new_duration: int) -> ConstantWaveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. Returns: - ConstantWaveform: The new waveform with the given duration. + The new waveform with the given duration. """ return ConstantWaveform(new_duration, self._value) @@ -506,9 +506,9 @@ class RampWaveform(Waveform): """A linear ramp waveform. Args: - duration (int): The waveform duration (in ns). - start (float): The initial value (in rad/µs). - stop (float): The final value (in rad/µs). + duration: The waveform duration (in ns). + start: The initial value (in rad/µs). + stop: The final value (in rad/µs). """ def __init__( @@ -534,7 +534,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return np.linspace(self._start, self._stop, num=self._duration) @@ -547,10 +547,10 @@ def change_duration(self, new_duration: int) -> RampWaveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. Returns: - RampWaveform: The new waveform with the given duration. + The new waveform with the given duration. """ return RampWaveform(new_duration, self._start, self._stop) @@ -575,8 +575,8 @@ class BlackmanWaveform(Waveform): """A Blackman window of a specified duration and area. Args: - duration (int): The waveform duration (in ns). - area (float): The integral of the waveform. Can be negative, in which + duration: The waveform duration (in ns). + area: The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values. """ @@ -617,11 +617,11 @@ def from_max_val( not surpassed, but approached as closely as possible. Args: - max_val (float): The maximum value threshold (in rad/µs). If + max_val: The maximum value threshold (in rad/µs). If negative, it is taken as the lower bound i.e. the minimum value that can be reached. The sign of `max_val` must match the sign of `area`. - area (float): The area under the waveform. + area: The area under the waveform. """ max_val = cast(float, max_val) area = cast(float, area) @@ -669,7 +669,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return cast(np.ndarray, self._norm_samples * self._scaling) @@ -677,10 +677,10 @@ def change_duration(self, new_duration: int) -> BlackmanWaveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. Returns: - BlackmanWaveform: The new waveform with the same area but a new + The new waveform with the same area but a new duration. """ return BlackmanWaveform(new_duration, self._area) @@ -702,13 +702,13 @@ class InterpolatedWaveform(Waveform): """Creates a waveform from interpolation of a set of data points. Args: - duration (int): The waveform duration (in ns). - values (ArrayLike): Values of the interpolation points (in rad/µs). - times (Optional[ArrayLike]): Fractions of the total duration (between 0 + duration: The waveform duration (in ns). + values: Values of the interpolation points (in rad/µs). + times: Fractions of the total duration (between 0 and 1), indicating where to place each value on the time axis. If not given, the values are spread evenly throughout the full duration of the waveform. - interpolator (str = "PchipInterpolator"): The SciPy interpolation class + interpolator: The SciPy interpolation class to use. Supports "PchipInterpolator" and "interp1d". **interpolator_kwargs: Extra parameters to give to the chosen interpolator class. @@ -806,10 +806,10 @@ def change_duration(self, new_duration: int) -> InterpolatedWaveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. Returns: - InterpolatedWaveform: The new waveform with the same coordinates + The new waveform with the same coordinates for interpolation but a new duration. """ return InterpolatedWaveform(new_duration, self._values, **self._kwargs) @@ -861,11 +861,11 @@ class KaiserWaveform(Waveform): https://numpy.org/doc/stable/reference/generated/numpy.kaiser.html Args: - duration (int): The waveform duration (in ns). - area (float): The integral of the waveform. Can be negative, + duration: The waveform duration (in ns). + area: The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values. - beta (Optional[float]): The beta parameter of the Kaiser window. + beta: The beta parameter of the Kaiser window. The default value is 14. """ @@ -923,12 +923,12 @@ def from_max_val( not surpassed, but approached as closely as possible. Args: - max_val (float): The maximum value threshold (in rad/µs). If + max_val: The maximum value threshold (in rad/µs). If negative, it is taken as the lower bound i.e. the minimum value that can be reached. The sign of `max_val` must match the sign of `area`. - area (float): The area under the waveform. - beta (Optional[float]): The beta parameter of the Kaiser window. + area: The area under the waveform. + beta: The beta parameter of the Kaiser window. The default value is 14. """ max_val = cast(float, max_val) @@ -1007,7 +1007,7 @@ def _samples(self) -> np.ndarray: """The value at each time step that describes the waveform. Returns: - numpy.ndarray: A numpy array with a value for each time step. + A numpy array with a value for each time step. """ return cast(np.ndarray, self._norm_samples * self._scaling) @@ -1015,11 +1015,11 @@ def change_duration(self, new_duration: int) -> KaiserWaveform: """Returns a new waveform with modified duration. Args: - new_duration(int): The duration of the new waveform. + new_duration: The duration of the new waveform. Returns: - KaiserWaveform: The new waveform with the same area and beta - but a new duration. + The new waveform with the same area and beta but a new + duration. """ return KaiserWaveform(new_duration, self._area, self._beta) diff --git a/pulser-simulation/pulser_simulation/noises.py b/pulser-simulation/pulser_simulation/noises.py index 5cff475d6..5323226cb 100644 --- a/pulser-simulation/pulser_simulation/noises.py +++ b/pulser-simulation/pulser_simulation/noises.py @@ -41,10 +41,10 @@ def amplitude( becoming local. Args: - reg (Register): A Pulser register - waist_width (float): The laser waist_width in µm - random (bool): Adds an additional random noise on the amplitude - seed (int): Optional, seed for the numpy.random.Generator + reg: A Pulser register + waist_width: The laser waist_width in µm + random: Adds an additional random noise on the amplitude + seed: seed for the numpy.random.Generator Return: NoiseModel: The function that applies the amplitude noise to some @@ -84,10 +84,10 @@ def doppler(reg: Register, std_dev: float, seed: Optional[int]) -> NoiseModel: ... Args: - reg (Register): A Pulser register - std_dev (float): The standard deviation of the normal distribution used + reg: A Pulser register + std_dev: The standard deviation of the normal distribution used to sample the random detuning shifts - seed (int): Optional, seed for the numpy.random.Generator + seed: seed for the numpy.random.Generator Return: NoiseModel: The function that applies the doppler noise to some diff --git a/pulser-simulation/pulser_simulation/simconfig.py b/pulser-simulation/pulser_simulation/simconfig.py index 8d175541f..ef748e6ee 100644 --- a/pulser-simulation/pulser_simulation/simconfig.py +++ b/pulser-simulation/pulser_simulation/simconfig.py @@ -49,7 +49,7 @@ class SimConfig: cannot be changed later on. Args: - noise (Union[str, tuple[str]]): Types of noises to be used in the + noise: Types of noises to be used in the simulation. You may specify just one, or a tuple of the allowed noise types: @@ -60,18 +60,18 @@ class SimConfig: - "SPAM": SPAM errors. Defined by **eta**, **epsilon** and **epsilon_prime**. - eta (float): Probability of each atom to be badly prepared. - epsilon (float): Probability of false positives. - epsilon_prime(float): Probability of false negatives. - runs (int): Number of runs needed : each run draws a new random + eta: Probability of each atom to be badly prepared. + epsilon: Probability of false positives. + epsilon_prime: Probability of false negatives. + runs: Number of runs needed : each run draws a new random noise. - samples_per_run (int): Number of samples per noisy run. + samples_per_run: Number of samples per noisy run. Useful for cutting down on computing time, but unrealistic. - temperature (float): Temperature, set in µK, of the Rydberg array. + temperature: Temperature, set in µK, of the Rydberg array. Also sets the standard deviation of the speed of the atoms. - laser_waist (float): Waist of the gaussian laser, set in µm, + laser_waist: Waist of the gaussian laser, set in µm, in global pulses. - solver_options (qutip.Options): Options for the qutip solver. + solver_options: Options for the qutip solver. """ noise: Union[NOISE_TYPES, tuple[NOISE_TYPES, ...]] = () diff --git a/pulser-simulation/pulser_simulation/simresults.py b/pulser-simulation/pulser_simulation/simresults.py index f8c689462..4929fbbe8 100644 --- a/pulser-simulation/pulser_simulation/simresults.py +++ b/pulser-simulation/pulser_simulation/simresults.py @@ -42,10 +42,10 @@ def __init__( """Initializes a new SimulationResults instance. Args: - size (int): The number of atoms in the register. - basis_name (str): The basis indicating the addressed atoms after + size: The number of atoms in the register. + basis_name: The basis indicating the addressed atoms after the pulse sequence ('ground-rydberg', 'digital' or 'all'). - sim_times (array): Array of times (µs) when simulation results are + sim_times: Array of times (µs) when simulation results are returned. """ self._dim = 3 if basis_name == "all" else 2 @@ -88,12 +88,11 @@ def expect( """Returns the expectation values of operators in obs_list. Args: - obs_list (list[Union[qutip.Qobj, ArrayLike]]): Input observable + obs_list: Input observable list. ArrayLike objects will be converted to qutip.Qobj. Returns: - list[Union[float, complex, ArrayLike]]: Expectation values of - obs_list. + Expectation values of obs_list. """ if not isinstance(obs_list, (list, np.ndarray)): raise TypeError("`obs_list` must be a list of operators.") @@ -135,13 +134,13 @@ def sample_state( """Returns the result of multiple measurements at time t. Args: - t (float): Time at which the state is sampled. - n_samples (int): Number of samples to return. - t_tol (float): Tolerance for the difference between t and + t: Time at which the state is sampled. + n_samples: Number of samples to return. + t_tol: Tolerance for the difference between t and closest time. Returns: - Counter: Sample distribution of bitstrings corresponding to + Sample distribution of bitstrings corresponding to measured quantum states at time t. """ t_index = self._get_index_from_time(t, t_tol) @@ -157,10 +156,10 @@ def sample_final_state(self, N_samples: int = 1000) -> Counter: """Returns the result of multiple measurements of the final state. Args: - N_samples (int): Number of samples to return. + N_samples: Number of samples to return. Returns: - Counter: Sample distribution of bitstrings corresponding to + Sample distribution of bitstrings corresponding to measured quantum states at the end of the simulation. """ return self.sample_state(self._sim_times[-1], N_samples) @@ -169,9 +168,9 @@ def plot(self, op: qutip.Qobj, fmt: str = "", label: str = "") -> None: """Plots the expectation value of a given operator op. Args: - op (qutip.Qobj): Operator whose expectation value is wanted. - fmt (str): Curve plot format. - label (str): Curve label. + op: Operator whose expectation value is wanted. + fmt: Curve plot format. + label: Curve label. """ plt.plot(self._sim_times, self.expect([op])[0], fmt, label=label) plt.xlabel("Time (µs)") @@ -181,8 +180,8 @@ def _get_index_from_time(self, t_float: float, tol: float = 1.0e-3) -> int: """Returns closest index corresponding to time t_float. Args: - t_float (float): Time value (in µs). - tol (float): Tolerance for the difference between t_float and + t_float: Time value (in µs). + tol: Tolerance for the difference between t_float and closest time. """ try: @@ -201,11 +200,11 @@ def _calc_pseudo_density(self, t_index: int) -> qutip.Qobj: probability of obtaining each possible state, after measurement. Args: - t_index (int): The index in the list of states/results to turn + t_index: The index in the list of states/results to turn into the pseudo-density matrix. Returns: - qutip.Qobj: The pseudo-density matrix as a Qobj. + The pseudo-density matrix as a Qobj. """ def _proj_from_bitstring(bitstring: str) -> qutip.Qobj: @@ -261,18 +260,18 @@ def __init__( distribution of bitstrings, not atomic states Args: - run_output (list[Counter]): Each Counter contains the + run_output: Each Counter contains the probability distribution of a multi-qubits state, represented as a bitstring. There is one Counter for each time the simulation was asked to return a result. - size (int): The number of atoms in the register. - basis_name (str): Basis indicating the addressed atoms after + size: The number of atoms in the register. + basis_name: Basis indicating the addressed atoms after the pulse sequence ('ground-rydberg' or 'digital' - 'all' basis makes no sense after projection on bitstrings). Defaults to 'digital' if given value 'all'. - sim_times (np.ndarray): Times at which Simulation object returned + sim_times: Times at which Simulation object returned the results. - n_measures (int): Number of measurements needed to compute this + n_measures: Number of measurements needed to compute this result when doing the simulation. """ basis_name_ = "digital" if basis_name == "all" else basis_name @@ -299,13 +298,12 @@ def get_state(self, t: float, t_tol: float = 1.0e-3) -> qutip.Qobj: way of computing expectation values of observables. Args: - t (float): Time (µs) at which to return the state. - t_tol (float): Tolerance for the difference between t and + t: Time (µs) at which to return the state. + t_tol: Tolerance for the difference between t and closest time. Returns: - qutip.Qobj: States probability distribution as a diagonal - density matrix. + States probability distribution as a diagonal density matrix. """ t_index = self._get_index_from_time(t, t_tol) return self._calc_pseudo_density(t_index) @@ -318,7 +316,7 @@ def get_final_state(self) -> qutip.Qobj: way of computing expectation values of observables. Returns: - qutip.Qobj: States probability distribution as a density matrix. + States probability distribution as a density matrix. """ return self.get_state(self._sim_times[-1]) @@ -341,10 +339,10 @@ def plot( The observable must be diagonal. Args: - op (qutip.Qobj): Operator whose expectation value is wanted. - fmt (str): Curve plot format. - label (str): y-Axis label. - error_bars (bool): Choose to display error bars. + op: Operator whose expectation value is wanted. + fmt: Curve plot format. + label: y-Axis label. + error_bars: Choose to display error bars. """ def get_error_bars() -> Tuple[ArrayLike, ArrayLike]: @@ -385,17 +383,17 @@ def __init__( """Initializes a new CoherentResults instance. Args: - run_output (list of qutip.Qobj): List of `qutip.Qobj` corresponding + run_output: List of `qutip.Qobj` corresponding to the states at each time step after the evolution has been simulated. - size (int): The number of atoms in the register. - basis_name (str): The basis indicating the addressed atoms after + size: The number of atoms in the register. + basis_name: The basis indicating the addressed atoms after the pulse sequence ('ground-rydberg', 'digital' or 'all'). - sim_times (list): Times at which Simulation object returned the + sim_times: Times at which Simulation object returned the results. - meas_basis (str): The basis in which a sampling measurement + meas_basis: The basis in which a sampling measurement is desired. - meas_errors (Optional[Mapping[str, float]]): If measurement errors + meas_errors: If measurement errors are involved, give them in a dictionary with "epsilon" and "epsilon_prime". """ @@ -438,23 +436,23 @@ def get_state( """Get the state at time t of the simulation. Args: - t (float): Time (µs) at which to return the state. - reduce_to_basis (str, default=None): Reduces the full state vector + t: Time (µs) at which to return the state. + reduce_to_basis: Reduces the full state vector to the given basis ("ground-rydberg" or "digital"), if the population of the states to be ignored is negligible. Doesn't apply to XY mode. - ignore_global_phase (bool, default=True): If True, changes the + ignore_global_phase: If True, changes the final state's global phase such that the largest term (in absolute value) is real. - tol (float, default=1e-6): Maximum allowed population of each + tol: Maximum allowed population of each eliminated state. - normalize (bool, default=True): Whether to normalize the reduced + normalize: Whether to normalize the reduced state. - t_tol (float): Tolerance for the difference between t and + t_tol: Tolerance for the difference between t and closest time. Returns: - qutip.Qobj: The resulting state at time t. + The resulting state at time t. Raises: TypeError: If trying to reduce to a basis that would eliminate @@ -506,24 +504,24 @@ def get_final_state( """Returns the final state of the Simulation. Args: - reduce_to_basis (str, default=None): Reduces the full state vector + reduce_to_basis: Reduces the full state vector to the given basis ("ground-rydberg" or "digital"), if the population of the states to be ignored is negligible. Doesn't apply to XY mode. - ignore_global_phase (bool, default=True): If True, changes the + ignore_global_phase: If True, changes the final state's global phase such that the largest term (in absolute value) is real. - tol (float, default=1e-6): Maximum allowed population of each + tol: Maximum allowed population of each eliminated state. - normalize (bool, default=True): Whether to normalize the reduced + normalize: Whether to normalize the reduced state. Returns: - qutip.Qobj: The resulting final state. + The resulting final state. Raises: - TypeError: If trying to reduce to a basis that would eliminate - states with significant occupation probabilites. + If trying to reduce to a basis that would eliminate states with + significant occupation probabilites. """ return self.get_state( self._sim_times[-1], @@ -609,14 +607,14 @@ def sample_state( """Returns the result of multiple measurements at time t. Args: - t (float): Time at which the state is sampled. - n_samples (int): Number of samples to return. - t_tol (float): Tolerance for the difference between t and + t: Time at which the state is sampled. + n_samples: Number of samples to return. + t_tol: Tolerance for the difference between t and closest time. Returns: - Counter: Sample distribution of bitstrings corresponding to - measured quantum states at time t. + Sample distribution of bitstrings corresponding to measured + quantum states at time t. """ sampled_state = super().sample_state(t, n_samples, t_tol) if self._meas_errors is None: diff --git a/pulser-simulation/pulser_simulation/simulation.py b/pulser-simulation/pulser_simulation/simulation.py index f8b4e9c6b..5fe22614a 100644 --- a/pulser-simulation/pulser_simulation/simulation.py +++ b/pulser-simulation/pulser_simulation/simulation.py @@ -49,13 +49,13 @@ class Simulation: r"""Simulation of a pulse sequence using QuTiP. Args: - sequence (Sequence): An instance of a Pulser Sequence that we + sequence: An instance of a Pulser Sequence that we want to simulate. - sampling_rate (float): The fraction of samples that we wish to + sampling_rate: The fraction of samples that we wish to extract from the pulse sequence to simulate. Has to be a value between 0.05 and 1.0. - config (SimConfig): Configuration to be used for this simulation. - evaluation_times (Union[str, ArrayLike, float]): Choose between: + config: Configuration to be used for this simulation. + evaluation_times: Choose between: - "Full": The times are set to be the ones used to define the Hamiltonian to the solver. @@ -150,7 +150,7 @@ def set_config(self, cfg: SimConfig) -> None: """Sets current config to cfg and updates simulation parameters. Args: - cfg (SimConfig): New configuration. + cfg: New configuration. """ if not isinstance(cfg, SimConfig): raise ValueError(f"Object {cfg} is not a valid `SimConfig`.") @@ -210,7 +210,7 @@ def add_config(self, config: SimConfig) -> None: former noise parameters. Args: - config (SimConfig): SimConfig to retrieve parameters from. + config: SimConfig to retrieve parameters from. """ if not isinstance(config, SimConfig): raise ValueError(f"Object {config} is not a valid `SimConfig`") @@ -264,7 +264,7 @@ def initial_state(self) -> qutip.Qobj: """The initial state of the simulation. Args: - state (Union[str, ArrayLike, qutip.Qobj]): The initial state. + state: The initial state. Choose between: - "all-ground" for all atoms in ground state @@ -301,7 +301,7 @@ def evaluation_times(self) -> np.ndarray: """The times at which the results of this simulation are returned. Args: - value (Union[str, ArrayLike, float]): Choose between: + value: Choose between: - "Full": The times are set to be the ones used to define the Hamiltonian to the solver. @@ -377,17 +377,17 @@ def draw( ) -> None: """Draws the input sequence and the one used by the solver. - Keyword Args: - draw_phase_area (bool): Whether phase and area values need + Args: + draw_phase_area: Whether phase and area values need to be shown as text on the plot, defaults to False. - draw_interp_pts (bool): When the sequence has pulses with waveforms + draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation on top of the respective waveforms (defaults to False). - draw_phase_shifts (bool): Whether phase shift and reference + draw_phase_shifts: Whether phase shift and reference information should be added to the plot, defaults to False. - fig_name(str, default=None): The name on which to save the figure. + fig_name: The name on which to save the figure. If None the figure will not be saved. - kwargs_savefig(dict, default={}): Keywords arguments for + kwargs_savefig: Keywords arguments for ``matplotlib.pyplot.savefig``. Not applicable if `fig_name` is ``None``. @@ -529,14 +529,14 @@ def build_operator(self, operations: Union[list, tuple]) -> qutip.Qobj: and ``[(X, 'global')]`` returns `XIII + IXII + IIXI + IIIX` Args: - operations (list): List of tuples `(operator, qubits)`. + operations: List of tuples `(operator, qubits)`. `operator` can be a ``qutip.Quobj`` or a string key for ``self.op_matrix``. `qubits` is the list on which operator will be applied. The qubits can be passed as their index or their label in the register. Returns: - qutip.Qobj: The final operator. + The final operator. """ op_list = [self.op_matrix["I"] for j in range(self._size)] @@ -642,7 +642,7 @@ def _construct_hamiltonian(self, update: bool = True) -> None: and refreshes potential noise parameters by drawing new at random. Args: - update(bool=True): Whether to update the noise parameters. + update: Whether to update the noise parameters. """ if update: self._update_noise() @@ -826,11 +826,11 @@ def get_hamiltonian(self, time: float) -> qutip.Qobj: """Get the Hamiltonian created from the sequence at a fixed time. Args: - time (float): The specific time at which we want to extract the + time: The specific time at which we want to extract the Hamiltonian (in ns). Returns: - qutip.Qobj: A new Qobj for the Hamiltonian with coefficients + A new Qobj for the Hamiltonian with coefficients extracted from the effective sequence (determined by `self.sampling_rate`) at the specified time. """ @@ -858,10 +858,10 @@ def run( Will return NoisyResults if the noise in the SimConfig requires it. Otherwise will return CoherentResults. - Keyword Args: - progress_bar (bool or None): If True, the progress bar of QuTiP's + Args: + progress_bar: If True, the progress bar of QuTiP's solver will be shown. If None or False, no text appears. - options (qutip.solver.Options): If specified, will override + options: If specified, will override SimConfig solver_options. If no `max_step` value is provided, an automatic one is calculated from the `Sequence`'s schedule (half of the shortest duration among pulses and delays). From 5b22b988f7e3a75a9bcfed6bc83782624a7ddc58 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Fri, 24 Jun 2022 17:12:03 +0200 Subject: [PATCH 05/18] Fixing mypy issues from numpy v1.23 (#381) * Fixing mypy issues from numpy v1.23 * Fix rounding error in UTs --- pulser-core/pulser/register/_reg_drawer.py | 2 +- pulser-core/pulser/register/register_layout.py | 2 +- tests/test_parametrized.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pulser-core/pulser/register/_reg_drawer.py b/pulser-core/pulser/register/_reg_drawer.py index d4b4d714d..d88d42f49 100644 --- a/pulser-core/pulser/register/_reg_drawer.py +++ b/pulser-core/pulser/register/_reg_drawer.py @@ -155,7 +155,7 @@ def _draw_2D( if len(bonds) > 0: lines = bonds[:, :, (ix, iy)] else: - lines = [] + lines = np.array([]) lc = mc.LineCollection(lines, linewidths=0.6, colors="grey") ax.add_collection(lc) diff --git a/pulser-core/pulser/register/register_layout.py b/pulser-core/pulser/register/register_layout.py index 744d79a81..f20f14f04 100644 --- a/pulser-core/pulser/register/register_layout.py +++ b/pulser-core/pulser/register/register_layout.py @@ -123,7 +123,7 @@ def get_traps_from_coordinates(self, *coordinates: ArrayLike) -> list[int]: """ traps = [] rounded_coords = np.round( - cast(ArrayLike, coordinates), decimals=COORD_PRECISION + np.array(coordinates), decimals=COORD_PRECISION ) for coord, rounded in zip(coordinates, rounded_coords): key = tuple(rounded) diff --git a/tests/test_parametrized.py b/tests/test_parametrized.py index 4c24c4dad..83875bec3 100644 --- a/tests/test_parametrized.py +++ b/tests/test_parametrized.py @@ -155,7 +155,7 @@ def test_opsupport(): np.testing.assert_almost_equal(y.build(), b.build()) y_ = y + 0.4 # y_ = [-0.6, 1.4] y = np.round(y_, 1) - np.testing.assert_array_equal(y.build(), y_.build()) + np.testing.assert_array_equal(y.build(), np.round(y_.build(), 1)) np.testing.assert_array_equal(round(y_).build(), np.round(y_).build()) np.testing.assert_array_equal(round(y_, 1).build(), y.build()) From c3dc5158db508063024dab902ba5c968ff3a1f8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 4 Jul 2022 17:30:10 +0200 Subject: [PATCH 06/18] Bugfix in simulation with SLM mask (#384) * Bugfix in simulation with SLM mask * Add dedicated test for the bug --- .../pulser_simulation/simulation.py | 2 ++ tests/test_simulation.py | 20 +++++++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/pulser-simulation/pulser_simulation/simulation.py b/pulser-simulation/pulser_simulation/simulation.py index 5fe22614a..986afc6e1 100644 --- a/pulser-simulation/pulser_simulation/simulation.py +++ b/pulser-simulation/pulser_simulation/simulation.py @@ -513,6 +513,8 @@ def write_samples( if self._seq._slm_mask_targets and self._seq._slm_mask_time: tf = self._seq._slm_mask_time[1] for qubit in self._seq._slm_mask_targets: + if qubit not in self.samples["Local"][basis]: + continue for x in ("amp", "det", "phase"): self.samples["Local"][basis][qubit][x][0:tf] = 0 diff --git a/tests/test_simulation.py b/tests/test_simulation.py index 169a3c18d..56493d894 100644 --- a/tests/test_simulation.py +++ b/tests/test_simulation.py @@ -857,6 +857,26 @@ def test_mask_two_pulses(): assert ham_masked == ham_three +def test_mask_local_channel(): + seq_ = Sequence(Register.square(2, prefix="q"), MockDevice) + seq_.declare_channel("rydberg_global", "rydberg_global") + pulse = Pulse.ConstantPulse(1000, 10, 0, 0) + seq_.config_slm_mask(["q0", "q3"]) + seq_.add(pulse, "rydberg_global") + + seq_.declare_channel("raman_local", "raman_local", initial_target="q0") + pulse2 = Pulse.ConstantPulse(1000, 10, -5, np.pi) + seq_.add(pulse2, "raman_local", protocol="no-delay") + + assert seq_._slm_mask_time == [0, 1000] + assert seq_._slm_mask_targets == {"q0", "q3"} + + sim = Simulation(seq_) + for qty in ("amp", "det", "phase"): + assert np.all(sim.samples["Local"]["digital"]["q0"][qty] == 0.0) + assert "q3" not in sim.samples["Local"]["digital"] + + def test_effective_size_intersection(): simple_reg = Register.square(2, prefix="atom") rise = Pulse.ConstantPulse(1500, 0, 0, 0) From 299a031f34504d239e6c3c3dfa08ef64a59c5035 Mon Sep 17 00:00:00 2001 From: Constantin Dalyac <58850838+cdalyac@users.noreply.github.com> Date: Tue, 5 Jul 2022 11:29:49 +0200 Subject: [PATCH 07/18] Updating the UD-MIS tutorial to include QAA solution (#383) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * added Quantum Adiabatic Algorithm to solve UD-MIS * added QAA to solve UD-MIS * Modified accordingly to remarks * Fixing broken tests * Saving the notebook with output * Fixing typos Co-authored-by: Constantin Co-authored-by: Henrique Silvério --- docs/source/tutorials/qaoa_mis.nblink | 2 +- ...QAOA and QAA to solve a MIS problem.ipynb} | 301 ++++++++++++++---- 2 files changed, 238 insertions(+), 65 deletions(-) rename tutorials/applications/{Using QAOA to solve a MIS problem.ipynb => QAOA and QAA to solve a MIS problem.ipynb} (74%) diff --git a/docs/source/tutorials/qaoa_mis.nblink b/docs/source/tutorials/qaoa_mis.nblink index 498c411c8..14bbd6c5d 100644 --- a/docs/source/tutorials/qaoa_mis.nblink +++ b/docs/source/tutorials/qaoa_mis.nblink @@ -1,3 +1,3 @@ { - "path": "../../../tutorials/applications/Using QAOA to solve a MIS problem.ipynb" + "path": "../../../tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb" } diff --git a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb b/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb similarity index 74% rename from tutorials/applications/Using QAOA to solve a MIS problem.ipynb rename to tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb index 76f1bdecb..4260a7c82 100644 --- a/tutorials/applications/Using QAOA to solve a MIS problem.ipynb +++ b/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using QAOA to solve a UD-MIS problem" + "# Using QAOA and QAA to solve a UD-MIS problem" ] }, { @@ -16,14 +16,13 @@ "import numpy as np\n", "import igraph\n", "from itertools import combinations\n", - "\n", "import matplotlib.pyplot as plt\n", - "\n", "from pulser import Pulse, Sequence, Register\n", "from pulser_simulation import Simulation\n", "from pulser.devices import Chadoq2\n", - "\n", - "from scipy.optimize import minimize" + "from pulser.waveforms import InterpolatedWaveform\n", + "from scipy.optimize import minimize\n", + "from scipy.spatial.distance import pdist, squareform" ] }, { @@ -37,7 +36,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we illustrate how to solve the Maximum Independent Set (MIS) problem using the Quantum Approximate Optimization Algorithm procedure on a platform of Rydberg atoms in analog mode, using Pulser. \n", + "In this tutorial, we illustrate how to solve the Maximum Independent Set (MIS) problem on a platform of Rydberg atoms in analog mode, using Pulser. \n", "\n", "For more details about this problem and how to encode it on a Rydberg atom quantum processor, see [Pichler, et al., 2018](https://arxiv.org/abs/1808.10816), [Henriet, 2020]( https://journals.aps.org/pra/abstract/10.1103/PhysRevA.101.012335) and [Dalyac, et al., 2020]( https://arxiv.org/abs/2012.14859)." ] @@ -96,7 +95,6 @@ "\n", "$$\n", "H= \\sum_{i=1}^N \\frac{\\hbar\\Omega}{2} \\sigma_i^x - \\sum_{i=1}^N \\frac{\\hbar \\delta}{2} \\sigma_i^z+\\sum_{j" ] @@ -189,14 +187,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Building the quantum loop " + "## 2. Building the quantum algorithm " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the graph is encoded in the Register and that we know there is a direct relation between the Ising Hamiltionian $H$ and the cost function $C$, we still have to build a quantum algorithm that outputs the maximal independent sets. To do so we present two different approaches, namely the Quantum Approximation Optimization Algorithm (QAOA) and the Quantum Adiabatic Algorithm (QAA)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### QAOA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we must build the quantum part of the QAOA. All atoms are initially in the groundstate $|00\\dots0\\rangle$ of the `ground-rydberg`basis. We then apply $p$ layers of alternating non-commutative Hamiltonians. The first one, called the mixing Hamiltonian $H_M$, is realized by taking $\\Omega = 1$ rad/µs, and $\\delta = 0$ rad/µs in the Hamiltonian equation. The second Hamiltonian $H_c$ is realized with $\\Omega = \\delta = 1$ rad/µs. $H_M$ and $H_c$ are applied turn in turn with parameters $\\tau$ and $t$ respectively. A classical optimizer is then used to estimate the optimal parameters. \n", + "This algorithm (see [Farhi, et al., 2014](https://arxiv.org/pdf/1411.4028.pdf)) has gained a lot of traction lately as a gate-based quantum algorithm. It has shown promising results in a number of applications and yields decent results for low-depth circuits.\n", + "\n", + "All atoms are initially in the groundstate $|00\\dots0\\rangle$ of the `ground-rydberg` basis. We then apply $p$ layers of alternating non-commutative Hamiltonians. The first one, called the mixing Hamiltonian $H_M$, is realized by taking $\\Omega = 1$ rad/µs, and $\\delta = 0$ rad/µs in the Hamiltonian equation. The second Hamiltonian $H_c$ is realized with $\\Omega =0$ rad/µs and $\\delta = 1.$ rad/µs. $H_M$ and $H_c$ are applied turn in turn with parameters $\\tau$ and $t$ respectively. A classical optimizer is then used to estimate the optimal parameters. \n", "\n", "Instead of creating a new `Sequence` everytime the quantum loop is called, we are going to create a parametrized `Sequence` and give that to the quantum loop." ] @@ -216,13 +230,9 @@ "t_list = seq.declare_variable(\"t_list\", size=LAYERS)\n", "s_list = seq.declare_variable(\"s_list\", size=LAYERS)\n", "\n", - "if LAYERS == 1:\n", - " t_list = [t_list]\n", - " s_list = [s_list]\n", - "\n", "for t, s in zip(t_list, s_list):\n", " pulse_1 = Pulse.ConstantPulse(1000 * t, 1.0, 0.0, 0)\n", - " pulse_2 = Pulse.ConstantPulse(1000 * s, 1.0, 1.0, 0)\n", + " pulse_2 = Pulse.ConstantPulse(1000 * s, 0.0, 1.0, 0)\n", "\n", " seq.add(pulse_1, \"ch0\")\n", " seq.add(pulse_2, \"ch0\")\n", @@ -241,7 +251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Experimentally, we don't have access to the state vector $|\\psi\\rangle$. We therefore make it more realistic by taking samples from the state vector that results from running the simulation with `simul.run()`. This is done with the built-in method `results.sample_final_state()`, in which we add the measurement basis which was declared at the end of the sequence, and the number of samples desired. Currently, the repetition rate of the machine is $5$Hz." + "Experimentally, we don't have access to the state vector $|\\psi\\rangle$. We therefore make it more realistic by taking samples from the state vector that results from running the simulation with `simul.run()`. This is done with the built-in method `results.sample_final_state()`, in which we add the measurement basis which was declared at the end of the sequence, and the number of samples desired. Currently, the repetition rate of the machine is $5$ Hz." ] }, { @@ -266,6 +276,7 @@ "metadata": {}, "outputs": [], "source": [ + "np.random.seed(123) # ensures reproducibility of the tutorial\n", "guess = {\n", " \"t\": np.random.uniform(8, 10, LAYERS),\n", " \"s\": np.random.uniform(1, 3, LAYERS),\n", @@ -313,7 +324,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -379,70 +390,124 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-3" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "get_cost_colouring(\"00111\", G)" + "def func(param, *args):\n", + " G = args[0]\n", + " C = quantum_loop(param)\n", + " cost = get_cost(C, G)\n", + " return cost" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### QAOA for depth $p = 2$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now use a classical optimizer `minimize` in order to find the best variational parameters. This function takes as arguments `func`, the graph `G`and an initial `x0` point for the simplex in Nelder-Mead minimization. As the optimizer might get trapped in local minima, we repeat the optimization 20 times and select the parameters that yield the best approximation ratio." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, + "outputs": [], + "source": [ + "scores = []\n", + "params = []\n", + "for repetition in range(20):\n", + " guess = {\n", + " \"t\": np.random.uniform(1, 10, LAYERS),\n", + " \"s\": np.random.uniform(1, 10, LAYERS),\n", + " }\n", + "\n", + " try:\n", + " res = minimize(\n", + " func,\n", + " args=G,\n", + " x0=np.r_[guess[\"t\"], guess[\"s\"]],\n", + " method=\"Nelder-Mead\",\n", + " tol=1e-5,\n", + " options={\"maxiter\": 10},\n", + " )\n", + " scores.append(res.fun)\n", + " params.append(res.x)\n", + " except Exception as e:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the sample that we woud obtain using the optimal variational parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { + "image/png": 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" ] }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "get_cost(example_dict, G)" + "optimal_count_dict = quantum_loop(params[np.argmin(scores)])\n", + "plot_distribution(optimal_count_dict)" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "def func(param, *args):\n", - " G = args[0]\n", - " C = quantum_loop(param)\n", - " cost = get_cost(C, G)\n", - " return cost" + "QAOA is capable of finding good variational parameters $\\tau$ and $t$. Now, sampling from this final state $|\\psi(t_{f})\\rangle$ will return both MISs of the graph with high probability. Note that listing all maximal independent sets of a graph is also NP, and can be used as a subroutine for solving many NP-complete graph problems. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### QAOA for depth $p = 2$" + "However, using QAOA to solve the problem is not the best idea; it's difficult to yield a >90% quality solution without going to high depths of the QAOA, implying that the growing closed-loop optimization can rapidly become expensive, with no guarantee of convergence. We therefore propose another approach called the Quantum Adiabatic Algorithm (QAA). This fast, reliant and exclusively analog method shows optimal convergence to the solution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now use a classical optimizer `minimize` in order to find the best variational parameters. This function takes as arguments `func`, the graph `G`and an initial `x0` point for the simplex in Nelder-Mead minimization." + "## Quantum Adiabatic Algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The idea behind the adiabatic algorithm (see [Albash, Lidar, 2018](https://arxiv.org/pdf/1611.04471.pdf)) is to slowly evolve the system from an easy-to-prepare groundstate to the groundstate of the cost Hamiltonian $H_C$. If done slowly enough, the system of atoms stays in the instantaneous ground-state.\n", + "\n", + "In our case, we continuously vary the parameters $\\Omega(t), \\delta(t)$ in time, starting with $\\Omega(0)=0, \\delta(0)<0$ and ending with $\\Omega(0)=0, \\delta>0$. The ground-state of $H(0)$ corresponds to the initial state $|00000\\rangle$ and the ground-state of $H(t_f)$ corresponds to the ground-state of $H_c$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure that we are not exciting the system to states that do not form independent sets, we have to estimate the minimal distance between atoms which are not connected in the graph (this yields $\\Omega_{\\text{min}}$), and estimate the furthest distance between two disconnected atoms $\\Omega_{\\text{max}}$. Keeping $\\Omega \\in [\\Omega_{\\text{min}}, \\Omega_{\\text{max}}]$ insures that only independent sets appear in the dynamics. " ] }, { @@ -451,31 +516,79 @@ "metadata": {}, "outputs": [], "source": [ - "res = minimize(\n", - " func,\n", - " args=G,\n", - " x0=np.r_[guess[\"t\"], guess[\"s\"]],\n", - " method=\"Nelder-Mead\",\n", - " tol=1e-5,\n", - " options={\"maxiter\": 100},\n", - ")" + "A = np.array(G.get_adjacency().data) # adjacency matrix of G\n", + "A_complement = -(np.array(G.get_adjacency().data) - 1) - np.eye(\n", + " len(A)\n", + ") # adjacency matrix of G complement\n", + "D = squareform(pdist(np.array(list(reg.qubits.values()))))\n", + "link_max = np.max(D * A)\n", + "no_link_min = np.min((D * A_complement)[np.nonzero(D * A_complement)])\n", + "Omega_min = Chadoq2.interaction_coeff / no_link_min**6\n", + "Omega_max = Chadoq2.interaction_coeff / link_max**6" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 15, "metadata": {}, + "outputs": [], "source": [ - "We can now plot the sample that we woud obtain using the variational parameters `res.x`." + "Omega = (\n", + " Omega_max - Omega_min\n", + ") / 2 # we choose a random value between the min and the max\n", + "delta_0 = -5 # just has to be negative\n", + "delta_f = -delta_0 # just has to be positive\n", + "T = 4500 # time in ns, we choose a time long enough to ensure the propagation of information in the system" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "adiabatic_pulse = Pulse(\n", + " InterpolatedWaveform(T, [1e-9, Omega, 1e-9]),\n", + " InterpolatedWaveform(T, [delta_0, 0, delta_f]),\n", + " 0,\n", + ")\n", + "seq = Sequence(reg, Chadoq2)\n", + "seq.declare_channel(\"ising\", \"rydberg_global\")\n", + "seq.add(adiabatic_pulse, \"ising\")\n", + "seq.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "simul = Simulation(seq)\n", + "results = simul.run()\n", + "final = results.get_final_state()\n", + "count_dict = results.sample_final_state()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -487,7 +600,6 @@ } ], "source": [ - "count_dict = quantum_loop(res.x)\n", "plot_distribution(count_dict)" ] }, @@ -495,14 +607,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "QAOA is capable of finding good variational parameters $\\tau$ and $t$. Now, sampling from this final state $|\\psi(t_{f})\\rangle$ will return both MISs of the graph with high probability. Note that listing all maximal independent sets of a graph is also NP, and can be used as a subroutine for solving many NP-complete graph problems. " + "See how fast and performant this method is! In only a few micro-seconds, we find an excellent solution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does the time evolution affect the quality of the results?" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "cost = []\n", + "for T in 1000 * np.linspace(1, 10, 10):\n", + " seq = Sequence(reg, Chadoq2)\n", + " seq.declare_channel(\"ising\", \"rydberg_global\")\n", + " adiabatic_pulse = Pulse(\n", + " InterpolatedWaveform(T, [1e-9, Omega, 1e-9]),\n", + " InterpolatedWaveform(T, [delta_0, 0, delta_f]),\n", + " 0,\n", + " )\n", + " seq.add(adiabatic_pulse, \"ising\")\n", + " simul = Simulation(seq)\n", + " results = simul.run()\n", + " final = results.get_final_state()\n", + " count_dict = results.sample_final_state()\n", + " cost.append(get_cost(count_dict, G) / 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "plt.plot(range(1, 11), -np.array(cost), \"--o\")\n", + "plt.xlabel(\"total time evolution (µs)\", fontsize=14)\n", + "plt.ylabel(\"approximation ratio\", fontsize=14)\n", + "plt.show()" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.5 ('pulser-dev')", "language": "python", "name": "python3" }, @@ -517,6 +685,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" + }, + "vscode": { + "interpreter": { + "hash": "e088768f7ff7b4294439f8ed10f7eed9e3b885124bc20d9d06cc2a37b1883330" + } } }, "nbformat": 4, From 58f5b0ccfbdcf9502dea48ea745a83c817ca93b3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 18 Jul 2022 11:22:23 +0200 Subject: [PATCH 08/18] Flexible Sequence drawing (#385) * Making detuning curve disappear whe not used * WIP: Plotting phase curves * Phase curve drawing accessible in public methods * Change color for hatch in output display * Prolonging the input signal when drawing output * Updating UTs and typing * Review notebooks with saved output * Small improvements * Implementing review suggestions * Adopting review suggestions --- pulser-core/pulser/_seq_drawer.py | 307 +++++++++++------- pulser-core/pulser/sequence.py | 4 + .../pulser_simulation/simulation.py | 4 + tests/test_sequence.py | 4 + ...ting Sequences with Errors and Noise.ipynb | 107 +++++- .../Building 1D Rydberg Crystals.ipynb | 7 - .../Spin chain of 3 atoms in XY mode.ipynb | 1 - 7 files changed, 292 insertions(+), 142 deletions(-) diff --git a/pulser-core/pulser/_seq_drawer.py b/pulser-core/pulser/_seq_drawer.py index ac675c0bf..054809f24 100644 --- a/pulser-core/pulser/_seq_drawer.py +++ b/pulser-core/pulser/_seq_drawer.py @@ -15,6 +15,7 @@ from __future__ import annotations from collections import defaultdict +from dataclasses import dataclass, field from itertools import combinations from typing import Any, Optional, Union, cast @@ -28,6 +29,43 @@ from pulser.pulse import Pulse from pulser.waveforms import ConstantWaveform, InterpolatedWaveform +# Color scheme +COLORS = ["darkgreen", "indigo", "#c75000"] + +CURVES_ORDER = ("amplitude", "detuning", "phase") + +SIZE_PER_WIDTH = {1: 3, 2: 4, 3: 5} +LABELS = [ + r"$\Omega$ (rad/µs)", + r"$\delta$ (rad/µs)", + r"$\varphi$ / 2π", +] + + +@dataclass +class ChannelDrawContent: + """The contents for drawingflake a single channel.""" + + time: list[int] + amplitude: list[float] + detuning: list[float] + phase: list[float] + target: dict[Union[str, tuple[int, int]], Any] + measurement: Optional[str] = None + interp_pts: dict[str, list[list[float]]] = field(default_factory=dict) + + def __post_init__(self) -> None: + self.curves_on = {"amplitude": True, "detuning": False, "phase": False} + + @property + def n_axes_on(self) -> int: + """The number of axes to draw for this channel.""" + return sum(self.curves_on.values()) + + def curves_on_indices(self) -> list[int]: + """The indices of the curves to draw.""" + return [i for i, qty in enumerate(CURVES_ORDER) if self.curves_on[qty]] + def gather_data(seq: pulser.sequence.Sequence) -> dict: """Collects the whole sequence data for plotting. @@ -45,16 +83,17 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: time = [-1] # To not break the "time[-1]" later on amp = [] detuning = [] + phase = [] # List of interpolation points interp_pts: defaultdict[str, list[list[float]]] = defaultdict(list) target: dict[Union[str, tuple[int, int]], Any] = {} - # phase_shift = {} for slot in sch: if slot.ti == -1: target["initial"] = slot.targets time += [0] amp += [0.0] detuning += [0.0] + phase += [0.0] continue if slot.type in ["delay", "target"]: time += [ @@ -63,6 +102,7 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: ] amp += [0.0, 0.0] detuning += [0.0, 0.0] + phase += [phase[-1]] * 2 if slot.type == "target": target[(slot.ti, slot.tf - 1)] = slot.targets continue @@ -71,12 +111,14 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: pulse.detuning, ConstantWaveform ): time += [slot.ti, slot.tf - 1] - amp += [float(pulse.amplitude._value)] * 2 - detuning += [float(pulse.detuning._value)] * 2 + amp += [float(pulse.amplitude[0])] * 2 + detuning += [float(pulse.detuning[0])] * 2 + phase += [float(pulse.phase) / (2 * np.pi)] * 2 else: time += list(range(slot.ti, slot.tf)) amp += pulse.amplitude.samples.tolist() detuning += pulse.detuning.samples.tolist() + phase += [float(pulse.phase) / (2 * np.pi)] * pulse.duration for wf_type in ["amplitude", "detuning"]: wf = getattr(pulse, wf_type) if isinstance(wf, InterpolatedWaveform): @@ -88,18 +130,14 @@ def gather_data(seq: pulser.sequence.Sequence) -> dict: time += [time[-1] + 1, total_duration - 1] amp += [0, 0] detuning += [0, 0] + phase += [phase[-1] if len(phase) else 0] * 2 # Store everything time.pop(0) # Removes the -1 in the beginning - data[ch] = { - "time": time, - "amp": amp, - "detuning": detuning, - "target": target, - } + data[ch] = ChannelDrawContent(time, amp, detuning, phase, target) if hasattr(seq, "_measurement"): - data[ch]["measurement"] = seq._measurement + data[ch].measurement = seq._measurement if interp_pts: - data[ch]["interp_pts"] = interp_pts + data[ch].interp_pts = dict(interp_pts) data["total_duration"] = total_duration return data @@ -113,6 +151,7 @@ def draw_sequence( draw_register: bool = False, draw_input: bool = True, draw_modulation: bool = False, + draw_phase_curve: bool = False, ) -> tuple[Figure, Figure]: """Draws the entire sequence. @@ -136,6 +175,8 @@ def draw_sequence( draw_modulation: Draws the expected channel output, defaults to False. If the channel does not have a defined 'mod_bandwidth', this is skipped unless 'draw_input=False'. + draw_phase_curve: Draws the changes in phase in its own curve (ignored + if the phase doesn't change throughout the channel). """ def phase_str(phi: float) -> str: @@ -154,6 +195,11 @@ def phase_str(phi: float) -> str: data = gather_data(seq) total_duration = data["total_duration"] time_scale = 1e3 if total_duration > 1e4 else 1 + for ch in seq._schedule: + if np.nonzero(data[ch].detuning)[0].size > 0: + data[ch].curves_on["detuning"] = True + if draw_phase_curve and np.nonzero(data[ch].phase)[0].size > 0: + data[ch].curves_on["phase"] = True # Boxes for qubit and phase text q_box = dict(boxstyle="round", facecolor="orange") @@ -204,39 +250,36 @@ def phase_str(phi: float) -> str: ) ax_reg.set_title("Masked register", pad=10) + ratios = [ + SIZE_PER_WIDTH[data[ch].n_axes_on] for ch in seq.declared_channels + ] fig = plt.figure( constrained_layout=False, - figsize=(20, 4.5 * n_channels), - ) - gs = fig.add_gridspec( - n_channels, - 1, - hspace=0.075, + figsize=(20, sum(ratios)), ) + gs = fig.add_gridspec(n_channels, 1, hspace=0.075, height_ratios=ratios) ch_axes = {} for i, (ch, gs_) in enumerate(zip(seq._channels, gs)): ax = fig.add_subplot(gs_) - ax.spines["top"].set_color("none") - ax.spines["bottom"].set_color("none") - ax.spines["left"].set_color("none") - ax.spines["right"].set_color("none") + for side in ("top", "bottom", "left", "right"): + ax.spines[side].set_color("none") ax.tick_params( labelcolor="w", top=False, bottom=False, left=False, right=False ) ax.set_ylabel(ch, labelpad=40, fontsize=18) - subgs = gs_.subgridspec(2, 1, hspace=0.0) - ax1 = fig.add_subplot(subgs[0, :]) - ax2 = fig.add_subplot(subgs[1, :]) - ch_axes[ch] = (ax1, ax2) + subgs = gs_.subgridspec(data[ch].n_axes_on, 1, hspace=0.0) + ch_axes[ch] = [ + fig.add_subplot(subgs[i, :]) for i in range(data[ch].n_axes_on) + ] for j, ax in enumerate(ch_axes[ch]): ax.axvline(0, linestyle="--", linewidth=0.5, color="grey") - if j == 0: - ax.spines["bottom"].set_visible(False) - else: + if j > 0: ax.spines["top"].set_visible(False) + if j < len(ch_axes[ch]) - 1: + ax.spines["bottom"].set_visible(False) - if i < n_channels - 1 or j == 0: + if i < n_channels - 1 or j < len(ch_axes[ch]) - 1: ax.tick_params( axis="x", which="both", @@ -273,77 +316,92 @@ def phase_str(phi: float) -> str: t_min = -final_t * 0.03 t_max = final_t * 1.05 - for ch, (a, b) in ch_axes.items(): + for ch, axes in ch_axes.items(): ch_obj = seq._channels[ch] + ch_data = data[ch] basis = ch_obj.basis - times = np.array(data[ch]["time"]) + times = np.array(ch_data.time) t = times / time_scale - ya = data[ch]["amp"] - yb = data[ch]["detuning"] + ys = [getattr(ch_data, qty) for qty in CURVES_ORDER] if sampling_rate: + cubic_splines = [] + yseff = [] t2 = 1 - ya2 = [] - yb2 = [] + t2s = [] for t_solv in solver_time: # Find the interval [t[t2],t[t2+1]] containing t_solv while t_solv > t[t2]: t2 += 1 - ya2.append(ya[t2]) - yb2.append(yb[t2]) - cs_amp = CubicSpline(solver_time, ya2) - cs_detuning = CubicSpline(solver_time, yb2) - yaeff = cs_amp(teff) - ybeff = cs_detuning(teff) + t2s.append(t2) + for i, y_ in enumerate(ys): + y2 = [y_[t_] for t_ in t2s] + cubic_splines.append(CubicSpline(solver_time, y2)) + yseff.append(cubic_splines[i](teff)) draw_output = draw_modulation and ( ch_obj.mod_bandwidth or not draw_input ) if draw_output: + ys_mod = [] t_diffs = np.diff(times) - input_a = np.repeat(ya[1:], t_diffs) - input_b = np.repeat(yb[1:], t_diffs) end_index = int(final_t * time_scale) - ya_mod = ch_obj.modulate(input_a)[:end_index] - yb_mod = ch_obj.modulate(input_b, keep_ends=True)[:end_index] - - a.set_xlim(t_min, t_max) - b.set_xlim(t_min, t_max) - - max_amp = np.max(ya) + for i, y_ in enumerate(ys): + input = np.repeat(y_[1:], t_diffs) + ys_mod.append( + ch_obj.modulate(input, keep_ends=i > 0)[:end_index] + ) + # Prolong the input samples + t = np.append(t, (t[-1] + 1 / time_scale, final_t)) + ys[0] += [0.0, 0.0] + ys[1] += [0.0, 0.0] + ys[2] += [ys[2][-1]] * 2 + + ref_ys = yseff if sampling_rate else ys + max_amp = np.max(ref_ys[0]) max_amp = 1 if max_amp == 0 else max_amp amp_top = max_amp * 1.2 - a.set_ylim(-0.02, amp_top) - det_max = np.max(yb) - det_min = np.min(yb) + amp_bottom = min(0.0, *ref_ys[0]) + det_max = np.max(ref_ys[1]) + det_min = np.min(ref_ys[1]) det_range = det_max - det_min if det_range == 0: det_min, det_max, det_range = -1, 1, 2 det_top = det_max + det_range * 0.15 det_bottom = det_min - det_range * 0.05 - b.set_ylim(det_bottom, det_top) - - if draw_input: - a.plot(t, ya, color="darkgreen", linewidth=0.8) - b.plot(t, yb, color="indigo", linewidth=0.8) - if sampling_rate: - a.plot(teff, yaeff, color="darkgreen", linewidth=0.8) - b.plot(teff, ybeff, color="indigo", linewidth=0.8, ls="-") - a.fill_between(teff, 0, yaeff, color="darkgreen", alpha=0.3) - b.fill_between(teff, 0, ybeff, color="indigo", alpha=0.3) - elif draw_input: - a.fill_between(t, 0, ya, color="darkgreen", alpha=0.3) - b.fill_between(t, 0, yb, color="indigo", alpha=0.3) - if draw_output: - a.plot(ya_mod, color="darkred", linewidth=0.8) - b.plot(yb_mod, color="gold", linewidth=0.8) - a.fill_between( - np.arange(ya_mod.size), 0, ya_mod, color="darkred", alpha=0.3 - ) - b.fill_between( - np.arange(yb_mod.size), 0, yb_mod, color="gold", alpha=0.3 - ) - a.set_ylabel(r"$\Omega$ (rad/µs)", fontsize=14, labelpad=10) - b.set_ylabel(r"$\delta$ (rad/µs)", fontsize=14) + ax_lims = [ + (amp_bottom, amp_top), + (det_bottom, det_top), + (min(0.0, *ref_ys[2]), max(1.1, *ref_ys[2])), + ] + ax_lims = [ax_lims[i] for i in ch_data.curves_on_indices()] + for ax, ylim in zip(axes, ax_lims): + ax.set_xlim(t_min, t_max) + ax.set_ylim(*ylim) + + for i, ax in zip(ch_data.curves_on_indices(), axes): + if draw_input: + ax.plot(t, ys[i], color=COLORS[i], linewidth=0.8) + if sampling_rate: + ax.plot( + teff, + yseff[i], + color=COLORS[i], + linewidth=0.8, + ) + ax.fill_between(teff, 0, yseff[i], color=COLORS[i], alpha=0.3) + elif draw_input: + ax.fill_between(t, 0, ys[i], color=COLORS[i], alpha=0.3) + if draw_output: + ax.fill_between( + np.arange(ys_mod[i].size), + 0, + ys_mod[i], + color=COLORS[i], + alpha=0.3, + hatch="////", + ) + special_kwargs = dict(labelpad=10) if i == 0 else {} + ax.set_ylabel(LABELS[i], fontsize=14, **special_kwargs) if draw_phase_area: top = False # Variable to track position of box, top or center. @@ -358,7 +416,7 @@ def phase_str(phi: float) -> str: if sampling_rate: area_val = ( np.sum( - cs_amp( + cubic_splines[0]( np.arange(seq_.ti, seq_.tf) / time_scale ) ) @@ -388,7 +446,7 @@ def phase_str(phi: float) -> str: else: phase_fmt = rf"$\phi$: {phase_str(phase_val)}" txt = "\n".join([phase_fmt, area_fmt]) - a.text( + axes[0].text( x_plot, y_plot, txt, @@ -399,8 +457,8 @@ def phase_str(phi: float) -> str: ) target_regions = [] # [[start1, [targets1], end1],...] - for coords in data[ch]["target"]: - targets = list(data[ch]["target"][coords]) + for coords in ch_data.target: + targets = list(ch_data.target[coords]) tgt_strs = [str(q) for q in targets] tgt_txt_y = max_amp * 1.1 - 0.25 * (len(targets) - 1) tgt_str = "\n".join(tgt_strs) @@ -408,7 +466,7 @@ def phase_str(phi: float) -> str: x = t_min + final_t * 0.005 target_regions.append([0, targets]) if seq._channels[ch].addressing == "Global": - a.text( + axes[0].text( x, amp_top * 0.98, "GLOBAL", @@ -419,7 +477,7 @@ def phase_str(phi: float) -> str: bbox=q_box, ) else: - a.text( + axes[0].text( x, tgt_txt_y, tgt_str, @@ -430,7 +488,7 @@ def phase_str(phi: float) -> str: phase = seq._phase_ref[basis][targets[0]][0] if phase and draw_phase_shifts: msg = r"$\phi=$" + phase_str(phase) - a.text( + axes[0].text( 0, max_amp * 1.1, msg, @@ -445,9 +503,9 @@ def phase_str(phi: float) -> str: [tf + 1 / time_scale, targets] ) # New one phase = seq._phase_ref[basis][targets[0]][tf * time_scale + 1] - a.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") - b.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") - a.text( + for ax in axes: + ax.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") + axes[0].text( tf + final_t * 5e-3, tgt_txt_y, tgt_str, @@ -459,7 +517,7 @@ def phase_str(phi: float) -> str: msg = r"$\phi=$" + phase_str(phase) wrd_len = len(max(tgt_strs, key=len)) x = tf + final_t * 0.01 * (wrd_len + 1) - a.text( + axes[0].text( x, max_amp * 1.1, msg, @@ -479,14 +537,14 @@ def phase_str(phi: float) -> str: # All targets have the same ref, so we pick q = targets_[0] ref = seq._phase_ref[basis][q] - if end != total_duration - 1 or "measurement" not in data[ch]: + if end != total_duration - 1 or ch_data.measurement is not None: end += 1 / time_scale for t_, delta in ref.changes(start, end, time_scale=time_scale): conf = dict(linestyle="--", linewidth=1.5, color="black") - a.axvline(t_, **conf) - b.axvline(t_, **conf) + for ax in axes: + ax.axvline(t_, **conf) msg = "\u27F2 " + phase_str(delta) - a.text( + axes[0].text( t_ - final_t * 8e-3, max_amp * 1.1, msg, @@ -498,13 +556,13 @@ def phase_str(phi: float) -> str: # Draw the SLM mask if seq._slm_mask_targets and seq._slm_mask_time: tf_m = seq._slm_mask_time[1] - a.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) - b.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) + for ax in axes: + ax.axvspan(0, tf_m, color="black", alpha=0.1, zorder=-100) tgt_strs = [str(q) for q in seq._slm_mask_targets] tgt_txt_x = final_t * 0.005 - tgt_txt_y = b.get_ylim()[0] + tgt_txt_y = axes[-1].get_ylim()[0] tgt_str = "\n".join(tgt_strs) - b.text( + axes[-1].text( tgt_txt_x, tgt_txt_y, tgt_str, @@ -513,33 +571,48 @@ def phase_str(phi: float) -> str: bbox=slm_box, ) - if "measurement" in data[ch]: - msg = f"Basis: {data[ch]['measurement']}" - b.text( + hline_kwargs = dict(linestyle="-", linewidth=0.5, color="grey") + if ch_data.measurement is not None: + msg = f"Basis: {ch_data.measurement}" + if len(axes) == 1: + mid_ax = axes[0] + mid_point = (amp_top + amp_bottom) / 2 + fontsize = 12 + else: + mid_ax = axes[-1] + mid_point = ( + ax_lims[-1][1] + if len(axes) == 2 + else ax_lims[-1][0] + sum(ax_lims[-1]) * 1.5 + ) + fontsize = 14 + + for ax in axes: + ax.axvspan(final_t, t_max, color="midnightblue", alpha=1) + + mid_ax.text( final_t * 1.025, - det_top, + mid_point, msg, ha="center", va="center", - fontsize=14, + fontsize=fontsize, color="white", rotation=90, ) - a.axvspan(final_t, t_max, color="midnightblue", alpha=1) - b.axvspan(final_t, t_max, color="midnightblue", alpha=1) - a.axhline(0, xmax=0.95, linestyle="-", linewidth=0.5, color="grey") - b.axhline(0, xmax=0.95, linestyle=":", linewidth=0.5, color="grey") - else: - a.axhline(0, linestyle="-", linewidth=0.5, color="grey") - b.axhline(0, linestyle=":", linewidth=0.5, color="grey") - - if "interp_pts" in data[ch] and draw_interp_pts: - all_points = data[ch]["interp_pts"] - if "amplitude" in all_points: - pts = np.array(all_points["amplitude"]) - a.scatter(pts[:, 0], pts[:, 1], color="darkgreen") - if "detuning" in all_points: - pts = np.array(all_points["detuning"]) - b.scatter(pts[:, 0], pts[:, 1], color="indigo") + hline_kwargs["xmax"] = 0.95 + + for i, ax in enumerate(axes): + if i > 0: + ax.axhline(ax_lims[i][1], **hline_kwargs) + if ax_lims[i][0] < 0: + ax.axhline(0, **hline_kwargs) + + if draw_interp_pts: + for qty in ("amplitude", "detuning"): + if qty in ch_data.interp_pts and ch_data.curves_on[qty]: + ind = CURVES_ORDER.index(qty) + pts = np.array(ch_data.interp_pts[qty]) + axes[ind].scatter(pts[:, 0], pts[:, 1], color=COLORS[ind]) return (fig_reg if draw_register else None, fig) diff --git a/pulser-core/pulser/sequence.py b/pulser-core/pulser/sequence.py index b9c8fe62a..9be44ba49 100644 --- a/pulser-core/pulser/sequence.py +++ b/pulser-core/pulser/sequence.py @@ -1167,6 +1167,7 @@ def draw( draw_interp_pts: bool = True, draw_phase_shifts: bool = False, draw_register: bool = False, + draw_phase_curve: bool = False, fig_name: str = None, kwargs_savefig: dict = {}, ) -> None: @@ -1191,6 +1192,8 @@ def draw( sequence, with a visual indication (square halo) around the qubits masked by the SLM, defaults to False. Can't be set to True if the sequence is defined with a mappable register. + draw_phase_curve: Draws the changes in phase in its own curve + (ignored if the phase doesn't change throughout the channel). fig_name: The name on which to save the figure. If `draw_register` is True, both pulses and register will be saved as figures, with a suffix ``_pulses`` and @@ -1238,6 +1241,7 @@ def draw( draw_register=draw_register, draw_input="input" in mode, draw_modulation="output" in mode, + draw_phase_curve=draw_phase_curve, ) if fig_name is not None and draw_register: name, ext = os.path.splitext(fig_name) diff --git a/pulser-simulation/pulser_simulation/simulation.py b/pulser-simulation/pulser_simulation/simulation.py index 986afc6e1..70176a7f9 100644 --- a/pulser-simulation/pulser_simulation/simulation.py +++ b/pulser-simulation/pulser_simulation/simulation.py @@ -372,6 +372,7 @@ def draw( draw_phase_area: bool = False, draw_interp_pts: bool = False, draw_phase_shifts: bool = False, + draw_phase_curve: bool = False, fig_name: str = None, kwargs_savefig: dict = {}, ) -> None: @@ -385,6 +386,8 @@ def draw( on top of the respective waveforms (defaults to False). draw_phase_shifts: Whether phase shift and reference information should be added to the plot, defaults to False. + draw_phase_curve: Draws the changes in phase in its own curve + (ignored if the phase doesn't change throughout the channel). fig_name: The name on which to save the figure. If None the figure will not be saved. kwargs_savefig: Keywords arguments for @@ -400,6 +403,7 @@ def draw( draw_phase_area=draw_phase_area, draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, + draw_phase_curve=draw_phase_curve, ) if fig_name is not None: plt.savefig(fig_name, **kwargs_savefig) diff --git a/tests/test_sequence.py b/tests/test_sequence.py index 81d5b59f4..b9871a638 100644 --- a/tests/test_sequence.py +++ b/tests/test_sequence.py @@ -410,6 +410,9 @@ def test_sequence(): with patch("matplotlib.pyplot.show"): seq.draw(draw_phase_area=True) + with patch("matplotlib.pyplot.show"): + seq.draw(draw_phase_curve=True) + s = seq.serialize() assert json.loads(s)["__version__"] == pulser.__version__ seq_ = Sequence.deserialize(s) @@ -556,6 +559,7 @@ def test_draw_register(): seq3d.declare_channel("ch_xy", "mw_global") seq3d.add(pulse, "ch_xy") seq3d.config_slm_mask([6, 15]) + seq3d.measure(basis="XY") with patch("matplotlib.pyplot.show"): seq3d.draw(draw_register=True) diff --git a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb index 0113b4bb9..982c0c174 100644 --- a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb +++ b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "91a245c9", "metadata": {}, "source": [ "# Simulation with Noise and Errors" @@ -9,6 +10,7 @@ }, { "cell_type": "markdown", + "id": "67e0251f", "metadata": {}, "source": [ "## Introduction\n", @@ -29,6 +31,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "aee2644a", "metadata": {}, "outputs": [], "source": [ @@ -36,14 +39,15 @@ "import matplotlib.pyplot as plt\n", "import qutip\n", "\n", - "from pulser import Register, Pulse, Sequence, Simulation\n", - "from pulser_simulation import SimConfig\n", + "from pulser import Register, Pulse, Sequence\n", + "from pulser_simulation import SimConfig, Simulation\n", "from pulser.devices import Chadoq2\n", "from pulser.waveforms import ConstantWaveform, RampWaveform" ] }, { "cell_type": "markdown", + "id": "0e7fff3e", "metadata": {}, "source": [ "## Single atom noisy simulations" @@ -51,6 +55,7 @@ }, { "cell_type": "markdown", + "id": "bafc3de4", "metadata": {}, "source": [ "### Sequence preparation" @@ -58,6 +63,7 @@ }, { "cell_type": "markdown", + "id": "556360fc", "metadata": {}, "source": [ "Prepare a single atom:" @@ -66,6 +72,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "46b32aac", "metadata": {}, "outputs": [], "source": [ @@ -74,6 +81,7 @@ }, { "cell_type": "markdown", + "id": "613dcffc", "metadata": {}, "source": [ "Act on this atom with a Constant Pulse, such that it oscillates towards the excited Rydberg state and back to the original state (Rabi oscillations):" @@ -82,15 +90,16 @@ { "cell_type": "code", "execution_count": 3, + "id": "e3b15936", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, "metadata": {}, @@ -108,6 +117,7 @@ }, { "cell_type": "markdown", + "id": "8bad66f1", "metadata": {}, "source": [ "We now run the noiseless simulation, to obtain a `CoherentResults` object in `clean_res`." @@ -116,6 +126,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "a68d7f41", "metadata": {}, "outputs": [], "source": [ @@ -125,6 +136,7 @@ }, { "cell_type": "markdown", + "id": "758ce4c0", "metadata": {}, "source": [ "Here we obtain the excited population using the projector onto the Rydberg state." @@ -133,6 +145,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "455644d3", "metadata": {}, "outputs": [], "source": [ @@ -142,11 +155,12 @@ { "cell_type": "code", "execution_count": 6, + "id": "59febbd8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -164,6 +178,7 @@ }, { "cell_type": "markdown", + "id": "71a84fc8", "metadata": {}, "source": [ "### The SimConfig object" @@ -171,6 +186,7 @@ }, { "cell_type": "markdown", + "id": "124ead51", "metadata": {}, "source": [ "Each simulation has an associated `SimConfig` object, which encapsulates parameters such as noise types, the temperature of the register... You may view it at any time using the following command." @@ -179,6 +195,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "1503db35", "metadata": {}, "outputs": [ { @@ -198,6 +215,7 @@ }, { "cell_type": "markdown", + "id": "cc20da6e", "metadata": {}, "source": [ "When creating a new `SimConfig`, you may choose several parameters. `'runs'` indicates the number of times a noisy simulation is run to obtain the average result of several simulations, `'samples_per_run'` is the number of delivered samples per run - this has no physical interpretation, this is used simply to cut down on calculation time." @@ -205,6 +223,7 @@ }, { "cell_type": "markdown", + "id": "b4d1a00e", "metadata": {}, "source": [ "We will also add `SPAM` noise to the simulation by creating a new `SimConfig` object, and assigning it to the `config` field of `sim` via the `Simulation.set_config` setter. We pass noise types as a tuple of strings to a SimConfig object. Possible strings are : `'SPAM', 'dephasing', 'doppler', 'amplitude'`." @@ -213,6 +232,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "73bf2544", "metadata": {}, "outputs": [], "source": [ @@ -222,6 +242,7 @@ }, { "cell_type": "markdown", + "id": "3bee68fc", "metadata": {}, "source": [ "We now show the new configuration to have an overview of the changes we made." @@ -230,6 +251,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "19022bb2", "metadata": {}, "outputs": [ { @@ -251,6 +273,7 @@ }, { "cell_type": "markdown", + "id": "87cf1ac2", "metadata": {}, "source": [ "Note that `SimConfig.spam_dict` is the spam parameters dictionary. `eta` is the probability of a badly prepared state, `epsilon` the false positive probability, `epsilon_prime` the false negative one." @@ -258,6 +281,7 @@ }, { "cell_type": "markdown", + "id": "8de7b636", "metadata": {}, "source": [ "When dealing with a `SimConfig` object with different noise parameters from the config in `Simulation.config`, you may \"add\" both configurations together, obtaining a single `SimConfig` with all noises from both configurations - on the other hand, the `runs` and `samples_per_run` will always be updated. This adds simulation parameters to noises that weren't available in the former `Simulation.config`. Noises specified in both `SimConfigs` will keep the noise parameters in `Simulation.config`. Try it out with `Simulation.add_config`:" @@ -266,6 +290,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "2601acb1", "metadata": {}, "outputs": [ { @@ -276,7 +301,7 @@ "----------\n", "Number of runs: 50\n", "Samples per run: 5\n", - "Noise types: doppler, dephasing, SPAM\n", + "Noise types: SPAM, dephasing, doppler\n", "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n", "Temperature: 1000.0µK\n", "Dephasing probability: 0.05\n" @@ -296,6 +321,7 @@ }, { "cell_type": "markdown", + "id": "c291268a", "metadata": {}, "source": [ "Note that we set the temperature in $\\mu K$. We also observe that the `eta` parameter wasn't changed, since both `SimConfig` objects had `'SPAM'` as a noise model already. This feature might be useful when running several simulations with distinct noise parameters to observe the influence of each noise independtly, then wanting to combine noises together without losing your tailored noise parameters." @@ -303,6 +329,7 @@ }, { "cell_type": "markdown", + "id": "9e13d45a", "metadata": {}, "source": [ "### Setting evaluation times" @@ -310,6 +337,7 @@ }, { "cell_type": "markdown", + "id": "f8d69070", "metadata": {}, "source": [ "As a `Simulation` field, `eval_times` refers to the times at which the result have to be returned. Choose `'Full'` for all the times the Hamiltonian has been sampled in the sequence, a list of times of your choice (has to be a subset of all times in the simulation), or a real number between $0$ and $1$ to sample the full return times array. Here, we choose to keep $\\frac{8}{10}$ of the Hamiltonian sample times for our evaluation times." @@ -318,6 +346,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "449e2cc1", "metadata": {}, "outputs": [], "source": [ @@ -326,6 +355,7 @@ }, { "cell_type": "markdown", + "id": "5d0bad77", "metadata": {}, "source": [ "We now obtain a `NoisyResults` object from our noisy simulation. This object represents the final result as a probability distribution over the sampled bitstrings, rather than a quantum state `QObj` in the `CleanResults` case." @@ -334,6 +364,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "a7d7df94", "metadata": {}, "outputs": [], "source": [ @@ -342,6 +373,7 @@ }, { "cell_type": "markdown", + "id": "4bdd9d97", "metadata": {}, "source": [ "### Plotting noisy and clean results" @@ -349,6 +381,7 @@ }, { "cell_type": "markdown", + "id": "d526f555", "metadata": {}, "source": [ "The new `res` instance has similar methods to the usual `SimResults` object. For example, we can calculate expectation values. Observe how different the Rydberg population in the clean case and noisy case are : we clearly see a damping due to all the noises we added." @@ -357,11 +390,12 @@ { "cell_type": "code", "execution_count": 13, + "id": "3f6c3c74", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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dzjTa47Fe6DNkt/x690V6h0Z47XQDu0ubOFrRzvrMaAyGqbu4LU0M57bVyTy+v4KWnsG52Za+WfV1qT06t89rxmg+C4OdnpmXnLZR3biHB9xtycJn1H/uJYKumSUt59SFnjb9yq2uo58/H6rig2tTWLwojG+/cJryll7WZ9p+dP/adUsYNI3w0LsX52Zb2gZAaLeLIxj1n3vYCh2UTSNDUHfC3ZYsfKoOQHQWRCS525IZ0YLuDKy+SxsX+v/tvoBE8o0dufzHnStp6FIrqckB0cnkxIfxgYJUnjxYSWPXHFZfwVEqlVLno8+fqkMQlgjRmQ7bpWnETEld1/x3ZP3b0370+SGlygrzxKewKdCC7gyqj0BILMRkT/12Wx/PHqnmnvXppEaHcEV2LHetSyUy2J9VKZEz7v5r1y5hxCx5cPeFudmXvkm5hEZMc/u8RlF1UPnPHTjo4CdvlHLzL/dS3TbPGEdYvOrBrwV9frRfgr4WSFvvbkvsQgu6M6g+BKkbpr3QnzlSjVlKvrx9bITVjz64ir99YxsBfjOfkvTYEO4qTOOpw1XUdfTP3r70K2C4F5pKZv9ZjaKzFjqrZly5vXKyjoffs889dqa2k0f3qbTUknoHrNLTr1B/iz6enzAvqo+on6kbqG7r47svn2HIZHavTTbQgu5oeluh9bzNvOQ3ixvYkBVDYmTQ6Gt+RgPx4fZPiv/81myGRyRvnGmYvY2phepnzZHZf1ajqLYUZ9lwq5nNkh+9dpYfvV7KgYutNndnGjHz7RdOExMaAEBZgwOmS6VthL5WNbxaMzdqDkNAOCxazq6SRp44UMnRiqkLtnoHTYw4ou/SPNCC7misIjnNhV7e3MP5ph6uz0uc12Ey40LJjgtlz/k5pPtHZUBoPNToTJc5U30Y/EMgcdW0mxytbKeucwB/o+A7M6zsHn+/gtO1nXzv9hWkRgdzrtFBgg66Mng+VB9W/XEMRposMauD5ZffnDv6htj2X7v5713nXG3hBLSgO5qaw2pYb3LBlG/vKmkE4PoVCfM+1NbceA6WtzIwPDK7DwqhXEI1ekL8nKk5Yqm89Z92k50nawnyN/Dzu/O50NTDb6fpxVPT3sd/7yrjmmWLuGVVEksTwjnf2DN/G+NyIShq7GlCMzuGeqGxePRp25qEcPDS5Sv0X7x9npaeIZ4+Uu1Wl4wWdEdTfVit2gJCpnz7zeIGVqZEkBo99fuzYVtuPAPD5tEK01mRWqgexXW/j9ljGlSzO1PXTbvJ8IiZV0/VsyMvkVtXJ3PTykR++fZ5atonBjullHz35WIAvn/HCoQQ5CaGc7G5Z/7CYDAoMdJPYnOj9jjIEbX4gdFMtKKqjgmLqPLmHp48UMmyxHDaeod4+2yjW8wFLeiOZcSkmjVN425p7BrgRFUHN8zT3WJlY3YMAX4G9pTNwe2Saona1x5ziC0+Rf0pleOdOn3mw77zLbT3DXPHmmQAvnNrHhL45dvnJ2z36ul63ilt4pvX547e5JcmhGMySypae+dva0ohNJ+DAQcEWX0N6xOsJebU1DVIRJAfQyNmTlR1jG7249dLCfQz8IfPbCAxIohnj1ZPsTPXoAXdkTSeUQ25pgmIWt0tN6x0jKCHBPixITOG9+Yi6Clr1VCGau12mTXWKtuUwmk3ebmolshgf7bmqtYPyVHBfHxjBs8fr+VSixLqzr5hvrezhJUpEdy3OXP0s0sSVOuHc44IjKauAyTUHZ//vnyN6iMQu2S0T09j1wDXr0jEIMb86AfLW9lV0siXti8mISKID69L5b2yZho63VOhqwXdkcxwoe8qbiAzNoQlixw38WRbbjznm3pmn74YEKoKjHSmy+ypOQIRqdNWDvYPjbCrpJGbVyVOSEP94tU5BBgN/OJvZXT2D/Op3x+mo2+IH39wNX7Gse1y4sMwCDjviMBoisUtpN0us0NKtUK3LM56Bk30Do2wZFEYK5IjOXSpFbNZ8sNXz5IcGcT9V2YBcFdhKmYJzx+vcYvZWtAdSc0xlT0SlX7ZW539wxy42MoNKxIRDixE2bZUrQDn5nbZoFwuZs/Nq/VIao7a9J//7WwjfUMj3L5m4hyY+PBAPrk5g5dP1nHXQ+9TXNfJrz+2lpWTismC/I1kxoU6JtMlOBpiF2vX2mxpK1cpnxa3mnXFnRARxMasGI5XdfDM0WpO13byjzcuJcjfCEBGbChXZMfw7NFqx84EthMt6I6k9phaEU0h2LtLmzCZJdevcIy7xcqSRWEkRQbx+PsVNHXP8jEvdT0MdqneMxr76GmGjkqb/vOXi+pIjAiasg3yF7bmEBrgR0VrH498onDav4elCeGUOSLTBdQTY81RXWA0G2otLqpR//mYoF+RHcuQycz3dhazOjWSOybduG9dnUxla9+oa82VaEF3FAOd0FI2rbvlzeIGFoUHUpAW5dDDCiH4/h0rqWzt4/Zf7edkdYf9H7aKkvaj288MbrWOviHeK2vi1tVJGKfomhkdGsDjn17P81/YzPZli6Y9TG5COBWtvbNPSZ2K1ELobYJO9wXrFhy1x1SdQfxyABq7rYIeyPqsGISAQZOZf70l77LuqFsWq9k+B6bIV3c2WtAdRd0JQKpg4yQGhkd491wzO/ISpm2NOx925CXwly9uwmgQ3PXwAV48Yaf/LjZH5SnrgJn91BxRdQZJa6Z8+/UzDQyPSO7Inzx2d4zCzBhWpdru2bM0MRwp4UKTA1bp2o8+e2qPqXNs9AOgsUu1q14UEURksD+bc2K5Iz95yqewzNgQEiOCeH+G6mBnoAXdUVh9lFMUFO0730L/8IjD3S3jWZEcyc6vbGFtehR//8xJ/vO1szOXIQuh7NX+VfupOQoJK9h1vovPPH6Ett6J06N2FtWRHRfKypSIeR0mNyEcgDJH+NETVoIxUJ9nexkZhvqTYzdClA89LNCPsEAl8H+8fyM//0j+lB8XQrA5J5aDF1td7kfXgu4oao+r7naWFKem7oHRi/3N4gbCg/zYlB3rVBNiwwJ58v6NfGpTBo/sKbevG2PKOmgsUdPrNbYxm9V5TinkvbJm3ilt4t5HDo4OG2noHODgpVZuz0+ed+A7MzaEAKPBMamLfgFqtakF3T4ai2FkcMLTdlP3AAkRY72WhBA2n7avyImltXfIcXEQO/Fz6dG8mdpjkLV19Nf7HjtCdVsf37p5GX8728g1yxbZ1UlxvvgbDfz7HSs5XNHO8ar2mT+QslZVwzWc9qxBx55I63kY6oaUdbSeGSIqxJ+qtj7ufvgAHyhIoaS+Cynhdksx0XzwMxpYvCiMUkcIOig/+tHfq9WnjXYFGsZckMljgt7YNUhCRNA0H7iczTlq8fb+xRaWJoY71Dxb6BW6I+ishe760Ue07oFhSuq7MBoF//LiGdr7hrnBie6WqciJD+Visx2rA+sfrV69zYw18yFlLa29gyxLDOfxT6+nrXeIn+0q47XTDWxZHEv2NDNhZ8uypHDOOqKNLqi/TVO/bplsD7XHIDhmwuCSxq6BWQl6anQI6TEhU/rRpZRO68qoV+iOwCqGFkE/XdsJwM/vzqexc4A955u5eun0w6KdQXZ8GK+ermdgeGQ0R3ZKIpIgPHlMrDTTU3cC/EMhLpfWnr0sT45gY3Ysx/51ByMWX6mfA4PeeUkRvHC8ltaeQWLD7G+tPCVW90HdiWkDuhoLtccnpB9LKWnqGmRRxOzOwabsWF47U8+IWSKl5PClNt4sbmBXSSP/ekset6x2/Eg7LeiOoPYYGPxV8Ak4XaMEfU1qFDFLA7hnw+WFRs4mJz4UKaGytW/mR76UtXqFbg91xyE5HwxGWnoGibP0LjcYBAYcn720LFEFVksbutmyeJ6CHp2lMppqj8O6++Ztm9cy2APNpbD8ttGX2vuGGRoxkziLFTrA5sWxPHO0ms8/eYyjlW109A0T6Gdga248cWEBjrYc0ILuGGqPQeJK8Fcn/FRNJ6nRwaPDCtxBjuWx/2Jzz8yCnlwApX+F/g41c1RzOSPDKs6w/rMMmcx0DZjmv2qegWVJ6rydre8azW2eM9aMJj002jb1J0GaJ2S4NI4rKpoNm3JiCfAzcPhSK9cuT+CGFQlszY0nJMB5sqsFfb6YzeqPYNVdoy+drOlgTWqU+2wCsuJCAdXac0asf7x1JyBnuxOtWsA0lYBpAJILRrOXYp20yrISFxZIfHig4wKjKWth/y9geGB08aGZxGj68fiA6FhR0WxYFB7E/n++hshgf5ckRIAOis6ftnJVPm/JP2/rHaKmvX/GwhFnExroR1JkEBeb7Sg/tubOa7fL9IwLiFrTFGNDnbtCB1iW6MDAaHIBmE2qK6hmamqPQWS6GrJtwSroi8JnfxOMDw90mZiDFvT5Y32EtYjiqZoOAFa7WdBBuV3sWqEHR6kGTvpxfHrqTqhGV9FZoyt0Z/lBx7M8KYLzjT2YRhzQQG00o0kHwKel7gSkTCwOHKsSdf4NfL5oQZ8vdSfALwjilwFjAdHJHfTcQXZ8KOXNvfZVqyXrwKhN6o6rm7YQtPZaVuhO9qEDLE8KZ2jEzKWWXsxmyR/er6B2tq2SrUQkQ+gifeOejr421XgtebKgDxATGkCgn41sMQ9BC/p8qTsBiatHez6crOkkOz6UiCD3F2/kxIfRPWiiuXtw5o2TC1Qufbf7xmd5LMP9qprWssJt7XGNDx3GMl1K6rt46kgV/7azmC/+8RjDc1mxC6H86Lp3z9TUF6mfSfkTXm7sGmRRuOevzkEL+vwwj6iA6Lg7+unaDlZ7wOoc1AodsNOPnq9+Wv+oNWM0nFbVtJbz3NIzRIDRQHig83MKcuLD8DcK9p1v4Sevl5IaHcypmk5+vfvi3HaYXKBG0g26tiR9QTDqPs1HSsmRija+t7OYg+WtJEYujCCyFvT50HIehntHL/TGrgEauwZZ7eYMFyvjUxdnJHE1IKCuyKk2LUisF3qKdYU+SGxYgEMHlUxHgJ+BnPgwnjtWw8CwmT98ZgN35Cfzq3fOj7r3ZkXyWkCqhYhmInVFEJ3FoH8E33r+NHc9dICnDlexKSeWr167xN3W2YUW9PlgXc1aBP3VU/UAFGZGu8mgiSRGBBHsb6TcnhV6YBjE5Wr/6lTUnVC+53BV2dfaO+TSGoPlScrt8oVt2eTEh/H921cSGxbAN54tmn2/dOvTpD7Pl1NXxMCi1dz7yEGeOVrNl7fncPw7O/jtJwtZm+4Z1/RM2CXoQogbhRDnhBAXhBDfmmabjwghSoQQxUKIPzvWTA9ltBR8CX1DJn797kU2Zcd6zArdYBAqMNpi5+N1cr52uUxFXdFoQBRwTCn+LLhpZSLbcuP50vbFAESG+POTD63mfFMP//NW2ex2Fhav5qFqQZ9Ibyt0VvHbC5Gcre/m1x9byz/esIxQF7jVHMmMgi6EMAIPAjcBecC9Qoi8SdssAb4NbJFSrgC+7nhTPRBrXwyDkScOVNLSM8g3r891t1UTyI4Ps8/lAuMCow3ONWohMdSrRvRZYwwoH3qcC1fo169I5A+f2TChJ8/VSxfx0Y3p/HZvOYcvtc1uh/rGfRn7970FQJlxMc9/cTM3r3J8nxVXYM8KfQNwQUpZLqUcAp4G7pi0zeeAB6WU7QBSyibHmumBjJig/hQkF9AzaOLh9y6yLTeewszLJ5i4k+y4UGra++17NLdG97UffYyGM6oU3PL/RkpJa++gSzJcZuJfbl5OWnQI33yuiN5Bk/0fTMqH1gsw4KCCpQVOeXMP7+95G4Dvf+Fj5CXPbziJO7FH0FOA8cMIayyvjScXyBVC7BdCHBRC3DjVjoQQDwghjgohjjY3z2FKvSfRck61I03O57F9l2jvG+YbOzxrdQ6QlxyBlHCkwo5VXOIqEAb9OD6e0ThJPgB9QyMMDJtd6nKZjtBAP370wVVUt/XzZvEsnqqsTxsNp5xi10LjzeJGVhkuYYrOITpmnj1z3IyjgqJ+wBLgauBe4LdCiKjJG0kpH5FSFkopC+PjXdtO1uFYVrFtEXk8/N5Frs9LYI2DB0A7gm258UQG+/PcUTvmjFoDo/pxfIy6ogkB0dE+Lm5svDaeK7JjCQ/042ilHcNMrOgnsQm8VdLAOv8K/CZViC5E7BH0WiBt3O+pltfGUwPslFIOSykvAWUogfde6osgIIz/Pj7CoMnMt25a5m6LpiTI38id+cm8UdxAZ9/wzB9ILtAXuoWa9j5MtSfUitYSELX2cYnzgBU6gNEgKMiI5ljFLAQ9LB4iUvSNG2juHqSqupJ4c/OEOMlCxR5BPwIsEUJkCSECgHuAnZO2eQm1OkcIEYdywZQ7zkwPpK6IvtgVPHWkho9fkeGwKTXO4K7CNIZMZl4+Ofk+PAVJ+dDTAF31TrfLkxk0jXDvg7sRLecwJ44NhHBllai9FGZEU9bUbd8N20pSvk/euKWUPHukevTG/PbZRlaICvXmFAPeFxozCrqU0gR8BXgTOAs8K6UsFkJ8Xwhxu2WzN4FWIUQJsBv4Rynl5bOXvAWzmsG5tyeZsEA/vubhRQcrUyJZkRzBM0eqZ95YV4wC8MrJeuJ7yzBiZnfX2IxQV/ZxsZfCjGikxL4ZslaS81VgdNBBrXkXCFVtffzT86f47B+OMmga4a2SRjaHWK6LxNXuNc4B2OVDl1K+JqXMlVLmSCl/aHntu1LKnZZ/SynlN6SUeVLKVVLKp51ptNtpKQNTP2+0JvJ31ywh2kP8qbb4SGEaxXVd7Clrprqtj+6BaVZzo4HRIpfa50lIKXl03yWuiawD4D+LgmjoVC1UW3o8y4cOkJ8ehdEgOFo5i/TFpHxUxahvBUabLH2Niqo7+Oe/nGLfhRa2htZCTA4ELdzsFiu6UnQOmGtVFkhLRB6f3JzhZmvs4878FAL9DHzyscNc9dPdXPXT3VO3ZA0ItQRGfbc0/GB5G2fru7glrpGRkHhqRqL4wV/VcOXWniFCA4y257S6mJAAP1YkR3B0Nn50H30Sszaqu2FFAi8V1TFoMpNluuAV/nPQgj4nzp/cR58M5N6br1kQLTVBVRf++XNX8LO71nDP+jQ6+oZpsDTuv4ykNT53oY/n0X2XiAkNIGPoPMaUAr6yfQmvnq7n3XNNlhx0z3G3WFmXEc3Jmg77uzCGLVLDwX3sScwq6D+4cyXX5yWwPHKYoN5arxmcrQV9lvQMmuirOE5VQA43rZqcju/ZrMuI5sPrUrl1tfIJ17RP01c7Kd9nW+lWtvbydmkj9xUuwtB8DpLyeWBbNtnxoXz35WJq2/s9KiBqpTAjhoFhM8V1sygW8sGK0ebuQYwGQWxoIA9/Yh0vflB1JJ3cMnehogV9lvzpQDm5spyYJRtc0m3PGaRGBwO2BN2yWvFBt8vzx2oQwMeyelTL3KQ1BPoZ+Y87VlLV1sfRynaXjJ6bLdaGcEftKSCzkrRGdQz1ocBoc/cgMaEBGA0CIQRBTafVG0kLPyAKWtBnTUf1WULFIItyN7rblDmTFBWEECrPekoSV6mfPiboUkpeLKplc04csd1n1YuWm9vmxXF8oEA9kbli9NxsSYgIIjU6mGOzLjCSqr2Bj9DcM0j8eJdZfRFEZ6rxgl6AFvRZEt1RrP6xgH1ugX5GEsKDpl+hB0WoGaM+9jh+vKqd6rZ+7ixIUb7l4BiITB19///dvJy4sAAWL/LMmoNN2bHsPd9Cu6WadUZ88EmspWeQ+PHTh+pPLuhreTJa0GdJQt85hkQAxC11tynzIjU6mOq2aVboYAmM+s6FDvDiiVqC/A3csCLBMokqf7RCFNQE933/fA33X5nlPiNt8Lmt2fQOmXhkr501fRFJEJbgU+e5uXucoPe3Q3uF1/jPQQv6rEkfPE9dYM7oDNGFSlpMyPQrdFB/5J3Vqk+0DzBkMvPXU/XsyEsk3M8MTWenXLkF+Rs9NnaSmxDObauTeXx/xWgl5Iz4UEaT2SwnrtCtOfh6he6jmM0sHimnOXy5uy2ZN6nRwTR0DUydiw7jHseLXGaTO3mvrJmOvmE+UJAMTSVgHl6QF/rXrlvCoGmE37xr58zRpDXQXApDNp7WvITO/mGGR+SYD32aodALGS3os2Cw+SLhop+uqLyZN/ZwUqODGTFL6jtt5KKDzzyOv3SilpjQAK5aEj/2nRegoOfEh/HBtan88WAljdPVGYwnaY3q995U4nzj3Eyz5allbIV+Uk1vCo11o1WORQv6LOirPArAYPwqN1syf1KjQwAbqYvBUSr67yMr9EOXWtm+dBH+RoO60AMjIdozfeUz8dVrljA8Yubx9ytm3tiHnsSsRUUTBN1LKkStaEGfBaaaIoakEWOid7hcwEbqIvhMYLSlZ5CWniGWJ4WrF+qKVF6yh/rKZyI9NoTr8xL586Eq+oZmmGQUmaayeXzgPE8Q9IEu1ZzMi9wtoAV9Vvg1neacTCM6PNzdpsybpMhghIDqmQKj7RUqG8CLKWtUhTVLE8NhZBgaixeku2U891+VRWf/MC8cn6FlshDqu/pAC4AJgt5gLSha2Od5MlrQ7UVKQlvPcMacRYwHddqbKwF+BhIjgmZeoYPXd+Qra7AIekI4NJ+DkcEF3xu7MCOaVSmRPLb/EmaztL1x0hqV1WOyMzNmgdLSM0ign4HwQL8FHSexhRZ0e+msJmC4kzPSOwQdIC16ptRF3wiMnmvsITLYX63crL7kBd4bWwjB/VdmUd7cy3tlM8zvTVqjsnqazrrGODfR3D1IXFigSjutPwlhiRCe4G6zHIoWdHuxPJIWy0yigv3da4uDSI0OptaWoIfGqSwALxf0843dLE0IH7vQA8JUpewC5+ZVSSREBPKnQ1W2Nxxtpevd57l5Qg66d1WIWtGCbi/1JxnBSFNQDgbDwgyWTSY1Opj6zn7bLVe9PDAqpeRcYze5iZZy/vqTanVuWPiXRoCfgWuWLeLQpVZGbLldorNUVo+XZ7qMVokO9UHLOS3oPk39SeoDMggJW/gBUSup0SGYJaPTeKbEy0eVNXQN0D1gUv5zy2hBb7rQ12fG0D1g4lyDjfMnhMrq8eIbN4wT9MZilXvvZSmLoAXdPqSE+iIuGLK9xn8OY6mL1TMGRuVYVoCXYRW63IRw1Up2uM/rBB2YeTxd0hrVdXFkFoOmFxDDI2ba+oZUlehohaj3nGcrWtDtobsBeps5I7M8apbkfBktLmrz3cCoNWUxNyF87Dt60cotNTqYxIggDl+aSdDzVXZP8zmX2OVq2nqHkJKxwHdILEQsrAE19qAF3R4sd/RjwxkLYiC0vSRFBWGw1RcdIDzRqzvynWvoIT48UJ3X+pPgFwyxS9xtlsMQQrA+K4YjFW1IacOP7uU37gk56NaA6AItHLOFFnR7qD+JRHC4P9mrVuj+RgNJkcG2UxfBqwOj55tUhgugbtyJKxd8J83JbMiMprFr0PZ5jl2ssnu8NDBqFfRFIUzbSdMb0IJuD3VFmGOX0CuDvMqHDpASbY+g53tlRz6zWVLW2K3cLWazKqDyslJwgEKLH92m28VgUJOqvPTGbW3MlTRQDmaTFnSfpr6I3tiVAF4n6KnRwbZdLjDWka+x2DVGuYjq9j4Ghs0sTQyD9ksw1O2VF/rShHAigvzsCIzmq+C3ecQldrkS6wo9tss6WjDffcY4ES3oM9HdCN31tEeohlzeJ+ghNHQNMGSaIRcdvO5x/K2SRgBWpkRC3Qn1ohcKusEgKMyMsSMwukZl+bScd41hLqSkvou4sAD8m05BUJTqJOqFaEGfCcsjaEPoMsAbBT145lz0yFSVFeBFgt47aOI3715ky+JYViRHqvNsDIBFC7+T5lQUZkZzsbmXVluTjLy0YrR7YJi/lTRy08okSydN7wyIghb0mbH8cVf45wDeJ+hpltRFm7noQqhH1DrvudD/cKCC1t4hvrHDMhu2/iQkrACjd7R1mMyG0Xx0G50zY5eoLB8vunEDvH6mgUGTmQ+uiVeDPLwoLXUyWtBnor4IYhfTNKiE3NsE3a6+6KAuguazMGzHFBwPp2tgmIffK2f70njWZURbCse8s7eHlVWpkQT4GThiy+1i9PPKwOiLx2vJigslP7AeRoa8+jxrQZ+J+pOQlE9r7xBhgX4E+hndbZFDSYoMwmgQ9mW6mE1eERh9bN8lOvuHx1bn7RUw0LHgW+baItDPSH5qFEdsrdDBkqJ6SmX9eAF1Hf0cvNTKnfkpiNGWuflutcmZaEG3RW8rdFZD0hra+4a8bnUO4Ge09kWfQdBH/asnnG6TM+noG+LRvZe4YUUCq1Ij1YujAdF8t9nlCtZnRVNc22l7ilFyvsr2abNzyLSH83JRHVLCnQXJ6mk7MBJist1tltPQgm4Lqy8xOZ+23iGvqhIdj12pi5FpEBy94CfbPLKnnJ4hE3+/I3fsxfoiS0B04Q//tkVhZgwms+REVcf0G1lvagv8PIPqpPniiRrWZUSTERu64EcL2oNdgi6EuFEIcU4IcUEI8S0b231ICCGFEIWOM9GNjBt20Noz5FVVouNJnWnQBYwFRhdwwKy1Z5DH36/gllVJLEuMGHujrkiJuZ93nl8r6zKiEQKOVNjwo8cvA7+gBXOeh0xmnj1SPWV74OK6Lsoae/hAQcrYaEEvDoiCHYIuhDACDwI3AXnAvUKIy5YyQohw4GvAIUcb6TbqilSv6OAor3W5gFqhN3QNMGiaoaAkOV+VTS/QwOhD711kYHiEr183bnVuDYh6+YUOEBHkz/LECNuCbg2M1i0M19o7pY380/On2Hv+8qlML56oxd8ouGVVkqp0Hhn0ereaPSv0DcAFKWW5lHIIeBq4Y4rtfgD8BFiYV/tU1BVBcgFSSlp7vVfQ02JClK51zHDqrIHRphKX2OVIOvuGeeJAJXcWpLB4UdjYG9aAqJdf6FbWZ0ZzoqpjhqEm+QsmMFpt6RR6uqZzwuumETM7T9axfeki5Sq1upC8/DzbI+gpQPW432ssr40ihFgLpEkpX7W1IyHEA0KIo0KIo83NM8w5dDe9rdBZBckF9A6NMGQye62gj6Uu2hsYLXKqPc7gQnMPgyYzt65OmvjGaJzEezNcxrM+K4a+oRFK6rqm32gBBUatsZ+TkwR9/8VWmrsH+eBai1TVF0FAuFcHRMEBQVEhhAH4H+CbM20rpXxESlkopSyMj4+f76GdiyWboyF0OZ/7w1EAsuJC3WmR07A7Fz0qQ5VNL8CAWX2nulklRQZPfKPuhE8ERK1YB17YdLtYb24LwO1iXYScru2Y8PqLx2uICPJj+7JF6oW6E+pG5QWjBW1hz7erBdLG/Z5qec1KOLASeFcIUQFcAexc8IFRyx/zLc93caa2k//8wCquz/OuCeFWEiPszEUXQl3sC+BCn4zVnZR8maAX+URA1EpCRBBpMcEcr7KRjx63VFWMLoAbt/VvtrFrkMYudY57B028WdzILauTVd2IaUhNY/KBOIk9gn4EWCKEyBJCBAD3ADutb0opO6WUcVLKTCllJnAQuF1KedQpFruKuiKaAtIgIIJd39jKRzemq6nwXoif0UBSZBA17X1IKWnqtjVjtED50BdYYLSus5/QACMRweN6nftQQHQ8q1OjODXJRTEBo5/qC+/hrjUpJTXtfay21BNYv9MbZxroHx4Zc7c0n1UBUR9wq80o6FJKE/AV4E3gLPCslLJYCPF9IcTtzjbQbdSdoIQc8pIjLn9M90JSo4PZd6GFrf+1mw0/fJvdpU1Tb5hcsCArRus7BkiKCp54U/axgKiV1SmR1LT3z9Coq0Dd7Dw4MNrZP0zv0AjX5yVgNAhO1XQA8OzRajJiQyjMiFYbWp8otaArpJSvSSlzpZQ5UsofWl77rpRy5xTbXr3gV+c9TdBVy6GBdLK91G8+mVUpkXT2D5MTH0Z4kB+vnKqbesOUtepn3XHXGecA6jr7SYoMmvSi9ULPd7k97mR1ahQAp2ttrNKT8mGoB1ovuMSmuWB1tyxeFM6SRWGcqumkoqWXQ5fa+Ehh2tjNu+6EpWVulvuMdRHeHSGYKxbf4bHhDHLGp7h5Md++aTmnv3cDj396A9ctT2B3aROmqVLbIlIgNH7B+dHrOgam8J8ftwREV7jHKDexMiUCIbDtdrHe5Dz4PFuD+KnRwaxOjeRUTQfPHavGIOBDa1PHNqw7oVbnXuoyHY8W9KmoO4FEUCwzyY7zDUE3GARB/qrx2I68BNr7hjk2VSMnISB5rUdf6JMZNI3Q0jNIUtSkFXrtCVVE4yMBUSvhQf5kx4WOuiimJG4p+Id49Hm2rtDTokNYnRpFe98wT7xfybbceBKtT2PDA9BY4hPuFtCCPjV1J+gMzaKXYHIW+YbLZTxbc+MJMBpGJ/pcRnKBZcZor2sNmyONncpXPGGFbh5RQb/kte4xys2ssScwmrTGo11rNe39hAf6ERHsNxoY7R408ZHCcUl5TcVgHtaC7tPUnaAyMJeQACOJEUEzb+9lhAX6sSknlrfONiLl5T0ySC5QM0brT7neuDlQZ8lBT44aJ+gt55WPOMU3BX1VaiRN3YO2J1Ulr1WB0ZFh1xk2C2ra+0iJVoHuZYkRBBgNxIQGcO3ycenFPhQQBS3ol9NVBz0NnDZnkR0f6rWpijOxIy+BytY+zjf1XP7maOGJ567exjNaVDTe5WK13UdX6NbAqE23S8paMA2o/j0eSE17/2hRXICfgY9uTOdr1y4hwG+crNWegJA4NUbRB9CCPpnaYwDs6cvwGf/5VOywFFFN6XYJT1DBUQ/2r46nbqqiotrjEBAGcUvcZJV7yUuKsKT62XC7eHBGk5SS2vZ+Ui0jFAG+d/sKPrU5c+KGPhQQBS3ol1N7HGnw473uRHLifVfQEyKCWJMWxctFtdO7XRaMoPcTFeJPcMC4aVN1J1RqnsG7JlDZS3CAkdyEcE7ZSl2MzlI98Gs9T9C7+k10D5pGV+hTMtSrYj0+lJaqBX0ytccYiFnOoAwgO973AqLj+eQVGZQ19vBu2RSN1JILVI5yf4fL7Zot9Z0DE4vDTEPQcBpSfMOvOh2rU1Sq35Q3bBhr9eCBgm4dap4SZUPQ60+CHIGUhd2FZDZoQR+P2Qx1J2gMV3nJvi7ot61JJikyiIffm6Lr3ujjuOev0us6+kkeX1TUVGIpBfdN/7mV1WmRdPQNU9Vmoylb8lr1/2tohsZtLsaasjje5XIZFvcpKetcYJFnoAV9PK0XYLCL8/5qeLAv+9BBBZruvzKLg+VtFFV3THzTKoa1nl8UXN85MDHDxeoT9tEMFysFaao0/rJzO56UdWqV23DaNUbZyfiiouk3OgpR6RDm4Z1dHYgW9PFY7uhHhzNJiQqe6HP1Ue7ZkE5EkB+P7Jm0Sg+OgtglHvk4Pp7eQROd/cMTM1xqj0NwjGoH7MPkJoQR7G+0PWPUQwOjtR2q2VpUiL+NjY771OoctKBPpPYYBIRxsDPW590tVsIC/fjEpgxeP9NAZeukQqLUQrUKms4H6wFYUxYvy3BJWeszmQ/T4Wc0sCo1khO2VujhiRCePOa+8BBqLBku06YV9zSpATU+5D8HLegTqT2GOSmfCy39Pp3hMpm7C9OREvZdaJn4Rso66G2CzuqpP+gBWFMWRxtzDXYrn3Dqejda5TkUpEVxtq7L9jzZlLUe9SRW39nP6ZpOUmy5W3zQfw5a0McwDULjGWpD8+gdGmFjVoy7LfIY0mKCCQ/yu3xsWapl9VPjuX70+slVorXHATlmu4+TnxbF0IjZ9ki61EI1jq7PxpQjF3G0oo3bfrWf7oFhPnuVje6JNUdBGFX7Ah9CC7qVxjMwMsS73WmEBhjHRldpEEKQlxTB2fpJF33CSjAGetzj+HjqOgYQQuXVA2NBXB9buU1HQbodgVHr04ybb9xPH67i3t8eJDTQyItf3sLmnLjpN649Bgl5EGAjC8YL0YJupUaJ0pPVsVy/InG086BGsTwpgtKGbkbM4/zlRn+1AvLgFXpJfRdp0SFj5eA1R1UwNzjavYZ5CImRQSRGBNkW9OQCEAaoOeIyu8YzPGLmuy+f4VsvnOaK7Fhe/vIWchPCp/+A2ayCuD5409aCbqXmMIPBiygbiOS2NUkzb+9j5CVH0Dc0MnVgtL7IIxs4DZnMvH+hhauWWFZyUipR0v7zCeSnRdkW9IBQSFjhNkH/91eKeeJAJQ9szeb3960nKmSGdsdtF2Gg0+cCoqAFfYzqw5T6LScyOIArF/tO3qq95CVFAGrFO4GUdZYGTiVusMo2xyrb6R0aYVuu5Xx2VEFvs/afTyI/PYrK1j7bI+lSNyg3hhtG0u0pa+H6vAT+383L8TPaIVk1vutW04IOKsWpo5I3O9O4cUXixG5tGgCWJIThZxCX+9E9ODD6XlkzfgbBppxY9YJ1hakFfQL5aVEAnLTVeTF1PQx2Qcs5l9hkpWfQRFVbH6tSIu3/UM1hCIyA+KXOM8xD0coFUH0YgIPDi7lVu1umJNDPyOJFYZdnQ0RlqPakHijoe8qaWZcRTXiQpfik5ij4BfvcyLmZWJ0aiUEw9YQqK6OBUde6Xc41dAOwzPKEaBfVR9RN2wcbr2lBB6g5jAk/6oNz2ZQd625rPJa8pIjLXS5CQNpGqD7kHqPGYR01B9DUNUBJfRfblo5zn9UeVTnVRj83WeiZhASogSYvHK9leKo5sgCxOWrQsosF3fpEuDzJRhB0PANdakpR6gYnWuW5aEEHRioPcUZmcu3qdPt8dD5KXnIEjV2Dl/ta0zaoQFTPFF0ZXch/7ypj84/fYVdxA3vOqyKoUf+5aVB139Pulin5zJYs6jsHeP1Mw9QbCKFW6S5+Eitt6CI80M92V8Xx1B5T07TStKD7JqYhqDvB0ZEl3LY62d3WeDTWwOjZ+u6Jb6RfoX7WHHaxRRPZU9bMkMnMF/90nAd3XyAuLJDliZZH9fqTMDKkM1ymYfvSRWTFhfLovkvTt9NNXa+mFw3YKEJyMKX13SxLCrd/clj1YUD47I1bC3rjaYzmQS4G5bE+U1eH2mL5aKbLpKEISflgDICqg643ykJn3zDnGrv5/LZsNmXHcqmll625cRgMFiGw2pa20W02ejIGg+DTWzI5Wd3B8emadaWtB6TLCsnMZklpQ/fo351dVB+CRXkQNIsgqhfh84LeX64u9EXLt45d/JopiQ4NICky6PLAqH+QEnU3+tGPVbUhJVydu4hH7yvkmzty+fL2xWMbVB2EmBwI0xXA0/GhtalEBPnx2L5LU2+QUqgKjFx0nms7+ukZNLEs0U5BN5uVj99H3S2gBZ3ms3upkzFsW+9bPR/mimoB0H35G2kb1LALk41cZidy+FI7/kZBfloUgX5G/u7aJWMN1qSEqgOQvsktti0UQgP9+OjGDF47U8/Th6su3yAoQhUYVb7vEnusAfhl9gZEm0tVaqUPP4X5tKBLKQluPMZZ4zIKLLm4GtvkJUdwobmHgeFJ3fnSr1A+6roit9h1pKKNlSmRU/ewbzkP/W2Q7rsXur18/bolbF0Sz7deOM2TByou3yB9swqMuqAyuLS+GyFgqa0y//FYnxz0Ct03eendg8SPNBGYc6X9QRcfJy8pghGz5Hxjz8Q3rKsiN7hdBoZHOFXTwYbpYiDVFv+5XqHPSJC/kUc+uY7rli/iOy8X8+hk90v6FTDcCw2nnG5LaUMXGTEhhAbamWZafVjVRMRkO9cwD8ZnBf1UTQf7334FgM3bb3OzNQuHaQOjYYvUlHg3CPrJ6g6GR+T0Qe2qgxASC7GLp35fM4FAPyO//tg6blqZyA/+WsJD42fKWm+KLgiAn63vmmVA9KBanfvw4swnBb2zf5gv/vE4VwWWYQ6MxJCoKwftJT0mhNAA4wQ/+q7iBsoau9XqreqgyycYHalQfbrXZUzTQdHqP/fhC322BPgZ+NW9Bdy2Jpkfv17Kb961iHpEEkRnOt2P3jdkorKtz/6AaFc9tJVDxman2uXp2CXoQogbhRDnhBAXhBDfmuL9bwghSoQQp4QQbwshPHpY4+7SJmo7+rk+9CKGjE0+WSI8VwwGwfKkiNFMl55BE1/58wl+/laZcrv0tahh2y7kSEU7uQlhRIdO0YWvp0ld6D4cKJsrfkYD/3t3PtfnJfDzv5XRO2hSb6RvcvqN++WiOqSEwkw72xxX7lc/M7Y4zaaFwIyCLoQwAg8CNwF5wL1CiLxJm50ACqWUq4G/AD91tKGO5GxDF4nGboK7yrVfdQ7kJasWAGaz5L1zzQyNmCmu64LMq9QGFXtdZsuIWXK8sp1CW+4W0Od5jhgNgk9vyWLIZGbveUslcPomp964B00j/Ort8+SnRbE5x85WHJX7ISAcElc7xaaFgj0r9A3ABSlluZRyCHgauGP8BlLK3VLKPsuvB4FUx5rpWErru7ktqkL94uN39LmwPCmCnkETNe39vFWiSsWr2vroDEmHsESo2O8yW0obuugeNE0fEK06AH5BPjeKzJGsz4wmMtifXSWN6oVRP/oBpxzvmSPV1HUO8M3rc+1PVqjYr1x+Pt6nxx5BTwHGTwGusbw2HfcDr0/1hhDiASHEUSHE0eZm9/X9KG3o4qrAMvAP0Rf6HLC2ADhV28E7pU0kWwYwl9R3Q+aVULHPZX70I5eU/3z9dDNgL+1VgTK/GYYiaKbFz2jgmmWL2F3ahGnEDHFLVJC50vGCPjA8wv+9c4ENmTFcudjGiLnx9DSrtr6ZenHm0KCoEOLjQCHwX1O9L6V8REpZKKUsjI93zxCJtt4hGrsGyRs6o3pT6At91ixNDMcg4In3K+kaMPElS0VmcV2nEvSeBmi9OMNeHMORinaSI4Ombt7U1waNpyFzq0ts8WZ25CXQ3jesWuwKoVbpTrhx//lQFU3dg7NbnY/6z690qC0LEXsEvRZIG/d7quW1CQghrgP+BbhdSumeckE7KG3oIpw+YnvKfD4iPleC/I3kxIdxuKKNAD8DHyhIISEiUAVKXehHl1JypKJt+tW51YYsLejzZWtuPAFGA29Z3S7ZV0NnFbRP0yZgjjx3rIb8tCg2zqaNdeV+8A+F5HyH2rIQsUfQjwBLhBBZQogA4B5g5/gNhBAFwMMoMW9yvJmOo7S+m0LDOQRSC/o8sOYHX7k4jtBAP1YkR3KmrlP1zQ5LVKs3J1PV1kdT9+D0+eeX9qoLPWWt023xdsIC/di8OJa3zjaqbozWm+SlPQ47xoWmbs7Wd3FH/iy7nlbsV241o7/DbFmozCjoUkoT8BXgTeAs8KyUslgI8X0hxO2Wzf4LCAOeE0IUCSF2TrM7t1Pa0MWOwBIVKPPRJviOIC9ZCfqOvAQAViZHcLG5lwGT2WV+9MNW//m0gr4HMjbpC91B7MhLoLK1j9KGbojLVTfu8vcctv9XTtYjBNy8ahZTw/ra1EAL7T8H7PShSylfk1LmSilzpJQ/tLz2XSnlTsu/r5NSJkgp8y3/3W57j+6jtKGbq4xnlA/QP8jd5ixYrlu+iA1ZMdy4IhGAvORIRiztTl3lRz9a0U5ksD9LFoVd/mZ3oyVQdpVTbfAlbliRSHigH996/hSDI2bI3qZumg4YHC2l5K+n6tiYFUNCxCyuS+0/n4BPVYqOmCXtDZWkmSohZ7u7zVnQLF4UzrOf3zRazLPCsmJXgVHX+NGPVLSxPjN66rbH2n/ucOLCAvmvu9ZwsqaT//jrWcjapvLRm8/Oe99n67u52NzLbWtm6W65+I7KP/fRgRaT8SlBr2jtZb3Z0lQoWwu6I0mNDiYy2J8ztV3Kjx6Roi42J9HcPUh5S+/0BUWX9kBgpE5LdTA3rkzkga3ZPHmwkl39uepFB7hd/nqqDqNBcNPKWbhbpIQLb6ubtnarAT4m6KX13WwxnsEUFAsJK91tjlchhFBDpOs6VVpbzjXqQh8xOeV4r52uB2byn2/WbR2cwD/dsJTVqZH8+ECv6mx4aX6C3j80ws6TdWxZHEfMVO0bpqOtHDr00/Z4fEzQO7nScAaRvQ0MPvXVXcLKlAjONnSryfGLr4PBTqh17FDh1p5Bvvb0Cf5tZzErUyJYlTLFqLG2SyqdLnubQ4+tUfgZDdy+Jpny5l56U65UWSZzvHF3DwzzqccOU9vRz32bZ9kCyvoEmHPNnI7tjfiUqnVWnSZBdGBcrP8AnMHq1CiGTGbONXQrMRUGuPA3h+1fSsnHfneI107X8/XrlvDCF7cQ4DfFn7D1mEuud9ixNRO5comq4jzpvwaGuqHu+Oh7ZrPkQtMUU60m0d47xMd/d4jjVe388p4CrlmWMDsjLr6jOj/G5szuc16Mzwi6lJLoBktEPPtqt9rirRSkRwFwoqodgqNVJe6Ftx22//NNPZQ2dPOdW/P4+nW5U4s5QNmbyhWgL3SnsTQhnPjwQF7uWqxu3OffGn3vP149y3X/s4f3L7ZM+/mm7gHueeQgZxu6eejj62YfDB0ZVm41vTqfgM8IelljD2uGTtAVmglRaTNur5k9KVHBxIcHcsI6NX7xdWrOaO/0F/ZssFYpXp+XOP1GQ30qw2XJDQ45pmZqhBBcuTiOty4NI1M3QtkbALx6qp7H9qvq0WeOVE/52dqOfu5++CBVbX38/r71XJc3y5U5qOlEQz1a0CfhM4K+72wlmwwlGJdc625TvBYhBGvTozhe1a5eWHwtIOHibofs/62SRlanRpIYaSNPuWIfmAZgyQ6HHFMzPVcujlO9kRK3QcMpKi6d55/+cpKC9CjuWZ/G62ca6OybOHt0T1kzH/r1+7T0DPLHz25gi70NuCZz8R0QRp2WOgmfEfTOM7sIFkOErtLj5pxJQXo0Fa19tPUOQVI+BMc4xI/e1DVAUXUHO5bPsJo7v0t10dRtkZ2O1Y/+nlgHwIvP/p5AfyMPfnQtH78igyGTmZ0nVdunroFh/uG5k3zyscOEBhp5+oErWJcxTYaSPVx8W7n0gqYIivswPiHofUMm0prfY8AYpqoYNU6jIC0KgKLqdpUymHONuvjmUE34/sUWdpeq1kBvW37uWGFD0KWE82+qghddBex0EiKCyE0I45XaCNr8E1nZc5Bf3JNPclQwK1MiyUuK4NmjNbT3DnHvIwd58UQtX9m+mFe/ehUrkuchxJ21ypWXq4Pek/EJQT94oYnt4jhdqdt1AYKTWZUaidEgOF7ZoV7IvQF6m2edvjgwPMJX/nyCTz9+hCcPVvJWSSNpMcEsTQif/kMt56GjSrtbXMiVi+PZd7GVV/pXcbV/MVdljrVh+EhhKqdrO7njwf2cb+rhd58q5B9uWEqQ/zxrA0pfVT+Xe2yHEbfhE4JefuJd4kQXUQX6D8DZhAT4sTwpnBPVFj967g1g8IeSl2e1n+eP19DWO8TKlAi+89IZ3itrZsfyRNs9ss/vUj91uqLLuMridmlO2o6/eWBCl807C1IIMBpo6h7gsU+tZ/vSRY45aOkrELdUDdrQTMAnBD2sYhcm/AhYpjMfXEFBWjQnqzsZMUvl48zZDiU77e6+aDZLHt17iVUpkbzwxS3ctDKREbPkxpU2slsAzu5UFcA6i8llXLUkjn+/fQWf+8QnVezi3NiwsqiQAB7+5Dr+8oXNo/72edPXpgqZlt/qmP15GV4v6JWtvawfPEhjTKEOoLiIgvQoegZNXGjqUS8sv10NQ6gvsuvzb5c2Ud7Sy+e2ZhPgZ+BX9xbwxtevYsN0gywAOmug+hCs+MD8v4DGbvyMBj61OZPIiHAVLyn9K5hHRt/fvnQRK6eq5p0r514HOQLLdXLDVHi9oL+wazc5hnpCVml3i6tYmx4NMDpAmmW3qBSzEvva5P92TzkpUcHcbFmR+xkNLEuMsP2h4hfVz5UfnJPNGgew8kPQ0+jc4Salf4XINJVBpbkMrxb0ssZujMXPIxFEr9MrN1eRERvCttx4frarjCcPVEBIDGRdpVwiM7hdTlZ3cLiijU9vycTPOIs/zzMvqIs8JntetmvmQe6NEBAGp59zzv4He1Tl8bJbVAM4zWV4taD/56slfNBvH6aMrRAxy9JizZwRQvDwJ9Zx3fJFfOflYh5676Jyu7RegCbbvbN/u7ec8CA/7tmQbv8B2y6pXiJ6de5eAkKU2J7dCSYnjBUuewNGBrW7xQZeK+h7yprpOb+PVJrwX/tRd5vjcwT5G/mNpUfHj18v5aHG5UhhgDN/mfYz1W19vHa6no9uSCcs0M/+g1ndLdp/7n5W3QUDnQ7t4TPKiT9CZDqk61nA0zGLq2bhMGKW/OdrZ/lSyAGkIQSxTEfE3YG/0cD/3p1PiL+RH++r5pqEK0g78iRfq76Bc819AAT6Gfj2TcvZvmwRv99fgUEI7tuSObsDFb+gqgajZrGq1ziH7KtVdfDp52DZzY7bb0c1lL8L2/5Zt762gVf+n3nuaDWXGlq5yXAQsfx2CJxi5qTGJRgNgh99cBX3bc7kZ80bCR5oIrLmHVanRlGQFoVZwgNPHuUvx2p45kgVt61JJiky2P4D1BVBw2m1MtS4H6O/elI69zoMztxC125OPgVIyNdP27bwuhV676CJ/36rjAcSyvDv7IY197jbJJ/HYBD822157FnyOYZ3/pGfphUh7v02AJ39asDBPzx3EoDPXpU1u50f+Z3Kf159t6PN1syVNffA0Ufh1LOw/v75789sVu6WrK0QPcshGD6G163QH37vIs3dg3wubD+EJ+tubB6CEIJty1PwX/cJxPld0FUHQGSwP0/ev4GtufHcujppdj0++tvh9F9g9UcgOMo5hmtmT+p6Ncv10MN2F5PZpHK/GjVX8In578vL8SpBb+0Z5JG95TywbICI2veg8DN6pqSnsfYTIM1w4k+jL4UH+fPEZzbwfx9dO7t9nfgTmPph/WcdbKRmXggBG78ILeeg3AGtk48/AYERoGNhM+JVgv5WSSMDw2a+5P+qegx3xOOexrHEZKunpmOPg2lo7vsxm9VjfdoVkLjKYeZpHMTKD0JovFqlz4e2S3DmeSj4uEqL1NjE6wS9ILKXyIsvw9pPqYIWjeex+avQVQNFf5p52+m4+I6a+r7hc46zS+M4/ALVE3LZm9B6ce772f+/6il781cdZpo34zWC3jdkYt+FFv4x8m2ElLDpS+42STMdi6+D1A2w52dzK0Axm+GdH0BEqi4y8WQKPwMGPzjwf3P7fGeNcqsVfAIikhxrm5fiNYK+93wLIaYONra9onpK6Jxkz0UI2P7/1Cr9+BOz//yZ51Wjr2u/o1aCGs8kPBHWfQqO/WHGCuEp2f8LQMKVX3e0ZV6L1wj6WyWN/HvQnzGYB+Gqb7rbHM1MZF+tKv72/jcM99v/ueEBePv7kLgaVn3EaeZpHMT2f4HAcHjj27PLeOmoUjeCNffqxdksWJCCbjZP/MMYMUt6zv6N29mD2PJ1WLTMPYZp7Me6Su+uh3d/bP/nDj+iWvFe/wNdMbgQCIlR57l894Re6TYxm+GlL6kipW3/5Fz7vIwFd0W8VdLIB369n3MNY1VoJy7W8W3TQ/SEZcDWf3SjdZpZkXWVCl7v/1/7en80nFbiv3iHWuFrFgaFn4H4ZfDmt1Wfl5k49BBU7IUbf6xX57NkwQm6lJKa9n5u/dVefvbmOX6+q5SmZ/6ODEMTxtt/qYcDLzRu/DHEL4cXPw/djdNv19MMT92rhpTcMccgm8Y9GP3hlv9RQc6nP2Y7EN5UCn/7HuTepFIVNbPCLkEXQtwohDgnhLgghPjWFO8HCiGesbx/SAiR6XBLLVy/IpG3vrGNW1Yl8Zvd58jY+01uNr3NheVfIjj3amcdVuMsAkLgrt+rXtdP3aOaME1mqBee+Tj0tsC9f1bBNs3CInML3PkbtfJ+4YEJU41GqToEf7hV+dxv/6XueT4HZhR0IYQReBC4CcgD7hVC5E3a7H6gXUq5GPg58BNHGzqemCAD/7uhi5O5v+eDxn1wzXdYfPePnHlIjTNZtBw+/Ci0nIeHtqgslp5mGBmGk8/Arwqh+iDc+WtILnC3tZq5svojcP1/QMlL8NgNKkfdbIb2Cjj0yJiYf/p1CHPQQGkfQ8gZIs9CiE3A96SUN1h+/zaAlPJH47Z507LNASGEH9AAxEsbOy8sLJRHjx6dvcXH/gB/+zfVx8MvGHb8O2z8/Oz3o/E82srhL5+BuhMTX08ugBt/Aukb3WOXxrEcfwLe+yl0VoMxAEYsFcNZW+GuP+iCwBkQQhyTUhZO9Z493RZTgPHPwTXA5CtrdBsppUkI0QnEAi2TDHkAeAAgPX2OwY6IFFhyvSooyblWlwN7EzHZ8JldUPa68qf3tUDsElVXoDNavIe1n1TpiKefU4Hu+KWwaAWkrNW9l+aJS9vnSikfAR4BtUKf006WXKf+03gnfgGQd4e7rdA4G6O/7m3uBOxZ9tQCaeN+T7W8NuU2FpdLJNDqCAM1Go1GYx/2CPoRYIkQIksIEQDcA+yctM1O4FOWf38YeMeW/1yj0Wg0jmdGl4vFJ/4V4E3ACDwmpSwWQnwfOCql3Ak8CjwphLgAtKFEX6PRaDQuxC4fupTyNeC1Sa99d9y/BwA91FGj0WjciE4d0Gg0Gi9BC7pGo9F4CVrQNRqNxkvQgq7RaDRewoyl/047sBDNQOUcPx7HpCpUH0B/Z99Af2ffYD7fOUNKGT/VG24T9PkghDg6XS8Db0V/Z99Af2ffwFnfWbtcNBqNxkvQgq7RaDRewkIV9EfcbYAb0N/ZN9Df2TdwyndekD50jUaj0VzOQl2hazQajWYSWtA1Go3GS/BoQfek4dSuwo7vfJ8QolkIUWT577PusNNRCCEeE0I0CSHOTPO+EEL80vL/45QQYq2rbXQ0dnznq4UQnePO8Xen2m4hIYRIE0LsFkKUCCGKhRBfm2IbrznXdn5fx59nKaVH/odq1XsRyAYCgJNA3qRtvgQ8ZPn3PcAz7rbbBd/5PuD/3G2rA7/zVmAtcGaa928GXgcEcAVwyN02u+A7Xw381d12Ovg7JwFrLf8OB8qm+Nv2mnNt5/d1+Hn25BX6BuCClLJcSjkEPA1Mnk12B/AHy7//AlwrhBAutNHR2POdvQop5R5UD/3puAN4QioOAlFCiCTXWOcc7PjOXoeUsl5Kedzy727gLGoW8Xi85lzb+X0djicL+lTDqSf/D5kwnBqwDqdeqNjznQE+ZHkk/YsQIm2K970Je/+feBubhBAnhRCvCyFWuNsYR2JxjRYAhya95ZXn2sb3BQefZ08WdM3UvAJkSilXA28x9oSi8R6Oo/p1rAF+BbzkXnMchxAiDHge+LqUssvd9jibGb6vw8+zJwu6Lw6nnvE7SylbpZSDll9/B6xzkW3uwp6/A69CStklpeyx/Ps1wF8IEedms+aNEMIfJW5/klK+MMUmXnWuZ/q+zjjPnizovjicesbvPMmneDvKN+fN7AQ+acmAuALolFLWu9soZyKESLTGgoQQG1DX6UJeqGD5Po8CZ6WU/zPNZl5zru35vs44z3bNFHUH0geHU9v5nb8qhLgdMKG+831uM9gBCCGeQkX744QQNcC/Af4AUsqHULNsbwYuAH3Ap91jqeOw4zt/GPiiEMIE9AP3LPCFCsAW4BPAaSFEkeW1/wekg1eea3u+r8PPsy7912g0Gi/Bk10uGo1Go5kFWtA1Go3GS9CCrtFoNF6CFnSNRqPxErSgazQajZegBV2j0Wi8BC3oGo1G4yX8f+Q3urRf0HfXAAAAAElFTkSuQmCC", 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" ] @@ -380,6 +414,7 @@ }, { "cell_type": "markdown", + "id": "7abb4133", "metadata": {}, "source": [ "You can also use the `SimResults.plot(obs)` method to plot expectation values of a given observable. Here we compute the `sigma_z` local operator expectation values. You may choose to add error bars using the argument `error_bars = True` (`True` by default for `NoisyResults`.) Be wary that computing the expectation value of non-diagonal operators will raise an error, as `NoisyResults` bitstrings are already projected on the $Z$ basis." @@ -388,13 +423,14 @@ { "cell_type": "code", "execution_count": 14, + "id": "47452cfb", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -413,11 +449,12 @@ { "cell_type": "code", "execution_count": 15, + "id": "2e2eb154", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -434,6 +471,7 @@ }, { "cell_type": "markdown", + "id": "422ec4e9", "metadata": {}, "source": [ "## SPAM effects" @@ -441,6 +479,7 @@ }, { "cell_type": "markdown", + "id": "20987d71", "metadata": {}, "source": [ "Compare both clean and noisy simulations for the default SPAM parameters (taken from [De Léséleuc, et al., 2018](https://arxiv.org/abs/1802.10424))" @@ -449,13 +488,14 @@ { "cell_type": "code", "execution_count": 16, + "id": "226b6667", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -480,6 +520,7 @@ }, { "cell_type": "markdown", + "id": "2e18bd3d", "metadata": {}, "source": [ "We will now modify the *SPAM* dictionary, as below, allowing for more ($40$%) badly prepared atoms." @@ -488,11 +529,12 @@ { "cell_type": "code", "execution_count": 17, + "id": "b4c33a09", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -513,6 +555,7 @@ }, { "cell_type": "markdown", + "id": "ed80950b", "metadata": {}, "source": [ "We can see here that the population doesn't go well above $0.6 = 1 - \\eta$, which is to be expected : badly prepared atoms don't reach state $\\Ket{r}$. We can expect this limit of $0.6$ in the Rydberg population to be more and more respected as the number of runs grows." @@ -520,6 +563,7 @@ }, { "cell_type": "markdown", + "id": "5f9e70bf", "metadata": {}, "source": [ "### Changing $\\eta$" @@ -527,6 +571,7 @@ }, { "cell_type": "markdown", + "id": "f856f2f6", "metadata": {}, "source": [ "Let us first initialize all spam error values to $0$. Then, we do a sweep over the parameter $\\eta$, probability of badly prepared states, to notice its effects." @@ -535,11 +580,12 @@ { "cell_type": "code", "execution_count": 18, + "id": "f0a44162", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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/4l4CxkRYff998XDVc+s117DKMAXDtpegvtqux1eU/ji85jVGNJ/kQMzDXDnB/uvb+acvIm/YZC4pfpWqip4v5hTFEdTvfJuRjaVsCn2E6GBfrcMZMKeshGWeyqTZ0Ja2oMXYQuapTBOv6JuUkrvvvvtCZenIkSP88Y9/7Lbdpk2bWLduHTt27GDfvn1MmjSJxsbGbtv1pyUsJCSEkpKSC/dLS0sJCel9UV+9Xs+iRYv4/PPPuz23aNEivvrqK/P+aDMZDQZc1/2eU/gz8VZtZq7MTwhmc8gDuBjqadj4D01iUBRTKs/W4r37/3FEH8Oti+7VJgghcL/2n3jRSNmnll30KYrNNNchNz3LbmMcs6/5sdbRWMQpK2FpwWm46d0AcNW5khbc47qaZpszZw6fffYZFRUVAFRXV1NUVNS2f1dXWlpaAKipqcHPzw8vLy8OHz7Mzp07e9xff1rCrr32WpYtW0ZTUxPHjh0jLy+P9PT0i7aRUpKfn3/h9xUrVjB+/HiAi1rivvvuO2JjYy36X3SV9e2/iW3No2Tyf+E5zLJM3wMlhOD+GxeywjATfebrcP6kJnEoSm+klHz13vOEUsGweb/Dw81Fs1jGxk1i08ibSa5cQW3+Ds3iUJTeNG19mWEtVWwIfZTxowdvKxg4aSUsJSiF1+e/DsDr818nJSjFov0lJCTw9NNPM3/+fJKTk5k3bx4nTpwA4MEHHyQ5OZk777yTBQsW0NraSnx8PEuWLGHatGmW/ikkJiZy6623kpCQwIIFC1i6dCl6fdu6cgsXLqS8vPxCS11SUhJJSUmcOHGCp556CoCXX36ZxMREUlJS+Oc//8m771ovp1ZD3XnC9/6NPJdYJl/9oNX2OxAxQT6UTnwcYWylapXt04YoSn/sLTrN3NPvc9pnPKFTb9A6HEKv+wMnpR/1X/1CZdJXHEtdFWLbC6w1pHL1wuu1jsZiom325OCRlpYmMzMv7j7Mzc0lPj6+3/tKejeJA3cfsFZoQ9pA/sc73v4N04v+xaEFy0mYtsBGkZnvXGML3z93O9fKjbg8noXwi9A6JEUB4MPXnuPO8r/SeNN7eCRdp3U4APz75ed46PRfabrxbdyTb9Q6HEUBoGXlEnS7/83/jH6Nvz50i9bhmEUIkSWl7LHLzSlbwhTbqyw/zsTjb7PH+1KHqIABDPdwxe2K32CUguIv/6h1OIoCQPX5BqaXvc0Jzxg8JlyrdTgXpF39U8qkP5Wb/q11KIrS5mwxuow3+Kz1Um5aMEfraKzC6SphnTPmAzbPmO+sCr/6Cy60Enxj98kEWrp6ZhqrPa8mtPhr6spztQ5HUdi76k2ixAnkJf8FNsrTNxCpkYFsG76Q0OqdNFXkax2OomBY/xdapOCHkPtIDR+cecG60m70p0YWpyxmccpircMY0loa64iv+I79PpeRFpWodTgX0ekEUTf8nsaPVlH02e9J+Hn3WaKKYi/G1laic1+l2CWCsdNu1TqcbiLmPkzrFx+Sv3opiT9RucMUDZ08iO7Act5pvZo759sn4bc9OF1LmGJ7RzZ9zHDqEKk/0TqUHiXFxbIj8FYSqtdReqjnGaqKYg+HNn5AhCylYtLPQed4X8dTkhPJdJ/KmMLPaG3unk5HUezFsPUF6vBkx+ifMD3KX+twrMbxSr0y6Lnu+4BSgki+5BqtQ+nVxNv+hxo5jMrv/qx1KIqzMhrx2/08hYSSPM8xL1iEELhO/Sl+nGPP2ve1DkdxVo01yEMr+Lp1OvfMnWSz5fW0oCphilWdLztKXMNejo6+DlcXx+3tDgwM5tCYG0iq3c7pkyWmX6AoVnY68zNCWo5zKPYh3NxctQ6nV5Nm38RJEYTr3ncxGgfXbHplaDAc+AIXYyN7Ri5k9rhArcOxKqerhFW+9DK54+O73Spfetkmx/vrX22fk+qZZ54hJiaGuLg41qxZ0+M2Ukp+97vfMW7cOOLj4y9aI3LTpk2kpKSQmJjIZZddZlEsRRtewyAFo2ffb9F+7GHMpT/FRRjJX/+21qEozkZKjJueo8A4mklX/VTraPqk0+upjr+DSYYDbNu1XetwFCd0dvs75BlDmD//6iHVCgZOWAkL/NljxB/OJf5w28y4jt8Df/aYTY5n60rYoUOHWLZsGTk5OaxevZrFixdjMHRPrvjOO+9QUlLC4cOHyc3NZdGiRQCcPXuWxYsXs2LFCnJycvj0008HHoyhlTHHviDDNZXx4+IGvh87CY9PJc8llsDCL7QORXEyzce3E1Sfz5agOwkZ6a11OCbFLXiEVvSc3vw6gy23pDK4GSuO4H8mm01e85mXMErrcKzO6SphtvLBBx+Qnp5OSkoKDz30EAaDgSVLltDQ0EBKSgp33nknANdffz2pqakkJiby2muvWXzcr7/+mkWLFuHu7k5kZCQxMTHs3r2723avvvoqTz31FLr2wb9BQUEAfPTRR9x4442MHTv2oscHomLvt4w0VnEmbtGguVqpjrmJaMMxCg6oAfqK/ZRtfpd66U705XdpHYpZ9MNHcWLUFcxu+J6deeVah6M4kWPrXqdV6hh7+b3odIPjvNIfTlsJq9+796KflsjNzWX58uVs27aN7Oxs9Ho9H374Ic8++yyenp5kZ2fz4YcfAvDWW2+RlZVFZmYmL774IlVVVd32158FvMvKyggLC7twPzQ0lLKysm7bFRQUsHz5ctLS0rjqqqsurBl59OhRzpw5w+zZs0lNTeW9994b8P/h/Pa3qJS+TLzitgHvw97i5t5Ls9RTuVV1SSp20tpMYNF3bHOZysz4cK2jMVvwFYvxE7UUb/lY61AUJyENLYzI+5wMl1TmTknWOhybcNyR0zZUv3cvxfe2jcMovvenjH37LbwmTRrw/tavX09WVhZTpkwBoKGhodcWpRdffJEvv/wSgJKSEvLy8vD3v3i67fPPWz8fT1NTEx4eHmRmZvLFF1/w05/+lC1bttDa2kpWVhbr16+noaGB6dOnM23aNMaNG9ev/cvzJwmv3sIqn5u4xn+41eO3lREBo9jjPYPYU6toaW7C1c1d65CUIa404ytCZS2tE24dVFf2bjGzqXANIbbkU1oNT+Kid9preMVO9m36ghRZjZz0FPpBVFb6wylLUf3uDGRzMwCypYX63RkW7a9jgezs7Gyys7M5cuQIf/zjH7ttt2nTJtatW8eOHTvYt28fkyZNorGxe+6d/rSEhYSEUFLyn9l9paWlhISEdNsuNDSUG29sW//thhtuYP/+/Rcev/LKKxk2bBgBAQFceuml7Nu3r9//g7JNb+GCEdcp9/T7tVrTpdyBPzXkbPlS61AUJ3B254eclr5Mm3uT1qH0j07Hmfg7mMxhsrN2aB2NMsRJKanf9S5nGM6U+bdrHY7NOGUlzCt9CsLNDQDh6opX+hSL9jdnzhw+++wzKioqAKiurqaoqAgAV1dXWlpaAKipqcHPzw8vLy8OHz7Mzp09j0N6/vnnL1ToOt+WLFnSbdtrr72WZcuW0dTUxLFjx8jLyyM9Pb3bdtdffz0bN24EYPPmzRdauq677jq2bt1Ka2sr9fX17Nq1q/+LoUuJx4EPyZTjmTVtWv9e6wASL7uJaoZj3POh1qEoQ1xtTRXjzm7lkP88/Hy8tA6n38KveIBm6ULTzje1DkUZ4nYePEpa005OhV+Lq5uH1uHYjHNWwiZNYuzbbwFY3BUJkJCQwNNPP838+fNJTk5m3rx5nDhxAoAHH3yQ5ORk7rzzThYsWEBrayvx8fEsWbKEaVaosCQmJnLrrbeSkJDAggULWLp0KXq9HoCFCxdSXt42iHbJkiV8/vnnJCUl8dvf/pY33ngDgPj4eBYsWEBycjLp6encf//9TJgwoV8xNBVsIaC5lCNjbsDbffD1cLu6uXM0aAETardTU3VK63CUIezA2ndwE60EX3KP1qEMiMeIYA74XkZS9SpaGmu1DkcZwo5vehc3YSBq/oNah2JTYrBNN05LS5OZmZkXPZabm9v/1hsgd3z8hVQVSt/6+h+XvvUThhet5cCi3cyMH2vnyKwjf982Yr5cyK74/2bqbb/ROhxlCJJScuDpmfhRQ+jvDiAccJkic2Ru+oa0TT/m8NRnGX/VI1qHowxB5xpbKH0mDT8vN0b/xrLhQo5ACJElpUzr6bnB+S2gOI6GswSVrOJ7/SVMiwszvb2Dik6aTqEuAr+8z7QORRmi9h08QLIhhzPRNwzaChhA0syrKJQheO4f+ExqRelLxo5NJIjjtCTfoXUoNjd4vwkGqHPGfMDmGfOHutrMj3GTzZyLv2NQz14ROh0VUTcyrvUoRYctT1uiKF2VbH4XgHFz79M4Esu4u7qwf9QNhDccoqWs/5N4FMUUuedDmnEh7FLHXFPVmpyuEtY5Y37nm60y5g91jbvfJccYzoxZc7QOxWLRc++lVeo48cNbWoeiDDGV5xpJqFxJkfdEPIIitQ7HYv4z7qZJunJq47+0DkUZYmrO1zH53PcU+F2CGOZv+gWDnNNVwhQrKs8m4HwuP3gvIG704MkN1pvAUWPJ8ZpCVPm3GFpbtQ5HGUJ2bNtAtCjHM3VoTLWfmhjDWjGdgMKvoEkN0FesJ2fTckaKWtymDP1WMFCVMMUCZ7e/RZN0ZVja0Om3b026nSCqObRthdahKEOIy8FPaMaFoGmLtA7FKtxcdBRH3oaHsZ6WfZ9oHY4yhHjlLOM0fkRNvUbrUOxCVcKUgWlpwCP3c1YZ01kwZbzW0VhN4uW3UsMwmrJUzjDFOs7W1jOldgPHR84CTz+tw7GaxKnzKDCO5lymqoQp1nH2VDFJDbvJG/0jhN5V63DswukqYbu/KWTpwxu63XZ/U2iT4/31r3+1yX47e+aZZ4iJiSEuLo41a9b0uM2GDRuYPHkyEyZM4O6776a1vbvtzJkz3HDDDRfyhB08eNCsYxpzvsbDUMuRMdcT5DN0Eul5eA7jsP88Emt+4HxNtdbhKEPAwa0rCBQ1uE4aGl2RHWbGBrJJN40RFbugXpUVxXJFG99CLyT+s36qdSh243SVsPRronj0X1fw6L+uALjwe/o1UTY5nq0rYYcOHWLZsmXk5OSwevVqFi9ejMFguGgbo9HI3XffzbJlyzh48CDh4eG8++67F+JLSUlh//79vPfeezz++ONmHff8jrc4bgwmYfpCq/9NWvOdfjeeopncdWoKvmI514Ofco5hhE+9XutQrMpVr6M+eiF6jLQc+k7rcJTBTkoC8j/jgG48sQmWJVAfTJyuEmYrH3zwAenp6aSkpPDQQw9hMBhYsmQJDQ0NpKSkcOeddwJtywelpqaSmJjIa6+9ZvFxv/76axYtWoS7uzuRkZHExMSwe/fui7apqqrCzc3twlJF8+bN4/PPPwfaKnFXXNFWIR0/fjzHjx/n1CkTWeNrK/E5tZuV4hLmJY62+G9wNHGTZ1OsC8HnsOpmUSzTWFdD0vktHPGfg24ILr2SMnU2ZdKf6qwvtA5FGeRq8rYT0lpCWcSNCDF40x31l9NWwk4W1lz00xK5ubksX76cbdu2kZ2djV6v58MPP+TZZ5/F09OT7OxsPvywbYzRW2+9RVZWFpmZmbz44otUVVV1219/FvAuKysjLOw/SVJDQ0MpKyu7aJuAgABaW1vpWGngs88+u7Do98SJE/nii7Yv0N27d1NUVERpaWmff2/Toe/QIWmOuRoPV30//lODg9DpKAu/nviWHMoKc7QORxnECn5YjpdownXS0Jm80tn06AA266Yy8uQWNUtSsUjFlrdokG5EXPpjrUOxK5su9CeEWAC8AOiBN6SUz3Z5fizwLjCifZslUsqVtowJ2ipeXz/flpDz6+f3ct0TkxgV5Tvg/a1fv56srCymTGlbCLyhoYGgoKAet33xxRf58ssvASgpKSEvLw9//4tzoTz//PMDjqUnQgiWLVvGE088QVNTE/Pnz7+wvuSSJUt4/PHHSUlJISkpiUmTJl14rjdnsr6kxRjI9BmXWjVORxI15z6Mr79C8ca3CIn6h9bhKIOUa84nlMpAEqbO0zoUm3DR66iLugrXgpU0HV6D+8SbtA5JGYwMrYwuXc1W1+nMDR+jdTR2ZbNKmBBCDywF5gGlQIYQYoWU8lCnzX4PfCKlfFUIkQCsBCJsFVOHsqNnMLQaATAYjJQdPWNRJUxKyd13380zzzzT53abNm1i3bp17NixAy8vL2bPnk1jY2O37Z544gk2btzY7fFFixaxZMmSix4LCQm50KoFUFpaSkhISLfXTp8+nS1btgCwdu1ajh49CsDw4cN5++23L/wdkZGRREX1MT6u6TwjT23jS9cF3BI5dBPpBYdGc8BjEuGlKzAa/g+diYqponRlqDlBdG0W3/vfwQLXwbewvbkSp17J6fzhNGd8xhhVCVMG4OzhzYyQtTREL3SqrkiwbXdkOpAvpSyUUjYDy4DrumwjgY4sn75AuQ3juSBknB96l7Y/Xa/XETLOsmnjc+bM4bPPPqOiogKA6upqioqKAHB1daWlpQWAmpoa/Pz88PLy4vDhw+zcubPH/T3//PNkZ2d3u3WtgAFce+21LFu2jKamJo4dO0ZeXh7p6endtuuIrampieeee46HH34YgLNnz9Lc3AzAG2+8waWXXsrw4b0nXj17YDVutMD4q9EN4mWKzNEQfzNjZAWF+7dpHYoyCJVv/QA9RtwnD61ZkV2lRweyRTcFv/JN0NqkdTjKIHRi1+c0SVfGz7pe61DszpaVsBCgpNP90vbHOvsj8GMhRCltrWA/s2E8F4yK8uW6J9pmX1jaFQmQkJDA008/zfz580lOTmbevHmcOHECgAcffJDk5GTuvPNOFixYQGtrK/Hx8SxZsoRp06ZZ/LckJiZy6623kpCQwIIFC1i6dOmF7sSFCxdSXt5Wr/3b3/5GfHw8ycnJXHPNNRcG4+fm5jJhwgTi4uJYtWoVL7zwQp/Hq8z4nGrpzZRLr7I4dkcXM/NGDFJQtedLrUNRBiHXnE/Yb4wiLW2q1qHYlItex7nIBXga62k8ukHrcJTBRkr8y9aR7TqR2NBgraOxOyGltM2OhbgZWCClvL/9/l3AVCnlY522ebI9hn8IIaYDbwITpJTGLvt6EHgQYOzYsakdrUwdcnNziY+P73eMSx/ecCFVhdK33Nxc4sfFUPvncHZ5TGfOks+1Dskucv4yEy9jHZH/k611KMogIk8dQrw6nQ9GPMKPf9F9Qs1QsyuvnIQPJlMdsZDwe9Xaq4r5qgr24P/+5ayL+S1zf9y9t2coEEJkSSnTenrOli1hZUBYp/uh7Y91dh/wCYCUcgfgAQR03ZGU8jUpZZqUMi0wMNBG4SqmFGWtxZs6dPE/0joUuzkTNpdIwzFOlx7VOhRlEKnK+BSjFHhMukXrUOwiLXo023SpjCxZBwa17qpivqLtnwEQPfNmjSPRhi0rYRlArBAiUgjhBiwCui7IVwzMARBCxNNWCau0YUwXZcwHbJ4xfyipyPicBulGymU3aB2K3QRPaftbi3eoPEiK+QxHVrNXxnDJpEStQ7ELvU5QE74AH2MNDQVbtQ5HGUSGF60lVx9HZGSM1qFowmZTdqSUrUKIx4A1tKWfeEtKmSOE+BOQKaVcAfwSeF0I8QRtg/TvkbbqH22Xfk2UzbLjD2VSSsZWbuSw9xQmjRihdTh2EzN+IscJwaNwLTA0m8oVKzt3guDzh1jrfTepw4degtbexMy8nsZj/8uJnZ8QNW621uEog8CpskJiWvPYHvGo1qFoxqbzpttzfq3s8thTnX4/BMy00rGcbmqrvUgpaW5uJJhqKhKcY2X7DkIIigIvY0bFcpprz+DmPXQWYFZso2b/t/gCLvFDf/JKZ5OiQ9miSyGpaC1ICer7WDEhf8unBANh05w3tcmQyJjv4eFBVVUVNm5Ec0pSSqqqqmiuLqUVHXGXOMcYl868kq7BVRgo3Nm1N11Ruju3/1tKZQBT0q1yfTlo6HSC6vAFjDRUUnd8t+kXKE7Ps3AN5brRhMVN1joUzQyJDIKhoaGUlpZSWWnT4WROy83NnRE7/kqRdwrRw7vNmxjyEtPnUL3eh+ZD38Lcu7UOR3FkLQ0EVe5gpdscrg/y0Toau4uccSOtx/5C6fZPiIsc2qk5FMuUnTzFhKZsDoUtYowTt5oOiUqYq6srkZGRWocxZO3K2MXUllzykn6vdSia8PJwJ8t7OinV25CtzQgXN61DUhxU3ZGNDJNNNEfOc8rhEckxkWTqJjD22CqQf1ddkkqvcrd+RYgwMCrdebsiYYh0Ryq2VZP9NQBhM5yvK7JDa+wCfKij/OAmrUNRHFhF5lfUSXdipzrXeLAOOp3gdNiVjGot43zJQa3DURyYS94qzonhjEq8TOtQNKUqYUqfpJSMPrGeIrcYPAIitA5HM7HTrqVJulCd9ZXWoSiOSkpGlG5gt24iEyNHaR2NZsJm3IJRCoq3LdM6FMVBVZ6tZVLjLsqCLgX9kOiQGzBVCVP6VFR0jETDEc6Mna91KJoKHRXIPteJBJZvaJv5pShdNJftw6+1kqoxV6Af4uuq9mVC3DgO6uLwPrZa61AUB3V412p8RT3Dkpxrtn1PVCVM6VPJzi/QCcloJ++3B6gOncsowwlqy3K0DkVxQGW72tYYDZ5yrcaRaEsIQWXoPMKb8zlXnqd1OIoDMhxeSROuhKZdrXUomlOVMKVP3sfWcEIEExybqnUomhs15ToASnc4x7qZSv+4FKxhv4xhSuJ4rUPRXMj0WwEo3LJc40gUR2MwGIk98wP53mnoPJxvBnFXqhKm9Op8TTWJjXsoCb5czXICJoyPJ4coPArXaB2K4mCM504RVp/L8YBL8HDVax2O5uLik8gTEXgVrjS9seJU8g7uIoRKWmIWaB2KQ1CVMKVX+Tu+xk20Mmzi9VqH4hBc9DqKAi5jbMMhjOdOaR2O4kBKdn8FgHeS6l6Bti7Jk2PmEdN4iLOnSrQOR3EgZ7K+wigF4dPVEBdQlTClL7nfUo0P49Lmah2Jw/Cc8CN0yAsnXUUBaMr5jhNyJKlTnHu6fWejpt2KTkiOqi5JpZPA8nUcdY3DLzhM61AcgqqEKT2SrU3E1mzn8PBZuLq6ah2Ow5g05RLKpT8th77VOhTFUbQ0EnZmFzne0/EdphL5dohJTKNUjMYjX82SVNrUnDpOTGs+lSFXaB2Kw1CVMKVHRVlr8aYeGae6VzobMcyd/cNmEFq9C1oatA5HcQDl+77Hk0ZkrBrj0pnQ6SgNmEVswz6aGuu0DkdxAMXtk5pGTr5B40gch6qEKT06l/0V9dKduBkqj0tXLTEL8KCJMwfXaR2K4gCq966gQbqRMOtHWoficDzGz8NTNJOX8b3WoSgOwC1/NUWMIi5RzbbvoCphSndSEnJqI9nuqQT4jdA6GocTN3UB56UnVXu+0joURWtSEnxiE/vcJhESMFLraBxObPoCmqQrdTmqS9LZycYaomr3kOd3KS4uagZxB1UJU7qpOb4Xf2MVteFztA7FIcWO8We3fhIB5RvBaNQ6HEVDVcf2EmisoC5cTV7pyTAfX464T2BUxTatQ1E0Vpb5La60ohuvhrh0piphSjclGd8AEJKquld6IoTgTOhcRhiqaCrJ0jocRUOlO9uy5I+dpsa49OZ86GWEG4upKivUOhRFQw0HvqFaepM4VV2wdObcK2cqPXI9vokCwogfF2f3Y7+S/Qqv7nu12+OPTHyExSmLzd7G1oLTfoSh6M+c2P0FEeFT7HJMxfEMK1pHri6G8dExdjumqc+/I5SPzgInXQ2F/4+ijBX4h/zC7sdXHIChldEVW9jhls68Ed5aR+NQZUTIQbYYcVpamszMzNQ6jCGrteE8xuci2OF/I5f97HVNY0l6N4kDdx+weBtbaGwxcODpmYR5tjBqiWoNc0Z11SfwfCGeLSH3cdmD/9AkBlOf/56et/cJyGgwUvnnaE74JJHyyxVW37/i+OoLduD1/gK+jHmaG378M63DuYg9ziFCiCwpZVpPz6mWMOUihVlrGUcrHuPnax2KQ/Nw1XPM/1KmVP8beaYI4ReudUiKneVv/5KJQuI3aXAt2L04ZfGFypY9TkA6vY5C32lMqNmEsbUFnYvKO+hsTuz5jkgpCJms0rh0pcaEKRc5d3A1jdKV+GlXah2Kw/NKXAhARbZaH88ZySOrqMCPhEmztA7F8UXPwYd6ivb/oHUkigZcj23kINGkjIvWOhSHoyphykWCK7Zx2COZ4T7DtQ7F4aWmplMuR1J7SOULczYtTQ3Ent9Ngd8sNd3eDFFTf4RBCqr3rdI6FMXOZMMZQuoPUeQ3DTcXVeXoSv1HlAsqSo4SZiyjPkz79e+yK7Iv+jnQbWxp9AgvDrinEnx6JxgNmsSgaOPI7jUMoxGPhIWaxWDq8691+egsOHgUh13GM6J8s9ahKHZ2at/36DHiNs7xUh45QhlRlTDlgmO72tZDHJNquzwur2S/QtK7Sd1ur2S/cmGb7IpsHlj7AAAPrH2gxwJizjb20Bw+G29Zy/nC3ZocX9FG7YGVNElXxmu0ooSpz7+5ZajzT1s7HTyTyOY8Gs5W2OV4imM4e2AN56Un8Wm2qYSZc07piaOcQ1QlTLnA5dgGKvAnPG6yzY6xOGUxB+4+cGEwcMfvnWdlZZ7KpNnQDECLsYXMU91nw5qzjT2MnXIVRikozfxOk+Mr2hh1ejtHPZLwHOZj9X2bc1Ix9fk39bwWJyCfCVehE5Jju76x+bEUByEl/ie3ss8lmbFBvjY5hDnnlJ44yjnE7NmRQggvKWW9LYNRtNPU3ERsbRZ5/pcTpNO2bp4WnIab3o1GQyOuOlfSgrvP7O1rG3tOwZ8QE0WuiMKjaJNV96s4roqyY0QYS9gZeqNN9m/O7EVTZcTU8z2dgFKCUmzy93RISL2UM2u8aTnyPVx5n02PpTiGpoo8Ag0n2R12p9ahdGPOecYeTJ5thRAzhBCHgMPt9ycKIfpu51MGndyMTQwX9biPn6d1KKQEpfD6/LYcZa/Pf73Hk0Nf2wz0ymgg9DrBCf9phDfkYGiosfr+FcdTlNHW6hk48SrNYjBVRkw933ECAux2AvJwd+PwsDTCzuxQy305ieKMtiEu/hqWld6Yc56xB3OaPJ4HrgSqAKSU+4BLbRmUYn81B1djkILoqdqMcemqo0D0VTDM2cYehiXMxwUjxzLUzC9nIAo3Us1wIhPTNY3D1Oe/r+e1OgE1hl/OSHmWygKV4NgZGPM3UCyDmJhsuyEulnCEc4hZ/U5SypIuD6mpYENMwKltHHePw9M3wC7Hc4RZKdaSkD6XOunOuZy1Woei2JjRYCDyXAaFw9PR6Qd3agotTkChaW2Tfk5kfmu3YyoaaW0m7Gwmed7peLrZvqwM1nOKOZWwEiHEDEAKIVyFEL8Ccm0cl2JHJWXljDcc5XyofRo4HWVWirX4+nhzxGMiQZU7tA5FsbFjhzLwpwYZNdvmxxqsJ5W+xETFcJQIPIo3aR2KYmMVh7fgRQPGqMttfqzBfE4xpxL2MPAoEAKUASnt95Uh4njGd+iFJGCifXIe2WtWij1PYo1jLyPUWM6J44dtfixFO5XtyUbDp/zIpscZzCeVvgghKPWfTmTDAQwN57QOR7Ghir2raJU6otNtPx7MUWY6DoTJSpiU8rSU8k4pZbCUMkhK+WMpZZU9glPsQ1e4gfN4ETLBPsuv2GNQcF8nsYHmlelLSFrbSblot+pmGcq8SrdQpAsjKCRywPuwRgqKwcx9/HxcMVCctVrrUBQbGlb6Azm6cUSGjrH5sbSYaGItJlNUCCHeBmTXx6WUP7VJRIpdGQ1Gos/t4phPGsl6yxfWNSc9RMeg4LtW3dVtUHDX1ye9m9Tt9eZs09cUfFssYDw2NplTIgD98U3Aryzen+J4GhvqGNewn+zgG7BkuXZLU1CY+vybUz76YusUL3Hp86jb6k5tzhqYdavF+1McT/O5SsIbj7J+1E8RQtj8eH2dU3piaRmxJiFlt/rVxRsIcVOnux7ADUC5lPLntgysN2lpaTIzc+hcFWot72AGsZ/NZe/EPzDphietum9TFRxrVYB60tES1mhoxEPv0WvBtGYMe176MdGnN+D222N4erhbZZ+K4zjww9ckbfgJ+y79NxOvWGSVffb1+cuuyOauVXfx/lXvW3XwfH8qWbYqo7v+Mo8IYzHBvz8MdjhJK/aVt/5dYrf8nB2XL2f6ZQvsdlxbnlMsIYTIklL22DxnsiVMSvl5l519DGy1UmyKxiqyVxELhE1xjNQU1tLfKyNr8Bw/F99t35CVuYnUWVfa/HiKfdXmrqVZ6omZYp+Tiq1mL3ZuiRsoS1vLakIuI/j436g9cQTvMeMtikVxPA2Hv6dGDiNxivbrEDu6gaRGjwWCrB2Iog3v0s2U6EIICI3VOhSrs/cU/Mj0hRil4PwhlapiKAqs2EG+ewLDfEZoHYrmLE2I7J/SNli7NEMtYTTkSMno0zs45DmJ4V6eWkfj8MzJmH9eCHGu4yfwDfAb24em2FrHGJdy/+lah6IJa8+e9PAN4phbLAGntltlf4rjqDpVSoyhgJox1pu8MhRTUJhrwoQUiuQodAXrtQ5FsbKq4/sJlKdpHKtawcxhzuxIHynl8E4/x3XtolQGp4LM7/EUzTZZqsjRTzC2SgFwdvQsxrce5vTp01bZn+IYjmWsBMA/2TpdkYMhBYUty7C7i5684VMJO7cHWhqtvn9FOyWZbct6jZl8tcaRDA69VsKEEJP7utkzSMU26g6toUm6EGPlPC7mpocArJIeYiBslQJgRNKVuAgjBe0nbWVoMOZvpIZhRCdbpyXM0VNQ2KOSKKPn4EkTpw5usvq+Fe24Ht/EccYwLi7BLsdzlHPKQPU1MP8ffTwngSusHItiZ0EV2zjqnkiSj69V92tueggt9ZUCwBIRKZdT/507hrz1cNVPrLJPRVvSaCT87C4KvFOZ7GJyLpNZLElBYQ99leHOOreW9XfsZdSUBTTtdaFq/0qCJ9lvBp1iO4bmRqLq9pI58hoirDTr1dQkEEc5pwxUr98oUkqL1xoQQiwAXgD0wBtSymd72OZW4I+0Vez2SSnvsPS4imnnThUTYShiS+i1Vt+3rSo45jLnJGar2ZN6V3fyvSYRdmYXUkq75MhRbKv4aDbhVHE8fLbV9tnX588RTirmlOGurWX9LUdRY4LI0sczquwHa4WtaKxwzzpiacZt3ByzX9OfSpajpqCwhFmXdUKICUACbXnCAJBSvmfiNXpgKTAPKAUyhBArpJSHOm0TC/wWmCmlPCOEULMu7aQo41uSAL+J1r8C1SI9RGfmnsRsNXuyKXw2YbnPUlKYS1i0fZrkFds5sXcV4UBYmnXHuGixgLa5zCnD5raW9UYIwenA6aSdeo3WmpO4+I6yUvSKVmoOrKFZ6hk31fwhLkO9kmWKObMj/wC81H67HPg/wJzmk3QgX0pZKKVsBpYB13XZ5gFgqZTyDICUsqIfsSsWkPnrOS19iUu2zcxIRz7B2NqYyW1fQGVZalzYUOBR/ANlYhRjIp0rn5WpMmyNpWJ8EtomBRXvUUsYDQUjT23liFsifn4jtQ5l0DAnT9jNwBzgpJTyXmAiYM4gohCgpNP90vbHOhsHjBNCbBNC7GzvvlRszWgg/OwujnpPwdVFr3U0Q86Y6GROEoBb0WatQ1Es1NzUSGx9NqUjp2kdisPpaC0DBtzinZh6CTXSi7rcDVaOTrG3sxWlRLUWWjWNizMwpxLWIKU0Aq1CiOFABRBmpeO70Jb8dTZwO/C6EGJE142EEA8KITKFEJmVlZVWOrTzqsjLwJfzNEdYPOxP6YHQ6Sj2m0pMXRaG1hatw1EskL9nI8NEI67j5modikOytMV7hLcnh9xTCD69A0wsoac4tsJd3wLgP9G6s+2HOnMqYZntFaPXgSxgD7DDjNeVcXFlLbT9sc5KgRVSyhYp5THgKG2VsotIKV+TUqZJKdMCAwPNOLTSlxN71wAQMlktrWMrIuYKhlNH4b4tWoeiWKAmZy0GKYi2choX5T/qQ2cSZKzgfHme1qEoFjDmb6AaH8ZNnGmT/Tt67smBMidZ62Ip5Vkp5b9oG2R/d3u3pCkZQKwQIlII4QYsAlZ02eYr2lrBEEIE0NY9WWh++MpAuBf/QAGhxETFaB2KJkzllen8fOdbf/LORE65GqMUVO9XY10Gs5Ent5HvGoevX4DV9jnY8xpZW9DEtovB4+1JPpXBRxqNRNbsotBnCnr9wIa49FXJGgzJjQfK5OxIIcQK2gbVfy2lPG7ujqWUrUKIx4A1tKWoeEtKmSOE+BOQKaVc0f7cfCHEIcAA/JeUsmoAf4diJtnSSET9fnb5XUO0DdInOEKOI1NMzZ60xmydgKDRHHGJYcQJtdb9YFVTXUlMy1Eywn5q1f06QgqKvti7DI9PnMypL0YiCzcDT1h9/4rtHTuUQRRnKYgc2BAXU+lOLJ2J68jMSVHxD+A24BkhRAZtFbJvpZQm15qQUq4EVnZ57KlOv0vgyfabYgcl+zczlmb00bNtsn9HP8HYU2XQTKaVv0fDuTN4DvfTOhylnwp2f8dkIfFNcq5ue3uXYVcXPQXD00io2YE0GhA6NVlosDm1by1RwNgpCwf0elOVLK1zT9qSOd2Rm6WUi4Eo4N/ArbQNzlcGoaoDbWNcoqY414lFC8MS5uIijBzLXKV1KMoAtORtoFZ6EjNpttahOBxrd6nKiMsYwXlKD2dYM0zFTrxKt1AixjAqbGBDXEylO7HGTFxHZW6yVk/gGtpaxCYD79oyKMV2fMq3cUQfS0JwsNahDHnjUudQv86d+iPr4Qq1EMRgE1q9k/xhKaS4uWsdisOxdmtZRNpCOPA7Tu5dRViCSgcymLQ0NxFTv4+DAQsGnDbBnOTAQzX3pDljwj6hLfHqauBlYHN7ygplkGmuO0tE0xG2jroLlcfd9oZ5eZHtMYGg07u0DkXpp7LCXELkKUrH3tOv15lagkXpWUh4FMdFKB4lagzlYFOQ/QPjRSOuMbMt2s9QrWSZYk5L2JvA7VJKg62DUWzreNZaxgkjXnFq7XVzWLI4cYfaMTNJOfYiZ04V4xc81nrBKTZVmvUdIcDoyf0b4+LsS7BY4oT/NCZWfkNzYwNuHp5ah6OY6UzOOoxSEDVFpXEZCHPGhK1RFbChoTZ3PY3Slbg08xdXdVbWmhI9csJ8AIoy1LiwwcT1+GZOEkBYTLLWoTgNj3GX4yWayNuzUetQlH4YfmIbhS5RjAhQa38OhDnJWpUhIqBiB4fdEvEd7qN1KA6vp9k6AzFu4nTOSm8MBZusGJ1iS4bWVqLrsigekY7Qqa9Ie4lJvwqDFJzN+V7rUBQz1dfWENuUy+lANY5voMwamK8MfrWnSxlrKOJYyI8s2o+zjHmx1pRoFxcX8odNIuzs7rZlWWyQm02xrsKDO4ilDl3UZVqH4lR8Rvhz1C2Okae2ax2KYqb8zHUki1a8xtuud2Uw5J60hLmzI0OA8M7bSyl/sFVQivUdy1hNEjBigmVr4DnLmBdzZuuYq2XsJQQf3sKJYzmMjppgvSAVm6jav5ZYICJ9YDmPlIE7GzydySXvcvZMFSP8/LUORzGh/vB6mqWemLSBn1dMVbKGeu5Jc2ZHPkdbaoqOrPYAElCVsEGkJX8jNXIYcRPVCvfmstZsnTGTF8Dhv1KWtUpVwgaBYWVbOa4bS8SogU+ksMakDmfkmzgPl9K3Kdi9mtQr79Q6HMWEgMqd5LsnkODtO+B9DPVKlinmDHi4HoiTUi6UUl7TfrvWxnEp1iQlIdW7OOo1CQ93N62jGfT6u7bk2JgkThKAvkhdtzi6xoY6YhoPcjJg4GNchvI6d7YWNflyGqUrjXlqcL6jO3v6FFGthdSMmqF1KIOaOd2RhYAr0GTjWBQbOV18mGBZydGw+7QOZUjob5es0Oko9k1jXM1WjAYDugEucKvYXv6eDUwQzXiMG3gal6G8zp2tubp7ccQzmTHVKreeoyvMXMVkIRkxYZ7WoQxq5lTC6oFsIcR6OlXEpJQ/t1lUilWVZK0iAAieqJYq0oqIms2IvaspyNlBdLLqEnZU5w+tp1XqiO5lWS9zJqYM5XXu7KE+ZCYTCl7kZFkRo0LCtQ5H6UVL3kbqpAcxKZdqHcqgZk535Argz8B2IKvTTRkkdMc2c4qRxIxPsdo+O495UUyLaE9kWLlvrcaRKH0ZeWo7+a5x+PiO7PH5xSmLOXD3gQutnx2/dx7TMpTXubOHwPaLxeNqzVWHNqZ6N/leE3FVy3pZxGRLmJTyXSGEGzCu/aEjUsoW24alWIs0Ggg/n8VhnxkE662T86jrmJehdqKxxZTowDERFOnC8CrbZpUYFes7d7aKmJajZITda/G+nHUJFmsIT5hGzRfeiMJNwMNah6P04GRJPmGynLLQ201u6yxpjQbKnNmRs2lbsPs4IIAwIcTdKkXF4FCSm8FYzmOMtF7Oo6E+5sVWs3VO+k8lqeIbmpoacHdXy7I4moLdq5gkJD4JaoyLlnQuLhR6Tya8JgNpNKqEuQ6oOHMVo4AgM4a4OEtao4Ey59P9D2C+lPIyKeWlwJXA87YNS7GWin1rAAhPtd66Xh1jXgCnHvPS3y5Z945lWbLUzC9H1Jy3kQbpRszk2VqH4vRawy9lFKcpzlcnbEekO7aZKnyJiHfO735rMqcS5iqlPNJxR0p5lLbZksog4FGyheMihJDwKKvtU415GVgagugpCzBIwfmcdTaOThmIUVW7yPNMxt3DS+tQnF7I5LaLxvK9azSOROlKGo2En8vkuE+qmultBeZUwjKFEG8IIWa3314HBraQnmJXrc2NRNXvp3zkVKvv29nHvAxkbUmfEQEUusYyQi3L4nAqy48TbiyhPmSmWduriSm2NSYqkVMiALdiNerF0RQf2UsgZzBEqFmR1mBOJewR2rLl/7z9dqj9McXBFWZvwks04Rp7udahDDkD7ZKtHjWDmJaj1JyttmV4Sj8dz1gJQECy6TEufbWCdk7kC5hM5Kv0QgiKfdOJrttLa4uaB+ZITmS3tU6GTrbeEBdnZs7syCbgn+03ZRA5e3AdBimImWJ+YVEzWcwz0LUlfRPm4lr6DvkZa0mdt8i2QSrmK9zMGXyImmA6U35fE1OcfQkWa9LFzGZE5kqOHNxJ3KRLtA5HaedesoVyEcyYyPH9ep1ayqtnvbaECSE+af95QAixv+vNfiEqA+V7Yhv5LjH4+Qea/Rpz8iApbQbSJRs16QqapCuNRzfYJiil36TRSHjNbo55TzZrjIuamGIfHbn1qvar3HqOorWlmej6bEpHTOnX69RSXr3rqyXs8fafP7JHIIp11Z8/Q1TzETLG/FjrUJRO3DyHccgzkVFValkWR1GSv5+xVHMs3LwxLgNtBXUm1mhR9w8ey3HdWHzKt1o7PGWACg9sZxz16GP6N8RlqKc1skSvLWFSyhPtvy6WUhZ1vgGqWcTB5WesxVUY8I6fY9X9qjEvlqsPmUW08Tgnyou1DkUBTuxdDUBoP9K4OPvEFFOs1aJ+MmAaMY0HaWyos0WYSj9VHWhrlYzsxxAXUK3HfTFnYH5PmQvViDwH13BkA03SldhU61bCOn+5dr6p7krzBSbPB+B45mqNI1EA3Iq3cIJAxkTEax2K0oXnuCvwFM3kZ6nue0fgU76NQl0EI4NC+vU6ldaod32NCXtECHEAiOsyHuwYoMaEObig0zvJc0/Ec5i31qEoXYydMINavJAFm7QOxekZWluJrttLiV+6yszugKKmXEmr1HE+d73WoTi9xvpaYhtzqAg0PXmlJ6r1uGd9fet8BFxD2wLe13S6pUop1UAjB1Z1qpRIw3HOm5nzqCcqD1LvLO2SFXpXjnlPYmxNBkajtGWoigkF+7cxnDp00bO1DkXpgY/vSPLd4hipcutpLj9rPe6iBc84lfLImnodmC+lrAFqgNsBhBBBgAfgLYTwllKqAS0O6ljmKvwB/6T5A3r9UF+g21LWSEPQGn4JoTnbyM/LISZugpUiU/qr6kBbzqOINDXCwlGdCZ5Jesmb1Jw5ja9fgNbhOK3zuetpkXqi00zn0lPMZ7L9XQhxjRAiDzgGbKZtIe9VNo5LsYAxfyPn8CIqacaAXj+QbPBK/4ROXgD8Z1C4og2f8m0c00UQMCrMrO3VxJT+sUaL+vDEueiFpDBDlRUt+VfsIN8tDu/hflqHMqSYMwjiaWAacFRKGQnMAXbaNCplwKTRyNgzuykYNhkXV7cB7UPNZLG9wKgUqoQfrsVbtA7FaXWMcTkVYP6yXmpiivmslRsqZvJs6qQ7LXlqcL5Was6cJrolj7OjBnZhr/TOnEpYi5SyCtAJIXRSyo2AOis7qNLCQ4yikuaxA1/XS81ksQMhKB0xhZi6vTS3GLSOxildGOMy/gqtQxmSrNWi7u7uSb5nMqOq1LW/VgozVqMXEt+Euf1+rWo97pvJZYuAs0IIb+AH4EMhRAWgkrY4qPI9KwkDxrR3dw2Umslie/ro2QScWcv+/TtJTh34JAplYNQYF9vqaFFvNDRa3KJeF3IJEwv+yemyQgJCoqwYpWKOpqPrqZfuxEzu/6B8tZRX38xpCbsOqAeeAFYDBags+g7LtegHThJAaHSS1qEoJoRPWQhA1YHvNY7EOakxLrZlzRb1wPaF1Ysy1XBkLYyp2kW+10Tc3D20DmXIMacS9pSU0iilbJVSviulfBH4ja0DU/rP0NpKTN0eikf0nvOoc9Nw55tqGrY/n+BIyvVj1LIsGqiprlRjXOzAWi3qUROmUIUvFG6yOCalf8qL8hgry2gIU4uo24LKmD+EXMh51Me6XmqBbsdSETCd+Kb9nKtTPfz2VNAxxiWxp683xdHo9XoKvFMJr9mNNBq1DsepFGeuBGD0JHXatwVzMuaP7yFj/gH7haiYq3p/e86j9m4uxfF5xc9lmGjiSIaa+WVPLXkb2sa4TJqtdSiKmVrDLyWAs5TlZWsdilPRH9tEFSMIi0vt8XnVu2KZvgbmf0RbPrBngCWdHj8vpay2aVTKgPiUb6VAF0l0cOiA9/FK9iu8uu/VC/c7ZrQ8MvER1VpmAxFpV2HYKKjP/R5mX6N1OE5jdPUu8jyTmajGuAwaIakLIeePnNi7itC4yVqH4xRaW1uJrs3kuO9U/HsZ4tJ54H3Su0kXelkU85jMmC+EeAGollKeBxBCDBdCTJVS7rJXkIppDXXniW3KYc+oW4i2YD9qJot9uXn7UeAeR2DlDq1DcRqnSgsYayyjPPRWrUNR+mFs5DhKxGg8Sn4Afqd1OE4h78Au4jlHUbRK42Ir5qSoeBXofNlR28NjisbyM78nSbTiNb7/eVwUbZ0bM4vkY29y4uRJRo8apXU4Q15x5iqCgcCJ3dO4dG0J7qBagvvHFi3qQghKRqSTfGYtxpZmdANMRq2Y7/T+tQBEpF+tcSRDlzmVMCGlvLDKsJTSKIQw53WKHdXlrqNZ6omZYt5A487LiahcYNryS7oS/fE3KMxYxehr7tU6nKHv2GbOMJzIhCndnlJdK9ZhqxZ1XfRsvDO/puDAFqInz7H6/pWL+ZRtoVgfxthR4VqHMmSZMzuyUAjxcyGEa/vtcaDQ1oEp/RNYuYM89wS8vH1Nbmut5UQU6wifeBn1eCALNmodypAnjUYiajIo9ElFp9drHY7ST9HpV2GUgtP71mgdypB3vvY8cU0HqAycrnUoQ5o5lbCHgRlAGVAKTAUetGVQSv9UV5QRbSjk3JhZZm2vFuh2LMLFnWPDUhh7dhedGp0VGyg+mk0gZzCED3xZL0U7gUGjyXeJwefENq1DGfIOZ6zHUzQzLL7/vSuK+UxWwqSUFVLKRVLKICllsJTyDillhTk7F0IsEEIcEULkCyGW9LHdTUIIKYRQa1IOwLGMtizSIyfMN2t7tUC342kOv4yxnKQwL1frUIa0E3tXAxCaqtK4DFZVwdOJbcrl/LkzWocypNXnrqNV6ogyY1kv1bsycCYrYUKIcUKI9UKIg+33k4UQvzfjdXpgKW2JXROA24UQCT1s5wM8DqjZlgNkyN/IObyInmheS5haoNvxjEltS4RYvmelxpEMbe4lWygXwYyJHK91KMoA+SbMxVUYyNu9VutQhrSg0zs55hGP2zDTQ1xU78rAmdMd+TrwW6AFQEq5H1hkxuvSgXwpZaGUshlYRts6lF39GXgOaDQrYuUi0mgk7MwuCrwm4dKP2UJqgW7b6m8Cw+CoFE6LkbgX/2DnSJ1Ha0szMXV7KfVLN7mt6lpxXDGpc2mSrtQfWa91KENWWXkZcYZ8akPMW6pI9a4MnDmVMC8p5e4uj7Wa8boQoKTT/dL2xy4QQkwGwqSU35mxP6UHZYWHGE0lzeGXaR2K0km/l4dqn34fW7eH5hZzipfSXwX7tuIjGtBHz+5zO9W14tjcPIdR4JnEqNNqDKWtFGasRidkj2lceqJ6VwbOnErYaSFENCABhBA3AycsPbAQQgf8E/ilGds+KITIFEJkVlZWWnroIaVsT9t4sDGTzSssiuPSx16BnzjP0X1q0LEtVB9s676KnNL3Gniqa8XxNYbNIkYep7j4uNahDEmyYCN1eBKSONPs16jelYExpxL2KPBv2taQLAN+QduMSVPKgLBO90PbH+vgA0wANgkhjgPTgBU9Dc6XUr4mpUyTUqYFBgaacWjn4Vr0AycJIDQ6SetQFAtFTGlLiHjmgJp+bwvDy7dToI9kZFBIn9uprhXbs3S9wY7FpI+3T0pSrKfVYCTi3G6KfCYjXFRCXFszZ3ZkoZRyLhAIjJdSzpJSFpmx7wwgVggRKYRwo20c2YpO+62RUgZIKSOklBHATuBaKaW67DSTobWV6LosikekI3pZ16urzl9+gFps1YEMDwzluD4C3xNbtQ5lyOlY1qsyYJrJbVXXiu31u7u+i9Hjp3IOb3THN9syTKd0+PBBxnIKY9RsrUNxCiYz3wsh/IE/ALMAKYTYCvxJSlnV1+uklK1CiMeANYAeeEtKmSOE+BOQKaVc0dfrFdMK9m9nHHXoTIxx6UytDenYTgfNIKn8E86dr2G4j+lZSYp58rPWkSRa8RxvXpZ11bXi4HR6SkakEX0mg6aWVtxd1SIu1nJq7yomAGEqjYtdmNN8sgyoBG4Cbm7/fbk5O5dSrpRSjpNSRksp/9L+2FM9VcCklLNVK1j/VB1oy3kUMUUVFkfV31l2w+Ln4i5ayctYZ7ugnFBt7gZapJ6YNPMSTyqOTxc1mzGiioMH9modypDiWbqF0zp/fMMSzeo2Vr0rljHn8mG0lPLPne4/LYS4zVYBKebzKd9GoS6CqFFhpjdW7K7rLDtzurai0ubRvN6FxsPr4Iqb7BClcwio2E6eWzwJPiO0DkWxkvApC2HPH6ncvxYmd18HVOm/8/WNxDfspSzoMgKEMGstVdW7YhlzWsLWCiEWCSF07bdbaetiVDTUUHeecY0HqVDrejmsgcyyc/caToFHIsGnd9g6PKdxpvIE0a0F1IyeoXUoSheW5GPzGjWOSn0Qw8rUGEprObhnK36iFvc4tTi6vZhTCXsA+Ahoar8tAx4SQpwXQpyzZXBK7/Kz1uHWjzEuiv0NdJZdbcglxBiPUVFebMvwnEbBrm/QCcnI5L5TU4DqWrGnvvKxmTV7UghOB00nqXkfp87W2Tn6oel8zvcAhKddrXEkzsNkd6SU0scegSj9U5u7jmapJ3aKeetFKvbXMcvurlV39WuWnX/ylVD4MscyVhJ0nTnZYJS+yLzvOYMPMSmmF+1WXSv201NLcUcZMacbDMAnfi4jTnzNuozNBM9TY2Mt5X9qOyWukYSNGKV1KE7DnLUj7+tyXy+E+IPtQlLMEVixgzz3BLy81Qw6RzaQWXYRE2ZQgzcUbrRNUE7EaDAQVbOLAp909C5qBp0jsUY+tpDUhRgRNB5WI2QsVVpRxQRDLjWju69BrJbxsh1zuiPnCCFWCiFGCyEm0JbPS7WOaehM5QliDAWcG21+NmNl8NC5uFDonUpETQbSaNQ6nEGt8OAO/KlBRs/VOhSlC2vkYxPDAijxGE9Y1TYMRrWEkSWOZnyPu2hhZNLFvStqGS/bMidZ6x3Au8AB4DvgF1LKX9k6MKV3hRkrAfBLUl2RQ5Uh4jKCqeL40X1ahzKoVe79FoDIaddoHInSE2vkY2sIn80EmU9O/jHrBOWkWvM20IILo5OvuOhxtYyXbZnTHRkLPA58DhQBdwkhvGwdmNK71qPrOYcXMRPNW+F+qKl86WVyx8d3u1W+9LLWoZnN1MDj0PYljE7uUcuyWGJE2Q/k66MJcKI0LkOhfHQwpxtsdNq16IWkLGulfYIagloNRsLO7qLYawLC3fui54biMl6OVEaEqVXohRCHgUellOuFEAJ4EviplDLRHgF2lZaWJjMznbcmbjQYqfpzFCU+E5n8y6+1DsfqKl96mdNLl3Z7PODRRwn82WPdHs8dH0/84Vx7hNYvr2S/wqv7Xu32+CMTH+k28Lu3gcdl/xtHpUckKb9ZbbM4h7KaM6cZ9v9iyQi9m+kP/D+tw7Ga/pSRnspHf8uYrfX2+e/oBms0NOKh9+i9y9Jo4NyfI8hyn8LlS76wfcBD0P4j+SR/nMrhhMcZf+ufuj2fXZHNXavu4v2r3h9yq0jY4xwihMiSUvZYezVnpGq6lPIcgGyrsf1DCPGNNQNUzHd0/3bGc4aSmO6Zv/tz4ndUgT977MKJYKCFwxFOMtaYZVc2cioJp9fS0tyEq5u7lSJzHgW7vmWyMDLCjNQUg4mlZcQaZcwe+po9eRGdnjL/aSRV7qSmrgnfYaqs9FfZntUkA2Mm9VxWHHkZL0f4vrdEr92RQohfA0gpzwkhbuny9D22DErpXccYl+jp13V7ztJFcYeKwJ89Rvzh3Asnl47fB0OB7Mw19gq8RQP5ezdpHcqg1HpkLefwInby5VqHogxAf7rB3OOvJECc40DWD/YKb0jxKN5ErRjG8Oh0rUPpt8H+fd/XmLBFnX7/bZfnFtggFsUMI8s2U+ASi2/Q4Bzj4kh98Y4uKv1qDFJw9uD3Wocy6EijkYgzO8gbloaLq5vW4Qwq9iij5iTF7c/sybFT2iZe1Oeorvv+Ot/QRFL9LkpGzgCdXutwnE5f3ZGil997uq/YwYmT5YxvzSU74n6tQxkwa3aF1O/de+Gn16RJVonP3joPPO56kvEdGchR11j8Tmyxf2CD3LHDmURRzfGowbeihLW6VwZaPuzRXWlud7253WAuw4M57h7HqIotSClpG76smCMnczPTxDnOjne+thVHOIf01RIme/m9p/uKHeTvWIFeSIJSf6R1KDbXuXD09nzxvT8FoPjen/a6nSMzJ/9O9ZjZjGs5QuWpUjtHN7hVZLV124dPHXypKcztXumrjJhTPkyVscGmNmw2icajFBaXaB3KoFJ/cBVGKRibfq3WodiVo5xD+qqETRRCnBNCnAeS23/vuJ9kp/iUTnT533MWH0ITu2c07kzL7MbW6Mow6wSyOwPZ3DZoV7a0UL87o8f9dP7paMzJvxOcdh06ISnY9pWdoxvcvEs3U6iLIDg0WutQbMJUGTFVPhzlBGRNwZOvQS8kxzNUqor+GF25hWMe43HzDer23GBaS7W/3/fmnEPsoddKmJRSL6UcLqX0kVK6tP/ecd/VnkEqUN/UTHztLopHzkDoe+9F1jq7sTUGSZpTOLzSpyDc2sb6CFdXvNKnXLyPQXCSMWfgccSE6ZzGD33BWnuHN2jVnj/LuMYDVAQN3RUlTJURk+XDQU5A1hQ4fgbn8Ma1cL3WoQwaZaVFxBvzqAm9osfnO0/26nxztMleA/m+N1VG7MWcZYsUB3Bg9yZGivO4x/fdbz9Yshv3ddViTuHwmjSJsW+/BcDYt9/q1p8/GE4y5gw8Fjo9xf6zGF+7m/qGBjtHODjl71yJmzDgPcExx7hYo7XYVBkxVT7MKWOO3pLcjU5Psd80xtftprG5RetoBoXiXW25JgNTB3dX5EC+702VEXtRlbBB4vyBlRilIHJq34VlMGQ3NnXVYm7h6Hi8p+f7Osk40gxNcwYeeyZehY9oIGenag0zR9PhNdRLd8ZNccxlvawx5sucMtJn+TB1EaNxS/JAu8H04+YRJM5yMGurPcIc9NwKv+c0foSOd9zUFOZ8Xw+0VauvMmIvqhI2CBiNklGVWznuGY/b8IA+t7XGori2ZlZ3o4WFo6+TzGDLKxMz7Uc040L9we+0DsXhSaORsOrtHBmWipu7h9bhDJg5lSBrlJHeXq91S/JAu8EiprblTzx3QC33ZUprcxPj6jIo9JuB0DluVcCc72tHadUaCMf9zysX5BYUkGDMpzG85377rhwhu7Gl3Y3W4AhXOdbg6uVLoVcKY6u2YjCqicl9KcnfzxhZQVOEeWXFUWldCXKU8TL95TlyNMdcYwg8pdK6mFK4dz0+NKAbd6XWoVjFYP2+V5WwQaB497fohCQk/XqtQzGradha3Y3Kf7TGzCeSMg4dzNY6FIdWntmWmqIjeedgpXUlaDCX0bMhs4lvPUz5yZNah+LQzu9fSbPUEz1tcJeVwU5VwgYBr6L1nNH54RuZqnUoZjUN26O70dmET78BgJNZQ2/RdmvyLN5EsS6EMZHjtQ7FJEvHfNnaYC2j/ilX4yKMFO5SSxz3JejkJg65JeHnN1LrUJyaqoQ5uJNnapnYlMXJoFngwP32ndn6Kr5zaxww4IH1vZ0EHWXZls58Ro+jzCUMv9KNVothqGmsryWuIZvygL7z6DkCW475slb5GKzCki7hHMMQ+eu0DsVh1Z7MJ8xQQnWI9uuqmvt9a83Zuo5URvpatkhxAPt3rWe+qKM++WqtQzFbx1V80e139PsqvuuSLR2FpPOSLZ2XVRmorifBznE60rItnZ0NuZyk4x9RWHaSqJBRVo9psDu6azXJogWvBMcf49JTa7G55cRUGTFVPswpY/2NoUN/l1ayBaF35fjwdGLP7aK11YCLi1oPsauSXV8RD4xM0X71FXO+b/v6vrb0mFpTlTAH15y7mlZ0jJ5kOufRK9mv8Oq+Vy/c72hleWTiI3ZPrjfQq3hrFA5zTjKWnAS1EpR2He5F75G34xuibn5A63AcTv2hNTRKV8alO34lrKO1WDY29ru12NIyYo0yZo8LFYvEziUoayM5+3eQONnxW0btTZ+/liI5ioQJk7UOxSx9fV9b46JCS6oS5sAamg1End1OqU8yEZ5+3SpZHToqWQNpXRkoR1j4tDfmnGQsOQlqJTDhMmq/GIZLwfeAqoR1FXJ6K0c8U5jo5a11KCZZ0lqsmBYx9TrI+h3V2StBVcIuYmyqI/z8Hrb5XUO4y+Aa4tLT97UjtWoNxOB4B5xU1oGDJIjjyJh5wMW5cwDNlpCwJJGjo/TFO8LA537Tu1I2cjpJ9buoOq+y53dWVphLmCynIfziMS6dx951vtlq7bv+jCfsrbVY6zJijeNrnRB5eFAYhS5R+JVvtsvxBpNjGatwpwX3+IVah2K2Qfl9bSbVEubATu1tW4g2JP06jSO5mCVdeY501WKqy9QRW/uGJV1N0KZ1fL9rM/PmOuayPFoozfiGECAk7eIxLp1bh5PeTbpwAWMr1uim07qMDJXuyqpRlzGp5F3OVJ/Gb2TfSa6dSc3+76iT7kyYfpXWoVzE1PftYJ2ta4pqCXNQUkpGlm2i2iUQt9ETtA7nIuYuCQSDd2aW1su29CYk7RqMCBpyVmodikNxL9pAmQgmNDpJ61CciiOvL+mbfBUuwkjeDpWq4gIpCan8gVyvVHyHO063vaN+39qDqoQ5qEMlp0kz7uPsmMtACK3DuYi5SwJ1vjlK65e5tM5Y3hvhHUipVyIR1VtpbDFoHY5DaGqsZ1zdHkr9Zzr08itDjaOfOKMnX8F5vJB532sdisMoPpxBsDxNS9Q8rUO5iKN+39qD+sZyUId3r8NHNBAwqfsU4uyK7It+amGoNg130DpjeV9jmQwx80kWBWQccLAZaRo5tO1bvEQTngmDo3t2yLQWO/iJU+/iSp73FCLO7kAajVqH4xBKd68AIHrGDRpHcjGtv2+1pCphDkoUfE8LLgxPmHvR49kV2Tywtm1m3ANrH9C0IjZYmXMS1HogaF+TMEKmXg/AqT2qmwWgaf+XnMeT8TOu7XUbe1+49NVNN1Rai805cWrdXWmIuoJgqinMcawKolZ8SzdQ6BJFUEik1qFcpK/v26Fy0dIbVQlzQBXnGkms28XJEZPB3eei5zJPZdJsaLv6bDG2kHkq06rH1npWkz2YexJ01NY+tzHJnHEJZGTZRoxOvqB3Q2MTcWe3kD9iFm4enj1uY+8LF0fvprMWUxcqjvB/iJx2A0YpqMj4wu7HdjSnTp0krjmX6jHaZ8nvSW/ft0PloqU3qhLmgHbtzSZOV4pbfPekk2nBabjp264+XXWupAWnWfXY5qwN6ew0r6gKwdnQy0k37mN/UYV9jumg9m39Dj9xHs/k3rtXbH3h0pWjd9NZU18XKn39H+xVhgLGhHPELYHg0tVW3e9gdHTbl7gII6PSem8xVuxPVcIcUM2BVQAETe6+un1KUAqvz38dgNfnv05KUIo9QxvyTcPmsGdFtbdutMDJ1+ItGjmye43VjzmYNO3/kno8iJ3ZeyXM1hcuXTnz+JbO+vo/2LMM1URdTZTxOEVHsq2+78FE5H/PWTGc0AmXaB2K0omqhDmYxhYDYys3Ue02BhEwrsdtOipe9q6AwdBvGnYkfXWjeY+fQzNuuBY478yv8/WNJNRs5tiIGejdvXrdzt4XLlqPJ3QUjvJ/iLr0DgDKt3+syfEdQU1tIwl1uyj1nwE6tZamI1GVMAeTkZvPdA5QG7VQ09QUWg+o1ZKjtPb12Y3m5sUp/ylMatxFUVWdXeNyFHu2rSFQ1OA50fRML2tduJjbjeao4wntzRH+D0EhkRx2TSCoxHlbjffuWs9IUYt3ovUTtJoqE/15Hpyvd0VlzHcwVRmf4yoMBM+4Q7MYrL1i/WBjbsbwvjI8d11UtkN/FpXt6EZrNDT22I02bMKPGLn5t3ySuZvwKx1zsK0tNe3/kiZciZh2vd2O6QiZ4B2BtRZNHuiqFKbW0e3qbORCph39O8V5+xkbm2z2cYaK+oMrMaBjbLr1x4OZKhP9ed4ZqZYwByKlZEzpKipcxuAeZpvV7c25knemgcUDZWrmlzXGvJjqRhvZnkOu8eB3A/0zBq0ztY0kndtM0Yjp6DyHax2O07HGsARLZk/2dx3dyEtvB6B82zKzjzFUNDa3Ele9geJhyeiGjdQ6HKULVQlzIEcLj5FqPMDp8Kt77IrsnMATGNBixOZUDtTAYtPsVVHtsxttxFgqPaOJrdnGmbpmmxzfUWVsX8doUY1XimMlnVTMZ8+LveDQGI64jCegxPlmSWZnbCFalNOaeJPWoSg9sGl3pBBiAfACoAfekFI+2+X5J4H7gVagEviplLLIljE5svIdy4kTkuCZt/f4fOfFiG2pY0Bt0e13OF1XpLk6KqqysVHTiqohbiHpe19m9b4crp7hPO9T0/6vaMGFkKl9V8K6dlt1XMD01m1lrt660azVTTfYmfN/sHUZ6tZlGdb2484Nf2TJFX+06rEcWcPe5bSgJ3xW9/NKf7t1+2Kqa3mgXc9DnpTSJjfaKl4FQBTgBuwDErpsczng1f77I8ByU/tNTU2VQ9X+p2fJ4j8lSmk02uV4h+LGW/S8s6vbs0ceihsv6/bssWgbUya8M6HX5wwnc6X8w3C5/OX/HvD+B5tTZ+tl0VPRsvD5KzU5ft2ePTJ3Yoo8FDde5k5Msei9dXaWlo+9p/bKCe9MkHtP7e1zuwnvTJAnio5K+Yfhcvs7zlNWXsp6SU54Z0K329K9S7tt29f3jCmmyoSzlxkgU/ZSp7Fld2Q6kC+lLJRSNgPLgOu6VAA3Sinr2+/uBEJtGI9DqzpRTGLzAcpDFthlVqQzz360FlMzv+yRMVwXPJ5yj1jiKlbT1OocC3rv3L6JsaISr5QbNTm+GjNpPZbMnuzvSgijxsZyxCWOwOJV/T7WYHV58zgOHCvmff9HAdNj5wbKVJlQZaZ3tqyEhQAlne6Xtj/Wm/sA5ykdXRRt/QidkPhPvc3mx3KE5UScgSVfPP0Z/1cfdwMTRT7Z2XusE7iDaz74FQZ0BE/RZoyLGjPpGAayEsKZiKuIMRRQVugcs1rrspbTKF2JvfRWmx7HVJlQZaZ3DjEwXwjxYyAN+Fsvzz8ohMgUQmRWVlbaNzg7GV7wDQViLNEJts3mDeqqxF4s+eLpPPur862nK9jQS+/CKAXnM4f+zK/S6jomnd/MiRGpMMxfkxgcJQmpszN3JYTOq06MndWW+qdk60d2iVFL0tBCTOX3HBw2jWHD+54VaekC96bKhCozvbNlJayMC0MhgbauxrKuGwkh5gK/A66VUjb1tCMp5WtSyjQpZVpgYKBNgtVSU1URMY0HKQyej7BDV2RflQNnT5xnTfb64vHwH0u+ZzIxJ1cijUabHMNRbN+5jWjdCbxStJ3p5QhJSJ2dOSshdO2yrPBq4KjLOPydoEuyKGsNI6mhafwNfVayrLXAvakyocpMz2xZCcsAYoUQkUIIN2ARsKLzBkKIScC/aauAOe1KxKXtuWt8Um3fFQl9Vw7UskSm9aeiaq8vnvPjrieCcvL2b7fpcbTWfOArjAhGpmozHkyxDmtd7JlaCaGnLsuq8KuIbc2j/PiRAcc/GNRmLue89MSQGNdnJcveC9wrF7NZigopZasQ4jFgDW0zJd+SUuYIIf5E20yBFbR1P3oDn7a3ABVLKZ1uiXe3w1+RIyNISUm12zHVVcnAOWKG56hL76B539Oc2/0xpMzSOhybKKysJbVuCxV+KYzyCdY6HMUC9ipDPa06ETRrFhS8QPHWjxgT8b82j0ETrU1EVK4ny3MGefWHu1WyOldaTa3ModiWTfOESSlXAiu7PPZUp9/n2vL4g0FL1XHC6g/xZcADbM75t9VytijOxS9gFJkeaYSfWAVGw5BcpHfLrl3crSvmXMr9NjuGqeWmVB4w+7DGsl/wny7Lu1bddVGXZZ4+hpHHVwJDsxJWmb2SQFlHQ9z1pAUn91nJ6u1/BOa9D6bKhCozfRNtKSwGj7S0NJmZOXSaS498/mfiDvyd7T9az4y0/xSOpHeTLizJYSvOvPadrVnrJNIf6z99lTk5S6i48XOCkofW9Y2Ukjef/Tn3N70HvzgII8JMv8hCqnw4hp7eh/4mGe36fbrjvf9heuGLnLh3N6PD46wftMYKXr0Vv5PbqH30EGODfMmuyOauVXfx/lXv99p1a+qco8rDwAkhsqSUPTYxqgW8NeZx5GsOiljSJ9lmrciu1FWJfWjRZRk962bqDv6BM7s+HHKVsCOnzjOlYSun/SYQ0KkCZs2M38rgYenqIWNnLYLCFynauozR4X+wYmTak021hJzaxHr3K7g6yBcwPXZO0Y6qhGmoqjiX8OY81o99nAl6y+dImNP64ojjmRTriBgdyPdu05levhZam8DFXeuQrGbzrkwe0hVSN/F/Lnq888nYHq3HytAQEpVIvj4av2MrgaFVCTu+/TMiacJl4s1ah6KYwSHyhDmrgo3vAxA9+86LHh9ozhZzFudWHEPn2WGdb5amAjkXfT3espaqfUNnCr6UkpaDbROrh9kpS75aUcIxWPI+mEp4XDl2AXGthzlZkm+9gB1AbdYnnJIjmTXH9Bw3c5NCq/JgO2pMmEaklBT+OYUWvRfjf7fjwuMdOVsaDY146D16zX9jiuq/HzwG8l711g13R8y9PLj2n1QFTmXcY59bK0RN7Ss5S8vr84geocPvyV29bmetlrCOFSVkYyPCw0Mll9SIrd+HkvwDhH0wi53jfsW0O/7H9AsGgcrKk/i+nEBm8C3MWPxvq3TXq/JgOTUmzAEdOpBJovE4e8YvuejxnnK2qH58pau+uuHW7djDJafX0Fpfg4uXr1YhWs2mjGwe1x2lceJve92mc+uxpeWlpxUl1EnH/mz9PoTFJFGgj2TEsZXA0KiE7Vv7PnOFgfBL7wIsHzsHqjzYmuqO1MiJbR9hlIK4K3580ePmLsXRF9V07Ny8027HnWaObB78yxgZjRKPnI8B8Ei5pcdtrJXxu4Na584x2ON9qAhbwPiWQ5wqLbD6vu2txWDEJ38Fp1zGEJI402r7VeXBtlQlTAP1TS1EnlzDsWETGRZw8VR7c5bi6HPfanHuQcUWFebUWQsoIwjj/k+ttk+tZB6r5NrWtVQEzQT/6J63sXLGb7XOnWOwx/sQMvN2AI798LHV921vm7IOkGY8QP2468GKy9+p8mBbqhKmge07thItytAl9TzI2JLpxGpx7sHDGhXmniZxuLroKQm5ivj6LE6WF1sjVM0UbPmE0aKa4Zc83Os21mg97kqtKOEYbP0+jI2dSIE+isD8zzAaBve6q8VbPkIvJGPbuyKtSZUH21GVMA2cy1yOAR0Rl9xu9X2rpuPBw9IKc1/dcBGz78FFGDm07j2rxWtvrQYjUceXUe0SjEfi1b1uZ2nrseLcziXdQ7TxGHu2rjS9sYPKKa9hYs0Gqr1j0Y9KMPt1tpqlrZhPVcLsrPh0HSnnNlI+YgrCO8jq+1dNx4OHpRXmvrrhRsVOptg1ksBjK2gdpFf4e/fsZioHOB13h8llmHprPVYnGcWUpAX3cQ5vmre/ymDLFtBhxaZdpOmO4jX5tn69TqU10p6qhNlZxvpPidKdxGfqj7s9Z27OFlNU0/HgYGmF2VQ3XEPcjSTJI+zKyrJOwHbWuOM1WqSesfN674o0pb8nmc6VNkBV2jRiz/fBxcOb4oibSW/czr6cHKvv39bO1jfjmvsFAB4p1k3QqsqD7akUFXZkMEpCD7/NGf1I/KYs6vb8LVuMXL60tdvjAY8aIcUOASp2Z0mFua+FdwGiLv8JHPwHJ7d/BIOsW7qp/hwp1SvZ73s5qSNG9bpd1zxIHRcwA122SK0o4Rjs/T7ELPwF4pX3KP3+ZVIm/Mtux7WG5buLWSi20xA0Cc+RkQPaR+cJQp2/i1R5sD1VCbOjvZnbmSqzOTz+Cfxc3Lo93/kDP5AEnmpdSOfT1yQOV/8ISn0mklS9hrIz9YT4edk3OAsUrH+bBBog/YE+tzM3D1JvJxlFAfAIiqTA/1JmnP6GwyX/y/iwYK1DMovBKNm/fRUP6Ypgys8HtI+uE4TUMBb7Ut2RdtSy7WUacCdygW0qRJ27XjrfVAXM8Zhq5rfWWCav1NsZpytj88bVVv8bbEZKfA++yxHCSZ42z+LdqbQtijmC5z7OSFHLnu/e1DoUs204XMGNDZ/T5OYHE+8Y0D7UjHptqUqYndRUlpJ6di0HAq7G3Segz21VstWhz1SF2dRYJnPHD46c/mPqhDdjDrw6aAboNxZuJ6SpgJzQ23B16XtAvjnUSUYxh/f4y6nwjCK5fDklVXVah2OW9Zs3Mke/F5fpj4DbwFq61Yx6balKmJ0cX/UCLhjwm/N4n9v1ddWuZnopHRanLObA3Qe63bp1zbn7cGL83cyWu9m9e0fPO3Mwpze+wjnpRcisn1hlf+oko5hFCNxnPMIE3XHWrv5K62hMyq84T1r5h7ToPNBP7bvbvi9qRr22VCXMHloaiDy2jF1uU4mNT+lz076u2tV0YudjjVbR8IVP0oA7bP2ntcKyGXn+FMGlq1ntegVp40Ktsk91knFOA7lo9Z16J/U6b0YfeY/TtU12jLb/vtq8m+t022hNuQu8Rlq0LzWjXjuqEmYH5T+8w3B5jrMTTV+tqKt2pYO1xjK5+gSQM/pG0ms3cLLoiDVDtLpja/+FK614z3wQvU5c1O3a+abStiimDOii1W0YTck/Zr7Yzecbd9kp0v4739hCwME30QnwvORnWoejWEBVwmzNaMQ141UOGKOYNvsak5ubc9Wuxow5B2uOZRpz1X9hRHBy1f9ZKzyrk4YWfHLeJ0M3kXmXXgJc3O0KdOt2VV30irX5XfYIOiERWW9T29Q9ZZAj+GbnIW5mPeeirwG/8AHvR+UB056qhNlYQ+4aAhuLyBx9O37e7ma9pq+rdjXTy3lYs1V0zNhotnvPJ/7k17TWnLBWiFZ1cOOnBBoraZp0L656876aTLV2qJOM0u+LVr8Izo+dx41yHZ/uyLNhZANjNErqtr+Ot2jEb96v+tzW1EWKmlGvPVUJs7ETq//OCTmSSQvvtcr+1Ewv52HtsUz6S36Bi2ylZNU/rBGeVUkpad31GqeEP1MX3Nnt+Z4WKjeHOsk4t4FetPrOfpQAcY7iLR/Q1GqwZYj9tu1IGdc3reBU0EwYldTntmocseNTyVptKH//dmLOZ7J6zCMsCA80ub05yVY7WkdkY6MaM+YEBjqWqWsm+Q7zRyby9JEPoeH34DnCGiFaxa7M3Uxr2cv+cY8R7HpxIuOuC5X3tDqASsaq9KSni9aOz0dvZeSRiY+weOIj1PnGcMOZ7/h6z0Pcmj7WrnH35di617lE1NA877+0DkWxAlUJsxGDUVL83d8ZgwfTb/klYKLQpyw2a4mIjtaRotvvUDO9lF51ziSf9G7ShTFV7335DZ5nfkz15lcYueC/tQzxAiklFRteoQUXxi/s/vnvaaHyzpUwlfHb+XS9YO3QdXWQvi5aeysjF147azHJ3z3J65u+4+a0h9HphI3+GvMVV57nksplnPSJZ1TMbLNfpy5SHJfqjrSRL3/IZFbjJk5E3YzvyLZWMFODjM2lZnoNfdbKqN+1G+/KOfPYLCfjsutfGBpr7fb39OWHQ0XMrl9L+eh5uI0Y3e15UwuVqy5652NuN5s5Xfq9dXWL5NtocfVh3vmvWXvolPX/iAHIWPM+kbqTuM9+EoR5lUI1jtixqUqYDVScb6R601JchJGoH3UfODnQ8S1qkLHzsDSjPnTvxsuuyCZ4uAfGWU8wXNaw+/P/Z/e/qyspJTmr3mC4qGfMvJ6n2ncsVA702BWp0ro4L3MG3fd10dpTGbnA3Rt96k9YqN/N8g27kVJaL/ABaGhqZVz+m1S6jsEv9SazX6cuUhybqoTZwN++2cMt8nsaoq5CdFnVvs9Cb4IaZKx01ddJqKduPIDZc6/hiEcyEUffJK+8yn7B9mDbgTxuPf8up0ck4xo5o9ft+lqoXCVjdU6mWnjMuWjtrYx00KU/gB4jkyo+Z0ehtmVl+4avSSKf2tTFoDN/OS91keLYVCXMyrbmncY95xP8RC3DZndfoshUoVcUc5k6CfXWjSeEIHjhfzNaVLPqoxdo0WhNSSkljSt/xwhRh+8tS3vsXjF3jUzVRe98TLXwmHPRaqqrm5GRGOOu5n6X1Xy2bqtt/6A+SCnxyXqFs8KXiCvu69dr1UWKY1OVMCtqbDHwh6/28ZDbGoxjJkPY1G7bmCz0imImUyehvrrxRiQtoGZEAlef+4RXNxy1W8yd7dvyDXMb13I06h5cQ5J73MbUGpmqi955WaOFx1RXN4B+4XO46PXcVPp/HCw9a0nIA3Zw7w7SWzMpjr0L0WmhbnPHhqqLFMelZkda0QMr/kpF0CcsxAU4De+1nVg6Zj9WvvQy7kuX8vsQyBkrSCyuxf3p26nsMqNHUcxhTrqSXrvxhMB33m/w/fRu8jZ9xMGEXzIhxNf2QbeTLQ0Ebf4NpWIUMbf8udftTM2CM2dGsTI0WWumeF9d3QD4hmKY9ydmrv4lH694gQmL/zCwgC1Qv/Gf1ONO7NVPXPT4p5foeHV499P4IxN1LMa8tEeKtoTWgw37Ky0tTWZmOl4XXmFlLUteeJOPXf6IPvkWks7v6DblubPc8fEXBlUrykDV791L0e13EP7xRxedhEylQwHAaMDw0hTyzxj52fD/x4qfXYKHq/ljTSxR9Ol/E56zlPVTXmPO1beZ9RpVZpSeDPRzYVYZ6SAlx56fS0BNDmfv2UxYZNxAw+23vL0/EPHV9ewddTPpj7zW4za9fQ8ojkEIkSWl7LHbS1XCrEBKyQOvb+CP5Q8zerg7B279N3etf5j3r3r/oqurjkIfWypJLJbkjBXkhYqeC72imMmiysneD+DrR/ll88MEXHIPv70q3rrB9UCeOoTh1Vl8r7uEOf/9JW4upkdFqJOM0pm5ecKs6XTJETzfuISy4RMZ9+Ras1NEWKKmupK6l2agkxKPx7YyImBUt23q9+7l+D33IJuaEe5uRLzzjiojDqavSpgaE2YFK/aVc1XJ84zhNAfm/ZYHNv0C6D77cXHKYnYlv8dfP3Xljs1G/vqpK7uS31MVMKXfrDYWKnkRhM/iWfe32LZlPZnHq20QbSdGI+c+fZRz0pP62f9rdgVM5TlSOtNipnhAWBzrQxYz7vxuzu18x2bH6SCNRgrf+AkBxipqfvRajxUwgIKNX2NsbkYHGJubKdj4tc1jU6xHVcIsVFPfwq4Vr3GTfgtc+l9k0tDn7EeVs0WxBqudhPQucMs76H0CecP9//GnT36gvrnVNkEDRWuX4nt6D6+638c1M3oejN+VKjOKLZg7qL2zpBt+yS7jeNzW/R7Olds0vl0f/S+T6rezJ+5J4tKu6HW7Q2N1tOrBIKBV33ZfGTzUu2Wh17/dxBLDa9QHTUZ32a9Nzn5UOVsUh+MdiG7RhwTpzvHb2ud47ruDNjnM5sx9jNzxF7L0yfzkod+Y1QoGqswottFxIRP+8UcAhH/80UUXMp3To3Tcrl05g+ejpyFbm6n4eDHYaDhP7q41pOW9yJ5hlzJ1Ud/Li8Vdei3P/diTTy4RPPdjT+IuvdYmMSm2ocaEWWDv8dO0vHU1E12KcX9sO5UffsfppUs5emH2o2RcWfdxCmp8i2JrAxozs28ZfPkQb7UuIPbul7kk1vSi8+b6aFcxft/exxX6bOrv+wG/sPh+DYxWZUaxhY6ubtnYiPDw6HGWZXZFNnetuuvCGN+6plaWv7SERpdPeNWv+4xiS8f4VlWUYXhlFs3CneGPb2P4CP9et+0o56bOOYq2+hoTplJUDFCrwci+Zf/LPbrDNC5cCiMjL0wX7hh4/+4VuvaB9z1PFy66/Q5AFRjF+jpSN/RVeemxEhQ5lkfO7GDD8hdJ/uVT+Hq6WhSHlJIX1ueRs+FjXnfbTfPs3+MX1jaOrWMB5a4nuc5UmVFsqaeu7o5yUvnSy2z/ail/ul2PTg/3rfgxT31sYMb1j3LTw09T9vwW7jhTwrqr/8X/5v6ux89vfxlaWyl/88eMk+cpvfFDPji+vM8LFVMpKhTHp1rCBujr71awcPc9VIZdyZj7ProwU8acK6uhbPc3hWR8d7zb41OujiD9miiLn7fHMcyJYTAw97N4USXIP5HaN36Ea3kWS6Ne5sm7Fw34+Aaj5PdfHeSb3YfZ6v1bfP0CEA9vAf1/KnYdy3g1Ghrx0Hv0mjBT+Q9H+PwPlTJkqoy8ceANVq94gfhiI4fD9Vx5zc+5P+l+AKqOH+DYx/N5cFQAzQI8XSz//O5467+YXvwau5P+SPpN/8kJ1teFylBni8+SvT+ffbWEIaUcVLfU1FSptRMVlbLoqVhZ+acYaayvvui5yn/9Wx6KT5CH4sbLQwmJsvJf/9YoSsucKDgrM1cdkycKzg74+ZcfWm+z5+1xDHNicGTmfBb3ntorFz0zSf7+wQS56JlJcu+pvVLWVsqav4yTZU9FyA2ZBwd07IbmVvnAuxly4m8+lkf/39XS+AdfKYt3d9vu9f2vy5ueniB//2CCvPkvSfL1/a8P6Hj2Zurzb419aF3G7FEGtVTx4kttZaPLreLFly5sk71+udybOF4eGD9e7k0cL7PXL79oHy9+fpdMfjtRTnhngkx+J9miz+/+zV9Kw1O+cvc/b5ZGg+HC43tP7ZVp76fJCe9MkGnvp7WVUSdkzmetv2XSXp9PIFP2UqdRLWH9JI1Gtj9/O9PPreH0zZ8TlDTnoucdoSXsZGENZUfPEDLOj1FR/cuCbo0r3JOFNXz9/F5aW4y4uOq47olJF8Vh6fP2OIY5MZjD1HthyXtliqnPYuVLL/P55qVMOwwuhraZVTvHw02XPcqI62difHM+B4kh4onv8ff1Nvu4Z+ubuf/dTEaWruWFYe9xRFdPZtLVpE352UVX8ZUvvcye5UsJPvuf458aAZNvs35Xo7X+z+ZeQfd1vMFQxuxRBh3dK9mvcOLVpdz2gxG9bJt9uPxSHaMfefTCmK/sk1ncv/oeWpEIqedvM19i7rhL+32sirJjuLx+Ked0vgQ9uQ0v7//8n/pqjXMkln7X9fV8X5+lgbZq2fPzqcaEWYE0Gtm/6TPcdzzPMF0+f4qYzfXB/gR12qbr+BXZ2EjR7XfYbfyKNZpY06+JQu+qY9fXhUgJQgdTr40idUGEWc8DlB09g6G1bVFog8FI2dEzF324LX3eHscwJwZbn2hNHaOv5835LAb+7DFmtpxG5CxHLwEDzJxx24Xny6/4O6kbfs6b792D8bIrmTJqismukPKzDTz2xjruOfcqpYEHSffzBYbB6d2w6i7g4vEsJw7qLpzkMMDWRB0Fl1hvPIslX9A9/V9Nff7NOd5gKGP2KIN9/Z/Nfd6WFqcspv7h6RTvaruQcXH34LcPv4VXStuFzIXk22UdybcNPLHjUe4tuIwnF7xkMplrTUMLJdX1VJbmc+6HRyjz1RM16ykivC8ux2HLl/L7sx0XKkZOrfwHlbc1WvWcouWFuzllpq/PkjnlpSfmfD7twaaVMCHEAuAFQA+8IaV8tsvz7sB7QCpQBdwmpTxuy5j6y9DaSvba9xmR9SLbfKp4NdSX2NJgEnfm8afyH1+U8b5jMLStvzgGeoIwZx8AIeP80LvoaG0xotfrCBnn51DPax2DPU60ln5xmbOm4ivZr7Cu4TOe0gPtLVF/aviMudlBLE5ZzFfD63g1ciyxpYdJXJrLKhMrPBw9dZ7XX3+Z11peZaS+Dl3Kf+E2YgSrv1va41V8x0nuQM7/Uu0Vzsj6In778B8unOSsob9f0Oa8t319Nsw9ntZlSOsyaK+LFEt0XMjUDI/kTFAsfmfzkJ0uZG7ZYuTypd1z6gUkfkXLqVLkVc9Rrg+luLqekjP1bT+r6ympbqCyqoqZzdtxDVzHdyObiW2WJOYPY139n/ivQ3/udqGyIDucc76xDK/JY2tiUbcLlYFWZq3xf7b0u86cMmONz2tXA3mNLdisO1IIoQeOAvOAUiADuF1KeajTNouBZCnlw0KIRcANUso+F5KzdXfklk++oCSjiJDUUDzdTzLqwKuMNZZRIsZQVZHGzlOZJJRFUusTi/f5PA6FHOOmy9oKpa1bN6zRTaH1oF5HGBRsjRiyVh83eaK1tLvG1DHMjaG3z9pFJ5kRbScZ33PHLpxkOroru37e5yA4Pe9yRqRcS+zky9nx5bfkb8sHuZd7R66k0T8Bj5v/TeVnP7Bn+VI8Df95fYP+2IXuxo3/s5xDlYEgjf8JSuhICKzk8j/fZvbfYer5/nY79PV/VWXQegPzLfl896eF05bft6f//Rr5b33NGd8Y/M4VoP/R5axqKuIXuk/xoIk3DQvZeSaRuHoDucPcCAz14Eb9D0xp2Iq7sYGyIyF80yL7PKfkLN9Go2cgRp0endGAR0MlibfNtMt3WX/Oabbq/rbFJBBHGphvy0rYdOCPUsor2+//FkBK+Uynbda0b7NDCOECnAQCZR9B2bIStuWTLzj4vdeFD/sEz78wJrCWs6k/J2X+XehdXNj2l3+w/3jihW2SI3KY+btfXtiHqS+Wjg+bodWI3qX3D2Nvz1ujwJhz8lb6ZusTrTnHMPf53j5L5mzT9fMeqv+SyPDDxDUewFUYWFc9hbyGX114flz0Oub86q/g4sYr2a9w+tXvCDT+7MLzlbqXCHjk6gstaf2pzA6kzAzky9aSsSKDZVagI7D1RUrnfdji+xbg+KoMVn1RdeHzfdWN/hRHR7Ah6yA3V7/BmSPFHGz43UXnlEtGH4fEGyDlDgibyra//rPXc0rlSy+T+U0ehZE/AqEHo4Go49+Sdk3shZZuSy/WLP0/q9nopmlVCbsZWCClvL/9/l3AVCnlY522Odi+TWn7/YL2bU532deDwIMAY8eOTS0qKrJ6vG/e8wbN8hRGj/QLH3Zd027cRDD3vdPWffLGYx9BrRtN7r4XtnFvrgHvZu5/+Q6TH6Yv/5FFeV5Nt+fHxPpywy9TTT5vjQ+rM3zgbc0e/0NLv7hMfZYAk9v09Xm/9ekr+eTxz2mhstcys/zJbzhf3dTt9T4j3bntn9eY9X+0tMz0lyof9mGPE7etv2+XP/kNp+uHtWXNF+LCzwCvOm775zV9nFOCuO+dBwDLzylaP6+YZ9BXwjqzS0uY0KOTBibMq+eSW2+8aJuThTV89Y8sjAaJTi+4/pep/RpseuHKy9DWD93rlVkvz5vav7m0HPCq2Ie5nyVTn8e+Pu+myoyl5cXcGE39ncrQZI/PjiXf5/Y4p1jjeVPU+cIyqjuyHzrGhIVNCe9WWDrY+gOtPvCKtZjzWbL082iqzNjjokGVGaU3WldQ7HFOURybVpUwF9oG5s8BymgbmH+HlDKn0zaPAkmdBubfKKW8ta/9ap0nTFEURVEUxVya5AmTUrYKIR4D1tCWouItKWWOEOJPtGWPXQG8CbwvhMgHqoGBr5GiKIqiKIoyiNg0T5iUciWwsstjT3X6vRG4xZYxKIqiKIqiOCKd1gEoiqIoiqI4I1UJUxRFURRF0YCqhCmKoiiKomhAVcIURVEURVE0oCphiqIoiqIoGlCVMEVRFEVRFA2oSpiiKIqiKIoGbJYx31aEEJWA9VfwvlgA0Ov6lYpm1PvieNR74pjU++J41HvimOzxvoRLKQN7emLQVcLsQQiR2dsSA4p21PvieNR74pjU++J41HvimLR+X1R3pKIoiqIoigZUJUxRFEVRFEUDqhLWs9e0DkDpkXpfHI96TxyTel8cj3pPHJOm74saE6YoiqIoiqIB1RKmKIqiKIqiAaeuhAkhFgghjggh8oUQS3p43l0Isbz9+V1CiAgNwnQ6Zrwv9wghKoUQ2e23+7WI05kIId4SQlQIIQ728rwQQrzY/p7tF0JMtneMzsaM92S2EKKmUzl5yt4xOhshRJgQYqMQ4pAQIkcI8XgP26iyYkdmviealRUXex3I0Qgh9MBSYB5QCmQIIVZIKQ912uw+4IyUMkYIsQh4DrjN/tE6DzPfF4DlUsrH7B6g83oHeBl4r5fnrwJi229TgVfbfyq28w59vycAW6SUP7JPOArQCvxSSrlHCOEDZAkhvu/y/aXKin2Z856ARmXFmVvC0oF8KWWhlLIZWAZc12Wb64B323//DJgjhBB2jNEZmfO+KHYmpfwBqO5jk+uA92SbncAIIcRo+0TnnMx4TxQ7k1KekFLuaf/9PJALhHTZTJUVOzLzPdGMM1fCQoCSTvdL6f7GXNhGStkK1AD+donOeZnzvgDc1N6U/5kQIsw+oSl9MPd9U+xruhBinxBilRAiUetgnEn78JVJwK4uT6myopE+3hPQqKw4cyVMGby+ASKklMnA9/yntVJRlP/YQ9tyKROBl4CvtA3HeQghvIHPgV9IKc9pHY9i8j3RrKw4cyWsDOjcghLa/liP2wghXABfoMou0Tkvk++LlLJKStnUfvcNINVOsSm9M6c8KXYkpTwnpaxt/30l4CqECNA4rCFPCOFK28n+QynlFz1sosqKnZl6T7QsK85cCcsAYoUQkUIIN2ARsKLLNiuAu9t/vxnYIFViNVsz+b50GT9xLW19/Iq2VgA/aZ/5NQ2okVKe0DooZyaEGNUxhlUIkU7b9726iLSh9v/3m0CulPKfvWymyoodmfOeaFlWnHZ2pJSyVQjxGLAG0ANvSSlzhBB/AjKllCtoe+PeF0Lk0zYAdpF2ETsHM9+XnwshrqVt1ks1cI9mATsJIcTHwGwgQAhRCvwBcAWQUv4LWAksBPKBeuBebSJ1Hma8JzcDjwghWoEGYJG6iLS5mcBdwAEhRHb7Y/8NjAVVVjRiznuiWVlRGfMVRVEURVE04MzdkYqiKIqiKJpRlTBFURRFURQNqEqYoiiKoiiKBlQlTFEURVEURQOqEqYoiqIoiqIBVQlTFGXQEEL4CyGy228nhRBl7b/XCiFesdExfyGE+MkAXucmhPihPdGzoihKNypFhaIog5IQ4o9ArZTy7zY8hgttS5pMbl8/tr+v/wNtC9J/aPXgFEUZ9FRLmKIog54QYrYQ4tv23/8ohHhXCLFFCFEkhLhRCPF/QogDQojV7UuYIIRIFUJsFkJkCSHWdFmJocMVwJ6OCpgQYpMQIq399wAhxPH23xOFELvbW+X2CyFi21//FXCnbf96RVEGK1UJUxRlKIqmrQJ1LfABsFFKmURbNuyr2ytiLwE3SylTgbeAv/Swn5lAlhnHexh4QUqZAqQBpe2PHwSmWPB3KIoyhKmxCoqiDEWrpJQtQogDtC1/tbr98QNABBAHTAC+b18yTg/0tH7faMxbm3QH8DshRCjwhZQyD0BKaRBCNAshfKSU5y35gxRFGXpUJUxRlKGoCUBKaRRCtHRaB85I2/eeAHKklNNN7KcB8OjymGj/6drxgJTyIyHELuBqYKUQ4iEp5Yb2p92BxoH/KYqiDFWqO1JRFGd0BAgUQkwHEEK4CiESe9guF4jp8lhH9+Js2lrQEEJEAYVSyheBr4Hk9sf9gdNSyhar/wWKogx6qhKmKIrTkVI2AzcDzwkh9gHZwIweNl0FXNrlsblCiAxgLlAthPg5cCtwUAiRTVs353vt214OfGf1P0BRlCFBpahQFEXpgxDiS+DXUso8IcQm4FdSykwzX/sFsERKedSWMSqKMjipljBFUZS+LaFtgH6/CCHcgK9UBUxRlN6oljBFURRFURQNqJYwRVEURVEUDahKmKIoiqIoigZUJUxRFEVRFEUDqhKmKIqiKIqiAVUJUxRFURRF0YCqhCmKoiiKomjg/wPsEweCaPX/9gAAAABJRU5ErkJggg==", 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" ] @@ -565,6 +611,7 @@ }, { "cell_type": "markdown", + "id": "fa88078c", "metadata": {}, "source": [ "As $\\eta$ grows, more qubits are not well-prepared (i.e, pumped into a state different from $\\Ket{g}$) and we stop seeing occupations at all. You may increase the number of runs to smooth the curves." @@ -572,6 +619,7 @@ }, { "cell_type": "markdown", + "id": "46ef2d98", "metadata": {}, "source": [ "### Changing $\\epsilon$" @@ -579,6 +627,7 @@ }, { "cell_type": "markdown", + "id": "c1579e00", "metadata": {}, "source": [ "Let's now run a sweep over $\\epsilon$." @@ -587,11 +636,12 @@ { "cell_type": "code", "execution_count": 19, + "id": "2202e805", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -617,6 +667,7 @@ }, { "cell_type": "markdown", + "id": "04570d01", "metadata": {}, "source": [ "As more false positives appear, it looks like the system is never captured, so always in a Rydberg state. Note that when $\\eta=0$, the object we obtain is a `CoherentResults` rather than a `NoisyResults`, since in this case, the randomness comes from measurements and the simulation is entirely deterministic. This results in smooth curves rather than scattered dots." @@ -624,6 +675,7 @@ }, { "cell_type": "markdown", + "id": "e2d78da0", "metadata": {}, "source": [ "### Changing $\\epsilon'$" @@ -631,6 +683,7 @@ }, { "cell_type": "markdown", + "id": "a9b8ef1f", "metadata": {}, "source": [ "Finally, we run a sweep over $\\epsilon'$." @@ -639,11 +692,12 @@ { "cell_type": "code", "execution_count": 20, + "id": "ceacfe1b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -669,6 +723,7 @@ }, { "cell_type": "markdown", + "id": "045815da", "metadata": {}, "source": [ "As there are more false negatives, all atoms seem to be recaptured, until no Rydberg occupation is detected." @@ -676,6 +731,7 @@ }, { "cell_type": "markdown", + "id": "c2260bb5", "metadata": {}, "source": [ "## Doppler Noise" @@ -683,6 +739,7 @@ }, { "cell_type": "markdown", + "id": "f22a5e46", "metadata": {}, "source": [ "As for any noise, Doppler noise is set via a `SimConfig` object. When averaging over several runs, it has the effect of damping the oscillations. Let's increase the number of runs in order to see this and get smoother curves." @@ -690,6 +747,7 @@ }, { "cell_type": "markdown", + "id": "9e3d4834", "metadata": {}, "source": [ "Note that you may change the standard deviation of the doppler noise, which is $k \\times \\sqrt{k_B T / m}$, where $k$ is the norm of the effective wavevector of the lasers, by changing the temperature field, setting it in $\\mu K$. We'll exaggerate the temperature field here to emphasize the effects of Doppler damping; the default value for temperature is 50$\\mu K$." @@ -698,6 +756,7 @@ { "cell_type": "code", "execution_count": 21, + "id": "fd4baccc", "metadata": {}, "outputs": [ { @@ -723,6 +782,7 @@ }, { "cell_type": "markdown", + "id": "962335eb", "metadata": {}, "source": [ "Let us now simulate the entire sequence with Doppler noise, much like what we did in the SPAM case. We should see damped oscillations if the standard deviation is high enough. This is the case here, as we exaggerated the temperature field." @@ -731,11 +791,12 @@ { "cell_type": "code", "execution_count": 22, + "id": "fcf16353", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -755,6 +816,7 @@ }, { "cell_type": "markdown", + "id": "78e4c8dc", "metadata": {}, "source": [ "## Multiple Atoms" @@ -762,6 +824,7 @@ }, { "cell_type": "markdown", + "id": "9885e2bc", "metadata": {}, "source": [ "We will now run the AFM preparation sequence from the Pulser tutorial with our noise models, and compare the results to the clean case. \n", @@ -772,6 +835,7 @@ { "cell_type": "code", "execution_count": 23, + "id": "4f6541ac", "metadata": {}, "outputs": [], "source": [ @@ -809,6 +873,7 @@ { "cell_type": "code", "execution_count": 24, + "id": "cb510f6c", "metadata": {}, "outputs": [], "source": [ @@ -825,6 +890,7 @@ }, { "cell_type": "markdown", + "id": "32e3a9f5", "metadata": {}, "source": [ "We now plot the simulation results by sampling the final states." @@ -833,11 +899,12 @@ { "cell_type": "code", "execution_count": 25, + "id": "fdc590ac", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -867,6 +934,7 @@ }, { "cell_type": "markdown", + "id": "0510aaad", "metadata": {}, "source": [ "The bars represent the simulation results as populations of bitstrings. They're colored blue for the noiseless simulation, and orange for the noisy one. We clearly identify the antiferromagnetic state as the most populated one in both cases, but it is slightly less populated in the noisy case, while some other bitstrings, not present in the noiseless case, appear." @@ -875,7 +943,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.5 ('pulser-dev')", "language": "python", "name": "python3" }, @@ -890,6 +958,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" + }, + "vscode": { + "interpreter": { + "hash": "e088768f7ff7b4294439f8ed10f7eed9e3b885124bc20d9d06cc2a37b1883330" + } } }, "nbformat": 4, diff --git a/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb b/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb index 203f16a82..994e50ade 100644 --- a/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb +++ b/tutorials/quantum_simulation/Building 1D Rydberg Crystals.ipynb @@ -835,13 +835,6 @@ "plot_evolution(occupations)\n", "heat_detuning(occupations, delta_0, delta_f)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb index fe68acb51..ee69203be 100644 --- a/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb +++ b/tutorials/quantum_simulation/Spin chain of 3 atoms in XY mode.ipynb @@ -23,7 +23,6 @@ "\n", "$$\n", "H= \\sum_{i=1}^N \\frac{\\hbar\\Omega}{2} \\sigma_i^x - \\sum_{i=1}^N \\frac{\\hbar \\delta}{2} \\sigma_i^z+H_{XY}.\n", - "\\label{eq:XY_Hamiltonian}\n", "$$\n", "\n", "The Rydberg states involved are different from the ones of the Ising interaction, they are $|0\\rangle = |62D_{3/2}, m_j=3/2 \\rangle$ and $|1\\rangle = |63P_{1/2}, m_j=1/2 \\rangle$. \n", From 4696b37c41a45de39b43eadd955203393abca24d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Mon, 18 Jul 2022 16:49:50 +0200 Subject: [PATCH 09/18] Refactoring the `sequence` module (#378) * Moving `sequence.py` to a `sequence` directory * Changing imports to pass all UTs * Refactoring `sequence.py` into multiple files * Remove _min_pulse_duration out of Sequence * Changing use of the sequence decorators * Moving `_check_allow_qubit_index` function to a decorator * Refactoring Sequence.add * Refactoring index methods in Sequence * Moving Sequence.__str__() to dedicated function * Import sorting * Updating docs source * Addressing review comments * Creating the `_ChannelSchedule` class * Creating the `_QubitRef` class * Adding the `block_if_measured` decorator * Creating the _Schedule class * Type hints and import sorting * Fixing unit tests * Moving auxiliary classes' locations * Getting rid of unused Sequence methods, attributes and properties * Getting rid of Sequence._channels * Getting rid of Sequence._phase_ref * Updating the module docstrings * Adressing review comments * Adressing review comments --- docs/source/apidoc/creation.rst | 2 +- pulser-core/pulser/devices/_device_datacls.py | 5 + pulser-core/pulser/json/supported.py | 2 +- pulser-core/pulser/sampler/sampler.py | 10 +- pulser-core/pulser/sampler/samples.py | 2 +- pulser-core/pulser/sequence/__init__.py | 16 + pulser-core/pulser/sequence/_basis_ref.py | 78 ++ pulser-core/pulser/sequence/_call.py | 18 + pulser-core/pulser/sequence/_decorators.py | 134 +++ pulser-core/pulser/sequence/_schedule.py | 211 +++++ .../pulser/{ => sequence}/_seq_drawer.py | 18 +- pulser-core/pulser/sequence/_seq_str.py | 69 ++ pulser-core/pulser/{ => sequence}/sequence.py | 770 ++++-------------- .../pulser_simulation/simulation.py | 17 +- tests/test_paramseq.py | 2 +- tests/test_sequence.py | 79 +- 16 files changed, 785 insertions(+), 648 deletions(-) create mode 100644 pulser-core/pulser/sequence/__init__.py create mode 100644 pulser-core/pulser/sequence/_basis_ref.py create mode 100644 pulser-core/pulser/sequence/_call.py create mode 100644 pulser-core/pulser/sequence/_decorators.py create mode 100644 pulser-core/pulser/sequence/_schedule.py rename pulser-core/pulser/{ => sequence}/_seq_drawer.py (97%) create mode 100644 pulser-core/pulser/sequence/_seq_str.py rename pulser-core/pulser/{ => sequence}/sequence.py (65%) diff --git a/docs/source/apidoc/creation.rst b/docs/source/apidoc/creation.rst index 7be6b3353..5b676ffc5 100644 --- a/docs/source/apidoc/creation.rst +++ b/docs/source/apidoc/creation.rst @@ -5,7 +5,7 @@ Pulse Sequence Creation Sequence ---------------------- -.. automodule:: pulser.sequence +.. automodule:: pulser.sequence.sequence :members: Register diff --git a/pulser-core/pulser/devices/_device_datacls.py b/pulser-core/pulser/devices/_device_datacls.py index 34a21ff77..ae3ebf7cb 100644 --- a/pulser-core/pulser/devices/_device_datacls.py +++ b/pulser-core/pulser/devices/_device_datacls.py @@ -188,6 +188,11 @@ def validate_pulse(self, pulse: Pulse, channel_id: str) -> None: channel_id: The channel ID used to index the chosen channel on this device. """ + if not isinstance(pulse, Pulse): + raise TypeError( + f"'pulse' must be of type Pulse, not of type {type(pulse)}." + ) + ch = self.channels[channel_id] if np.any(pulse.amplitude.samples > ch.max_amp): raise ValueError( diff --git a/pulser-core/pulser/json/supported.py b/pulser-core/pulser/json/supported.py index a82188b71..ac4d1fb2e 100644 --- a/pulser-core/pulser/json/supported.py +++ b/pulser-core/pulser/json/supported.py @@ -75,7 +75,7 @@ "InterpolatedWaveform", "KaiserWaveform", ), - "pulser.sequence": ("Sequence",), + "pulser.sequence.sequence": ("Sequence",), "pulser.parametrized.variable": ("Variable",), "pulser.parametrized.paramobj": ("ParamObj",), } diff --git a/pulser-core/pulser/sampler/sampler.py b/pulser-core/pulser/sampler/sampler.py index 04ebab7a3..533fde98d 100644 --- a/pulser-core/pulser/sampler/sampler.py +++ b/pulser-core/pulser/sampler/sampler.py @@ -27,7 +27,7 @@ from pulser.pulse import Pulse from pulser.sampler.noise_model import NoiseModel, apply_noises from pulser.sampler.samples import QubitSamples -from pulser.sequence import Sequence, _TimeSlot +from pulser.sequence.sequence import Sequence, _ChannelSchedule, _TimeSlot def sample( @@ -214,7 +214,9 @@ def _sample_slots(N: int, *slots: _TimeSlot) -> list[QubitSamples]: return qs -TimeSlotExtractionStrategy = Callable[[List[_TimeSlot]], List[List[_TimeSlot]]] +TimeSlotExtractionStrategy = Callable[ + [_ChannelSchedule], List[List[_TimeSlot]] +] """Extraction strategy of _TimeSlot's of a Channel. It's an alias for functions that returns a list of lists of _TimeSlots. @@ -228,13 +230,13 @@ def _sample_slots(N: int, *slots: _TimeSlot) -> list[QubitSamples]: """ -def _regular(ts: list[_TimeSlot]) -> list[list[_TimeSlot]]: +def _regular(ts: _ChannelSchedule) -> list[list[_TimeSlot]]: """No grouping performed, return only the pulses.""" return [[x] for x in ts if isinstance(x.type, Pulse)] def _group_between_retargets( - ts: list[_TimeSlot], + ts: _ChannelSchedule, ) -> list[list[_TimeSlot]]: """Filter and group _TimeSlots together. diff --git a/pulser-core/pulser/sampler/samples.py b/pulser-core/pulser/sampler/samples.py index 4a5da3326..a45de2ccc 100644 --- a/pulser-core/pulser/sampler/samples.py +++ b/pulser-core/pulser/sampler/samples.py @@ -18,7 +18,7 @@ import numpy as np -from pulser.sequence import QubitId +from pulser.register.base_register import QubitId @dataclass diff --git a/pulser-core/pulser/sequence/__init__.py b/pulser-core/pulser/sequence/__init__.py new file mode 100644 index 000000000..4bdbb3dc8 --- /dev/null +++ b/pulser-core/pulser/sequence/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Module containing the sequence class definition.""" + +from pulser.sequence.sequence import Sequence diff --git a/pulser-core/pulser/sequence/_basis_ref.py b/pulser-core/pulser/sequence/_basis_ref.py new file mode 100644 index 000000000..3606035df --- /dev/null +++ b/pulser-core/pulser/sequence/_basis_ref.py @@ -0,0 +1,78 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Class for tracking the phase and usage of a qubit over time.""" +from __future__ import annotations + +from typing import Generator, Union + +import numpy as np + + +class _QubitRef: + def __init__(self) -> None: + self.phase = _PhaseTracker(0) + self.last_used = 0 + + def increment_phase(self, phi: float) -> None: + self.phase[self.last_used] = self.phase.last_phase + phi + + def update_last_used(self, new_t: int) -> None: + self.last_used = max(self.last_used, new_t) + + +class _PhaseTracker: + """Tracks a phase reference over time.""" + + def __init__(self, initial_phase: float): + self._times: list[int] = [0] + self._phases: list[float] = [self._format(initial_phase)] + + @property + def last_time(self) -> int: + return self._times[-1] + + @property + def last_phase(self) -> float: + return self._phases[-1] + + def changes( + self, + ti: Union[float, int], + tf: Union[float, int], + time_scale: float = 1.0, + ) -> Generator[tuple[float, float], None, None]: + """Changes in phases within ]ti, tf].""" + start, end = np.searchsorted( + self._times, (ti * time_scale, tf * time_scale), side="right" + ) + for i in range(start, end): + change = self._phases[i] - self._phases[i - 1] + yield (self._times[i] / time_scale, change) + + def _format(self, phi: float) -> float: + return phi % (2 * np.pi) + + def __setitem__(self, t: int, phi: float) -> None: + phase = self._format(phi) + if t in self._times: + ind = self._times.index(t) + self._phases[ind] = phase + else: + ind = int(np.searchsorted(self._times, t, side="right")) + self._times.insert(ind, t) + self._phases.insert(ind, phase) + + def __getitem__(self, t: int) -> float: + ind = int(np.searchsorted(self._times, t, side="right")) - 1 + return self._phases[ind] diff --git a/pulser-core/pulser/sequence/_call.py b/pulser-core/pulser/sequence/_call.py new file mode 100644 index 000000000..fb52bcc2a --- /dev/null +++ b/pulser-core/pulser/sequence/_call.py @@ -0,0 +1,18 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Encodes a sequence building call.""" + +from collections import namedtuple + +_Call = namedtuple("_Call", ["name", "args", "kwargs"]) diff --git a/pulser-core/pulser/sequence/_decorators.py b/pulser-core/pulser/sequence/_decorators.py new file mode 100644 index 000000000..cce2da1e8 --- /dev/null +++ b/pulser-core/pulser/sequence/_decorators.py @@ -0,0 +1,134 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Custom decorators used by the Sequence class.""" +from __future__ import annotations + +from collections.abc import Callable, Iterable +from functools import wraps +from itertools import chain +from typing import TYPE_CHECKING, Any, TypeVar, cast + +from pulser.parametrized import Parametrized +from pulser.sequence._call import _Call + +if TYPE_CHECKING: # pragma: no cover + from pulser.sequence.sequence import Sequence + +F = TypeVar("F", bound=Callable) + + +def screen(func: F) -> F: + """Blocks the call to a function if the Sequence is parametrized.""" + + @wraps(func) + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + if self.is_parametrized(): + raise RuntimeError( + f"Sequence.{func.__name__} can't be called in" + " parametrized sequences." + ) + return func(self, *args, **kwargs) + + return cast(F, wrapper) + + +def verify_variable(seq: Sequence, x: Any) -> None: + """Checks if a variable has been declared in a sequence.""" + if isinstance(x, Parametrized): + # If not already, the sequence becomes parametrized + seq._building = False + for name, var in x.variables.items(): + if name not in seq._variables: + raise ValueError(f"Unknown variable '{name}'.") + elif seq._variables[name] is not var: + raise ValueError( + f"{x} has variables that don't come from this " + "Sequence. Use only what's returned by this" + "Sequence's 'declare_variable' method as your" + "variables." + ) + elif isinstance(x, Iterable) and not isinstance(x, str): + # Recursively look for parametrized objs inside the arguments + for y in x: + verify_variable(seq, y) + + +def verify_parametrization(func: F) -> F: + """Checks and updates the sequence status' consistency with the call. + + - Checks the sequence can still be modified. + - Checks if all Parametrized inputs stem from declared variables. + """ + + @wraps(func) + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + for x in chain(args, kwargs.values()): + verify_variable(self, x) + func(self, *args, **kwargs) + + return cast(F, wrapper) + + +def store(func: F) -> F: + """Checks and stores the call to call it when building the Sequence.""" + + @wraps(func) + @verify_parametrization + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + storage = self._calls if self._building else self._to_build_calls + func(self, *args, **kwargs) + storage.append(_Call(func.__name__, args, kwargs)) + + return cast(F, wrapper) + + +def check_allow_qubit_index(func: F) -> F: + """Checks if using qubit indices is allowed.""" + + @wraps(func) + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + if not self.is_parametrized() and self.is_register_mappable(): + raise RuntimeError( + f"Sequence.{func.__name__} cannot be called in" + " non-parametrized sequences using a mappable register." + ) + func(self, *args, **kwargs) + + return cast(F, wrapper) + + +def mark_non_empty(func: F) -> F: + """Marks the sequence as non-empty.""" + + @wraps(func) + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + func(self, *args, **kwargs) + self._empty_sequence = False + + return cast(F, wrapper) + + +def block_if_measured(func: F) -> F: + """Blocks the call if the sequence has been measured.""" + + @wraps(func) + def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: + if self.is_measured(): + raise RuntimeError( + "The sequence has been measured, no further " + "changes are allowed." + ) + func(self, *args, **kwargs) + + return cast(F, wrapper) diff --git a/pulser-core/pulser/sequence/_schedule.py b/pulser-core/pulser/sequence/_schedule.py new file mode 100644 index 000000000..d1e12573f --- /dev/null +++ b/pulser-core/pulser/sequence/_schedule.py @@ -0,0 +1,211 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Special containers to store the schedule of operations in the Sequence.""" +from __future__ import annotations + +import warnings +from collections.abc import Iterator +from dataclasses import dataclass +from typing import Dict, NamedTuple, Optional, Union, cast, overload + +import numpy as np + +from pulser.channels import Channel +from pulser.pulse import Pulse +from pulser.register.base_register import QubitId + + +class _TimeSlot(NamedTuple): + """Auxiliary class to store the information in the schedule.""" + + type: Union[Pulse, str] + ti: int + tf: int + targets: set[QubitId] + + +@dataclass +class _ChannelSchedule: + channel_id: str + channel_obj: Channel + + def __post_init__(self) -> None: + self.slots: list[_TimeSlot] = [] + + def last_target(self) -> int: + """Last time a target happened on the channel.""" + for slot in self.slots[::-1]: + if slot.type == "target": + return slot.tf + return 0 # pragma: no cover + + def get_duration(self, include_fall_time: bool = False) -> int: + temp_tf = 0 + for i, op in enumerate(self.slots[::-1]): + if i == 0: + # Start with the last slot found + temp_tf = op.tf + if not include_fall_time: + break + if isinstance(op.type, Pulse): + temp_tf = max( + temp_tf, op.tf + op.type.fall_time(self.channel_obj) + ) + break + elif temp_tf - op.tf >= 2 * self.channel_obj.rise_time: + # No pulse behind 'op' with a long enough fall time + break + return temp_tf + + def adjust_duration(self, duration: int) -> int: + """Adjust a duration for this channel.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return self.channel_obj.validate_duration( + max(duration, self.channel_obj.min_duration) + ) + + @overload + def __getitem__(self, key: int) -> _TimeSlot: + pass + + @overload + def __getitem__(self, key: slice) -> list[_TimeSlot]: + pass + + def __getitem__( + self, key: Union[int, slice] + ) -> Union[_TimeSlot, list[_TimeSlot]]: + if key == -1 and not self.slots: + raise ValueError("The chosen channel has no target.") + return self.slots[key] + + def __iter__(self) -> Iterator[_TimeSlot]: + for slot in self.slots: + yield slot + + +class _Schedule(Dict[str, _ChannelSchedule]): + def get_duration( + self, channel: Optional[str] = None, include_fall_time: bool = False + ) -> int: + if channel is None: + channels = tuple(self.keys()) + if not channels: + return 0 + else: + channels = (channel,) + + return max(self[id].get_duration(include_fall_time) for id in channels) + + def find_slm_mask_times(self) -> list[int]: + # Find tentative initial and final time of SLM mask if possible + mask_time: list[int] = [] + for ch_schedule in self.values(): + if ch_schedule.channel_obj.addressing != "Global": + continue + # Cycle on slots in schedule until the first pulse is found + for slot in ch_schedule: + if not isinstance(slot.type, Pulse): + continue + ti = slot.ti + tf = slot.tf + if mask_time: + if ti < mask_time[0]: + mask_time = [ti, tf] + else: + mask_time = [ti, tf] + break + return mask_time + + def add_pulse( + self, + pulse: Pulse, + channel: str, + phase_barrier_ts: list[int], + protocol: str, + ) -> None: + pass + last = self[channel][-1] + t0 = last.tf + current_max_t = max(t0, *phase_barrier_ts) + phase_jump_buffer = 0 + for ch, ch_schedule in self.items(): + if protocol == "no-delay" and ch != channel: + continue + this_chobj = self[ch].channel_obj + for op in ch_schedule[::-1]: + if not isinstance(op.type, Pulse): + if op.tf + 2 * this_chobj.rise_time <= current_max_t: + # No pulse behind 'op' needing a delay + break + elif ch == channel: + if op.type.phase != pulse.phase: + phase_jump_buffer = this_chobj.phase_jump_time - ( + t0 - op.tf + ) + break + elif op.tf + op.type.fall_time(this_chobj) <= current_max_t: + break + elif op.targets & last.targets or protocol == "wait-for-all": + current_max_t = op.tf + op.type.fall_time(this_chobj) + break + + delay_duration = max(current_max_t - t0, phase_jump_buffer) + if delay_duration > 0: + delay_duration = self[channel].adjust_duration(delay_duration) + self.add_delay(delay_duration, channel) + + ti = t0 + delay_duration + tf = ti + pulse.duration + self[channel].slots.append(_TimeSlot(pulse, ti, tf, last.targets)) + + def add_delay(self, duration: int, channel: str) -> None: + last = self[channel][-1] + ti = last.tf + tf = ti + self[channel].channel_obj.validate_duration(duration) + self[channel].slots.append(_TimeSlot("delay", ti, tf, last.targets)) + + def add_target(self, qubits_set: set[QubitId], channel: str) -> None: + channel_obj = self[channel].channel_obj + if self[channel].slots: + fall_time = ( + self[channel].get_duration(include_fall_time=True) + - self[channel].get_duration() + ) + if fall_time > 0: + self.add_delay( + self[channel].adjust_duration(fall_time), channel + ) + + last = self[channel][-1] + if last.targets == qubits_set: + return + ti = last.tf + retarget = cast(int, channel_obj.min_retarget_interval) + elapsed = ti - self[channel].last_target() + delta = cast(int, np.clip(retarget - elapsed, 0, retarget)) + if channel_obj.fixed_retarget_t: + delta = max(delta, channel_obj.fixed_retarget_t) + if delta != 0: + delta = self[channel].adjust_duration(delta) + tf = ti + delta + + else: + ti = -1 + tf = 0 + + self[channel].slots.append( + _TimeSlot("target", ti, tf, set(qubits_set)) + ) diff --git a/pulser-core/pulser/_seq_drawer.py b/pulser-core/pulser/sequence/_seq_drawer.py similarity index 97% rename from pulser-core/pulser/_seq_drawer.py rename to pulser-core/pulser/sequence/_seq_drawer.py index 054809f24..ffc4ace28 100644 --- a/pulser-core/pulser/_seq_drawer.py +++ b/pulser-core/pulser/sequence/_seq_drawer.py @@ -189,7 +189,7 @@ def phase_str(phi: float) -> str: else: return rf"{value:.2g}$\pi$" - n_channels = len(seq._channels) + n_channels = len(seq.declared_channels) if not n_channels: raise RuntimeError("Can't draw an empty sequence.") data = gather_data(seq) @@ -260,7 +260,7 @@ def phase_str(phi: float) -> str: gs = fig.add_gridspec(n_channels, 1, hspace=0.075, height_ratios=ratios) ch_axes = {} - for i, (ch, gs_) in enumerate(zip(seq._channels, gs)): + for i, (ch, gs_) in enumerate(zip(seq.declared_channels, gs)): ax = fig.add_subplot(gs_) for side in ("top", "bottom", "left", "right"): ax.spines[side].set_color("none") @@ -308,7 +308,7 @@ def phase_str(phi: float) -> str: # Make sure the time axis of all channels are aligned final_t = total_duration / time_scale if draw_modulation: - for ch, ch_obj in seq._channels.items(): + for ch, ch_obj in seq.declared_channels.items(): final_t = max( final_t, (seq.get_duration(ch) + 2 * ch_obj.rise_time) / time_scale, @@ -317,7 +317,7 @@ def phase_str(phi: float) -> str: t_max = final_t * 1.05 for ch, axes in ch_axes.items(): - ch_obj = seq._channels[ch] + ch_obj = seq.declared_channels[ch] ch_data = data[ch] basis = ch_obj.basis times = np.array(ch_data.time) @@ -465,7 +465,7 @@ def phase_str(phi: float) -> str: if coords == "initial": x = t_min + final_t * 0.005 target_regions.append([0, targets]) - if seq._channels[ch].addressing == "Global": + if seq.declared_channels[ch].addressing == "Global": axes[0].text( x, amp_top * 0.98, @@ -485,7 +485,7 @@ def phase_str(phi: float) -> str: ha="left", bbox=q_box, ) - phase = seq._phase_ref[basis][targets[0]][0] + phase = seq._basis_ref[basis][targets[0]].phase[0] if phase and draw_phase_shifts: msg = r"$\phi=$" + phase_str(phase) axes[0].text( @@ -502,7 +502,9 @@ def phase_str(phi: float) -> str: target_regions.append( [tf + 1 / time_scale, targets] ) # New one - phase = seq._phase_ref[basis][targets[0]][tf * time_scale + 1] + phase = seq._basis_ref[basis][targets[0]].phase[ + tf * time_scale + 1 + ] for ax in axes: ax.axvspan(ti, tf, alpha=0.4, color="grey", hatch="//") axes[0].text( @@ -536,7 +538,7 @@ def phase_str(phi: float) -> str: end = cast(float, end) # All targets have the same ref, so we pick q = targets_[0] - ref = seq._phase_ref[basis][q] + ref = seq._basis_ref[basis][q].phase if end != total_duration - 1 or ch_data.measurement is not None: end += 1 / time_scale for t_, delta in ref.changes(start, end, time_scale=time_scale): diff --git a/pulser-core/pulser/sequence/_seq_str.py b/pulser-core/pulser/sequence/_seq_str.py new file mode 100644 index 000000000..a53e36080 --- /dev/null +++ b/pulser-core/pulser/sequence/_seq_str.py @@ -0,0 +1,69 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Function for representing the sequence in a string.""" +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pulser.pulse import Pulse + +if TYPE_CHECKING: # pragma: no cover + from pulser.sequence.sequence import Sequence + + +def seq_to_str(sequence: Sequence) -> str: + """Generates the string representation of a sequence.""" + full = "" + pulse_line = "t: {}->{} | {} | Targets: {}\n" + target_line = "t: {}->{} | Target: {} | Phase Reference: {}\n" + delay_line = "t: {}->{} | Delay \n" + # phase_line = "t: {} | Phase shift of: {:.3f} | Targets: {}\n" + for ch, seq in sequence._schedule.items(): + basis = sequence.declared_channels[ch].basis + full += f"Channel: {ch}\n" + first_slot = True + for ts in seq: + if ts.type == "delay": + full += delay_line.format(ts.ti, ts.tf) + continue + + tgts = list(ts.targets) + tgt_txt = ", ".join([str(t) for t in tgts]) + if isinstance(ts.type, Pulse): + full += pulse_line.format(ts.ti, ts.tf, ts.type, tgt_txt) + elif ts.type == "target": + phase = sequence._basis_ref[basis][tgts[0]].phase[ts.tf] + if first_slot: + full += ( + f"t: 0 | Initial targets: {tgt_txt} | " + + f"Phase Reference: {phase} \n" + ) + first_slot = False + else: + full += target_line.format(ts.ti, ts.tf, tgt_txt, phase) + full += "\n" + + if hasattr(sequence, "_measurement"): + full += f"Measured in basis: {sequence._measurement}" + + if sequence.is_parametrized(): + prelude = "Prelude\n-------\n" + full + lines = ["Stored calls\n------------"] + for i, c in enumerate(sequence._to_build_calls, 1): + args = [str(a) for a in c.args] + kwargs = [f"{key}={str(value)}" for key, value in c.kwargs.items()] + lines.append(f"{i}. {c.name}({', '.join(args+kwargs)})") + full = prelude + "\n\n".join(lines) + + return full diff --git a/pulser-core/pulser/sequence.py b/pulser-core/pulser/sequence/sequence.py similarity index 65% rename from pulser-core/pulser/sequence.py rename to pulser-core/pulser/sequence/sequence.py index 9be44ba49..7b9e49507 100644 --- a/pulser-core/pulser/sequence.py +++ b/pulser-core/pulser/sequence/sequence.py @@ -19,28 +19,16 @@ import json import os import warnings -from collections import namedtuple -from collections.abc import Callable, Generator, Iterable, Mapping, Set -from functools import wraps -from itertools import chain +from collections.abc import Iterable, Mapping from sys import version_info -from typing import ( - Any, - NamedTuple, - Optional, - Tuple, - TypeVar, - Union, - cast, - overload, -) +from typing import Any, Optional, Tuple, Union, cast, overload import matplotlib.pyplot as plt import numpy as np from numpy.typing import ArrayLike import pulser -from pulser._seq_drawer import draw_sequence +import pulser.sequence._decorators as seq_decorators from pulser.channels import Channel from pulser.devices import MockDevice from pulser.devices._device_datacls import Device @@ -49,8 +37,13 @@ from pulser.parametrized import Parametrized, Variable from pulser.parametrized.variable import VariableItem from pulser.pulse import Pulse -from pulser.register.base_register import BaseRegister +from pulser.register.base_register import BaseRegister, QubitId from pulser.register.mappable_reg import MappableRegister +from pulser.sequence._basis_ref import _QubitRef +from pulser.sequence._call import _Call +from pulser.sequence._schedule import _ChannelSchedule, _Schedule, _TimeSlot +from pulser.sequence._seq_drawer import draw_sequence +from pulser.sequence._seq_str import seq_to_str if version_info[:2] >= (3, 8): # pragma: no cover from typing import Literal, get_args @@ -65,91 +58,7 @@ ) -QubitId = Union[int, str] PROTOCOLS = Literal["min-delay", "no-delay", "wait-for-all"] -F = TypeVar("F", bound=Callable) - - -class _TimeSlot(NamedTuple): - """Auxiliary class to store the information in the schedule.""" - - type: Union[Pulse, str] - ti: int - tf: int - targets: set[QubitId] - - -# Encodes a sequence building calls -_Call = namedtuple("_Call", ["name", "args", "kwargs"]) - - -def _screen(func: F) -> F: - """Blocks the call to a function if the Sequence is parametrized.""" - - @wraps(func) - def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: - if self.is_parametrized(): - raise RuntimeError( - f"Sequence.{func.__name__} can't be called in" - " parametrized sequences." - ) - return func(self, *args, **kwargs) - - return cast(F, wrapper) - - -def _verify_variable(seq: Sequence, x: Any) -> None: - if isinstance(x, Parametrized): - # If not already, the sequence becomes parametrized - seq._building = False - for name, var in x.variables.items(): - if name not in seq._variables: - raise ValueError(f"Unknown variable '{name}'.") - elif seq._variables[name] is not var: - raise ValueError( - f"{x} has variables that don't come from this " - "Sequence. Use only what's returned by this" - "Sequence's 'declare_variable' method as your" - "variables." - ) - elif isinstance(x, Iterable) and not isinstance(x, str): - # Recursively look for parametrized objs inside the arguments - for y in x: - _verify_variable(seq, y) - - -def _verify_parametrization(func: F) -> F: - """Checks and updates the sequence status' consistency with the call. - - - Checks the sequence can still be modified. - - Checks if all Parametrized inputs stem from declared variables. - """ - - @wraps(func) - def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: - if self._is_measured and self.is_parametrized(): - raise RuntimeError( - "The sequence has been measured, no further " - "changes are allowed." - ) - for x in chain(args, kwargs.values()): - _verify_variable(self, x) - func(self, *args, **kwargs) - - return cast(F, wrapper) - - -def _store(func: F) -> F: - """Checks and stores the call to call it when building the Sequence.""" - - @wraps(func) - @_verify_parametrization - def wrapper(self: Sequence, *args: Any, **kwargs: Any) -> Any: - storage = self._calls if self._building else self._to_build_calls - func(self, *args, **kwargs) - storage.append(_Call(func.__name__, args, kwargs)) - - return cast(F, wrapper) class Sequence: @@ -220,18 +129,11 @@ def __init__( self._in_xy: bool = False self._mag_field: Optional[tuple[float, float, float]] = None self._calls: list[_Call] = [_Call("__init__", (register, device), {})] - self._channels: dict[str, Channel] = {} - self._schedule: dict[str, list[_TimeSlot]] = {} - # The phase reference of each channel - self._phase_ref: dict[str, dict[QubitId, _PhaseTracker]] = {} - # Stores the names and dict ids of declared channels - self._taken_channels: dict[str, str] = {} + self._schedule: _Schedule = _Schedule() + self._basis_ref: dict[str, dict[QubitId, _QubitRef]] = {} # IDs of all qubits in device self._qids: set[QubitId] = set(self._register.qubit_ids) # Last time each qubit was used, by basis - self._last_used: dict[str, dict[QubitId, int]] = {} - # Last time a target happened, by channel - self._last_target: dict[str, int] = {} self._variables: dict[str, Variable] = {} self._to_build_calls: list[_Call] = [] self._building: bool = True @@ -239,11 +141,19 @@ def __init__( self._empty_sequence: bool = True # SLM mask targets and on/off times self._slm_mask_targets: set[QubitId] = set() - self._slm_mask_time: list[int] = [] # Initializes all parametrized Sequence related attributes self._reset_parametrized() + @property + def _slm_mask_time(self) -> list[int]: + """The initial and final time when the SLM mask is on.""" + return ( + [] + if not self._slm_mask_targets + else self._schedule.find_slm_mask_times() + ) + @property def qubit_info(self) -> dict[QubitId, np.ndarray]: """Dictionary with the qubit's IDs and positions.""" @@ -267,7 +177,7 @@ def register(self) -> BaseRegister: @property def declared_channels(self) -> dict[str, Channel]: """Channels declared in this Sequence.""" - return dict(self._channels) + return {name: cs.channel_obj for name, cs in self._schedule.items()} @property def declared_variables(self) -> dict[str, Variable]: @@ -280,17 +190,15 @@ def available_channels(self) -> dict[str, Channel]: # Show all channels if none are declared, otherwise filter depending # on whether the sequence is working on XY mode # If already in XY mode, filter right away - if not self._channels and not self._in_xy: + if not self._schedule and not self._in_xy: return dict(self._device.channels) else: # MockDevice channels can be declared multiple times + occupied_ch_ids = [cs.channel_id for cs in self._schedule.values()] return { id: ch for id, ch in self._device.channels.items() - if ( - id not in self._taken_channels.values() - or self._device == MockDevice - ) + if (id not in occupied_ch_ids or self._device == MockDevice) and (ch.basis == "XY" if self._in_xy else ch.basis != "XY") } @@ -336,7 +244,15 @@ def is_register_mappable(self) -> bool: """ return isinstance(self._register, MappableRegister) - @_screen + def is_measured(self) -> bool: + """States whether the sequence has been measured.""" + return ( + self._is_measured + if self.is_parametrized() + else hasattr(self, "_measurement") + ) + + @seq_decorators.screen def get_duration( self, channel: Optional[str] = None, include_fall_time: bool = False ) -> int: @@ -352,36 +268,12 @@ def get_duration( Returns: The duration of the channel or sequence, in ns. """ - if channel is None: - channels = tuple(self._channels.keys()) - if not channels: - return 0 - else: + if channel is not None: self._validate_channel(channel) - channels = (channel,) - last_ts = {} - for id in channels: - this_chobj = self._channels[id] - temp_tf = 0 - for i, op in enumerate(self._schedule[id][::-1]): - if i == 0: - # Start with the last slot found - temp_tf = op.tf - if not include_fall_time: - break - if isinstance(op.type, Pulse): - temp_tf = max( - temp_tf, op.tf + op.type.fall_time(this_chobj) - ) - break - elif temp_tf - op.tf >= 2 * this_chobj.rise_time: - # No pulse behind 'op' with a long enough fall time - break - last_ts[id] = temp_tf - - return max(last_ts.values()) - - @_screen + + return self._schedule.get_duration(channel, include_fall_time) + + @seq_decorators.screen def current_phase_ref( self, qubit: QubitId, basis: str = "digital" ) -> float: @@ -402,10 +294,10 @@ def current_phase_ref( "this sequence's register." ) - if basis not in self._phase_ref: + if basis not in self._basis_ref: raise ValueError("No declared channel targets the given 'basis'.") - return self._phase_ref[basis][qubit].last_phase + return self._basis_ref[basis][qubit].phase.last_phase def set_magnetic_field( self, bx: float = 0.0, by: float = 0.0, bz: float = 30.0 @@ -427,29 +319,29 @@ def set_magnetic_field( bz: The magnetic field in the z direction (in Gauss). """ if not self._in_xy: - if self._channels: + if self._schedule: raise ValueError( - "The magnetic field can only be set in 'XY " "Mode'." + "The magnetic field can only be set in 'XY Mode'." ) # No channels declared yet self._in_xy = True elif not self._empty_sequence: # Not all channels are empty raise ValueError( - "The magnetic field can only be set on an empty " "sequence." + "The magnetic field can only be set on an empty sequence." ) mag_vector = (bx, by, bz) if np.linalg.norm(mag_vector) == 0.0: raise ValueError( - "The magnetic field must have a magnitude greater" " than 0." + "The magnetic field must have a magnitude greater than 0." ) self._mag_field = mag_vector # No parametrization -> Always stored as a regular call self._calls.append(_Call("set_magnetic_field", mag_vector, {})) - @_store + @seq_decorators.store def config_slm_mask(self, qubits: Iterable[QubitId]) -> None: """Setup an SLM mask by specifying the qubits it targets. @@ -460,10 +352,10 @@ def config_slm_mask(self, qubits: Iterable[QubitId]) -> None: try: targets = set(qubits) except TypeError: - raise TypeError("The SLM targets must be castable to set") + raise TypeError("The SLM targets must be castable to set.") if not targets.issubset(self._qids): - raise ValueError("SLM mask targets must exist in the register") + raise ValueError("SLM mask targets must exist in the register.") if self.is_parametrized(): return @@ -474,23 +366,7 @@ def config_slm_mask(self, qubits: Iterable[QubitId]) -> None: # If checks have passed, set the SLM mask targets self._slm_mask_targets = targets - # Find tentative initial and final time of SLM mask if possible - for channel in self._channels: - if not self._channels[channel].addressing == "Global": - continue - # Cycle on slots in schedule until the first pulse is found - for slot in self._schedule[channel]: - if not isinstance(slot.type, Pulse): - continue - ti = slot.ti - tf = slot.tf - if self._slm_mask_time: - if ti < self._slm_mask_time[0]: - self._slm_mask_time = [ti, tf] - else: - self._slm_mask_time = [ti, tf] - break - + @seq_decorators.block_if_measured def declare_channel( self, name: str, @@ -521,7 +397,7 @@ def declare_channel( target will have to be set manually as the first addition to this channel. """ - if name in self._channels: + if name in self._schedule: raise ValueError("The given name is already in use.") if channel_id not in self._device.channels: @@ -556,16 +432,11 @@ def declare_channel( if ch.basis == "XY" and not self._in_xy: self._in_xy = True self.set_magnetic_field() - self._channels[name] = ch - self._taken_channels[name] = channel_id - self._schedule[name] = [] - self._last_target[name] = 0 - - if ch.basis not in self._phase_ref: - self._phase_ref[ch.basis] = { - q: _PhaseTracker(0) for q in self._qids - } - self._last_used[ch.basis] = {q: 0 for q in self._qids} + + self._schedule[name] = _ChannelSchedule(channel_id, ch) + + if ch.basis not in self._basis_ref: + self._basis_ref[ch.basis] = {q: _QubitRef() for q in self._qids} if ch.addressing == "Global": self._add_to_schedule(name, _TimeSlot("target", -1, 0, self._qids)) @@ -659,7 +530,9 @@ def declare_variable( self._variables[name] = var return var - @_store + @seq_decorators.store + @seq_decorators.mark_non_empty + @seq_decorators.block_if_measured def add( self, pulse: Union[Pulse, Parametrized], @@ -697,41 +570,17 @@ def add( if self.is_parametrized(): if not isinstance(pulse, Parametrized): - self._validate_pulse(pulse, channel) - # Sequence is marked as non-empty on the first added pulse - if self._empty_sequence: - self._empty_sequence = False + self._validate_and_adjust_pulse(pulse, channel) return - if not isinstance(pulse, Pulse): - raise TypeError( - f"'pulse' must be of type Pulse, not of type {type(pulse)}." - ) - - channel_obj = self._channels[channel] - _duration = channel_obj.validate_duration(pulse.duration) - if _duration != pulse.duration: - try: - pulse = Pulse( - pulse.amplitude.change_duration(_duration), - pulse.detuning.change_duration(_duration), - pulse.phase, - pulse.post_phase_shift, - ) - except NotImplementedError: - raise TypeError( - "Failed to automatically adjust one of the pulse's " - "waveforms to the channel duration constraints. Choose a " - "duration that is a multiple of " - f"{channel_obj.clock_period} ns." - ) - - self._validate_pulse(pulse, channel) + pulse = cast(Pulse, pulse) + channel_obj = self._schedule[channel].channel_obj last = self._last(channel) - t0 = last.tf # Preliminary ti basis = channel_obj.basis - ph_refs = {self._phase_ref[basis][q].last_phase for q in last.targets} + ph_refs = { + self._basis_ref[basis][q].phase.last_phase for q in last.targets + } if len(ph_refs) != 1: raise ValueError( "Cannot do a multiple-target pulse on qubits with different " @@ -740,90 +589,24 @@ def add( else: phase_ref = ph_refs.pop() - if phase_ref != 0: - # Has to recreate the original pulse with a new phase - pulse = Pulse( - pulse.amplitude, - pulse.detuning, - pulse.phase + phase_ref, - post_phase_shift=pulse.post_phase_shift, - ) + pulse = self._validate_and_adjust_pulse(pulse, channel, phase_ref) phase_barriers = [ - self._phase_ref[basis][q].last_time for q in last.targets + self._basis_ref[basis][q].phase.last_time for q in last.targets ] - current_max_t = max(t0, *phase_barriers) - if protocol != "no-delay": - for ch, seq in self._schedule.items(): - if ch == channel: - continue - this_chobj = self._channels[ch] - for op in self._schedule[ch][::-1]: - if not isinstance(op.type, Pulse): - if op.tf + 2 * this_chobj.rise_time <= current_max_t: - # No pulse behind 'op' needing a delay - break - elif ( - op.tf + op.type.fall_time(this_chobj) <= current_max_t - ): - break - elif ( - op.targets & last.targets or protocol == "wait-for-all" - ): - current_max_t = op.tf + op.type.fall_time(this_chobj) - break - - delay_duration = current_max_t - t0 - # Find last pulse and compare phase - for op in self._schedule[channel][::-1]: - if isinstance(op.type, Pulse): - if op.type.phase != pulse.phase: - delay_duration = max( - delay_duration, - # Considers that the last pulse might not be at t0 - channel_obj.phase_jump_time - (t0 - op.tf), - ) - break - - if delay_duration > 0: - # Delay must not be shorter than the min duration of this channel - # and a multiple of the clock period (forced by validate_duration) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - delay_duration = channel_obj.validate_duration( - max(delay_duration, channel_obj.min_duration) - ) - self._delay(delay_duration, channel) - - ti = t0 + delay_duration - tf = ti + pulse.duration - self._add_to_schedule(channel, _TimeSlot(pulse, ti, tf, last.targets)) + self._schedule.add_pulse(pulse, channel, phase_barriers, protocol) - true_finish = tf + pulse.fall_time(channel_obj) + true_finish = self._last(channel).tf + pulse.fall_time(channel_obj) for qubit in last.targets: - if self._last_used[basis][qubit] < true_finish: - self._last_used[basis][qubit] = true_finish + self._basis_ref[basis][qubit].update_last_used(true_finish) if pulse.post_phase_shift: self._phase_shift( pulse.post_phase_shift, *last.targets, basis=basis ) - # Sequence is marked as non-empty on the first added pulse - if self._empty_sequence: - self._empty_sequence = False - - # If the added pulse starts earlier than all previously added pulses, - # update SLM mask initial and final time - if self._slm_mask_targets: - try: - if self._slm_mask_time[0] > ti: - self._slm_mask_time = [ti, tf] - except IndexError: - self._slm_mask_time = [ti, tf] - - @_store + @seq_decorators.store def target( self, qubits: Union[QubitId, Iterable[QubitId]], @@ -840,7 +623,8 @@ def target( """ self._target(qubits, channel) - @_verify_parametrization + @seq_decorators.store + @seq_decorators.check_allow_qubit_index def target_index( self, qubits: Union[int, Iterable[int], Parametrized], @@ -863,19 +647,9 @@ def target_index( Cannot be used on non-parametrized sequences using a mappable register. """ - self._check_allow_qubit_index(self.target_index.__name__) - - qubits = cast(int, qubits) - self._target_index(qubits, channel) - - def _check_allow_qubit_index(self, method_name: str) -> None: - if not self.is_parametrized() and self.is_register_mappable(): - raise RuntimeError( - f"Sequence.{method_name} cannot be called in" - " non parametrized sequences using a mappable register." - ) + self._target(qubits, channel, _index=True) - @_store + @seq_decorators.store def delay( self, duration: Union[int, Parametrized], @@ -889,7 +663,8 @@ def delay( """ self._delay(duration, channel) - @_store + @seq_decorators.store + @seq_decorators.block_if_measured def measure(self, basis: str = "ground-rydberg") -> None: """Measures in a valid basis. @@ -917,15 +692,12 @@ def measure(self, basis: str = "ground-rydberg") -> None: "available options are: " + ", ".join(list(available)) ) - if hasattr(self, "_measurement"): - raise RuntimeError("The sequence has already been measured.") - if self.is_parametrized(): self._is_measured = True else: self._measurement = basis - @_store + @seq_decorators.store def phase_shift( self, phi: Union[float, Parametrized], @@ -947,7 +719,8 @@ def phase_shift( """ self._phase_shift(phi, *targets, basis=basis) - @_verify_parametrization + @seq_decorators.store + @seq_decorators.check_allow_qubit_index def phase_shift_index( self, phi: Union[float, Parametrized], @@ -975,10 +748,10 @@ def phase_shift_index( Cannot be used on non-parametrized sequences using a mappable register. """ - self._check_allow_qubit_index(self.phase_shift_index.__name__) - self._phase_shift_index(phi, *targets, basis=basis) + self._phase_shift(phi, *targets, basis=basis, _index=True) - @_store + @seq_decorators.store + @seq_decorators.block_if_measured def align(self, *channels: str) -> None: """Aligns multiple channels in time. @@ -992,9 +765,9 @@ def align(self, *channels: str) -> None: """ ch_set = set(channels) # channels have to be a subset of the declared channels - if not ch_set <= set(self._channels): + if not ch_set <= set(self._schedule): raise ValueError( - "All channel names must correspond to declared" " channels." + "All channel names must correspond to declared channels." ) if len(channels) != len(ch_set): raise ValueError("The same channel was provided more than once.") @@ -1014,13 +787,10 @@ def align(self, *channels: str) -> None: for id in channels: delta = tf - last_ts[id] if delta > 0: - channel_obj = self._channels[id] - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - delta = channel_obj.validate_duration( - max(delta, channel_obj.min_duration) - ) - self._delay(delta, id) + self._delay( + self._schedule[id].adjust_duration(delta), + id, + ) def build( self, @@ -1159,7 +929,7 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: return cast(Sequence, json.loads(obj, cls=PulserDecoder, **kwargs)) - @_screen + @seq_decorators.screen def draw( self, mode: str = "input+output", @@ -1251,27 +1021,15 @@ def draw( fig.savefig(fig_name, **kwargs_savefig) plt.show() - @overload - def _precheck_target_qubits_set( - self, qubits: Union[Iterable[int], int, Parametrized], channel: str - ) -> Union[Set[int]]: - pass - - @overload - def _precheck_target_qubits_set( - self, - qubits: Union[Iterable[QubitId], QubitId], - channel: str, - ) -> Union[Set[QubitId]]: - pass - - def _precheck_target_qubits_set( + @seq_decorators.block_if_measured + def _target( self, qubits: Union[Iterable[QubitId], QubitId, Parametrized], channel: str, - ) -> Union[Set[QubitId], Set[int]]: + _index: bool = False, + ) -> None: self._validate_channel(channel) - channel_obj = self._channels[channel] + channel_obj = self._schedule[channel].channel_obj try: qubits_set = ( set(cast(Iterable, qubits)) @@ -1288,170 +1046,77 @@ def _precheck_target_qubits_set( f"This channel can target at most {channel_obj.max_targets} " "qubits at a time." ) - - return qubits_set - - def _target( - self, - qubits: Union[Iterable[QubitId], QubitId], - channel: str, - ) -> None: - qubits_set = self._precheck_target_qubits_set(qubits, channel) - self._check_ids(*qubits_set) + qubit_ids_set = self._check_qubits_give_ids(*qubits_set, _index=_index) if not self.is_parametrized(): - self._perform_target_non_parametrized(qubits_set, channel) - - def _check_indices( - self, indices: Iterable[Union[int, Parametrized]] - ) -> None: - nb_of_indices = len(self._register.qubit_ids) - allowed_indices = range(nb_of_indices) - for i in indices: - if i not in allowed_indices and not isinstance(i, Parametrized): + basis = channel_obj.basis + phase_refs = { + self._basis_ref[basis][q].phase.last_phase + for q in qubit_ids_set + } + if len(phase_refs) != 1: raise ValueError( - f"All non-variable targets must be indices valid " - f"for the register, between 0 and " - f"{nb_of_indices - 1}. Wrong index: {i!r}." + "Cannot target multiple qubits with different " + "phase references for the same basis." ) + self._schedule.add_target(qubit_ids_set, channel) - @_store - def _target_index( - self, qubits: Union[Iterable[int], int, Parametrized], channel: str - ) -> None: - - qubits_set = self._precheck_target_qubits_set(qubits, channel) - if self.is_parametrized(): - self._check_indices(qubits_set) - else: - try: - qubit_ids_set = { - self.register.qubit_ids[index] for index in qubits_set - } - except IndexError: - raise IndexError("Indices must exist for the register.") - self._perform_target_non_parametrized(qubit_ids_set, channel) - - def _perform_target_non_parametrized( - self, qubits_set: Set[QubitId], channel: str - ) -> None: - channel_obj = self._channels[channel] - basis = channel_obj.basis - phase_refs = {self._phase_ref[basis][q].last_phase for q in qubits_set} - if len(phase_refs) != 1: + def _check_qubits_give_ids( + self, *qubits: Union[QubitId, Parametrized], _index: bool = False + ) -> set[QubitId]: + if _index: + if self.is_parametrized(): + nb_of_indices = len(self._register.qubit_ids) + allowed_indices = range(nb_of_indices) + for i in qubits: + if i not in allowed_indices and not isinstance( + i, Parametrized + ): + raise ValueError( + f"All non-variable targets must be indices valid " + f"for the register, between 0 and " + f"{nb_of_indices - 1}. Wrong index: {i!r}." + ) + return set() + else: + qubits = cast(Tuple[int, ...], qubits) + try: + return {self.register.qubit_ids[index] for index in qubits} + except IndexError: + raise IndexError("Indices must exist for the register.") + ids = set(cast(Tuple[QubitId, ...], qubits)) + if not ids <= self._qids: raise ValueError( - "Cannot target multiple qubits with different " - "phase references for the same basis." + "All given ids have to be qubit ids declared" + " in this sequence's register." ) + return ids - try: - fall_time = self.get_duration( - channel, include_fall_time=True - ) - self.get_duration(channel) - if fall_time > 0: - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - self.delay( - max(fall_time, channel_obj.min_duration), - channel, - ) - - last = self._last(channel) - if last.targets == qubits_set: - return - ti = last.tf - retarget = cast(int, channel_obj.min_retarget_interval) - elapsed = ti - self._last_target[channel] - delta = cast(int, np.clip(retarget - elapsed, 0, retarget)) - if channel_obj.fixed_retarget_t: - delta = max(delta, channel_obj.fixed_retarget_t) - if delta != 0: - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - delta = channel_obj.validate_duration( - max(delta, channel_obj.min_duration) - ) - tf = ti + delta - - except ValueError: - ti = -1 - tf = 0 - - self._last_target[channel] = tf - self._add_to_schedule( - channel, _TimeSlot("target", ti, tf, set(qubits_set)) - ) - + @seq_decorators.block_if_measured def _delay(self, duration: Union[int, Parametrized], channel: str) -> None: self._validate_channel(channel) if self.is_parametrized(): return - - duration = cast(int, duration) - last = self._last(channel) - ti = last.tf - tf = ti + self._channels[channel].validate_duration(duration) - self._add_to_schedule( - channel, _TimeSlot("delay", ti, tf, last.targets) - ) - - def _check_basis(self, basis: str) -> None: - if basis not in self._phase_ref: - raise ValueError("No declared channel targets the given 'basis'.") - - def _check_ids(self, *ids: Union[QubitId, Parametrized]) -> None: - if not set(ids) <= self._qids: - raise ValueError( - "All given ids have to be qubit ids declared" - " in this sequence's register." - ) - - def _phase_shift_non_parametrized( - self, - phi: Union[float, Parametrized], - *targets: QubitId, - basis: str, - ) -> None: - phi = cast(float, phi) - if phi % (2 * np.pi) == 0: - return - - for qubit in targets: - last_used = self._last_used[basis][qubit] - new_phase = self._phase_ref[basis][qubit].last_phase + phi - self._phase_ref[basis][qubit][last_used] = new_phase + self._schedule.add_delay(cast(int, duration), channel) def _phase_shift( self, phi: Union[float, Parametrized], - *targets: QubitId, + *targets: Union[QubitId, Parametrized], basis: str, + _index: bool = False, ) -> None: - self._check_basis(basis) - self._check_ids(*targets) + if basis not in self._basis_ref: + raise ValueError("No declared channel targets the given 'basis'.") + target_ids = self._check_qubits_give_ids(*targets, _index=_index) if not self.is_parametrized(): - self._phase_shift_non_parametrized(phi, *targets, basis=basis) + phi = cast(float, phi) + if phi % (2 * np.pi) == 0: + return - @_store - def _phase_shift_index( - self, - phi: Union[float, Parametrized], - *targets: Union[int, Parametrized], - basis: str, - ) -> None: - self._check_basis(basis) - if self.is_parametrized(): - self._check_indices(targets) - else: - targets = cast(Tuple[int], targets) - try: - target_ids = [ - self.register.qubit_ids[index] for index in targets - ] - except IndexError: - raise IndexError("Indices must exist for the register.") - self._phase_shift_non_parametrized(phi, *target_ids, basis=basis) + for qubit in target_ids: + self._basis_ref[basis][qubit].increment_phase(phi) def _to_dict(self) -> dict[str, Any]: d = obj_to_dict(self, *self._calls[0].args, **self._calls[0].kwargs) @@ -1462,76 +1127,15 @@ def _to_dict(self) -> dict[str, Any]: return d def __str__(self) -> str: - full = "" - pulse_line = "t: {}->{} | {} | Targets: {}\n" - target_line = "t: {}->{} | Target: {} | Phase Reference: {}\n" - delay_line = "t: {}->{} | Delay \n" - # phase_line = "t: {} | Phase shift of: {:.3f} | Targets: {}\n" - for ch, seq in self._schedule.items(): - basis = self._channels[ch].basis - full += f"Channel: {ch}\n" - first_slot = True - for ts in seq: - if ts.type == "delay": - full += delay_line.format(ts.ti, ts.tf) - continue - - tgts = list(ts.targets) - tgt_txt = ", ".join([str(t) for t in tgts]) - if isinstance(ts.type, Pulse): - full += pulse_line.format(ts.ti, ts.tf, ts.type, tgt_txt) - elif ts.type == "target": - phase = self._phase_ref[basis][tgts[0]][ts.tf] - if first_slot: - full += ( - f"t: 0 | Initial targets: {tgt_txt} | " - + f"Phase Reference: {phase} \n" - ) - first_slot = False - else: - full += target_line.format( - ts.ti, ts.tf, tgt_txt, phase - ) - full += "\n" - - if hasattr(self, "_measurement"): - full += f"Measured in basis: {self._measurement}" - - if self.is_parametrized(): - prelude = "Prelude\n-------\n" + full - lines = ["Stored calls\n------------"] - for i, c in enumerate(self._to_build_calls, 1): - args = [str(a) for a in c.args] - kwargs = [ - f"{key}={str(value)}" for key, value in c.kwargs.items() - ] - lines.append(f"{i}. {c.name}({', '.join(args+kwargs)})") - full = prelude + "\n\n".join(lines) - - return full + return seq_to_str(self) def _add_to_schedule(self, channel: str, timeslot: _TimeSlot) -> None: - if hasattr(self, "_measurement"): - raise RuntimeError( - "The sequence has already been measured. " - "Nothing more can be added." - ) - self._schedule[channel].append(timeslot) - - def _min_pulse_duration(self) -> float: - duration_list = [] - for ch_schedule in self._schedule.values(): - for slot in ch_schedule: - if isinstance(slot.type, Pulse): - duration_list.append(slot.tf - slot.ti) - return min(duration_list) + # Maybe get rid of this + self._schedule[channel].slots.append(timeslot) def _last(self, channel: str) -> _TimeSlot: """Shortcut to last element in the channel's schedule.""" - try: - return self._schedule[channel][-1] - except IndexError: - raise ValueError("The chosen channel has no target.") + return self._schedule[channel][-1] def _validate_channel(self, channel: str) -> None: if isinstance(channel, Parametrized): @@ -1539,11 +1143,32 @@ def _validate_channel(self, channel: str) -> None: "Using parametrized objects or variables to refer to channels " "is not supported." ) - if channel not in self._channels: + if channel not in self._schedule: raise ValueError("Use the name of a declared channel.") - def _validate_pulse(self, pulse: Pulse, channel: str) -> None: - self._device.validate_pulse(pulse, self._taken_channels[channel]) + def _validate_and_adjust_pulse( + self, pulse: Pulse, channel: str, phase_ref: Optional[float] = None + ) -> Pulse: + self._device.validate_pulse(pulse, self._schedule[channel].channel_id) + channel_obj = self._schedule[channel].channel_obj + _duration = channel_obj.validate_duration(pulse.duration) + new_phase = pulse.phase + (phase_ref if phase_ref else 0) + if _duration != pulse.duration: + try: + new_amp = pulse.amplitude.change_duration(_duration) + new_det = pulse.detuning.change_duration(_duration) + except NotImplementedError: + raise TypeError( + "Failed to automatically adjust one of the pulse's " + "waveforms to the channel duration constraints. Choose a " + "duration that is a multiple of " + f"{channel_obj.clock_period} ns." + ) + else: + new_amp = pulse.amplitude + new_det = pulse.detuning + + return Pulse(new_amp, new_det, new_phase, pulse.post_phase_shift) def _reset_parametrized(self) -> None: """Resets all attributes related to parametrization.""" @@ -1558,13 +1183,13 @@ def _set_register(self, seq: Sequence, reg: BaseRegister) -> None: self._device.validate_register(reg) qids = set(reg.qubit_ids) used_qubits = set() - for ch, ch_obj in self._channels.items(): + for ch, ch_schedule in self._schedule.items(): # Correct the targets of global channels - if ch_obj.addressing == "Global": + if ch_schedule.channel_obj.addressing == "Global": for i, slot in enumerate(self._schedule[ch]): stored_values = slot._asdict() stored_values["targets"] = qids - seq._schedule[ch][i] = _TimeSlot(**stored_values) + seq._schedule[ch].slots[i] = _TimeSlot(**stored_values) else: # Make sure all explicit targets are in the register for slot in self._schedule[ch]: @@ -1578,50 +1203,3 @@ def _set_register(self, seq: Sequence, reg: BaseRegister) -> None: seq._register = reg seq._qids = qids seq._calls[0] = _Call("__init__", (seq._register, seq._device), {}) - - -class _PhaseTracker: - """Tracks a phase reference over time.""" - - def __init__(self, initial_phase: float): - self._times: list[int] = [0] - self._phases: list[float] = [self._format(initial_phase)] - - @property - def last_time(self) -> int: - return self._times[-1] - - @property - def last_phase(self) -> float: - return self._phases[-1] - - def changes( - self, - ti: Union[float, int], - tf: Union[float, int], - time_scale: float = 1.0, - ) -> Generator[tuple[float, float], None, None]: - """Changes in phases within ]ti, tf].""" - start, end = np.searchsorted( - self._times, (ti * time_scale, tf * time_scale), side="right" - ) - for i in range(start, end): - change = self._phases[i] - self._phases[i - 1] - yield (self._times[i] / time_scale, change) - - def _format(self, phi: float) -> float: - return phi % (2 * np.pi) - - def __setitem__(self, t: int, phi: float) -> None: - phase = self._format(phi) - if t in self._times: - ind = self._times.index(t) - self._phases[ind] = phase - else: - ind = int(np.searchsorted(self._times, t, side="right")) - self._times.insert(ind, t) - self._phases.insert(ind, phase) - - def __getitem__(self, t: int) -> float: - ind = int(np.searchsorted(self._times, t, side="right")) - 1 - return self._phases[ind] diff --git a/pulser-simulation/pulser_simulation/simulation.py b/pulser-simulation/pulser_simulation/simulation.py index 70176a7f9..dbb2dfdb5 100644 --- a/pulser-simulation/pulser_simulation/simulation.py +++ b/pulser-simulation/pulser_simulation/simulation.py @@ -29,9 +29,9 @@ from numpy.typing import ArrayLike from pulser import Pulse, Sequence -from pulser._seq_drawer import draw_sequence from pulser.register import QubitId -from pulser.sequence import _TimeSlot +from pulser.sequence._seq_drawer import draw_sequence +from pulser.sequence.sequence import _TimeSlot from pulser_simulation.simconfig import SimConfig from pulser_simulation.simresults import ( CoherentResults, @@ -89,7 +89,10 @@ def __init__( ) if not sequence._schedule: raise ValueError("The provided sequence has no declared channels.") - if all(sequence._schedule[x][-1].tf == 0 for x in sequence._channels): + if all( + sequence._schedule[x][-1].tf == 0 + for x in sequence.declared_channels + ): raise ValueError( "No instructions given for the channels in the sequence." ) @@ -875,7 +878,13 @@ def run( if "max_step" in options.keys(): solv_ops = qutip.Options(**options) else: - auto_max_step = 0.5 * (self._seq._min_pulse_duration() / 1000) + min_pulse_duration = min( + slot.tf - slot.ti + for ch_schedule in self._seq._schedule.values() + for slot in ch_schedule + if isinstance(slot.type, Pulse) + ) + auto_max_step = 0.5 * (min_pulse_duration / 1000) solv_ops = qutip.Options(max_step=auto_max_step, **options) meas_errors: Optional[Mapping[str, float]] = None diff --git a/tests/test_paramseq.py b/tests/test_paramseq.py index c8ff31505..5f1707efb 100644 --- a/tests/test_paramseq.py +++ b/tests/test_paramseq.py @@ -67,7 +67,7 @@ def test_stored_calls(): sb.declare_channel("ch1", "rydberg_local") sb.target_index(var, "ch1") assert sb._calls[-1].name == "declare_channel" - assert sb._to_build_calls[-1].name == "_target_index" + assert sb._to_build_calls[-1].name == "target_index" assert sb._to_build_calls[-1].args == (var, "ch1") with pytest.raises(ValueError, match="name of a declared channel"): sb.delay(1000, "rydberg_local") diff --git a/tests/test_sequence.py b/tests/test_sequence.py index b9871a638..f2a9eab7c 100644 --- a/tests/test_sequence.py +++ b/tests/test_sequence.py @@ -24,7 +24,7 @@ from pulser.devices import Chadoq2, MockDevice from pulser.devices._device_datacls import Device from pulser.register.special_layouts import TriangularLatticeLayout -from pulser.sequence import _TimeSlot +from pulser.sequence.sequence import _TimeSlot from pulser.waveforms import ( BlackmanWaveform, CompositeWaveform, @@ -70,16 +70,18 @@ def test_channel_declaration(): seq2 = Sequence(reg, MockDevice) available_channels = set(seq2.available_channels) - seq2.declare_channel("ch0", "raman_local", initial_target="q1") - seq2.declare_channel("ch1", "rydberg_global") - seq2.declare_channel("ch2", "rydberg_global") - assert set(seq2.available_channels) == (available_channels - {"mw_global"}) - assert seq2._taken_channels == { + channel_map = { "ch0": "raman_local", "ch1": "rydberg_global", "ch2": "rydberg_global", } - assert seq2._taken_channels.keys() == seq2._channels.keys() + for channel, channel_id in channel_map.items(): + seq2.declare_channel(channel, channel_id) + assert set(seq2.available_channels) == (available_channels - {"mw_global"}) + assert set( + seq2._schedule[channel].channel_id + for channel in seq2.declared_channels + ) == set(channel_map.values()) with pytest.raises(ValueError, match="type 'Microwave' cannot work "): seq2.declare_channel("ch3", "mw_global") @@ -215,28 +217,12 @@ def test_delay_min_duration(): seq.add(pulse0, "ch0") seq.target("q1", "ch1") seq.add(pulse1, "ch1") - min_duration = seq._channels["ch1"].min_duration + min_duration = seq.declared_channels["ch1"].min_duration assert seq._schedule["ch1"][3] == _TimeSlot( "delay", 220, 220 + min_duration, {"q1"} ) -def test_min_pulse_duration(): - seq = Sequence(reg, device) - seq.declare_channel("ch0", "rydberg_global") - seq.declare_channel("ch1", "rydberg_local") - seq.target("q0", "ch1") - pulse0 = Pulse.ConstantPulse(60, 1, 1, 0) - pulse1 = Pulse.ConstantPulse(80, 1, 1, 0) - seq.add(pulse1, "ch1") - assert seq._min_pulse_duration() == 80 - seq.add(pulse0, "ch0") - seq.delay(52, "ch0") - seq.target("q1", "ch1") - seq.add(pulse1, "ch1") - assert seq._min_pulse_duration() == 60 - - def test_phase(): seq = Sequence(reg, device) seq.declare_channel("ch0", "raman_local", initial_target="q0") @@ -288,9 +274,10 @@ def test_measure(): with pytest.raises(ValueError, match="not supported"): seq.measure(basis="XY") seq.measure() - with pytest.raises(RuntimeError, match="already been measured"): - seq.measure(basis="digital") - with pytest.raises(RuntimeError, match="Nothing more can be added."): + with pytest.raises( + RuntimeError, + match="sequence has been measured, no further changes are allowed.", + ): seq.add(pulse, "ch0") seq = Sequence(reg, MockDevice) @@ -301,6 +288,34 @@ def test_measure(): seq.measure(basis="XY") +@pytest.mark.parametrize( + "call, args", + [ + ("declare_channel", ("ch1", "rydberg_global")), + ("add", (Pulse.ConstantPulse(1000, 1, 0, 0), "ch0")), + ("target", ("q1", "ch0")), + ("target_index", (2, "ch0")), + ("delay", (1000, "ch0")), + ("align", ("ch0", "ch01")), + ("measure", tuple()), + ], +) +def test_block_if_measured(call, args): + seq = Sequence(reg, MockDevice) + seq.declare_channel("ch0", "rydberg_local", initial_target="q0") + # For the align command + seq.declare_channel("ch01", "rydberg_local", initial_target="q0") + # Check there's nothing wrong with the call + if call != "measure": + getattr(seq, call)(*args) + seq.measure(basis="ground-rydberg") + with pytest.raises( + RuntimeError, + match="sequence has been measured, no further changes are allowed.", + ): + getattr(seq, call)(*args) + + def test_str(): seq = Sequence(reg, device) seq.declare_channel("ch0", "raman_local", initial_target="q0") @@ -328,7 +343,6 @@ def test_sequence(): seq.declare_channel("ch2", "rydberg_global") assert seq.get_duration("ch0") == 0 assert seq.get_duration("ch2") == 0 - seq.phase_shift(np.pi, "q0", basis="ground-rydberg") with patch("matplotlib.pyplot.show"): with patch("matplotlib.figure.Figure.savefig"): @@ -356,6 +370,7 @@ def test_sequence(): seq.add( Pulse.ConstantPulse(500, 2 * np.pi, -2 * np.pi * 100, 0), "ch0" ) + seq.phase_shift(np.pi, "q0", basis="ground-rydberg") with pytest.raises(ValueError, match="qubits with different phase ref"): seq.add(pulse2, "ch2") with pytest.raises(ValueError, match="Invalid protocol"): @@ -387,8 +402,8 @@ def test_sequence(): assert seq.current_phase_ref("q0", "digital") == 0 seq.phase_shift(np.pi / 2, "q1") seq.target("q1", "ch0") - assert seq._last_used["digital"]["q1"] == 0 - assert seq._last_target["ch0"] == 1000 + assert seq._basis_ref["digital"]["q1"].last_used == 0 + assert seq._schedule["ch0"].last_target() == 1000 assert seq._last("ch0").ti == 1000 assert seq.get_duration("ch0") == 1000 seq.add(pulse1, "ch0") @@ -834,13 +849,13 @@ def test_non_parametrized_mappable_register_index_functions_failure( with pytest.raises( RuntimeError, match="Sequence.target_index cannot be called in" - " non parametrized sequences", + " non-parametrized sequences", ): seq.target_index(index, channel="ch0") with pytest.raises( RuntimeError, match="Sequence.phase_shift_index cannot be called in" - " non parametrized sequences", + " non-parametrized sequences", ): seq.phase_shift_index(phi, index) From 4592d39765e6c864f35d264bbf0ccb182562bf96 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Thu, 21 Jul 2022 18:23:01 +0200 Subject: [PATCH 10/18] Serialization to the abstract representation (#355) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Abstract representation for pulses and waveforms * Initial version of abstract seq format (#346) * Preliminary AbstractReprEncoder * added abstract_repr tests (#347) * WIP: Sequence to abstract_repr conversion * Abstract representation schema check (#350) * added json schema test for abstract repr * changed export POC to fit schema * separate tests for abstract repr * sequence misc cleanup and comment * WIP: Updates to the format * Moving custom serialization exceptions * Handling variables in the serialization * Format updates * WIP: Unit tests * Updates to the JSON schema * Restrict export of InterpolatedWaveform * More updates to the JSON Schema * Adding support for new operations * Changing how signatures are stored for abstract representation * Adding support for `set` and `np.ndarray` * Import sorting + myp * Fixing `InterpolatedWaveform` export * Preparing for merge * Adding support for phase shifts and a sequence name * Adding support for "delay" and updating the JSON schema * Updates for the latest schema * Feat: deserialize abstract repr for non param sequences (+refacto abstract serialization) * Fix: Get rid of inline import statements + type-hint fixes * Moving pulser.json.signatures into pulser.json.abstract_repr * Removing `str` variable handling logic * Feat: Deserialize parametrized objects * Make CI run on all PRs * Fix mypy errors * Fix flake8 errors * Fix format and import sorting * Serializer support for args as kwargs * Add function to test the serialization roundtrip * Post-merge fixes * Dropping usage of pos-only args due to python 3.7 conflict * Reaching 100% coverage of the deserializer * Updating test ids * Fixing the path to the json schema * Finish serializer coverage * Adding comments + small typo fixes Co-authored-by: Piotr Migdał Co-authored-by: MB --- .flake8 | 4 +- .github/workflows/ci.yml | 3 - .mypy.ini | 2 +- pulser-core/MANIFEST.in | 1 + pulser-core/pulser/devices/_device_datacls.py | 2 +- .../pulser/json/abstract_repr/__init__.py | 14 + .../pulser/json/abstract_repr/deserializer.py | 267 ++++ .../pulser/json/abstract_repr/schema.json | 725 +++++++++++ .../pulser/json/abstract_repr/serializer.py | 233 ++++ .../pulser/json/abstract_repr/signatures.py | 118 ++ pulser-core/pulser/json/exceptions.py | 30 + pulser-core/pulser/json/supported.py | 11 +- pulser-core/pulser/parametrized/paramobj.py | 82 +- pulser-core/pulser/parametrized/variable.py | 18 +- pulser-core/pulser/pulse.py | 13 +- pulser-core/pulser/register/base_register.py | 4 +- pulser-core/pulser/register/register.py | 26 +- pulser-core/pulser/sequence/sequence.py | 82 +- pulser-core/pulser/waveforms.py | 43 + pulser-core/setup.py | 1 + requirements.txt | 1 + tests/test_abstract_repr.py | 1077 +++++++++++++++++ tests/test_json.py | 3 +- tests/test_paramseq.py | 4 +- tests/test_register.py | 4 +- 25 files changed, 2718 insertions(+), 50 deletions(-) create mode 100644 pulser-core/pulser/json/abstract_repr/__init__.py create mode 100644 pulser-core/pulser/json/abstract_repr/deserializer.py create mode 100644 pulser-core/pulser/json/abstract_repr/schema.json create mode 100644 pulser-core/pulser/json/abstract_repr/serializer.py create mode 100644 pulser-core/pulser/json/abstract_repr/signatures.py create mode 100644 pulser-core/pulser/json/exceptions.py create mode 100644 tests/test_abstract_repr.py diff --git a/.flake8 b/.flake8 index c1ecc28d2..dffd549d2 100644 --- a/.flake8 +++ b/.flake8 @@ -8,8 +8,10 @@ extend-ignore = E203, per-file-ignores = # D100 Missing docstring in public module + # D101 Missing docstring in public class + # D102 Missing docstring in public method # D103 Missing docstring in public function # F401 Module imported but unused - tests/*: D100, D103 + tests/*: D100, D101, D102, D103 __init__.py: F401 setup.py: D100 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d191c3137..8f825195b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,9 +2,6 @@ name: build on: pull_request: - branches: - - master - - develop push: branches: - master diff --git a/.mypy.ini b/.mypy.ini index e65456272..62caff8ae 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -7,7 +7,7 @@ warn_unused_ignores = True disallow_untyped_defs = True # 3rd-party libs without type hints nor stubs -[mypy-matplotlib.*,scipy.*,qutip.*] +[mypy-matplotlib.*,scipy.*,qutip.*,jsonschema.*] follow_imports = silent ignore_missing_imports = true diff --git a/pulser-core/MANIFEST.in b/pulser-core/MANIFEST.in index c6be02f03..842ff617d 100644 --- a/pulser-core/MANIFEST.in +++ b/pulser-core/MANIFEST.in @@ -1,3 +1,4 @@ include README.md include LICENSE include pulser/devices/interaction_coefficients/C6_coeffs.json +include pulser/json/abstract_repr/schema.json diff --git a/pulser-core/pulser/devices/_device_datacls.py b/pulser-core/pulser/devices/_device_datacls.py index ae3ebf7cb..b3ff0c537 100644 --- a/pulser-core/pulser/devices/_device_datacls.py +++ b/pulser-core/pulser/devices/_device_datacls.py @@ -20,10 +20,10 @@ import numpy as np from scipy.spatial.distance import pdist, squareform -from pulser import Pulse from pulser.channels import Channel from pulser.devices.interaction_coefficients import c6_dict from pulser.json.utils import obj_to_dict +from pulser.pulse import Pulse from pulser.register.base_register import BaseRegister, QubitId from pulser.register.register_layout import COORD_PRECISION, RegisterLayout diff --git a/pulser-core/pulser/json/abstract_repr/__init__.py b/pulser-core/pulser/json/abstract_repr/__init__.py new file mode 100644 index 000000000..9698aced3 --- /dev/null +++ b/pulser-core/pulser/json/abstract_repr/__init__.py @@ -0,0 +1,14 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Serialization and deserialization tools for the abstract representation.""" diff --git a/pulser-core/pulser/json/abstract_repr/deserializer.py b/pulser-core/pulser/json/abstract_repr/deserializer.py new file mode 100644 index 000000000..b58af40b0 --- /dev/null +++ b/pulser-core/pulser/json/abstract_repr/deserializer.py @@ -0,0 +1,267 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Deserializer from JSON in the abstract representation.""" +from __future__ import annotations + +import json +from pathlib import Path +from typing import TYPE_CHECKING, Any, Union, cast, overload + +import jsonschema + +import pulser +import pulser.devices as devices +from pulser.json.abstract_repr.signatures import ( + BINARY_OPERATORS, + UNARY_OPERATORS, +) +from pulser.json.exceptions import AbstractReprError +from pulser.parametrized import ParamObj, Variable +from pulser.pulse import Pulse +from pulser.register.register import Register +from pulser.waveforms import ( + BlackmanWaveform, + CompositeWaveform, + ConstantWaveform, + CustomWaveform, + InterpolatedWaveform, + KaiserWaveform, + RampWaveform, + Waveform, +) + +if TYPE_CHECKING: # pragma: no cover + from pulser.sequence import Sequence + +with open(Path(__file__).parent / "schema.json") as f: + schema = json.load(f) + +VARIABLE_TYPE_MAP = {"int": int, "float": float} + +ExpReturnType = Union[int, float, ParamObj] + + +@overload +def _deserialize_parameter(param: int, vars: dict[str, Variable]) -> int: + pass + + +@overload +def _deserialize_parameter(param: float, vars: dict[str, Variable]) -> float: + pass + + +@overload +def _deserialize_parameter( + param: dict[str, str], vars: dict[str, Variable] +) -> Variable: + pass + + +def _deserialize_parameter( + param: Union[int, float, dict[str, Any]], + vars: dict[str, Variable], +) -> Union[ExpReturnType, Variable]: + """Deserialize a parameterized object. + + A parameter can be either a literal, a variable or an expression. + In the first case, return the literal. Otherwise, return a reference + to the variable, or build an expression referencing variables. + + Args: + param: The JSON parametrized object to deserialize + vars: The references to the sequence variables + + Returns: + A literal (int | float), a ``Variable``, or a ``ParamObj``. + """ + if not isinstance(param, dict): + # This is a literal + return param + + if "variable" in param: + # This is a reference to a variable. + if param["variable"] not in vars: + raise AbstractReprError( + f"Variable '{param['variable']}' used in operations " + "but not found in declared variables." + ) + return vars[param["variable"]] + + if "expression" not in param: + # Can't deserialize param if it is a dict without a + # `variable` or an `expression` key + raise AbstractReprError( + f"Parameter '{param}' is neither a literal nor " + "a variable or an expression." + ) + + # This is a unary or a binary expression + expression = ( + param["expression"] if param["expression"] != "div" else "truediv" + ) + + if expression in UNARY_OPERATORS: + return cast( + ExpReturnType, + UNARY_OPERATORS[expression]( + _deserialize_parameter(param["lhs"], vars) + ), + ) + elif expression in BINARY_OPERATORS: + return cast( + ExpReturnType, + BINARY_OPERATORS[expression]( + _deserialize_parameter(param["lhs"], vars), + _deserialize_parameter(param["rhs"], vars), + ), + ) + else: + raise AbstractReprError(f"Expression '{param['expression']}' invalid.") + + +def _deserialize_waveform(obj: dict, vars: dict) -> Waveform: + + if obj["kind"] == "constant": + return ConstantWaveform( + duration=_deserialize_parameter(obj["duration"], vars), + value=_deserialize_parameter(obj["value"], vars), + ) + if obj["kind"] == "ramp": + return RampWaveform( + duration=_deserialize_parameter(obj["duration"], vars), + start=_deserialize_parameter(obj["start"], vars), + stop=_deserialize_parameter(obj["stop"], vars), + ) + if obj["kind"] == "blackman": + return BlackmanWaveform( + duration=_deserialize_parameter(obj["duration"], vars), + area=_deserialize_parameter(obj["area"], vars), + ) + if obj["kind"] == "blackman_max": + return BlackmanWaveform.from_max_val( + max_val=_deserialize_parameter(obj["max_val"], vars), + area=_deserialize_parameter(obj["area"], vars), + ) + if obj["kind"] == "interpolated": + return InterpolatedWaveform( + duration=_deserialize_parameter(obj["duration"], vars), + values=_deserialize_parameter(obj["values"], vars), + times=_deserialize_parameter(obj["times"], vars), + ) + if obj["kind"] == "kaiser": + return KaiserWaveform( + duration=_deserialize_parameter(obj["duration"], vars), + area=_deserialize_parameter(obj["area"], vars), + beta=_deserialize_parameter(obj["beta"], vars), + ) + if obj["kind"] == "kaiser_max": + return KaiserWaveform.from_max_val( + max_val=_deserialize_parameter(obj["max_val"], vars), + area=_deserialize_parameter(obj["area"], vars), + beta=_deserialize_parameter(obj["beta"], vars), + ) + if obj["kind"] == "composite": + wfs = [_deserialize_waveform(wf, vars) for wf in obj["waveforms"]] + return CompositeWaveform(*wfs) + if obj["kind"] == "custom": + return CustomWaveform( + samples=_deserialize_parameter(obj["samples"], vars) + ) + + raise AbstractReprError("The object does not encode a known waveform.") + + +def _deserialize_operation(seq: Sequence, op: dict, vars: dict) -> None: + if op["op"] == "target": + seq.target_index( + qubits=_deserialize_parameter(op["target"], vars), + channel=op["channel"], + ) + elif op["op"] == "align": + seq.align(*op["channels"]) + elif op["op"] == "delay": + seq.delay( + duration=_deserialize_parameter(op["time"], vars), + channel=op["channel"], + ) + elif op["op"] == "phase_shift": + seq.phase_shift_index( + _deserialize_parameter(op["phi"], vars), + *[_deserialize_parameter(t, vars) for t in op["targets"]], + ) + elif op["op"] == "pulse": + pulse = Pulse( + amplitude=_deserialize_waveform(op["amplitude"], vars), + detuning=_deserialize_waveform(op["detuning"], vars), + phase=_deserialize_parameter(op["phase"], vars), + post_phase_shift=_deserialize_parameter( + op["post_phase_shift"], vars + ), + ) + seq.add( + pulse=pulse, + channel=op["channel"], + protocol=op["protocol"], + ) + + +def deserialize_abstract_sequence(obj_str: str) -> Sequence: + """Deserialize a sequence from an abstract JSON object. + + Args: + obj_str: the JSON string representing the sequence encoded + in the abstract JSON format. + + Returns: + Sequence: The Pulser sequence. + """ + obj = json.loads(obj_str) + + # Validate the format of the data against the JSON schema. + jsonschema.validate(instance=obj, schema=schema) + + # Device + device_name = obj["device"] + device = getattr(devices, device_name) + + # Register + qubits = obj["register"] + reg = Register({q["name"]: (q["x"], q["y"]) for q in qubits}) + + seq = pulser.Sequence(reg, device) + + # Channels + for name, channel_id in obj["channels"].items(): + seq.declare_channel(name, channel_id) + + # Variables + vars = {} + for name, desc in obj["variables"].items(): + v = seq.declare_variable( + cast(str, name), + size=len(desc["value"]), + dtype=VARIABLE_TYPE_MAP[desc["type"]], + ) + vars[name] = v + + # Operations + for op in obj["operations"]: + _deserialize_operation(seq, op, vars) + + # Measurement + if obj["measurement"] is not None: + seq.measure(obj["measurement"]) + + return seq diff --git a/pulser-core/pulser/json/abstract_repr/schema.json b/pulser-core/pulser/json/abstract_repr/schema.json new file mode 100644 index 000000000..fde39413e --- /dev/null +++ b/pulser-core/pulser/json/abstract_repr/schema.json @@ -0,0 +1,725 @@ +{ + "$ref": "#/definitions/PulserSequence", + "$schema": "http://json-schema.org/draft-07/schema#", + "definitions": { + "Atom": { + "additionalProperties": false, + "properties": { + "name": { + "description": "Name of the atom.", + "type": "string" + }, + "x": { + "description": "x-position in µm", + "type": "number" + }, + "y": { + "description": "y-position in µm", + "type": "number" + } + }, + "required": [ + "name", + "x", + "y" + ], + "type": "object" + }, + "Basis": { + "enum": [ + "ground-rydberg", + "digital" + ], + "type": "string" + }, + "BlackmanMaxWaveform": { + "additionalProperties": false, + "description": "A Blackman window of a specified max value and area.", + "properties": { + "area": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values." + }, + "kind": { + "const": "blackman_max", + "type": "string" + }, + "max_val": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform peak value." + } + }, + "required": [ + "kind", + "max_val", + "area" + ], + "type": "object" + }, + "BlackmanWaveform": { + "additionalProperties": false, + "description": "A Blackman window of a specified duration and area.", + "properties": { + "area": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values." + }, + "duration": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform duration (in ns)." + }, + "kind": { + "const": "blackman", + "type": "string" + } + }, + "required": [ + "kind", + "duration", + "area" + ], + "type": "object" + }, + "ChannelName": { + "description": "Name of declared channel.", + "type": "string" + }, + "CompositeWaveform": { + "additionalProperties": false, + "properties": { + "kind": { + "const": "composite", + "type": "string" + }, + "waveforms": { + "description": "List of waveforms to compose one after another, in specified order.", + "items": { + "$ref": "#/definitions/Waveform" + }, + "type": "array" + } + }, + "required": [ + "kind", + "waveforms" + ], + "type": "object" + }, + "ConstantWaveform": { + "additionalProperties": false, + "description": "A waveform of constant value.", + "properties": { + "duration": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform duration (in ns)." + }, + "kind": { + "const": "constant", + "type": "string" + }, + "value": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The constant modulation value (in rad/µs)." + } + }, + "required": [ + "kind", + "duration", + "value" + ], + "type": "object" + }, + "CustomWaveform": { + "additionalProperties": false, + "properties": { + "kind": { + "const": "custom", + "type": "string" + }, + "samples": { + "description": "List of waveform value samples, one per timestep.", + "items": { + "type": "number" + }, + "type": "array" + } + }, + "required": [ + "kind", + "samples" + ], + "type": "object" + }, + "ExprArgument": { + "anyOf": [ + { + "type": "number" + }, + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "$ref": "#/definitions/VariableRef" + }, + { + "$ref": "#/definitions/ExprBinary" + }, + { + "$ref": "#/definitions/ExprUnary" + } + ], + "description": "Expression argument" + }, + "ExprBinary": { + "additionalProperties": false, + "description": "Simple binary expression involving variables and constants.\n\nThe array access behaviour depends on expression:\n- index: - the lhs array is indexed using rhs indices, resulting in an array of the same length as rhs. - out of bounds indexing is a runtime error - NOTE: Pulser only supports variable references on lhs of index expression. This limitation might be lifted in the future.\n- everything else: - the expression is applied element-wise - operating on arrays of different lengths is a runtime error", + "properties": { + "expression": { + "description": "Expresion operation", + "enum": [ + "add", + "sub", + "mul", + "div", + "mod", + "pow", + "index" + ], + "type": "string" + }, + "lhs": { + "$ref": "#/definitions/ExprArgument", + "description": "Left-hand side of an operation" + }, + "rhs": { + "$ref": "#/definitions/ExprArgument", + "description": "Right-hand side of an operation" + } + }, + "required": [ + "expression", + "lhs", + "rhs" + ], + "type": "object" + }, + "ExprUnary": { + "additionalProperties": false, + "description": "Simple arithmetic binary expression involving variables and constants.", + "properties": { + "expression": { + "description": "Expresion operation", + "enum": [ + "neg", + "abs", + "floor", + "ceil", + "round", + "sqrt", + "exp", + "log2", + "log", + "sin", + "cos", + "tan" + ], + "type": "string" + }, + "lhs": { + "$ref": "#/definitions/ExprArgument", + "description": "Argument of an unary operation" + } + }, + "required": [ + "expression", + "lhs" + ], + "type": "object" + }, + "Expression": { + "anyOf": [ + { + "$ref": "#/definitions/ExprBinary" + }, + { + "$ref": "#/definitions/ExprUnary" + } + ], + "description": "Mathematical expression involving variables and constants.\n\nThe expression is evaluated in the context of any parametrizable field.\n\nIf the context requires an integer value, the float result is rounded at the end. If the expression type differs from expected by the context (e.g. channel_name), it is a runtime error. If an expression result array length differs from expected, a it is a runtime error." + }, + "HardwareChannel": { + "description": "Hardware channel name.", + "enum": [ + "raman_local", + "rydberg_local", + "rydberg_global" + ], + "type": "string" + }, + "InterpolatedWaveform": { + "additionalProperties": false, + "description": "Creates a waveform from interpolation of a set of data points. Uses pchip interpolation algorithm.", + "properties": { + "duration": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform duration (in ns)." + }, + "kind": { + "const": "interpolated", + "type": "string" + }, + "times": { + "$ref": "#/definitions/ParametrizedNumArray", + "description": "Fractions of the total duration (between 0 and 1), indicating where to place each value on the time axis. The array size must be the same as `values` array size." + }, + "values": { + "$ref": "#/definitions/ParametrizedNumArray", + "description": "Values of the interpolation points (in rad/µs)." + } + }, + "required": [ + "kind", + "duration", + "values", + "times" + ], + "type": "object" + }, + "KaiserMaxWaveform": { + "additionalProperties": false, + "description": "A Kaiser window of a specified max value, area and beta parameter.", + "properties": { + "area": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values." + }, + "beta": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The beta parameter of the Kaiser window. A typical value is 14." + }, + "kind": { + "const": "kaiser_max", + "type": "string" + }, + "max_val": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform peak value." + } + }, + "required": [ + "kind", + "max_val", + "area", + "beta" + ], + "type": "object" + }, + "KaiserWaveform": { + "additionalProperties": false, + "description": "A Kaiser window of a specified duration, area and beta parameter.", + "properties": { + "area": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The integral of the waveform. Can be negative, in which case it takes the positive waveform and changes the sign of all its values." + }, + "beta": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The beta parameter of the Kaiser window. A typical value is 14." + }, + "duration": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform duration (in ns)." + }, + "kind": { + "const": "kaiser", + "type": "string" + } + }, + "required": [ + "kind", + "duration", + "area", + "beta" + ], + "type": "object" + }, + "OpAlign": { + "additionalProperties": false, + "description": "Aligns multiple channels in time.\n\nIntroduces delays that align the provided channels with the one that finished the latest, such that the next action added to any of them will start right after the latest channel has finished.", + "properties": { + "channels": { + "items": { + "$ref": "#/definitions/ChannelName" + }, + "type": "array" + }, + "op": { + "const": "align", + "type": "string" + } + }, + "required": [ + "op", + "channels" + ], + "type": "object" + }, + "OpDelay": { + "additionalProperties": false, + "description": "Adds extra fixed delay before starting the pulse.", + "properties": { + "channel": { + "$ref": "#/definitions/ChannelName", + "description": "Channel on which to insert a delay" + }, + "op": { + "const": "delay", + "type": "string" + }, + "time": { + "$ref": "#/definitions/ParametrizedNum", + "description": "Delay time" + } + }, + "required": [ + "op", + "channel", + "time" + ], + "type": "object" + }, + "OpPhaseShift": { + "additionalProperties": false, + "description": "Adds a separate phase shift to atoms. If possible, OpPulse phase and post_phase_shift are preferred.", + "properties": { + "basis": { + "$ref": "#/definitions/Basis", + "description": "Phase shift basis" + }, + "op": { + "const": "phase_shift", + "type": "string" + }, + "phi": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The intended phase shift (in rads)." + }, + "targets": { + "description": "Target atom indices", + "items": { + "$ref": "#/definitions/ParametrizedNum" + }, + "type": "array" + } + }, + "required": [ + "op", + "basis", + "targets", + "phi" + ], + "type": "object" + }, + "OpPulse": { + "additionalProperties": false, + "description": "Pulse is a modulation of a frequency signal in amplitude and/or frequency, with a specific phase, over a given duration.\n\nNote: We define the ``amplitude`` of a pulse to be its Rabi frequency, `ω`, in rad/µs. Equivalently, the ``detuning`` is `Δ`, also in rad/µs.", + "properties": { + "amplitude": { + "$ref": "#/definitions/Waveform", + "description": "Pulse amplitude waveform (in rad/µs)" + }, + "channel": { + "$ref": "#/definitions/ChannelName", + "description": "Device channel to use for this pulse." + }, + "detuning": { + "$ref": "#/definitions/Waveform", + "description": "Shift in frequency from the channel's central frequency over time" + }, + "op": { + "const": "pulse", + "type": "string" + }, + "phase": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The pulse phase (in radians)" + }, + "post_phase_shift": { + "$ref": "#/definitions/ParametrizedNum", + "description": "A phase shift (in radians) immediately after the end of the pulse" + }, + "protocol": { + "description": "Stipulates how to deal with eventual conflicts with other channels, specifically in terms of having multiple channels act on the same target simultaneously.\n\n- ``'min-delay'``: Before adding the pulse, introduces the smallest possible delay that avoids all exisiting conflicts.\n\n- ``'no-delay'``: Adds the pulse to the channel, regardless of existing conflicts.\n\n- ``'wait-for-all'``: Before adding the pulse, adds a delay that idles the channel until the end of the other channels' latest pulse.", + "enum": [ + "min-delay", + "no-delay", + "wait-for-all" + ], + "type": "string" + } + }, + "required": [ + "op", + "protocol", + "channel", + "amplitude", + "detuning", + "phase", + "post_phase_shift" + ], + "type": "object" + }, + "OpTarget": { + "additionalProperties": false, + "description": "Adds a waveform to the pulse.", + "properties": { + "channel": { + "$ref": "#/definitions/ChannelName", + "description": "Channel to retarget. Must be local" + }, + "op": { + "const": "target", + "type": "string" + }, + "target": { + "$ref": "#/definitions/ParametrizedNum", + "description": "New target atom index" + } + }, + "required": [ + "op", + "channel", + "target" + ], + "type": "object" + }, + "Operation": { + "anyOf": [ + { + "$ref": "#/definitions/OpAlign" + }, + { + "$ref": "#/definitions/OpDelay" + }, + { + "$ref": "#/definitions/OpTarget" + }, + { + "$ref": "#/definitions/OpPulse" + }, + { + "$ref": "#/definitions/OpPhaseShift" + } + ], + "description": "Sequence operation. All operations are performed in specified order." + }, + "ParametrizedNum": { + "anyOf": [ + { + "type": "number" + }, + { + "$ref": "#/definitions/Expression" + } + ], + "description": "Numeric scalar value that can be parametrized" + }, + "ParametrizedNumArray": { + "anyOf": [ + { + "items": { + "type": "number" + }, + "type": "array" + }, + { + "$ref": "#/definitions/Expression" + }, + { + "$ref": "#/definitions/VariableRef" + } + ], + "description": "Numeric array value that can be parametrized" + }, + "PulserSequence": { + "additionalProperties": false, + "description": "Pulser import/export data structure.", + "properties": { + "$schema": { + "type": "string" + }, + "channels": { + "additionalProperties": { + "$ref": "#/definitions/HardwareChannel" + }, + "description": "Channels declared in this Sequence.", + "type": "object" + }, + "device": { + "const": "Chadoq2", + "description": "A valid device in which to execute the Sequence", + "type": "string" + }, + "measurement": { + "anyOf": [ + { + "$ref": "#/definitions/Basis" + }, + { + "type": "null" + } + ], + "description": "Type of measurement to perform after all pulses are executed" + }, + "name": { + "description": "User-assigned sequence name. Can be autogenerated on export if not provided.", + "type": "string" + }, + "operations": { + "description": "Sequence of pulses, delays and target changes, performed in specified order.", + "items": { + "$ref": "#/definitions/Operation" + }, + "type": "array" + }, + "register": { + "description": "A 2D quantum register containing a set of atoms.", + "items": { + "$ref": "#/definitions/Atom" + }, + "type": "array" + }, + "variables": { + "additionalProperties": { + "$ref": "#/definitions/Variable" + }, + "description": "Variables and expressions that can be used in expressions or parametrized values.", + "type": "object" + }, + "version": { + "const": "1", + "type": "string" + } + }, + "required": [ + "version", + "device", + "name", + "register", + "channels", + "variables", + "operations", + "measurement" + ], + "type": "object" + }, + "RampWaveform": { + "additionalProperties": false, + "description": "A linear ramp waveform.", + "properties": { + "duration": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The waveform duration (in ns)." + }, + "kind": { + "const": "ramp", + "type": "string" + }, + "start": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The initial value (in rad/µs)." + }, + "stop": { + "$ref": "#/definitions/ParametrizedNum", + "description": "The final value (in rad/µs)." + } + }, + "required": [ + "kind", + "duration", + "start", + "stop" + ], + "type": "object" + }, + "Variable": { + "additionalProperties": false, + "description": "Variable representing a typed value assigned during sequence build. variables can be used in expressions and parametrized values.", + "properties": { + "type": { + "description": "Variable type", + "enum": [ + "int", + "float" + ], + "type": "string" + }, + "value": { + "description": "Default variable value. The default array length determins the variable array size.", + "items": { + "type": "number" + }, + "type": "array" + } + }, + "required": [ + "type", + "value" + ], + "type": "object" + }, + "VariableName": { + "description": "Name of declared variable.", + "type": "string" + }, + "VariableRef": { + "additionalProperties": false, + "description": "References a declared variable by name.", + "properties": { + "variable": { + "$ref": "#/definitions/VariableName", + "description": "variable name, must reference declared variable" + } + }, + "required": [ + "variable" + ], + "type": "object" + }, + "Waveform": { + "anyOf": [ + { + "$ref": "#/definitions/CompositeWaveform" + }, + { + "$ref": "#/definitions/CustomWaveform" + }, + { + "$ref": "#/definitions/ConstantWaveform" + }, + { + "$ref": "#/definitions/RampWaveform" + }, + { + "$ref": "#/definitions/BlackmanWaveform" + }, + { + "$ref": "#/definitions/BlackmanMaxWaveform" + }, + { + "$ref": "#/definitions/InterpolatedWaveform" + }, + { + "$ref": "#/definitions/KaiserWaveform" + }, + { + "$ref": "#/definitions/KaiserMaxWaveform" + } + ], + "description": "Modulation waveform of any kind" + } + } +} diff --git a/pulser-core/pulser/json/abstract_repr/serializer.py b/pulser-core/pulser/json/abstract_repr/serializer.py new file mode 100644 index 000000000..bc078392d --- /dev/null +++ b/pulser-core/pulser/json/abstract_repr/serializer.py @@ -0,0 +1,233 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utility functions for JSON serialization to the abstract representation.""" +from __future__ import annotations + +import json +from itertools import chain +from typing import TYPE_CHECKING, Any +from typing import Sequence as abcSequence +from typing import Union, cast + +import numpy as np + +from pulser.json.abstract_repr.signatures import SIGNATURES +from pulser.json.exceptions import AbstractReprError +from pulser.register.base_register import QubitId + +if TYPE_CHECKING: # pragma: no cover + from pulser.sequence import Sequence + from pulser.sequence._call import _Call + + +class AbstractReprEncoder(json.JSONEncoder): + """The custom encoder for abstract representation of Pulser objects.""" + + def default(self, o: Any) -> Union[dict[str, Any], list[Any]]: + """Handles JSON encoding of objects not supported by default.""" + if hasattr(o, "_to_abstract_repr"): + return cast(dict, o._to_abstract_repr()) + elif isinstance(o, np.ndarray): + return cast(list, o.tolist()) + elif isinstance(o, set): + return list(o) + else: + return cast(dict, json.JSONEncoder.default(self, o)) + + +def abstract_repr(name: str, *args: Any, **kwargs: Any) -> dict[str, Any]: + """Generates the abstract repr of an object with a defined signature.""" + try: + signature = SIGNATURES[name] + except KeyError: + raise ValueError(f"No signature found for '{name}'.") + arg_as_kwarg: tuple[str, ...] = tuple() + if len(args) < len(signature.pos): + # If less arguments than those in the signature were given, that might + # be because they were provided with a keyword and thus stored as + # kwargs instead (unless var_pos is defined) + arg_as_kwarg = signature.pos[len(args) :] + if signature.var_pos is not None or not set(arg_as_kwarg) <= set( + kwargs + ): + raise ValueError( + f"Not enough arguments given for '{name}' (expected " + f"{len(signature.pos)}, got {len(args)})." + ) + res: dict[str, Any] = {} + res.update(signature.extra) # Starts with extra info ({} if undefined) + # With PulseSignature.all_pos_args(), we safeguard against the opposite + # case where an expected keyword argument is given as a positional argument + res.update( + { + arg_name: arg_val + for arg_name, arg_val in zip(signature.all_pos_args(), args) + } + ) + # Account for keyword arguments given as pos args + max_pos_args = len(signature.pos) + len( + set(signature.keyword) - set(kwargs) + ) + if signature.var_pos: + res[signature.var_pos] = args[len(signature.pos) :] + elif len(args) > max_pos_args: + raise ValueError( + f"Too many positional arguments given for '{name}' (expected " + f"{max_pos_args}, got {len(args)})." + ) + for kw in kwargs: + if kw in signature.keyword or kw in arg_as_kwarg: + res[kw] = kwargs[kw] + else: + raise ValueError( + f"Keyword argument '{kw}' is not in the signature of '{name}'." + ) + return res + + +def serialize_abstract_sequence( + seq: Sequence, seq_name: str = "pulser-exported", **defaults: Any +) -> str: + """Serializes the Sequence into an abstract JSON object. + + Keyword Args: + seq_name: A name for the sequence. If not defined, defaults + to "pulser-exported". + defaults: The default values for all the variables declared in this + Sequence instance, indexed by the name given upon declaration. + Check ``Sequence.declared_variables`` to see all the variables. + + Returns: + str: The sequence encoded as an abstract JSON object. + """ + res: dict[str, Any] = { + "version": "1", + "name": seq_name, + "register": {}, + "channels": {}, + "variables": {}, + "operations": [], + "measurement": None, + } + + seq._cross_check_vars(defaults) + try: + seq.build(**defaults) + except Exception: + raise ValueError("The given 'defaults' produce an invalid sequence.") + + for var in seq._variables.values(): + value = var._validate_value(defaults[var.name]) + res["variables"][var.name] = dict( + type=var.dtype.__name__, value=value.tolist() + ) + + def convert_targets( + target_ids: Union[QubitId, abcSequence[QubitId]] + ) -> Union[int, list[int]]: + target_array = np.array(target_ids) + og_dim = target_array.ndim + if og_dim == 0: + target_array = target_array[np.newaxis] + indices = seq.register.find_indices(target_array.tolist()) + return indices[0] if og_dim == 0 else indices + + def get_all_args( + pos_args_signature: tuple[str, ...], call: _Call + ) -> dict[str, Any]: + return {**dict(zip(pos_args_signature, call.args)), **call.kwargs} + + operations = res["operations"] + for call in chain(seq._calls, seq._to_build_calls): + if call.name == "__init__": + data = get_all_args(("register", "device"), call) + res["device"] = data["device"].name + res["register"] = data["register"] + elif call.name == "declare_channel": + data = get_all_args( + ("channel", "channel_id", "initial_target"), call + ) + res["channels"][data["channel"]] = data["channel_id"] + if "initial_target" in data and data["initial_target"] is not None: + operations.append( + { + "op": "target", + "channel": data["channel"], + "target": convert_targets(data["initial_target"]), + } + ) + elif "target" in call.name: + data = get_all_args(("qubits", "channel"), call) + if call.name == "target": + target = convert_targets(data["qubits"]) + elif call.name == "target_index": + target = data["qubits"] + else: + raise AbstractReprError(f"Unknown call '{call.name}'.") + operations.append( + { + "op": "target", + "channel": data["channel"], + "target": target, + } + ) + elif call.name == "align": + operations.append({"op": "align", "channels": list(call.args)}) + elif call.name == "delay": + data = get_all_args(("duration", "channel"), call) + operations.append( + { + "op": "delay", + "channel": data["channel"], + "time": data["duration"], + } + ) + elif call.name == "measure": + data = get_all_args(("basis",), call) + res["measurement"] = data["basis"] + elif call.name == "add": + data = get_all_args(("pulse", "channel", "protocol"), call) + op_dict = { + "op": "pulse", + "channel": data["channel"], + "protocol": "min-delay" + if "protocol" not in data + else data["protocol"], + } + op_dict.update(data["pulse"]._to_abstract_repr()) + operations.append(op_dict) + elif "phase_shift" in call.name: + try: + basis = call.kwargs["basis"] + except KeyError: + basis = "digital" + targets = call.args[1:] + if call.name == "phase_shift": + targets = convert_targets(targets) + elif call.name == "phase_shift_index": + pass + else: + raise AbstractReprError(f"Unknown call '{call.name}'.") + operations.append( + { + "op": "phase_shift", + "phi": call.args[0], + "targets": targets, + "basis": basis, + } + ) + else: + raise AbstractReprError(f"Unknown call '{call.name}'.") + + return json.dumps(res, cls=AbstractReprEncoder) diff --git a/pulser-core/pulser/json/abstract_repr/signatures.py b/pulser-core/pulser/json/abstract_repr/signatures.py new file mode 100644 index 000000000..0708fb63e --- /dev/null +++ b/pulser-core/pulser/json/abstract_repr/signatures.py @@ -0,0 +1,118 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Defines the signatures of objects for the abstract representation.""" +from __future__ import annotations + +import operator +from collections.abc import Callable +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Optional + +import numpy as np + +if TYPE_CHECKING: # pragma: no cover + from pulser.parametrized.variable import Variable, VariableItem + + +@dataclass +class PulserSignature: + """The signature of a Pulser object.""" + + pos: tuple[str, ...] = field(default_factory=tuple) + var_pos: Optional[str] = None + keyword: tuple[str, ...] = field(default_factory=tuple) + extra: dict[str, str] = field(default_factory=dict) + + def all_pos_args(self) -> tuple[str, ...]: + """All potential positional arguments. + + Includes the keyword args if var_pos is None. + """ + if self.var_pos is not None: + return self.pos + return (*self.pos, *self.keyword) + + +SIGNATURES: dict[str, PulserSignature] = { + # Waveforms + "CompositeWaveform": PulserSignature( + var_pos="waveforms", extra=dict(kind="composite") + ), + "CustomWaveform": PulserSignature( + pos=("samples",), extra=dict(kind="custom") + ), + "ConstantWaveform": PulserSignature( + pos=("duration", "value"), extra=dict(kind="constant") + ), + "RampWaveform": PulserSignature( + pos=("duration", "start", "stop"), extra=dict(kind="ramp") + ), + "BlackmanWaveform": PulserSignature( + pos=("duration", "area"), extra=dict(kind="blackman") + ), + "BlackmanWaveform.from_max_val": PulserSignature( + pos=("max_val", "area"), extra=dict(kind="blackman_max") + ), + "InterpolatedWaveform": PulserSignature( + pos=("duration", "values"), + keyword=("times",), + extra=dict(kind="interpolated"), + ), + "KaiserWaveform": PulserSignature( + pos=("duration", "area"), keyword=("beta",), extra=dict(kind="kaiser") + ), + "KaiserWaveform.from_max_val": PulserSignature( + pos=("max_val", "area"), + keyword=("beta",), + extra=dict(kind="kaiser_max"), + ), + # Pulse + "Pulse": PulserSignature( + pos=("amplitude", "detuning", "phase"), keyword=("post_phase_shift",) + ), + # Special case operators + "truediv": PulserSignature( + pos=("lhs", "rhs"), extra=dict(expression="div") + ), + "round_": PulserSignature(pos=("lhs",), extra=dict(expression="round")), +} + + +def _index_var(lhs: Variable, rhs: int) -> VariableItem: + return lhs[rhs] + + +BINARY_OPERATORS: dict[str, Callable] = { + "add": operator.add, + "sub": operator.sub, + "mul": operator.mul, + "truediv": operator.truediv, + "pow": operator.pow, + "mod": operator.mod, + "index": _index_var, +} + +UNARY_OPERATORS: dict[str, Callable] = { + "neg": operator.neg, + "abs": operator.abs, + "ceil": np.ceil, + "floor": np.floor, + "sqrt": np.sqrt, + "exp": np.exp, + "log2": np.log2, + "log": np.log, + "sin": np.sin, + "cos": np.cos, + "tan": np.tan, +} diff --git a/pulser-core/pulser/json/exceptions.py b/pulser-core/pulser/json/exceptions.py new file mode 100644 index 000000000..fb1a718f2 --- /dev/null +++ b/pulser-core/pulser/json/exceptions.py @@ -0,0 +1,30 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Custom exceptions for serialization errors.""" + + +class SerializationError(Exception): + """Exception raised when sequence serialization fails.""" + + pass + + +class AbstractReprError(Exception): + """Exception raised for abstract representation errors. + + Raised when an error occurs during the serialization to or deserialization + from the abstract representation. + """ + + pass diff --git a/pulser-core/pulser/json/supported.py b/pulser-core/pulser/json/supported.py index ac4d1fb2e..611a73053 100644 --- a/pulser-core/pulser/json/supported.py +++ b/pulser-core/pulser/json/supported.py @@ -17,7 +17,8 @@ from typing import Any, Mapping -import pulser +import pulser.devices as devices +from pulser.json.exceptions import SerializationError SUPPORTED_BUILTINS = ("float", "int", "str", "set") @@ -63,7 +64,7 @@ ), "pulser.register.mappable_reg": ("MappableRegister",), "pulser.devices": tuple( - [dev.name for dev in pulser.devices._valid_devices] + ["MockDevice"] + [dev.name for dev in devices._valid_devices] + ["MockDevice"] ), "pulser.pulse": ("Pulse",), "pulser.waveforms": ( @@ -81,12 +82,6 @@ } -class SerializationError(Exception): - """Exception raised when sequence serialization fails.""" - - pass - - def validate_serialization(obj_dict: Mapping[str, Any]) -> None: """Checks if 'obj_dict' can be serialized.""" try: diff --git a/pulser-core/pulser/parametrized/paramobj.py b/pulser-core/pulser/parametrized/paramobj.py index 0d9335eb6..53a2c7743 100644 --- a/pulser-core/pulser/parametrized/paramobj.py +++ b/pulser-core/pulser/parametrized/paramobj.py @@ -24,6 +24,13 @@ import numpy as np +from pulser.json.abstract_repr.serializer import abstract_repr +from pulser.json.abstract_repr.signatures import ( + BINARY_OPERATORS, + SIGNATURES, + UNARY_OPERATORS, +) +from pulser.json.exceptions import AbstractReprError from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized @@ -34,6 +41,7 @@ class OpSupport: """Methods for supporting operators on parametrized objects.""" + # TODO: Make operator methods' args pos-only when python 3.7 is dropped # Unary operators def __neg__(self) -> ParamObj: return ParamObj(operator.neg, self) @@ -216,6 +224,69 @@ def class_to_dict(cls: Callable) -> dict[str, Any]: return obj_to_dict(self, cls_dict, *args, **self.kwargs) + def _to_abstract_repr(self) -> dict[str, Any]: + op_name = self.cls.__name__ + if isinstance(self.cls, Parametrized): + raise ValueError( + "Serialization of calls to parametrized objects is not " + "supported." + ) + elif ( + self.args # If it is a classmethod the first arg will be the class + and hasattr(self.args[0], op_name) + and inspect.isfunction(self.cls) + ): + # Check for parametrized methods + if inspect.isclass(self.args[0]): + # classmethod + cls_name = self.args[0].__name__ + name = f"{cls_name}.{op_name}" + if cls_name == "Pulse": + signature = ( + "amplitude", + "detuning", + "phase", + "post_phase_shift", + ) + all_args = { + **dict(zip(signature, self.args[1:])), + **self.kwargs, + } + if "post_phase_shift" not in all_args: + all_args["post_phase_shift"] = 0.0 + if name == "Pulse.ConstantAmplitude": + all_args["amplitude"] = abstract_repr( + "ConstantWaveform", 0, all_args["amplitude"] + ) + return abstract_repr("Pulse", **all_args) + elif name == "Pulse.ConstantDetuning": + all_args["detuning"] = abstract_repr( + "ConstantWaveform", 0, all_args["detuning"] + ) + return abstract_repr("Pulse", **all_args) + else: + return abstract_repr(name, *self.args[1:], **self.kwargs) + + raise NotImplementedError( + "Instance or static method serialization is not supported." + ) + elif op_name in SIGNATURES: + return abstract_repr(op_name, *self.args, **self.kwargs) + + elif op_name in UNARY_OPERATORS: + return dict(expression=op_name, lhs=self.args[0]) + + elif op_name in BINARY_OPERATORS: + return dict( + expression=op_name, + lhs=self.args[0], + rhs=self.args[1], + ) + else: + raise AbstractReprError( + f"No abstract representation for '{op_name}'." + ) + def __call__(self, *args: Any, **kwargs: Any) -> ParamObj: """Returns a new ParamObj storing a call to the current ParamObj.""" obj = ParamObj(self, *args, **kwargs) @@ -246,7 +317,8 @@ def __str__(self) -> str: if isinstance(self.cls, Parametrized): name = str(self.cls) elif ( - hasattr(self.args[0], self.cls.__name__) + self.args + and hasattr(self.args[0], self.cls.__name__) and inspect.isfunction(self.cls) and inspect.isclass(self.args[0]) ): @@ -255,3 +327,11 @@ def __str__(self) -> str: else: name = self.cls.__name__ return f"{name}({', '.join(args+kwargs)})" + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, ParamObj): + return False + return self.args == other.args and self.kwargs == other.kwargs + + def __hash__(self) -> int: + return id(self) diff --git a/pulser-core/pulser/parametrized/variable.py b/pulser-core/pulser/parametrized/variable.py index 773cf9702..971790ce7 100644 --- a/pulser-core/pulser/parametrized/variable.py +++ b/pulser-core/pulser/parametrized/variable.py @@ -66,15 +66,20 @@ def _clear(self) -> None: object.__setattr__(self, "_count", self._count + 1) def _assign(self, value: Union[ArrayLike, float, int]) -> None: + val = self._validate_value(value) + object.__setattr__(self, "value", val) + object.__setattr__(self, "_count", self._count + 1) + + def _validate_value( + self, value: Union[ArrayLike, float, int] + ) -> np.ndarray: val = np.array(value, dtype=self.dtype, ndmin=1) if val.size != self.size: raise ValueError( f"Can't assign array of size {val.size} to " + f"variable of size {self.size}." ) - - object.__setattr__(self, "value", val) - object.__setattr__(self, "_count", self._count + 1) + return val def build(self) -> ArrayLike: """Returns the variable's current value.""" @@ -88,6 +93,9 @@ def _to_dict(self) -> dict[str, Any]: d.update(dataclasses.asdict(self)) return d + def _to_abstract_repr(self) -> dict[str, str]: + return {"variable": self.name} + def __str__(self) -> str: return self.name @@ -126,6 +134,10 @@ def _to_dict(self) -> dict[str, Any]: self, self.var, self.key, _module="operator", _name="getitem" ) + def _to_abstract_repr(self) -> dict[str, Any]: + indices = list(range(self.var.size))[self.key] + return {"expression": "index", "lhs": self.var, "rhs": indices} + def __str__(self) -> str: if isinstance(self.key, slice): items = [ diff --git a/pulser-core/pulser/pulse.py b/pulser-core/pulser/pulse.py index 01f55a546..c95258707 100644 --- a/pulser-core/pulser/pulse.py +++ b/pulser-core/pulser/pulse.py @@ -24,6 +24,7 @@ import numpy as np from pulser.channels import Channel +from pulser.json.abstract_repr.serializer import abstract_repr from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize @@ -154,7 +155,6 @@ def ConstantAmplitude( return cls(amplitude_wf, detuning, phase, post_phase_shift) @classmethod - @parametrize def ConstantPulse( cls, duration: Union[int, Parametrized], @@ -166,7 +166,7 @@ def ConstantPulse( """Pulse with a constant amplitude and a constant detuning. Args: - duration: The pulse duration (in multiples of 4 ns). + duration: The pulse duration (in ns). amplitude: The pulse amplitude value (in rad/µs). detuning: The detuning value (in rad/µs). phase: The pulse phase (in radians). @@ -206,6 +206,15 @@ def _to_dict(self) -> dict[str, Any]: post_phase_shift=self.post_phase_shift, ) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr( + "Pulse", + self.amplitude, + self.detuning, + self.phase, + post_phase_shift=self.post_phase_shift, + ) + def __str__(self) -> str: return ( f"Pulse(Amp={self.amplitude!s}, Detuning={self.detuning!s}, " diff --git a/pulser-core/pulser/register/base_register.py b/pulser-core/pulser/register/base_register.py index 4a3420083..127618919 100644 --- a/pulser-core/pulser/register/base_register.py +++ b/pulser-core/pulser/register/base_register.py @@ -121,8 +121,8 @@ def find_indices(self, id_list: abcSequence[QubitId]) -> list[int]: """ if not set(id_list) <= set(self.qubit_ids): raise ValueError( - "The IDs list must be selected among" - "the IDs of the register's qubits." + "The IDs list must be selected among the IDs of the register's" + " qubits." ) return [self.qubit_ids.index(id_) for id_ in id_list] diff --git a/pulser-core/pulser/register/register.py b/pulser-core/pulser/register/register.py index 159ecb43b..e1947a265 100644 --- a/pulser-core/pulser/register/register.py +++ b/pulser-core/pulser/register/register.py @@ -15,8 +15,9 @@ from __future__ import annotations +import warnings from collections.abc import Mapping -from typing import Any, Optional +from typing import Any, Optional, Union import matplotlib.pyplot as plt import numpy as np @@ -24,8 +25,9 @@ import pulser import pulser.register._patterns as patterns +from pulser.json.exceptions import AbstractReprError from pulser.register._reg_drawer import RegDrawer -from pulser.register.base_register import BaseRegister +from pulser.register.base_register import BaseRegister, QubitId class Register(BaseRegister, RegDrawer): @@ -346,3 +348,23 @@ def draw( def _to_dict(self) -> dict[str, Any]: return super()._to_dict() + + def _to_abstract_repr(self) -> list[dict[str, Union[QubitId, float]]]: + not_str = [id for id in self._ids if not isinstance(id, str)] + names = [str(id) for id in self._ids] + if not_str: + warnings.warn( + "Register serialization to an abstract representation " + "irreversibly converts all qubit ID's to strings.", + stacklevel=7, + ) + if len(set(names)) < len(names): + collisions = [id for id in not_str if str(id) in self._ids] + raise AbstractReprError( + "Name collisions encountered when converting qubit IDs to " + f"strings for IDs: {[(id, str(id)) for id in collisions]}" + ) + return [ + {"name": name, "x": x, "y": y} + for name, (x, y) in zip(names, self._coords) + ] diff --git a/pulser-core/pulser/sequence/sequence.py b/pulser-core/pulser/sequence/sequence.py index 7b9e49507..3e5123209 100644 --- a/pulser-core/pulser/sequence/sequence.py +++ b/pulser-core/pulser/sequence/sequence.py @@ -32,6 +32,10 @@ from pulser.channels import Channel from pulser.devices import MockDevice from pulser.devices._device_datacls import Device +from pulser.json.abstract_repr.deserializer import ( + deserialize_abstract_sequence, +) +from pulser.json.abstract_repr.serializer import serialize_abstract_sequence from pulser.json.coders import PulserDecoder, PulserEncoder from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, Variable @@ -512,11 +516,10 @@ def declare_variable( To avoid confusion, it is recommended to store the returned Variable instance in a Python variable with the same name. """ - if name == "qubits": - # Necessary because 'qubits' is a keyword arg in self.build() + if name in ("qubits", "seq_name"): raise ValueError( - "'qubits' is a protected name. Please choose a different name " - "for the variable." + f"'{name}' is a protected name. Please choose a different name" + " for the variable." ) if name in self._variables: @@ -658,7 +661,7 @@ def delay( """Idles a given channel for a specific duration. Args: - duration: Time to delay (in multiples of 4 ns). + duration: Time to delay (in ns). channel: The channel's name provided when declared. """ self._delay(duration, channel) @@ -839,22 +842,7 @@ def build( "a concrete register." ) - all_keys, given_keys = self._variables.keys(), vars.keys() - if given_keys != all_keys: - invalid_vars = given_keys - all_keys - if invalid_vars: - warnings.warn( - "No declared variables named: " + ", ".join(invalid_vars), - stacklevel=2, - ) - for k in invalid_vars: - vars.pop(k, None) - missing_vars = all_keys - given_keys - if missing_vars: - raise TypeError( - "Did not receive values for variables: " - + ", ".join(missing_vars) - ) + self._cross_check_vars(vars) if not self.is_parametrized(): if not self.is_register_mappable(): @@ -902,6 +890,26 @@ def serialize(self, **kwargs: Any) -> str: """ return json.dumps(self, cls=PulserEncoder, **kwargs) + def to_abstract_repr( + self, seq_name: str = "pulser-exported", **defaults: Any + ) -> str: + """Serializes the Sequence into an abstract JSON object. + + Keyword Args: + seq_name (str): A name for the sequence. If not defined, defaults + to "pulser-exported". + defaults: The default values for all the variables declared in this + Sequence instance, indexed by the name given upon declaration. + Check ``Sequence.declared_variables`` to see all the variables. + + Returns: + str: The sequence encoded as an abstract JSON object. + + See Also: + ``serialize`` + """ + return serialize_abstract_sequence(self, seq_name, **defaults) + @staticmethod def deserialize(obj: str, **kwargs: Any) -> Sequence: """Deserializes a JSON formatted string. @@ -929,6 +937,19 @@ def deserialize(obj: str, **kwargs: Any) -> Sequence: return cast(Sequence, json.loads(obj, cls=PulserDecoder, **kwargs)) + @staticmethod + def from_abstract_repr(obj_str: str) -> Sequence: + """Deserialize a sequence from an abstract JSON object. + + Args: + obj_str (str): the JSON string representing the sequence encoded + in the abstract JSON format. + + Returns: + Sequence: The Pulser sequence. + """ + return deserialize_abstract_sequence(obj_str) + @seq_decorators.screen def draw( self, @@ -1203,3 +1224,22 @@ def _set_register(self, seq: Sequence, reg: BaseRegister) -> None: seq._register = reg seq._qids = qids seq._calls[0] = _Call("__init__", (seq._register, seq._device), {}) + + def _cross_check_vars(self, vars: dict[str, Any]) -> None: + """Checks if values are given to all and only declared vars.""" + all_keys, given_keys = self._variables.keys(), vars.keys() + if given_keys != all_keys: + invalid_vars = given_keys - all_keys + if invalid_vars: + warnings.warn( + "No declared variables named: " + ", ".join(invalid_vars), + stacklevel=3, + ) + for k in invalid_vars: + vars.pop(k, None) + missing_vars = all_keys - given_keys + if missing_vars: + raise TypeError( + "Did not receive values for variables: " + + ", ".join(missing_vars) + ) diff --git a/pulser-core/pulser/waveforms.py b/pulser-core/pulser/waveforms.py index cba1c4fe3..2bdefbbbf 100644 --- a/pulser-core/pulser/waveforms.py +++ b/pulser-core/pulser/waveforms.py @@ -32,6 +32,8 @@ from numpy.typing import ArrayLike from pulser.channels import Channel +from pulser.json.abstract_repr.serializer import abstract_repr +from pulser.json.exceptions import AbstractReprError from pulser.json.utils import obj_to_dict from pulser.parametrized import Parametrized, ParamObj from pulser.parametrized.decorators import parametrize @@ -213,6 +215,10 @@ def _modulated_samples(self, channel: Channel) -> np.ndarray: def _to_dict(self) -> dict[str, Any]: pass + @abstractmethod + def _to_abstract_repr(self) -> dict[str, Any]: + pass + @abstractmethod def __str__(self) -> str: pass @@ -388,6 +394,9 @@ def _validate(self, waveform: Waveform) -> None: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, *self._waveforms) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr("CompositeWaveform", *self._waveforms) + def __str__(self) -> str: contents_list = ["{!r}"] * len(self._waveforms) contents = ", ".join(contents_list) @@ -433,6 +442,9 @@ def _samples(self) -> np.ndarray: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._samples) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr("CustomWaveform", self._samples) + def __str__(self) -> str: return "Custom" @@ -489,6 +501,9 @@ def change_duration(self, new_duration: int) -> ConstantWaveform: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._value) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr("ConstantWaveform", self._duration, self._value) + def __str__(self) -> str: return f"{self._value:.3g} rad/µs" @@ -557,6 +572,11 @@ def change_duration(self, new_duration: int) -> RampWaveform: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._start, self._stop) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr( + "RampWaveform", self._duration, self._start, self._stop + ) + def __str__(self) -> str: return f"Ramp({self._start:.3g}->{self._stop:.3g} rad/µs)" @@ -688,6 +708,9 @@ def change_duration(self, new_duration: int) -> BlackmanWaveform: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._area) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr("BlackmanWaveform", self._duration, self._area) + def __str__(self) -> str: return f"Blackman(Area: {self._area:.3g})" @@ -839,6 +862,21 @@ def _plot( def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._values, **self._kwargs) + def _to_abstract_repr(self) -> dict[str, Any]: + if self._kwargs["interpolator"] != "PchipInterpolator" or set( + self._kwargs + ) - {"times", "interpolator"}: + raise AbstractReprError( + "Export of an InterpolatedWaveform is only supported for the " + "'PchipInterpolator' and without any 'interpolator_kwargs'." + ) + return abstract_repr( + "InterpolatedWaveform", + self._duration, + self._values, + times=self._times, + ) + def __str__(self) -> str: coords = [f"({int(x)}, {y:.4g})" for x, y in self.data_points] return f"InterpolatedWaveform(Points: {', '.join(coords)})" @@ -1026,6 +1064,11 @@ def change_duration(self, new_duration: int) -> KaiserWaveform: def _to_dict(self) -> dict[str, Any]: return obj_to_dict(self, self._duration, self._area, self._beta) + def _to_abstract_repr(self) -> dict[str, Any]: + return abstract_repr( + "KaiserWaveform", self._duration, self._area, beta=self._beta + ) + def __str__(self) -> str: return ( f"Kaiser({self._duration} ns, " diff --git a/pulser-core/setup.py b/pulser-core/setup.py index 3f68c5fd2..b4791e1e8 100644 --- a/pulser-core/setup.py +++ b/pulser-core/setup.py @@ -43,6 +43,7 @@ "matplotlib", "numpy>=1.20", "scipy", + "jsonschema==4.4.0", ], extras_require={ ":python_version == '3.7'": [ diff --git a/requirements.txt b/requirements.txt index 0ba7b5d10..2b5a0746e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,6 +16,7 @@ isort mypy == 0.921 pytest pytest-cov +jsonschema # CI pre-commit diff --git a/tests/test_abstract_repr.py b/tests/test_abstract_repr.py new file mode 100644 index 000000000..e68444ed1 --- /dev/null +++ b/tests/test_abstract_repr.py @@ -0,0 +1,1077 @@ +# Copyright 2020 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import json +from collections.abc import Callable +from typing import Any +from unittest.mock import patch + +import jsonschema +import numpy as np +import pytest + +from pulser import Pulse, Register, Register3D, Sequence +from pulser.devices import Chadoq2, MockDevice +from pulser.json.abstract_repr.deserializer import VARIABLE_TYPE_MAP +from pulser.json.abstract_repr.serializer import ( + AbstractReprEncoder, + abstract_repr, +) +from pulser.json.exceptions import AbstractReprError +from pulser.parametrized.decorators import parametrize +from pulser.parametrized.paramobj import ParamObj +from pulser.parametrized.variable import VariableItem +from pulser.sequence._call import _Call +from pulser.waveforms import ( + BlackmanWaveform, + CompositeWaveform, + ConstantWaveform, + CustomWaveform, + InterpolatedWaveform, + KaiserWaveform, + RampWaveform, + Waveform, +) + +SPECIAL_WFS: dict[str, tuple[Callable, tuple[str, ...]]] = { + "kaiser_max": (KaiserWaveform.from_max_val, ("max_val", "area", "beta")), + "blackman_max": (BlackmanWaveform.from_max_val, ("max_val", "area")), +} + + +class TestSerialization: + @pytest.fixture + def sequence(self): + qubits = {"control": (-2, 0), "target": (2, 0)} + reg = Register(qubits) + + seq = Sequence(reg, Chadoq2) + seq.declare_channel("digital", "raman_local", initial_target="control") + seq.declare_channel( + "rydberg", "rydberg_local", initial_target="control" + ) + + target_atom = seq.declare_variable("target_atom", dtype=int) + duration = seq.declare_variable("duration", dtype=int) + amps = seq.declare_variable("amps", dtype=float, size=2) + + half_pi_wf = BlackmanWaveform(200, np.pi / 2) + + ry = Pulse.ConstantDetuning( + amplitude=half_pi_wf, detuning=0, phase=-np.pi / 2 + ) + + seq.add(ry, "digital") + seq.target_index(target_atom, "digital") + seq.phase_shift_index(-1.0, target_atom) + + pi_2_wf = BlackmanWaveform(duration, amps[0] / 2) + pi_pulse = Pulse.ConstantDetuning( + CompositeWaveform(pi_2_wf, pi_2_wf), 0, 0 + ) + + max_val = Chadoq2.rabi_from_blockade(8) + two_pi_wf = BlackmanWaveform.from_max_val(max_val, amps[1]) + two_pi_pulse = Pulse.ConstantDetuning(two_pi_wf, 0, 0) + + seq.align("digital", "rydberg") + seq.add(pi_pulse, "rydberg") + seq.phase_shift(1.0, "control", "target", basis="ground-rydberg") + seq.target("target", "rydberg") + seq.add(two_pi_pulse, "rydberg") + + seq.delay(100, "digital") + seq.measure("digital") + return seq + + @pytest.fixture + def abstract(self, sequence): + return json.loads( + sequence.to_abstract_repr( + target_atom=1, + amps=[np.pi, 2 * np.pi], + duration=200, + ) + ) + + def test_schema(self, abstract): + with open("pulser-core/pulser/json/abstract_repr/schema.json") as f: + schema = json.load(f) + jsonschema.validate(instance=abstract, schema=schema) + + def test_values(self, abstract): + assert set(abstract.keys()) == set( + [ + "name", + "version", + "device", + "register", + "variables", + "channels", + "operations", + "measurement", + ] + ) + assert abstract["device"] == "Chadoq2" + assert abstract["register"] == [ + {"name": "control", "x": -2.0, "y": 0.0}, + {"name": "target", "x": 2.0, "y": 0.0}, + ] + assert abstract["channels"] == { + "digital": "raman_local", + "rydberg": "rydberg_local", + } + assert abstract["variables"] == { + "target_atom": {"type": "int", "value": [1]}, + "amps": {"type": "float", "value": [np.pi, 2 * np.pi]}, + "duration": {"type": "int", "value": [200]}, + } + assert len(abstract["operations"]) == 11 + assert abstract["operations"][0] == { + "op": "target", + "channel": "digital", + "target": 0, + } + + assert abstract["operations"][2] == { + "op": "pulse", + "channel": "digital", + "protocol": "min-delay", + "amplitude": { + "area": 1.5707963267948966, + "duration": 200, + "kind": "blackman", + }, + "detuning": { + "kind": "constant", + "duration": 200, + "value": 0.0, + }, + "phase": 4.71238898038469, + "post_phase_shift": 0.0, + } + + assert abstract["operations"][3] == { + "op": "target", + "channel": "digital", + "target": { + "expression": "index", + "lhs": {"variable": "target_atom"}, + "rhs": 0, + }, + } + + assert abstract["operations"][5] == { + "op": "align", + "channels": ["digital", "rydberg"], + } + + duration_ref = { + "expression": "index", + "lhs": {"variable": "duration"}, + "rhs": 0, + } + amp0_ref = { + "expression": "index", + "lhs": {"variable": "amps"}, + "rhs": 0, + } + blackman_wf_dict = { + "kind": "blackman", + "duration": duration_ref, + "area": {"expression": "div", "lhs": amp0_ref, "rhs": 2}, + } + composite_wf_dict = { + "kind": "composite", + "waveforms": [blackman_wf_dict, blackman_wf_dict], + } + + assert abstract["operations"][6] == { + "op": "pulse", + "channel": "rydberg", + "protocol": "min-delay", + "amplitude": composite_wf_dict, + "detuning": {"kind": "constant", "duration": 0, "value": 0.0}, + "phase": 0.0, + "post_phase_shift": 0.0, + } + + assert abstract["operations"][10] == { + "op": "delay", + "channel": "digital", + "time": 100, + } + + assert abstract["measurement"] == "digital" + + def test_exceptions(self, sequence): + with pytest.raises(TypeError, match="not JSON serializable"): + Sequence(Register3D.cubic(2), MockDevice).to_abstract_repr() + + with pytest.raises( + ValueError, match="No signature found for 'FakeWaveform'" + ): + abstract_repr("FakeWaveform", 100, 1) + + with pytest.raises(ValueError, match="Not enough arguments"): + abstract_repr("ConstantWaveform", 1000) + + with pytest.raises(ValueError, match="Too many positional arguments"): + abstract_repr("ConstantWaveform", 1000, 1, 4) + + with pytest.raises(ValueError, match="'foo' is not in the signature"): + abstract_repr("ConstantWaveform", 1000, 1, foo=0) + + with pytest.raises( + AbstractReprError, match="Name collisions encountered" + ): + Register({"0": (0, 0), 0: (20, 20)})._to_abstract_repr() + + with pytest.raises( + AbstractReprError, + match="Export of an InterpolatedWaveform is only supported for the" + " 'PchipInterpolator'", + ): + InterpolatedWaveform( + 1000, [0, 1, 0], interpolator="interp1d" + )._to_abstract_repr() + + with pytest.raises( + AbstractReprError, match="without any 'interpolator_kwargs'" + ): + InterpolatedWaveform( + 1000, [0, 1, 0], extrapolate=False + )._to_abstract_repr() + + with pytest.raises( + ValueError, + match="The given 'defaults' produce an invalid sequence.", + ): + sequence.to_abstract_repr( + target_atom=1, + amps=[-np.pi, 2 * np.pi], + duration=200, + ) + + @pytest.mark.parametrize( + "call", + [ + _Call("targets", ({"q0", "q1"}, "ch0"), {}), + _Call( + "phase_shifts", (1.0, "q2", "q3"), dict(basis="ground-rydberg") + ), + _Call("wait", (100,), {}), + ], + ) + def test_unknown_calls(self, call): + seq = Sequence(Register.square(2, prefix="q"), Chadoq2) + seq.declare_channel("ch0", "rydberg_global") + seq._calls.append(call) + with pytest.raises( + AbstractReprError, match=f"Unknown call '{call.name}'." + ): + seq.to_abstract_repr() + + @pytest.mark.parametrize( + "obj,serialized_obj", + [ + (Register({"q0": (0.0, 0.0)}), [dict(name="q0", x=0.0, y=0.0)]), + (np.arange(3), [0, 1, 2]), + ({"a"}, ["a"]), + ], + ids=["register", "np.array", "set"], + ) + def test_abstract_repr_encoder(self, obj, serialized_obj): + assert json.dumps(obj, cls=AbstractReprEncoder) == json.dumps( + serialized_obj + ) + + def test_paramobj_serialization(self, sequence): + var = sequence._variables["duration"][0] + ser_var = { + "expression": "index", + "lhs": {"variable": "duration"}, + "rhs": 0, + } + wf = BlackmanWaveform(1000, 1.0) + ser_wf = wf._to_abstract_repr() + with pytest.raises( + ValueError, match="Serialization of calls to parametrized objects" + ): + param_obj_call = BlackmanWaveform(var, 1)() + json.dumps(param_obj_call, cls=AbstractReprEncoder) + + s = json.dumps( + Pulse.ConstantAmplitude(var, wf, 1.0, 1.0), cls=AbstractReprEncoder + ) + assert json.loads(s) == dict( + amplitude={"kind": "constant", "duration": 0, "value": ser_var}, + detuning=ser_wf, + phase=1.0, + post_phase_shift=1.0, + ) + + s = json.dumps( + Pulse.ConstantDetuning(wf, 0.0, var, post_phase_shift=1.0), + cls=AbstractReprEncoder, + ) + assert json.loads(s) == dict( + amplitude=ser_wf, + detuning={"kind": "constant", "duration": 0, "value": 0.0}, + phase=ser_var, + post_phase_shift=1.0, + ) + + s = json.dumps( + Pulse.ConstantPulse(var, 2.0, 0.0, 1.0, 1.0), + cls=AbstractReprEncoder, + ) + assert json.loads(s) == dict( + amplitude={"kind": "constant", "duration": ser_var, "value": 2.0}, + detuning={"kind": "constant", "duration": ser_var, "value": 0.0}, + phase=1.0, + post_phase_shift=1.0, + ) + + method_call = parametrize(BlackmanWaveform.change_duration)(wf, var) + with pytest.raises( + NotImplementedError, + match="Instance or static method serialization is not supported.", + ): + method_call._to_abstract_repr() + + with pytest.raises( + AbstractReprError, match="No abstract representation for 'Foo'" + ): + + class Foo: + def __init__(self, bar: str): + pass + + ParamObj(Foo, "bar")._to_abstract_repr() + + +def _get_serialized_seq( + operations: list[dict] = None, + variables: dict[str, dict] = None, +) -> dict[str, Any]: + + return { + "version": "1", + "name": "John Doe", + "device": "Chadoq2", + "register": [ + {"name": "q0", "x": 0.0, "y": 2.0}, + {"name": "q42", "x": -2.0, "y": 9.0}, + {"name": "q666", "x": 12.0, "y": 0.0}, + ], + "channels": {"digital": "raman_local", "global": "rydberg_global"}, + "operations": operations or [], + "variables": variables or {}, + "measurement": None, + } + + +def _check_roundtrip(serialized_seq: dict[str, Any]): + s = serialized_seq.copy() + # Replaces the special wfs when they are not parametrized + for op in s["operations"]: + if op["op"] == "pulse": + for wf in ("amplitude", "detuning"): + if op[wf]["kind"] in SPECIAL_WFS: + wf_cls, wf_args = SPECIAL_WFS[op[wf]["kind"]] + for val in op[wf].values(): + if isinstance(val, dict): + # Parametrized + break + else: + reconstructed_wf = wf_cls( + *(op[wf][qty] for qty in wf_args) + ) + op[wf] = reconstructed_wf._to_abstract_repr() + + seq = Sequence.from_abstract_repr(json.dumps(s)) + defaults = {name: var["value"] for name, var in s["variables"].items()} + rs = seq.to_abstract_repr(seq_name=serialized_seq["name"], **defaults) + assert s == json.loads(rs) + + +# Needed to replace lambdas in the pytest.mark.parametrize calls (due to mypy) +def _get_op(op: dict) -> Any: + return op["op"] + + +def _get_kind(op: dict) -> Any: + return op["kind"] + + +def _get_expression(op: dict) -> Any: + return op["expression"] + + +class TestDeserialization: + def test_deserialize_device_and_channels(self) -> None: + s = _get_serialized_seq() + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # Check device name + assert seq._device.name == s["device"] + + # Check channels + assert len(seq.declared_channels) == len(s["channels"]) + for name, chan_id in s["channels"].items(): + seq.declared_channels[name] == chan_id + + def test_deserialize_register(self): + s = _get_serialized_seq() + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # Check register + assert len(seq.register.qubits) == len(s["register"]) + for q in s["register"]: + assert q["name"] in seq.qubit_info + assert seq.qubit_info[q["name"]][0] == q["x"] + assert seq.qubit_info[q["name"]][1] == q["y"] + + def test_deserialize_variables(self): + s = _get_serialized_seq( + variables={ + "yolo": {"type": "int", "value": [42, 43, 44]}, + "zou": {"type": "float", "value": [3.14]}, + } + ) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # Check variables + assert len(seq.declared_variables) == len(s["variables"]) + for k, v in s["variables"].items(): + assert k in seq.declared_variables + assert seq.declared_variables[k].name == k + assert ( + seq.declared_variables[k].dtype == VARIABLE_TYPE_MAP[v["type"]] + ) + assert seq.declared_variables[k].size == len(v["value"]) + + @pytest.mark.parametrize( + "op", + [ + {"op": "target", "target": 2, "channel": "digital"}, + {"op": "delay", "time": 500, "channel": "global"}, + {"op": "align", "channels": ["digital", "global"]}, + { + "op": "phase_shift", + "phi": 42, + "targets": [0, 2], + "basis": "digital", + }, + { + "op": "pulse", + "channel": "global", + "phase": 1, + "post_phase_shift": 2, + "protocol": "min-delay", + "amplitude": { + "kind": "constant", + "duration": 1000, + "value": 3.14, + }, + "detuning": { + "kind": "ramp", + "duration": 1000, + "start": 1, + "stop": 5, + }, + }, + ], + ids=_get_op, + ) + def test_deserialize_non_parametrized_op(self, op): + s = _get_serialized_seq(operations=[op]) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # init + declare channels + 1 operation + offset = 1 + len(s["channels"]) + assert len(seq._calls) == offset + 1 + # No parametrized call + assert len(seq._to_build_calls) == 0 + + c = seq._calls[offset] + if op["op"] == "target": + assert c.name == "target_index" + assert c.kwargs["qubits"] == op["target"] + assert c.kwargs["channel"] == op["channel"] + elif op["op"] == "align": + assert c.name == "align" + assert c.args == tuple(op["channels"]) + elif op["op"] == "delay": + assert c.name == "delay" + assert c.kwargs["duration"] == op["time"] + assert c.kwargs["channel"] == op["channel"] + elif op["op"] == "phase_shift": + assert c.name == "phase_shift_index" + assert c.args == tuple([op["phi"], *op["targets"]]) + elif op["op"] == "pulse": + assert c.name == "add" + assert c.kwargs["channel"] == op["channel"] + assert c.kwargs["protocol"] == op["protocol"] + pulse = c.kwargs["pulse"] + assert isinstance(pulse, Pulse) + assert pulse.phase == op["phase"] + assert pulse.post_phase_shift == op["post_phase_shift"] + assert isinstance(pulse.amplitude, Waveform) + assert isinstance(pulse.detuning, Waveform) + else: + assert False, f"operation type \"{op['op']}\" is not valid" + + @pytest.mark.parametrize( + "wf_obj", + [ + {"kind": "constant", "duration": 1200, "value": 3.14}, + {"kind": "ramp", "duration": 1200, "start": 1.14, "stop": 3}, + {"kind": "blackman", "duration": 1200, "area": 2 * 3.14}, + {"kind": "blackman_max", "max_val": 5, "area": 2 * 3.14}, + { + "kind": "interpolated", + "duration": 2000, + "values": [1, 1.5, 1.7, 1.3], + "times": [0, 0.4, 0.8, 0.9], + }, + {"kind": "kaiser", "duration": 2000, "area": 12, "beta": 1.1}, + {"kind": "kaiser_max", "max_val": 6, "area": 12, "beta": 1.1}, + { + "kind": "composite", + "waveforms": [ + {"kind": "constant", "duration": 104, "value": 1}, + {"kind": "constant", "duration": 208, "value": 2}, + {"kind": "constant", "duration": 312, "value": 3}, + ], + }, + {"kind": "custom", "samples": [i / 10 for i in range(0, 20)]}, + ], + ids=_get_kind, + ) + def test_deserialize_non_parametrized_waveform(self, wf_obj): + s = _get_serialized_seq( + operations=[ + { + "op": "pulse", + "channel": "global", + "phase": 1, + "post_phase_shift": 2, + "protocol": "min-delay", + "amplitude": wf_obj, + "detuning": wf_obj, + } + ] + ) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # init + declare channels + 1 operation + offset = 1 + len(s["channels"]) + assert len(seq._calls) == offset + 1 + # No parametrized call + assert len(seq._to_build_calls) == 0 + + c = seq._calls[offset] + pulse: Pulse = c.kwargs["pulse"] + wf = pulse.amplitude + + if wf_obj["kind"] == "constant": + assert isinstance(wf, ConstantWaveform) + assert wf.duration == wf_obj["duration"] + assert wf._value == wf_obj["value"] + + elif wf_obj["kind"] == "ramp": + assert isinstance(wf, RampWaveform) + assert wf.duration == wf_obj["duration"] + assert wf._start == wf_obj["start"] + assert wf._stop == wf_obj["stop"] + + elif wf_obj["kind"] == "blackman": + assert isinstance(wf, BlackmanWaveform) + assert wf.duration == wf_obj["duration"] + assert wf._area == wf_obj["area"] + + elif wf_obj["kind"] == "blackman_max": + assert isinstance(wf, BlackmanWaveform) + assert wf._area == wf_obj["area"] + expected_duration = BlackmanWaveform.from_max_val( + wf_obj["max_val"], wf_obj["area"] + ).duration + assert wf.duration == expected_duration + + elif wf_obj["kind"] == "interpolated": + assert isinstance(wf, InterpolatedWaveform) + assert np.array_equal(wf._values, wf_obj["values"]) + assert np.array_equal(wf._times, wf_obj["times"]) + + elif wf_obj["kind"] == "kaiser": + assert isinstance(wf, KaiserWaveform) + assert wf.duration == wf_obj["duration"] + assert wf._area == wf_obj["area"] + assert wf._beta == wf_obj["beta"] + + elif wf_obj["kind"] == "kaiser_max": + assert isinstance(wf, KaiserWaveform) + assert wf._area == wf_obj["area"] + assert wf._beta == wf_obj["beta"] + expected_duration = KaiserWaveform.from_max_val( + wf_obj["max_val"], wf_obj["area"], wf_obj["beta"] + ).duration + assert wf.duration == expected_duration + + elif wf_obj["kind"] == "composite": + assert isinstance(wf, CompositeWaveform) + assert all(isinstance(w, Waveform) for w in wf._waveforms) + + elif wf_obj["kind"] == "custom": + assert isinstance(wf, CustomWaveform) + assert np.array_equal(wf._samples, wf_obj["samples"]) + + def test_deserialize_measurement(self): + s = _get_serialized_seq() + _check_roundtrip(s) + s["measurement"] = "ground-rydberg" + + seq = Sequence.from_abstract_repr(json.dumps(s)) + + assert seq._measurement == s["measurement"] + + var1 = { + "expression": "index", + "lhs": {"variable": "var1"}, + "rhs": 0, + } + + var2 = { + "expression": "index", + "lhs": {"variable": "var2"}, + "rhs": 0, + } + + var3 = { + "expression": "index", + "lhs": {"variable": "var3"}, + "rhs": 0, + } + + @pytest.mark.parametrize( + "op", + [ + {"op": "target", "target": var1, "channel": "digital"}, + {"op": "delay", "time": var2, "channel": "global"}, + { + "op": "phase_shift", + "phi": var1, + "targets": [2, var1], + "basis": "digital", + }, + { + "op": "pulse", + "channel": "global", + "phase": var1, + "post_phase_shift": var2, + "protocol": "min-delay", + "amplitude": { + "kind": "constant", + "duration": var2, + "value": 3.14, + }, + "detuning": { + "kind": "ramp", + "duration": var2, + "start": 1, + "stop": 5, + }, + }, + ], + ids=_get_op, + ) + def test_deserialize_parametrized_op(self, op): + s = _get_serialized_seq( + operations=[op], + variables={ + "var1": {"type": "int", "value": [0]}, + "var2": {"type": "int", "value": [42]}, + }, + ) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + # init + declare channels + 1 operation + offset = 1 + len(s["channels"]) + assert len(seq._calls) == offset + # No parametrized call + assert len(seq._to_build_calls) == 1 + + c = seq._to_build_calls[0] + if op["op"] == "target": + assert c.name == "target_index" + assert isinstance(c.kwargs["qubits"], VariableItem) + assert c.kwargs["channel"] == op["channel"] + elif op["op"] == "delay": + assert c.name == "delay" + assert c.kwargs["channel"] == op["channel"] + assert isinstance(c.kwargs["duration"], VariableItem) + elif op["op"] == "phase_shift": + assert c.name == "phase_shift_index" + # phi is variable + assert isinstance(c.args[0], VariableItem) + # qubit 1 is fixed + assert c.args[1] == 2 + # qubit 2 is variable + assert isinstance(c.args[2], VariableItem) + elif op["op"] == "pulse": + assert c.name == "add" + assert c.kwargs["channel"] == op["channel"] + assert c.kwargs["protocol"] == op["protocol"] + pulse = c.kwargs["pulse"] + assert isinstance(pulse, ParamObj) + assert pulse.cls == Pulse + assert isinstance(pulse.kwargs["phase"], VariableItem) + assert isinstance(pulse.kwargs["post_phase_shift"], VariableItem) + + assert isinstance(pulse.kwargs["amplitude"], ParamObj) + assert issubclass(pulse.kwargs["amplitude"].cls, Waveform) + assert isinstance(pulse.kwargs["detuning"], ParamObj) + assert issubclass(pulse.kwargs["detuning"].cls, Waveform) + else: + assert False, f"operation type \"{op['op']}\" is not valid" + + @pytest.mark.parametrize( + "wf_obj", + [ + {"kind": "constant", "duration": var1, "value": var2}, + { + "kind": "ramp", + "duration": var1, + "start": var2, + "stop": var3, + }, + {"kind": "blackman", "duration": var1, "area": var2}, + {"kind": "blackman_max", "max_val": var3, "area": var2}, + { + "kind": "interpolated", + "duration": var1, + "values": {"variable": "var_values"}, + "times": {"variable": "var_times"}, + }, + { + "kind": "kaiser", + "duration": var1, + "area": var3, + "beta": var2, + }, + { + "kind": "kaiser_max", + "max_val": var2, + "area": var2, + "beta": var2, + }, + { + "kind": "composite", + "waveforms": [ + { + "kind": "constant", + "duration": var1, + "value": var2, + }, + { + "kind": "constant", + "duration": var1, + "value": var2, + }, + { + "kind": "constant", + "duration": var1, + "value": var2, + }, + ], + }, + ], + ids=_get_kind, + ) + def test_deserialize_parametrized_waveform(self, wf_obj): + # var1,2 = duration 1000, 2000 + # var2,4 = value - 2, 5 + s = _get_serialized_seq( + operations=[ + { + "op": "pulse", + "channel": "global", + "phase": 1, + "post_phase_shift": 2, + "protocol": "min-delay", + "amplitude": wf_obj, + "detuning": wf_obj, + } + ], + variables={ + "var1": {"type": "int", "value": [1000]}, + "var2": {"type": "int", "value": [2]}, + "var3": {"type": "int", "value": [5]}, + "var_values": {"type": "float", "value": [1, 1.5, 1.7, 1.3]}, + "var_times": {"type": "float", "value": [0, 0.4, 0.8, 0.9]}, + }, + ) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + + seq_var1 = seq._variables["var1"] + seq_var2 = seq._variables["var2"] + seq_var3 = seq._variables["var3"] + seq_var_values = seq._variables["var_values"] + seq_var_times = seq._variables["var_times"] + + # init + declare channels + 1 operation + offset = 1 + len(s["channels"]) + assert len(seq._calls) == offset + # No parametrized call + assert len(seq._to_build_calls) == 1 + + c = seq._to_build_calls[0] + pulse: Pulse = c.kwargs["pulse"] + wf = pulse.kwargs["amplitude"] + + if wf_obj["kind"] == "constant": + assert wf.cls == ConstantWaveform + assert wf.kwargs["duration"] == seq_var1[0] + assert wf.kwargs["value"] == seq_var2[0] + + elif wf_obj["kind"] == "ramp": + assert wf.cls == RampWaveform + assert wf.kwargs["duration"] == seq_var1[0] + assert wf.kwargs["start"] == seq_var2[0] + assert wf.kwargs["stop"] == seq_var3[0] + + elif wf_obj["kind"] == "blackman": + assert wf.cls == BlackmanWaveform + assert wf.kwargs["duration"] == seq_var1[0] + assert wf.kwargs["area"] == seq_var2[0] + + elif wf_obj["kind"] == "blackman_max": + assert wf.cls == BlackmanWaveform.from_max_val.__wrapped__ + assert wf.kwargs["area"] == seq_var2[0] + assert wf.kwargs["max_val"] == seq_var3[0] + + elif wf_obj["kind"] == "interpolated": + assert wf.cls == InterpolatedWaveform + assert wf.kwargs["duration"] == seq_var1[0] + assert wf.kwargs["values"] == seq_var_values + assert wf.kwargs["times"] == seq_var_times + + elif wf_obj["kind"] == "kaiser": + assert wf.cls == KaiserWaveform + assert wf.kwargs["duration"] == seq_var1[0] + assert wf.kwargs["area"] == seq_var3[0] + assert wf.kwargs["beta"] == seq_var2[0] + + elif wf_obj["kind"] == "kaiser_max": + assert wf.cls == KaiserWaveform.from_max_val.__wrapped__ + assert wf.kwargs["area"] == seq_var2[0] + assert wf.kwargs["beta"] == seq_var2[0] + assert wf.kwargs["max_val"] == seq_var2[0] + + elif wf_obj["kind"] == "composite": + assert wf.cls == CompositeWaveform + assert all(isinstance(w, ParamObj) for w in wf.args) + assert all(issubclass(w.cls, Waveform) for w in wf.args) + + @pytest.mark.parametrize( + "json_param", + [ + {"expression": "neg", "lhs": {"variable": "var1"}}, + {"expression": "abs", "lhs": var1}, + {"expression": "ceil", "lhs": {"variable": "var1"}}, + {"expression": "floor", "lhs": var1}, + {"expression": "sqrt", "lhs": var1}, + {"expression": "exp", "lhs": var1}, + {"expression": "log", "lhs": var1}, + {"expression": "log2", "lhs": {"variable": "var1"}}, + {"expression": "sin", "lhs": {"variable": "var1"}}, + {"expression": "cos", "lhs": var1}, + {"expression": "tan", "lhs": {"variable": "var1"}}, + {"expression": "index", "lhs": {"variable": "var1"}, "rhs": 0}, + {"expression": "add", "lhs": var1, "rhs": 0.5}, + {"expression": "sub", "lhs": {"variable": "var1"}, "rhs": 0.5}, + {"expression": "mul", "lhs": {"variable": "var1"}, "rhs": 0.5}, + {"expression": "div", "lhs": var1, "rhs": 0.5}, + {"expression": "pow", "lhs": {"variable": "var1"}, "rhs": 0.5}, + {"expression": "mod", "lhs": {"variable": "var1"}, "rhs": 2}, + ], + ids=_get_expression, + ) + def test_deserialize_param(self, json_param): + s = _get_serialized_seq( + operations=[ + { + "op": "pulse", + "channel": "global", + "phase": 1, + "post_phase_shift": 2, + "protocol": "min-delay", + "amplitude": { + "kind": "constant", + "duration": 1000, + "value": 2.0, + }, + "detuning": { + "kind": "constant", + "duration": 1000, + "value": json_param, + }, + } + ], + variables={ + "var1": {"type": "float", "value": [1.5]}, + }, + ) + _check_roundtrip(s) + seq = Sequence.from_abstract_repr(json.dumps(s)) + seq_var1 = seq._variables["var1"] + + # init + declare channels + 1 operation + offset = 1 + len(s["channels"]) + assert len(seq._calls) == offset + # No parametrized call + assert len(seq._to_build_calls) == 1 + + c = seq._to_build_calls[0] + pulse: ParamObj = c.kwargs["pulse"] + wf = pulse.kwargs["detuning"] + param = wf.kwargs["value"] + + expression = json_param["expression"] + rhs = json_param.get("rhs") + + if expression == "neg": + assert param == -seq_var1 + if expression == "abs": + assert param == abs(seq_var1[0]) + if expression == "ceil": + assert param == np.ceil(seq_var1) + if expression == "floor": + assert param == np.floor(seq_var1[0]) + if expression == "sqrt": + assert param == np.sqrt(seq_var1[0]) + if expression == "exp": + assert param == np.exp(seq_var1[0]) + if expression == "log": + assert param == np.log(seq_var1[0]) + if expression == "log2": + assert param == np.log2(seq_var1) + if expression == "sin": + assert param == np.sin(seq_var1) + if expression == "cos": + assert param == np.cos(seq_var1[0]) + if expression == "tan": + assert param == np.tan(seq_var1) + + if expression == "index": + assert param == seq_var1[rhs] + if expression == "add": + assert param == seq_var1[0] + rhs + if expression == "sub": + assert param == seq_var1 - rhs + if expression == "mul": + assert param == seq_var1 * rhs + if expression == "div": + assert param == seq_var1[0] / rhs + if expression == "pow": + assert param == seq_var1**rhs + if expression == "mod": + assert param == seq_var1 % rhs + + @pytest.mark.parametrize( + "param,msg,patch_jsonschema", + [ + ( + var1, + "Variable 'var1' used in operations but not found in declared " + "variables.", + False, + ), + ( + {"abs": 1}, + f"Parameter '{dict(abs=1)}' is neither a literal nor a " + "variable or an expression.", + True, + ), + ( + {"expression": "floordiv", "lhs": 0, "rhs": 0}, + "Expression 'floordiv' invalid.", + True, + ), + ], + ids=["bad_var", "bad_param", "bad_exp"], + ) + def test_param_exceptions(self, param, msg, patch_jsonschema): + s = _get_serialized_seq( + [ + { + "op": "delay", + "time": param, + "channel": "global", + } + ] + ) + extra_params = {} + if patch_jsonschema: + std_error = jsonschema.exceptions.ValidationError + with patch("jsonschema.validate"): + with pytest.raises(AbstractReprError, match=msg): + Sequence.from_abstract_repr(json.dumps(s)) + else: + std_error = AbstractReprError + extra_params["match"] = msg + with pytest.raises(std_error, **extra_params): + Sequence.from_abstract_repr(json.dumps(s)) + + def test_unknow_waveform(self): + s = _get_serialized_seq( + [ + { + "op": "pulse", + "channel": "global", + "phase": 1, + "post_phase_shift": 2, + "protocol": "min-delay", + "amplitude": { + "kind": "constant", + "duration": 1000, + "value": 2.0, + }, + "detuning": { + "kind": "gaussian", + "duration": 1000, + "value": -1, + }, + } + ] + ) + with pytest.raises(jsonschema.exceptions.ValidationError): + Sequence.from_abstract_repr(json.dumps(s)) + + with pytest.raises( + AbstractReprError, + match="The object does not encode a known waveform.", + ): + with patch("jsonschema.validate"): + Sequence.from_abstract_repr(json.dumps(s)) diff --git a/tests/test_json.py b/tests/test_json.py index df0d91688..c46b21918 100644 --- a/tests/test_json.py +++ b/tests/test_json.py @@ -20,7 +20,8 @@ from pulser import Register, Register3D, Sequence from pulser.devices import Chadoq2, MockDevice from pulser.json.coders import PulserDecoder, PulserEncoder -from pulser.json.supported import SerializationError, validate_serialization +from pulser.json.exceptions import SerializationError +from pulser.json.supported import validate_serialization from pulser.parametrized.decorators import parametrize from pulser.register.register_layout import RegisterLayout from pulser.register.special_layouts import ( diff --git a/tests/test_paramseq.py b/tests/test_paramseq.py index 5f1707efb..cf75a1b83 100644 --- a/tests/test_paramseq.py +++ b/tests/test_paramseq.py @@ -219,8 +219,8 @@ def test_str(): sb.add(pls, "ch1") s = ( f"Prelude\n-------\n{str(seq)}Stored calls\n------------\n\n" - + "1. add(Pulse.ConstantPulse(mul(var[0], 100), var[0]," - + " -1, var[0]), ch1)" + + "1. add(Pulse(ConstantWaveform(mul(var[0], 100), var[0]), " + + "ConstantWaveform(mul(var[0], 100), -1), var[0], 0.0), ch1)" ) assert s == str(sb) diff --git a/tests/test_register.py b/tests/test_register.py index 368ef9449..251eac0b8 100644 --- a/tests/test_register.py +++ b/tests/test_register.py @@ -386,8 +386,8 @@ def test_find_indices(): with pytest.raises( ValueError, - match="IDs list must be selected among" - "the IDs of the register's qubits", + match="IDs list must be selected among the IDs of the register's " + "qubits", ): reg.find_indices(["c", "e", "d"]) From 87f21f747808641750656694d53e5ded658096ef Mon Sep 17 00:00:00 2001 From: CdeTerra <102144942+CdeTerra@users.noreply.github.com> Date: Fri, 22 Jul 2022 14:11:36 +0200 Subject: [PATCH 11/18] Keep register atom ids type in when encode decode (#389) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Keep register atom ids type in when encode decode * Black * Add Register3D UT and check ids order * Fix flake8 Co-authored-by: Henrique Silvério --- pulser-core/pulser/json/supported.py | 8 +++- pulser-core/pulser/register/base_register.py | 41 +++++++++++++++++--- tests/test_json.py | 14 +++++++ tests/test_register.py | 5 ++- 4 files changed, 60 insertions(+), 8 deletions(-) diff --git a/pulser-core/pulser/json/supported.py b/pulser-core/pulser/json/supported.py index 611a73053..c38cedf8c 100644 --- a/pulser-core/pulser/json/supported.py +++ b/pulser-core/pulser/json/supported.py @@ -48,7 +48,13 @@ "tan", ) -SUPPORTS_SUBMODULE = ("Pulse", "BlackmanWaveform", "KaiserWaveform") +SUPPORTS_SUBMODULE = ( + "Pulse", + "BlackmanWaveform", + "KaiserWaveform", + "Register", + "Register3D", +) SUPPORTED_MODULES = { "builtins": SUPPORTED_BUILTINS, diff --git a/pulser-core/pulser/register/base_register.py b/pulser-core/pulser/register/base_register.py index 127618919..1506cff57 100644 --- a/pulser-core/pulser/register/base_register.py +++ b/pulser-core/pulser/register/base_register.py @@ -67,6 +67,9 @@ def __init__(self, qubits: Mapping[Any, ArrayLike], **kwargs: Any): self._coords = [np.array(v, dtype=float) for v in qubits.values()] self._dim = self._coords[0].size self._layout_info: Optional[_LayoutInfo] = None + self._init_kwargs(**kwargs) + + def _init_kwargs(self, **kwargs: Any) -> None: if kwargs: if kwargs.keys() != {"layout", "trap_ids"}: raise ValueError( @@ -133,6 +136,7 @@ def from_coordinates( center: bool = True, prefix: Optional[str] = None, labels: Optional[abcSequence[QubitId]] = None, + **kwargs: Any, ) -> T: """Creates the register from an array of coordinates. @@ -172,7 +176,7 @@ def from_coordinates( qubits = dict(zip(cast(Iterable, labels), coords)) else: qubits = dict(cast(Iterable, enumerate(coords))) - return cls(qubits) + return cls(qubits, **kwargs) def _validate_layout( self, register_layout: RegisterLayout, trap_ids: tuple[int, ...] @@ -202,16 +206,41 @@ def _validate_layout( @abstractmethod def _to_dict(self) -> dict[str, Any]: - qs = dict(zip(self._ids, map(np.ndarray.tolist, self._coords))) - if self._layout_info is not None: - return obj_to_dict(self, qs, **(self._layout_info._asdict())) - return obj_to_dict(self, qs) + """Serializes the object. + + During deserialization, it will be reconstructed using + 'from_coordinates', so that it uses lists instead of a dictionary + (in JSON, lists elements keep their types, but dictionaries keys do + not). + """ + cls_dict = obj_to_dict( + None, + _build=False, + _name=self.__class__.__name__, + _module=self.__class__.__module__, + ) + + kwargs = ( + {} if self._layout_info is None else self._layout_info._asdict() + ) + + return obj_to_dict( + self, + cls_dict, + [np.ndarray.tolist(qubit_coords) for qubit_coords in self._coords], + False, + None, + self._ids, + **kwargs, + _submodule=self.__class__.__name__, + _name="from_coordinates", + ) def __eq__(self, other: Any) -> bool: if type(other) is not type(self): return False - return set(self._ids) == set(other._ids) and all( + return list(self._ids) == list(other._ids) and all( ( np.allclose( # Accounts for rounding errors self._coords[i], diff --git a/tests/test_json.py b/tests/test_json.py index c46b21918..b1c2405cc 100644 --- a/tests/test_json.py +++ b/tests/test_json.py @@ -90,6 +90,20 @@ def test_register_from_layout(): assert new_reg._layout_info.trap_ids == (1, 0) +@pytest.mark.parametrize( + "reg", + [ + Register(dict(enumerate([(2, 3), (5, 1), (10, 0)]))), + Register3D({3: (2, 3, 4), 4: (3, 4, 5), 2: (4, 5, 7)}), + ], +) +def test_register_numbered_keys(reg): + j = json.dumps(reg, cls=PulserEncoder) + decoded_reg = json.loads(j, cls=PulserDecoder) + assert reg == decoded_reg + assert all([type(i) == int for i in decoded_reg.qubit_ids]) + + def test_mappable_register(): layout = RegisterLayout([[0, 0], [1, 1], [1, 0], [0, 1]]) mapp_reg = layout.make_mappable_register(2) diff --git a/tests/test_register.py b/tests/test_register.py index 251eac0b8..67413f39d 100644 --- a/tests/test_register.py +++ b/tests/test_register.py @@ -405,7 +405,8 @@ def assert_ineq(left, right): def test_equality_function(): reg1 = Register({"c": (1, 2), "d": (8, 4)}) assert_eq(reg1, reg1) - assert_eq(reg1, Register({"d": (8, 4), "c": (1, 2)})) + assert_eq(reg1, Register({"c": (1, 2), "d": (8, 4)})) + assert_ineq(reg1, Register({"d": (8, 4), "c": (1, 2)})) assert_ineq(reg1, Register({"c": (8, 4), "d": (1, 2)})) assert_ineq(reg1, Register({"c": (1, 2), "d": (8, 4), "e": (8, 4)})) assert_ineq(reg1, 10) @@ -413,6 +414,8 @@ def test_equality_function(): reg2 = Register3D({"a": (1, 2, 3), "b": (8, 5, 6)}) assert_eq(reg2, reg2) assert_eq(reg2, Register3D({"a": (1, 2, 3), "b": (8, 5, 6)})) + assert_eq(reg2, Register3D({"a": (1, 2, 3), "b": (8, 5, 6)})) + assert_ineq(reg2, Register3D({"b": (8, 5, 6), "a": (1, 2, 3)})) assert_ineq(reg2, Register3D({"b": (1, 2, 3), "a": (8, 5, 6)})) assert_ineq( reg2, Register3D({"a": (1, 2, 3), "b": (8, 5, 6), "e": (8, 5, 6)}) From d9fa7366ca8fdd75010014bfecc3baaab76c1073 Mon Sep 17 00:00:00 2001 From: Louis Vignoli <97944962+lvignoli@users.noreply.github.com> Date: Fri, 29 Jul 2022 18:30:09 +0200 Subject: [PATCH 12/18] New sampler implementation (#388) * wip: new sampler prototype * fix: use a custom pairwise function itertools.pairwise is >=3.10. * chore: use stirng literals for dict keys * fix: update correct import after rebase * fix: delete unused import * delete the old sampler * rename the new sampler to sampler * fix: adapt after changes on Sequence * split between sampler.py and samples.py Necessary to avoid circular imports * refactor SequenceSamples to dataclass * move samples.py to root of pulser-core * feat: add get_samples() method to _ChannelSchedule * docs: add docstring to samples module * use get_samples() in sampler.samples() * delete irrelevant files: noises and broken tests noise models were made void with the change from QubitSamples to ChannelSamples * untrack new_sampler_demo.py * add demo notebook * style: fix formatting * restore the sequence sampler tests * tests: update sampler test to new architecture * fix: correct the duration of samples from ChannelSchedule.get_samples() Now we need to pass the duration of the parent sequence as a parameter to embed the samples in an array of the right length * style: sort imports * fix: add dummy modulation kwarg to samples() for mypy compliance * Restructuring the sampler * Enable modulation of samples * Update unit tests * Adding ChannelSamples.modulate() * Incorporating ChannelSamples in sequence drawing * Change the sampling and display of the phase * Completing tests * Add missing docstring * Removing test tutorial * Addressing review comments Co-authored-by: HGSilveri --- pulser-core/pulser/sampler/noise_model.py | 65 ---- pulser-core/pulser/sampler/sampler.py | 307 +----------------- pulser-core/pulser/sampler/samples.py | 196 +++++++++-- pulser-core/pulser/sequence/_schedule.py | 34 ++ pulser-core/pulser/sequence/_seq_drawer.py | 211 +++++------- pulser-core/pulser/sequence/sequence.py | 3 +- pulser-simulation/pulser_simulation/noises.py | 110 ------- tests/test_sampling_noises.py | 106 ------ tests/test_sequence_sampler.py | 93 ++++-- 9 files changed, 372 insertions(+), 753 deletions(-) delete mode 100644 pulser-core/pulser/sampler/noise_model.py delete mode 100644 pulser-simulation/pulser_simulation/noises.py delete mode 100644 tests/test_sampling_noises.py diff --git a/pulser-core/pulser/sampler/noise_model.py b/pulser-core/pulser/sampler/noise_model.py deleted file mode 100644 index d7b661496..000000000 --- a/pulser-core/pulser/sampler/noise_model.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2022 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Defines a NoiseModel and how to apply it to the samples.""" -from __future__ import annotations - -import functools -from typing import Callable - -from pulser.sampler.samples import QubitSamples - -NoiseModel = Callable[[QubitSamples], QubitSamples] -"""A function that apply some noise on a list of QubitSamples. - -A NoiseModel corresponds to a source of noises present in a device which is -relevant when sampling the input pulses. Physical effects contributing to -modifications of the shined amplitude, detuning and phase felt by qubits of the -register are susceptible to be implemented by a NoiseModel. -""" - - -def compose_local_noises(*functions: NoiseModel) -> NoiseModel: - """Helper to compose multiple NoiseModel. - - Args: - *functions: a list of functions - - Returns: - The mathematical composition of *functions. The last element is applied - first. If *functions is [f, g, h], it returns f∘g∘h. - """ - return functools.reduce( - lambda f, g: lambda x: f(g(x)), functions, lambda x: x - ) - - -def apply_noises( - samples: list[QubitSamples], noises: list[NoiseModel] -) -> list[QubitSamples]: - """Apply a list of NoiseModel on a list of QubitSamples. - - The noises are composed using the compose_local_noises function, such that - the last element is applied first. - - Args: - samples: A list of QubitSamples. - noises: A list of NoiseModel. - - Return: - A list of QubitSamples on which each element of noises has been - applied. - """ - tot_noise = compose_local_noises(*noises) - - return [tot_noise(s) for s in samples] diff --git a/pulser-core/pulser/sampler/sampler.py b/pulser-core/pulser/sampler/sampler.py index 533fde98d..b9b4f0b1a 100644 --- a/pulser-core/pulser/sampler/sampler.py +++ b/pulser-core/pulser/sampler/sampler.py @@ -1,299 +1,26 @@ -# Copyright 2022 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Exposes the sample() functions. - -It contains many helpers. -""" +"""Defines the main function for sequence sampling.""" from __future__ import annotations -import itertools -from collections import defaultdict -from typing import Callable, List, Optional, cast - -import numpy as np +from typing import TYPE_CHECKING -from pulser.channels import Channel -from pulser.pulse import Pulse -from pulser.sampler.noise_model import NoiseModel, apply_noises -from pulser.sampler.samples import QubitSamples -from pulser.sequence.sequence import Sequence, _ChannelSchedule, _TimeSlot +from pulser.sampler.samples import SequenceSamples +if TYPE_CHECKING: # pragma: no cover + from pulser import Sequence -def sample( - seq: Sequence, - modulation: bool = False, - common_noises: Optional[list[NoiseModel]] = None, - global_noises: Optional[list[NoiseModel]] = None, -) -> dict: - """Samples the given Sequence and returns a nested dictionary. - It is intended to be used like the json.dumps() function. +def sample(seq: Sequence, modulation: bool = False) -> SequenceSamples: + """Construct samples of a Sequence. Args: - seq: A pulser.Sequence instance. - modulation: Flag to account for the modulation of AOM/EOM - before sampling. - common_noises: A list of the noise sources - for all channels. - global_noises: A list of the noise sources - for global channels. - - Returns: - A nested dictionnary of the samples of the amplitude, detuning and - phase at every nanoseconds for all channels. - """ - if common_noises is None: - common_noises = [] - if global_noises is None: - global_noises = [] - - # 1. determine if the global channel decay to a local one - # 2. extract samples - # 3. modulate - # 4. apply noises/SLM - # 5. write samples - # - # NOTE(perf): it not very efficient to hold copies of the same data for - # every qubits in a global channel, but it remains manageable for registers - # with less than 100 qubits. - - samples: dict[str, list[QubitSamples]] = {} - addrs: dict[str, str] = {} - - for ch_name, ch in seq.declared_channels.items(): - s: list[QubitSamples] - - addr = seq.declared_channels[ch_name].addressing - - ch_noises = list(common_noises) - - slm_on = seq._slm_mask_targets and seq._slm_mask_time - - if addr == "Global": - decay = slm_on or len(global_noises) > 0 or len(common_noises) > 0 - if decay: - addr = "Decayed" - ch_noises.extend(global_noises) - - addrs[ch_name] = addr - - strategy = _group_between_retargets if modulation else _regular - s = _sample_channel(seq, ch_name, strategy) - if modulation: - s = _modulate(ch, s) - - s = apply_noises(s, ch_noises) - - if slm_on: # Update the samples of masked qubits during SLM on times - for i, _ in enumerate(s): - if s[i].qubit in seq._slm_mask_targets: - ti, tf = seq._slm_mask_time[0], seq._slm_mask_time[1] - s[i].amp[ti:tf] = 0.0 - # apply only on amp since it's just a shutter - - samples[ch_name] = s - - # format the samples in the simulation dict form - d = _write_dict(seq, samples, addrs) - - return d - - -def _prepare_dict(seq: Sequence, N: int) -> dict: - """Constructs empty dict of size N. - - Usually N is the duration of seq. - """ - - def new_qty_dict() -> dict: - return { - "amp": np.zeros(N), - "det": np.zeros(N), - "phase": np.zeros(N), - } - - def new_qdict() -> dict: - return defaultdict(new_qty_dict) - - if seq._in_xy: - return { - "Global": {"XY": new_qty_dict()}, - "Local": {"XY": new_qdict()}, - } - else: - return { - "Global": defaultdict(new_qty_dict), - "Local": defaultdict(new_qdict), - } - - -def _write_dict( - seq: Sequence, - samples: dict[str, list[QubitSamples]], - addrs: dict[str, str], -) -> dict: - """Export the given samples to a nested dictionary.""" - # Get the duration - if not _same_duration(samples): - raise ValueError("All the samples do not share the same duration.") - N = list(samples.values())[0][0].amp.size - - d = _prepare_dict(seq, N) - - for ch_name, some_samples in samples.items(): - basis = seq.declared_channels[ch_name].basis - addr = addrs[ch_name] - if addr == "Global": - # Take samples on only one qubit and write them - a_qubit = next(iter(seq._qids)) - to_write = [x for x in some_samples if x.qubit == a_qubit] - for s in to_write: - d["Global"][basis]["amp"] += s.amp - d["Global"][basis]["det"] += s.det - d["Global"][basis]["phase"] += s.phase - else: - for s in some_samples: - d["Local"][basis][s.qubit]["amp"] += s.amp - d["Local"][basis][s.qubit]["det"] += s.det - d["Local"][basis][s.qubit]["phase"] += s.phase - return d - - -def _same_duration(samples: dict[str, list[QubitSamples]]) -> bool: - durations: list[int] = [] - flatten_samples: list[QubitSamples] = [] - for some_samples in samples.values(): - flatten_samples.extend(some_samples) - for s in flatten_samples: - durations.extend((s.amp.size, s.det.size, s.phase.size)) - return durations.count(durations[0]) == len(durations) - - -def _sample_channel( - seq: Sequence, ch_name: str, strategy: TimeSlotExtractionStrategy -) -> list[QubitSamples]: - """Compute a list of QubitSamples for a channel.""" - qs: list[QubitSamples] = [] - grouped_slots = strategy(seq._schedule[ch_name]) - - for group in grouped_slots: - ss = _sample_slots(seq.get_duration(), *group) - qs.extend(ss) - return qs - - -def _sample_slots(N: int, *slots: _TimeSlot) -> list[QubitSamples]: - """Gather samples of a list of _TimeSlot in a single Samples instance.""" - # Same target in one group, guaranteed by the strategy (this seems - # weird, it's not enforced by the structure,bad design?) - qubits = slots[0].targets - amp, det, phase = np.zeros(N), np.zeros(N), np.zeros(N) - pulse_slots = [s for s in slots if isinstance(s.type, Pulse)] - for s in pulse_slots: - pulse = cast(Pulse, s.type) - amp[s.ti : s.tf] += pulse.amplitude.samples - det[s.ti : s.tf] += pulse.detuning.samples - phase[s.ti : s.tf] += pulse.phase - qs = [ - QubitSamples( - amp=amp.copy(), det=det.copy(), phase=phase.copy(), qubit=q - ) - for q in qubits - ] - return qs - - -TimeSlotExtractionStrategy = Callable[ - [_ChannelSchedule], List[List[_TimeSlot]] -] -"""Extraction strategy of _TimeSlot's of a Channel. - -It's an alias for functions that returns a list of lists of _TimeSlots. -_TimeSlots in the same group MUST share the same targets. - -NOTE: - This strategy type is used mostly for the necessity to extract samples - differently when taking into account the modulation of AOM/EOM. Despite - there are only two cases, whether it's necessary to modulate a local - channel or not, this pattern can accomodate for future needs. -""" - - -def _regular(ts: _ChannelSchedule) -> list[list[_TimeSlot]]: - """No grouping performed, return only the pulses.""" - return [[x] for x in ts if isinstance(x.type, Pulse)] - - -def _group_between_retargets( - ts: _ChannelSchedule, -) -> list[list[_TimeSlot]]: - """Filter and group _TimeSlots together. - - Group the input slots by groups of successive Pulses and delays between - two target operations. Consider the following sequence consisting of pulses - A B C D E F, targeting different qubits: - - .---A---B------.---C--D--E---.----F-- - ^ ^ ^ - | | | - target q0 target q1 target q0 - - It will group the pulses' _TimeSlot's in batches (A B), (C D E) and (F), - returning the following list of list of _TimeSlot instances: - - [[A, B], [C, D, E], [F]] - - Args: - ts: A list of TimeSlot from a Sequence schedule. - - Returns: - A list of list of _TimeSlot. _TimeSlot instances are successive and - share the same targets. They are of type either Pulse or "delay", all - "target" ones are discarded. - """ - TO_KEEP = "pulses_and_delays" - - def key_func(x: _TimeSlot) -> str: - if isinstance(x.type, Pulse) or x.type == "delay": - return TO_KEEP - else: - return "other" - - grouped_slots: list[list[_TimeSlot]] = [] - - for key, group in itertools.groupby(ts, key_func): - g = list(group) - if key != TO_KEEP: - continue - grouped_slots.append(g) - - return grouped_slots - - -def _modulate(ch: Channel, samples: list[QubitSamples]) -> list[QubitSamples]: - """Modulate local samples according to the hardware specs. - - Additional parameters will probably be needed (keep_end, etc). + seq: The sequence to sample. + modulation: Whether to modulate the samples. """ - modulated_samples: list[QubitSamples] = [] - for s in samples: - modulated_samples.append( - QubitSamples( - amp=ch.modulate(s.amp), - det=ch.modulate(s.det), - phase=ch.modulate(s.phase), - qubit=s.qubit, - ) - ) - return modulated_samples + return SequenceSamples( + list(seq.declared_channels.keys()), + [ + ch_schedule.get_samples(modulated=modulation) + for ch_schedule in seq._schedule.values() + ], + seq.declared_channels, + ) diff --git a/pulser-core/pulser/sampler/samples.py b/pulser-core/pulser/sampler/samples.py index a45de2ccc..e9e265ee2 100644 --- a/pulser-core/pulser/sampler/samples.py +++ b/pulser-core/pulser/sampler/samples.py @@ -1,37 +1,187 @@ -# Copyright 2022 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Defines samples dataclasses.""" +"""Contains dataclasses for samples and some helper functions.""" from __future__ import annotations -from dataclasses import dataclass +from collections import defaultdict +from dataclasses import dataclass, field import numpy as np -from pulser.register.base_register import QubitId +from pulser.channels import Channel +from pulser.register import QubitId + +"""Literal constants for addressing.""" +_GLOBAL = "Global" +_LOCAL = "Local" +_AMP = "amp" +_DET = "det" +_PHASE = "phase" + + +def _prepare_dict(N: int, in_xy: bool = False) -> dict: + """Constructs empty dict of size N. + + Usually N is the duration of seq. + """ + + def new_qty_dict() -> dict: + return { + _AMP: np.zeros(N), + _DET: np.zeros(N), + _PHASE: np.zeros(N), + } + + def new_qdict() -> dict: + return defaultdict(new_qty_dict) + + if in_xy: + return { + _GLOBAL: {"XY": new_qty_dict()}, + _LOCAL: {"XY": new_qdict()}, + } + else: + return { + _GLOBAL: defaultdict(new_qty_dict), + _LOCAL: defaultdict(new_qdict), + } + + +def _default_to_regular(d: dict | defaultdict) -> dict: + """Helper function to convert defaultdicts to regular dicts.""" + if isinstance(d, dict): + d = {k: _default_to_regular(v) for k, v in d.items()} + return d @dataclass -class QubitSamples: - """Gathers samples concerning a single qubit.""" +class _TargetSlot: + """Auxiliary class to store target information. + + Recopy of the sequence._TimeSlot but without the unrelevant `type` field, + unrelevant at the sample level. + + NOTE: While it store targets, targets themselves are insufficient to + conclude on the addressing of the samples. Additional info is needed: + compare against a known register or the original sequence information. + """ + + ti: int + tf: int + targets: set[QubitId] + + +@dataclass +class ChannelSamples: + """Gathers samples of a channel.""" amp: np.ndarray det: np.ndarray phase: np.ndarray - qubit: QubitId + slots: list[_TargetSlot] = field(default_factory=list) def __post_init__(self) -> None: - if not len(self.amp) == len(self.det) == len(self.phase): - raise ValueError( - "ndarrays amp, det and phase must have the same length." - ) + assert len(self.amp) == len(self.det) == len(self.phase) + self.duration = len(self.amp) + + for t in self.slots: + assert t.ti < t.tf # well ordered slots + for t1, t2 in zip(self.slots, self.slots[1:]): + assert t1.tf <= t2.ti # no overlaps on a given channel + + def extend_duration(self, new_duration: int) -> ChannelSamples: + """Extends the duration of the samples. + + Pads the amplitude and detuning samples with zeros and the phase with + its last value (or zero if empty). + + Args: + new_duration: The new duration for the samples (in ns). + Must be greater than or equal to the current duration. + + Returns: + The extend channel samples. + """ + extension = new_duration - len(self.amp) + if new_duration < self.duration: + raise ValueError("Can't extend samples to a lower duration.") + + new_amp = np.pad(self.amp, (0, extension)) + new_detuning = np.pad(self.det, (0, extension)) + new_phase = np.pad( + self.phase, + (0, extension), + mode="edge" if self.phase.size > 0 else "constant", + ) + return ChannelSamples(new_amp, new_detuning, new_phase, self.slots) + + def modulate(self, channel_obj: Channel) -> ChannelSamples: + """Modulates the samples for a given channel. + + It assumes that the phase starts at its initial value and is kept at + its final value.The same could potentially be done for the detuning, + but it's not as safe of an assumption so it's not done for now. + + Args: + channel_obj: The channel object for which to modulate the samples. + + Returns: + The modulated channel samples. + """ + new_amp = channel_obj.modulate(self.amp) + new_detuning = channel_obj.modulate(self.det) + new_phase = channel_obj.modulate(self.phase, keep_ends=True) + return ChannelSamples(new_amp, new_detuning, new_phase, self.slots) + + +@dataclass +class SequenceSamples: + """Gather samples of a sequence with useful info.""" + + channels: list[str] + samples_list: list[ChannelSamples] + _ch_objs: dict[str, Channel] + + @property + def channel_samples(self) -> dict[str, ChannelSamples]: + """Mapping between the channel name and its samples.""" + return dict(zip(self.channels, self.samples_list)) + + @property + def max_duration(self) -> int: + """The maximum duration among the channel samples.""" + return max(samples.duration for samples in self.samples_list) + + def to_nested_dict(self) -> dict: + """Format in the nested dictionary form. + + This is the format expected by `pulser_simulation.Simulation()`. + """ + bases = {ch_obj.basis for ch_obj in self._ch_objs.values()} + in_xy = False + if "XY" in bases: + assert bases == {"XY"} + in_xy = True + d = _prepare_dict(self.max_duration, in_xy=in_xy) + for chname, samples in zip(self.channels, self.samples_list): + cs = samples.extend_duration(self.max_duration) + addr = self._ch_objs[chname].addressing + basis = self._ch_objs[chname].basis + if addr == _GLOBAL: + d[_GLOBAL][basis][_AMP] += cs.amp + d[_GLOBAL][basis][_DET] += cs.det + d[_GLOBAL][basis][_PHASE] += cs.phase + else: + for s in cs.slots: + for t in s.targets: + times = slice(s.ti, s.tf) + d[_LOCAL][basis][t][_AMP][times] += cs.amp[times] + d[_LOCAL][basis][t][_DET][times] += cs.det[times] + d[_LOCAL][basis][t][_PHASE][times] += cs.phase[times] + + return _default_to_regular(d) + + def __repr__(self) -> str: + blocks = [ + f"{chname}:\n{cs!r}" + for chname, cs in zip(self.channels, self.samples_list) + ] + return "\n\n".join(blocks) diff --git a/pulser-core/pulser/sequence/_schedule.py b/pulser-core/pulser/sequence/_schedule.py index d1e12573f..f470db7e8 100644 --- a/pulser-core/pulser/sequence/_schedule.py +++ b/pulser-core/pulser/sequence/_schedule.py @@ -24,6 +24,7 @@ from pulser.channels import Channel from pulser.pulse import Pulse from pulser.register.base_register import QubitId +from pulser.sampler.samples import ChannelSamples, _TargetSlot class _TimeSlot(NamedTuple): @@ -76,6 +77,39 @@ def adjust_duration(self, duration: int) -> int: max(duration, self.channel_obj.min_duration) ) + def get_samples(self, modulated: bool = False) -> ChannelSamples: + """Returns the samples of the channel. + + Args: + modulated: Whether to return the modulated samples. + """ + # Keep only pulse slots + channel_slots = [s for s in self.slots if isinstance(s.type, Pulse)] + dt = self.get_duration() + amp, det, phase = np.zeros(dt), np.zeros(dt), np.zeros(dt) + slots: list[_TargetSlot] = [] + + for ind, s in enumerate(channel_slots): + pulse = cast(Pulse, s.type) + amp[s.ti : s.tf] += pulse.amplitude.samples + det[s.ti : s.tf] += pulse.detuning.samples + ph_jump_t = self.channel_obj.phase_jump_time + t_start = max(0, (s.ti - ph_jump_t)) + t_end = ( + channel_slots[ind + 1].ti - ph_jump_t + if ind < len(channel_slots) - 1 + else dt + ) + phase[t_start:t_end] += pulse.phase + slots.append(_TargetSlot(s.ti, s.tf, s.targets)) + + ch_samples = ChannelSamples(amp, det, phase, slots) + + if modulated: + ch_samples = ch_samples.modulate(self.channel_obj) + + return ch_samples + @overload def __getitem__(self, key: int) -> _TimeSlot: pass diff --git a/pulser-core/pulser/sequence/_seq_drawer.py b/pulser-core/pulser/sequence/_seq_drawer.py index ffc4ace28..98cd3ea97 100644 --- a/pulser-core/pulser/sequence/_seq_drawer.py +++ b/pulser-core/pulser/sequence/_seq_drawer.py @@ -26,8 +26,10 @@ import pulser from pulser import Register, Register3D +from pulser.channels import Channel from pulser.pulse import Pulse -from pulser.waveforms import ConstantWaveform, InterpolatedWaveform +from pulser.sampler.samples import ChannelSamples +from pulser.waveforms import InterpolatedWaveform # Color scheme COLORS = ["darkgreen", "indigo", "#c75000"] @@ -44,17 +46,19 @@ @dataclass class ChannelDrawContent: - """The contents for drawingflake a single channel.""" + """The contents for drawing a single channel.""" - time: list[int] - amplitude: list[float] - detuning: list[float] - phase: list[float] + samples: ChannelSamples target: dict[Union[str, tuple[int, int]], Any] - measurement: Optional[str] = None interp_pts: dict[str, list[list[float]]] = field(default_factory=dict) def __post_init__(self) -> None: + self.samples.phase = self.samples.phase / (2 * np.pi) + self._samples_from_curves = { + "amplitude": "amp", + "detuning": "det", + "phase": "phase", + } self.curves_on = {"amplitude": True, "detuning": False, "phase": False} @property @@ -62,82 +66,87 @@ def n_axes_on(self) -> int: """The number of axes to draw for this channel.""" return sum(self.curves_on.values()) + def get_input_curves(self) -> list[np.ndarray]: + """The samples for the curves, as programmed.""" + return self._give_curves_from_samples(self.samples) + + def get_output_curves(self, ch_obj: Channel) -> list[np.ndarray]: + """The modulated samples for the curves.""" + mod_samples = self.samples.modulate(ch_obj) + return self._give_curves_from_samples(mod_samples) + + def get_interpolated_curves( + self, sampling_rate: float + ) -> list[np.ndarray]: + """The curves with a fractional sampling rate.""" + indices = np.linspace( + 0, + self.samples.duration - 1, + int(sampling_rate * (self.samples.duration + 1)), + dtype=int, + ) + sampled_curves = [curve[indices] for curve in self.get_input_curves()] + t = np.arange(self.samples.duration) + return [CubicSpline(indices, curve)(t) for curve in sampled_curves] + def curves_on_indices(self) -> list[int]: """The indices of the curves to draw.""" return [i for i, qty in enumerate(CURVES_ORDER) if self.curves_on[qty]] + def _give_curves_from_samples( + self, samples: ChannelSamples + ) -> list[np.ndarray]: + return [ + getattr(samples, self._samples_from_curves[qty]) + for qty in CURVES_ORDER + ] + -def gather_data(seq: pulser.sequence.Sequence) -> dict: +def gather_data(seq: pulser.sequence.Sequence, gather_output: bool) -> dict: """Collects the whole sequence data for plotting. Args: seq: The input sequence of operations on a device. + gather_output: Whether to gather the modulated output curves. Returns: The data to plot. """ # The minimum time axis length is 100 ns - total_duration = max(seq.get_duration(), 100) + total_duration = max( + seq.get_duration(include_fall_time=gather_output), 100 + ) data: dict[str, Any] = {} for ch, sch in seq._schedule.items(): - time = [-1] # To not break the "time[-1]" later on - amp = [] - detuning = [] - phase = [] # List of interpolation points interp_pts: defaultdict[str, list[list[float]]] = defaultdict(list) target: dict[Union[str, tuple[int, int]], Any] = {} for slot in sch: if slot.ti == -1: target["initial"] = slot.targets - time += [0] - amp += [0.0] - detuning += [0.0] - phase += [0.0] continue - if slot.type in ["delay", "target"]: - time += [ - slot.ti, - slot.tf - 1 if slot.tf > slot.ti else slot.ti, - ] - amp += [0.0, 0.0] - detuning += [0.0, 0.0] - phase += [phase[-1]] * 2 - if slot.type == "target": - target[(slot.ti, slot.tf - 1)] = slot.targets + if slot.type == "target": + target[(slot.ti, slot.tf - 1)] = slot.targets + continue + if slot.type == "delay": continue pulse = cast(Pulse, slot.type) - if isinstance(pulse.amplitude, ConstantWaveform) and isinstance( - pulse.detuning, ConstantWaveform - ): - time += [slot.ti, slot.tf - 1] - amp += [float(pulse.amplitude[0])] * 2 - detuning += [float(pulse.detuning[0])] * 2 - phase += [float(pulse.phase) / (2 * np.pi)] * 2 - else: - time += list(range(slot.ti, slot.tf)) - amp += pulse.amplitude.samples.tolist() - detuning += pulse.detuning.samples.tolist() - phase += [float(pulse.phase) / (2 * np.pi)] * pulse.duration - for wf_type in ["amplitude", "detuning"]: - wf = getattr(pulse, wf_type) - if isinstance(wf, InterpolatedWaveform): - pts = wf.data_points - pts[:, 0] += slot.ti - interp_pts[wf_type] += pts.tolist() - - if time[-1] < total_duration - 1: - time += [time[-1] + 1, total_duration - 1] - amp += [0, 0] - detuning += [0, 0] - phase += [phase[-1] if len(phase) else 0] * 2 + for wf_type in ["amplitude", "detuning"]: + wf = getattr(pulse, wf_type) + if isinstance(wf, InterpolatedWaveform): + pts = wf.data_points + pts[:, 0] += slot.ti + interp_pts[wf_type] += pts.tolist() + # Store everything - time.pop(0) # Removes the -1 in the beginning - data[ch] = ChannelDrawContent(time, amp, detuning, phase, target) - if hasattr(seq, "_measurement"): - data[ch].measurement = seq._measurement + samples = sch.get_samples() + data[ch] = ChannelDrawContent( + samples.extend_duration(total_duration), target + ) if interp_pts: data[ch].interp_pts = dict(interp_pts) + if hasattr(seq, "_measurement"): + data["measurement"] = seq._measurement data["total_duration"] = total_duration return data @@ -161,7 +170,8 @@ def draw_sequence( the solver. If present, plots the effective pulse alongside the input pulse. draw_phase_area: Whether phase and area values need to be shown - as text on the plot, defaults to False. + as text on the plot, defaults to False. If `draw_phase_curve=True`, + phase values are ommited. draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation on top of the respective waveforms (defaults to True). @@ -192,13 +202,13 @@ def phase_str(phi: float) -> str: n_channels = len(seq.declared_channels) if not n_channels: raise RuntimeError("Can't draw an empty sequence.") - data = gather_data(seq) + data = gather_data(seq, gather_output=draw_modulation) total_duration = data["total_duration"] time_scale = 1e3 if total_duration > 1e4 else 1 for ch in seq._schedule: - if np.nonzero(data[ch].detuning)[0].size > 0: + if np.count_nonzero(data[ch].samples.det) > 0: data[ch].curves_on["detuning"] = True - if draw_phase_curve and np.nonzero(data[ch].phase)[0].size > 0: + if draw_phase_curve and np.count_nonzero(data[ch].samples.phase) > 0: data[ch].curves_on["phase"] = True # Boxes for qubit and phase text @@ -278,7 +288,6 @@ def phase_str(phi: float) -> str: ax.spines["top"].set_visible(False) if j < len(ch_axes[ch]) - 1: ax.spines["bottom"].set_visible(False) - if i < n_channels - 1 or j < len(ch_axes[ch]) - 1: ax.tick_params( axis="x", @@ -292,27 +301,9 @@ def phase_str(phi: float) -> str: unit = "ns" if time_scale == 1 else r"$\mu s$" ax.set_xlabel(f"t ({unit})", fontsize=12) - if sampling_rate: - indexes = np.linspace( - 0, - total_duration - 1, - int(sampling_rate * total_duration), - dtype=int, - ) - times = np.arange(total_duration, dtype=np.double) / time_scale - solver_time = times[indexes] - delta_t = np.diff(solver_time)[0] - # Compare pulse with an interpolated pulse with 100 times more samples - teff = np.arange(0, max(solver_time), delta_t / 100) - - # Make sure the time axis of all channels are aligned - final_t = total_duration / time_scale - if draw_modulation: - for ch, ch_obj in seq.declared_channels.items(): - final_t = max( - final_t, - (seq.get_duration(ch) + 2 * ch_obj.rise_time) / time_scale, - ) + # The time axis of all channels is the same + t = np.arange(total_duration) / time_scale + final_t = t[-1] t_min = -final_t * 0.03 t_max = final_t * 1.05 @@ -320,41 +311,15 @@ def phase_str(phi: float) -> str: ch_obj = seq.declared_channels[ch] ch_data = data[ch] basis = ch_obj.basis - times = np.array(ch_data.time) - t = times / time_scale - ys = [getattr(ch_data, qty) for qty in CURVES_ORDER] + ys = ch_data.get_input_curves() if sampling_rate: - cubic_splines = [] - yseff = [] - t2 = 1 - t2s = [] - for t_solv in solver_time: - # Find the interval [t[t2],t[t2+1]] containing t_solv - while t_solv > t[t2]: - t2 += 1 - t2s.append(t2) - for i, y_ in enumerate(ys): - y2 = [y_[t_] for t_ in t2s] - cubic_splines.append(CubicSpline(solver_time, y2)) - yseff.append(cubic_splines[i](teff)) + yseff = ch_data.get_interpolated_curves(sampling_rate) draw_output = draw_modulation and ( ch_obj.mod_bandwidth or not draw_input ) if draw_output: - ys_mod = [] - t_diffs = np.diff(times) - end_index = int(final_t * time_scale) - for i, y_ in enumerate(ys): - input = np.repeat(y_[1:], t_diffs) - ys_mod.append( - ch_obj.modulate(input, keep_ends=i > 0)[:end_index] - ) - # Prolong the input samples - t = np.append(t, (t[-1] + 1 / time_scale, final_t)) - ys[0] += [0.0, 0.0] - ys[1] += [0.0, 0.0] - ys[2] += [ys[2][-1]] * 2 + ys_mod = ch_data.get_output_curves(ch_obj) ref_ys = yseff if sampling_rate else ys max_amp = np.max(ref_ys[0]) @@ -383,19 +348,19 @@ def phase_str(phi: float) -> str: ax.plot(t, ys[i], color=COLORS[i], linewidth=0.8) if sampling_rate: ax.plot( - teff, + t, yseff[i], color=COLORS[i], linewidth=0.8, ) - ax.fill_between(teff, 0, yseff[i], color=COLORS[i], alpha=0.3) + ax.fill_between(t, 0, yseff[i], color=COLORS[i], alpha=0.3) elif draw_input: ax.fill_between(t, 0, ys[i], color=COLORS[i], alpha=0.3) if draw_output: ax.fill_between( - np.arange(ys_mod[i].size), + t, 0, - ys_mod[i], + ys_mod[i][:total_duration], color=COLORS[i], alpha=0.3, hatch="////", @@ -405,7 +370,7 @@ def phase_str(phi: float) -> str: if draw_phase_area: top = False # Variable to track position of box, top or center. - draw_phase = any( + print_phase = not draw_phase_curve and any( seq_.type.phase != 0 for seq_ in seq._schedule[ch] if isinstance(seq_.type, Pulse) @@ -415,13 +380,7 @@ def phase_str(phi: float) -> str: if isinstance(seq_.type, Pulse): if sampling_rate: area_val = ( - np.sum( - cubic_splines[0]( - np.arange(seq_.ti, seq_.tf) / time_scale - ) - ) - * 1e-3 - / np.pi + np.sum(yseff[0][seq_.ti : seq_.tf]) * 1e-3 / np.pi ) else: area_val = seq_.type.amplitude.integral / np.pi @@ -441,7 +400,7 @@ def phase_str(phi: float) -> str: if round(area_val, 2) == 1 else rf"A: {area_val:.2g}$\pi$" ) - if not draw_phase: + if not print_phase: txt = area_fmt else: phase_fmt = rf"$\phi$: {phase_str(phase_val)}" @@ -539,7 +498,7 @@ def phase_str(phi: float) -> str: # All targets have the same ref, so we pick q = targets_[0] ref = seq._basis_ref[basis][q].phase - if end != total_duration - 1 or ch_data.measurement is not None: + if end != total_duration - 1 or "measurement" in data: end += 1 / time_scale for t_, delta in ref.changes(start, end, time_scale=time_scale): conf = dict(linestyle="--", linewidth=1.5, color="black") @@ -574,8 +533,8 @@ def phase_str(phi: float) -> str: ) hline_kwargs = dict(linestyle="-", linewidth=0.5, color="grey") - if ch_data.measurement is not None: - msg = f"Basis: {ch_data.measurement}" + if "measurement" in data: + msg = f"Basis: {data['measurement']}" if len(axes) == 1: mid_ax = axes[0] mid_point = (amp_top + amp_bottom) / 2 diff --git a/pulser-core/pulser/sequence/sequence.py b/pulser-core/pulser/sequence/sequence.py index 3e5123209..ba1a904df 100644 --- a/pulser-core/pulser/sequence/sequence.py +++ b/pulser-core/pulser/sequence/sequence.py @@ -972,7 +972,8 @@ def draw( case only the input is drawn. draw_phase_area: Whether phase and area values need to be shown as text on the plot, defaults to False. Doesn't work in - 'output' mode. + 'output' mode. If `draw_phase_curve=True`, phase values are + ommited. draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation on top of the respective input waveforms (defaults to True). diff --git a/pulser-simulation/pulser_simulation/noises.py b/pulser-simulation/pulser_simulation/noises.py deleted file mode 100644 index 5323226cb..000000000 --- a/pulser-simulation/pulser_simulation/noises.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright 2022 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Contains noise models. - -For now, only the amplitude and doppler noises are implemented in the form a -NoiseModel, which are the laser-atom interaction related noises relevant at -sampling time. -""" -from __future__ import annotations - -from typing import Optional - -import numpy as np - -from pulser.register import Register -from pulser.sampler.noise_model import NoiseModel -from pulser.sampler.samples import QubitSamples - - -def amplitude( - reg: Register, - waist_width: float, - random: bool = True, - seed: Optional[int] = None, -) -> NoiseModel: - """Generate a NoiseModel for the gaussian amplitude profile of laser beams. - - The laser of a global channel has a non-constant amplitude profile in the - register plane. It makes global channels act differently on each qubit, - becoming local. - - Args: - reg: A Pulser register - waist_width: The laser waist_width in µm - random: Adds an additional random noise on the amplitude - seed: seed for the numpy.random.Generator - - Return: - NoiseModel: The function that applies the amplitude noise to some - QubitSamples. - """ - rng = np.random.default_rng(seed) - - def f(s: QubitSamples) -> QubitSamples: - r = np.linalg.norm(reg.qubits[s.qubit]) - - noise_amp = rng.normal(1.0, 1.0e-3) if random else 1.0 - noise_amp *= np.exp(-((r / waist_width) ** 2)) - - amp = s.amp.copy() - amp *= noise_amp - - return QubitSamples( - amp=amp, - det=s.det.copy(), - phase=s.phase.copy(), - qubit=s.qubit, - ) - - return f - - -def doppler(reg: Register, std_dev: float, seed: Optional[int]) -> NoiseModel: - """Generate a NoiseModel for the Doppler effect detuning shifts. - - Example usage: - - MASS = 1.45e-25 # kg - KB = 1.38e-23 # J/K - KEFF = 8.7 # µm^-1 - sigma = KEFF * np.sqrt(KB * 50.0e-6 / MASS) - doppler_noise = doppler(reg, sigma) - ... - - Args: - reg: A Pulser register - std_dev: The standard deviation of the normal distribution used - to sample the random detuning shifts - seed: seed for the numpy.random.Generator - - Return: - NoiseModel: The function that applies the doppler noise to some - QubitSamples. - """ - rng = np.random.default_rng(seed) - errs = rng.normal(0.0, std_dev, size=len(reg.qubit_ids)) - detunings = dict(zip(reg.qubit_ids, errs)) - - def f(s: QubitSamples) -> QubitSamples: - det = s.det.copy() - det[np.nonzero(s.det)] += detunings[s.qubit] - return QubitSamples( - amp=s.amp.copy(), - det=det, - phase=s.phase.copy(), - qubit=s.qubit, - ) - - return f diff --git a/tests/test_sampling_noises.py b/tests/test_sampling_noises.py deleted file mode 100644 index 203671be4..000000000 --- a/tests/test_sampling_noises.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2022 Pulser Development Team -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from __future__ import annotations - -import numpy as np -import pytest - -import pulser -import pulser_simulation.noises as noises -from pulser.devices import MockDevice -from pulser.pulse import Pulse -from pulser.sampler import sample -from pulser.waveforms import ConstantWaveform - - -def test_amplitude_noise(): - """Test the noise related to the amplitude profile of global pulses.""" - N = 100 - amplitude = 1.0 - waist_width = 2.0 # µm - - coords = np.array([[-2.0, 0.0], [0.0, 0.0], [2.0, 0.0]]) - reg = pulser.Register.from_coordinates(coords, prefix="q") - seq = pulser.Sequence(reg, MockDevice) - seq.declare_channel("ch0", "rydberg_global") - seq.add( - Pulse.ConstantAmplitude(amplitude, ConstantWaveform(N, 0.0), 0.0), - "ch0", - ) - seq.measure() - - def expected_samples(vec: np.ndarray) -> np.ndarray: - """Defines the non-noisy effect of a gaussian amplitude profile.""" - r = np.linalg.norm(vec) - a = np.ones(N) - a *= amplitude - a *= np.exp(-((r / waist_width) ** 2)) - return a - - s = sample( - seq, global_noises=[noises.amplitude(reg, waist_width, random=False)] - ) - - for q, coords in reg.qubits.items(): - got = s["Local"]["ground-rydberg"][q]["amp"] - want = expected_samples(coords) - np.testing.assert_equal(got, want) - - -@pytest.mark.xfail( - reason="Test a different doppler effect than the one implemented; " - "no surprise it fails." -) -def test_doppler_noise(): - """What is exactly the doppler noise here? - - A constant detuning shift per pulse seems weird. A global shift seems more - reasonable, but how can it be constant during the all sequence? It is not - clear to me here, I find the current implementation in the simulation - module to be unsatisfactory. - - No surprise I make it fail on purpose right now 😅 - """ - N = 100 - det_value = np.pi - - reg = pulser.Register.from_coordinates(np.array([[0.0, 0.0]]), prefix="q") - seq = pulser.Sequence(reg, MockDevice) - seq.declare_channel("ch0", "rydberg_global") - for _ in range(3): - seq.add( - Pulse.ConstantDetuning(ConstantWaveform(N, 1.0), det_value, 0.0), - "ch0", - ) - seq.delay(100, "ch0") - seq.measure() - - MASS = 1.45e-25 # kg - KB = 1.38e-23 # J/K - KEFF = 8.7 # µm^-1 - doppler_sigma = KEFF * np.sqrt(KB * 50.0e-6 / MASS) - seed = 42 - rng = np.random.default_rng(seed) - - shifts = rng.normal(0, doppler_sigma, 3) - want = np.zeros(6 * N) - want[0:100] = det_value + shifts[0] - want[200:300] = det_value + shifts[1] - want[400:500] = det_value + shifts[2] - - local_noises = [noises.doppler(reg, doppler_sigma, seed=seed)] - samples = sample(seq, common_noises=local_noises) - got = samples["Local"]["ground-rydberg"]["q0"]["det"] - - np.testing.assert_array_equal(got, want) diff --git a/tests/test_sequence_sampler.py b/tests/test_sequence_sampler.py index 5031163a4..575c39848 100644 --- a/tests/test_sequence_sampler.py +++ b/tests/test_sequence_sampler.py @@ -25,8 +25,6 @@ from pulser.devices import Device, MockDevice from pulser.pulse import Pulse from pulser.sampler import sample -from pulser.sampler.sampler import _write_dict -from pulser.sampler.samples import QubitSamples from pulser.waveforms import BlackmanWaveform, RampWaveform # Helpers @@ -34,7 +32,7 @@ def assert_same_samples_as_sim(seq: pulser.Sequence) -> None: """Check against the legacy sample extraction in the simulation module.""" - got = sample(seq) + got = sample(seq).to_nested_dict() want = pulser_simulation.Simulation(seq).samples.copy() def truncate_samples(samples_dict): @@ -49,7 +47,7 @@ def truncate_samples(samples_dict): assert_nested_dict_equality(got, truncate_samples(want)) -def assert_nested_dict_equality(got, want: dict) -> None: +def assert_nested_dict_equality(got: dict, want: dict) -> None: for basis in want["Global"]: for qty in want["Global"][basis]: np.testing.assert_array_equal( @@ -80,7 +78,7 @@ def test_one_pulse_sampling(): seq.add(Pulse(amp_wf, det_wf, phase), "ch0") seq.measure() - got = sample(seq)["Global"]["ground-rydberg"] + got = sample(seq).to_nested_dict()["Global"]["ground-rydberg"] want = (amp_wf.samples, det_wf.samples, np.ones(N) * phase) for i, key in enumerate(["amp", "det", "phase"]): np.testing.assert_array_equal(got[key], want[i]) @@ -115,10 +113,31 @@ def test_modulation(mod_seq: pulser.Sequence) -> None: blackman = np.clip(np.blackman(N), 0, np.inf) input = (np.pi / 2) / (np.sum(blackman) / N) * blackman - want = chan.modulate(input) - got = sample(mod_seq, modulation=True)["Global"]["ground-rydberg"]["amp"] - - np.testing.assert_array_equal(got, want) + want_amp = chan.modulate(input) + mod_samples = sample(mod_seq, modulation=True) + got_amp = mod_samples.to_nested_dict()["Global"]["ground-rydberg"]["amp"] + np.testing.assert_array_equal(got_amp, want_amp) + + want_det = chan.modulate(np.ones(N)) + got_det = mod_samples.to_nested_dict()["Global"]["ground-rydberg"]["det"] + np.testing.assert_array_equal(got_det, want_det) + + want_phase = np.ones(mod_seq.get_duration(include_fall_time=True)) + got_phase = mod_samples.to_nested_dict()["Global"]["ground-rydberg"][ + "phase" + ] + np.testing.assert_array_equal(got_phase, want_phase) + + input_samples = sample(mod_seq) + input_ch_samples = input_samples.channel_samples["ch0"] + output_ch_samples = mod_samples.channel_samples["ch0"] + + for qty in ("amp", "det", "phase"): + np.testing.assert_array_equal( + getattr(input_ch_samples.modulate(chan), qty), + getattr(output_ch_samples, qty), + ) + assert input_ch_samples.modulate(chan).slots == output_ch_samples.slots @pytest.fixture @@ -146,6 +165,7 @@ def seq_with_SLM() -> pulser.Sequence: return seq +@pytest.mark.xfail(reason="SLM not handled by `sample()` for now") def test_SLM_samples(seq_with_SLM): pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi / 2), 0.0, 0.0) a_samples = pulse.amplitude.samples @@ -166,7 +186,7 @@ def z() -> np.ndarray: want["Local"]["ground-rydberg"]["superman"]["amp"][0:200] = a_samples want["Local"]["ground-rydberg"]["superman"]["amp"][200:400] = a_samples - got = sample(seq_with_SLM) + got = sample(seq_with_SLM).to_nested_dict() assert_nested_dict_equality(got, want) @@ -186,30 +206,39 @@ def test_SLM_against_simulation(seq_with_SLM): assert_same_samples_as_sim(seq_with_SLM) -def test_corner_cases(): - """Test corner cases of helper functions.""" - with pytest.raises( - ValueError, - match="ndarrays amp, det and phase must have the same length.", - ): - _ = QubitSamples( - amp=np.array([1.0]), - det=np.array([1.0]), - phase=np.array([1.0, 1.0]), - qubit="q0", - ) +def test_samples_repr(seq_rydberg): + samples = sample(seq_rydberg) + assert repr(samples) == "\n\n".join( + [ + f"ch0:\n{samples.samples_list[0]!r}", + f"ch1:\n{samples.samples_list[1]!r}", + ] + ) - reg = pulser.Register.square(1, prefix="q") - seq = pulser.Sequence(reg, MockDevice) - N, M = 10, 11 - samples_dict = { - "a": [QubitSamples(np.zeros(N), np.zeros(N), np.zeros(N), "q0")], - "b": [QubitSamples(np.zeros(M), np.zeros(M), np.zeros(M), "q0")], - } + +def test_extend_duration(seq_rydberg): + samples = sample(seq_rydberg) + short, long = samples.samples_list + assert short.duration < long.duration + assert short.extend_duration(short.duration).duration == short.duration with pytest.raises( - ValueError, match="All the samples do not share the same duration." + ValueError, match="Can't extend samples to a lower duration." ): - _write_dict(seq, samples_dict, {}) + long.extend_duration(short.duration) + + extended_short = short.extend_duration(long.duration) + assert extended_short.duration == long.duration + for qty in ("amp", "det", "phase"): + new_qty_samples = getattr(extended_short, qty) + old_qty_samples = getattr(short, qty) + np.testing.assert_array_equal( + new_qty_samples[: short.duration], old_qty_samples + ) + np.testing.assert_equal( + new_qty_samples[short.duration :], + old_qty_samples[-1] if qty == "phase" else 0.0, + ) + assert extended_short.slots == short.slots # Fixtures @@ -278,7 +307,7 @@ def mod_seq(mod_device: Device) -> pulser.Sequence: seq = pulser.Sequence(reg, mod_device) seq.declare_channel("ch0", "rydberg_global") seq.add( - Pulse.ConstantDetuning(BlackmanWaveform(1000, np.pi / 2), 0.0, 0.0), + Pulse.ConstantDetuning(BlackmanWaveform(1000, np.pi / 2), 1.0, 1.0), "ch0", ) seq.measure() From 43dcb4de81e9a69ab3eb19710f24fb2817b75d02 Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Wed, 3 Aug 2022 16:33:25 +0200 Subject: [PATCH 13/18] Updates from flake8 5.0 --- pulser-core/pulser/register/register.py | 2 +- pulser-core/pulser/register/special_layouts.py | 2 +- pulser-core/pulser/sequence/sequence.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pulser-core/pulser/register/register.py b/pulser-core/pulser/register/register.py index e1947a265..5f7f90930 100644 --- a/pulser-core/pulser/register/register.py +++ b/pulser-core/pulser/register/register.py @@ -214,7 +214,7 @@ def max_connectivity( spacing: float = None, prefix: str = None, ) -> Register: - """Initializes the register with maximum connectivity for a given device. + """Initializes the register with maximum connectivity for a device. In order to maximize connectivity, the basic pattern is the triangle. Atoms are first arranged as layers of hexagons around a central atom. diff --git a/pulser-core/pulser/register/special_layouts.py b/pulser-core/pulser/register/special_layouts.py index 4b2d02441..1f56b56bc 100644 --- a/pulser-core/pulser/register/special_layouts.py +++ b/pulser-core/pulser/register/special_layouts.py @@ -101,7 +101,7 @@ def _to_dict(self) -> dict[str, Any]: class TriangularLatticeLayout(RegisterLayout): - """A RegisterLayout with a triangular lattice pattern in an hexagonal shape. + """A RegisterLayout with a triangular lattice pattern in a hexagonal shape. Args: n_traps: The number of traps in the layout. diff --git a/pulser-core/pulser/sequence/sequence.py b/pulser-core/pulser/sequence/sequence.py index ba1a904df..c48cdb304 100644 --- a/pulser-core/pulser/sequence/sequence.py +++ b/pulser-core/pulser/sequence/sequence.py @@ -707,7 +707,7 @@ def phase_shift( *targets: QubitId, basis: str = "digital", ) -> None: - r"""Shifts the phase of a qubit's reference by 'phi', for a given basis. + r"""Shifts the phase of a qubit's reference by 'phi', on a given basis. This is equivalent to an :math:`R_z(\phi)` gate (i.e. a rotation of the target qubit's state by an angle :math:`\phi` around the z-axis of the @@ -730,7 +730,7 @@ def phase_shift_index( *targets: Union[int, Parametrized], basis: str = "digital", ) -> None: - r"""Shifts the phase of a qubit's reference by 'phi', for a given basis. + r"""Shifts the phase of a qubit's reference by 'phi', on a given basis. This is equivalent to an :math:`R_z(\phi)` gate (i.e. a rotation of the target qubit's state by an angle :math:`\phi` around the z-axis of the From 094ab4b9ee4ce78ee33c2fb069203ee8fbba82d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Fri, 5 Aug 2022 09:34:28 +0200 Subject: [PATCH 14/18] Simulation with modulated output (#390) * wip: simulation with sample from pulser.sampler.sample() * wip: convert defaultdict to dicts in samples() * wip: determine the basis with empty or zeros samples dicts * wip: update notebook to demonstrate previous fixes * wip: add flag for final zero samples * wip: update Simulation sample extraction * wip: update notebook to showcase changes * Adds sampling with SLM mask and simulation with modulation * Noisy simulations from sampled sequences * Type hinting * Simulation drawing with modulation + bug fixes * Change application of amplitude noise fluctuations * Small fixes to sampling and simulation logic * Updating the unit tests * Updating the tutorials * Incorporating review comments and suggestions Co-authored-by: Louis Vignoli --- pulser-core/pulser/sampler/sampler.py | 32 ++- pulser-core/pulser/sampler/samples.py | 80 +++++-- pulser-core/pulser/sequence/_schedule.py | 20 +- pulser-core/pulser/sequence/_seq_drawer.py | 43 ++-- .../pulser_simulation/simconfig.py | 18 +- .../pulser_simulation/simulation.py | 189 ++++++---------- tests/conftest.py | 70 ++++++ tests/test_sequence_sampler.py | 102 ++++----- tests/test_simconfig.py | 4 + tests/test_simresults.py | 20 +- tests/test_simulation.py | 214 +++++++++++++----- ...ting Sequences with Errors and Noise.ipynb | 94 ++------ 12 files changed, 518 insertions(+), 368 deletions(-) create mode 100644 tests/conftest.py diff --git a/pulser-core/pulser/sampler/sampler.py b/pulser-core/pulser/sampler/sampler.py index b9b4f0b1a..e50e57ea8 100644 --- a/pulser-core/pulser/sampler/sampler.py +++ b/pulser-core/pulser/sampler/sampler.py @@ -1,26 +1,44 @@ """Defines the main function for sequence sampling.""" from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Optional -from pulser.sampler.samples import SequenceSamples +from pulser.sampler.samples import SequenceSamples, _SlmMask if TYPE_CHECKING: # pragma: no cover from pulser import Sequence -def sample(seq: Sequence, modulation: bool = False) -> SequenceSamples: +def sample( + seq: Sequence, + modulation: bool = False, + extended_duration: Optional[int] = None, +) -> SequenceSamples: """Construct samples of a Sequence. Args: seq: The sequence to sample. modulation: Whether to modulate the samples. + extended_duration: If defined, extends the samples duration to the + desired value. """ + samples_list = [ + ch_schedule.get_samples(modulated=modulation) + for ch_schedule in seq._schedule.values() + ] + if extended_duration: + samples_list = [ + cs.extend_duration(extended_duration) for cs in samples_list + ] + optionals = {} + if seq._slm_mask_targets and seq._slm_mask_time: + optionals["_slm_mask"] = _SlmMask( + seq._slm_mask_targets, + seq._slm_mask_time[1], + ) return SequenceSamples( list(seq.declared_channels.keys()), - [ - ch_schedule.get_samples(modulated=modulation) - for ch_schedule in seq._schedule.values() - ], + samples_list, seq.declared_channels, + **optionals, ) diff --git a/pulser-core/pulser/sampler/samples.py b/pulser-core/pulser/sampler/samples.py index e9e265ee2..e94456253 100644 --- a/pulser-core/pulser/sampler/samples.py +++ b/pulser-core/pulser/sampler/samples.py @@ -3,6 +3,7 @@ from collections import defaultdict from dataclasses import dataclass, field +from typing import Optional import numpy as np @@ -69,6 +70,14 @@ class _TargetSlot: targets: set[QubitId] +@dataclass +class _SlmMask: + """Auxiliary class to store the SLM mask configuration.""" + + targets: set[QubitId] = field(default_factory=set) + end: int = 0 + + @dataclass class ChannelSamples: """Gathers samples of a channel.""" @@ -100,8 +109,8 @@ def extend_duration(self, new_duration: int) -> ChannelSamples: Returns: The extend channel samples. """ - extension = new_duration - len(self.amp) - if new_duration < self.duration: + extension = new_duration - self.duration + if extension < 0: raise ValueError("Can't extend samples to a lower duration.") new_amp = np.pad(self.amp, (0, extension)) @@ -113,7 +122,17 @@ def extend_duration(self, new_duration: int) -> ChannelSamples: ) return ChannelSamples(new_amp, new_detuning, new_phase, self.slots) - def modulate(self, channel_obj: Channel) -> ChannelSamples: + def is_empty(self) -> bool: + """Whether the channel is effectively empty. + + We consider the channel to be empty if all amplitude and detuning + samples are zero. + """ + return np.count_nonzero(self.amp) + np.count_nonzero(self.det) == 0 + + def modulate( + self, channel_obj: Channel, max_duration: Optional[int] = None + ) -> ChannelSamples: """Modulates the samples for a given channel. It assumes that the phase starts at its initial value and is kept at @@ -122,13 +141,17 @@ def modulate(self, channel_obj: Channel) -> ChannelSamples: Args: channel_obj: The channel object for which to modulate the samples. + max_duration: The maximum duration of the modulation samples. If + defined, truncates them to have a duration less than or equal + to the given value. Returns: The modulated channel samples. """ - new_amp = channel_obj.modulate(self.amp) - new_detuning = channel_obj.modulate(self.det) - new_phase = channel_obj.modulate(self.phase, keep_ends=True) + times = slice(0, max_duration) + new_amp = channel_obj.modulate(self.amp)[times] + new_detuning = channel_obj.modulate(self.det)[times] + new_phase = channel_obj.modulate(self.phase, keep_ends=True)[times] return ChannelSamples(new_amp, new_detuning, new_phase, self.slots) @@ -139,6 +162,7 @@ class SequenceSamples: channels: list[str] samples_list: list[ChannelSamples] _ch_objs: dict[str, Channel] + _slm_mask: _SlmMask = field(default_factory=_SlmMask) @property def channel_samples(self) -> dict[str, ChannelSamples]: @@ -150,10 +174,24 @@ def max_duration(self) -> int: """The maximum duration among the channel samples.""" return max(samples.duration for samples in self.samples_list) - def to_nested_dict(self) -> dict: + def used_bases(self) -> set[str]: + """The bases with non-zero pulses.""" + return { + ch_obj.basis + for ch_obj, ch_samples in zip( + self._ch_objs.values(), self.samples_list + ) + if not ch_samples.is_empty() + } + + def to_nested_dict(self, all_local: bool = False) -> dict: """Format in the nested dictionary form. This is the format expected by `pulser_simulation.Simulation()`. + + Args: + all_local: Forces all samples to be distributed by their + individual targets, even when applied by a global channel. """ bases = {ch_obj.basis for ch_obj in self._ch_objs.values()} in_xy = False @@ -162,17 +200,33 @@ def to_nested_dict(self) -> dict: in_xy = True d = _prepare_dict(self.max_duration, in_xy=in_xy) for chname, samples in zip(self.channels, self.samples_list): - cs = samples.extend_duration(self.max_duration) + cs = ( + samples.extend_duration(self.max_duration) + if samples.duration != self.max_duration + else samples + ) addr = self._ch_objs[chname].addressing basis = self._ch_objs[chname].basis - if addr == _GLOBAL: - d[_GLOBAL][basis][_AMP] += cs.amp - d[_GLOBAL][basis][_DET] += cs.det - d[_GLOBAL][basis][_PHASE] += cs.phase + if addr == _GLOBAL and not all_local: + start_t = self._slm_mask.end + d[_GLOBAL][basis][_AMP][start_t:] += cs.amp[start_t:] + d[_GLOBAL][basis][_DET][start_t:] += cs.det[start_t:] + d[_GLOBAL][basis][_PHASE][start_t:] += cs.phase[start_t:] + if start_t == 0: + # Prevents lines below from running unnecessarily + continue + unmasked_targets = cs.slots[0].targets - self._slm_mask.targets + for t in unmasked_targets: + d[_LOCAL][basis][t][_AMP][:start_t] += cs.amp[:start_t] + d[_LOCAL][basis][t][_DET][:start_t] += cs.det[:start_t] + d[_LOCAL][basis][t][_PHASE][:start_t] += cs.phase[:start_t] else: for s in cs.slots: for t in s.targets: - times = slice(s.ti, s.tf) + ti = s.ti + if t in self._slm_mask.targets: + ti = max(ti, self._slm_mask.end) + times = slice(ti, s.tf) d[_LOCAL][basis][t][_AMP][times] += cs.amp[times] d[_LOCAL][basis][t][_DET][times] += cs.det[times] d[_LOCAL][basis][t][_PHASE][times] += cs.phase[times] diff --git a/pulser-core/pulser/sequence/_schedule.py b/pulser-core/pulser/sequence/_schedule.py index f470db7e8..ab1760ae0 100644 --- a/pulser-core/pulser/sequence/_schedule.py +++ b/pulser-core/pulser/sequence/_schedule.py @@ -94,19 +94,33 @@ def get_samples(self, modulated: bool = False) -> ChannelSamples: amp[s.ti : s.tf] += pulse.amplitude.samples det[s.ti : s.tf] += pulse.detuning.samples ph_jump_t = self.channel_obj.phase_jump_time - t_start = max(0, (s.ti - ph_jump_t)) + t_start = s.ti - ph_jump_t if ind > 0 else 0 t_end = ( channel_slots[ind + 1].ti - ph_jump_t if ind < len(channel_slots) - 1 else dt ) phase[t_start:t_end] += pulse.phase - slots.append(_TargetSlot(s.ti, s.tf, s.targets)) + tf = s.tf + if modulated: + # Account for the extended duration of the pulses + # after modulation, which is at most fall_time + fall_time = pulse.fall_time(self.channel_obj) + tf += ( + min(fall_time, channel_slots[ind + 1].ti - s.tf) + if ind < len(channel_slots) - 1 + else fall_time + ) + + slots.append(_TargetSlot(s.ti, tf, s.targets)) ch_samples = ChannelSamples(amp, det, phase, slots) if modulated: - ch_samples = ch_samples.modulate(self.channel_obj) + ch_samples = ch_samples.modulate( + self.channel_obj, + max_duration=self.get_duration(include_fall_time=True), + ) return ch_samples diff --git a/pulser-core/pulser/sequence/_seq_drawer.py b/pulser-core/pulser/sequence/_seq_drawer.py index 98cd3ea97..6080e70f1 100644 --- a/pulser-core/pulser/sequence/_seq_drawer.py +++ b/pulser-core/pulser/sequence/_seq_drawer.py @@ -75,19 +75,20 @@ def get_output_curves(self, ch_obj: Channel) -> list[np.ndarray]: mod_samples = self.samples.modulate(ch_obj) return self._give_curves_from_samples(mod_samples) - def get_interpolated_curves( - self, sampling_rate: float + def interpolate_curves( + self, curves: list[np.ndarray], sampling_rate: float ) -> list[np.ndarray]: """The curves with a fractional sampling rate.""" indices = np.linspace( 0, - self.samples.duration - 1, - int(sampling_rate * (self.samples.duration + 1)), + self.samples.duration, + num=int(sampling_rate * self.samples.duration), + endpoint=False, dtype=int, ) - sampled_curves = [curve[indices] for curve in self.get_input_curves()] + sampled_curves = [curve[indices] for curve in curves] t = np.arange(self.samples.duration) - return [CubicSpline(indices, curve)(t) for curve in sampled_curves] + return [CubicSpline(indices, sc)(t) for sc in sampled_curves] def curves_on_indices(self) -> list[int]: """The indices of the curves to draw.""" @@ -312,15 +313,15 @@ def phase_str(phi: float) -> str: ch_data = data[ch] basis = ch_obj.basis ys = ch_data.get_input_curves() - if sampling_rate: - yseff = ch_data.get_interpolated_curves(sampling_rate) - draw_output = draw_modulation and ( ch_obj.mod_bandwidth or not draw_input ) if draw_output: ys_mod = ch_data.get_output_curves(ch_obj) + if sampling_rate: + curves = ys_mod if draw_output else ys + yseff = ch_data.interpolate_curves(curves, sampling_rate) ref_ys = yseff if sampling_rate else ys max_amp = np.max(ref_ys[0]) max_amp = 1 if max_amp == 0 else max_amp @@ -357,14 +358,22 @@ def phase_str(phi: float) -> str: elif draw_input: ax.fill_between(t, 0, ys[i], color=COLORS[i], alpha=0.3) if draw_output: - ax.fill_between( - t, - 0, - ys_mod[i][:total_duration], - color=COLORS[i], - alpha=0.3, - hatch="////", - ) + if not sampling_rate: + ax.fill_between( + t, + 0, + ys_mod[i][:total_duration], + color=COLORS[i], + alpha=0.3, + hatch="////", + ) + else: + ax.plot( + t, + ys_mod[i][:total_duration], + color=COLORS[i], + linestyle="dotted", + ) special_kwargs = dict(labelpad=10) if i == 0 else {} ax.set_ylabel(LABELS[i], fontsize=14, **special_kwargs) diff --git a/pulser-simulation/pulser_simulation/simconfig.py b/pulser-simulation/pulser_simulation/simconfig.py index ef748e6ee..489031fb8 100644 --- a/pulser-simulation/pulser_simulation/simconfig.py +++ b/pulser-simulation/pulser_simulation/simconfig.py @@ -17,7 +17,7 @@ from dataclasses import dataclass, field from sys import version_info -from typing import Any, Union +from typing import Any, Optional, Union import numpy as np import qutip @@ -69,8 +69,10 @@ class SimConfig: Useful for cutting down on computing time, but unrealistic. temperature: Temperature, set in µK, of the Rydberg array. Also sets the standard deviation of the speed of the atoms. - laser_waist: Waist of the gaussian laser, set in µm, - in global pulses. + laser_waist: Waist of the gaussian laser, set in µm, in global + pulses. + amp_sigma: Dictates the fluctuations in amplitude as a standard + deviation of a normal distribution centered in 1. solver_options: Options for the qutip solver. """ @@ -79,11 +81,12 @@ class SimConfig: samples_per_run: int = 5 temperature: float = 50.0 laser_waist: float = 175.0 + amp_sigma: float = 5e-2 eta: float = 0.005 epsilon: float = 0.01 epsilon_prime: float = 0.05 dephasing_prob: float = 0.05 - solver_options: qutip.Options = None + solver_options: Optional[qutip.Options] = None spam_dict: dict[str, float] = field( init=False, default_factory=dict, repr=False ) @@ -92,6 +95,12 @@ class SimConfig: ) def __post_init__(self) -> None: + if not 0.0 <= self.amp_sigma < 1.0: + raise ValueError( + "The standard deviation in amplitude (amp_sigma=" + f"{self.amp_sigma}) must be greater than or equal" + " to 0. and smaller than 1." + ) self._process_temperature() self._change_attribute( "spam_dict", @@ -118,6 +127,7 @@ def __str__(self, solver_options: bool = False) -> str: lines.append(f"SPAM dictionary: {self.spam_dict}") if "doppler" in self.noise: lines.append(f"Temperature: {self.temperature*1.e6}µK") + lines.append(f"Amplitude standard dev.: {self.amp_sigma}") if "amplitude" in self.noise: lines.append(f"Laser waist: {self.laser_waist}μm") if "dephasing" in self.noise: diff --git a/pulser-simulation/pulser_simulation/simulation.py b/pulser-simulation/pulser_simulation/simulation.py index dbb2dfdb5..a20735666 100644 --- a/pulser-simulation/pulser_simulation/simulation.py +++ b/pulser-simulation/pulser_simulation/simulation.py @@ -17,9 +17,8 @@ import itertools import warnings -from collections import Counter +from collections import Counter, defaultdict from collections.abc import Mapping -from copy import deepcopy from dataclasses import asdict from typing import Any, Optional, Union, cast @@ -28,10 +27,11 @@ import qutip from numpy.typing import ArrayLike +import pulser.sampler as sampler from pulser import Pulse, Sequence from pulser.register import QubitId +from pulser.sampler.samples import _TargetSlot from pulser.sequence._seq_drawer import draw_sequence -from pulser.sequence.sequence import _TimeSlot from pulser_simulation.simconfig import SimConfig from pulser_simulation.simresults import ( CoherentResults, @@ -67,6 +67,8 @@ class Simulation: those specific times. - A float to act as a sampling rate for the resulting state. + with_modulation: Whether to simulated the sequence with the programmed + input or the expected output. """ def __init__( @@ -75,6 +77,7 @@ def __init__( sampling_rate: float = 1.0, config: Optional[SimConfig] = None, evaluation_times: Union[float, str, ArrayLike] = "Full", + with_modulation: bool = False, ) -> None: """Instantiates a Simulation object.""" if not isinstance(sequence, Sequence): @@ -100,7 +103,22 @@ def __init__( self._interaction = "XY" if self._seq._in_xy else "ising" self._qdict = self._seq.qubit_info self._size = len(self._qdict) - self._tot_duration = self._seq.get_duration() + self._modulated = with_modulation + if self._modulated and sequence._slm_mask_targets: + raise NotImplementedError( + "Simulation of sequences combining an SLM mask and output " + "modulation is not supported." + ) + self._tot_duration = self._seq.get_duration( + include_fall_time=self._modulated + ) + self.samples_obj = sampler.sample( + self._seq, + modulation=self._modulated, + # The samples are extended by 1 to improve the ODE + # solver convergence + extended_duration=self._tot_duration + 1, + ) # Type hints for attributes defined outside of __init__ self.basis_name: str @@ -133,6 +151,10 @@ def __init__( self._bad_atoms: dict[Union[str, int], bool] = {} self._doppler_detune: dict[Union[str, int], float] = {} + # Stores the qutip operators used in building the Hamiltonian + self.operators: dict[str, defaultdict[str, dict]] = { + addr: defaultdict(dict) for addr in ["Global", "Local"] + } # Sets the config as well as builds the hamiltonian self.set_config(config) if config else self.set_config(SimConfig()) if hasattr(self._seq, "_measurement"): @@ -386,7 +408,8 @@ def draw( to be shown as text on the plot, defaults to False. draw_interp_pts: When the sequence has pulses with waveforms of type InterpolatedWaveform, draws the points of interpolation - on top of the respective waveforms (defaults to False). + on top of the respective waveforms (defaults to False). Can't + be used if the sequence is modulated. draw_phase_shifts: Whether phase shift and reference information should be added to the plot, defaults to False. draw_phase_curve: Draws the changes in phase in its own curve @@ -400,9 +423,16 @@ def draw( See Also: Sequence.draw(): Draws the sequence in its current state. """ + if draw_interp_pts and self._modulated: + raise ValueError( + "Can't draw the interpolation points when the sequence is " + "modulated; `draw_interp_pts` must be `False`." + ) draw_sequence( self._seq, self._sampling_rate, + draw_input=not self._modulated, + draw_modulation=self._modulated, draw_phase_area=draw_phase_area, draw_interp_pts=draw_interp_pts, draw_phase_shifts=draw_phase_shifts, @@ -414,116 +444,51 @@ def draw( def _extract_samples(self) -> None: """Populates samples dictionary with every pulse in the sequence.""" - self.samples: dict[str, dict[str, dict]] - if self._interaction == "ising": - self.samples = { - addr: {basis: {} for basis in ["ground-rydberg", "digital"]} - for addr in ["Global", "Local"] - } - else: - self.samples = {addr: {"XY": {}} for addr in ["Global", "Local"]} - - if not hasattr(self, "operators"): - self.operators = deepcopy(self.samples) - - def prepare_dict() -> dict[str, np.ndarray]: - # Duration includes retargeting, delays, etc. - # Also adds extra time step for final instruction - return { - "amp": np.zeros(self._tot_duration + 1), - "det": np.zeros(self._tot_duration + 1), - "phase": np.zeros(self._tot_duration + 1), - } + local_noises = True + if set(self.config.noise).issubset({"dephasing", "SPAM"}): + local_noises = "SPAM" in self.config.noise and self.config.eta > 0 + samples = self.samples_obj.to_nested_dict(all_local=local_noises) - def write_samples( - slot: _TimeSlot, - samples_dict: Mapping[str, np.ndarray], + def add_noise( + slot: _TargetSlot, + samples_dict: Mapping[QubitId, dict[str, np.ndarray]], is_global_pulse: bool, - *qid: Union[int, str], ) -> None: """Builds hamiltonian coefficients. Taking into account, if necessary, noise effects, which are local and depend on the qubit's id qid. """ - _pulse = cast(Pulse, slot.type) - noise_det = 0.0 - noise_amp = 1.0 - if "doppler" in self.config.noise: - noise_det += self._doppler_detune[qid[0]] - # Gaussian beam loss in amplitude for global pulses only - # Noise is drawn at random for each pulse - if "amplitude" in self.config.noise and is_global_pulse: - position = self._qdict[qid[0]] - r = np.linalg.norm(position) - w0 = self.config.laser_waist - noise_amp = np.random.normal(1.0, 1.0e-3) * np.exp( - -((r / w0) ** 2) - ) - - samples_dict["amp"][slot.ti : slot.tf] += ( - _pulse.amplitude.samples * noise_amp - ) - samples_dict["det"][slot.ti : slot.tf] += ( - _pulse.detuning.samples + noise_det + noise_amp_base = max( + 0, np.random.normal(1.0, self.config.amp_sigma) ) - samples_dict["phase"][slot.ti : slot.tf] += _pulse.phase - - for channel in self._seq.declared_channels: - addr = self._seq.declared_channels[channel].addressing - basis = self._seq.declared_channels[channel].basis - - # Case of coherent global simulations - if addr == "Global" and ( - set(self.config.noise).issubset({"dephasing"}) - ): - slm_on = bool(self._seq._slm_mask_targets) - for slot in self._seq._schedule[channel]: - if isinstance(slot.type, Pulse): - # If SLM is on during slot, populate local samples - if slm_on and self._seq._slm_mask_time[1] > slot.ti: - samples_dict = self.samples["Local"][basis] - for qubit in slot.targets: - if qubit not in samples_dict: - samples_dict[qubit] = prepare_dict() - write_samples( - slot, samples_dict[qubit], True, qubit - ) - self.samples["Local"][basis] = samples_dict - # Otherwise, populate corresponding global - else: - slm_on = False - samples_dict = self.samples["Global"][basis] - if not samples_dict: - samples_dict = prepare_dict() - write_samples(slot, samples_dict, True) - self.samples["Global"][basis] = samples_dict - - # Any noise : global becomes local for each qubit in the reg - # Since coefficients are modified locally by all noises - else: - is_global = addr == "Global" - samples_dict = self.samples["Local"][basis] - for slot in self._seq._schedule[channel]: - if isinstance(slot.type, Pulse): - for qubit in slot.targets: - if qubit not in samples_dict: - samples_dict[qubit] = prepare_dict() - # We don't write samples for badly prep qubits - if not self._bad_atoms[qubit]: - write_samples( - slot, samples_dict[qubit], is_global, qubit - ) - self.samples["Local"][basis] = samples_dict - - # Apply SLM mask if it was defined - if self._seq._slm_mask_targets and self._seq._slm_mask_time: - tf = self._seq._slm_mask_time[1] - for qubit in self._seq._slm_mask_targets: - if qubit not in self.samples["Local"][basis]: - continue - for x in ("amp", "det", "phase"): - self.samples["Local"][basis][qubit][x][0:tf] = 0 + for qid in slot.targets: + if "doppler" in self.config.noise: + noise_det = self._doppler_detune[qid] + samples_dict[qid]["det"][slot.ti : slot.tf] += noise_det + # Gaussian beam loss in amplitude for global pulses only + # Noise is drawn at random for each pulse + if "amplitude" in self.config.noise and is_global_pulse: + position = self._qdict[qid] + r = np.linalg.norm(position) + w0 = self.config.laser_waist + noise_amp = noise_amp_base * np.exp(-((r / w0) ** 2)) + samples_dict[qid]["amp"][slot.ti : slot.tf] *= noise_amp + + if local_noises: + for ch, ch_samples in self.samples_obj.channel_samples.items(): + addr = self._seq.declared_channels[ch].addressing + basis = self._seq.declared_channels[ch].basis + samples_dict = samples["Local"][basis] + for slot in ch_samples.slots: + add_noise(slot, samples_dict, addr == "Global") + # Delete samples for badly prepared atoms + for basis in samples["Local"]: + for qid in samples["Local"][basis]: + if self._bad_atoms[qid]: + for qty in ("amp", "det", "phase"): + samples["Local"][basis][qid][qty] = 0.0 + self.samples = samples def build_operator(self, operations: Union[list, tuple]) -> qutip.Qobj: """Creates an operator with non-trivial actions on some qubits. @@ -613,19 +578,13 @@ def _build_basis_and_op_matrices(self) -> None: basis = ["u", "d"] projectors = ["uu", "du", "ud", "dd"] else: - # No samples => Empty dict entry => False - if ( - not self.samples["Global"]["digital"] - and not self.samples["Local"]["digital"] - ): + used_bases = self.samples_obj.used_bases() + if "digital" not in used_bases: self.basis_name = "ground-rydberg" self.dim = 2 basis = ["r", "g"] projectors = ["gr", "rr", "gg"] - elif ( - not self.samples["Global"]["ground-rydberg"] - and not self.samples["Local"]["ground-rydberg"] - ): + elif "ground-rydberg" not in used_bases: self.basis_name = "digital" self.dim = 2 basis = ["g", "h"] diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..adaf1cc2c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,70 @@ +# Copyright 2022 Pulser Development Team +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pytest + +from pulser.channels import Raman, Rydberg +from pulser.devices import Device + + +@pytest.fixture +def mod_device() -> Device: + return Device( + name="ModDevice", + dimensions=3, + rydberg_level=70, + max_atom_num=2000, + max_radial_distance=1000, + min_atom_distance=1, + _channels=( + ( + "rydberg_global", + Rydberg( + "Global", + 1000, + 200, + clock_period=1, + min_duration=1, + mod_bandwidth=4.0, # MHz + ), + ), + ( + "rydberg_local", + Rydberg( + "Local", + 2 * np.pi * 20, + 2 * np.pi * 10, + max_targets=2, + phase_jump_time=0, + fixed_retarget_t=0, + min_retarget_interval=220, + mod_bandwidth=4.0, + ), + ), + ( + "raman_local", + Raman( + "Local", + 2 * np.pi * 20, + 2 * np.pi * 10, + max_targets=2, + phase_jump_time=0, + fixed_retarget_t=0, + min_retarget_interval=220, + mod_bandwidth=4.0, + ), + ), + ), + ) diff --git a/tests/test_sequence_sampler.py b/tests/test_sequence_sampler.py index 575c39848..4ff9c36d9 100644 --- a/tests/test_sequence_sampler.py +++ b/tests/test_sequence_sampler.py @@ -13,7 +13,6 @@ # limitations under the License. from __future__ import annotations -import textwrap from copy import deepcopy import numpy as np @@ -21,7 +20,6 @@ import pulser import pulser_simulation -from pulser.channels import Rydberg from pulser.devices import Device, MockDevice from pulser.pulse import Pulse from pulser.sampler import sample @@ -137,7 +135,48 @@ def test_modulation(mod_seq: pulser.Sequence) -> None: getattr(input_ch_samples.modulate(chan), qty), getattr(output_ch_samples, qty), ) - assert input_ch_samples.modulate(chan).slots == output_ch_samples.slots + + +def test_modulation_local(mod_device): + seq = pulser.Sequence(pulser.Register.square(2), mod_device) + seq.declare_channel("ch0", "rydberg_local", initial_target=0) + ch_obj = seq.declared_channels["ch0"] + pulse1 = Pulse.ConstantPulse(500, 1, -1, 0) + pulse2 = Pulse.ConstantPulse(200, 2.5, 0, 0) + partial_fall = pulse1.fall_time(ch_obj) // 3 + seq.add(pulse1, "ch0") + seq.delay(partial_fall, "ch0") + seq.add(pulse2, "ch0") + seq.target(1, "ch0") + seq.add(pulse1, "ch0") + + input_samples = sample(seq) + output_samples = sample(seq, modulation=True) + assert input_samples.max_duration == seq.get_duration() + assert output_samples.max_duration == seq.get_duration( + include_fall_time=True + ) + + # Check that the target slots account for fall time + in_ch_samples = input_samples.channel_samples["ch0"] + out_ch_samples = output_samples.channel_samples["ch0"] + expected_slots = deepcopy(in_ch_samples.slots) + # The first slot should extend to the second + expected_slots[0].tf += partial_fall + assert expected_slots[0].tf == expected_slots[1].ti + # The next slots should fully account for fall time + expected_slots[1].tf += pulse2.fall_time(ch_obj) + expected_slots[2].tf += pulse1.fall_time(ch_obj) + + assert out_ch_samples.slots == expected_slots + + # Check that the samples are fully extracted to the nested dict + samples_dict = output_samples.to_nested_dict() + for qty in ("amp", "det", "phase"): + combined = sum( + samples_dict["Local"]["ground-rydberg"][t][qty] for t in range(2) + ) + np.testing.assert_array_equal(getattr(out_ch_samples, qty), combined) @pytest.fixture @@ -165,7 +204,6 @@ def seq_with_SLM() -> pulser.Sequence: return seq -@pytest.mark.xfail(reason="SLM not handled by `sample()` for now") def test_SLM_samples(seq_with_SLM): pulse = Pulse.ConstantDetuning(BlackmanWaveform(200, np.pi / 2), 0.0, 0.0) a_samples = pulse.amplitude.samples @@ -174,34 +212,20 @@ def z() -> np.ndarray: return np.zeros(seq_with_SLM.get_duration()) want: dict = { - "Global": {}, + "Global": {"ground-rydberg": {"amp": z(), "det": z(), "phase": z()}}, "Local": { "ground-rydberg": { - "batman": {"amp": z(), "det": z(), "phase": z()}, "superman": {"amp": z(), "det": z(), "phase": z()}, } }, } - want["Local"]["ground-rydberg"]["batman"]["amp"][200:400] = a_samples + want["Global"]["ground-rydberg"]["amp"][200:400] = a_samples want["Local"]["ground-rydberg"]["superman"]["amp"][0:200] = a_samples - want["Local"]["ground-rydberg"]["superman"]["amp"][200:400] = a_samples got = sample(seq_with_SLM).to_nested_dict() assert_nested_dict_equality(got, want) -slm_reason = textwrap.dedent( - """ -If the SLM is on, Global channels decay to local ones in the -sampler, such that the Global key in the output dict is empty and -all the samples are written in the Local dict. On the contrary, the -simulation module use the Local dict only for the first pulse, and -then write the remaining in the Global dict. -""" -) - - -@pytest.mark.xfail(reason=slm_reason) def test_SLM_against_simulation(seq_with_SLM): assert_same_samples_as_sim(seq_with_SLM) @@ -312,41 +336,3 @@ def mod_seq(mod_device: Device) -> pulser.Sequence: ) seq.measure() return seq - - -@pytest.fixture -def mod_device() -> Device: - return Device( - name="ModDevice", - dimensions=3, - rydberg_level=70, - max_atom_num=2000, - max_radial_distance=1000, - min_atom_distance=1, - _channels=( - ( - "rydberg_global", - Rydberg( - "Global", - 1000, - 200, - clock_period=1, - min_duration=1, - mod_bandwidth=4.0, # MHz - ), - ), - ( - "rydberg_local", - Rydberg( - "Local", - 2 * np.pi * 20, - 2 * np.pi * 10, - max_targets=2, - phase_jump_time=0, - fixed_retarget_t=0, - min_retarget_interval=220, - mod_bandwidth=4.0, - ), - ), - ), - ) diff --git a/tests/test_simconfig.py b/tests/test_simconfig.py index ac64f59f2..9a09325c0 100644 --- a/tests/test_simconfig.py +++ b/tests/test_simconfig.py @@ -36,3 +36,7 @@ def test_init(): SimConfig(temperature=-1.0) with pytest.raises(ValueError, match="SPAM parameter"): SimConfig(eta=-1.0) + with pytest.raises( + ValueError, match="The standard deviation in amplitude" + ): + SimConfig(amp_sigma=-0.001) diff --git a/tests/test_simresults.py b/tests/test_simresults.py index c95cf7084..6f83fa9ca 100644 --- a/tests/test_simresults.py +++ b/tests/test_simresults.py @@ -45,7 +45,7 @@ seq.measure("ground-rydberg") sim = Simulation(seq) -cfg_noisy = SimConfig(noise=("SPAM", "doppler", "amplitude")) +cfg_noisy = SimConfig(noise=("SPAM", "doppler", "amplitude"), amp_sigma=1e-3) sim_noisy = Simulation(seq, config=cfg_noisy) results = sim.run() results_noisy = sim_noisy.run() @@ -152,12 +152,12 @@ def test_get_final_state_noisy(): assert isdiagonal(final_state) res3._meas_basis = "ground-rydberg" assert ( - final_state[0, 0] == 0.06666666666666667 + 0j - and final_state[2, 2] == 0.92 + 0j + final_state[0, 0] == 0.12 + 0j + and final_state[2, 2] == 0.8666666666666667 + 0j ) assert res3.states[-1] == final_state assert res3.results[-1] == Counter( - {"10": 0.92, "00": 0.06666666666666667, "11": 0.013333333333333334} + {"10": 0.8666666666666667, "00": 0.12, "11": 0.013333333333333334} ) @@ -242,7 +242,7 @@ def test_expect_noisy(): with pytest.raises(ValueError, match="non-diagonal"): results_noisy.expect([bad_op]) op = qutip.tensor([qutip.qeye(2), qutip.basis(2, 0).proj()]) - assert np.isclose(results_noisy.expect([op])[0][-1], 0.6933333333333334) + assert np.isclose(results_noisy.expect([op])[0][-1], 0.7333333333333334) def test_plot(): @@ -285,7 +285,7 @@ def test_sample_final_state(): def test_sample_final_state_noisy(): np.random.seed(123) assert results_noisy.sample_final_state(N_samples=1234) == Counter( - {"00": 140, "01": 227, "10": 221, "11": 646} + {"11": 725, "10": 265, "01": 192, "00": 52} ) res_3level = Simulation( seq_no_meas_noisy, config=SimConfig(noise=("SPAM", "doppler"), runs=10) @@ -295,10 +295,10 @@ def test_sample_final_state_noisy(): final_state.full(), np.array( [ - [0.64 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.14 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.0 + 0.0j, 0.1 + 0.0j, 0.0 + 0.0j], - [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.12 + 0.0j], + [0.54 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.0 + 0.0j, 0.18 + 0.0j, 0.0 + 0.0j], + [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j, 0.1 + 0.0j], ] ), ).all() diff --git a/tests/test_simulation.py b/tests/test_simulation.py index 56493d894..2d950dd90 100644 --- a/tests/test_simulation.py +++ b/tests/test_simulation.py @@ -25,53 +25,53 @@ from pulser.waveforms import BlackmanWaveform, ConstantWaveform, RampWaveform from pulser_simulation import SimConfig, Simulation -q_dict = { - "control1": np.array([-4.0, 0.0]), - "target": np.array([0.0, 4.0]), - "control2": np.array([4.0, 0.0]), -} -reg = Register(q_dict) - -duration = 1000 -pi = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0.0, 0) -twopi = Pulse.ConstantDetuning(BlackmanWaveform(duration, 2 * np.pi), 0.0, 0) -pi_Y = Pulse.ConstantDetuning( - BlackmanWaveform(duration, np.pi), 0.0, -np.pi / 2 -) - -seq = Sequence(reg, Chadoq2) - -# Declare Channels -seq.declare_channel("ryd", "rydberg_local", "control1") -seq.declare_channel("raman", "raman_local", "control1") - -d = 0 # Pulse Duration - -# Prepare state 'hhh': -seq.add(pi_Y, "raman") -seq.target("target", "raman") -seq.add(pi_Y, "raman") -seq.target("control2", "raman") -seq.add(pi_Y, "raman") -d += 3 - -prep_state = qutip.tensor([qutip.basis(3, 2) for _ in range(3)]) - -# Write CCZ sequence: -seq.add(pi, "ryd", protocol="wait-for-all") -seq.target("control2", "ryd") -seq.add(pi, "ryd") -seq.target("target", "ryd") -seq.add(twopi, "ryd") -seq.target("control2", "ryd") -seq.add(pi, "ryd") -seq.target("control1", "ryd") -seq.add(pi, "ryd") -d += 5 - -# Add a ConstantWaveform part to testout the drawing procedure -seq.add(Pulse.ConstantPulse(duration, 1, 0, 0), "ryd") -d += 1 + +@pytest.fixture +def reg(): + q_dict = { + "control1": np.array([-4.0, 0.0]), + "target": np.array([0.0, 4.0]), + "control2": np.array([4.0, 0.0]), + } + return Register(q_dict) + + +@pytest.fixture +def seq(reg): + duration = 1000 + pi = Pulse.ConstantDetuning(BlackmanWaveform(duration, np.pi), 0.0, 0) + twopi = Pulse.ConstantDetuning( + BlackmanWaveform(duration, 2 * np.pi), 0.0, 0 + ) + pi_Y = Pulse.ConstantDetuning( + BlackmanWaveform(duration, np.pi), 0.0, -np.pi / 2 + ) + seq = Sequence(reg, Chadoq2) + # Declare Channels + seq.declare_channel("ryd", "rydberg_local", "control1") + seq.declare_channel("raman", "raman_local", "control1") + + # Prepare state 'hhh': + seq.add(pi_Y, "raman") + seq.target("target", "raman") + seq.add(pi_Y, "raman") + seq.target("control2", "raman") + seq.add(pi_Y, "raman") + + # Write CCZ sequence: + seq.add(pi, "ryd", protocol="wait-for-all") + seq.target("control2", "ryd") + seq.add(pi, "ryd") + seq.target("target", "ryd") + seq.add(twopi, "ryd") + seq.target("control2", "ryd") + seq.add(pi, "ryd") + seq.target("control1", "ryd") + seq.add(pi, "ryd") + + # Add a ConstantWaveform part to testout the drawing procedure + seq.add(Pulse.ConstantPulse(duration, 1, 0, 0), "ryd") + return seq def test_bad_import(): @@ -85,7 +85,7 @@ def test_bad_import(): assert pulser.simulation.SimConfig is SimConfig -def test_initialization_and_construction_of_hamiltonian(): +def test_initialization_and_construction_of_hamiltonian(seq): fake_sequence = {"pulse1": "fake", "pulse2": "fake"} with pytest.raises(TypeError, match="sequence has to be a valid"): Simulation(fake_sequence) @@ -93,7 +93,7 @@ def test_initialization_and_construction_of_hamiltonian(): assert sim._seq == seq assert sim._qdict == seq.qubit_info assert sim._size == len(seq.qubit_info) - assert sim._tot_duration == duration * d + assert sim._tot_duration == 9000 # seq has 9 pulses of 1µs assert sim._qid_index == {"control1": 0, "target": 1, "control2": 2} with pytest.raises(ValueError, match="too small, less than"): @@ -131,7 +131,7 @@ def test_initialization_and_construction_of_hamiltonian(): Simulation(seq_) -def test_extraction_of_sequences(): +def test_extraction_of_sequences(seq): sim = Simulation(seq) for channel in seq.declared_channels: addr = seq.declared_channels[channel].addressing @@ -172,7 +172,7 @@ def test_extraction_of_sequences(): ).all() -def test_building_basis_and_projection_operators(): +def test_building_basis_and_projection_operators(seq, reg): # All three levels: sim = Simulation(seq, sampling_rate=0.01) assert sim.basis_name == "all" @@ -211,7 +211,8 @@ def test_building_basis_and_projection_operators(): # Global ground-rydberg seq2 = Sequence(reg, Chadoq2) seq2.declare_channel("global", "rydberg_global") - seq2.add(pi, "global") + pi_pls = Pulse.ConstantDetuning(BlackmanWaveform(1000, np.pi), 0.0, 0) + seq2.add(pi_pls, "global") sim2 = Simulation(seq2, sampling_rate=0.01) assert sim2.basis_name == "ground-rydberg" assert sim2.dim == 2 @@ -228,7 +229,7 @@ def test_building_basis_and_projection_operators(): # Digital seq2b = Sequence(reg, Chadoq2) seq2b.declare_channel("local", "raman_local", "target") - seq2b.add(pi, "local") + seq2b.add(pi_pls, "local") sim2b = Simulation(seq2b, sampling_rate=0.01) assert sim2b.basis_name == "digital" assert sim2b.dim == 2 @@ -245,7 +246,7 @@ def test_building_basis_and_projection_operators(): # Local ground-rydberg seq2c = Sequence(reg, Chadoq2) seq2c.declare_channel("local_ryd", "rydberg_local", "target") - seq2c.add(pi, "local_ryd") + seq2c.add(pi_pls, "local_ryd") sim2c = Simulation(seq2c, sampling_rate=0.01) assert sim2c.basis_name == "ground-rydberg" assert sim2c.dim == 2 @@ -262,7 +263,7 @@ def test_building_basis_and_projection_operators(): # Global XY seq2 = Sequence(reg, MockDevice) seq2.declare_channel("global", "mw_global") - seq2.add(pi, "global") + seq2.add(pi_pls, "global") sim2 = Simulation(seq2, sampling_rate=0.01) assert sim2.basis_name == "XY" assert sim2.dim == 2 @@ -281,7 +282,7 @@ def test_building_basis_and_projection_operators(): ) -def test_empty_sequences(): +def test_empty_sequences(reg): seq = Sequence(reg, MockDevice) with pytest.raises(ValueError, match="no declared channels"): Simulation(seq) @@ -383,7 +384,7 @@ def test_add_max_step_and_delays(): assert np.isclose(occ_auto[-1], 0.5, 1e-4) -def test_run(): +def test_run(seq): sim = Simulation(seq, sampling_rate=0.01) sim.set_config(SimConfig("SPAM", eta=0.0)) with patch("matplotlib.pyplot.show"): @@ -434,7 +435,7 @@ def test_run(): sim.run() -def test_eval_times(): +def test_eval_times(seq): with pytest.raises( ValueError, match="evaluation_times float must be between 0 " "and 1." ): @@ -561,18 +562,26 @@ def test_config(): ) -def test_noise(): +def test_noise(seq): sim2 = Simulation( - seq, sampling_rate=0.01, config=SimConfig(noise=("SPAM"), eta=0.4) + seq, sampling_rate=0.01, config=SimConfig(noise=("SPAM"), eta=0.9) ) sim2.run() with pytest.raises(NotImplementedError, match="Cannot include"): sim2.set_config(SimConfig(noise="dephasing")) assert sim2.config.spam_dict == { - "eta": 0.4, + "eta": 0.9, "epsilon": 0.01, "epsilon_prime": 0.05, } + assert sim2.samples["Global"] == {} + assert any(sim2._bad_atoms.values()) + for basis in ("ground-rydberg", "digital"): + for t in sim2._bad_atoms: + if not sim2._bad_atoms[t]: + continue + for qty in ("amp", "det", "phase"): + assert np.all(sim2.samples["Local"][basis][t][qty] == 0.0) def test_dephasing(): @@ -626,7 +635,7 @@ def test_add_config(): assert sim.config.laser_waist == 172.0 -def test_cuncurrent_pulses(): +def test_concurrent_pulses(): reg = Register({"q0": (0, 0)}) seq = Sequence(reg, Chadoq2) @@ -920,3 +929,84 @@ def test_effective_size_disjoint(): assert sim.get_hamiltonian(0) == 0 * sim.build_operator( [("I", "global")] ) + + +def test_simulation_with_modulation(mod_device, reg): + seq = Sequence(reg, mod_device) + seq.declare_channel("ch0", "rydberg_global") + seq.config_slm_mask({"control1"}) + pulse1 = Pulse.ConstantPulse(120, 1, 0, 2.0) + seq.add(pulse1, "ch0") + + with pytest.raises( + NotImplementedError, + match="Simulation of sequences combining an SLM mask and output " + "modulation is not supported.", + ): + Simulation(seq, with_modulation=True) + + seq = Sequence(reg, mod_device) + seq.declare_channel("ch0", "rydberg_global") + seq.declare_channel("ch1", "raman_local", initial_target="target") + seq.add(pulse1, "ch1") + seq.target("control1", "ch1") + seq.add(pulse1, "ch1") + seq.add(pulse1, "ch0") + ch1_obj = seq.declared_channels["ch1"] + pulse1_mod_samples = ch1_obj.modulate(pulse1.amplitude.samples) + mod_dt = pulse1.duration + pulse1.fall_time(ch1_obj) + assert pulse1_mod_samples.size == mod_dt + + sim_config = SimConfig(("amplitude", "doppler")) + sim = Simulation(seq, with_modulation=True, config=sim_config) + + assert sim.samples["Global"] == {} # All samples stored in local + raman_samples = sim.samples["Local"]["digital"] + # Local pulses + for qid, time_slice in [ + ("target", slice(0, mod_dt)), + ("control1", slice(mod_dt, 2 * mod_dt)), + ]: + np.testing.assert_allclose( + raman_samples[qid]["amp"][time_slice], pulse1_mod_samples + ) + np.testing.assert_equal( + raman_samples[qid]["det"][time_slice], sim._doppler_detune[qid] + ) + np.testing.assert_equal( + raman_samples[qid]["phase"][time_slice], pulse1.phase + ) + + def pos_factor(qid): + r = np.linalg.norm(reg.qubits[qid]) + w0 = sim_config.laser_waist + return np.exp(-((r / w0) ** 2)) + + # Global pulse + time_slice = slice(2 * mod_dt, 3 * mod_dt) + rydberg_samples = sim.samples["Local"]["ground-rydberg"] + noise_amp_base = rydberg_samples["target"]["amp"][time_slice] / ( + pulse1_mod_samples * pos_factor("target") + ) + for qid in reg.qubit_ids: + np.testing.assert_allclose( + rydberg_samples[qid]["amp"][time_slice], + pulse1_mod_samples * noise_amp_base * pos_factor(qid), + ) + np.testing.assert_equal( + rydberg_samples[qid]["det"][time_slice], sim._doppler_detune[qid] + ) + np.testing.assert_equal( + rydberg_samples[qid]["phase"][time_slice], pulse1.phase + ) + + with pytest.raises( + ValueError, + match="Can't draw the interpolation points when the sequence " + "is modulated", + ): + sim.draw(draw_interp_pts=True) + + # Drawing with modulation + with patch("matplotlib.pyplot.show"): + sim.draw() diff --git a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb index 982c0c174..69cac5eb6 100644 --- a/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb +++ b/tutorials/advanced_features/Simulating Sequences with Errors and Noise.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "91a245c9", "metadata": {}, "source": [ "# Simulation with Noise and Errors" @@ -10,7 +9,6 @@ }, { "cell_type": "markdown", - "id": "67e0251f", "metadata": {}, "source": [ "## Introduction\n", @@ -25,13 +23,14 @@ "\n", "- Waist of the laser : For global pulses, the laser amplitude has a Gaussian profile and atoms at the border of the waist feel a slightly lower amplitude than those at the focus.\n", "\n", + "- Amplitude fluctuations: The `amp_sigma` parameter dictates fluctuations in the laser amplitude from pulse to pulse. \n", + "\n", "- Dephasing / phase-damping: Each qubit interacts with its environment, and we can model this interaction with random $Z$-rotations on each qubit. Given a dephasing probability $p$, this noise model adds two collapse operators $M_0 = \\sqrt{1-\\frac{p}{2}} \\times \\mathbb{1}$, $M_1 = \\sqrt{\\frac{p}{2}} \\sigma_z = \\sqrt{\\frac{p}{2}} (\\Ket{r}\\Bra{r} - \\Ket{g}\\Bra{g})$ and forces the solver to adopt a density matrix formalism. See [here](https://ocw.mit.edu/courses/nuclear-engineering/22-51-quantum-theory-of-radiation-interactions-fall-2012/lecture-notes/MIT22_51F12_Ch8.pdf) for a more thorough explanation.\n" ] }, { "cell_type": "code", "execution_count": 1, - "id": "aee2644a", "metadata": {}, "outputs": [], "source": [ @@ -47,7 +46,6 @@ }, { "cell_type": "markdown", - "id": "0e7fff3e", "metadata": {}, "source": [ "## Single atom noisy simulations" @@ -55,7 +53,6 @@ }, { "cell_type": "markdown", - "id": "bafc3de4", "metadata": {}, "source": [ "### Sequence preparation" @@ -63,7 +60,6 @@ }, { "cell_type": "markdown", - "id": "556360fc", "metadata": {}, "source": [ "Prepare a single atom:" @@ -72,7 +68,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "46b32aac", "metadata": {}, "outputs": [], "source": [ @@ -81,7 +76,6 @@ }, { "cell_type": "markdown", - "id": "613dcffc", "metadata": {}, "source": [ "Act on this atom with a Constant Pulse, such that it oscillates towards the excited Rydberg state and back to the original state (Rabi oscillations):" @@ -90,14 +84,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "e3b15936", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -117,7 +110,6 @@ }, { "cell_type": "markdown", - "id": "8bad66f1", "metadata": {}, "source": [ "We now run the noiseless simulation, to obtain a `CoherentResults` object in `clean_res`." @@ -126,7 +118,6 @@ { "cell_type": "code", "execution_count": 4, - "id": "a68d7f41", "metadata": {}, "outputs": [], "source": [ @@ -136,7 +127,6 @@ }, { "cell_type": "markdown", - "id": "758ce4c0", "metadata": {}, "source": [ "Here we obtain the excited population using the projector onto the Rydberg state." @@ -145,7 +135,6 @@ { "cell_type": "code", "execution_count": 5, - "id": "455644d3", "metadata": {}, "outputs": [], "source": [ @@ -155,7 +144,6 @@ { "cell_type": "code", "execution_count": 6, - "id": "59febbd8", "metadata": {}, "outputs": [ { @@ -178,7 +166,6 @@ }, { "cell_type": "markdown", - "id": "71a84fc8", "metadata": {}, "source": [ "### The SimConfig object" @@ -186,7 +173,6 @@ }, { "cell_type": "markdown", - "id": "124ead51", "metadata": {}, "source": [ "Each simulation has an associated `SimConfig` object, which encapsulates parameters such as noise types, the temperature of the register... You may view it at any time using the following command." @@ -195,7 +181,6 @@ { "cell_type": "code", "execution_count": 7, - "id": "1503db35", "metadata": {}, "outputs": [ { @@ -215,7 +200,6 @@ }, { "cell_type": "markdown", - "id": "cc20da6e", "metadata": {}, "source": [ "When creating a new `SimConfig`, you may choose several parameters. `'runs'` indicates the number of times a noisy simulation is run to obtain the average result of several simulations, `'samples_per_run'` is the number of delivered samples per run - this has no physical interpretation, this is used simply to cut down on calculation time." @@ -223,7 +207,6 @@ }, { "cell_type": "markdown", - "id": "b4d1a00e", "metadata": {}, "source": [ "We will also add `SPAM` noise to the simulation by creating a new `SimConfig` object, and assigning it to the `config` field of `sim` via the `Simulation.set_config` setter. We pass noise types as a tuple of strings to a SimConfig object. Possible strings are : `'SPAM', 'dephasing', 'doppler', 'amplitude'`." @@ -232,7 +215,6 @@ { "cell_type": "code", "execution_count": 8, - "id": "73bf2544", "metadata": {}, "outputs": [], "source": [ @@ -242,7 +224,6 @@ }, { "cell_type": "markdown", - "id": "3bee68fc", "metadata": {}, "source": [ "We now show the new configuration to have an overview of the changes we made." @@ -251,7 +232,6 @@ { "cell_type": "code", "execution_count": 9, - "id": "19022bb2", "metadata": {}, "outputs": [ { @@ -273,7 +253,6 @@ }, { "cell_type": "markdown", - "id": "87cf1ac2", "metadata": {}, "source": [ "Note that `SimConfig.spam_dict` is the spam parameters dictionary. `eta` is the probability of a badly prepared state, `epsilon` the false positive probability, `epsilon_prime` the false negative one." @@ -281,7 +260,6 @@ }, { "cell_type": "markdown", - "id": "8de7b636", "metadata": {}, "source": [ "When dealing with a `SimConfig` object with different noise parameters from the config in `Simulation.config`, you may \"add\" both configurations together, obtaining a single `SimConfig` with all noises from both configurations - on the other hand, the `runs` and `samples_per_run` will always be updated. This adds simulation parameters to noises that weren't available in the former `Simulation.config`. Noises specified in both `SimConfigs` will keep the noise parameters in `Simulation.config`. Try it out with `Simulation.add_config`:" @@ -290,7 +268,6 @@ { "cell_type": "code", "execution_count": 10, - "id": "2601acb1", "metadata": {}, "outputs": [ { @@ -301,9 +278,10 @@ "----------\n", "Number of runs: 50\n", "Samples per run: 5\n", - "Noise types: SPAM, dephasing, doppler\n", + "Noise types: SPAM, doppler, dephasing\n", "SPAM dictionary: {'eta': 0.005, 'epsilon': 0.01, 'epsilon_prime': 0.05}\n", "Temperature: 1000.0µK\n", + "Amplitude standard dev.: 0.05\n", "Dephasing probability: 0.05\n" ] } @@ -321,7 +299,6 @@ }, { "cell_type": "markdown", - "id": "c291268a", "metadata": {}, "source": [ "Note that we set the temperature in $\\mu K$. We also observe that the `eta` parameter wasn't changed, since both `SimConfig` objects had `'SPAM'` as a noise model already. This feature might be useful when running several simulations with distinct noise parameters to observe the influence of each noise independtly, then wanting to combine noises together without losing your tailored noise parameters." @@ -329,7 +306,6 @@ }, { "cell_type": "markdown", - "id": "9e13d45a", "metadata": {}, "source": [ "### Setting evaluation times" @@ -337,7 +313,6 @@ }, { "cell_type": "markdown", - "id": "f8d69070", "metadata": {}, "source": [ "As a `Simulation` field, `eval_times` refers to the times at which the result have to be returned. Choose `'Full'` for all the times the Hamiltonian has been sampled in the sequence, a list of times of your choice (has to be a subset of all times in the simulation), or a real number between $0$ and $1$ to sample the full return times array. Here, we choose to keep $\\frac{8}{10}$ of the Hamiltonian sample times for our evaluation times." @@ -346,7 +321,6 @@ { "cell_type": "code", "execution_count": 11, - "id": "449e2cc1", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +329,6 @@ }, { "cell_type": "markdown", - "id": "5d0bad77", "metadata": {}, "source": [ "We now obtain a `NoisyResults` object from our noisy simulation. This object represents the final result as a probability distribution over the sampled bitstrings, rather than a quantum state `QObj` in the `CleanResults` case." @@ -364,7 +337,6 @@ { "cell_type": "code", "execution_count": 12, - "id": "a7d7df94", "metadata": {}, "outputs": [], "source": [ @@ -373,7 +345,6 @@ }, { "cell_type": "markdown", - "id": "4bdd9d97", "metadata": {}, "source": [ "### Plotting noisy and clean results" @@ -381,7 +352,6 @@ }, { "cell_type": "markdown", - "id": "d526f555", "metadata": {}, "source": [ "The new `res` instance has similar methods to the usual `SimResults` object. For example, we can calculate expectation values. Observe how different the Rydberg population in the clean case and noisy case are : we clearly see a damping due to all the noises we added." @@ -390,12 +360,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "3f6c3c74", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -414,7 +383,6 @@ }, { "cell_type": "markdown", - "id": "7abb4133", "metadata": {}, "source": [ "You can also use the `SimResults.plot(obs)` method to plot expectation values of a given observable. Here we compute the `sigma_z` local operator expectation values. You may choose to add error bars using the argument `error_bars = True` (`True` by default for `NoisyResults`.) Be wary that computing the expectation value of non-diagonal operators will raise an error, as `NoisyResults` bitstrings are already projected on the $Z$ basis." @@ -423,14 +391,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "47452cfb", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -449,12 +416,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "2e2eb154", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -471,7 +437,6 @@ }, { "cell_type": "markdown", - "id": "422ec4e9", "metadata": {}, "source": [ "## SPAM effects" @@ -479,7 +444,6 @@ }, { "cell_type": "markdown", - "id": "20987d71", "metadata": {}, "source": [ "Compare both clean and noisy simulations for the default SPAM parameters (taken from [De Léséleuc, et al., 2018](https://arxiv.org/abs/1802.10424))" @@ -488,14 +452,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "226b6667", "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -520,7 +483,6 @@ }, { "cell_type": "markdown", - "id": "2e18bd3d", "metadata": {}, "source": [ "We will now modify the *SPAM* dictionary, as below, allowing for more ($40$%) badly prepared atoms." @@ -529,12 +491,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "b4c33a09", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -555,7 +516,6 @@ }, { "cell_type": "markdown", - "id": "ed80950b", "metadata": {}, "source": [ "We can see here that the population doesn't go well above $0.6 = 1 - \\eta$, which is to be expected : badly prepared atoms don't reach state $\\Ket{r}$. We can expect this limit of $0.6$ in the Rydberg population to be more and more respected as the number of runs grows." @@ -563,7 +523,6 @@ }, { "cell_type": "markdown", - "id": "5f9e70bf", "metadata": {}, "source": [ "### Changing $\\eta$" @@ -571,7 +530,6 @@ }, { "cell_type": "markdown", - "id": "f856f2f6", "metadata": {}, "source": [ "Let us first initialize all spam error values to $0$. Then, we do a sweep over the parameter $\\eta$, probability of badly prepared states, to notice its effects." @@ -580,12 +538,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "f0a44162", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -611,7 +568,6 @@ }, { "cell_type": "markdown", - "id": "fa88078c", "metadata": {}, "source": [ "As $\\eta$ grows, more qubits are not well-prepared (i.e, pumped into a state different from $\\Ket{g}$) and we stop seeing occupations at all. You may increase the number of runs to smooth the curves." @@ -619,7 +575,6 @@ }, { "cell_type": "markdown", - "id": "46ef2d98", "metadata": {}, "source": [ "### Changing $\\epsilon$" @@ -627,7 +582,6 @@ }, { "cell_type": "markdown", - "id": "c1579e00", "metadata": {}, "source": [ "Let's now run a sweep over $\\epsilon$." @@ -636,7 +590,6 @@ { "cell_type": "code", "execution_count": 19, - "id": "2202e805", "metadata": {}, "outputs": [ { @@ -667,7 +620,6 @@ }, { "cell_type": "markdown", - "id": "04570d01", "metadata": {}, "source": [ "As more false positives appear, it looks like the system is never captured, so always in a Rydberg state. Note that when $\\eta=0$, the object we obtain is a `CoherentResults` rather than a `NoisyResults`, since in this case, the randomness comes from measurements and the simulation is entirely deterministic. This results in smooth curves rather than scattered dots." @@ -675,7 +627,6 @@ }, { "cell_type": "markdown", - "id": "e2d78da0", "metadata": {}, "source": [ "### Changing $\\epsilon'$" @@ -683,7 +634,6 @@ }, { "cell_type": "markdown", - "id": "a9b8ef1f", "metadata": {}, "source": [ "Finally, we run a sweep over $\\epsilon'$." @@ -692,7 +642,6 @@ { "cell_type": "code", "execution_count": 20, - "id": "ceacfe1b", "metadata": {}, "outputs": [ { @@ -723,7 +672,6 @@ }, { "cell_type": "markdown", - "id": "045815da", "metadata": {}, "source": [ "As there are more false negatives, all atoms seem to be recaptured, until no Rydberg occupation is detected." @@ -731,7 +679,6 @@ }, { "cell_type": "markdown", - "id": "c2260bb5", "metadata": {}, "source": [ "## Doppler Noise" @@ -739,7 +686,6 @@ }, { "cell_type": "markdown", - "id": "f22a5e46", "metadata": {}, "source": [ "As for any noise, Doppler noise is set via a `SimConfig` object. When averaging over several runs, it has the effect of damping the oscillations. Let's increase the number of runs in order to see this and get smoother curves." @@ -747,7 +693,6 @@ }, { "cell_type": "markdown", - "id": "9e3d4834", "metadata": {}, "source": [ "Note that you may change the standard deviation of the doppler noise, which is $k \\times \\sqrt{k_B T / m}$, where $k$ is the norm of the effective wavevector of the lasers, by changing the temperature field, setting it in $\\mu K$. We'll exaggerate the temperature field here to emphasize the effects of Doppler damping; the default value for temperature is 50$\\mu K$." @@ -756,7 +701,6 @@ { "cell_type": "code", "execution_count": 21, - "id": "fd4baccc", "metadata": {}, "outputs": [ { @@ -768,7 +712,8 @@ "Number of runs: 100\n", "Samples per run: 1\n", "Noise types: doppler\n", - "Temperature: 5000.0µK\n" + "Temperature: 5000.0µK\n", + "Amplitude standard dev.: 0.05\n" ] } ], @@ -782,7 +727,6 @@ }, { "cell_type": "markdown", - "id": "962335eb", "metadata": {}, "source": [ "Let us now simulate the entire sequence with Doppler noise, much like what we did in the SPAM case. We should see damped oscillations if the standard deviation is high enough. This is the case here, as we exaggerated the temperature field." @@ -791,12 +735,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "fcf16353", "metadata": {}, "outputs": [ { "data": { - "image/png": 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DERs26sPub+D7gJ8rpQqAtwG/EJFLZFJKPayUKldKlc+aZf8s2UqUUuw83uqtAGr35fGwrigXt4KqJqfdosQMXf1DHGvpprzIfrMQQGpSIuVFM3R/ApOJ5PpC/lh5p3EAhX6vC7zb/Lkb+C2AUmo3kAbYbxi3kdNtvTicfVwVAWYhH+u8K5OD2jxkGgcaOlEKyovtdRT7s3lxHkfPddPRq/0EZrGnroP52WksmJFutyhjYqUi2AcsFZGFIpKCxxm8PWDMGeA6ABFZiUcRxL7tZwxe9RZ522pj/kAgOekpLJqVwYEG3ajGLCrqO0lMENZGiPkPYNMiz+rkDb0qMAWlFHvq2tm0KLL9A2ChIlBKDQOfAnYAR/BEB9WKyFdF5FbvsH8EPiEilcBvgI8opeK6sM2rJ1opyktnQV5kzSDWL8jlYKOTOL88prGvvoPV87PISLW+LaVRSgtySE9J1PkEJnG8pYf23kE2RbhZCMDSb6FS6lk8TmD/bV/ye34Y2GKlDNGE263Ye7qDm0vn2S3KJaxfkMsT+5toaL9AcQSHwUUDg8NuKpuc3LWxyG5R3kRyYgLlxdpPYBa7T0V2fSF/IsMbqQGgrq2Xrv5h1i2IHLuxj3ULcgA4cEabh6ZKbbOL/iE3l0WQf8DHpkUzON7iiXTRTI3dde0U5E6jMML9A6AVQURx0HuTXe+96UYSy+ZkMj01STuMTaCi3nOdN0SgIvDNXvWqYGoo5SkvvnFhZESFjYdWBBHEwUYnmWlJLJoZeRUKExOEssJsvSIwgX31HRTlpTM7M81uUS5hTX4201OTtCKYIg5nH209g6MRd5GOVgQRxMEzTtYW5kRkByOAdYW5HD3XzYVBnVg2WXwzxUjJHwgkKTGBy4p13aGpUtno6eERKUmh46EVQYTQOzDMsXNdET2DWF+Uw4hbUdWkG9VMltNtvbT3Dkakf8DHpkV5nGrt5XxXv92iRC2VTU5SEhNYMTdr/MERgFYEEUJVkwu3IiIdxT7WFXpk0+ahyePzD0RCoblQ+LJg9+i6Q5PmUKOTVfOzSEmKjltsdEgZBxxs9NwgIinBKJDcjBQWzszgQIPTblGiln31HeSmJ9vWftQIq+dnk5mapM1Dk2R4xE11kyuif8uBaEUQIRw842ThzAxyM+ztPzAe6xbkcKixUyeWTZKKhk42FEVupyrwBAZsKM7VmeST5GRrD31DI5QVZtstimG0IogAlFIcPOOMaP+Aj/ULcmnrGaSxo89uUaKOtp4BTrf1RrR/wEdpQQ4nzuvAgMlQ2egEoKwgx1Y5JoJWBBGAJ9RsYDRpK5LRiWWTJxr8Az7KCrJxK6ht7rJblKjjUKOLrLQkivMi1/wXiGFFICKRnx4XpfiStCLZUexj+ZxM0lMSR5PfNMapqO8gNSmBkvzIjyRZU+Axa/hmtxrjVDY6KYvgMPBgjKsIROQKETkMHPW+LhMR3VLSRA6ecZKWnMDyCOhUNR5JiQmUFeiOZZNhX0MnZQU5pCYl2i3KuMzOTGNedpoOFZ4gfYMjHGvpjiqzEBhbEfwXcCPQDqCUqgSutlKoeONgYyel+TkR04hmPNYtyOHI2S76BkfsFiVq6Bscodbhiqj+A+NRWpCtmxFNkNpmFyNuFTWJZD4M3XmUUo0Bm/QdwCQGhkeodXRFhX/Ax/oFuQy7FdUOPVs0yqFGJ8NuxWVR4B/wUVqQQ337BVwXhuwWJWo45HMUR1HEEBhTBI0icgWgRCRZRD6Hp7+AxgQON3cxOOKOKkWgHcYTp6K+AxGPEo0WfOYNrfCNU9nkIj9nWkTWkRoLI4rgk8B9eBrPO4C13tcaE4gmR7GPvOmpFOWl6zjzCbCvoZPlczLJTk+2WxTDrMn3Ooy1ecgwHkdxdK0GwEBjGqVUG/D+MMgSlxxsdDI/O405WdE1g1i/IJfXTrahlIro5KhIwO1WHGzo5Na18+0WZUJkpydTnJeu/QQG6egd5EzHBd5/+QK7RZkw4yoCEfkZcEkaqVLqY5ZIFGccPNMZVasBH+sX5PDUQQdNnX1R0XjDTuraeukeGI6qkgM+SgtyqKjXNYeM4Fs5RZujGIy1qvyT3/M04B1AszXixBfnu/tp6uzjI1cUjz3wpYfglf+4dPvW+2HbA5bINh4+5XXgTKdWBONQ7XACnptqtFFakM32ymZauweYlZlqtzgRTWWjkwS5aFKLJoyYhn7v/1pEfgO8ZplEccShUf9AztgDtz1w8Yb/YDY8aL/zbsXcTKYlJ3LwjJPb1ubbLU5EU9XkYlpyYkQXmguFT3lVNTm5buUce4WJcCobnSydnUlGqqWt4C1hMoHrS4HZZgsSjxxsdJKcKKyeH4UziMQESguydYaxAWocLlbPzyIpSvJE/CnJzyJBPNEwmtAopahsckWloxiMZRZ3i0iX73/gj8DnrRct9jl0xsmKuVmkJUd+pmkw1hflUtvcRf+QTisJxYhbUePoGi3ZEG2kpySxdHYm1dphPCZNnX109A5GpX8AjJmGIr/uQRSilKKm2cUtZSZGkoTZl7CuMIdht6LG4YqKQmp2cMpbkrg0ShUBePwELx49ryPExuBQFFYc9SekIhCR9WN9UCl1wHxx4oeG9gt09w+b61gKsy9hfdFFh7FWBMHx1epZk59jryBToLQwh9/tb8Lh7KMgVwcGBKOy0UlqUnTUCwvGWCuCb43xngKuNVmWuMKXrXmJIojACKFQzJyeyoIZ6bpj2RhUNznJSElk0czocxT7KPV+R6uaXFoRhKCyyUlJfnbU1AsLJKQiUEptC6cg8UaNw0VKYgLL5gTMIMaa1Tfuvfh/4cbwCDoO6xbksKdOtzQMRZXDxer87KgqSRzIinmZJCcKlU1O3rZmnt3iRBzDI26qHS7u2lhktyiTxpD6EpESEXmPiHzI97BasFin2uFi+dxM482tG/fCo7d6nj9660WlYDNr8rNp6RrgfHe/3aJEHEMjbg43d43OqKOV1KREVs7LolpHDgXleEsP/UPuqI0YAmNRQ18Gvud9bAO+AdxqsVwxjVIeB2vJRG4Q9a/CyKDn+cig53UE4DNt1Tp0J6tATrT0MDDsjtqIIX9KC7KpbnLhdute1YH4MoqjMXPch5Hp6B3AdcA5pdRHgTIg+r/ZNnKm4wJdE3UUF18Fid7G9okpntcRwOr8bER0hcpgRHNGcSCl+Tl0Dwxzur3XblEijspGJznpySyI4gx7IylwfUopt4gMi0gWcB4otFiumCako3gsCjfCh7fDT2/w/H/yr57ngWy9H5Zc53keBl/C9NQkFs7M0IogCFVNLjLTkiiK4huEj9JCn8PYyeJZ022WJrI41OikrCAnqkNrjawIKkQkB/gxsB84AOw2snMRuUlEjonISRG5P8SY94jIYRGpFZFfGxU8mql2uEhOFJbNneAPyndTL9zocSg/6LroTPY9X3Jd2H0Ja/KzqdGK4BKqHS7WRLmj2MeSWdOZlpyoW1cGcGFwmOMt3VGbSObDSELZvd6nPxKRPwNZSqmq8T4nIonAD4AbgCZgn4hsV0od9huzFHgA2KKU6hSRuChdUevoYvncTFJf/UboUNHJzuoDfQkvfg1OvxL8GCaFo5bMz+YPh5pp6xlg5nRdmAw8neeOnO3iY1cutFsUU0hKTKAkP0srggBqHF24FayNYkcxGHMWbxeRu0QkQylVb0QJeNkInFRK1SmlBoHHgdsCxnwC+IFSqhNAKXV+IsJHI0qp0ZmiJbP6QF/CtV8IfgwTcxJ8Tm9tHrrI8XM9DI0oSqM4kSyQNfk51Da7GB5x2y1KxFDpzSiOdj+QEdPQt4ArgcMi8oSI3CEiRrqo5AP+vY6bvNv8WQYsE5HXRWSPiNwUbEcico+IVIhIRWtrq4FDRy5NnX24+obGjhiaSoSQz5cAnv/DkG+wOj8LgBo9WxylatRRbMNM8aWHvDkoAY+XHprSbssKs+kfcnO8pcckQaOfQ01OCnKnRf1K2Ihp6BXgFa+p51o8s/hHgCyTjr8UuAYoAHaKyBqllDNAhoeBhwHKy8ujOn7NkKPYN6sf7ptchJC/LyEMZKUla4dxANVNLnLSkynInRb+g1tUaqR0tIexk1Xzzfj5Rz+e1pQ5dosxZYwmlE0D3oWnf/FlwKMGPubgzdFFBd5t/jQB25VSQ0qp08BxPIohZql2uEhKkEsziv2xYVY/VUq0w/hN+Mx/0RxJEkhxXjqZaUm6JLWXtp4Bmjr7KIuBPBEjPoLfAkfwrAa+DyxWSn3awL73AUtFZKGIpAB3AtsDxjyNZzWAiMzEYyqqMyp8NFLjcLFsTub4pafDPKufKmvys2h29dPeM2C3KLbTPzTCsXPdUV1xNBgiQmlBtu5h7MU38Yl2/wAYyyP4KfA+pdSEis4rpYZF5FPADiAReEQpVSsiXwUqlFLbve+9RUQOAyPAPymlYrZwjc9RfOOquRP7YGAhuge9N5gw5wyMhb/D+JrlcRH8FZKj57oZditPxdEoKiJohNKCHH68s47+oZGo7aNhFj5FEAtmMiM+gh2T3blS6lng2YBtX/J7roDPeh8xT1NnH84LQ5RMdKbob/P1J7D+kI1mJJ8iqNGKYLSJS2lBNpREXpvRqVBWkM2wW3H0XHdUl1QwgxpHFwtnZpCVlmy3KFMm+pprRjE1oRzFk60qGiy6KNTnLa5cmpWWTHFeunYY48konjk9hXnZIYLroniV4N/DOO4VQbPr4t8giq8paEUQVnyO4hX+zSumMqs3Gl0UppVDSX42B884Td9vtFHtLSgY0lEc5gZCZjIvO42Z01OpbHTBZrulsY/O3kGaOvv4wCZv6ekovqZgUBGISD5Q5D9eKbXTKqFilSWHv8fJlF/CvwW+471hjDerD2S8+kM+X8LCrcZXDlNgTX42f6o6S0fvIDMyUkzffzTQPzTCifM93LBqjt2iWLIK1A5jD7XNnmq7Y4aBR9EqYVxFICJfB94L+By64OlQphXBBFBK8a89t7Gn5B6+cUfZxVmDb7ZuRs6ArwZRIFM9hkHW+PkJrl42y5JjRDqHz3Yx4lYTKzFuBRauAksLsnnp2Hl6B4bJSI1Po4LPBLp6LEdxFK0SjOQR3A4sV0q9TSl1i/eh+xFMEIezj84LQ5fOIAJzBk7+9WImKJiTFRqmvITVutQEtZOpLGsFFvavWJOfjVJw5Gz89qCoaXZRkDuNnPTYWPkaUed1QDKgA8SngM9RHHSmaGRWP1XCkJeQPS2Zorz0uE4sq3a4mJExhqM4XEw1O30M/EOFy4tnmLbfaKLWVy8sRjCiCC4Ah0Tkr/gpA6XUZyyTKgapdrhITBBWzouAmGMLbZcl87NHOzbFIzWOruCO4snY66dynQL9RyZOAOZkpTErM5WaOO1K19U/RH37Bd5dHjttWYwogu1cmhGsmSDVji6Wzp4eGUk4FtouS/Kzeab6LJ29g+TGmcO4f2iE4y3dXLM8wD8yWXv9VK+ThavAkvlZ1DbH58rP15Z1TP9AlGEkoexRb4mIZd5Nx5RSQ9aKFVsopah1uLh2RewnWo06jJtdXLU0vhzGx0YzigNMBqHyPfxXCSf/GjURJuBR+DtPtMVlhrFPAQY184Z75WcSRqKGrsFTZK4eT5xjoYh8WIePGuesq5/23sGYaGI+HiXektTVjvhTBDWhbhDB7PXBVglREmECnnMccSuOnO1i3YJcu8UJK9UO12g+xZuwa+VnAkZMQ98C3qKUOgYgIsuA3wAbrBQslqgOdBSbEd89Vv0hG2eQOekpFM6YFpcO4xqHi+xpQUpPB7PXv/qtsOR2WMVoSZHm+FMENQ4XD0x7Gh58Z5B3g+QEWZzVbwZGFEGyTwkAKKWOi0j0F9cIIzUOFwkCK+dmmRffHar+UASwJj87LkNIaxxdoUtPB9rrLYzqCQfzs9PITU+Ou2ZEPQPD1LX1cvq6z8D13/NsHCsnKILqgY2F0eb1PxGRa7yPHwMVVgsWS1Q7XCydncm0lMQ324uH+z2zRDNzBgLx71Zl1TECKMnPprGjD+eFQcuOEWkMDrs5dq57tFvbuIS754TJXctExNODIs4cxkfOdqHURRPomwh2TS3M5zATIyuCvwXuA3zhoq8CP7RMohhDKUWNw8XWZV5Hsf9MMCnN+puADSsHX5/eGkcXVy6dGdZj28Xxlm4GR9wTiy0PZ88JC+zQJfnZ/OTVOgaGR0hNig+H8Zj5QBC1Kz8jUUMDwLe9D80EOdfVT1vPIGt8MwgL47snjEW2S3+HcUwpgjGiO2oy3g9YlFE80esUJv9RyfxshkYUx8/1xEUgBHi+07MyU5mTZTBhMJJ+72MQUhGIyG+VUu8RkWo8tYXehFKq1FLJYoRqrw31TT+USOg+ZqHt0ucwrvY2cI8ZxphVVz9VTWZaEgtmpJt7zMlcpzCtAv1DheNFEdQ6uiiZaP5AJPzex2GsFcHfef9/ezgEiVV8juJV8yLshzKRXgaTIN4cxjXNXZTMN6FHceDsP5hPKRCbIsUKZ0wjMy0pbiLE+gZHOHG+m7estqiyrI3RRSGdxUqps96n9yqlGvwfwL3hES/6qXa4WDJ7usdRHEn4bJdgie0ynhzGQyNujpztCu5AnAiBs//GvW++TklpcPcLF1ciD7o8D5uix0SEkvnZcaMIjpzrwq3G8A9MhWDXPowYiRoKMgXhrWYLEot4ehR32V+SOBgWR61cLEkd+/VoTrT0MDjsDn6dJxK1FWqVFs7oogmypiCbI+e6GRpx2y2KdXiv4fpHiqlPu4sbf7f84jX0n8VPYF+XPF78mq3RRWP5CP4Wz8x/kYhU+b2VCbxutWCxQEvXAG09A5FbpdDSWjQXK1TGlMM4CCEzimFi9vpQESYRbGNePT+LwWE3J8/3REZBRSvwXsN/fqKSFyqOcOCh93pMgFP13/j7mcLUMyQUY60Ifg3cgqfg3C1+jw1KqQ+EQbaopzpSatPbQG5G/GQY1zhcTE9NYmFehrEPhFolnPyr9bP/ic5gx8G/JHWsU+PooiTh9EU/kJk5QTav/EKuCJRSLsAFvA9ARGYDacB0EZmulDoTHhGjl2qfoziGqhROhHhxGNc4XKyan0VCgkFHsZFVglVKwORIsYV5GWSkJHoa8sRQWeZABoY9lWU/IacvbjQ7J8jGlZ+RonO34MkhmA+cx9O7+Aiw2lrRopyXHuKzr/8Hn00F/j3EmAipD2QVJfnZPFt9DteFIbLTo7AqSai8gbUf9PzfuJfh+eUcPtvFXRuLwivbZLAgUiwhQVg9P5ua5tj2BR0966ksW5Jcf3HjeDkCEVoPLBhGMou/BmwC/qKUWici2wBtGhqPbQ9w2a7LuGrpTL59eGvEV5O0gjX5Ue4nCGbPDZhVO97+OP1DbtYURMGqz6Is19X5WTy+t5ERtyLR6KooyqjyrmxL5dSb3xhrFh/B9cACMaIIhpRS7SKSICIJSqmXROS/rRYs2mnp6qe12+soPmy3NPYQkw7jgFl199GXgI2j5xoWJtvHwKIs1zX52fxsqJ661h6Wzsk0ZZ+RRnWTkxkZKRQMt4X/4GHoV2BEEThFZDqwE/iViJwHek05egxT1RS/jmIfuRkpFOTGmMM4YFa9x72S9JREFs2aHp7jT7WPgQV26BK/DONYVQRVTZ4exWKHZzQM/QqMKILbgD7gH4D3A9nAV0yXJMaIaEdxKNtlMKY46ygtiDGHccCsesczw6yahzUmkZDXKUjNextZNDODtOQEqpu6eMc6W0WxhL5Bj6P4w4Utng1mZP5GWI8CI4rgS0qpzwNuPJ3KEJGvA5+3UrBop8bhYvGs6aSnGPkTh5nxbJcmzjqi3mEcDO8PdyT/Mmqbd/Aeq6Jlgl0nm+PNg5GUmMCqeVkxW5K6ttnFWo7znsNepTzViKsI7FFg5C51A5fe9N8aZFv8EsSG9wiwY+ZHgK12SBQx+Bcm27IkRvwEXk639XBhcORiItlUbLlGI0witJplSX42Tx5w4HYr42G0UUJVk4tNCUdIcHtbtU91JRYYvfXi1+D0KxfftyG6yEhm8eIgmcW7rBYsqgiw4bV8toXL//2vfHndKnvligD8Hcaxpgh85TNGawxNxZY7kQiTCMw0XpOfzWO7G6hr62XJ7DD5S8JEtcNF17QyhO3mrMQCo7eu/QIUbjdP4Ekw1org18BzwEPA/X7bu5VSHZZKFeVU+zuKI8wWOCFMiFbwOYyj2k8Q4hpWO1ykJSewJFyO4gimrDAHgMpGZ8wpgsomJ4sKL4dr/VZiJ//65kqwE5nFR+CqbtzMYhH5DtChlOoGEJEsEblcKfXGeDsXkZuA7wCJwE+UUkHuKiAi7wKeAC5TSkV9G8wqr6O4xH0MfvkOz8YIsQVOCJOiFdbkZ48qx6hjDHtujcPFynlZJCUaqd0Y2yyeNZ2MlESqmpy8a0OB3eKYRnf/EHWtvdy+Nh8Kl3o2Fm70PKZitomwVZ0RH8H/AOv9XvcE2XYJIpII/ACPj6EJ2Cci25VShwPGZeLpfTCuYokWfI7iNMcuS2v+Rwsl+dk8VxOlDuMQ2bhuJdQ2d/GOdfn2yudjvJWnxVmuiQmeHsaHoknhh1rx+pEJ1KdBfeen8eTW2oiF1gUjikCUUqMdypRSbhEx8rmNwEmlVB2AiDyOJxQ1ML3qX4GvA/9kTOTIp9rh4qqlM6OmX6nVlBZEscM4xDWsV3PoGRyOjDwRI1EoYchyXVuYw89er2dw2E1KUhSsksZb8T6Yzf9uO8BDzx3lwFuDVeMPIxZHGhm5WnUi8hkRSfY+/g6oM/C5fKDR73WTd9soIrIeKFRKPTPWjkTkHhGpEJGK1tZWA4e2jxaVczGjOMJryQfF5OqU8GaHcdThfw1L7hitMLko4Rz1aXfxnmdKjFeYtIpgqxYjhKqNP8nzKS3IYXDEzdFzsVN3qMrhoiB3GjMyUuwVZLLX2CBGFMEngSsAB56b+eXAPVM9sIgk4Clm94/jjVVKPayUKldKlc+aNWuqh7aUavciwC+jOMJsgWNiUZekqHcY+67d7d8f7Qr27xv3sGz4cYa+2Gl/PZnJdpvb9sDFLmcw5Y5nZYWe73xlNJmHxqGqyTm6orUVizsKjqsIlFLnlVJ3KqVmK6XmKKXuUkqdN7BvB+CfaVPg3eYjEygBXhaRejyF7baLSLlx8SOMxr2kMcCGhOORmVE8HhbOOtbkx1ZLwxqHi5VzM0kO5ii2YFUF2NvHwAD5OdPIy0ihstFpy/HNplNNp7GjjzX5OXaLYrl1wUgZ6mV4nMNzlFIlIlIK3KqUGs9zsg9YKiIL8SiAO4G7fG96o5JGDcYi8jLwuaiNGvLOpjcn9FOe+O+ktWyJjlWAPxb6NEYdxn1DZE+LModxAEopahwu3l42/9I3rbTl2tXHwCAiQllhDlVNTttkMJNq90IAyiJhRQCWWheMmIZ+DDwADAEoparw3NTHRCk1DHwK2IGnf8FvlVK1IvJVEbl18iJHKN7ZdKIokhkOe89RUwg16zBhhuszldXGwKrgTMcFuvpDOIottuVazhT9BqUF2Zw430PPwLDFglpPlfKYedef/pHxvtPjMZEe1mHESPRPulJq72h7Ng+GrrJS6lng2YBtXwox9hoj+4xYiq9CJSYzMqQgMTl6I4QCZx0mzXB9N80qh4sroi1yKACfryNo6elojxSbYu5IWWEOSnlMZ5sW5VkgYPioci/yFNS74QtwwxfM2WmE9igwsiJoE5HFgAIQkTuAs5ZKFY0UbqTi6kf59vC7OXHTr6PPLBQKk2a4Ue8w9uPQGScpSQmsmBek5HI0RoqZSFlBDkB0+QmCrXgb97JOTnJLXpM9MoUZIyuC+4CHgRUi4gBO4ylHrQngtYFF/GjkFj619hq7RTEPE2e4seIwrmxyUjI/K7ijGKIrUsxkZmSkUDhj2mg/jogn2IoXUI/ewieSBqHxeWhcFvPXclxF4E0Iu15EMoAEX6kJzaXUOFwslmZP6eko6lc6JibWRYk6h3GIa7jV/S46LvucTUJZgMkZq2UFORw845zyfsJCiBWvGh4kSdwoNRwXFQGMRA3lAV8GrgSUiLwGfFUp1W61cNFGtcPFleLNtYtQW+CkMGmG6+8wjgo/QZBrWNvs4pvffY3vFEZIJMlUsSDKqawghz9VnaWtZ4CZ01NNENJCQqx4RxKScY8MkZgUhX6eSWDER/A40Aq8C7jD+/z/rBQqGml29nG+e4CyBCNJ1/GJfzP7aKWy0SP7Wm+1TduZahSKBVFOvkqkURFGGsynU7iRr8/+Bo+NvAWJBD9PGCKNjPgI5iml/tXv9ddE5L2mSRAj7G/oBGB9wgmbJZkkYTBl5WakkJ8T3Q7jykYnuenJLJiRbrcoHqa68rQgyqkkP4sEgUONLq5dMWfK+7OcgBWvUoqn2/K5RhXZrwQgLNYFI4rgeRG5E/it9/UdeHIDNH4cONPJtOREVtjS3doEwmTKKi2IbodxZZOTssIcAsKpo5dQPqAp+A3SU5JYNiczOlYEQWh29dPWM0hpUvys7o0ogk8Afw/8wvs6EegVkb8BlFIqCmspTIEQpWu/DHw50fsiWh3DYSDqHMZ+9A4Mc7ylmxtXzw0+IFoDBCzIHSktyOaFwy0opaxVmgZKSY9JkGtT7VVgpXFk5jUSNRQkWDqOCZJw0z80QsmXd/CJqxfx+ZtW2CtfhOMfZ371ssguIBhItcOFW43hH4iVAIEQPRgmQllhDr+taKKps49CK81oBkpJG9rmR1WTi6QEid7V/SQwEjV0t1Lqp36vE4EvKKW+YqlkUUS1w8WwW7FhQa7dophLqBluMAzOetcuyCFBoKKhM7IUgYG2nL4kqYioRmklJvgNfAr/UKPTWkVgAVVNLpbPzSStY8huUcKGEdPQdd5WkncDecDPgFcslSrK8DmK1y3IsVcQsxlvhjuJEgTTU5NYMTeLA96/WcRgoLRCZZOTBTPSyYv0kMipYkLuyPK5maQkJVDV5OSWYMX5IhSlFFVNTm4unQ9x1JndiGnoLm+UUDXQC9yllHrdcsmiiAMNnRTnxcENwiTKi3P5/f4mhkfcUdXv99AZJxuKZ9gtRniYYu5IcmICq+dnjYbbRiRBVrwCVOH7h+jx80wRI6ahpXh6Cv8eWAl8UEQOKqUuWC1cNKCU4sCZCDNzRDgbinJ5bHcDR891UxIJrR4NcL6rn2ZXPx+LdbOQiZQV5PB/+xojV+EHWfFur2zmM785yDOfuZLVwYoKxihGTEN/BO5TSv1VPO7/z+LpNbDaUsmihMaOPtp6Blkfa/4BCyn3zqr3N3TaowgM+AMC8XXdiphEMjOwOMqprDCbn++q52RrDyvmRkdwYXWTp6DgsjnxFSNjqAm9UqoLPLGiwLdE5I/WihUFeOOsTx96ERA2FGlFYJT8nGnMy06joqGTD19RHH4BJlFqubLRSWKCRM0KxhAWRzn5HMZVjS7rFUGwvAej2/yobHKxat4YBQVjlJBnKyL/DKCU6hKRdwe8/RErhYp4/OKsN7/2Mbak1sXdDGKqbCjKZX999HjjDjU6WTE3k7TkxPEHawAozssgMy2JQ1YnlgXrtW10mx8jbkWtwxU5HcnCyFhqz78LWeC04SYLZIke/OKsE9QQt2afIjEhRjJNjTLFrmUbinJpdvXT7OwzUSgTCHJebrcazSjWGCchQSgrCEPrymB5D0a3+XG6rYfewRHWeFcy8cRYpiEJ8TzY6/jCG2ethvoYIgl30Ra7JQovJmSelhd5/AQVDZ3cmjPNbAknR4jzOt3eS3f/MGvj4QYxkdyRYAT4F0oLsnl4Zx39QyPWraZC5T0Y3ebFF+EU83kiQRhLEagQz4O9ji+8cdZnfvx+/mHoXj5dstVuicKLCZmnK+dlkp6SyIGGTm6NlDjzEOd1yFtbf22s5YkEYzK5I2P4WcoKcxh2Kw6f7bIuoCJU3oPRbV4qGjrJTEti8azp1sgZwYxlGioTkS4R6QZKvc99r9eESb7IpXAjf3Rv5oBaxvrCOHMU+2ZgMOnM06TEBNYW5lDREEF+ghDnVdnkJCMlMS5vEFPFF2VleevKYHkPRrd5qajvYENRbvyZeRlDESilEpVSWUqpTKVUkve573V0VQuziAPupSyZPZ3s9Dj7c5jUl7e8KJcjZ7vpHRg2UbgJEOgPCHFelY1O1hRkx+UNYqrMyUpjTlZqxPcw7uwd5MT5Hi6Ll4TBAOIrRspElFIccC9lfTyYC4IxXuapfzMN/4dfM40NxTMYcSsO2XGTCBVBEnBeA8MjHD7bpR3FU8DjMI7gDGM8ZiFAKwLNxKhr68VJps4fCMW2Bzx2Y5/t2Pfcz/68bkEOIlBRb0PdIYOduaqaXAyNKNbFm/nPRMoKc6hr68XVF7lF3CrqO0hJTIhLRzFoRTBpfEXTdEbx5MlKS2b5nEx7/AQG/Ry7TrYjApsWxedM8U0ECxk2EEY8mlgWwY1q9tV3UFqQHbd5IloRTJIDZzrJolc7EKfIhqJcDp5xMuIOcyCaQT/HrlNtrJ6fRU56ShiFi0AmkaDlo6wwmwSBfacjKDDAj/6hEaodrtHSJ/GIkRITGn+8cdYPAQ+lAV/9hGd7jFcntIry4lx+9cYZjp3rZtX8MNejGcfP0Tc4wsEzTj6ypTh8MkUqoUxpBsKIM9OSWZOfzR6fIphEraeQGMl7GGvb1vs5VHQPQyOKy4rjd3WvFcFE2fYAXZs/R9lXnufvr1vG312/1G6JwovJhcp8iWX7z3SGXxH4E+S8pgH3yjspW/x128SKGCaStBWEyxfl8fPX6z2JZZOo9RQSE+olVbx4Arj4XYxHtCKYBIfOOFGK+HQUT/SHN06Rr4LcaczOTGV/fQcf3FRkkpCTIMh5fePPR3l4Zx2H4thkMMpEkraCsGnRDB7eWcfBM042L84Lk9DG2FvfyfI5mfEXBu6HVgSTYH9DJyIe26dmDAyUohARyotzR8P3DDORpuWTXK3sOtVOWWEO01P1zwSYcIKWP+XFM0gQ2FPXHlGKYMStONDQyW1rIyS73Sb0N3wSHDjjmUFkpsXvDMIQBktRbCiawbPV52jp6mdOVpqxfU+mafkE6OofoqrJyX3blkx6H5qLZKUls3p+Nnvq2u0W5U0cOdtFz8Bw3OYP+LBUEYjITcB3gETgJ0qp/wh4/7PAx4FhoBX4mFKqwUqZporbrTh0xsktcT6DeBOhZudrP2jIhuwzsVXUd3Jz6TwrJfVgwM+x73QHbkVEzV4jnnGcwJcvnMFjexqMFaAz06E8BhXeUuiXLdSKwBJEJBH4AXAD0ATsE5HtSqnDfsMOAuVKqQsi8rfAN4D3WiWTGZw430P3wLDOH/BnrNn5hg+Na0NePT+LtOQEKho6wqMIDPg5dp1qJyUpQV/niTDOKm3Tojx+8tppDjU62bQob2z/kZkO5THY19DJ/Ow08iOlAq5NWLki2AicVErVAYjI48BtwKgiUEq95Dd+D/ABC+UxhQNnPLbsuHQUTwYDNuTkxATKCnLYP1E/gYXsOtVOeVFu3CYYmYbfzP56oD4NeAzParHmCc+Yn70V3EHqTVkckq2UYt/pDo9SinOsTCjLBxr9Xjd5t4XibuC5YG+IyD0iUiEiFa2trSaKOHH2N3QyIyOF4rx0W+WINcqLc6lt7uLC4AQL0E0y23UsOnoHOXK2iyu0WWjqBJQauXnGn3jf/B2Qt/Ci/0gpuO5LY5YjsYLGjj7Odw/EvVkIIsRZLCIfAMqBoIX9lVIPAw8DlJeX29oL4cCZTtYV5iCiK1GaSXnRDEbcp6hsdBm3yweLSvI99/1fcgcc+sWlnx1jtulzaG5ePHMipxC7TCRpa5xZ/OUL8/jVGw0MvuUKUgzmIISUw8ckVw77fP6BOE4k82GlInAAhX6vC7zb3oSIXA/8C7BVKTVgoTxTprN3kLrWXt61vsBuUSKTcXIGxsJni9/f0GFcERjJds1b6DfTNGZr3nWqjYyUxLgtQHYJJuaObFo0g0deP80htZyNRnIQ/Pdlst9gX30HWWlJLJut+41baRraBywVkYUikoKnB/J2/wEisg74X+BWpdR5C2UxhQpdaC40BuvOhCI7PZllc6ZPLJ8gWOE4E5rm7DrVzsaFM0hO1KW4Jsw434ONC2cgAm/UtY/vP5rid2o89tV3ePIbdJ8J6xSBUmoY+BSwAzgC/FYpVSsiXxUR79XlP4HpwO9E5JCIbA+xu4jglePnSU9JZH1Rjt2iRB4GyzqPxYaiXA40dOI2WoAuWOG4KTbNOefqp661ly1LtFloUoT6Hnhv4jnth1gxN4s9p4PkEwT6dkz4ToWivWeAU629lGuzEGCxj0Ap9SzwbMC2L/k9v97K45uJUoqXj7VyxeI8UpN0JMklBNaiaT/9ZluyARvyhqIZ/GZvIyfO97B8rt9y3YhteBLZrsHYXdcG6PyBSROsJlHAzP6Oxf/Nfx7OZnDYzWhN12D+nlD1jUxgT53HP7AxzhPJfOi1r0FOtfbS1NnH1uWz7RYlMgmcid/+/YvRH/6PMWzN5d6QXJ8TbxQDTW7GxWAk0a6T7eSkJ7Nyro0F8KKZYCuygJn9VcnH6B9yv7k/Qags9GCruylGhQE8f/gcuenJoz2V4x2tCAzy8jGPC+OaZbNsliSCmcJMHKAoL538nGm8ctzkEGGDtmalFLtOtbN5UZ62G0+FwO9BgN9mbqnHEPCGf3+CUL6dwH2Z4DcYHHbz4pHz3LBqDknaDwRoRWCYl4+1smT2dApn6PwBqxARrl0xm9dOtNE/NGLejg3amhs7+nA4+3T+gNkEzOyzHDupT7uL+17Z4Nn2YLYneqjkjtExIScTJvgNdp1qo3tgmBtXz53wZ2MVrQgM0DswzN7THXo1EAauWzmbvqERdp8ysTiZ0baUp3z+Ae0oNh3/mf22B3hw/S5WjvwfQ1/svGjqu/37bx4bDBOiwnbUtpCRkqgDAvyIiISySGf3qXYGR9xsW6H9A6YSxAl8DfBPqXfwlyMLLv17+9uGT/51YklOBmLWd51qZ3ZmKotnZUzufDSGuXzhDH6+q56qJicbJtIQJlRfBIOMuBUvHD7HNStm6/IhfmhFYICXjnnCRnWomcmESBCq+kUFVUfPo5S6mMEdLKpkMiUIQtw4fP6BK5fk6azxMHDF4pkkJwp/rjk3MUUAU/JFHTjTSVvPoDYLBaAVwThcDBudqcNGQ2GkfaXR8gAvPcT/nvKO+4rfuIVbDfU2mCwnz/fQ1jPAFdosNHkm0MY0Oz2Zrctms72ymfvfupLEQOe8yS1Rffy55hwpiQlsW67NvP5oRTAOp1p7cDj7uHfbYrtFiVyMlCAwWh5g2wO0ln+Wjf/+F/4h8Xd85ms/92z3rQgsiCkHj1kIdP7AlJhgKYrb1s7nL0da2Hs6SFkRE3oRB6KUYkftObYsydNNpQLQimAsXnqIJa/8h6d07nNcrI1qcXncuCOgNs2szFTKCnL4a9N6PuMbM0Xb8HjsOtVG4YxpOirMbMaY2V+/5Z9JT0mkd8e/wvmfBR1j5u/s8Nkumjr7+PS1uutcIFoRjMW2B3j/qW2c7xrgha5bLWuOEdeE6Gt8/crZfLNxMee7+pnta185xTyFUIy4FXvqOrhJ243NZ4yZ/TTgxtVz+cejb2PfF75NSpK1QYw7as6RIHD9yjmWHica0eGjY9A7MMy+0506WshMDNaTeXtuE/cm/oHK3c9P7XgvPeQ1RXlnmb7nLz00OuTI2S5cfUNcsUSbhcLNrWXzcfUNsdNIEqGBazkWO2pbuKx4BnnTU6cgcWyiVwRjsMsbNnrNsllgbuHD+MRoPZnGvRQ98z4+m9SPe8/TsOpPk18FGLA1v37Smz+gO1WFnSuXziQ3PZk/VDZz/apxZupT8BucbuvlWEs3X3r7qkl9PtbRK4IxePnYeTJSErks6aRng8llcOMOo/Vk6l9FRgZJEkWCe4ihUzstFeuZ6rOsnp910QSlCRvJiQncXDqPFw6fo3dggt3pJsCO2nMAvGW1NgsFQyuCEPjCRj9Q0ELyL2/3bLSgJnpcYbSejHecW8EQSbQ2HJ6SSWAsjp3rpqrJpZsN2chta/PpH3LzwuEWy46xo/Yca/KzKcjVwQDB0IogBCfOe8JG3zr9lGU10eMOo70CvOPcJHC3+iLfz/qHCVcyNcrvDzSRlCDctnb+lPelmRwbFuSSnzON7ZXNluy/paufg2ec3KhXAyHRiiAEv9zTQHKisGD9DVOubaLxw2jkT+FGksRN1tItvHjEk2VsNsMjbp484ODaFbO1A9FGEhKEt5fNY+fxVjp6B03f//Nes9BNJToqLBRaEQShvWeA31Y0cvvafGasuGpKHa80U+PalbM519VPbXOX6fveeaKVtp4B7tigzUJ2c1tZPsNuxbPVZ03f947aFhbNymCJ7k0cEq0IgvDo7gb6h9z8zdZFng0Wxa/HPQbCAa9dMZsEgScPOEw//BP7m8jLSNHhwRHAynmZLJ09ne2HzDUPOS8MsruuXdcWGgcdPhrAhcFhHttdz/Ur5+gZhJkYrR3jG+cdO/Obs6lLhe/texctW3/IHJMie5wXBvnL4fO8f9MC3aQ+AhDx+Gm++fxxHM4+8nOmmbLfvx45z4hb6WTBcdCKIID/29eI88IQf3vNIrtFiS2MxoAHGdfQ3st/f+sV2l46yVduKzFFnD9WNjM44tZmoQji1rJ8vvn8cf5U2czfbDWntteO2nPMy06jtCBImXLNKFoR+PDORD8KfDQN+FmQMRbVQNGMTVFeBu/eUMBv9jZyz9bFpswWn9jfxMp5Wayer28QkcKCvHTWFubwh0PmKIILg8O8cryV921coEuLj4NeE/vY9gBP33aY4v5f85eR9cHDFU0MW9RMjE9duwSF4vsvnpzyvo63dFPZ5NKrgQjktrXzOXy2ixMt3VPe187jrQwMu3USmQG0IvCilOJHr5zi1hlNXJtwQCeORRgFuencedkCflfRyJn2C1Pa1+/369yBSOXm0nkkCKbkFPyx8iy56clsLJ5g45s4RCsCL68cbyW9ZT/f7v8SCYLOIo5A7tu2hIQE4bsvnpj0PoZH3Dx50MG2FbOZqXMHIo7ZmWlsWTKTPxxqnlLuSFWTk2eqz/LeyxaQpIMBxkX/hbz86JVTXJ9+gkQ15Nmgs4gjjrnZaXzg8iKePNBEXWvPpPbxlyPnae0e0CUlIphby+ZzpuMClU2TK/uulOKrfzzMzOkp3KcbShkiPhWBf/y69/F4801cO6sH0VnEEc3fXrOY1KREvvPXia8KzrRf4IEnq1g8K4Nrde5AxHJjyVxSkhL45Z6GSX3+meqzVDR08rm3LNedyAwSn4pg2wMep+/dLwDwjfzvsYbfUvDRR3QWcYQzKzOVD11RxPbKZo5PwKHY3T/E3Y/uw63gJx++zPImKJrJk5WWzEeuKOaJ/U28eHRihej6h0Z46NmjrJyXxbvLCy2SMPaI31+Dtza+UvDppn/k86u7mJ6apLOIo4C/uXox6cmJfOcvxlYFI27Fp39zkNNtvfzPB9azcGaGxRJqpso/vmUZK+Zm8s9PVNHWM2D4cz997TQOZx9ffPtKEhN0yKhR4lcReGvji0Ayw7yn+1HLSh1rzGVGRgof3bKQZ6rPcthADaJ/f/YILx9r5Su3reaKxTPDIKFmqqQmJfKdO9fR1T/M55+oMuQ4Pt/Vzw9eOslbVs3R13mCxG9CWfFVqMRkRoYU7oRkUm74kl4FRBGfuGoRj+6u57/+cpwff6g85LjH957hp6+d5iNXFPP+y4vCKKFmqiyfm8n9N63gq386zK/3nhn3+v3njmMMjbj5f29bGSYJY4e4XRGczynl32Z+nW8P30HbO3+nlUCUkZ2ezMevXMQLh1uoanIGHbOnrp0vPF3DVUtn8oWb9c0hGvnIFcVctXQm//qnw5wKESnW2TvIc9VneeJAEx/dspBibfqbMGJFnffRnYvcBHwHSAR+opT6j4D3U4HHgA1AO/BepVT9WPssLy9XFRUVk5ZJKcXhXz/A6hP/c+mbunREVNHdP8RV33iJrLRk7r5yIbevyyd7midKpKG9l9t/8Dq5GSk8de+W0e2a6KOlq58b/3snC2ak87XbSzh2rtvzaOnm6LluWrs9PoQ5Wam88NmtZOlIoaCIyH6lVNDls2WKQEQSgePADUATsA94n1LqsN+Ye4FSpdQnReRO4B1KqfeOtd9JK4LGvXTUvsh/n5zNY01z2LhwBg+9cw2LZ02f+L40EcPO4638545jVDtcpCUn8PbS+bxzXT5f2l5La/cAf7hvi54hxgB/rjnLJ395YPR1alICS+dMZ/mcLFbMzWTZ3EzWFuZohT8GdimCzcCDSqkbva8fAFBKPeQ3Zod3zG4RSQLOAbPUGEJNShE07mX4Z7fAyCBDksSrm3/K9TfcQoKOKogZqptc/HrvGbYfctA7OEJSgvDY3Ru10zCG+HPNWdzK4zsozsvQUUETxC5FcAdwk1Lq497XHwQuV0p9ym9MjXdMk/f1Ke+YtoB93QPcA7BgwYINDQ0TSDT52dug4fVLtxdtgY8+O8Gz0kQ6PQPDPFt1llmZqbrhjEbjx1iKICqihpRSDwMPg2dFMKEPf/TZ0ZwBRgY9GcM6WSxmmZ6axHsu04lEGs1EsFIROAD/X2SBd1uwMU1e01A2HqexuRRu9Nz861/1lI3QSkCj0WhGsVIR7AOWishCPDf8O4G7AsZsBz4M7AbuAF4cyz8wJQo3agWg0Wg0QbBMESilhkXkU8AOPOGjjyilakXkq0CFUmo78FPgFyJyEujAoyw0Go1GE0Ys9REopZ4Fng3Y9iW/5/3Au62UQaPRaDRjE7eZxRqNRqPxoBWBRqPRxDlaEWg0Gk2coxWBRqPRxDmWFp2zAhFpBSbXww5mAm3jjoot9DnHB/qc44OpnHORUmpWsDeiThFMBRGpCJViHavoc44P9DnHB1adszYNaTQaTZyjFYFGo9HEOfGmCB62WwAb0OccH+hzjg8sOee48hFoNBqN5lLibUWg0Wg0mgC0ItBoNJo4JyYVgYjcJCLHROSkiNwf5P1UEfk/7/tviEixDWKaioFz/oiItIrIIe/j43bIaRYi8oiInPd2uQv2vojId71/jyoRWR9uGc3GwDlfIyIuv2v8pWDjogkRKRSRl0TksIjUisjfBRkTM9fa4Pmaf52VUjH1wFPy+hSwCEgBKoFVAWPuBX7kfX4n8H92yx2Gc/4I8H27ZTXxnK8G1gM1Id5/G/AcIMAm4A27ZQ7DOV8D/MluOU0+53nAeu/zTOB4kO92zFxrg+dr+nWOxRXBRuCkUqpOKTUIPA7cFjDmNuBR7/MngOtEJJo7YRs555hCKbUTTw+LUNwGPKY87AFyRGReeKSzBgPnHHMopc4qpQ54n3cDR4D8gGExc60Nnq/pxKIiyAca/V43cekfcnSMUmoYcAF5YZHOGoycM8C7vEvnJ0Qk1hv7Gv2bxBqbRaRSRJ4TkdV2C2MmXhPuOuCNgLdi8lqPcb5g8nWORUWgCc4fgWKlVCnwAhdXRJrY4QCeejJlwPeAp+0VxzxEZDrwe+DvlVJddstjNeOcr+nXORYVgQPwn+0WeLcFHSMiSUA20B4W6axh3HNWSrUrpQa8L38CbAiTbHZh5HsQUyilupRSPd7nzwLJIjLTZrGmjIgk47kp/kop9WSQITF1rcc7Xyuucywqgn3AUhFZKCIpeJzB2wPGbAc+7H1+B/Ci8nphopRxzznAZnorHttjLLMd+JA3omQT4FJKnbVbKCsRkbk+X5eIbMTz+47mCQ7e8/kpcEQp9e0Qw2LmWhs5Xyuus6U9i+1AKTUsIp8CduCJpnlEKVUrIl8FKpRS2/H8oX8hIifxON/utE/iqWPwnD8jIrcCw3jO+SO2CWwCIvIbPNETM0WkCfgykAyglPoRnl7ZbwNOAheAj9ojqXkYOOc7gL8VkWGgD7gzyic4AFuADwLVInLIu+3/AQsgJq+1kfM1/TrrEhMajUYT58SiaUij0Wg0E0ArAo1Go4lztCLQaDSaOEcrAo1Go4lztCLQaDSaOEcrAk1cISJ5flUbz4mIw/u8R0R+aNEx/15EPjSJz6WIyE5v0qNGYxk6fFQTt4jIg0CPUuqbFh4jCU9JgPXeulYT/fyX8RQU/JXpwmk0XvSKQKNhtMb7n7zPHxSRR0XkVRFpEJF3isg3RKRaRP7sLQGAiGwQkVdEZL+I7AhR8fJa4IBPCYjIyyJS7n0+U0Tqvc9Xi8he7+qkSkSWej//NPB+a89eE+9oRaDRBGcxnpv4rcAvgZeUUmvwZHLe7FUG3wPuUEptAB4B/i3IfrYA+w0c75PAd5RSa4FyPBU0AWqAy6ZwHhrNuGjbo0YTnOeUUkMiUo2nbMefvdurgWJgOVACvOAt+5IIBKtvMw9jdZ12A/8iIgXAk0qpEwBKqRERGRSRTG99eo3GdLQi0GiCMwCglHKLyJBfLRc3nt+NALVKqc3j7KcPSAvY5muClOzboJT6tYi8AdwMPCsif6OUetH7dirQP/lT0WjGRpuGNJrJcQyYJSKbwVM6OESDkCPAkoBtPlPPNXhWEojIIqBOKfVd4A9AqXd7HtCmlBoy/Qw0Gi9aEWg0k8DbEvQO4OsiUgkcAq4IMvQ5PL2G/bleRPYB1wMdIvIZ4D1AjbfiZAnwmHfsNuAZ009Ao/FDh49qNBYjIk8B/6yUOiEiLwOfU0pVGPzsk8D9SqnjVsqoiW/0ikCjsZ778TiNJ4S3ydDTWglorEavCDQajSbO0SsCjUajiXO0ItBoNJo4RysCjUajiXO0ItBoNJo4RysCjUajiXP+P1lQa/riyMm8AAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -816,7 +759,6 @@ }, { "cell_type": "markdown", - "id": "78e4c8dc", "metadata": {}, "source": [ "## Multiple Atoms" @@ -824,7 +766,6 @@ }, { "cell_type": "markdown", - "id": "9885e2bc", "metadata": {}, "source": [ "We will now run the AFM preparation sequence from the Pulser tutorial with our noise models, and compare the results to the clean case. \n", @@ -835,7 +776,6 @@ { "cell_type": "code", "execution_count": 23, - "id": "4f6541ac", "metadata": {}, "outputs": [], "source": [ @@ -873,7 +813,6 @@ { "cell_type": "code", "execution_count": 24, - "id": "cb510f6c", "metadata": {}, "outputs": [], "source": [ @@ -890,7 +829,6 @@ }, { "cell_type": "markdown", - "id": "32e3a9f5", "metadata": {}, "source": [ "We now plot the simulation results by sampling the final states." @@ -899,12 +837,11 @@ { "cell_type": "code", "execution_count": 25, - "id": "fdc590ac", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -934,7 +871,6 @@ }, { "cell_type": "markdown", - "id": "0510aaad", "metadata": {}, "source": [ "The bars represent the simulation results as populations of bitstrings. They're colored blue for the noiseless simulation, and orange for the noisy one. We clearly identify the antiferromagnetic state as the most populated one in both cases, but it is slightly less populated in the noisy case, while some other bitstrings, not present in the noiseless case, appear." From d60fc2cff6c68c13cf84fe279b80c5af19146950 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Fri, 5 Aug 2022 16:59:46 +0200 Subject: [PATCH 15/18] Adding the sampler to the API reference (#392) --- docs/source/apidoc/{creation.rst => core.rst} | 12 +++++++++++- docs/source/apidoc/pulser.rst | 4 ++-- .../apidoc/{emulation.rst => simulation.rst} | 0 docs/source/installation.rst | 2 +- pulser-core/pulser/sampler/__init__.py | 9 +++------ pulser-core/pulser/sampler/sampler.py | 2 +- pulser-core/pulser/sampler/samples.py | 15 ++++++++++----- 7 files changed, 28 insertions(+), 16 deletions(-) rename docs/source/apidoc/{creation.rst => core.rst} (94%) rename docs/source/apidoc/{emulation.rst => simulation.rst} (100%) diff --git a/docs/source/apidoc/creation.rst b/docs/source/apidoc/core.rst similarity index 94% rename from docs/source/apidoc/creation.rst rename to docs/source/apidoc/core.rst index 5b676ffc5..05ea56199 100644 --- a/docs/source/apidoc/creation.rst +++ b/docs/source/apidoc/core.rst @@ -1,5 +1,5 @@ ************************ -Pulse Sequence Creation +Core Features ************************ Sequence @@ -99,3 +99,13 @@ Channels .. automodule:: pulser.channels :members: :show-inheritance: + + +Sampler +------------------ +.. automodule:: pulser.sampler.sampler + :members: + +.. automodule:: pulser.sampler.samples + :members: + diff --git a/docs/source/apidoc/pulser.rst b/docs/source/apidoc/pulser.rst index 58d6beb69..42ce1e873 100644 --- a/docs/source/apidoc/pulser.rst +++ b/docs/source/apidoc/pulser.rst @@ -4,5 +4,5 @@ API Reference .. toctree:: :maxdepth: 3 - creation - emulation + core + simulation diff --git a/docs/source/apidoc/emulation.rst b/docs/source/apidoc/simulation.rst similarity index 100% rename from docs/source/apidoc/emulation.rst rename to docs/source/apidoc/simulation.rst diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 53274c563..a6eab3a81 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -17,7 +17,7 @@ installed, then use ``pip``: :: The standard ``pulser`` distribution will install the core ``pulser`` package and the ``pulser_simulation`` extension package, which is required if you want -to access the :doc:`apidoc/emulation` features. +to access the :doc:`apidoc/simulation` features. If you wish to install only the core ``pulser`` features, you can instead run: :: diff --git a/pulser-core/pulser/sampler/__init__.py b/pulser-core/pulser/sampler/__init__.py index 0f75e41c4..273043ae8 100644 --- a/pulser-core/pulser/sampler/__init__.py +++ b/pulser-core/pulser/sampler/__init__.py @@ -12,12 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Module sampler enables the sampling of pulser sequences. +"""The sampler module enables the sampling of pulser sequences. -Samples of a sequence are needed for plotting and simulation. - - Typical usage: - - sampler.sample(sequence) +The samples of a sequence are organized in channels and are used for +plotting and simulation. """ from pulser.sampler.sampler import sample diff --git a/pulser-core/pulser/sampler/sampler.py b/pulser-core/pulser/sampler/sampler.py index e50e57ea8..bedb5d231 100644 --- a/pulser-core/pulser/sampler/sampler.py +++ b/pulser-core/pulser/sampler/sampler.py @@ -1,4 +1,4 @@ -"""Defines the main function for sequence sampling.""" +"""The main function for sequence sampling.""" from __future__ import annotations from typing import TYPE_CHECKING, Optional diff --git a/pulser-core/pulser/sampler/samples.py b/pulser-core/pulser/sampler/samples.py index e94456253..4459a0488 100644 --- a/pulser-core/pulser/sampler/samples.py +++ b/pulser-core/pulser/sampler/samples.py @@ -1,4 +1,4 @@ -"""Contains dataclasses for samples and some helper functions.""" +"""Dataclasses for storing and processing the samples.""" from __future__ import annotations from collections import defaultdict @@ -107,7 +107,7 @@ def extend_duration(self, new_duration: int) -> ChannelSamples: Must be greater than or equal to the current duration. Returns: - The extend channel samples. + The extended channel samples. """ extension = new_duration - self.duration if extension < 0: @@ -125,7 +125,7 @@ def extend_duration(self, new_duration: int) -> ChannelSamples: def is_empty(self) -> bool: """Whether the channel is effectively empty. - We consider the channel to be empty if all amplitude and detuning + The channel is considered empty if all amplitude and detuning samples are zero. """ return np.count_nonzero(self.amp) + np.count_nonzero(self.det) == 0 @@ -136,7 +136,7 @@ def modulate( """Modulates the samples for a given channel. It assumes that the phase starts at its initial value and is kept at - its final value.The same could potentially be done for the detuning, + its final value. The same could potentially be done for the detuning, but it's not as safe of an assumption so it's not done for now. Args: @@ -157,7 +157,7 @@ def modulate( @dataclass class SequenceSamples: - """Gather samples of a sequence with useful info.""" + """Gather samples for each channel in a sequence.""" channels: list[str] samples_list: list[ChannelSamples] @@ -192,6 +192,11 @@ def to_nested_dict(self, all_local: bool = False) -> dict: Args: all_local: Forces all samples to be distributed by their individual targets, even when applied by a global channel. + + Returns: + A nested dictionary splitting the samples according to their + addressing ('Global' or 'Local'), the targeted basis + and, in the 'Local' case, the targeted qubit. """ bases = {ch_obj.basis for ch_obj in self._ch_objs.values()} in_xy = False From 4ecad789b2d998af27c7ef5eb483543c6f8f17b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Tue, 9 Aug 2022 18:40:28 +0200 Subject: [PATCH 16/18] Automatic packaging and CI test updates (#386) * Reorganizing the requirements locations * WIP: Trying out composite Github actions * Incorporating composite Github action in CI suite * Make CI run on all PRs * Bug: Requirements files were switched * Moving jsonschema to requirements * Installing only the necessary requirements in CI * Adding pytest to type-checking required packages * Complementing the CI test description * WIP: Test version check * WIP: Check new version validity * WIP: Test version validity [fail 1] * WIP: Test version check [fail 2] * Bring version back * Trigger new workflow run * WIP: Test stable version validity [fail 3] * WIP: Test stable version validity [pass] * Finish version checker tests * Adding full test workflow * Make full tests run on push only * Centralizing the different packages * Add packaging script * WIP: Test publish workflow * Test: Trigger 'publish' workflow * WIP: 2nd 'publish' workflow test * WIP: 3rd 'publish' workflow test * Finish the 'publish' workflow * Documenting the Release procedure * Review comments + Details on merge commit messages * Adding review suggestions --- .github/scripts/package.sh | 27 ++++++++ .github/workflows/ci.yml | 70 +++++++------------- .github/workflows/publish.yml | 79 +++++++++++++++++++++++ .github/workflows/pulser-setup/action.yml | 31 +++++++++ .github/workflows/test.yml | 25 +++++++ .github/workflows/version.yml | 48 ++++++++++++++ .readthedocs.yml | 2 +- CONTRIBUTING.md | 2 +- MANIFEST.in | 1 - README.md | 2 +- VERSION.txt | 2 +- dev_requirements.txt | 17 +++++ docs/source/installation.rst | 2 +- packages.txt | 2 + pulser-core/requirements.txt | 6 ++ pulser-core/setup.py | 16 ++--- pulser-simulation/requirements.txt | 2 + pulser-simulation/setup.py | 14 ++-- release.md | 73 +++++++++++++++++++++ requirements.txt | 27 -------- setup.py | 8 +-- 21 files changed, 351 insertions(+), 105 deletions(-) create mode 100755 .github/scripts/package.sh create mode 100644 .github/workflows/publish.yml create mode 100644 .github/workflows/pulser-setup/action.yml create mode 100644 .github/workflows/test.yml create mode 100644 .github/workflows/version.yml create mode 100644 dev_requirements.txt create mode 100644 packages.txt create mode 100644 pulser-core/requirements.txt create mode 100644 pulser-simulation/requirements.txt create mode 100644 release.md delete mode 100644 requirements.txt diff --git a/.github/scripts/package.sh b/.github/scripts/package.sh new file mode 100755 index 000000000..93f5d66b2 --- /dev/null +++ b/.github/scripts/package.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash + +# Exit if something fails +set -e + +# Find and change to the repository directory +repo_dir=$(git rev-parse --show-toplevel) +cd "${repo_dir}" + +# Removing existing files in /dist +rm -rf dist + +packages=$(cat packages.txt) +# Build the pulser packages +for pkg in $packages +do + echo "Packaging $pkg" + python $pkg/setup.py -q bdist_wheel -d "../dist" + rm -r $pkg/build +done + +# Build the pulser metapackage +python setup.py -q bdist_wheel -d "dist" +rm -r build + +echo "Built wheels:" +ls dist diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8f825195b..0c028fa1a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,83 +12,59 @@ jobs: runs-on: ubuntu-latest steps: - name: Check out Pulser - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/checkout@v3 + - name: Pulser + flake8 install + uses: ./.github/workflows/pulser-setup with: - python-version: 3.8 - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install -e ./pulser-core -e ./pulser-simulation - pip install -r requirements.txt + extra-packages: flake8 - name: Lint with flake8 run: flake8 black: runs-on: ubuntu-latest steps: - name: Check out Pulser - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/checkout@v3 + - name: Pulser + black install + uses: ./.github/workflows/pulser-setup with: - python-version: 3.8 - - name: Install black - run: | - python -m pip install --upgrade pip - pip install black - pip install 'black[jupyter]' + extra-packages: black - name: Check formatting with black run: black --check --diff . isort: runs-on: ubuntu-latest steps: - name: Check out Pulser - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/checkout@v3 + - name: Pulser + isort install + uses: ./.github/workflows/pulser-setup with: - python-version: 3.8 - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install -e ./pulser-core -e ./pulser-simulation - pip install -r requirements.txt + extra-packages: isort - name: Check import sorting with isort run: isort --check-only --diff . typing: runs-on: ubuntu-latest steps: - name: Check out Pulser - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/checkout@v3 + - name: Pulser + mypy install + uses: ./.github/workflows/pulser-setup with: - python-version: 3.8 - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install -e ./pulser-core -e ./pulser-simulation - pip install -r requirements.txt + extra-packages: '''mypy\|pytest''' - name: Type check with mypy run: mypy test: - runs-on: ${{ matrix.os }} + if: github.event_name != 'push' + runs-on: ubuntu-latest strategy: matrix: - os: [ubuntu-latest] - python-version: [3.7, 3.8, 3.9, "3.10"] + python-version: ['3.7', '3.9'] steps: - name: Check out Pulser - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/checkout@v3 + - name: Pulser + pytest install + uses: ./.github/workflows/pulser-setup with: python-version: ${{ matrix.python-version }} - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install -e ./pulser-core -e ./pulser-simulation - pip install -r requirements.txt + extra-packages: pytest - name: Run the unit tests & generate coverage report run: pytest --cov --cov-fail-under=100 diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 000000000..3ed52addc --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,79 @@ +name: Upload Release Package to PyPI + +on: + release: + types: [released] + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Check out Pulser + uses: actions/checkout@v3 + with: + ref: ${{ github.ref }} + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.9 + - name: Install Python dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build packages + shell: bash + run: ./.github/scripts/package.sh + - name: Publish to TestPyPI + env: + TWINE_USERNAME: ${{ secrets.TESTPYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.TESTPYPI_PASSWORD }} + run: twine upload --repository testpypi dist/* + - name: Install from TestPyPI + timeout-minutes: 5 + shell: bash + run: | + version="$(head -1 VERSION.txt)" + until pip install -i https://test.pypi.org/simple/ pulser==$version --extra-index-url https://pypi.org/simple + do + echo "Failed to install from TestPyPI, will wait for upload and retry." + sleep 30 + done + - name: Test the installation + # Installs pytest from dev_requirements.txt (in case it has a version specifier) + run: | + grep -e pytest dev_requirements.txt | sed 's/ //g' | xargs pip install + pytest + - name: Publish to PyPI + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: twine upload dist/* + + check-release: + needs: deploy + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ['3.7', '3.8', '3.9', '3.10'] + steps: + - name: Check out Pulser + uses: actions/checkout@v3 + with: + ref: ${{ github.ref }} + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install Pulser from PyPI + shell: bash + run: | + python -m pip install --upgrade pip + pip install pulser + - name: Test the installation + shell: bash + run: | + version="$(head -1 VERSION.txt)" + python -c "import pulser; assert pulser.__version__ == '$version'" + grep -e pytest dev_requirements.txt | sed 's/ //g' | xargs pip install + pytest \ No newline at end of file diff --git a/.github/workflows/pulser-setup/action.yml b/.github/workflows/pulser-setup/action.yml new file mode 100644 index 000000000..51b90b144 --- /dev/null +++ b/.github/workflows/pulser-setup/action.yml @@ -0,0 +1,31 @@ +name: Pulser setup +description: "Sets up Python and installs Pulser." +inputs: + python-version: + description: Python version + required: false + default: '3.9' + extra-packages: + description: Extra packages to install (give to grep) + required: false + default: '' +runs: + using: 'composite' + steps: + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ inputs.python-version }} + cache: 'pip' + - name: Install Pulser + shell: bash + run: | + python -m pip install --upgrade pip + pip install -e ./pulser-core -e ./pulser-simulation + - name: Install extra packages from the dev requirements + if: "${{ inputs.extra-packages != '' }}" + shell: bash + run: | + grep -e ${{ inputs.extra-packages }} dev_requirements.txt \ + | sed 's/ //g' \ + | xargs pip install \ No newline at end of file diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 000000000..338561c05 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,25 @@ +name: test + +on: + push: + branches: + - master + - develop + +jobs: + full-tests: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ['3.7', '3.8', '3.9', '3.10'] + steps: + - name: Check out Pulser + uses: actions/checkout@v3 + - name: Pulser + pytest setup + uses: ./.github/workflows/pulser-setup + with: + python-version: ${{ matrix.python-version }} + extra-packages: pytest + - name: Run the unit tests & generate coverage report + run: pytest --cov --cov-fail-under=100 \ No newline at end of file diff --git a/.github/workflows/version.yml b/.github/workflows/version.yml new file mode 100644 index 000000000..d07e59df2 --- /dev/null +++ b/.github/workflows/version.yml @@ -0,0 +1,48 @@ +name: version + +on: + pull_request: + paths: + - 'VERSION.txt' + +jobs: + validate-version: + runs-on: ubuntu-latest + steps: + - name: Check out base branch + uses: actions/checkout@v3 + with: + ref: ${{ github.event.pull_request.base.ref }} + - name: Get old version + run: | + old_version="$(head -1 VERSION.txt)" + echo "Old version: $old_version" + echo "old_version=$old_version" >> $GITHUB_ENV + - name: Check out head branch + uses: actions/checkout@v3 + - name: Get new version + run: | + new_version="$(head -1 VERSION.txt)" + echo "New version: $new_version" + echo "new_version=$new_version" >> $GITHUB_ENV + - name: Compare versions + run: dpkg --compare-versions "${{ env.old_version }}" lt "${{ env.new_version }}" + - name: Check stable version validity + if: github.event.pull_request.base.ref == 'master' + run: | + pattern=^\(0\|[1-9]\d*\)\.\(0\|[1-9]\d*\)\.\(0\|[1-9]\d*\)$ + if [[ ${{ env.new_version }} =~ $pattern ]]; then + echo "New version is valid."; exit 0 + else + echo "New version is invalid."; exit 1 + fi + - name: Check development version validity + if: github.event.pull_request.base.ref != 'master' + run: | + pattern=^\(0\|[1-9]\d*\)\.\(0\|[1-9]\d*\)dev\(0\|[1-9]\d*\)$ + if [[ ${{ env.new_version }} =~ $pattern ]]; then + echo "New version is valid."; exit 0 + else + echo "New version is invalid."; exit 1 + fi + diff --git a/.readthedocs.yml b/.readthedocs.yml index 716d9c9b9..61cab3a91 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -18,6 +18,6 @@ sphinx: python: install: - requirements: docs/requirements.txt - - requirements: requirements.txt + - requirements: dev_requirements.txt - requirements: pulser-core/rtd_requirements.txt - requirements: pulser-simulation/rtd_requirements.txt diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f1d15872c..c0ac85f51 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -70,7 +70,7 @@ Here are the steps you should follow to make your contribution: We enforce some continuous integration standards in order to maintain the quality of Pulser's code. Make sure you follow them, otherwise your pull requests will be blocked until you fix them. To check if your changes pass all CI tests before you make the PR, you'll need additional packages, which you can install by running ```shell -pip install -r requirements.txt +pip install -r dev_requirements.txt ``` - **Tests**: We use [`pytest`](https://docs.pytest.org/en/latest/) to run unit tests on our code. If your changes break existing tests, you'll have to update these tests accordingly. Additionally, we aim for 100% coverage over our code. Try to cover all the new lines of code with simple tests, which should be placed in the `Pulser/pulser/tests` folder. To run all tests and check coverage, run: diff --git a/MANIFEST.in b/MANIFEST.in index 76adac73d..03d8c7343 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,3 @@ include README.md -include requirements.txt include LICENSE include VERSION.txt diff --git a/README.md b/README.md index 610f549c2..ef6607edf 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ your installation will change accordingly. To run the tutorials or the test suite locally, after installation first run the following to install the development requirements: ```bash -pip install -r requirements.txt +pip install -r dev_requirements.txt ``` Then, you can do the following to run the test suite and report test coverage: diff --git a/VERSION.txt b/VERSION.txt index b8aacd3fb..2551af7e6 100644 --- a/VERSION.txt +++ b/VERSION.txt @@ -1 +1 @@ -0.6.0.dev +0.7dev0 diff --git a/dev_requirements.txt b/dev_requirements.txt new file mode 100644 index 000000000..038d95e3c --- /dev/null +++ b/dev_requirements.txt @@ -0,0 +1,17 @@ +# tests +black +black[jupyter] +flake8 +flake8-docstrings +isort +mypy == 0.921 +pytest +pytest-cov + +# CI +pre-commit + +# tutorials +notebook +python-igraph +scikit-optimize diff --git a/docs/source/installation.rst b/docs/source/installation.rst index a6eab3a81..5ccc60f7c 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -42,4 +42,4 @@ your installation will change accordingly. If you want to install the development requirements, stay inside the same ``Pulser`` directory and follow up by running: :: - pip install -r requirements.txt + pip install -r dev_requirements.txt diff --git a/packages.txt b/packages.txt new file mode 100644 index 000000000..888e92d31 --- /dev/null +++ b/packages.txt @@ -0,0 +1,2 @@ +pulser-core +pulser-simulation \ No newline at end of file diff --git a/pulser-core/requirements.txt b/pulser-core/requirements.txt new file mode 100644 index 000000000..146f8af7a --- /dev/null +++ b/pulser-core/requirements.txt @@ -0,0 +1,6 @@ +jsonschema == 4.4.0 +matplotlib +numpy >= 1.20 +scipy +backports.cached-property; python_version == '3.7' +typing-extensions; python_version == '3.7' diff --git a/pulser-core/setup.py b/pulser-core/setup.py index b4791e1e8..7667c387e 100644 --- a/pulser-core/setup.py +++ b/pulser-core/setup.py @@ -27,6 +27,9 @@ # Changes to the directory where setup.py is os.chdir(current_directory) +with open("requirements.txt") as f: + requirements = f.read().splitlines() + # Stashes the source code for the local version file local_version_fpath = Path(package_name) / "_version.py" with open(local_version_fpath, "r") as f: @@ -39,18 +42,7 @@ setup( name=distribution_name, version=__version__, - install_requires=[ - "matplotlib", - "numpy>=1.20", - "scipy", - "jsonschema==4.4.0", - ], - extras_require={ - ":python_version == '3.7'": [ - "backports.cached-property", - "typing-extensions", - ], - }, + install_requires=requirements, packages=find_packages(), package_data={package_name: ["py.typed"]}, include_package_data=True, diff --git a/pulser-simulation/requirements.txt b/pulser-simulation/requirements.txt new file mode 100644 index 000000000..7ccd7f816 --- /dev/null +++ b/pulser-simulation/requirements.txt @@ -0,0 +1,2 @@ +qutip>=4.6.3 +typing-extensions; python_version == '3.7' diff --git a/pulser-simulation/setup.py b/pulser-simulation/setup.py index 484fd835e..bd9ec0476 100644 --- a/pulser-simulation/setup.py +++ b/pulser-simulation/setup.py @@ -27,6 +27,10 @@ # Changes to the directory where setup.py is os.chdir(current_directory) +with open("requirements.txt") as f: + requirements = f.read().splitlines() +requirements.append(f"pulser-core=={__version__}") + # Stashes the source code for the local version file local_version_fpath = Path(package_name) / "_version.py" with open(local_version_fpath, "r") as f: @@ -39,15 +43,7 @@ setup( name=distribution_name, version=__version__, - install_requires=[ - f"pulser-core=={__version__}", - "qutip>=4.6.3", - ], - extras_require={ - ":python_version == '3.7'": [ - "typing-extensions", - ], - }, + install_requires=requirements, packages=find_packages(), package_data={package_name: ["py.typed"]}, include_package_data=True, diff --git a/release.md b/release.md new file mode 100644 index 000000000..591e67b44 --- /dev/null +++ b/release.md @@ -0,0 +1,73 @@ +# Release Procedure + + +## Versioning + +Pulser version follows the [Semantic Versioning 2.0.0 specifcation](https://semver.org/spec/v2.0.0.html), which means its versions are numbered as MAJOR.MINOR.PATCH. + +Currently (as of July 2022), Pulser is still in an early development phase and has a MAJOR of 0 - as such, both breaking and backwards compatible changes and additions to the API will be introduced through increments in the MINOR until version 1.0.0 is released. + +During this phase, only two type of releases are envisioned: + +- A scheduled release, where the MINOR is bumped and the PATCH is reset (`0.{x}.{y} -> 0.{x+1}.0`) +- A hotfix, where the PATCH is bumped (`0.{x}.{y} -> 0.{x}.{y+1}`) + +Only releases are tracked and tagged in the `master` branch, while development is done in the `develop` branch. To signal this, the version in the `develop` branch should always be one MINOR ahead of `master` and follow the `MAJOR.{MINOR+1}dev{PATCH}` format (e.g. if the latest release tagged in `master` was `0.4.3`, then the version in `develop` should be `0.5dev3`). Through this format, we mark which release is under development and how many patches have occured since its development started (which tells us how many times we brought in changes done directly in `master` through an hotfix). + +The version number is centralized in the `VERSION.txt` file and is shared between all the Pulser packages. + + +## Preparing a scheduled release + +A scheduled release is the result of a series of features that were added to the `develop` branch over time. The release process starts out with the creation of a release branch, which should be branched out from `develop` to contain all the desired features and be named `release/v{x}.{y}.{z}`, where `x, y, z` are the MAJOR, MINOR and PATCH of the version to be released (though usually a scheduled release will have PATCH=0). + +In the release branch, no other features can be added. Changes to the documentation and bug fixes are allowed, but should only be done when the development in the `develop` branch needs to continue while the release is being prepared; otherwise, do all the changes in `develop` before checking out the release branch. Note that the release branch will ultimately be *merged* to the `master` branch *without being squashed*, so keep the ammount of commits small and document them well to preserve the quality of the history. + +Crucially, the release branch must feature a commit changing the development version in `VERSION.txt` to the desired version of the release. For a minor release, this should be of the form `{x}.{y}dev{z} -> {x}.{y}.0`. + +Finally, open a PR from the `release/v{x}.{y}.{z}` branch to `master`, have someone review and accept the changes introduced in the release branch (all the changes done in `develop` will be there as well, but those have already been reviewed) and merge the branch to `master` **without squashing the commits**. To keep the `master` branch's history clean and informative, replace Github's default merge commit message with `Release v{x}.{y}.{z}`. Optionally, you can also include a summary of the most important changes introduced in the release. + + +## Preparing a hotfix + +Unlike with a scheduled release, a hotfix serves only to fix bugs found in the latest release. The hotfix branch must be branched out from `master` and feature only the changes required to fix any bugs. + +Along with the bug fixes, the hotfix branch must also have a commit updating the version with an increment of the PATCH, ie `{x}.{y}.{z} -> {x}.{y}.{z+1}`. + +When ready, open a PR to merge the hotfix branch to `master` and, once that is reviewed and accepted, **squash and merge the commits** (note the difference with respect to the scheduled release procedure). + + +## Writing the release notes + +In the [Pulser Releases](https://github.com/pasqal-io/Pulser/releases), draft a new release where you **tag the HEAD of `master`** with **`v{new-version}`** (eg for version 1.2.3, the tag will be `v1.2.3`). + +The release notes should include: + +- A summary of the main changes introduced (for scheduled releases) +- A list of the bug fixes (if any) +- The full list of changes since the last release. When on the `master` branch, you can get the list of changes since the last tag (which should be the last release) by running: + ```bash + previous_version=$(git describe --tags --abbrev=0) + git log $previous_version..HEAD "--pretty=%h %s" + ``` + If you've tagged the latest version already, just manually replace `previous_version` with the previously released version number. +- A thank you to all the contributors. Reusing the `previous_version` variable defined before, you can get this list by running: + ```bash + git log $previous_version..HEAD --pretty="%an" | sort | uniq + ``` + Note that this will list only the authors of the PRs to `develop`. If you know of other contributors that do not appear listed, make sure to add them. + + +## Deploying the release + +The publication of the release notes will trigger a Github Actions workflow that automatically builds all the packages, publishes them to PyPI and runs some tests to check the publication succeed. + +Make sure this workflow ran without errors - if not, assess why it failed and, if it was a third-party problem (e.g. a network connection issue), try to rerun the workflow. +However, in the unlikely scenario that the deployment failed, it is more likely that there is something that needs to be fixed, in which case you should make an hotfix right away. + + +## Merging the changes back to `develop` + +Finally, you must open a PR from `master` to `develop` to merge the changes that occured in `master`. In this PR, you must also bump the version you just released, `{x}.{y}.{z}`, to the new development version, `{x}.{y+1}dev{z}` (e.g. `0.8.3 -> 0.9dev3`). + +Once the PR is accepted, merge it **without squashing** (again, replacing the merge commit message with something more informative) and that's it, you're done! \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 2b5a0746e..000000000 --- a/requirements.txt +++ /dev/null @@ -1,27 +0,0 @@ -matplotlib -numpy >= 1.20 -qutip >= 4.6.3 -scipy - -# version specific -backports.cached-property; python_version == '3.7' -typing-extensions; python_version == '3.7' - -# tests -black -black[jupyter] -flake8 -flake8-docstrings -isort -mypy == 0.921 -pytest -pytest-cov -jsonschema - -# CI -pre-commit - -# tutorials -notebook -python-igraph -scikit-optimize diff --git a/setup.py b/setup.py index e6017f250..fc1a95c21 100644 --- a/setup.py +++ b/setup.py @@ -25,14 +25,14 @@ "`pip install -e ./pulser-core -e ./pulser-simulation` instead." ) +with open("packages.txt", "r") as f: + requirements = [f"{pkg.strip()}=={__version__}" for pkg in f.readlines()] + # Just a meta-package that requires 'pulser-core' and 'pulser-simulation' setup( name="pulser", version=__version__, - install_requires=[ - f"pulser-core=={__version__}", - f"pulser-simulation=={__version__}", - ], + install_requires=requirements, description="A pulse-level composer for neutral-atom quantum devices.", long_description=open("README.md").read(), long_description_content_type="text/markdown", From 30ce366d7dca3aaf7da2f69a80086dcea33551f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henrique=20Silv=C3=A9rio?= Date: Wed, 10 Aug 2022 10:10:11 +0200 Subject: [PATCH 17/18] Small tutorial updates (#393) --- .../QAOA and QAA to solve a MIS problem.ipynb | 33 ++++--- ...iltonians in arrays of Rydberg atoms.ipynb | 89 ++++++++++--------- 2 files changed, 66 insertions(+), 56 deletions(-) diff --git a/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb b/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb index 4260a7c82..cd150af3d 100644 --- a/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb +++ b/tutorials/applications/QAOA and QAA to solve a MIS problem.ipynb @@ -507,7 +507,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To ensure that we are not exciting the system to states that do not form independent sets, we have to estimate the minimal distance between atoms which are not connected in the graph (this yields $\\Omega_{\\text{min}}$), and estimate the furthest distance between two disconnected atoms $\\Omega_{\\text{max}}$. Keeping $\\Omega \\in [\\Omega_{\\text{min}}, \\Omega_{\\text{max}}]$ insures that only independent sets appear in the dynamics. " + "To ensure that we are not exciting the system to states that do not form independent sets, we have to estimate the minimal distance between atoms which are not connected in the graph (this yields $\\Omega_{\\text{min}}$), and estimate the furthest distance between two disconnected atoms $\\Omega_{\\text{max}}$. Keeping $\\Omega \\in [\\Omega_{\\text{min}}, \\Omega_{\\text{max}}]$ ensures that only independent sets appear in the dynamics. " ] }, { @@ -516,13 +516,19 @@ "metadata": {}, "outputs": [], "source": [ - "A = np.array(G.get_adjacency().data) # adjacency matrix of G\n", - "A_complement = -(np.array(G.get_adjacency().data) - 1) - np.eye(\n", - " len(A)\n", - ") # adjacency matrix of G complement\n", - "D = squareform(pdist(np.array(list(reg.qubits.values()))))\n", + "# Adjacency matrix of G\n", + "A = np.array(G.get_adjacency().data)\n", + "# Adjacency matrix of G complement\n", + "A_complement = -(A - 1) - np.eye(len(A))\n", + "# Coordinates of all the qubits\n", + "coordinates = list(reg.qubits.values())\n", + "# Distance matrix between two qubits\n", + "D = squareform(pdist(coordinates))\n", + "# Maximum distance between linked atoms\n", "link_max = np.max(D * A)\n", + "# Minimum distances between unlinked atoms\n", "no_link_min = np.min((D * A_complement)[np.nonzero(D * A_complement)])\n", + "# Valid ranges for Omega\n", "Omega_min = Chadoq2.interaction_coeff / no_link_min**6\n", "Omega_max = Chadoq2.interaction_coeff / link_max**6" ] @@ -533,12 +539,11 @@ "metadata": {}, "outputs": [], "source": [ - "Omega = (\n", - " Omega_max - Omega_min\n", - ") / 2 # we choose a random value between the min and the max\n", + "# We choose a random value between the min and the max\n", + "Omega = (Omega_max + Omega_min) / 2\n", "delta_0 = -5 # just has to be negative\n", "delta_f = -delta_0 # just has to be positive\n", - "T = 4500 # time in ns, we choose a time long enough to ensure the propagation of information in the system" + "T = 4000 # time in ns, we choose a time long enough to ensure the propagation of information in the system" ] }, { @@ -548,9 +553,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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" ] @@ -647,7 +652,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb index a9d85d53a..0fc65becb 100644 --- a/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb +++ b/tutorials/quantum_simulation/Microwave-engineering of programmable XXZ Hamiltonians in arrays of Rydberg atoms.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -135,14 +135,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, "metadata": {}, @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -179,12 +179,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -376,12 +376,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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ADZJWRMRtVZu9GXg0Ip4s6TjgbOB1ZY4XESdWPf6FpJOBb5SqfAMd2DFvZjY+BHSp8TKKg4DVEXFXNlDta6Q+gmpHs6PF5RvAS9S8Nq1lwIuatC+gQKoNSV2kE/AkYKdbqbJbXs3MJrAxpdpYDNxX9XwN8JxG20TEgKTHSBOyFU/OtrNPkpqYcvUJ55G3k/oAUh6P/aBuqsOgcSrwMVk/Yw4XPeXFhcpc86LaoJ3Pvn+2e+Eye+06o9SxVs8vnjtr7oziSeMWzp1WuAzAE5sH2naseTP7CpfZd7fiCf4e21L8dwIYHCre77e0RAJCgJm9xS/qF84qft7LHAdgdl/xcjN6yh3rsS3FbnHf2lX8c1RLBF0j9/MuyFJVVCyPiOVjPnDzvD4bMT2fdBvsd0h1/HWZneW9gjg/2/ZY4BZSMigzs8ln5OyzD0fEsgbr1gJ7Vz3fK3ut3jZrJPWQ/uNfX7Km9exDysl0B7AIeC3wVkmnRsTni+4sb4A4EDghIr5V9ABmZhNHoKFyV5rADcD+kpaSAsFxwOtrtlkBHA9cB7yGlFivmbemfjgiPlZ5knUNnA58TtK9EXFFkZ3lDRAPAyUSwZuZTSARMFQue0PWp3AycDXpNteLIuJWSR8B+iNiBfAF4CuSVgOPkIJIs2wjBZ7qOg0BH5e0GDgNaEmA+DTwTklXRoRzX5jZpDWGKwiy/9CvqHntzKrHm0nNPq1wL2l8xQ/qrPsu6cqlkLwBYnfgKcBtkr5HinzVIiKa1nNuZjY+ovQMeB3gW8D7JfVHxA9r1u1HiQSpeQPE+6se719nfdDEW6vMzMZFBAyWv4IYZx8mTR60MvtH/kpgHfBU4B+B2qAxqrwjqT2gzsymhLE0MY2niNgIvEzSO0hz9XymavUvgZOL7tNzUpuZVUSkZQKLiPOB8yUtBJYAf4iIO8vsywHCzKzKRL2CqBURDwIPjmUfbjoyM9suYGig8dJBJC2V9BNJ75E06rTPkp4i6Z8kXStpzzzHcIAwM6uI7C6mRktneQi4EXgnsErSrZI+LumgygaSnifpLEm3A7cBbwF+SppsaFRuYjIzywjQBLmLKSI2AKcCp0p6BnAUKVvsaZIeIP06C0kjvC8GvluTenxUkzJA3HP91aXKPfibXFddw8xd/KeljtU3s/jc4gNbi49RjKFy//VMm1E88VlXT7ksmNNnFT/WzNnFy2zZVO4PP0ok65s+q3hiRYD5uxRP/lgmmeCSBeWSCe4+t/jndlrJZH2zpxf7enps87ZSxxlmDCOpx1NE3ELKk/d/Je0F/BUwCFwWEevK7rdUgMjye9RWsOOuv8zMitIE/yqLiDXABc3YV67QLmlG1o71W0lbSDk/qhfnaTKzSWDidFK3Q5F0328ALiPNklQ6IEjaBbgQeDppBPaJEXHdiIXMzNohghhoQlPVJJE3QBwFvDciPtuEY54LXBURr5HUB5RrDDUza7qJ2QfRKnkDxBbg9rEeTNI84GDSMHCyeVvdPGVmHSEiiG3+SqrIe3vBl2hO3vKlpHt3vyjpl5IulDSrCfs1Mxu7CBjY1niZYvJeQXwAuEDSStJkGI/WbhARF+U83oHAuyLieknnkmY7+kD1RpJOAk4CoNfxw8zaJIJwE9N2eQPEX5D6IRYCh9VZH0CeALEGWBMR12fPv0EKEMN3liYBXw7QNXPBxM6cZWYTiDupq+UNEJ8nTaz9VtJk2KUa6SLifkn3SXpKRKwCXkIa/m1mNv4qTUwG5A8QTwVeU3TC6wbeBVyS3cF0F/CmJuzTzGzsAmLQTUwVeQPEKqApnQERcROwrBn7MjNrqhiCAd/FVJE3QJwOfELSzyPinlZWyMxs/LgPolqROakXAr+R9Bt2vospIuKQptbMzKzNwiOph8kbIAZJndMTwuDWzaXKbVy/tnAZdXWXOtb0+XsULtM3c17hMoNbNxUuA/D4hkcKl+mdPrvUsQa2FW+93PTElsJlps0ol2F17m7FB/sPbCvXjv2Hx4t/dnunFc+5eecDGwqXAbjrwT8WLjOjr9zfyLP3mV9o+4ESWW13EngkdZVcn6yIOLTF9TAzG38eST3MpJwPwsysHN/mWi13gJC0CPgH4BBgV+AR4EfAORFxf2uqZ2bWRh5JPUze+SD+FLgJ+HtgA/Dz7Oe7gZsk7d+qCpqZtU0EQ9sGGi5TTd4riLOBx4HnRMTdlRclLQFWZutf3fTamZm1UWQBwpK8AeIvgbdXBweAiLhH0odIEwqZmU1sATE4saccbaa8AaIPeKLBuiey9WZmE1pEMLi1NZ3UknYFLgX2Ae4Gjo2InTJjSxoEbsme3hsRR7WkQjnknQ/iJuBdkoZtL0nAO7L1ZmYTW2v7IE4HfhAR+wM/oE4m68ymiHhWtoxbcID8VxAfAf4LuF3SpcA6YE/gtcD+wCtbUz0zs/ZqYRPT0cCh2eMvA9cAp7XqYM2Qd6DcVZKOBD4GvA8QaczhjcCREbGydVU0M2uPiGCwdZ3Ue0TEuuzx/UCjdArTJfUDA8BZEfGdVlVoNLnHQUTEVcBVkmYC84FHI2Jjy2pmZtZuQ8HQ1hEDxILsy7tieTbBGQCSvk9qXan1vuonERGSGuUGWRIRayXtC/xQ0i0R8ducv0FT5QoQki4CPhoRv8uCwsaqdUuAD0bEiS2qo5lZWwQQQyM2MT0cEQ2nK4iIejNuAiDpAUmLImJdNvD4wQb7WJv9vEvSNcCzgc4NEMAJpFnlfldn3QLgeKBjAkR33/RS5abN271wmXl7P7XUsfpKJFgbLNE2GkPlbjCb1jejcJl5C9o3f3jfjOLnL0o2LW/bUrzJIaJc4riFJc7h1oHiv9ius8t9LnYrUW5GX7mMPg8VTFw4MNiMZH2jXkGMxQrSd+VZ2c/v1m4gaT6wMSK2SFoAvAD4RKsqNJoi71yjs78nUC5lqJlZB2lxH8RZwNclvRm4BzgWQNIy0jiztwB/BvyrpCHSXaZnRcS4TcvcMEBIOgY4puqlD0t6uGazGcCLSJ3VZmYTWwunHI2I9cBL6rzeD7wle/xT4BktqUAJI11BPIn05Q/p6uFZQG0S/i3AT4Ez8h5Q0qmkkxGkwSBviohyEziYmTWTU20M0zBARMS5wLkAkn4HvCoibh7LwSQtJiX8OyAiNkn6OnAc8KWx7NfMrBnSSGoHiIpcI6kjYmmj4CDpkOwup7x6gBmSeoCZwO8LlDUza6EghoYaLlNNqdsLJD0Z+Dvgb4ElpNteR72LKbu395PAvaSO7ZUeZGdmHWMIhrZ6PoiKvLmYkDRP0kmSrgVWkQZ+PAr8b+BPcu5jPmm4+dKszCxJb6yz3UmS+iX1x4C7J8ysPdJdTIMNl6lmxAAhqUvSK6ryL32edMVwXrbJKRHxrxHxeM7jHQb8LiIeiohtwLeA59duFBHLI2JZRCxTT7kxDWZmRUXA4NbBhstUM9Jtrp8CXg8sBDYD3yYlmPo+MBc4ucTx7gWem6Xr2ES65at/5CJmZm0SQTRjwN0kMVIfxKmkW1GvAE7I7uEFYIQcIiOKiOslfQP4BSkR1S+B5SOXMjNrk+wKwpKRmpi+QJoM6JXAKkmfk3TQWA8YER+MiKdGxNMj4m8jonZshZnZuEh9EEMNl6mmYYCIiLeS0mi8gdQM9DbgOkm3k3KY+zrMzCadocGhhstUM2IndURsjoivRsQRpJHVZwCDpJmQBJwl6Y2S3JNsZhNeDMHQ1qGGy1RTZD6IdaSsgp/IkksdTxoFfTHwL6Q5IppPoqunt1CRvQ96aalDPfmAhaXKldHXk/sO4+0Gh4pftO0+d1rhMgBzphcfIjNvZrkMoXvOLf7/xVCJbKmDJTOszimRjXTPOeXOe7eKl1lQ4rzvWiIbLpT7DE4r8VkHGCh4rB+XPOfDZLe5WlLqnYuI/oh4F2ksw1+Tps4zM5vQAhgajIbLVFPu34hMNpbh29liZjaxRXgkdZUxBQgzs8kkAjcxVXGAMDOriKnZlNSIA4SZWSbCyfqqOUCYmVVEMDgFb2dtxAHCzCwTUe4W6snKAcLMLBPA1hJjPSYrBwgzs4wDxHAOEGZmmYjyI+4nIwcIM7NMEL6CqOIAYWaWSU1M412LztHxASI2Pvzwlv7l99RZtQB4uF6ZO/vLzUF0Z6lSjevRRp1QB+iMenRCHaAz6tEJdYD21WPJWHfgJqbhOj9AROxe73VJ/RGxrN316cR6dEIdOqUenVCHTqlHJ9Shk+qRhzuph+v4AGFm1i4OEMM5QJiZZSIcIKpN5ABRrqOh+TqhHp1QB+iMenRCHaAz6tEJdYDOqceoAnAf9Q4Kd8iYmQGwqGtavKl3r4br/3nrXTdOlP6UZpjIVxBmZk3lJqbhyk0W20aSjpC0StJqSafXWT9N0qXZ+usl7dOCOuwt6UeSbpN0q6R319nmUEmPSbopW85sQT3ulnRLtv/+Ousl6bPZufiVpANbUIenVP2ON0l6XNIpNds0/VxIukjSg5J+XfXarpK+J+nO7GfdedElHZ9tc6ek41tQj/8n6Y7snH9b0i4Nyo74/o2xDh+StLbqnL+iQdkR/56aUI9Lq+pwt6SbGpRtyrlotiDd5tpomXIiomMXoBv4LbAv0AfcDBxQs807gM9nj48DLm1BPRYBB2aP5wC/qVOPQ4H/avH5uBtYMML6VwBXAgKeC1zfhvfnfmBJq88FcDBwIPDrqtc+AZyePT4dOLtOuV2Bu7Kf87PH85tcj5cCPdnjs+vVI8/7N8Y6fAh4b473a8S/p7HWo2b9p4AzW3kumr3sTl+8Q0saLkD/eNexnUunNzEdBKyOiLsAJH0NOBq4rWqbo0l/HADfAD4nSZF9CpshItYB67LHT0i6HVhcU49OcDRwcfa7/0zSLpIWZfVvhZcAv42IegMZmyoiflLn6vBoUjAC+DJwDXBazTYvA74XEY8ASPoecATw1WbVIyJWVj39GfCaMvseSx1yyvP31JR6SBJwLPDiMvseLw+x9erz454FI2zSCQMP26bTm5gWA/dVPV+TvVZ3m4gYAB4DdmtVhbI/iGcD19dZ/TxJN0u6UtLTWnD4AFZKulHSSXXW5zlfzXQcjb9oW30uAPaoCn73A3vU2abd5+RE0lVcPaO9f2N1ctbMdVGD5rZ2nosXAQ9ERKMEBa0+F6VExBERsWyE5YjxrmM7dXqA6CiSZgPfBE6JiMdrVv+C1NTyTOBfgO+0oAovjIgDgZcD75R0cAuOkYukPuAo4D/rrG7HuRgmu2oa10ZiSe8DBoBLGmzSyvfvAmA/4Fmkq91PNXHfZfwNI1+ldcxn2Rrr9ACxFti76vle2Wt1t5HUA8wD1je7IpJ6ScHhkoj4Vu36iHg8IjZkj68AeiWNdKlaWESszX4+CHyb1GRQLc/5apaXA7+IiAfq1LPl5yLzgKRFANnPB+ts05ZzIukE4EjgDY2aN3O8f6VFxAMRMRgRQ8C/Ndh3u85FD/Bq4NJG27TyXFjzdHqAuAHYX9LS7D/W44AVNdusACp3prwG+GEz+x9ge3vqF4DbI+KcBtvsmW2HpINI57ZpgUrSLElzKo9JHaO/rtlsBfB32d1MzwUea2H/Q8P/EFt9LqpUv/fHA9+ts83VwEslzc+aXV6avdY0ko4A/gk4KiI2Ntgmz/s3ljosqnp6TIN95/l7aobDgDsiYk29la0+F9ZE491LPtpCujPnN6S7L96XvfYR0h8jwHRSM8dq4OfAvi2owwtJzRe/Am7KllcAbwfenm1zMnAr6c6QnwHPb3Id9s32fXN2nMq5qK6DgPOyc3ULsKxF78ks0hf+vKrXWnouSMFoHbCN1Hb+ZlJf0w9IiXi/D+yabbsMuLCq7InZ52M18KYW1GM1qW2/8tmo3FX3J8AVI71/TazDV7L3/FekL/1FtXVo9PfUzHpkr3+p8lmo2rYl58JLaxePpDYzs7o6vYnJzMzGiQOEmZnV5QBhZmZ1OUCYmVldDhBmZlaXA4S1jaTnSfq6pN9L2ippfZaF9XhJ3U08zj6SIhu8ZmYlOUBYWyilBL+WlFX1NNJgqhNJ9+RfQBqFbGYdpNOzudokkOXZOQf4XET8fc3q70o6hzT4zsw6iK8grB1OAx4hpaPYSUT8lpSvKSQdXbte0pckraluhpL0Vkm/kLRJ0qOSfizp+SNVQtIhkn4g6QlJf5R0taSnj/F3M5u0HCCspbIv9b8EVkbE5kbbRcSNpFxBb6spvwtpXoELI2Iwe+2TwHJS1thjgTcCPwGeNEI9XklKy7Eh2/71pMmf/lvS3o3KmU1lbmKyVlsAzADyTCp0PvAFSUtixyREf0ea/exCAElPBk4FPh0R76kqe/ko+z4X+HFEbL9CkfQj0gxz/wCckqN+ZlOKryCsk3wN+APw1qrX3gZcHjsygx5G+twuz7tTSfuT5kq4RFJPZQE2AteRps40sxoOENZq64FNwJLRNsyaoL4InJh9ib8IOAD4fNVmldkC66aSbmBh9vMLpMyj1cuRtHAGQrOJzE1M1lIRMSDpGuBwSdMiYssoRS4A3kOaK/kY0uT21fM3VOYEXgysylmNylwUZ5DSgtfamnM/ZlOKryCsHc4i/Zf+iXorswls/hy239G0EvhH0gRQ/xZplrSK7wNDQJF5jFeRAs3TIqK/zvKrwr+R2RTgKwhruYj4iaT3AOdIOoA0ocy9wHzgJcBbSHcVVb6ozyfNDreN1CxUva/fSvo08J5sVrIVwCBpyso7ImKnaS4jIiS9kzTmog/4OulKZA/g+cC90WCmQLOpzAHC2iIiPiPp56Q7kD5JurvpCaCf1BF9WdXml5P6LS6P+nNev1fSauAdpKlG/0gKLitHOP4V2YC995HuiJoB3E+a8a7h3MlmU5lnlLOOI+lw0pf9YRHxg/Guj9lU5QBhHUPSfqT5ij8NbImIvxjnKplNae6ktk7yAeBKYAtpgJyZjSNfQZiZWV2+gjAzs7ocIMzMrC4HCDMzq8sBwszM6nKAMDOzuhwgzMysrv8PCfH6I7dUF2IAAAAASUVORK5CYII=", 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" ] @@ -432,22 +432,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -479,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -510,12 +510,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -544,12 +544,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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zivLxwBsi4r7Ac4H3zovdDzi/x7wkSZIkSRrLOB3cVwG/BTyB0tn9x3mxpwIn9ZiXJEmSJEljWXIHNzN/Bfx5Q+yhvWUkSZIkSVIHS74GNyLOj4j7N8TuGxGeoixJkiRJWjHjDDK1M7BxQ+y3gLsvOxtJkiRJkjoap4MLkA3T9wSuWV4qkiRJkiR113oNbkS8HHh59d8EPhsRN468bBNgC+DY/tOTJEmSJGlpFhtk6nzgS9Xzg4DTgbUjr7kBOAt4f7+pSZIkSZK0dK0d3Mw8DjgOICIA/j4zfzxAXpIkSZIkjWWc2wQdPMlEJEmSJElajiV3cAEi4h7AM4GdKCMnz5eZeUhfiUmSJEmSNI4ld3AjYj/gE5SRl6+gXHs7X9MIy5IkSZIkTdw4R3D/L/BV4FmZOTrQlCRJkiRJK2qcDu49gFfYuZUkSZIkTaMNxnjtOcCWk0pEkiRJkqTlGKeD+0rg1dVAU5IkSZIkTZVxTlE+gnIE9+yIWANcNRLPzHxUX4lJkiRJkjSOcTq4twA/nFQikiRJkiQtx5I7uJm5zwTzkCRJkiRpWca5BleSJEmSpKm15CO4EfHIxV6TmV9fXjqSJEmSJHUzzjW4XwVykdds2D0VSZIkSZK6G6eD++iaaVsCTwEeBby4l4wkSZIkSepgnEGmvtYQ+lREvA34Q+CLvWQlSZIkSdKY+hpk6vPAM3ualyRJkiRJY+urg7s7cGtP85IkSZIkaWzjjKJ8YM3k2wP3BQ4BPtVXUpIkSZIkjWucQaY+1DD9BuDjwEuXnY0kSZIkSR2Nc4ryLjWPbTNzk8x8bmb+fLEZRMQHI+KKiPj+vGlHRMQlEfGd6vHkebHDI+K8iPhhRDxhjFwlSZIkSeuZcUZRvrCH5X0IeBfw4ZHpb8vMN8+fEBH3AQ4AfhvYDjg5Iu6Vmbf0kIckSZIkacaMc4oyABExd9/bLYCrgK9m5ueX0jYzvx4ROy9xUfsCx2bmDcCPI+I8YC/gf8bNWZIkSZI0+5Z8inJE3CkivgYcT7ne9snVv8dHxFcjYtNl5PHiiDizOoV582ra9sBF815zcTWtLrdDI+L0iDh97dq1y0hDkiRJkrRajXMN7uuBBwHPATbJzG2BTYADq+mv75jDe4B7Ag8ALgXeMu4MMvOozNwzM/fceuutO6YhSZIkSVrNxungPgN4TWZ+dO462My8JTM/CvxtFR9bZl5ezedW4H2U05ABLgF2nPfSHappkiRJkiQtME4Hd0vgrIbYWVV8bBGx7bz/Pg2YG2H5eOCAiNg4InYBdgO+1WUZkiRJkqTZN84gUz8GngKcVBN7chVvFREfA/YBtoqIi4G/A/aJiAcACVwAPB8gM38QEZ+gdJ5vBl7kCMqSJEmSpCbjdHDfC7ylGkzqo5TrZe9GuZXP84C/WmwGmfknNZM/0PL6fwT+cYwcJUmSJEnrqXHug/u2iNia0pF9bjU5gBuBIzPzHf2nJ0mSJEnS0ox1H9zMfHVE/BOwN7fdB/fUzLx6EslJkiRJkrRUS+7gRsRhwA6Z+ZfAF0di7wQuysx/6jk/SZIkSZKWZJxRlA8GzmyIfbeKS5IkSZK0Isbp4O4ErGmI/Qi4+/LTkSRJkiSpm3E6uNcB2zfEdgBuWH46kiRJkiR1M04H97+A/xMRG8+fWP3/FVVckiRJkqQVMc4oykcA3wDOjYiPAJdQjug+G9iS224dJEmSJEnS4Ma5D+53I+LRwJuBwyhHf28FTgGekZnfnUyKkiRJkiQtbtz74H4LeGREbAJsDlydmb+eSGaSJEmSJI1hrA7unKpTa8dWkiRJkjQ1xhlkSpIkSZKkqWUHV5IkSZI0E+zgSpIkSZJmgh1cSZIkSdJMsIMrSZIkSZoJdnAlSZIkSTOh022CJE1QRP30zGHzkCRJklYZj+BKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZYAdXkiRJkjQT7OBKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNhEE7uBHxwYi4IiK+P2/aFhFxUkSsqf7dvJoeEfHOiDgvIs6MiAcNmaskaUIiFj4kSZJ6MPQR3A8BTxyZ9irgS5m5G/Cl6v8ATwJ2qx6HAu8ZKEdJkiRJ0io0aAc3M78OXDUyeV/gmOr5McB+86Z/OItTgc0iYttBEpUkSZIkrTrTcA3uNpl5afX8MmCb6vn2wEXzXndxNW2BiDg0Ik6PiNPXrl07uUwlSZIkSVNrGjq4v5GZCWSHdkdl5p6ZuefWW289gcwkSZIkSdNuGjq4l8+delz9e0U1/RJgx3mv26GaJkmSJEnSAtPQwT0eOKh6fhBw3LzpB1ajKe8N/HzeqcySJEmSJK1joyEXFhEfA/YBtoqIi4G/A44EPhERhwAXAs+sXv4F4MnAecB1wMFD5ipJkiRJWl0G7eBm5p80hB5b89oEXjTZjCRJkiRJs2IaTlGWJEmSJGnZ7OBKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZYAdXkiRJkjQT7OBKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZYAdXkiRJkjQT7OBKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZYAdXkiRJkjQT7OBKkiRJkmaCHVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZsNFKJzAnIi4ArgVuAW7OzD0jYgvg48DOwAXAMzPz6pXKUZIkSZI0vabtCO6jM/MBmbln9f9XAV/KzN2AL1X/lyRJkiRpgWnr4I7aFzimen4MsN/KpSJJkiRJmmbT1MFN4MSIOCMiDq2mbZOZl1bPLwO2qWsYEYdGxOkRcfratWuHyFWSJEmSNGWm5hpc4OGZeUlE3BU4KSLOmR/MzIyIrGuYmUcBRwHsueeeta+RJEmSJM22qTmCm5mXVP9eAXwa2Au4PCK2Baj+vWLlMpQkSZIkTbOp6OBGxB0j4k5zz4HHA98HjgcOql52EHDcymQoSZIkSZp203KK8jbApyMCSk7/npknRMRpwCci4hDgQuCZK5ijJEmSJGmKTUUHNzPPB+5fM/1K4LHDZyRJkiRJWm2m4hRlSZIkSZKWyw6uJEmSJGkm2MGVJEmSJM0EO7iSJEmSpJlgB1eSJEmSNBPs4EqSJEmSZoIdXEmSJEnSTLCDK0mSJEmaCRutdAJSVxGxYFpmrkAmkiRJkqaBR3AlSZIkSTPBDq4kSZIkaSZ4irIkSSO8BEKSpNXJI7iSJEmSpJlgB1eSJEmSNBPs4EqSJEmSZoIdXEmSJEnSTHCQKUlaX9QMnIQDJ0mSpBniEVxJkiRJ0kywgytJkiRJmgl2cCVJkiRJM8EOriRJkiRpJtjBlSRJkiTNBEdRllZI1Ixom45oK0mzq24kc3A0c0nqkUdwJUmSJEkzwQ6uJEmSJGkmeIqyJM1Td+o4ePr4UDx1X+qPnydJ6yOP4EqSJEmSZoJHcCVplfEos6Q5HqWVtKj1bIA7j+BKkiRJkmaCR3AlacKGPMLi0RxpGH7WplTdkaolvC++n/LsqNlhB3cF+CW6/ljpjs0klydpZfm3ZOnattVq2I6rIcdZ5d9WafWxgytpVfOH3/rD91qzzP1bmj0e6FgZq+Ia3Ih4YkT8MCLOi4hXrXQ+0yYiah/ScrlfSQu1fS6aYqvhe3o15Dg0t4e0UKfPRUT9Q7X87lmeqT+CGxEbAv8CPA64GDgtIo7PzLNWNrP1l1VmafKsxE7eatjGk8hxNXyHN+W4Gt4zTa/VsO934edidZrV/XEaTH0HF9gLOC8zzweIiGOBfYGZ7OD2/Ue9rd2Q1ySNm8dSchx3eV2WNckcu+hzP1jOez30dhwyx3HnOakchzbkZ6Zv05JHV6uhEzuJz+6QOQ5pWvJoM/T39Gr+fmkzLdtx6BzHXd5K5Nj7d0/TEdQp+nvX5zyn6fuqi9XQwd0euGje/y8Gfm/+CyLiUODQ6r+/jIgfDpRbH7YCflazc20F/Axqd7yJxRo+OMvKseZLoe8cV2RbDZ2j29EcJ5Fj3/tV59i05NESm5Y8VkOOk/hbMss5Nu3/05TjatiOY3y/TF2Oq2E7rtLfiMvOse/8p+m9HvO7Z9rcvTGSmVP9APYH3j/v/88B3rXSefW4fqePM32aYuZhjuY4vXmYozlOUx7maI7TlIc5muM05bFaclxNj9UwyNQlwI7z/r9DNU2SJEmSpN9YDR3c04DdImKXiLg9cABw/ArnJEmSJEmaMlN/DW5m3hwRLwb+E9gQ+GBm/mCF0+rTUWNOn6aYeSw9Ni15tMWmJY+22LTk0RabljzaYtOSR1tsWvJoi01LHm2xacmjLTYtebTFpiWPtti05NEWm5Y82mLTkkdbbFryaItNSx5tsWnJoy02LXmsKlGdby1JkiRJ0qq2Gk5RliRJkiRpUXZwJUmSJEkzwQ6uJEmSJGkm2MGVJEmSJM2EqR9FeX0QEffOzHO6xIBLgScC21eTL6GMOJ110zPzmoi4S9+xlnU7GPhUnzl2WVbf7ZazrMw8umWePwX2G5nncZl5Qss8a/eRZW6PLnm0rltdrEubCeb4FuAOdfOrPmv7jsSOz8yz25Y1cI5PaFpWW6wpD0mSpNXIUZSnQET8JDN36hC7ErgGOJHygxVgB+Bp1fNPjUx/HHAy8Ps1bZYTe11mfnigHLssq/d2y1hW2/v5C+AU4MPAxfPmeSCwJjNfOs48l5Fjr3kskmPXfb/vbfV24PnAwTXzuzOwKXDsSOwA4NjMPHIKctyWUuyqW1ZbrDGPNl0Lb8sp/vRdIBmqeLISOY5bIJyWbVXFGos4HefXOccu7Vrm1/aZmcg+MuT+OGSODFSAXSzHofMYd3mT2vfHLZhOat/vum5Dvp9t82zajpSDP2O1sci9kB3cgUTEO5tCwKHAezvEXghsOfqjJSLWUN7bXUembw5cDty1ps1yYpcBP2zI8beBLXrMscuyurb7AbBrTbvlLuv7TbHMXHDZQEQEcDWlgzLqT4HNgdF7Qy8rx5Y8rm9rR/267QZsXBNbdHu0xXrOcXeAzNy4Zn43AHfMzJtGYrcHrm1b1pA5ZubtG5bVFjs3M3erWRYR8b3MvF9DrEvhbTnFn74LJEMWT4bOcUPK99Y4Bbtp2VZvp7mI07W41jXHsdtFxFGZeWiHHAeLTUsey8hxsALsIjkOnUeX/XES+/7YBdNV8j09ifezy3Z8Q/X88DHaTKTI3fZ9thrYwR1IRFwLvILyY3nU0cBfdIh9kNKB+fnIss6jvLf3HJl+F2AtsHVNm+XErgT2pHTC1gkD51E64X3l2GVZXdtdAfwaeGSPy/oR8LsNsTXAQzPztJF57gX8D/ACFu4H7wRuBR7QY45tefw38OAx1+10SkXywWO0Wc626pLjCcBWmbl1zfy+DuyemReOxO5ezW+o7diW49eARzYsqy32/4CXs1AA/wa8vyHWpfDWtfgzkQJJW6xDYWKacryB+gLhYgW7aS80dZnfsnJsaLcFcBr132cXUP5m183vhSwsRC4p/w6xwffHIXPseZ/rnOOAebQt70zK56b2c93zvt9WMH0npcM0WkCb2L7f9/f0JN7PDtvxXMpv493GaLOc2Hks/C6by/+7mblDTWxV8Brc4ZwGfD8zvzEaiIh/7Rh7M/DtiDgRuKiavBNwpxKO94xMfxxwTEOb5cS+AWyamd+pyfG0nnPssqyu7X4LeN9ox2aZy7qgJXYy8K6IuBO3Vdl2BH4OfJua/SAiHgk8qecc2/L4/LjrFhHHAzs15FjbZgmxvnN8FnBiRJxVM7+/Br4U5ayD+dtxV8rp9NOQ4/NaltUW2w54KqUAMWoTyh/uuuLai1piNMzvVsrRxQNp7tjXxU6nHCn/wzHaLCe2JiIePFoQoPwI2GAV5JjUb/+tKQWGPnPssq3a5nkCsFVN7l3nt5wcm9qtBW4Bzpg3Las2m9L8uYiO+XeJDb0/Dplj3/tc1xyHzKNtedsDP+k5x7Z2tzTEDqnmdQYLTWLf7/t7ehLvZ5ftGNVjVFub5cTuQdku85c5931215o8Vg2P4A6kqvpen5nX9RWr4psDT2DhAE7UTc/Mq5vaLCe2yLr3mmOXZU2iXddlLSYi7jZ/npl52WL7wSRyrMtjnGUPoe8cm+YXERsAe7HudjwtM2+Zlhy7xCLiDOCgzFxQgY6I64HHNBTXfg08tiG2FvgF5RTZ0cLKJcCrM/OUmnY/qnI5ZWT6BygFkscttc0yY18EtqAU4UYLApcBb57yHL9I+cE1uv3/FPjnzPybgfKo3VZt84yIB1V5X9HH/JaZY1O7nYH9M/PzNW3aPjPXUoqRfe4H0/KZGTLHXve5ZeQ4WB6LLG9L4PDM/ECPOba1+2fgJTWx7YCXZf31rZPY9/v+np7E+9llO95E6VxuOEab5cS2Ax6emT+pyf+izNxxdPpqYQdXY4mIbVj3R/Lli7x+08z85eQz676sLu3m2nTZHsCvWNhZ+la2fBijZZCGxXJsiUefebQtrym2WBt63lYdc7w35fSk2jwmsR0Zc70XmV/baOv3Ay5s+OP2GODUuqJK18Lbcos/Q1rNBZ6ht/+QRZyh5znaDngGcEpmfrfmtYdRighjFSI1vmn5fA6dR5fl9bXvZ0vBFLiRDkX45Vrt3z0t3+G9FbLbYhHxIpq/z/4yM/+500pPATu4UyAivpiZT+o5VjtATNP0JcTWUK7bvAulAhSUi9SvAV6Ymd9uaNd2AX7fOY69rGXkeClwIeNvjysoR7fWsO4AMLtW7U7skGPtfrBImx9TTrPrM4++BxOZxLbqkmNbHu+nnALcZ45jr/ci87uSDoM+DW0SBZJpKJ6sVI7jFN9WYluNO8++5zc3T9qLV3dhzNHAu1jOPrLI+/bbjLc9JpJHlxynoQC7WI6TyIMeP2uLLKt1329bFouMkl8VQMnMq5rms8Qce/9cV897+7xP6PtxT+CeY+bR+r4M9V02TbwGdyBRTr2qDQF7NsQXi+0dEU+vie0N7FQTi4bpS4ntAjw7M7+5TiBib+CzUW7tUNdu855z3CUi/mrMZXVt99SGdkG5nm2/DttjS2CvzLxgpN0uwBkR8ZGGdls07AfPBh7WkGPb9tgR2LVDHndt2Y5bNcQe1RBra7OcbdUlx6dV7UZHO18sj3MoA1D1mWPT8j4EHFszzwC2qcl9LrYZcM/RP2RRjvJ9k/rRuYmI12bm3zfEei28Vc6inEo7TqxLG4DzaSgiRERjYYJSJJj2HL8SEQuKbxFxDc3Ft6G3VZd59j0/KAPINbX7FOVo7fzC0KOB10dE02jUj8vMk+oW1Pa5oPs+UhuLiMdTrvM/mfG2R695LCPHxsLhBD6fXXOcRB59f9baltW277ct679ZWDB9NPCmKIX/e1bxiIg7A18GXjX692yJOfb9uZ7723NmzfzG/rwvM8fa9ywiDgQ+QCmej5NH0/vy+ijjgfx+07pROv77sm7n9/jMPLthWauCHdzhnEYZzbTu4vGtgDd3iG1G/QAxB1GOzo1eSA/lnp5Ng8q0xTYY7cwBZOapEbEt5VY1N9e027TnHO/YYVld2z27yvFONW2i4/aA266BmO8SyvvZNEDJi6nfDx5JGbynLse27bHBMvJoWrdNGmJPpnk7NrWZM1SOe7Fw4Jil5BETyLFpec+gjOpdl2PXQZ/qvlfmvCAiPlczvWvhbbFCU98FkiGLJ1OVI/XFt3dQX3xbiUJTlyJOl/ktluNixattxiwMfTgi/qBhWXWFyLlY132kKXYY8KvRDnW1XqdGxBsHyqNrjpMoHPad4yTy6LIfP7BheXN5NBU+u35mNqO+YPotSoftblmNTRERGwJ/BHy1JY9JfPc0tTsPoGWfa/q8/zAituo5x6ai9HOAX2fmC2ryOD/K0d26+W1GcyG77dab51J+rxzLbUe4dwA+FhHHZuaRNctbFezgDuds4PmZuWY0EBE3dYzdSLnw/fsj038H2C4zD65p86y6NkuI7R8Rn6f8UZ8buGRHymAmlwOfycwFP7wj4m96zvFPx11W13YRsTtwj8x8XU2bv+64PV4OnBYRx460OwD4Mc0jZr+Pmv0gIr7RkmPb9nhaxzze27Jur66LRbnBeFOOtW2qWNdt1SXHR1GG9D+mps3bWvL48gRybFpvgE825NhltPXHAdtFuVffgmaUAkmfhTdoLzT1XSAZsngyTTlSV3yj3F/2ug45ds2j7yJOl/ktuq1a2gX1+/BHKZ+b40emB3A3mj8Xd6LbftAl1vTb7hLK2UdD5dE1x0kUDvvOcRJ5wPjr/RfVvOo+13O3xBl3329a1py6z8WWlGLALb95UXl+bET8OwN/Bza0mxsduG5+TZ/3Wyl/7/rOsako/Uzgdg153KVlftCcf1vsLpTf4jfND0TEWym3dlq1HVyvwR1IROwPfC8zF9xLq6qmfrBD7HDgozkyQExEPAK4c9aP8vg84MTRNkuI7Un5w7jgNAbKMOlXZubPato9FfhOjzk+A/jaOMvq2q7q4O6QmV+qabMN8CCat8dVmbm2od0WDe0uo3k07dr9p8pxr8z8t6WuVxXbk3LdyLh57E7ze/1Q4NzRWNVmg7rTXao2a3reVovluGB5Ua4ZunPTaVQRsUddHpl5VlNsCTmOu498jXJfuj5HW/8u8OCsuT6zKq7dp6Xw1hS7EXhQQ2HlBsq9ges69rWxeUWcu/Uxvyp2DaUAUVeYuDPwnIZiwfXAw6Y8x58Dp7Cw+PavlO/VPx4oj9pttcg8XwN8ZPToxTLmt1iOV1HGUqhrdx7lO360MPTnwDuA40ZnRzmVdY+Gz0XX/aDLZ+Zw4HXA39as1xbA04fIYxk5Xg5sQ7/7XN85TiKPaxhzP66+e+6emdszItpH9W7b99vWrWmU/GdRjgC+emR+B1E6bU8Y8Duwad1eWj1/R838mj7vj6N0BA/oOcfaOxFExEGU05PfX5PHDcDzxnxfHkcpxD+mIXYHYO8cuY1jRNyd8vdi99FlrRZ2cCVphsR4A3z8A6Wj/q2a2GeAw/oqvFWxtkJT1wLJgjbzYoMUT6Ypx6rtk2ranQF8epxtv5w8Fik0dSnidCkKLWVb3YdyxkFd8aquMHQI8PrM/ErNvM4CntbwuXgh8IkO27/r/vNw4OGj60U502CsIuUSltVrjsssHPa9bk3bsdciaxUbez9e5HOxO3DRuPt+27pV7eo+F1+mnIFRl/vXgcsG/g5s2n+oW+eWz/t/Ui776PugRWNRuiWPWGSda9tly603gd8D3kW5hnh+53dX4MWZeULdslYDO7hTICIOzpp7h83FgJ8C+7Hujnlc244XDQPENE2vYkdQRmF7GuXeWL9ZFvCBHDmFYV67ozLz0A6xphwnkUfbenfJ8WhKlXlfSqU5KfduPA54N/BCynt215HYkdkwal0sMnAP8HbG2A+WsT26DiA09n4wbzvuR4/bqiU29vKWsay22EmUDseS82ibZ0R8pVqvx1IN8EGpHrcO8BHtgz5J641xCkPSLGnb92f1czFN69Ull45takfWj4gNWDgK9Gk573Tz1cgO7hSI9lt8/ILbTjebO89/B8q1nmsy86UN7brcluVX1XKOGVnWQZRriw6pa0YZka7uR3JQqvE7jJlj1zzalnUx8DvjtKu+QM6saReUL4C/A47Jde9b9lzgr4F/qokdROnQvKghjxMoFba62NcpRzdG94NDKKdEv3qp61XlcynQNBhKWx6fB+7TEGvaDzav8h+NtW3H5Wyrthyblnc45Qt+dHmLLatr7BvAa2vyaFvvPShFjtF5BvA/lMEp/iMXDvDxumr96vL4V0qldtxbI7QW5VpiXQtNTQWStjZDF09mNce22CSKYU1FnF6LQlVsrjD0GODnTLAwtMjnouv277tgN4l9dVqKikNux16LrG3zjHL7l28DN405v7Z9/53ASxijYBoRG1FOCz6TkSI87QcmBvsOjIidgFMp11aP83n/AfDZnnMcuygdEecA3xmnTdVuDd1u89l4C6fVwA7uQCLizKYQ5R5rC65Xm4tl5gY18wvKBe+/qmk3N9jAtTXz27Rm+m9imVl3AT4RkZTrDObH5y7Y33mRWN3yWnNcRh6N67aMHC+oa9OS442ZefuW/L8yksecfVpij2rYD26hXB8yf5CDpW6PLnnsw/j7wS7V8wvq2izyXveeY93yqu14PbeNIjjOsnp7P6tcmtZ7H8p7/fUO8zuG+kEmDqCcITLWPXIXKZS1xdoKTV0KJG3FtSGLJ7Oc41lA3Y+05RSamubZVsTpUhRaLMfOhaHM3HpBoP1+6132/a77T1vBrkuRcrF99QnA/j3m2LVw2Pe6dc2xS5G16378L5TfkPcac35t+/77KLdpGudz8XJgT+DRjHdgYhLfgU2xD1Guj91qzM/7xyjXYQ/x3fMm4Ck1uQTlOt9n17RZLP+PU64J/+Y6gXJby/dm5v1r2rX+LV8N7OAOJCIup+zoV4+GKEfgfrchtoZyAf5pI/Pbi3Jkd8ccGSAmIn4CbJj1gw7cDGw/2qaK3UD58HwyM2+tpm1A+fAcQ/kCrbuu7ibK8OR1sdrlLZJj1zza1q1LjmuAO2bmdjVtrue2H1tzp3lsQzmC+0rgjQ2xvwUemN0G7nlYzX7wE+C6zLx3h+3RZQChtu1YG1vGduy6rdpybFred4EfZebDOiyrS2zs9Y6I7wNbZua2NfO7jvLH+xgWDvBxAPDIrB/06SZg69EKdJTrdS4DFlxLCIsW5e5L90LT2AWStvkNWDyZ6Rx7zqNtnvvQvYgziUJTXWHo7sBDKIPqrNOE8kPyrLrZUT4XXYvEfRfseltWtbzWwvPARcUht2NvRdZqeV3249+j/JbfZMz5te37ixXp6z4XT6eMQ7BgNOcu+8cS8t+nQ2yxbVW3XgAHDfXdU/1N/jXwyQ55dMn/Z8Dr60LA32TmFnXtVoXM9DHAg3Lj5oc3xH7UEvsi5Z57Z1GOspxIueXQqZRR1vaqafMPwIcb5veNujZV7D2UP9BrKffGOpdyGsbHKT/G79/Q7hMtsRM65Ng1j9plVbETO+T4IspAOnVt5jqx5wBXVY+zq2m7tMSeS7m/X90839gS+6uG/eDHlNH9xt0en+mYx3vH3Q+q7fiGDtux67Zqy7FpeZ9p2VZty+oae9a8PK6uHq3rTTlC8sKG+e0PvKB6z79XPb5IuRb8McBODe0uBO5SM/0ulDNEHkD5QT//sTNloJqm2M2U+wnWLe+mllxqY5Qi3087zO/66v3eZt60bSj3tvwlsFvLPNti61uOiy1rrDza2lGKJpcOuK2uo4yZ8HuU8R62q56/m/LdcN+G+f0KOLrmcesin4ux9v1l7D+XAf890LIOq7Zj3zlOYp8bMsdBPvOU3wLXdJhf277/kw6fi1OBn1E6uXPTNgD+uFrnob8D67bVsZTi65LXa4I5Nm3/K4ATOrxnbflfSzmr4I+Bh1aPP66m3Qz8X8pR9NHHNXXzWy0P74M7kMysOz1jLnbPltiTgLnTIeZfHH5ZS5vXtMQe2hJ7wdzziNiymnZl0+vntXtmS+yJHXLsmkftsqrY4zvk+C8tbd5UPT2s4SWHtcSa5tn4+sx8K/DWMfeDtu2xX8c8nt8Sq90Plrkdm9p1zXHs5S2yrK6xj1LupzlOHv+xhNh7ljq/ymtpvkfuNyiXC3xntFFEXNASO4fyo37BmQOUavbmlB9Qoz7dEHs75VS3Ok1toKzblsDXIuKu1bTLKaNbvpjy46vOW1tiH1wPc/xwz3m0zfMIyjVudQ6hnOL7tepsBygdkOVsqwMpt797Hbd9r15Mud5uf8ptN0adCbwpMz8+GoiIP6L5c/G/jL/vLxZr2n9Opf6oDLS/L1331b/rOce296zrPtf3duyaR9+f+T+mHEAZ93PRtu+/knL67DifiwOAo4DLI+JqyhHAzSjXh76eYb8Dm2IHUq4vHme9oFxO8USG+e75N5r/jj8GeOBIm7kRm9vyfzT1t/n8F8r78pmsvwXS8xrmtyp4ivIUiIhNKRXh0VHMvpUtb1BE3Dszzxkn1qVNFXtcZp7Uc2xP4J6MN7hN12W1rhvlFKQFA+1Qrh0Z/VI4PmtuBzJvfl0H4Okaa3qvG9drkW3cNY+27f9nlC/Yld6ObTnWtpvU+0nL6OgR8YS6GOX0o9o2dcupltU2sNNrgX+m4dYCTfOU1ldR7uF+YTbcXzwzT1+BtKSpMc6BCa2sWOR2bllzidtqYQd3CkTEFZTKyxrWHehlV8opiSc2tBt7oJcubSYRi4gDKVXH9zPM4DZtsSspp/iMDrTzR5TTT97DuoMmHAAcm5lHDphjl23ctF6DbuOIOIxyCsxrWZ3bcRLLahsdfVvKbbJGY2+onh9e02bsEdXnx6Lh9gFNFivKRRnZc9zCytjFq6qIcxHTUYQyx5EYZSCVXubZtSi0WKwlj9bCUEssGL9Y3brv0+N7s9j+0XVZTMl73bKsIbdjr9tjCbG3AHdg5ff9d1bzH13WORFxbwb8DmTM22suoRD8zab5DfXdE7fdQnPB/ChjcBxEj7fXXO3s4A4kIv6qKUQZNe2eOTLEd0TsQrktwkca2h1Kud5w1KOAe9fE2toE8BeU6/bqYk8CvtAh9kTqT7d4DmWQqTuv06AMbnMR5bSWvpa12Lr9AbDF6I/viDgPuDUz7zUy/faUaxq6DMDTNbYH5TqLUU+hDCY0um5t67U53QcQug/luo26WNN78xjKd80dR/KY1HZsy/EPGtrtBmxcE5vU+9k2OvoNWTPAR0ScS9mOu9W0aRpRPWgfOX0T4HTGv31AW1HuU8AzGKCwEuVUuAsp11ZNZfFkPc6x13l2LAotFltWYahm+jHAw+i3WN33dpzEsqblvW57P1fDduyyz70deD5wMCu777cVsi+tchnqO7Bt/6ld7wkUpHvf/tF+C80nU35/1cXaRrFuu53kqu782sEdSJRRU/+J8kN01GuBjTPz5pE2t6dc3P4XwA017Y5uiP1rtZwXj9FmLvaHlKOW66RC6XB2jdUt743A7TJzy3UalCM/VwFP7XFZi63byZTRaX8+ksu5VY67jEy/O2VgsD0Zf1TsrrHzG9bt3ZT3+qljrNddKPdE65L/+Yy/H/wbsEmO3FZjgtuxLccvNbQ7nXL674N7zKPr6Ohfo4x6PBpbQ/nO3rWmTe2I6lW8bTTtG4FHZP3tAz4JvGW0DYsX5c6hDMZxzUisa/FqL2CrmthcwWLj0er0ChRPzLE+Nu48WwtNHYpCi8U6F4Yyc8EYJlFGQN2t4XNxJuUa9Lr5tRVuu7w3bdtxsQJg1/1gGt7ra2n+ful7O/a973f9zO8OkJkbjyxrUvt+U2xTygGBdT4X1Tr/knInhaG+A5v2n18Ad2Thei+6zlk/yvYkvnu63kJzsZGvf1zNY05W/9+e0gFe0IyWzu9q4CBTw/k2zRdyvxw4LSKOZd1bfBxA2Sm/n5nfqGn3r3WxiDgIuHdmHrPUNlXsPZRbznytJnZNx9gvGnIEeH+1zNHBbX7Q57KWsG4/on6gnU2BDSPiiyPTd6V0HrsMwNM1dkXdukXEAcADxlyv5QwgdBljvjcRcSjw6QG3Y1uOa+vaRcTxlFEeL+wxj7bYycC7IuJO3FZt3ZFy8/nnNcR+XZrGWTVtPkzzwE7faon9crRzC5CZp0bEtpQBKOqKcszLYb5LKH8Y6yqnt1KOGL+X+uLDHzTEPkf5Yz/a2Z7rGGxHOVoy37bAhpRKeVPxoUvsfHNcch5rOszz9CrHP6ybX0Q8eLTwQylK3dI1RumQNhWGFsSqvzMbVP+O5rgRzZ+LOzD+vt/1vWnbjk37x3L2g2l5r9u+X/rejn3v+10/8ydQClejet/322JRBhe88+jrKeucDPsd2LT/XEe5BGePpa5XFbtxqO+eWOQWmlEGsqu7hWZrDNgn68cNSMp+Xtf5bRrwb1WwgzucgylHzersDmxBuT7hIdW0Syi3ErmMchS3zvYNsf07tCEz79DQhmy5F1ZbDLhH3fIy85iqUzF/cJuvAodny+A2XZZVtWtbt92ro0sLcqF0Hkavpzotq5tsN8yvbVTsTjFK1b1uOz6pZX6N67XINm7LccG9bOfFat+bLNeh3JHhtmNbjnWVyuWMct41ttjo6B9tirW0aVpW48jpwEci4vOUDvL84tqBlA5xl6LccTQXVsYuXkXEKcB2DW3OBr5UHd1eseKJOdbGThl3nosUmroUhRaLdSkMXUO5f/Vf1uR4Dc2fi/PoVrgd+71ZZDu2FQC77gfT8l6f2ue6LaPwOfb2WMI8a2MR8SzgxIbCZ9/7flvsZcDHGgrZf8+w34FN+8/N3DaWxVLXC+DfG+Y3ie+eD1O2TZ0PUn7fvzvKafBQRqr+CqWA85cNsbZRrNfS3Pm9qOb1q4anKK/HImILgMy8aqVz6VuXdYsxBtqJiE0zc7RKPEhs3HUbZ72Wk2OX5U1yW/W13rH4gErRJdaSx5Cjo9+bct/mBYN/UCrkV2Xm2pp223BbUW500JCzagorExuZuapSj1U8Gdr6muOE5tlY4OkaG3P5/0DZz79VE3sj5Rq4p1LzueiyvGq+g+0/XZc1Te/1kDlOw7Kq5U18319CDo3rvBLfgX2v90p/94zk0jhSdVus5rUvAk7JzO/WxP4yM/95ubmuFDu4UyBaLuSOiC82HaXrEouIr1AqRo+lVKKDclrJl4FX5ci1Q/PafS8z79dzrCnHrvM7B/gO46/bGsrR9SUPtBPDj/B7CfBf46xbl/VaZo7zlzd/gJXG5U1oW11KORVqyXm0zTPaB1R6P6VKO25s6kdHnzZDFWqWU+CZphybchkyRzoUhqrnEy8KTSo27zUTLbJWrx/7vem7ADgXY4re6yG3Y1MbJlAUHbdguhKfC8rf3UHWuc8C8hLW61Ia7gwQLXcN6BKjnCJc26ZlvXq/ledq5ynKA5n7Y1cXAvaNcm1sXWzPiHjQmLE9GmJBGeHxOcCz5ipnEbEh5Tz9/4yIwxuWtVNEPL1DbIeGHB8D7F3TbjnLuiflhvPjrtsuwLNz4UA77wA+G2UI/tE2W0X9yNiTit2VcqP60XV7U8O6ta3X3g3rtZQ8Nm/Z/tOyHbcG9htzvR/VMM+g3IR+r9EiQtw2oNLuHWJnRMRHGvLfJsrtFupyrIu1tVkstllEHEk5ErsN5Q/rFZTTjN8NvJByS4K7jsSObPpjG+2Ft7GLVxHxQOC/KUWL3xQKopzW2ViwAM6i+VSvXmNTluOaiFhQ4IkyoBjA7QbK8XwaCkMR0VT8mXv/z6xp01gUoozY3ZTHYLGIeDRlRNvHUE5DjIgYt8g66femaf9YzrKm5b3+ygTWrSnW9/ZYTqxpew257z+eMnjZyT2uV9/bo23d2tb5v1l4y8VHA6+Pcjr07/cYm/tb/amaNo13IaDcdrMp/06x1d75tYM7nLWUHz9NF3K/eSQ2Z6sOsX0oA7rU/ZDfKDM/Pn9C1WE6NiI+Rjm1qq76deeOsc0acnxUlePo4A3LWdaGHddtg9HOUOX5lEEJ7lQT24TmAXgmEdugbt0i4iWUwYfqtmPteuXiAwi15bEp078do8N6P5ky6ENdjtA+oFKX2GaUESDrRvx+UUPsz4EbKbcOW2qbxWLPpwzS8ehc9xrf51IGxvsnyvU582MHAV+McmrTqKC+cDUX61K8egtlNMp1BgZZZqGma6ypwDNNOTYVeJpuMzWpHLsUhs6DhWMLLKMoNKnYZjXToQz4cyD9FVm7vjdtBbsuBcDV8l7fted1a9uOfW+PrrEPUX7jjG6voT8Xzwaub3g/+17nrgXkrkXizSh3DbhmZFmbU86IvGuPscdSvqdfUNPmoojYvyHH7aJcMz5ubMua6XPaOsZTzw7ucM4HHpv1F3LfBDw/M9f0EYuI71NuD/PomjbXRcS7KdcJzR8E4yDKD903Z+aCodejDGTQJfbshhzPoAx4cnCPy/rjjuu2f9QPtPNr4KTMfF1Nm1fTPADPJGKHNazb1cC3G7Zj03otNoBQWx5/w/Rvx78ed72j3Iz9Hg05vpzmgWO+3DE25OjobbH3ZeYb50+rOrNHRsTfN8TeGOWob1PhbTP6LZRtASy45cMyCzVdY00FnmnKsbbAQ3mvFrxfE8wRxi/+zBV969psRrciTt+xZwG3a/iR3LWA3Hcxsq1g16UA2LasOdPwXjPgdux7e3SNPYPyN7bPwmeX2DMpBy3GyX1Ssc0acuxaJIbmOwP0Hav9nqb7XQgWiz2uY+d36nkN7kCi/ULuDwBvyswf1sTeCHxwnFhV4blrZr67ps3+lAru/MFhLgY+S6mIndfQCX8ecGKH2CuB42pyfASl+vb+Hpe1N/BA6gfMaVu3PVm4TS6hfAl+OjN/VtPmocC5A8Z2oBylHX3fzgLek5nnjbFecwMIXdkhj6cC35ny7bgN8KCW9V4wcFJE7E45A2DBQDCx+IBKe4wboxodPTOvq1neFnWxpunLjJ1IOaXsmFz3Gs3nAq+k3K+6Lva3wAMbCm83Ag9qKIKMHas6En9eLXe0YPEg4CkNhY4bKPca7jO2GnK8Fvg6Cws8R1J+tBw2UI7XUAo5dQWeyymnxI/GXlo9f0dNmzsDz2ko1PyaUkCeeKzavjcArxhtAxxFOepRV2Q9gHJ/67p9v+k96/TeRMQ3KAW7BaPG972sKnYN0/Fe/5xyT/AhtmPf26Nr7DXAR0aP+FV5DPm5OBw4AnjtAOu8WKx2/4mIL1OKxAvutLDIOq+lnHJed2eAL1MuSegr9jTK9/Qna9r8CnhpZi64n3ZEXAU8o0PsJsplSHWd349n5jajbVYLO7iStJ6KctrTq7jtGlwone/jgX8F/qIhdjbwPw2Ft8OBj/ZcKHsJ5VZZfRVqusbaCjzTkmNbgScbptcWfuYta02HWKfCUEuOYxeFJhGrfiS/puGH8I8p4yL0VWTttP9UBbsNMvPsmjZjFwDnLWuq3+sq/qS+1m2Rwmfv26Nj7GvAd1f6c1HF7kPDCOJDxmjYf7quVxXfnIY7A/Qdq54PdReCL1IOsNV1fr+emY/se5lDsYM7oCgjsdV9cS34IzSvzcGZeXSH2FsoN5afv6zjMvOElmW9NjP/vu8Y8E1KhWhJuSxjWUdQRrpbsCzgQ5Qq+tMoNxyfH/tAZt7UMM/aEa6bpq9QrHabLLKtui7rfZSbgk/zdjyaUt0dZ+CkuX3xSfQ3oNJgsUksS9JCi/0QltZ30TKC+LTEus5Pq4cd3IFExGHAn1BOp5i7bmAHyukUx2bmkQ3tutwy5O2UwWMOHlnWgZSK40tH23Rd1hJiv+C2U4aWlMsylvWrajnHjCzrIMq1NF9oiN0NOKRmlptTTuUaHdk1KCM/1o0GO6nYdzNzh5pY235wMfA7PedxEWUbTvN2vIQymvYxuXDgpL+mDJw0Gvt61f6RuXBApf0o1+fULesESqV1iNgewNtrYstZ1uco22U/agpQUa5NHjtWs6yywKUVqJZcPFmBYtLYBZ4VyLGpwDP3vjyR8UbM7hrrtTA0LcWkRXLciPIduB8TLrJ2jXUsAK6W9/okyiUxfa1bW+FzsO3RNTbwsnYCTqWM7fNzWOdWhu8EXsK80cUHio3eVvHU6vneLLzdYlObLzP8LTSb7igw2LJmgR3cgUQZwfK3R/+IRcTtgWuBBaf6UT5gv0258H2c2O4AmbnxyLKCMiDCrxrmt2mVS6+xzFxwwXzV8b1jTS69L6ta3o2ZefuGWFKul5nfNikjWwJcMDI9gJ0b2kwyVrfec4NfjMbmtlXvebRs46nZjuPuBxHxQ4DM3L1hvb4ysqw5+wwY24cy0MTXWajrsh5BuQaorgC1LaXDOW6saxGtqUB1KKVIMlo8WYlCU1OBZ5pybCrwNBVxnktz4ecg4P90jO3H+IWhSRRxJhH7XGZuuyAQ8VNKB6euANilyNp1P2grKnYpAK6W9/oblGtA+1q3tsJn39tjmgqfXWIfolwDu1UuHEH8fZRb+vzHCsfOrtbh3mPO73XA4Q3b44OUg0l9xfbmtgNUk15WAP+amVvXxFZ959cO7kAi4hzgCZl54cj0u1OuDdmTMiLuOuEq9rtjxk6gfMmss9NGxF6Uo6k7Zs2NzyPiZmD7nmM3Ag/LzNNGpl8GXJ0jt9VY5rJuoAxV/8nMvLWatgHlC+r9wJ81xI4B7pUj10ZFuTfhHbN+QIKbKMPG111PNYlY7XpHxE8o1whtX9NmEnm0beNp2Y7Xc9uPnKUOnPRflM/Uw2vatA2odBNwnyFicdvo6HU/rDsvKzNvVzM9KLe9qSsGLBbrXERrKIbdQhnJ9JJ5k1es0LSKc2wr4rQVALvGuhSG9qH/Ik7fsU0pHceHjkwP4JuZWXt3io5F1q77wS7V8wvq2nQsBK+G9/pRmblBQ45jr9syPjNDFkX3YTo+M79H6VNsMtpgQt8vXd7PNQA5cqu0JcwvKb9v6jpMz+05dhDlb0ndbY76XhaUs0ifVTO9tfO7KmSmjwEelNPCzgO+SBlp8ShKR/S86t+HN7T70bgxygASP6OMsHti9TibcnrG+yn3Z6ub3zcmEPsQ5Rrc0VwuBg7seVnvAT5OuefwudXjimraw1tirwXuXzO/FwFvaFjWJ+raTDB2Qt16A/8AfLihzYkTyOPvRrbjmur5NG3HuU7sOcBV1ePsatouDbG3U0bzrGvzXMqI33XLeuNQMWB/4IU9L+sC4ME10/ei3HqiS+xGYJuG5d3cEruBUijZYN60DShHiv+3oc1NwE4DxlZDjtdXn4Ft5k3bhts+r6PTDwOubGiznNgvgd1a8l8Qo5yRdOk4bYaOUX58Xk/5kT/6uLVh//hjSmGnKXZ9n/tB9T7/dMz9Yxbe677XretnZuztsYz9cSo+M5TL766ldHS3qx6/RzmV+yfVvysdW0P5zT3u/K4C7tuwPW7sM0Y5xb7p/ex1WVUsKb/Tj655XFvXZrU8vA/uQLJcs3Yvyg/A+dfmnJbV6RAN7e45biwzv025Cfnd5i8rq9NoWuY3WpHuI/ZcgHFyWcayfjNMfkRsWU27ct5LTmmJ1c3vX1pizxw49sSG6a9pafP4CeTxurnnU7wd31Q9PazhJYe1xMaSmY3z6TuWmf/R97KApwPviYg7cdtpkztSrml6HvCuDrEPA3enXOc36lstsQ9SOvHvjoirq2mbUU4LbtrPP005HXPBkfwJxV4/kmNUOf5kinJ8LeX+hV+LiLtW0y6nnB4bNdOPp5xB9Bc9x15Mzb2BK29tiB1BuZ5xnDZDx84G/iUz3zMaqE5Rrts/vky5jvMvG2Kvp9/94O2U05/rNO0fs/BeH0IZd+Jr1Rk4cNvI7/PzX2ps/mdmnPl12R5dY0cwHZ+ZAynXsb6OhbegfCXwnCmIvbN6Pu789qfcJqjOC3uOvYxy7e8Qy4JS1H9z1t++7Pcb2qwKnqI8oOr0vdEO7rcyMycRa8jh3pl5zpAxytGNJ7JwKPSsm56Z10TEXbrE6nKo8nhcZp7UV6zv+S0lRukcjLUd6+bVQx4XsXA08OMy85xoGCmchttBZObZTW2WE2tZ77FHJe/SZujYcufXVoDqGluOpRZPVtJqyFH9i3Iv+e9l/S2y9svMz1TPG/cP9x1J0yAiHgFcmA23L8vM01cgrV7YwR1IRDyecqrDGm67TmsHYFfKacPP6zn2wsw8sSaPSYyU3Ba7kjIi3YkjOT6tev6pkemPA04Gfr+mzWKx12Xmh4dYt1WyHSexPa4GLqR+NPBLKYMPjcZeUj1/5xhtlhPre1Tyod/rQXOkXE/Ya6FpqALVChWaags81fPBijh9FnimpVCz2nOsYnXvS1sBsLU42HU/oKWoWJf3BLfHoO818FN6HBWesh17m98kYtOUIzViQreg7DM2LXlMU46rnR3cgUTE2cCTcmSo8YjYhXKKwO49xj5Eufn16EXqQRnt8711KU4o9kLKwDjXjOS4hrL/7ToyfXPKaU93rWmzWOwiyuledXk8iXK60TixvYCtamJd57ec2B8AW4y5HfveHnN5bJz1o4H/kjKY1Gjs3CrH3cZos5xYl1HJdwM2rom1tRk6Nokc7005vXHcYtIkYmMXZFagwNBU4Bm6iNNrgWdaCjUzkOMngHsx3HvdpajYawFwErFlzO8XNN+WsMuo8G+onh/e0/wmEZumHPu+9eNq2OdmOcdV3fm1gzuQqiOyR2bePDJ97sf6HXqMXUsZBOb/1KRyNOW6kRsGin2Q0jH7+UiO51H2v3uOTL8LZdCirWvaLBa7itKx/+VIDkHp6P3hmLHPUQbF2a+n+S0ndjKlUDDOdux7e8zlcc+sHw38XMooyqOxphzb2iwn1mVU8tMpVfAHj9Fm6NgkcjyPMuL6NesElldo6rtANW2FproCz9BFnC4FnvW1iDOJ2L1y5DZ8AFFGLL/DgO91l6Ji37clnKr3OmtGUY7oPCp803bsOr9JxKYlx18wgVs/9hxb7LaK62OOm2TzyO+Nnd/VoHalNBEfBE6LiGMpP+SgDMpyAOVHXZ8xKLdxOWY0iYj4V+D7mfmNgWJvBr4dESfOy3Enyoc4IuI9I9MfRxnOvK7NYrEfANdl5tdq8rhm3FhEnAJs19f8lhn7EeNvx163RxU7G/hSVbCZv7xdgb9viN2havvFMdosJ3Yy5ZYz36nJ/4K6WEQcTxmN9MKlthk6NqEcr6P+1gG3Vv8OGduEciZIXaHpWuAtI9PnOpx1bSYVexJldM3R92CDKj5qW8r61rWZVGxDytGUugLJtZTi1XxzRZC6NkPHVkOOJwAbRMSZNW02ZNj3uinWtj827R+z8F6viYgH58htCSlFwVs6xIL67dh1fpOITUuO11HGYWi69eNu2Xzrx0Fi0X5bxfU1x1ur4sSCEOVv8qplB3cgmfmGiDiOckTtIdXkS4BnZeZZEXGfHmOPB77bkMr2lKH0B4ll5tbV0ZkncNv1Gl/ltptmL5iemVc3tVks1pAfmbnFuLHMfFKf81tmbPcu23ECedwvyn1va0cDj4g31MUoP8TGarOcWEv+TSOPHzJum6Fjk8iRMqprl2LSJGK1BZkpKzQ1FXiGLuKMXeBZj4s4fee4BXAS8ILRJpRbfAz5XncpKo5dAJxEbELv9cl0G/m9KfbrMts4q6f5TSI2LTnezG2nS49qGz1/yNiHKZ8Dc7zNL2nu/F5U8/pVw1OUV0D1B5LMvGrSsSGXtVhMyxfldgS/6dDNfSk1TZ9UrCG3TTNz9KhXa6xLm6Fj05LHpHIEbse6BZK5QZ/qikkTjdXlOG2aCjy0F3HaikK9x3pfaf1GRHwAODozT6mJ/TvwbKZgP6Blf1z2Rphy0fOo8H3Pb9Zz1OoREf9AGXzuWzWxN2b7bQanmh3cgUTETsCbgMdQqmBBudfVlykDQbykx9ip1fO9KSPv1rV57ECxV+XIIFjztsn3MvN+S50+TbEVyGMN5cbyd6FUTYMysMON1UtuNzL9GuAdwEurNvMH9FlO7IVZ7rNcl+NqGDTBHGtiQxdIusSmJY8m62uBxBwXxIIBbwfYFKuer3geQ+dIg+j5Noh9z2+Wc5yWPMxxvNhq5ynKw/k45ebrz8qqghoRGwJ/RLmX6fN6jJ1N6ejcbYBlLRqLiLnTaOfbG9gpIp4+Mj0apq9EbJpy3AV4dmZ+c51A8wATewNfAfapabOc2GcjYvRayLkct4qIv6qJPaoh1tZm6Nj6muNdIuJURgonUU7TfTvlpvNDxWoLKxExv4gzf3pbm0nFGgs8wFnUn1rWNH2aYtOSx6rOMSKeCryVmlv3RUTjbf0mEJsrlJ65wnkMnWPt7RErJ9L8fnaJ9T2/Wc5xWvIwxzFiq73zawd3OFtl5sfnT6g6g8dGxIf7jFXVzbnXTXRZS4h9jHKN8Ghl9SDgFhYOMAHl6G9dm6Fj05TjBqMdzkrtABOZeWpEbFjXZpmxbYHNKdfbjNqkIfZkyna804IWzW2Gjq2vOW4KvLTnIkjfsaGLOF0KPOtrgWR9zDEon5s6n6Rcz3bBOg0mczvAtth5sHAciRXIY+gcz4iIj7BQANtExDvHjD2qIdZ1fpOITUuO05KHOY4X26xm+py2jvHUs4M7nDMi4t2UQVbmj3h8EHBZzzGAiIjfG2BZi8WuBt6cmesM6x8Rv0MZOObg0Q0VEc+qazN0bMpy3D8iPk8ZgGD+Nt6ohOOPR6YfCJzd0GY5scuBz2TmGTU5vrouFuWm8PfIzNcttc3QsfU4x9dMoAjSa4zhizhdCjzra4FkfczxIcAmDZ3wDbht4J35LqniQ8WSms/MCuQxdI6bUW4hdENN/EUdYn9OuQxowXdnx/lNIjYtOU5LHuY4Xux5HTu/U89rcAcS5f5zhwD7su51I8dTOhLP6TH2+er5HwywrMVi5wDnZeZPRrbHI4A7Z+bnGRERzwNOHG0zdGzKctwT2Jr6bZx10zPzCxHxpD5jlFs0XJmZP6vJ8aHAuaOxiNidcgT67KW2GTq2Huf4PsptRuqKGTtQfkiudOxIyh/bw6YgxwcBT6kpPnyDUny4GyMi4gbgoQ0FhsFi5thbjtdTfii+dbQN8CrK37y62/pdDmwzUOyl1fN3rHAeQ+d4Z+A5WX/Lwl8Djx0nFhFfBu6dmdv1Mb9JxKYlx2nJwxzHjt0K/AX1nd+3ZOZWNdNXBTu4krQe67sIMqHCymBFnCUUeK7KzLUj23B3yr0Lz6rZvg8F1oy2GTpmjr3l+A3g7zLzpJo2F1Fu01e3X50VEXsMFaP5MzNoHkPmCFwGXJ+Z1zEiyh0exop1aTN0zDzMcZmxLwOvaej8/jgzdxmdvlrYwR1IRGxEOYK7H+t+KR8HfIhyOm9fsc9S/nA8dYBlLTX2NMqRotEc9x2Z3tZm6Ng05fiBzLyJGhFxVGYeutTp0xSbljzMUdJSVJ3wBUWOKrZNLjLatiRNi7bO76qXmT4GeAAfA95DGZl3h+qxdzXtwp5ja4DzBlqWOQ4T+zSwRc3jnpRO8Oj0LRumr0TMHKc7xyMpI69fRbkV1dnVtJ2mJPa26rHSecyPndOQ4+j0tjZDx8yxv9hmHX4DfHEaYtOShzmuPzlOSx7mOF5stT8cZGo4v5uZ9xqZdjFwakTcmJkv6CsWEQlkZp466WWZ42A5JnB/WGdQjqTcPgjWHZAgq9dtB5xe02bomDlOb47bUgaCe3RmXgYQEXcDngt8G/inKYh9vcr5kVOU4z5LzPGgljZDx8yxn9jzgf+Nco3uXSmfpSsoZ9p8Aai7P24Ae0bEgwaK7dEQGzoPc1x/cpyWPMxxvNgDaqaXYMQXc2SU89XEU5QHEuVek28BPpmZt1bTNqDcK/b9wJ/1GPthtdjdB1iWOQ4TOwa4Vy4cCGsNcMesH5DgJuCeo22GjpnjdOeYmbcbnV7FbszM2690LCJ+CJCZu5ujOU5Bjv9JGWzsfjWd4iMpt5mK0XbAPgPG9gFu5baCwUrlYY7rT47Tkoc5jhd7CPDQmukBfC4zt62JrQ45BYeR14cHsDPwcUql99zqcUU17eE9xz4LfG6gZZnjMLHXAvev2a9eBLyhYZ/7RF2boWPmONU5ng28Ethm3rRtKCMWXzklsXMplwusdB7maI7bAGuBkxs+TzdQ7oNbF7tpqBjlliCXrnQe5rj+5DgteZjj2LEEvkzpAI8+fl3XZrU8PEV5IJl5QUQcAfwvIwMxZebZEXFln7Hq+b5DLMscB4vdOyIOY+HIkVk3PTOf2dRm6Jg5TmeOlMrtq4CvRcRdq9jlVex3gRdMQewL1fOVzsMczfFyyinI/xXzBpSKiG0op7D/kHIv3DpvHTB2BOX06ZXOoy12BObYR+wIpiPHacmjLXYE5jjqJ8DzM3PNaCDKqPCrlqcoD6T6cXkA5d5tl1STd6imXQrcrcfYSyhVmXcOsCxzHCb2U8q1lMdy203u53KkyvHiJbYZOmaO05vjsZl5JDUi4uDMPHqaY9OShzmuPzlGxObAR4F7cNsP0blO8ZGZefVK57hI/lORhzmuPzlOSx7mWBv7F+CdmfnDmth+mfmZunarQpfDvj7Gf1BOvbpdzfTbAzf2HDuXcv++IZZljuZojqs3xwXzmxf/ybTHpiUPczTHavrB05DHDGzHqcjDHGcrD3McO9b4fbYaHp6iPJxbKUdRLhyZvi3lKGGfsQ2ov5h8EssyR3M0x9Wb444RcWbNPAPYYUpiuwEb18TM0RxXKrZNzXSA90bEy6cgx9WwHc1xtnKcljzMcbxY03cZwOuAo1viU81TlAcSEU8E3kUZQGPuvPadgF0pO9DBPcZ+p3r+3QGWZY7maI6rN8ctgUdTbhU0XwA/olyHuNKx0ymd9AebozlOQY4nUD47Z9e0uS/wwCnIcTVsR3OcrRynJQ9zHC+2hoXfZXOxe2XmxjWxVcEjuAPJzBMi4l7AXqw70MtpmXlLRLyhzxjlAzLIsszRHM1xdeYIHAVsmpnfYUREXDANsYg4HtgpMy80R3Ocghy3AE6iDEC1Tgj4wZTkuBq2oznOUI7Tkoc5jh27GTiQ+s7vN0Zfv5p4BFeSJGkJIuIDwNGZeUpN7N8z809XIC1JGtssf5/ZwZUkSZIkzYSm+yJJkiRJkrSq2MGVJEmSJM0EO7iSJI0hIo6IiIyIxoEaI2Kf6jX7zJv2soh4eoflPaBa5hZjtFmwfEmS1gd2cCVJ6t+3gYdU/855GTB2Bxd4APB3wJI7uA3LlyRp5nmbIEmSepaZvwBOHXq5EbEhZQDJFVm+JEkrzSO4kiR1s0dEfCUirouISyPi7yNiA1h4inB1L8K7A8+qpmdEfKiK3SsiPh0RV0TE9RHxk4j4fxGxUUQ8Fzi6Wt6aeW13rtpmRPxjRLwqIn4M3Ajcr+EU6a9GxCkR8fsR8e0q7+9HxNNGVywi/iQizqny+V5EPLVq/9V5r9k0Iv65yveGKv+TI+LevW5lSZLG4BFcSZK6+QzwQeANwBOAvwVuBY6oee3TgC8A350XX1v9+3ngauAFwM+A7YEnU4rQnwf+AXgN8EfAxVWbS+fN+7nA+cBfA78CfgrcpSHnewLvqHL+GfAK4P9FxL0z8zyAiHgc8FHgeOCvgK2BtwO/BZw7b15vA54KvBpYA2wJPAzYrGHZkiRNnB1cSZK6eV9mHlk9PzEi7gy8IiLePvrCzPzfiLgB+Flm/ubU4YjYCtgV2Dczj5/X5N+rf9dGxI+q59+Z64SOCODxmfnrefPdoyHnrYBHZuaa6nXfpnSWnwm8vnrN64CzgKdlZlav+z5wOut2cB8CfDQzPzBv2qcblitJ0iA8RVmSpG4+MfL/Y4FNgfuOMY8rKUdfj4yIP4+I3TrkccL8zu0i1sx1bgEy8wrgCmAn+M01vHsCn5zr3FavOwP48ci8TgOeGxGvjog9q7aSJK0oO7iSJHVzecP/t1/qDKpO5OMoR0ffAJwbEedHxAvGyOPSxV/yG1fVTLuBcvoxlCO8t6N0ekeNru9fAu8F/ozS2b0iIt4WEXcYIx9JknplB1eSpG62afj/JePMJDPPz8wDKde6PhD4MvDuiHjSUmcxzvIW8TPgJuCuNbF11jczf5mZh2fmrsDOlFOcX0y5pZEkSSvCDq4kSd08c+T/BwC/BL7X8PobgE2aZpbFdygDO8FtpzrfUP3b2LYvmXkL5WjyMyIi5qZHxO8Cu7S0uzAz30JZ93FO0ZYkqVcOMiVJUjd/Xt0W6DTKKMrPA47IzJ/P6xvOdxbwiIh4CnAZ5WjpnSmjGn8cOA/YkDIq8s2UI7lz7QBeFBHHUI6wnpmZN05ipShHYE8EPh0RR1FOWz6iyvnWuRdFxP9QRlr+HqVj/yjg/sAxE8pLkqRFeQRXkqRu9qVcP3s88GzK7Xz+b8vrDwd+SBmc6jRu6zT+hHLU9njgY8B2wFOqgZ3IzLlbC/0hcErVdru+V2ZOZp4EPAvYgzIq8mGU2wldBvx83ku/TjmK/VHK7Yz2B16eme+YVG6SJC0m5g2SKEmStEBE7EA5wvyPmdnWiZckaUXZwZUkSb8REZsAbwVOppxGfQ/glZRBpn47M8cZtVmSpEF5Da4kSZrvFuBuwLuALYFfAf8F/JGdW0nStPMIriRJkiRpJjjIlCRJkiRpJtjBlSRJkiTNBDu4kiRJkqSZYAdXkiRJkjQT7OBKkiRJkmbC/wfdOwFRarkvmgAAAABJRU5ErkJggg==\n", 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", 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\n", + "image/png": 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", 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kSZIkdTDMc3AvioiHNsQeHBFeoixJkiRJWjbDTDK1M3DnhthdgHsvuTWSJEmSJHU0zAAXIBuWrwKuX1pTJEmSJEnqrvUe3Ih4JfDK6tcEPhcRN/W9bBNgC+CY0TdPkiRJkqTFGTTJ1EXAV6v/HwCcDqzte82NwNnAvy1mhRFxJPBU4OrMfHC1bAvgWMpl0JcAz87M6yIigHcDTwZ+DRyYmd9bzHokSZIkSStL6wA3M48HjgcoY03emJkXL3GdHwHeC3y0Z9mhwFcz8y0RcWj1+yHAk4D7VT+/Dby/+leSJEmSpHUs+h7czHzhCAa3ZOY3gGv7Fj8dOLr6/9HAfj3LP5rFqcBmEbH9UtsgSZIkSZo/i34OLkBE3Ad4NrATZebkXpmZL+rYjm0z84rq/1cC21b/3wFY0/O6y6plV/QsIyIOBg4G2GmnnTo2QZIkSZI0yxY9wI2I/YDjKGd9r6bce9uraYbloWRmRsRQdWXmEcARAKtWrRpJOyRJkiRJs2WYM7h/D5wCPDcz+yeaWqqrImL7zLyiugT56mr55cC9el63Y7VMkiRJkqR1DPMc3PsAbx/D4Bbgs5RZmqn+Pb5n+Qui2Av4Wc+lzJIkSZIk3W6YM7jnAlsudYUR8Ulgb2CriLgM+DvgLcBxEfEi4FLKfb4AX6Q8IugCymOCXrjU9UuSJEmS5tMwA9zXAO+KiO9k5kVdV5iZz2kIPb7mtQm8tOu6JEmSJEkrxzAD3MMpZ3DPiYjVrP+on8zM3x9VwyRJkiRJGsYwA9xbgfPG1RBJkiRJkpZi0QPczNx7jO2QJEmSJGlJhplFWZIkSZKkqbXoM7gR8ZhBr8nMbyytOZIkSZIkdTPMPbinADngNRt2b4okSZIkSd0NM8B9bM2yLYGnAr8PvGwkLZIkSZIkqYNhJpn6ekPo0xHxTuAPgRNG0ipJkiRJkoY0qkmmvgA8e0R1SZIkSZI0tFENcHcHbhtRXZIkSZIkDW2YWZRfULN4Y+DBwIuAT4+qUZIkSZIkDWuYSaY+0rD8RuBY4K+X3BpJkiRJkjoaZoC7S82yGzLzqlE1RpIkSZKkroaZRfnScTZEkiRJkqSlGOYMLgARsfDc2y2Aa4FTMvMLo26YJEmSJEnDGGaSqbsBnwceDdwCXANsCbwqIv4beGpm/nIsrZQkSZIkaYBhHhP0JuDhwPOBTTJze2AT4AXV8jeNvnmSJEmSJC3OMAPcZwKvy8yPZ+atAJl5a2Z+HPh/VVySJEmSpGUxzAB3S+DshtjZVVySJEmSpGUxzAD3YuCpDbEnV3FJkiRJkpbFMLMofxB4R0RsCnwcuALYDtgfOAh41eibJ0mSJEnS4gzzHNx3RsTWlIHsgdXiAG4C3pKZ7x598yRJkiRJWpyhnoObma+NiH8C9uKO5+CempnXjaNxkiRJkiQt1jDPwT0E2DEzXw6c0Bd7D7AmM/9pxO2TJEmSJGlRhplk6oXADxpi36/ikiRJkiQti2EGuDsBqxtiFwL3XnpzJEmSJEnqZpgB7q+BHRpiOwI3Lr05kiRJkiR1M8wA97+B/xsRd+5dWP3+6ireSUTsHhFn9vz8PCJeERGHR8TlPcuf3HUdkiRJkqT5NswsyocD3wLOj4iPAZdTzug+D9iSOx4dNLTMPA/YAyAiNqzq/gzlvt53Zubbu9YtSZIkSVoZhnkO7vcj4rHA24FDKGd/bwO+CTwzM78/ojY9HrgwMy+NiBFVKUmSJEmad8NcokxmfjczHwPcjXLf7d0yc+/MPH2Ebdof+GTP7y+LiB9ExJERsXldgYg4OCJOj4jT165dO8KmSJIkSZJmxVAD3AWZ+ZvM/Elm/maUjYmIjYGnAf9RLXo/sCvl8uUrgHc0tOeIzFyVmau23nrrUTZJkiRJkjQjOg1wx+hJwPcy8yqAzLwqM2/NzNuADwF7LmvrJEmSJElTa9oGuM+h5/LkiNi+J/YM4KyJt0iSJEmSNBOGmUV5rCLirsA+wIt7Fr8tIvYAErikLyZJkiRJ0u2mZoCbmb+iPG6od9nzl6k5kiRJkqQZM22XKEuSJEmS1IkDXEmSJEnSXHCAK0mSJEmaCw5wJUmSJElzwQGuJEmSJGkuOMCVJEmSJM0FB7iSJEmSpLkwNc/BlSRJGomI9ZdlTr4dkqSJ8wyuJEmSJGkuOMCVJEmSJM0FB7iSJEmSpLngAFeSJEmSNBcc4EqSJEmS5oIDXEmSJEnSXHCAK0mSJEmaCw5wJUmSJElzwQGuJEmSJGkuOMCVJEmSJM0FB7iSJEmSpLngAFeSJEmSNBcc4EqSJEmS5oIDXEmSJEnSXHCAK0mSJEmaCw5wJUmSJElzwQGuJEmSJGkuOMCVJEmSJM0FB7iSJEmSpLmw0XI3oFdEXAL8ArgVuCUzV0XEFsCxwM7AJcCzM/O65WqjJEmSJGk6TeMZ3Mdm5h6Zuar6/VDgq5l5P+Cr1e+SJEmSJK1jGge4/Z4OHF39/2hgv+VriiRJkiRpWk3bADeBEyPijIg4uFq2bWZeUf3/SmDb/kIRcXBEnB4Rp69du3ZSbZUkSZIkTZGpugcX+L3MvDwitgG+EhHn9gYzMyMi+wtl5hHAEQCrVq1aLy5JkiRJmn9TdQY3My+v/r0a+AywJ3BVRGwPUP179fK1UJIkSZI0raZmgBsRd42Iuy38H/gD4Czgs8AB1csOAI5fnhZKkiRJkqbZNF2ivC3wmYiA0q5PZOaXIuI04LiIeBFwKfDsZWyjJEmSJGlKTc0ANzMvAh5as/wa4PGTb5EkSZIkaZZMzSXKkiRJkiQthQNcSZIkSdJccIArSZIkSZoLDnAlSZIkSXPBAa4kSZIkaS44wJUkSZIkzYWpeUyQJKlBeT74ujIn3w5JkqQp5xlcSZIkSdJccIArSZIkSZoLDnAlSZIkSXPBAa4kSZIkaS44wJUkSZIkzQUHuJIkSZKkueAAV5IkSZI0FxzgSpIkSZLmggNcSZIkSdJccIArSZIkSZoLDnAlSZIkSXPBAa4kSZIkaS44wJUkSZIkzQUHuJIkSZKkueAAV5IkSZI0FxzgSpIkSZLmggNcSZIkSdJccIArSZIkSZoLDnAlSZIkSXPBAa4kSZIkaS5MxQA3Iu4VESdHxNkR8aOI+Otq+eERcXlEnFn9PHm52ypJkiRJmk4bLXcDKrcAr87M70XE3YAzIuIrVeydmfn2ZWybJEmSJGkGTMUANzOvAK6o/v+LiDgH2GF5WyVJkiRJmiVTcYlyr4jYGXgY8J1q0csi4gcRcWREbN5Q5uCIOD0iTl+7du2kmipJkiRJmiJTNcCNiE2BTwGvyMyfA+8HdgX2oJzhfUdducw8IjNXZeaqrbfeelLNlSRJkiRNkakZ4EbEnSiD249n5qcBMvOqzLw1M28DPgTsuZxtlCRJkiRNr6kY4EZEAB8GzsnMf+5Zvn3Py54BnDXptkmStCAi1vuRJEnTYyommQJ+F3g+8MOIOLNa9lrgORGxB5DAJcCLl6NxkiRJkqTpNxUD3Mz8JlCXBv/ipNsiSZIkSZpNUzHAlSRJmoS6y8ozcxlaIkkaBwe4K1zT/WP+sZckSZI0a6ZikilJkiRJkpbKAa4kSZIkaS54ibIkaWp5G4UkSRqGZ3AlSZIkSXPBAa4kSZIkaS44wJUkSZIkzQUHuJIkSZKkueAkU1rRnMBG08DjUJIkaTQ8gytJkiRJmguewZVmgGf4pOlX9z5dKe9RP6MkSdPCM7iSJEmSpLngGVxpjnlWRZKml5/R3azkqyUkDeYAV5ImwC+ykiRJ4+clypIkSZKkueAZXElD8/IwSfPIzzZJmn2ewZUkSZIkzQUHuJIkSZKkueAlypIkSZJmW8NkjnibwYrjAFfSxHh/m7TyrPQZxFf69ksajt+Vls5LlCVJkiRJc8EzuJoZZrQ0LM+cSJKkeeF34cVxgCtNkAOu+TZL/esfSUmaM96DKgEOcGfKLH15libF94XqeFxIM67uPbxM718/T5qZLK038JiZouN7HnkPriRJkiRpLngGV2pgxlbSPJqmz7Zpasu8aNunXWOaD9N0ttXjbcJW2BnjmRjgRsQTgXcDGwL/lplvWeYmTZ1xfFCM68Nn0h+w07C+ca9zHLrut3Hs71k6vlVvmvb3NLVlKSa9HfOy37qapsHBLJn3v8ErIaGwkvtQs2nqL1GOiA2BfwWeBDwQeE5EPHB5WyVJkiRJmjazcAZ3T+CCzLwIICKOAZ4OnL2srVqCSWf0Jl3nUtY3KxnyWemnLuv0cqXJv0eXoq0PR3kWfqHsNB3709SWLvUupp/G0YfjKDcOg9oy759f0/Q5NK6+mKYzg7Ny7M/S1XXTdEXXOMpN0zEDk/97Mc1mYYC7A7Cm5/fLgN/ufUFEHAwcXP36y4g4b0JtG4WtgJ9C7QFmrD52e7wt1jBd/uByo23rssVqtn8q+2kl9MUU9O/t8eXoiyk4Fqcpdnt82vpp3mOz9J4xth7/ri8hNsLP4Nvjs/SemZW/QRP/jBq+L6bNvRsjmTnVP8CzKPfdLvz+fOC9y92uEW7f6caGi01be4zZT9Mem7b2rOTYtLXHmP007bFpa89Kjk1be1ZybLnWOSs/U38PLnA5cK+e33eslkmSJEmSdLtZGOCeBtwvInaJiI2B/YHPLnObJEmSJElTZurvwc3MWyLiZcCXKY8JOjIzf7TMzRqlI4wNHVuOdRobPrYc6zQ2Pes0Nj3rNDZ8bDnWaWx61mlsetZpbLrWOROiut5akiRJkqSZNguXKEuSJEmSNJADXEmSJEnSXHCAK0mSJEmaCw5wJUmSJElzYepnUZ43EbEtsEP16+WZedViYg113T8zz+1aFrgCeGJvOeDLmXn9YtY5yjrbykXEPTrU+cLMPKrDfnlhZh7VFAM+PWxbllJ2Ee35CbBfX53HZ+aXutY5jvWNo51d6lxkvZ36qUt7BpWLiH27bKMkSdJK5SzKExIRewAfAO5B+ZIKsCNwPfAu4BUNsZdk5vca6vwx8LSWetvKXlO95sS+cvsAb8jMjzatMzN3GnGdbeVOAp7Qoc4rgEvpsE9HvX1LKTugPT8Hvgl8FLisp84XAKsz86871DmO9Y2jnZ3qXES9XftpHPtme0riZ9g6j8jMg6v/d06a1cXolsBqSyi0rq8l1rXOiSZ3ltpWWpItXZIfS9n+ceyblnaO/DhcaOs4tn9MScG57vvlSNDOerK4Kts54d91O7q0dVzJ21Fv/zRtX8+JmcayS9hvKy5Z7gB3QiLiTODFmfmdvuV7AScDezfEPgd8sq5K4ADgopZ628q+BNiy/0MhIjav6vz3hnIHAx8ccZ1t5a4CtmmIXQmc11Dng4Dfadnfq1vKndUS26KhLd8Bbqgpt5iyg7ajsT2Zud5tBhERVVs61TmO9Y2jnS11ng/8pqbcouqlYz+NYd/cmJkbN8QuAB7ZUOf3gafSLfE1jsF/14TKNMXGlVDpur8vAG4dtj3TtG96EzFDrm8picaZOG7mve+XUnaatmMZ1ncysBMj/AweY1vHkbwd+fZP2fb9mJLY2m3E9f4QWNNQ7lLgZ5TB7zZAAlcDxwNvGZQ0nGYOcCckIlZn5v0aYjfVfZGtYrcBfwHcWBN+B3BdS71tZY+kfJH/WV+ZewDXtZQ7agx1tpVbC2zdELsGWFXVvU4YuCAzay/Bj4hbWspdCDyiqU7KQLyuLacDdwf27VC2bTva2rMaeFRmntZX557A/1AGQMPWOY71jaOdbXV+mPJB3dQXXfu4rZ/GsW++DjymIfYd4OKqjgVZ/b4DcDbdEl9dE1htg/8HAO/ruL4fdaizrdw4kjuDEipLaWtTsuWqluTHWJJbbbGWfdOWiLmE8vekLtb1OFxMonHk298WW0Jyb9b7fmCicdIJ2rbYFCWL294zV1K+D13fV27QZ/BuDbGltnXSyduu2z9N29dW527ApZm5W4d6W/dbZt6podwvgTcCR2fmldXy7Sgn0B6fmX9QU+dM8B7cyTkhIr5AyaCsqZbdi5JBOacl9hPgrMz8Vn+FEXH4gHrbyr4d+F5EnNhTbidKJuzclnIfGEOdbeWObol9C9g0M8+sqfPClv1yfku5S1pip7W05e+BR3cs27Ydbe05CXhvRNyNO7Jy96Jk477Qsc5xrG8c7Wyr80DKl+BR93FbP41j3xzUEltDuerjxzV1rgHu2j+4BcjMUyNiS8of+7pk00tbYlAG0f1uAzakvLfqBvEXdVxfdKyzrdyFHWOrI+KR/ckGyheKG4BtaU6odG3rBTTv72xpzwYtdXbd/q775j6U5F9dImZTRn8cBu19MY7t77pv2vppHvp+0Pui676Zpn6a9HtmI7p9Bn+L7u+LrvumrdytE97+adq+tjq/Bdwwhv22YUu5jTLzrb0Lq4HuWyPiz5hhnsGdoIh4EvB01r0G/rOZ+cWmGHAqcENm/nrYegeVrbJe+/aV+zLlDVFbLiK2GHWdbeUy87q2WF1dPXU27u+2cgPq7NSWpZYdUO92vXUuZOHGpev6xtHOMdW5lD4e+b6pi0XES4FvZub3a+p6OXA/YFfqEzwPBZ7dkGz6DSVrWxdbC/yccnlY/+D/cuC1mfnNmnJXAn/UYX2/AJ7Uoc62chcCB3SInQBsAdQlG15KSagcNeK2fptyJULd/v4o8OSG9lwJvH3E299139wT+L2GRMwNwONGfBwuJBqb+mIc299137T10zz0/aD3Rdd9M039NOn3TO+l68N8Bn+Ccja9y/ui675pK/cvwF9NcPunafva6vwE8Hbg/R3qbdtvV1IuRa4rl8BnKGdwr6pevy3lRME+mfmE/vpmhQPcFaIamJKZ105znS3rGnaynE0z85cd1tNYrmudvWW7bAfwK2BP1h1wfTdb3rzRPklLp20cELs/5bKbaWhnY52LrXeUx9tS9g0dJ9Opyg+d+OqawGob/LfVOWh9Xeocpy4JjKW0ddD+nnRyq02HRMwhwL9M6jhcTh2Pm5nu+0mUHbVpSBYvInn5MZbh2J+i5O1Ytn9S2zeK9nTZb5n5Lw3lNgcOpXxX2KaKXUX5rvDWSXy/HxcHuFMg2ifbaIudAOwPHEY5OLdl3RvEPwa8Dng8JesVlPtEvwYcmpmXNNT7w8x8SEPsZMrBP8o6T8jMJzXEVlPufbwHJfMULG6ynDXAx6nfL403zkf3SSMat6+K987qPMx2XE05W7GadSdVuG9V7sQRb0fX2Ey0cxFlu/bTOPZN10md9snMr9TFhjGqBNZSEkOLqXOYdg5KbjDiZNIo2toh2dI1odI58UW5PLBTIqarYfdLVWYcyb2xJSG79P04EpssYfsiIprKRrcZcaeun7qsr8u299Q90oT/MuybpSZvJ3aigDEkp9veEwO2o/N+W8rxNqu8B3dCFr7Y1IWApzTEA3h6lPte62J7AMdRBpePzXVvED+QMsPbi4HnZuatVWxD4I+BL0fEYQ317hgRD2+I/S7w/BHXuVdE/FFDbBfgedkwWU5EvKOh3HaUexzq9st3IqJpApOtIuJVDbHNW9q5XUO5hfjWwH4dtmNLYM/+xEFE7AKcEREfayi3Tct2tG1jW2ybiHjPlLSzrS2bDeiLtnq79tM49s1mwK79f4Dijsl0mmaL/XBEPITmxFdbgme9BFZELCmBRZnwqmnw35bcaowB50XEfw/bzra2UO6VrU1ERERjIoKSgGhMqCyhrasjYr1kS0RcT0uyBfhGx+1o2zdtsUtZPxHzWOBNEdEpETPguPgm5bvLsPtl0HZ0jY3juOna9211Tnz7IuIgygRrdWU/DTyTIY+b5diOMayv07ZHxF9QvsMMe1y0tXNQW8exb/6Hbp8ZXbe/6/Z1/Sxt275PAX/Uoc5B9bbtt7cCz+pQrvXxUtPOAe7krKV8Eai7AfyeNN8cvg3lmvze2ILNgJ2z/gbxt0TEGzPz2L7YrcAxEfFJyjN06zJGm7Wsc6Mx1LlZS7kN+gcb1TpPjYjtgc2BWxrKNe2XN7WU26QltmlLO+8CvAn4p4ay0XE74I57JnpdTtlvTROxvKylzrZtbIvduWV9k25nW1ueQ3tftNXbtZ/GsW+geUKNe0bEZ2tiCwPqtsTXCVEuZ6or2zWBtUtL0uAeLcmtVW3JtLYEB+W+obp2ntKSbGhLbnRORAxIqHRta1uy5cvV5+2w23Fq9WWnrlzXxNdmdEvEfDQintJQZ9tx8duUe83q9stREXF0w/qWso3jOG7akntd+34cic2lJOjeDTyhoey5wLYNx815EbHVlGxH133atr62bW97z7wHeHSHBOymAz6jxrFvWpPQdPvM6Lr9XbdvHMnpK4HdG+r8YkQ0XX21lP32Kppnn24r9wbqZ7qfCQ5wJ+ciyqQZdTeA3wyNM6LeTHncx+qa2Brg0oh4DfU3iF8bEe+jzETcO9HMAZSzm2/PzLNq6n1eyzp/PYY6b2op96xong35KuC/MvOMmnKHteyXn7eUe21L7G9b2vmEqn1NZf+m43a8EjgtIo7pK7c/5TExTTNTf7DjNrbFDmtZ3zsn3M62thxOmSm7S71d+2kc+6ZtdvGgPEal/xKwoFz61Jb4ejOjT2DdlebB/91b1rdVS+xuLXVu0NLOT7SUa0tuQLdExKCESte2tiVb2mbCbtuOrVvW1zXxBd0SMdvR7bjYsGW/3JXuya2lbP+ok3td+34ciU3o/r7YqKVs0HzcbNWxrZPup67ra9v2tvfMRl0T/rS/L9rauhnd9s2gpO8ktx+6J5lHnZyOljrvBLwQeHWHetv224Yt5e4dET9oKLdtQxtmgvfgTki03wB+HPCPDbEPA2/LzPWemxUR+1Gelblwg/jCwXgl5Qbxf6ZcCtE70cxllOdgnkt5VmzdoPo1wPEN63wW5UvSKOs8DPh4Q7lVNetbmCznQuCazPxpTbndgBdRv18+BVzUUO5RwPkNsacBZ7a08xct7dkWeHjLdlybmWsbym3RUO5Kmifv2b2lLW3b2BbbHVhTt74q/oAxtHN1w34Z1Jau9Xbtp0HteSBlcLjofVOV25z6Wck/QflcOLmmzDcoj+g4ifoEz/8DHtaUwAI+Qn0Ca3/Kc3nrEjw3Up7nWzf4vxl4YMP62mJtdba189nAvg3l2uq8npJwqEtE3B14fkMi4mLKfVEvH3Fbf0G5RK4u2dI2E/a1lKuF6rZjC8qMzsPum7ZY28ym21XbWJeIOQl4QIfjom2/XEx5/zb1RddtHMdxcwPwuyPu+7Y6J719FwNHUPq/ruwFlL6qO25uA/afku3ouk/b1te27W3vmS8CJ1N/XDwceGpDW9ZUr296X7S1teu+uYHmWdLbZkIfx/Z33b62z9K2cm3b9z3K/Bt1dR5XveZ1I95vxwM/bSi3FfD7NDy2KDPv2d+OWeEAV5ImJFomGWqLdVjP5jTPjHgO8O0RJ7CeCXy9IaHwIkpyr259bwWObIi9BDiuoc4dgT9saOc3KA+2HzaB0ymZVJVtS6h0bWtbsqVxJuyqbFOy6daWdrYlfgYlhW5i+ETM2cAzOhwX+1HObjQ9cm8cyb1B2z/qJGSnvl9CYm/k29dTvja5l5lntyTwthmwHdPST4PWt2WHbR+UvHxzwza0Jfy3pZxxHEcivWvSdxzbP9Ltq8qONDmd5dGXbe+JLbrUy+D99vSGcm+n+fFRn8jMP61rxyxwgDsFImIfSlal7g/2OS3lXpiZR0XEvsB+fWWPz8wvtZR9fWa+sUOs8abzZaizdfZpypna/Rhuv7TV+SHKvdLPoNw3fXudwIcz8+aWeo+iDDDqJv15H+V5gftR/rAvdkKgThP0DNpvHWNd29IWW9hn+zGi/bKIehf6YpT9tDBx0+Moz50L7ph5/D2U59qNbFZySZo2o0zgzZqVvO3zYhzJ6UHlRn3cRIeZ52daZvqzzD+USwPOpJxxeV71c+jCspZyPwbeRblsY3/g96qf/atl724rOyOxyyiZt/6fLSmDy6bYL1r2ywc61vlrygO496LMerdj9f/3A8c2lFsoewNwCLBdz7ZtV/XzTxtihwDfpmTy+38eQZm4rCl2ZcdtbItd1bEtXdvZtM8G7ZcrOvbFIS19sZR+ugn4E8q9gwvlNqQcj79oiZ1HucWg/+eZwNqW98wPB3zevLBj7PUdY0e0rY+SVX4/JSv+2er/TxywDW11dm1LW+yELrEltvUo4C2UM+7XUh6Xdk61bLNRt7VnfefWrG+nllhbW1qPxUkeF0vs/7Z9M46+GMdxOo7+bd2+qt5jKInA1ZRLc6+ulu3c5biZsn5qW9+DR73tSzm2KTMPj3rffGUMdbb1/cI21H0mjuMYPrmlDx9V/bt2yP49t+246HnPDFtv237bg3LlxzlVn51UteNU4OEt5TbtcixOy49ncCck6m/+hnLG5inAnbPvDGBEbEz5ArzeZVpVud2ASzNzt5r1BeVm+181lN20qrsp9sOG2IMHlBtHnRdX/1+Q1e87t8Uyc70JSqr9ctso66zqPR/YleaZstvK3pSZGzfEkvIhW1d27wGxTts4INa1LZ3a2bLP2vbLXsDGjL4vuvbT72fmBh3rPJr6ySH2B55bVwz4QGZuXVdnVe84nj18GfB/Gtrz/czcsaHczymPM/sod0y8sSPlnqofA6/tUGdbW34A1J35DspjLerO/AfwJcqAqy72eeBBdW1ZYlsvB/6Och/1ldXrt6PcR/10oGkm7La2fgF44JDrOwD4v5QJaupizwHqrrJpPRbbrkJYynFB/f5ciLf1f1usbd/sx+j7outxOo7+7bp9nwcuoSTh/zPXn0H8DZRHmdWV/SDl+01dbJr6qW19h1Ee0zjstre9Z86mJOvryrXtl+8DP6JcGTTKffMt4PUd6jyJMkdKXaxt+38B/EPN+g4E/obRH8PfpjxVoK4PPwQc1BBr699jKCex6sq9gvL3/l0d6m3bb7+hTGRbN/v0BzPzoQ3lGv/mzwIHuBMSEddRDuqmyTZ2zcxL+8rcm3JfwSoabgCnnFV6UWae1ld2T8oXhHtlzWUIEXELsEND7FbKmae6dV7QUm4cdd5M2Tc/HjJ2E2Xyg7r98t/A/TrUeSOlDz+VmbdVyzagfPC8inKGsGmm7Bu44w9B/6Q/rwHe2hBrmxBo0OQ9XfbboFiXyYK6trNtn7XtlzWUs7Sj7ouu/dQ2ydBTKV8E62L70zypU9vg91mUpEGdoAzG1quzii0lgdWUqLh3y/oeVDf4X2QiauRJMbolaQYmVLq2tWOCp62te3dc3zgSMcdSBmt1sa7HxQ6UKyDa+mLUyc3lSEJOun+7vi8uy8z7tdTbdNwc2LYdbbE5f8/8Z1tbWmI7ABdn5u4t7emyb9qSt4Pq7PSZkZm1T38Z0zE8juT0AS11rgbo+J7put9+Splhu67c32bmFnXlZoGPCZqcU4FfZ+bX+wMRcQ7w1erg7p3h7L6Uwe+mmXlmTblTKDeIvz8i7sYdme57Ue71+yjly2XddfbfbYmd1bLOcydc58mUqd/XG6hQni3ZFHsn8N6G/fLOjnW+iTJ4eF+VsAjKxA1fo3y4PLml7Ospl8d+PSL6J/1ZBfxFQ+xllCn+6/xzS+zIjtvYFvtox7Z0bWfbPmvbLy+n/EFfal/0z77dFmtrzwsoEze9gfUnGXoNJUNcF3sWZcbEOufS/siqbSlZ6bqE0oVVm5qSTfdrSWA1xW6Gxked3dqyvtUR8cj+RBTwSMql3U11dm3LoFjTo8zaYoMSKl3bekM0P+rsVx3b2nV9v2iL0XwsHkjLM84Z/XExqC+6HhuT7oulHKej7t+u27eG8qzQro8VnIV+altf18c0HkjzeyZb2tLWzkGPlOy6b7ru77ZHQ7ZtPxM+htsei3llx/79k5Zy/wvcNobj5rZofvzhZrQ/XmpmOcCdkGyZ9CYzHxLlTOCerDsh0mlZXaLQUG5hdrPfjnIpRu/N41cOaM+jWmK1lytUsQdPuM4/aIk9uyV2CECH/dJW5xsW/h/lWYRk5jU9L/nXlrJvq/57SMNLDmmJNdXZ+PrMfHFLrG0b22J1lxQtpi1d2zlonzWV+68B8aX0RZd++s/qv+9veMn7W2JNDqZ58PsM4C9pTihd0hLrmsBqS0R9t2V9J9EtEXVSx7aMI4EzKKHSta1dEzzjSCi1JXf2p1si5o8Z/XHxNsoZ3lEn9ybdF12P03H0b9ftezllzosXMXwC759btmOa+ql3ff377aGUORNGmbzcr6UtbfvlbcDHKHNJjHLfvIhyS8Cwx83f0W3796F5f4/jGB5HcvpxwMP6yi3MzPxhygC1/z2zEF9K0vu9rD+R7b9Sjpn/yvrHKx3UsK6Z4CXKUy4iNs3M/suaB8aq+P0z89wRx9ra01on5V7i/kH8d7PlIBxQ5z6Z+ZUOsc51Uj/b9fEL9VXb2R9f1GzYE4x12cbl2Ia2dg6ss2tfAD+hYfbtaJmxvC3Wsr6uM483xmbRsIkozYaIeDRlnoi6s0qrMvP0AeU9LrSiLPU9M+tW+vZ31XW/xYDHleUMz7TsAHcKRPtkG50mfVlK2THFrqZknlZTvvhDmTTkvsBLMvPEKWlnW+w67njod+/EJ/tXy5Iy4UptPDPfMiXb0XkbZ2QbfkzJTHbpi5/TPLnN9pQZmoeNrc7Mvx71NrbEWge/XZNmS0iK7UM5iznS5BblzPAT++r8cjY8rqmn3MQSOItp66STYpSJU0aa+GkpN+hYDBqOi4i4Bx36tykpNig+bcm9rm1hDP076sRetc6uyb2J9xMt+3SSic0q3vh50eWzZGEbl7BvOh03LW0ZtP2djsUpSk4fTvmusN76WPcMbl38I5RLlZ/BkI+qbNmOxsc/zjoHuBMSEX/UFKJca//6htjfA69riP0t5bKTpnoPpsxGOGzsz2mepfIfKVPOD1vnSyiTCV2yTiBiF+AM6rcjKJecnNAQexLlEqi62BOpv+xzKXU+hebZrn9E+WB6UEO8bTbstkl/usYeSJn9sS7WdRvHsQ1t7XxKxzp3owzSO/VFNk9uc2PWTCqxiNhSZjNvim2SzZNGTFviaxzJrWsozw0+sa/OfYA3ZOZHG8pNNIGziLaeDuw+qfYsYfvbEj9dEzhHA79L/XHxacrjsIbt36Uc+9OU3OvalnH078gTe4vY/mnqp7b1/ZLy2TapxOaJlGew17XlCkp/DLVfltiersdN133zbcp9qKM8TiednP5Vta6j+9Z3AGWi0tsofy/q4k+mfHeri20JfJX6we+nqb/HNmif5X+mB78OcCckyo3sH6d5xsB/oP4APLwl9krKjJGvBm6siR9FGcwNG/vIgPa8uEOdR1IGTuvUWQ04bhjQzj+kfvbprw2IjbrOttmuT6T07b4N8bbZsC+keYbprrGLxrCN49iGtnZ+tWOd36J86ejSF6uBR2X97Ntfp8xqPGys62zmbbHbWH+fLWzDJpT7g+oMSpp1TWC1JY2eAtxnxMmtpwBb9J/Ni4jNKWd3vtZSbtQJnN2AL9fEFtPWq4C7Tjgp1mn7WxI/bQmctkTMzZTJty7pW74L5X6ybTv07+Monxu1q2S2kntd2zLq/u2a2DufMtldnUEJvLtSZpevi026n9r26S/HkNgc9J75raa20PxZ8iPgN3V1ssR90/G46fyZkZl36rC+aUpOb5rtj5skax79WcXbZm7+OWWMUTf4fTHlkV29683q9x2A7Rra2jj4nQVOMjU5P6D5BvA/pfkm79e2xA6ifCE/KzO/VRP/QMfYB1vWeVjHOt8JnBYRx7DuLG77U6a2byr3fppnn76+JfbzMdTZNtv1y6rfu8yGfckYYleOYRvHsQ1t7Vzbsc5TKBnSLtvRNrnNQR1jH6XbxE1tsV/SPDPvGsqM3/9EfZJqE5pnTbwz5ctMXWLopS2xDSmD37pExZO4Y5/0upwyg2PXOmtni6RsX1u5e1LOyPTavlpf06y+bTNPfwt4NM2PgWtrK2NoT1tsdcf1tc1qfCvNx+Jt1WfxeiHK94+m4yLo1r970t4XT2kpO45joy120RjaMpb+7Ri7gXIm6pEtCbzG44bp6ae2fXpbx/dF67YPeM80tSXb2kn3mfVb903X44aO2z+G47RrO9v6sC12Y5QJ9uoeN3kdkC3xtrJ3ysy/7FvdZcCpEfEimmfYTsqVRHWD3236Xz9LHOBOzitonv3sT1n/Q2nBY1tiq4CbKX9I6uzQMfZQ4NqW2JqGWGOdmblFRDyQMo3571SLL6c8t+vKlnK/1bAusv35XPcZdZ25iNmuI2K3tnhDvbuOIXbPltiStnGC7azLKi6mzj+Fzn3xpKps0+Q2H+8Ya1pf28zjjTHgPTQPfj9B+ZLfJWnWNYHVljRaw+iTWxcC34tyyV5vAmMfytmKSSZwTqEkDLq09V/G0J622Dc7rq8t8dOWwGlLxFxP83FxPN369zzKGZdZT+51bcs4+rdrYu9AyuWSXRJ4lzA9/dS2T1/N6BObbe+ZtS1teWNL7GWUQdCo903X46brZ8aVHdc3TcnpI1n3cZNQEr0nUz77EnhrQ/xJlNnJ62Lntgx+L6d5hu21tD9eamZ5ibImLiK2AMjM9QbRbbFpFoNntO46sc/IY12Ns51RpvXvHRi2zty3yDoXzujUTWDTGGtZ5zhmJR95rIrvDlybmWtrYo+i3FtUF9sdWJOZv66JbQHcUBcbpCe5tc7kJ1TJrY51bk45I9Fb55czs/9MQ3+5oRM4S9XW1km3ZynrGzaBExH/QJnk5rs1sbdSLqdb77jIzLO79u9SLMexMeq2jKt/u8bGYdreM6Pc/kW8Zw5rastyHb+jPDYGbX9mHjKO43TSx3C1zrrHTS4q3h+LiJ0pA+PHccfZ9s0og99DM/PihnW8FPhmZn6/JvbyzPyXoTZqijjAnZCI2IgyM9ozGGL2s2i5ybstVsVPyIbn7w6IfYVyb9x+lEsUEri6autbsmEWywF1nkzJZj2OkhkL4O6Ue6neA/wV8HjKJU29sUOz7x6tnjrbZp9ui7W1s2ud0zaxT1ts5Nu4hNgVlCsU7sG6k8lcT5mA6Htd1kfJyr6P+gls/q2Kj3LSo2mKtQ5+l9Ook1ttiZFxJE2GjS22rZNqT0RsSjnDueyJn77XTTSxOS19MY7kHlPSv4tIwg0sOy39RMs+bWnLuPZb58Rt17JTdtw0zq4+K7GWbVvSLPALsUED52HWOcu8RHly/p3ypf1w1r8B/Lgo18j3C+ApC3/8a2JPjoiHN6wvgFUN8UGxx1Au9dl7IYtVZbcOAE6oMj7D1vm7lIdiP7cn67kh5fKJL1MGHLWxKJdN1tW5U9TPTh3Aji1t2aulXFudu0TEqxpimzbEFuJbtZQdR2zzMWzjONq5NbBfZn5nnUDEXsDnIuIdHercFHg38IT+5EjcMYHN7g2xMyKiadKjbSPiPVMS26xm+YITI+IhlEz/fqyfpHofZVbzuljXBFZb7FzgTHqSWxHRmNzqibUlt1YD11ASI5dxx3v+ekrf/zV9SZMq1pg0Ac6mXNI3ytigtk66PRfRMKN1RDQmfiKiMfFDmWCvaX2NsYh4LGXyk7rjYuSJzSre2xfLfWysjoj1kntLbMvU9O+A2KD4yWPYN+N4z0x0v1XfEw9raEtr/1a/1yZ9B5SdmuMmIg6hTHbYO7v6Y4E3Rblc+gkzEGucBZ7ymKC290xb/PZY/8B2wCC2sc5ZH/w6wJ2cR+T6M6Mt3ACelHtb627yviftN4CfRpnBtTe+YCvg7R1id8rMt/YuqAa6b42It3Ssc6PMPLavzluBYyLioy2xT1IuYavLFN69JbZZS1s261jnXWmenGcDuk/sM47Ypox+G8fRzugf3AJk5qkRsX3HOjdg8AQ2XSY9aptkadKxgwYMfo+jDBTqklTfoxyno0xgtSWNdgX+jtEmt3YBnteQGDm52rZRJk1aEyoN276Yto6jPW2xLYE9J5j42axm+YIvUSavGWVic7sZ6otxJPcm3b+tfd8QW0zZbRj9vhnHe2Ys+61m+YIP0D7zeFP/Lswc3iXpO/Hjpmb5gn8Atu5PwsYdM9JvMwOxNRHxrJptC2DLiPhs7ZZX44GG+MJx2uT4atA9bLlBA+6p5gB3cq6NlpnRaL7J++aW2BpK5vvFmbm6oWyX2A0R8Rrg6IVLgqJcKnQg5VKdLnX+OiLeR7nnqndCkQOAK1ti19E8+/RzW2LPa2nLTR3rbJvt+iBKFrjLxD7jiP3tGLZxHO38m4j4AmUyh96+fwHlj0TX2cWPpHkCm6+1xNomPeo6K/k4YkfRPPh9DrBzS5Lq78eQwNqM5qTJhmNIbm3QkhjZcAxJk0EJlWNpfgxcW1vH0Z62GEw28dOWiGlLenZNbN6F2emLcST3YHoSe88BXkjzYwzbyjJF/QST3W9t75kNWtrS1r93orwfupSd9HEzaPvbZqSfhdgmjGcW+H1aBr9t62wr1zb4nXregzshsf4N4AuZqq9RZoU8Putv8j4O+MeG2MspD6j+YWaeVxN/K3Bkh9hzgf8DPJ07nmN3JWVSmHOAb3eo81mUjPXTueOehMuAz1EGN89viJ0LXNAwwD8IOLEh9hrKPq1ry2HAxzvU+Uzg65n505rYtpT+vKYh/ijg/AnGngacOeJtHEc7twUezrp9vzAB0YV0myhp28y8KiIeUFdvlglsamO0THoULZMsLUPsa8DrGga/F1MuJzuJ+iTVayifRXWx/wc8rCWB9cCWpNHDG5Imv6Y8W7sugfVUyvMu62L7U54tXFfnL4BvUJ8Y2ZHy+VEXezjw1IbEyI2UZyAPG1tDucT7gA5tHUd72mLXU5I4dcmdqyif93WxuwPPbzjefgM8viF2G83PIz+CcoZg2L5vO9ZmqS/G0ZbrmWz/tsUurtrS9DnVVvZnlOeHT0M/XU/zPh3Hfmt7z7yP8nzhYfv3uOr3Z3coO+njpm3730t5f9fNrv41yvfraY/9CvjrzDy5Ztu/UcXf1hC/FnhmQ+xmym1HdYPYLwNP7FDu2Mxsepb11HOAuwxiiBvAJalf2+C3im8OHEp9kuoDlC8Qo0xgtSWN9gIeRn1CoS65tRBrS26tYv2E2UIC44sR8aSGOi9k9EmobYHdgEuHbeuY2tOa/AG2qGvLmBI/gxIxb2tYX9fE5irK2Ypp6ouJJveYbP8O+hxaStm293CXfdM11rhPGU9CdNB75ik0zzxeO1t9Zp5dlW+Md4kt0/Y/nOYZ6TefhVj/do1CRJxA88D4G5n5mFGWmwUOcCcoIu7P+h+Sx2eZLbAu9tnMPKctNmB9L8zMo7rEgJ9Qsjr9bf1SROzbJdbSztdn5htHHQO+06Eth1POij+DEc12PSi+DLHDGfE2LiF2FCVDvDDYWuxkSJ0mSqrW2XWypJmISdNi0CBG0roW+56JJcxI37XsJGJ+ZmhUHOBOSJTZ355Ducyjdxbl/SmDje07xI7JzLe0rLPrY0Z+zh2XB/Wu8wVVW67oEFudmX894nZ23Ya2tvyqKnM06892vR3lUU/rFQO+T7msu7Za4AdA3eye44p9PzN3rG1M920cRzsvp0xAdHSuO+HRgcDfUCZD6o8dAPzfltjjKWcvazefMrnNvjMe+3xmbl8Tu33w2zURNYYE1uGUz4X1ylEuXT5gQGwaEjGtMUqyZWKPgVtk0mg/JjNLdtfYwqPz9mO4vm+Lfbja3mnpi6lJ7k1bEm5A2a9QHlU4yn0zD/t0J8pVD4+j+XGLdbFDKfd/dim7mFjdIx67xm6fQX3QQL1v33R9/OFMxJZatkuds84B7oRExPnAg/r/uEbExpRr3+/aIfYj4DdNqwQeRLmRf+hYZm5Qsw0B3JiZG3eI3UK5t6BufZsCvxh1LDPXmwxnMW2pK1eVTcq9OL3xhRmtdwA2pDzTtS6+c0vZccWG2jeL2MaxtLOlLTfVHU+LiJ1Heabt1/vWuWBvyky7sxz7HeBRNcuDck/rsZTLZkeZpOqawGpLqDyZMsPnsLFJJ2Jak0nAf1O+qE1DW5uSRguJhKZZsiediPkJZQAxyuNiC8oX+esb4tN03Iwjubcfk+3f1r6nXE5bZ1DZbwGvZ7T7pmtsP6bnPXMd5faS/8z1Zx7/EGVG+rrYKyh/c9/VoeykY6+h3GdcN/g9GVjvMvLqNUdSJjWb5dgHKI9OqzOwbGZuXVuwPHLrlR3KzfTg1wHuhER5FuS+mXlp3/J7U97Mu3WInUiZ2GhfysRV67yEcq/KIzrEVlMmXDitb517UgYNj+kQ+yZwr6x5UHtE3ALsMOLYTcDvdmjLjZQZ7Opmuz6a0hd193atAW6gTJxQF78Z2HWCsbZ903Ubx9HOG7jji8wwkyG1xfahfJF9Rg4/WdKsxJLmwe9ewJpc/7FkS01SdU5gjSGJMfFETEtsB+CSuv29XG0dkMCalkTMdzKz9kkOSzguzgeYpr5oi43pfTEtSbi9gI3plmj8/bokOyxp38zKPh3He2Y1QGber0PZScd+Q/k7Xjf4/QQlWVo3cDmQ8v1llmPPoswE3zQLfFvZ/YHn1iwP4D87lmsc/M6EzPRnAj/AE4ELgBMos0ceQcniXQD8bcfYEymXZP1ewzov7Bg7gXL/6tmUQfSJlMlnTqW8EbrE/o3yLLm69X1rDLGPdGzL+ylnv9ZSkgvnUy5VOpYyEHtoQ7mXU7K8TfHjJhz70hi2cRztXBiongtcW/2cUy3bpWNsC8ofit0b1vnWOYhdSnkeYl1sDeXM0SNrYntSrvoYdewmYNuG9txI+XKyQc+yDYA/oVxl0CV2A7BTw/punnBsDeVzZVraekP1vtq2Z9m2wCGUK4KajpubxxBL7jjz0v9z2xiOi+9MWV907adrOsYm3b9tsTWUq8S6lB3HvpmVfdr2nrmFcjn1b1Muv79n9f/3AT9uiR1Huc2tS9lJx35Rt196josHN8RumoPYGsql+V3KJuV771E1P7d2LNfYF7Pws+wNWEk/lD+yewHPrH72ojwjsnNszO3djnKW9xHAdqOILcM+79wWyjPAtlzu42bM+2fut3Eef2gfwO9HmWly1EmqrgmstoTK73WMTToR0xZ7OeVMXW9bV1f/n7ak0YEtx82kEzE/adln/X2/2NguU9YXk07uTbp/22L70T3R+Nyebbyu+qnb/knEJr1PByUv/5KSwP5h9XMC5f7hTVtid6acTe9SdtKx42ge/H6N5oTRQXMQW0V5Dm6XsufQPIi9qmO5NXXLZ+XHS5QnqLqUb0/WnVDju5mZXWPjrLdhG+6fmefOQoxyv+ATWX+a9usj4h5Nsbr6qjr3ycyvDBtbStmlxIDvMqFtrNa3hhHOBB5LmAW8KTaueqcp1ve67ejZ51ndXzau2CLa0/iItK6xaTNLbR23KM8/b3pO+36Z+V/V/8dyXNgXmjWLfc/Mqyjzy7yIdb8vXAZ8jjJJ3I3L1bZpFhGPpuXxaJl5+ijLzQIHuBMSEX9AyUCtpny5hzLxxX0pZz8O6hB7SfX7yOvNzBMbtmMcMx6PI3YNZYKCE1l3+/YBTgKe0BB7Q2Z+dJRtWeJ2jGP7R76NUSa/uJQRzgQ+jv0yrnqnLPbCzDyqLYkz6VhdO6u2TjzxM65YQxJnSY+B6xqra2fV1qlKxHTYZwNjHesdW1/Mej8tV6KRMTyqcFZiTfusTXR8pOJSyk461maa2jmubZ/0fpt1DnAnJCLOAZ6U1fTnPct3oVwWs3uH2BerX0dd7xnAx+o2AzgY+OAMxF5CufT2+nUC5eHbVwHbNMTWUC6DqavzSdyxz/tjjwO+WhNbTNlxxJ4CbDHibRy0vjvn8DOB/wJYL1Nd1fkgus0CvltDnUutd5piu2XmnWtiRMSPgddRZmgdNsEzjtg4EipTFQP+ldE/Bq5rbKJJoyXEjqO8V0e67ZR7yqalL+ahn5Yj0TiORxXOSmw15e/sfgz3OLaVkPRtG+BNUzunLTnfdb/N9ODXAe6ERJnF7gGZeUvf8oUBwG91iJ1N+WM+6npvoExFX3cpyFEzEjuSMsD7We/CKGeZ1gJbN8SuBZ5G2T/rhCmDwj9siB1LucfleR3KjiN2EmWAP8ptHLS+XXP4mcAvpNx3cl1NnV1nAf8W5ZFNo55dfJpiqyn3zvRbGOBfAvx2hwTPOGLjSKhMU+xxlC+go34MXNfYOJJG40jE3Ez5GzTKbf8R5W/itPTFrPTTWPqeJSQac/SPKpyV2LXAt6kf/D6e+u87QftjEzcBfl0TW0zZScc2yeaZom9j/e8fy9XOsWw73fup635rK9c64J52tRulsTgSOC0ijqF84QO4FyWT+7WOsQ9Xv4+63ouBszLzW/0bEREfmJHY24HvRcSJPdu3E+WM0tEtsR8Bv87Mr9fUeX1L7DzKY1K6lB1H7MIxbGNb7Bzgq1Uip3d99wXe2BI7ifIYmTNr6rykY+wUyoy/o653mmK3UL7wNA3wg/pHAtxW/TvJ2CaUKy3qEiNPmYPYnpTtvCflMv1e21P2yyRjG9J8bFw44djqiPgB64uqnaPe9tuWUHYl99M4Yt+izETclmhsO24emX2P+QMeCdw657Hfyswn9y0nIo6lzKJ7v2x+bGJTbA3lWHxkh7KTjt0W5Qz+eqHqZ1raOY7YUvqp635rK7dJzfKZ4QB3QjLzzRFxPOXM2e9Uiy8HnpuZZ0fEA7vEAEZdL3Al5SxunR1mIZaZW1dnj/bljst8TgEOy8zr2mIN6yIzt2iJPaYptoiy44jtPoZtbIs9JMqzdPsnLTstM2+NiDc3xVrq3LVj7E+bYkusd2pilOcBtg3wv0y3BM84YuNIqExT7DzgTXRL8IwjNo6kUddYWyLmjDFs+8uq36elL2aln8YRO4XuicaTgPdGxN244yzmvYCfUeYOmefYj1sGv9cB96ZcMdPvuy2xT1AeTdSl7KRjv6R5MPazKWrnOGJL6aeu+62t3Jqa188ML1FeBhGxBUBmXjuq2LjqnYfYShYR27LujLdXjTPW0IZNM7Pu8piJx6atPePaxuo1/QmOhUmf6hI8Y421tXNeDEjwTDQ2vq0cTkR8GDgqM79ZE/sE5ZaOkW/7NPXFLPTTtIoJzwI/DbGIeDjl0Wp1g9+XZuYZ6++p+RER/0CZhO27NbG3ZuYhy9Csqdd1v83z/naAOyERsRPwNsr9Wj+jZLDvTrlU+D3AX3WIHUq5JGvU9S7EHk+ZiXcWY4dm3wRaPX3xw8x8yKRiy7HO6ozBNcA9KH8kg3Ifz/XAu4G/rmK9EwItJfaSzPxeQ1vmZaKGWYndPviddIJjJceaTFPyY5piVXzhEu/ewd9UPjrP2GT7ggYxZY8jHFesbWA8yvWNeztG3dYm09TOcW37pPfbrPMS5ck5FngX5fLghSzzhsAfUy4nPKhDbGHGyFHXOxexiDisph8C2Cki/mjEse0aYuNcZ1tsF+B5mfmddQIRewEnA3uPOPa5iHhHQ1u2iohXTTC2aUNsudozlm2sWb7g7Ih4GvAB+hIcUS61fRfwignGahMjcxRrTO5UzqZclmqsR3WM/jM1j6qLiMbH2A2ItT46bwn1GptsXzQ+qpAyU3vT8TY3sWpAu86gdsBgpOv6ltzWScXGtP2zEutctut+m/XBrwPcydkqM4/tXVANyo6JiI92jP199fuo652H2Ccp9xfXZYLvPobYXShJjI9PcJ1tsQ36B6IAmXlqRGw4htj2wObALf1xykQFk4xtQLkn8p+mpD1jiQ0Y/H4EePEEExwrOXZURBxNvWlLjExTIuZTlHu/Llmn0GgenfeEEddrbLJ9cUZEfIz1BbBtRLxnjmOb1SxfcFpEHNWlzobYUts6K9s/K7Gl9NNmDeWgfb+1lRs04J5qXqI8IVFmK76WMhlL76zFBwBPBT7fIbYV5RLlUdc7D7H9gcdk5ln0iYibgIePOLYGuBo4YILrbIv9AvgG5VEDvfvmBZQs+WUjjj0ceGrW3B8UETcCj5pgbE3VvpdPSXvGEUvgH6gf/L4SWJuZ96uJERE3Zc0jKox1jl1AeW80JVQOp7mv5j12KPC3NcuDcmvNnXM2Hp1nbLJ9MQ+PKuwa+wDwoZrlQZlE7cUd6nwHcGfg1SNu66xs/6zEltJPXfdbW7kDMvPuNbGZ4AB3QqoP7RcBT2fd+00+SxkwPL9D7MOUPyCjrnceYucCF2Tmj+kTEQcBJ444topyxu3SCa5zUHu2pmbfZOYXI+JJo4xRHvlwTWb+tKYtjwLOn2BsW0pWclraM47YacBftAzwPwPsyuQSHCs5djElwTPPCZVxJGIOpXxOH8P6j6q7ivKImWFjx1W/P3vE9RqbbF/cHXh+1j8C8DfA4+c4dhvNg5gPUa4kGbbOiymfU6+bkm2c9PbPSmwp/dR1v7WVe0dmblWzfCY4wJWkGRMRu9M8gN82M68adRLDWGtsd+DazFxb0x+PAlav0NigRMwfUL9Pz46IB3SJVXV3KmtsOvqC6lGFmflr+kR5YsI8x75G8wDnUsoZ8aHqnMJtnOj2z0psifV23W9t5S7OzF3q2jkLHOBOSERsRDnTuh/rfpgfT7lf7oAOsd4zuKOsd55izwDuOYFYb19Map2t7cnMm6kREUdk5sErMTZt7RnXNkrTYMDAf9tcxCzU0koyaAA071b69nfVdb/N9f7OTH8m8AN8kvJss70ol7vtWP3//cClHWPHjqleY7PfF58Btqj52ZIyCJ7n2GUNsWlsa+dtbPmsOYIy2+9bgHMo9+hfU/3/LZRJI4yNLrZZz/4+t6XsSoxt1vHv5Qmjjo2rXmP2xST320rui3nYhuXop651zvqPsyhPziMyc7e+ZZcBp0aZwOQvO8TOBxhDvcaGj01bXyTwUCB6Yln9fk/g9DmObQOspSQApqE9Y9nGKvPaL4AnA/emPA/6sVk9PzHKcxUPBL5HmRDJ2Ghix1X98jXKfU698QN6yq7E2Kcj4ruUq2y2qfbT1ZSrTL5ImWioXwCrIuLhHWJ7NMSWWq+x4WP2Rcf9VrO8BCP+B3h5lzpnqC/2aGjnUrZ/VmJL6ac9GsoN2m9t5U7IzCc1xaedlyhPSEScSpkh7VOZeVu1bAPKM1v/DfizDrFXUb4wjLpeY7PfF0cDu2X9BFQ3A7vOcWwNZRbOx09Je8YRS8pkFHWD3x2AizNz9/5yVdlpmoF4HmLnAbi/a2O/At4IHF0z+H0L5fFLUVN0746xvYCNga+PuF5j9sWkYr8DPKpmeVASnvPeF+PY/lmJLaWfuu63tnKfz8zta2KzYamngP1Z3A+wM+Uy1quB86ufq6tlv9cxtsuY6jU2+33xeuChDcficXMeeznw0ilqzzhiVwM7NcTWUJ5f9xpg257l2wKHUC4hNTa62Enu78bYr+qO0eo1N1Keg1sXu7ljbA1w1hjqNWZfTCqWlKtBTq75uW0F9MU4tn9WYkvpp677ra3cb+rKzMqPlyhPSGZeEhGHA/9L34RImXlORFzTIXYxwBjqNTYffXH/iDikL/bZzHz2nMf+peqLaWnPyGPAG4DNgfXO7lKeL/oxymNYvh4R21TLr6rKPgL4S2Mji/1x9bv7e/3YdyLiNZQzuFcBRHmM14HAecAG1PvnjrGXAxuNoV5jw8fsi26xHwMvzszV/YGI+GnHOmepL8ax/bMSW0o/dd1vbeXW1Lx+ZjjAnZDqS+r+lGe+fadavCPwyYi4AtiuQ+wYSvZl1PUam/2++AnlHs5jgO+usNgxlIzln05Je0Yeq5bd2DbAp5xBO4Q+EfHCzDQ22thRuL/rYi+lTEJVN/j9/cy8rr9M5dzMPK9DbPOqL5p0rdfY8DH7olvs8zQPRg5aAX0xju2fldhS+qnrfmsrV3ff7uzoctrXn+F/KJeP3qlm+cbATR1jq8dUrzH7YpZjK6EvfgqcSTlr+Lzq59CFZf1l+sr/2NhkYtPWnimLvdC+WJmxaWvPDMVG/p6Zwm2cms+MaYot035rLDcLP57BnZzbKGdjLu1bvj3lzF+X2G1LKGvMvpjX2Eroi7sDj8y+Zx1HxD8DP4qIP6VeADtGxA+MjSy2bUNsGts68X1Ts3zBByPilaNen30xNTH7YnreM7PUF5P+zJim2FL6qet+ayv3BuColvhUcxblCYmIJwLvpZxdWriufSfgvpQD6IUdYi+rfh91vcbsi1mOrYS+uDPwe5m5zuA3Iu5NmfBoM2Bf4DrWFcCFlPsmjY0m9i1gQ9zfdbHVlGfi9gvgwcDDRrw++2J6YvbF9LxnZqkvJv2ZMU2xpfRT1/3WVm63zLxzTWwmeAZ3QjLzSxGxG7An694zd1pm3hoRb+4SAxhHvcbsi1mOzXtfAPsAX42IpgH+HwObZuaZ9ImIS4yNNHYK8Jspas80xW4BXkD9F6sfjWF9p2BfTEvsFOyLaXnPnMLs9MWkPzOmKXYK3fup635rK/et/tfPEs/gStIMivLM48YBvrTcIuLDwFGZ+c2a2Ccys+lSemlFWunvmZW+/V113W/zvL8d4EqSJEmS5kLT1NCSJEmSJM0UB7iSJEmSpLngAFeSpCFExOERkRHROFFjROxdvWbvnmWviIg/6rC+Pap1bjFEmfXWL0nSSuAAV5Kk0fse8DvVvwteAQw9wAX2AP4OWPQAt2H9kiTNPR8TJEnSiGXmz4FTJ73eiNiQMoHksqxfkqTl5hlcSZK6eUBEnBwRv46IKyLijdXjm9a7RLh6huG9gedWyzMiPlLFdouIz0TE1RFxQ0T8OCL+IyI2iogDgaOq9a3uKbtzVTYj4h8j4tCIuBi4CXhIwyXSp0TENyPiCRHxvardZ0XEM/o3LCKeExHnVu35YUQ8rSp/Ss9rNo2If6nae2PV/pMi4v4j3cuSJA3BM7iSJHXzX8CRwJuBfYH/B9wGHF7z2mcAXwS+3xNfW/37BeA64C+Bn1KebfxkShL6C8A/AK8D/hi4rCpzRU/dBwIXAX8D/Ar4CXCPhjbvCry7avNPgVcD/xER98/MCwAiYh/g48BngVcBWwPvAu4CnN9T1zuBpwGvBVYDWwK/C2zWsG5JksbOAa4kSd18KDPfUv3/xIi4O/DqiHhX/wsz838j4kbgp5l5+6XDEbEVcF/g6Zn52Z4in6j+XRsRF1b/P3NhENongD/IzN/01PuAhjZvBTwmM1dXr/seZbD8bOBN1WveAJwNPCMzs3rdWcDprDvA/R3g45n54Z5ln2lYryRJE+ElypIkdXNc3+/HAJsCDx6ijmsoZ1/fEhF/HhH369COL/UObgdYvTC4BcjMq4GrgZ3g9nt4VwGfWhjcVq87A7i4r67TgAMj4rURsaoqK0nSsnKAK0lSN1c1/L7DYiuoBpH7UM6Ovhk4PyIuioi/HKIdVwx+ye2urVl2I+XyYyhneO9EGfT269/elwMfBP6MMti9OiLeGRG/NUR7JEkaKQe4kiR1s23D75cPU0lmXpSZL6Dc6/ow4GvA+yLiSYutYpj1DfBT4GZgm5rYOtubmb/MzMMy877AzpRLnF9GeaSRJEnLwgGuJEndPLvv9/2BXwI/bHj9jcAmTZVlcSZlYie441LnG6t/G8uOSmbeSjmb/MyIiIXlEfEIYJeWcpdm5jso2z7MJdqSJI2Uk0xJktTNn1ePBTqNMovyQcDhmfmznrFhr7OBR0fEU4ErKWdL706Z1fhY4AJgQ8qsyLdQzuQulAN4aUQcTTnD+oPMvGkcG0U5A3si8JmIOIJy2fLhVZtvW3hRRHybMtPyDykD+98HHgocPaZ2SZI0kGdwJUnq5umU+2c/CzyP8jifv295/WHAeZTJqU7jjkHjjylnbT8LfBK4J/DUamInMnPh0UJ/CHyzKnvPUW/Mgsz8CvBc4AGUWZEPoTxO6ErgZz0v/QblLPbHKY8zehbwysx897jaJknSINEzSaIkSdJ6ImJHyhnmf8zMtkG8JEnLygGuJEm6XURsAvwzcBLlMur7AK+hTDL1oMwcZtZmSZImyntwJUlSr1uB7YD3AlsCvwL+G/hjB7eSpGnnGVxJkiRJ0lxwkilJkiRJ0lxwgCtJkiRJmgsOcCVJkiRJc8EBriRJkiRpLjjAlSRJkiTNhf8Pb5YGnjNq4JcAAAAASUVORK5CYII=\n", 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PzPzRYrclSZIkSVo5hnkO7rkR8cCG2AMiYrGXKH8KeNLAsn2Bb2bmvYBvVr8D7Arcq3rtBXxksfWVJEmSJK0sw0wytQNw64bYHwB3W8xKMvM7wJUDi58OHFz9fDCw27zlh2RxArBFRGw3RJ0lSZIkSSvEMAkuQDYsXw1cvYR6rMrMi6ufLwFWVT/fGVg7730XVsvWExF7RcRJEXHSunXrllANSZIkSdK0ar0HNyL+Hvj76tcEvhQR1w+8bTNgK+CwUVQoMzMimhLppjIHAAcArF69eqiykiRJkqTZsNAkU+dS7okF2AM4CRg8RXodcBrwiSXU49KI2C4zL64uQb6sWn4RcNd577tLtUySJEmSpPW0JriZeSRwJECZ0Ji3ZuZ5Y6jHFykJ9P7Vv0fOW753RBwG/DHwy3mXMkuSJEmSdLNhHhP0olFsMCI+C+wCbBMRFwJvoiS2R0TEi4ELgOdUb/8q5RFBZ1MeEzSSOkiSJEmSZs+iE1yAiLg7JfncnjJz8nyZmS9eaB2Z+dyG0GNr3pvAK4epoyRJkiRpZVp0ghsRuwFHUGZevoxy7+18Tu4kSZIkSVo2w5zB/RfgWOB5memzeCRJkiRJE2WYBPfuwOtMbiVJkiRJk2ijId57BrD1uCoiSZIkSdJSDJPgvh54QzXRlCRJkiRJE2WYS5TfTDmDe3pErAGuHIhnZv75qComSZIkSdIwhklwbwTOHFdFJEmSJElaikUnuJm5yxjrIUmSJEnSkgxzD64kSZIkSRNr0WdwI+JRC70nM7+ztOpIkiRJktTNMPfgHgvkAu/ZuHtVJEmSJEnqbpgE99E1y7YGngr8ObD3SGokSZIkSVIHw0wydVxD6PMR8T7gL4CjRlIrSZIkSZKGNKpJpr4CPGdE65IkSZIkaWijSnDvDdw0onVJkiRJkjS0YWZRfmHN4k2BBwAvBj4/qkpJkiRJkjSsYSaZ+lTD8uuAw4FXL7k2kiRJkiR1NEyCu2PNsmsz89JRVUaSJEmSpK6GmUX5gnFWRJIkSZKkpRjmDC4AETH33NutgCuBYzPzK6OumCRJkiRJwxhmkqnbAl8GHgncAFwBbA28NiK+Czw1M38zllpKkiRJkrSAYR4T9HbgwcALgM0ycztgM+CF1fK3j756kiRJkiQtzjAJ7rOAf87MQzPzRoDMvDEzDwX+bxWXJEmSJGlZDJPgbg2c1hA7rYpLkiRJkrQshklwzwOe2hB7chWXJEmSJGlZDDOL8seA90TE5sChwMXAHYHdgZcArx199SRJkiRJWpxhnoP7vojYlpLI7lktDuB6YP/M/PfRV0+SJEmSpMUZ6jm4mfmGiPg34OHc8hzcEzLzqnFUTpIkSZKkxRrmObj7AHfJzFcBRw3EPgCszcx/61KJiLg3cPi8RXcH3ghsAfwtsK5a/obM/GqXbUiSJEmSZtswk0y9CPhJQ+zHVbyTzDwzM3fOzJ2BhwDXAF+owu+bi5ncSpIkSZKaDJPgbg+saYidA9xt6dUB4LHAOZl5wYjWJ0mSJElaAYZJcK8B7twQuwtw3dKrA5RZmT877/e9I+InEXFgRGw5om1IkiRJkmbMMAnud4F/jIhbz19Y/f66Kr4kEbEp8DTgP6tFHwHuAexMeSzRexrK7RURJ0XESevWrat7iyRJkiRpxg0zi/Kbge8BZ0XEp4GLKGd0nw9szS2PDlqKXYEfZealAHP/AkTEx4Ev1xXKzAOAAwBWr16dI6iHJEmSJGnKDPMc3B9HxKOBdwP7UM7+3gQcDzwrM388gvo8l3mXJ0fEdpl5cfXrM4BTR7ANSZIkSdIMGvY5uD8EHhURmwFbAldl5u9GUZGI+EPg8cBL5y1+V0TsDCRw/kBMkiRJkqSbDZXgzqmS2pEktvPW+VvKpc7zl71glNuQJEmSJM2uYSaZkiRJkiRpYpngSpIkSZJmggmuJEmSJGkmmOBKkiRJkmaCCa4kSZIkaSaY4EqSJEmSZoIJriRJkiRpJpjgSpIkSZJmggmuJEmSJGkmmOBKkiRJkmaCCa4kSZIkaSaY4EqSJEmSZoIJriRJkiRpJpjgSpIkSZJmggmuJEmSJGkmmOBKkiRJkmaCCa4kSZIkaSaY4EqSJEmSZoIJriRJkiRpJpjgSpIkSZJmwibLXQFJkiQi6pdn9lsPSdJU8wyuJEmSJGkmmOBKkiRJkmaCCa4kSZIkaSaY4EqSJEmSZoIJriRJkiRpJpjgSpIkSZJmgo8JkqRp4+NUJEmSankGV5IkSZI0EybqDG5EnA/8GrgRuCEzV0fEVsDhwA7A+cBzMvOq5aqjJEmSJGkyTeIZ3Edn5s6Zubr6fV/gm5l5L+Cb1e+SJEmSJK1nEhPcQU8HDq5+PhjYbfmqIkmSJEmaVJOW4CZwdEScHBF7VctWZebF1c+XAKsGC0XEXhFxUkSctG7dur7qKkmSJEmaIBN1Dy7wZ5l5UUTcAfhGRJwxP5iZGREbTBOamQcABwCsXr3aaUQlSZIkaQWaqDO4mXlR9e9lwBeAhwGXRsR2ANW/ly1fDSVJkiRJk2piEtyI+MOIuO3cz8ATgFOBLwJ7VG/bAzhyeWooSZIkSZpkk3SJ8irgCxEBpV6fycyvRcSJwBER8WLgAuA5y1hHSZIkSdKEmpgENzPPBR5Ys/wK4LH910iSJEmSNE0m5hJlSZIkSZKWwgRXkiRJkjQTTHAlSZIkSTPBBFeSJEmSNBNMcCVJkiRJM8EEV5IkSZI0E0xwJUmSJEkzwQRXkiRJkjQTTHAlSZIkSTPBBFeSJEmSNBNMcCVJkiRJM8EEV5IkSZI0E0xwJUmSJEkzwQRXkiRJkjQTTHAlSZIkSTPBBFeSJEmSNBNMcCVJkiRJM8EEV5IkSZI0E0xwJUmSJEkzwQRXkiRJkjQTTHAlSZIkSTPBBFeSJEmSNBNMcCVJkiRJM8EEV5IkSZI0E0xwJUmSJEkzwQRXkiRJkjQTTHAlSZIkSTPBBFeSJEmSNBMmIsGNiLtGxLcj4rSI+FlEvLpa/uaIuCgiTqleT17uukqSJEmSJtMmy12Byg3A6zLzRxFxW+DkiPhGFXtfZr57GesmSZIkSZoCE5HgZubFwMXVz7+OiNOBOy9vrSRJkiRJ02QiLlGeLyJ2AB4E/KBatHdE/CQiDoyILRvK7BURJ0XESevWreurqpIkSZKkCTJRCW5EbA58DnhNZv4K+AhwD2Bnyhne99SVy8wDMnN1Zq7edttt+6quJEmSJGmCTEyCGxG3oiS3h2bm5wEy89LMvDEzbwI+DjxsOesoSZIkSZpcE5HgRkQAnwROz8z3zlu+3by3PQM4te+6zbqIqH1JkiRJ0rSZiEmmgD8FXgD8NCJOqZa9AXhuROwMJHA+8NLlqJwkSZIkafJNRIKbmccDdacNv9p3XSRJkiRJ02kiLlGWJEmSJGmpJuIMriRp9jTdz5+ZPddEkiStFJ7BlSRJkiTNBBNcSZIkSdJM8BJlSZpAXt4rSZI0PM/gSpIkSZJmggmuJEmSJGkmeImyJEmaaF6yL0laLM/gSpIkSZJmgmdwJUkzzzOAkiStDCa4Uk/8gi1JkiSNl5coS5IkSZJmgmdwJY2EZ6il5ePnT5IWUPd30r+RM8kzuJIkSZKkmWCCK0mSJEmaCSa4kiRJkqSZ4D24mmnelyZJkiStHJ7BlSRJkiTNBM/gSpIkaSLVXYnlVViS2pjgStISdb0U3kvo1Qf7WbO25MnEStLY+MiisfISZUmSJEnSTPAMrjSjJumszSTVpW8rue2S1udZ4cmwoo+DZw61ApjgSpo5JpX1pmW/TEs9JUnqk/8/Lo6XKEuSJEmSZoJncKeIozaToe04eIxml8d28nmMJEmSCa565RdQSdKozfo9lbPePu8LXZkmqV9Pel1gxj7zY+YlypIkSZKkmeAZXEmqzMKo6Sy0QRqlSTozMy3cZ9It/DxMn6k4gxsRT4qIMyPi7IjYd7nrs1JERO1LkpbLLPxdmoU2rHTTcvympZ7jsJLbPg5t+7NrbBzbU4OI+hez+X/SxCe4EbEx8P+AXYH7Ac+NiPstb60kSZIkSZNmGi5RfhhwdmaeCxARhwFPB05b1lotwThm4e17Zt9Z2N60zIY8SW2fpGM7jravZH3vz0k6DuPog9Pyd6nvtk/ScR+XrpczzkK5rrGu+l7nLMTazMKluH23oe8+P2xdxr3NSTUNCe6dgbXzfr8Q+OP5b4iIvYC9ql9/ExFn9lS3UdgGuBxqO6axEpyYuth2Y5Mc67u/2D+N9RGzn63M2IQf95vjsxCr2dcTWc9Zb3tbXWAiPmeT5m6Nkcyc6BfwbOAT835/AfCh5a7XCNt3kjFjxowZM2bMmLHpiE1afYwZ6yM2Ta+JvwcXuAi467zf71ItkyRJkiTpZtOQ4J4I3CsidoyITYHdgS8uc50kSZIkSRNm4u/BzcwbImJv4OvAxsCBmfmzZa7WKB1gzJgxY8aMGTNmbGpiy7FNY8aWOzY1orreWpIkSZKkqTYNlyhLkiRJkrQgE1xJkiRJ0kwwwZUkSZIkzQQTXEmSJEnSTJj4WZRnTUSsAu5c/XpRZl66mFjDuu6TmWd0KQdcDDxpfjng65l59ULb67LOiLh9h+29KDMP6lKXltiLgO8DTx+oyxcz8/S2ugCfH3EbXpSZB0XEE4HdBtZ7ZGZ+rWW9ndvfVh86thH4RYc2tB5fuvXR1rq09cOOx2Gh/dlpvyzQhqHrKUmStFI4i3JPImJn4KPA7SlfSAHuAlwNvB94TUPsFZn5o4Z1Xgxc0KHcFdV7jh4o93jgLZl5SEO5n2fm9h3WeQzwuBFvr2vsKso+Owy4cF5ddgcOy8z9O7SvcxsoCeVOwCED9XkhsCYzX91lvR1jXdv4K+D4EbdhHHXZGLhnwzrPBm4ccRu67pe2cttREv9h13lAZu7VEFvKYELXAZGRDxYx+kGWsayz7wGRpnJdLWFw7fHADxnDIGGXGP0e286Djk2xpbajS7k2SxkAb6vnOI79tMTodmzHsa8fn5nf6LENI19nm0n6uzyuAe6VODBugtuTiDgFeGlm/mBg+cOBbwO7NMS+BHy2bpXAK4BHdCy39eAfu4jYEjgX+I+GcnsBH+uwzkuBOzTELgHObFjnfYEPd6xL3bOSA7g/cOvM/P1AXTYFft1Sl/sDW3Vow/2BUxtiOwEXZOZOGwQjAriKksjUlV1K+5vq07mNmbnB7Q5VG65tKdd2fNv6U9e6XEdzP7w0Mzft0IbW/dlxv7S2oaWeZwMPbVjnjzPzLjWxpQwm9F1uHIMJfa+z7wGRroMeP83MPxpx+8YySNjzvh5HXTrFltiOcfSZaTn20xLreowmaV9P0t/lrn2378/YOAa4fwqs7VCucZ9NAxPcnkTEmsy8V0Ps+rovrVXsJuBllC/ngw7MzI27lKMkMr8cKHN7SlLVVO6gjutcB2zbELsCWF1td70wJdnuUpeDgAc3rHMNcM/MvGCgLncDzmmpy9mUhGvYNpwDPKQh9j3gcuDFmXniwHofRrmU+uUtbezS/rb6dG3jGspAS10b/oeSdA17fNv6U9e6fJeS4Nat8zLgzzq0oW1/dt0vbeWOAx7VEPsBcF61jjlZ/b498P/Y0JIGE+g+6DPqwaLOgwl9r3Mc22sp1zbocSZloKwu9h/AJxpibYNrLwOOaog9hTEc966xMRzbhQbsugw67tRQl5vL9txnzqf8/1IX6zoA3vuxn5ZYyzFqG/zuuq/vB3ylIfaUMbRhHH+X2/ruxUDdFQbL8Xe57wHuSzLzVh3KNQ6MTwPvwe3PURHxFcofpbXVsrtSRlBOb4n9Ajg1M783uMKI+EDHcu8GfhQRR88rtz1lhO+MlnIf7bjOg1ti3wM2z8xTatZ5Wce6fKhlnccD34yINQN1uSflUuqmcid2bMP5LbFjgXcDH4mI23LLyNpdgV8CPxpD+9vq07WNxwAfamjDV1rKtR3ftv7UtS5vb1nn2zu24fwx7Je2ci9pia2lXAny85p13kT5D7xuMAFKIjzoJspl3S+keUCkz3LntMTWRMRDBxN/yn/cG03QOsexvbZydwdOon7QY2vgadQfi81o7i+vbIltTElyflNTz10btrWU4973vm5b57k075fouM7vAauAJ464HV37zOYtbWzrF3PrGLQcx35aYm3H6LaMfl+fS/Nn98ljaMM4/i639d1b9dyGcZS7sWPbN+5Y7g5MMc/g9igidqV+cqOvNsWAE4BrM/OaYda5iHJbUv7TXO8eDUqnri0XEVt1WWdmXtUWq1vXQttbqC5tImIj4GEDdTkxM29coNzQbRiiTnecv97MvGRc7V+gHp3bWNeGBd7fuT91rctC6xy2DYvRdZ1t5Rr6yyuB4zPzxzXrOgvYs2EwYR3wK8olbYOJ/0XAGzLz+Jpy36f8B9hXuXOAPRpiRwFbUb74DSb+lwDvnpB1jmN7beXuRLkyoW7Q43rgwZm5wZmLiLgWeExDf/kd8NiG2DXAUzLz2zWxMymD6qM87n3v67Z1XgI8s2G//BrYtcM6PwP8DjhoQvpM137R9W/MOI79tMTajtHGwKtHvK9/ATyv4bN7CfDsCfmcde27vwGeNCF/l7uW+yDwdx3afgll7plhy63NzLsOLp8WJrgrQJVIkJlXLuc6Y/jZnjfPzMHRxCXVpW2di4l1aQPwWzZMqH+YC3z4omXSjq7G1caWdbZNPLLg8W2qS0QEQ+7TWMKs413asNB+oVyiNHQb6DaByFgGE/out5BJGqDoe3sdBj3eD7y34YvNY4AT6vrLEgcXxzZI2MUoj+24Bh0Xue2++sw+wAe79ItJO/bTYtjB76rMRO3rvv4uL9B3X5WZHxzl9parXNe2973PlpsJ7gSIiIMoEzE9nXI5UlLuCTwS2L/pi2tEfAM4uaHcp4F/Bh5LmXAggNsB3wL2zczzG9bZNrnIt6t6DrvONZT7Jm9PGT0KFjfbc9uN/13r0nWyhPkzVg/Thssoo6lrWH/Ch3tW5Y6uK7eI+hyVmbt2iI2jjeOYnOJ4ytmeurocAryWIffphLWvU7+I7hOINM6C2dU4BkS6llvKIAsdBqDGNOjReUAkOjyGbbmMeACtNcYyHds+Bl0XUXbZ+kxT+6fg2I9lnW3l2gZrF3OMRr2v+xrEX8rf5VH33aW0gY6fsa6D9HQY4K7KTs3/EaPiPbg9mfsjVBcCngu8CXh03nIp5R2BPSn37r6yodyjKPeN1pU7Hngp5ZKTG6vYxsBfAl+PiP0a1nmXiHhwQ+xPgRd0WOeOwPOzYbbniHhPQ7nbd6zLsRHxgYZy20TEazvEtgV269CGrYGHDSbcEbEj8NVqkKJOAFu1tH91277puY13aNnfd2jZXtvx/WPKZTN1dTkWuE/DPj05Ij7dVJeW9n09IppmHW9rQ9v+bNsvbf2irQ1bAPcY/E8pyoj9D6ifeATgk5TL1DZcafuASOOAF+VeoQ0GDCLialoGDIDTWurStr3GcgvEjm6JnUvDQENEtA1Ata2zaz3bYt9pqefngWex/qDHo4G3R0TboMcTgR2AZ1AuU6MqfyTwyRyYaX5eua6Da7UDnUvpLwvE+j62Z0bEd5k36BoRCw66LrDOtthC8XH0mbbHxWww6Dyv/YcAb2Tyj/041tlW7hOUeRWGPUYfBB7OaPf1/L/nN9dlEeXG8TlrW+fI++4S2tD1M/Y54Jkdtvc/bDjAvZi2vxN4dlM5yuPb6m6fPL2hHlPBBLc/6yhnj+pu5L51Zr5z/purhHX/iHgHZSKi+eXm3Kql3Fsz8/CB2I3AYdUX+abJRbZo2d4mHde50WBSUZU9ISK2A7YEbqgpd7uOdflMyzo36xiLjm2AW+55mO8iyqQHLwJeR/1kEXvT3P5tWmK3banPONp4a5onvNi7pVzb8d24pS4b0bxPt2ipCy3r3LpjG9r2Z9t+gY5toHkCkTtFxBdrYgFs2zKY8PCIeGZDbMcxDIhs2XF7XQcT2gYoug40dB30GMeAyBnAqg6DHp+vYm9m/UdH7AF8uWXAsm1wrbUv0W2gs+v+7PvY3gH4AqMddN28ITa/rn32mUMi4ikN22sbdP5Pyr27k3DsT6i+8Pe1zoWOw707HKNfUE5kjHJfj2MQv+vnbBx994iI+JcRt6FrPS+h+biPY4D7tZQnmdSVO4vyveMwSqIL5f+Bz0bEYZm5f8M6J54Jbn/OpUzAUDtxQ0S8Hjg4b7nHcBXlD9hvKc/PXTNkuSsj4sOUGYznz7C8B2Xmtndn/eQiz2/Z3jUd1/nsaJ7t+VLgvzPz5Jpy/9yxLle2rPMNHWP/0LENfw+cGBGHDZTbnXJW7fE0zyT88Zb2/74ldl3PbdyvpQ0faynXdnx/3VKXk2nep+e11KXrrONtbWjbn2375X0d29A2u3TQPAvmU2keTNiC5sGpP2T0AyKbd9xe18GEtgEK6DbQ0HXQYxwDIkG3QY/NMvPlNds4ISKSMgA37ODaFox+oLPr/oR+j+1GYxh03Ygyw/u/NcT77jN3pNug8yETdOy37XmdbeVigVjjbMhj2NfjGMTfgtH/Xe7ad7fouL0tGP1nrO24L7S9Lm3fuKXc7YE7DV6xExHvpTzabGoTXO/B7Um038j9espI0NO5ZVruSymzIZ8OfD8zz6wp9zzg/zSUey/lEoj5lx1cCHyJMrJ0dkOy/XrgyIbtPZvyh3zYda6uKXdRVc9zgCsy8/Kaci+m7LNh6/IdynO/6tb5COCsDrFVlGfLNrXhysxc11Buq7pymXlatM+U/Gzgpw3tfyflOch1sVcAR/TYxnsDaxvacG+6Hd/dKH/gm2Ydv29DPS9hxLOOL9CGR1AelD7Ufqni96MkA8O2YUvqZ0D/DPCurJ8F87fAzg2DCW2z6V5HeSZvXQL/a8pnrW7A4MHAUxvKdd1eW6xtdtdrgT9tKHc1ZUChbqDhdsALOqxzHG24knIFUF09z6bs77pBjzsCz6F+0ONo4HnA5zLzpmo7G1HOAh0EPLBlcO1+HfpS1/7SdX9eTb/H9hrgU9QPuj4HeGKHda6t1vWqCekzxwD3bRp0bmn/4ylniibh2G9Fme26r3W2lbuUMn/KsMfocspA7yj3ddfPZ1v7un7O2ta5lL93fzLiNnT9jP2IMt/GsNtbR/MM2W1tP5LSZ+rK3QZ4eGZeMLCtuwFHZ+a9B+sxLUxwJWkZRcuENG2xIbfRNliyH3Bow+DUs4DjOg6INA0KPA04pcP22gZnug6yNA5A0TLQsIRBj5EPiFQDZV0GPX4AnA88hnIFztxZjm8BxwHf7DC41taXug50LmVQchzHtml7dwH+omF7Cw26NvWJVZRjMvTgYhUfdZ85DXhGh0HnT1L62aiPfdt+azr2Ny6wva7r7DrI3eUY/RZ4cUNduu7rkQ/is/DnrG2dc8/pHlXfPRF48ijbUJUd+vhleXRmnwPc36naVlfuj4EPUe4Hnp/83hPYOzO/VlePaWCCOwGifeKGF2XmQSOOvTEz39ohNo51HpCZe3WIda1L1+21zXT9YeAVwG6UM+mLnQW7cUKWheJLOPYT08aIOAD4XLXO+X90jwS+D+zXUJeu2/sGzbOOd13n3D7bjeH2y9yELI+hPIsuuGUW8A9QnnU3shnQNTtGNegxsM6tq3VeMap1anKMo89otDxG9aZlv3StZx8D3PPW1/TYxY3YcEbnE7O6v3tqZaavZX4BP5/x2IWU0bHB19aUD1JT7MKet9cWuxbYB7jjvO3cEdiXculHXWwfSqL24JrXQyjTvdfFbo733P5xtPGSlu39Gvgq5bKcP6teu1fLLmipS9v21rXEruu4zrY2NO2zhfbL9cBfUe6lmiu3cdX+X7fEzqTcejD4ehawrqVP/LQl9qKW2BtbYgeMITaO7R01hnJtsYMo9y2dQZkP4ArKrSb7U0bGm2JbtKzz25TL2S6jjLSfXf18GLBDl+Nexe9T9dUPVK99KDOUt5V5EeVMwEcoZxy+WP38pAXK9d2XxnFs29b5ccqTC44CflK9jgJeRpkQskt/2YJyj9wk9ZlJOfat+63j8e26zm/0fYw69rXF7M/Ta9rQ9e9W275u22cPGHXfXcLnqK0NXY/fGS3lHlH9u26Ebd+ZchvW6dV+P6aqwwmUW0qaym3e9rme9JdncHsS9Td/Qzk78xTKTeV1sft3jD2A8kW5Lrb5ArGfjmGd51U/z8nq9x1aYneje/u6bK81lpl1E2sQEddn5qYNsaT8Eawr+3BgU8rlgHXxP6f7sZ+UNu4y7PYiIoDrlrC9ptifZ+ZGfbRhEetsq8tC+/pg6ieM2J1yT+UGxYCPZua2Detse17vhZT7/OvW+ROg7ozxQrEfZ+ZdRry904C6s+wBfAW4X4dyX6N8mR92nRdRHvt2cK7/+LY9gH+kTBhUF9sNaHok3PcpM9T+V244a+pbKFc71JVrO+4XU64iOIz1Z1HeHWicPTMifkV5DN0hA+VeSLm889UN5cbRl7r2ia7Htm17aymfzYPZcFbqO1IuKa0r19ZfHkv5rH+rIb4b/faZyykzrU7CsW/bb7vRvF+6fnbb1vk9yqN5+jpGh1IS0mH7Wpf9uSfwD3T7u9X2OWvbZ/tREvhR9t1fA/864jZ0PX6HAc9vKPdxyqOjRtn23wG7ZP0M2R/LzAc2lGv8bjANTHB7EhFXUTp03Q3g36Sc1bmqJnZOx9jZwJ2z5iHdEXFDS+zGMazz95SpzX8+ZKxrXbpury12Lbf8MR6csfr1wDsbYv8XeFDWT8qxlnJ56jMa4m3tX+jY331C2ti2vespk0ycOLD8YZTPxL903F7TBDht7evahq7rbJuQ5anAlxtiuwOPyvpJfNqS3xdSZkTcoBjLMFg0pu11GqBYoNyoBz26DhQtNDjTZdDjcMpMyoOzZ25KOT4b3GdZlbt/XV2qgakbKPcH1pUbV1/q9di2xRYY7OpS7kyAbJjoZTn6TGZu8PSN5Tr2HQcXdxnDOrsOnnY9RnssUG6SBrgnZZC3tu/OW+c4Bs27HL++2345Zab2unL/lJlb1ZWbBj4mqD8nANdk5nGDgSgzo22emafUxM7vGDuDcgZ0gwSQMgLbFDt1DOv8NmV69g0SBMpzA5tiP+xYl67ba4u9kXJJ6nERMThj9WrKpUF1sb0pj3qo8yrKZ7Ap/i26Hfv/bWlH3208sGV77wM+FBG35ZZR6LtSkv6/oJx9GXZ7722JvZhyFmGUbZi/z1ZVyy5ZxDpfSJmQ5S1sOCHL6ymjwnWxZ1NmUaxzBs2P6nphtc2mAZF7tQwW7dIyWNIldsOYttf2SK2u5bqss+3xbb9uibU9Eq7rI9r2pPmxPQHciXI7wHzbUS6Jb+ovayLioYMDU8BDKZPt9NmX+j62bbHrIuIvqZ+V+rqWcm39ZS1w0yT1mQk69l0frziOdXYt1/UY/XXHvtb33622z9k4HnO5J819lzG0oevx+6uWcpeMoe03RfMjEreg/TFlU8szuJKWVZTLhOZPfHDJctZnWkXEI4ELGr68HAn8W2YeXxM7FfibzPxhTexo4B+z/vFmRwBv6xD7GuWevFFu75OUGSTrZnf9GPDhDuXaZgtuW2fbY98+ShkoejplojO4ZUCk7ZFwXR/Rdjrwlw1fiNZRBpPqZs88G/jXhv5yFOUe9LqBqVMp9/n11Ze69omux7Zte2+iXPr6GDaclfpnlMfvDdtf3kn5wrovk9FnLqEMiEzCse/6eMWun93FPrKxj2P0F5QrAofta1335/y/W8Psl7bPWds+6/qYy7a+eyHl0u5hj1FbG7oev4cDD6J+1uZDKAPcdbGubV8L7NWwzn+m+TFkazPzroPLp4UJ7oSLiM0zc/Cy5ptjlJGnwdnPfpgtBzYi7pOZZ3SItdWl6zrbZpBui21OOcvwJDacgv3qujJz66R8mdvgg56Zp0fEfZpiLesc+UzXiyjb9dh33d/jmM378ZSz9MMew3HUpTVGudemqc88kZqZoDPza22xlvZ1nZG8MaaVaYFBj9WU5zF2mj3TgalmMcWzUi/UZzLzJI/95JjmvjZqi+m7y1CtXnRteyzwGL2suSJjWpjgToBoebxHtE8AcxnlcsU1lC8mUCYauCfwisw8usM6pyV2BeURKkezftsfD7wlMw9pKHcVtzyYe3BilYspl+cNO+nKyNu3iLKzcOy7HsO+69nWZ35DOQ51k65sR+lTw07IMo42tCXGXQeuug6WdB3YaB2cainXOsjSdVCHlkGPtroAv2ACBkSqeNAwSLZA7PY0HL+2WEs9ltKXRt0nFhqUG3aA9MjMPGMpg6eTMohWxXs99ozh+NLxs7vAOnv9XANH1LRhwb42KYP4C+2zlrospe92+hyN4fi9mfLdYIN1Uubm2GOB2DMot5fMj30yB+ZTWIxoeXTktDPB7UlEPLMpRLnW/o0NsX+hXEJQF3sXZfKb8we2tSPleZ+fbii3F/CxhtjfAm9oiL0NOKDDOl9GmcK+LrYr5bEwdbEn0Dxj3LuArQf/44zyEOy1lEt16so9Bbj14B+CKBOr/Ab4w4ZY66QrdJvteKeGdc7F70u5H7Mu1nbsf0K5D7muXNv+fkpLXbu28X6UWSubtrdVwzG8hNHv76XEGvtM1kwIUSUHtTNBV7GFJmTpMlv5Ztk8kcQsDGy0DTT0OgC1hLr8iuYZiPseEDkY+FPqB8k+QZnJsy72ecpjqeqO3zHA4xpi4xi06rtPdB0g7Tx4StnfOzEZfeZzlEeO9HXsx3F8x7HOvj/XvffDMfwNbdtn4+i736dczj3sMRrH8ftttb6DB9a5B/Bkyne0YWNbUybnrEt+P0/9PbZB+1MNpjr5NcHtSZQb1Q+l/gbwPSnTl9d1wDe3xN5I+eK9Xqz64n0tJbG8rqbcQS2xTy1Ql5d2WOdBlEmD6maQ/lZL7NstdXkjJTn65XqFygjylZSb7evWeQwlMbxgoNzdgLOAnRpi51AmWqqbdKXrTNffo1xm/cSG+Hl0O/a/o7n9bft7HLN5n9uyvWMogxR1x/AKRr+/u8bWAPds6BdnAo/M+pmgj6PMeFwXOx64aw4/I3lb7CY23M9zbbgN5RE1dbG2gauug1MLDaQ0DWx0HZxqG4BaaJBl1IMeXWcgHseASNugx+8pkwKdP7B8R8q9Xvduia1qOH6XAnfocGyX0pc6HQe6D8p1GSDtOni6E+Wyw502CC5fn9m2x2M/juPb+bPbts6eP9ebA5v22A/HMnDcss/G0ncz81YN22s7RmM5ftltxuq22K8oOUZd8vtS4Pxq23Oy+v3OlEdL1dWzMfmdBs6i3J+f0DIjHvDfWX+T9xtaYn8PnBgRh7H+zGi7U5KjUzPzezXlPtoS+1jL9vbruM6P0DyD9NUtsV+11OXvgB9Fmbxi/gQpj6dMstC0ztOBb0ZE3cQqb22JHcPoZ7o+lpKMNsUvb2l/27E/u6X9V7fE1o2hjZe0bO8cmo/h98ZQl66x42nuF6+jeSbol7TEDqHbjORtsd/QPItp0jxT4q0pX0LqBqc2plyZUTdA8ZSOsV1pmOkR2GyBcl1m/T23ZZ1Pbil3TktsTce6tM5A3Baj+di2zUp9U/V3dIMQ5f//C2tiF1XxtljT8aMl1nZsl9KXuhyHtmPb1l/atpcdY231/B5w7YT1mT6P/TiOb9fPbus6ez5Gv29pwzj6YVvbu8a6zsbeue92PUZjqGfrjOsdY7fKzJcPbOpC4ISIeDHNs2cncBL1ye8dBt8/TUxw+/Mamh/v8dds+AdnzqNbYvemzGb5dOBPqmUXUZ6FdQnlLG6dO7fEHkg5A9oUW9sQa1xnZt6moQzZ/oyth7XU5X7A9ZSzn3P3KRwL7JeZg39M52/vj6o/CLUTq0TEO5piLeu8R8fYXzfFKn9Gc/sbj31mntayzcb9nZl1o3hzsa5tvFNL7N7V6P6wx7BrXbrGHt3WZ4D/F82TrhzaEmva3iO6xIAP0Jz8/oJuA1ddB6faYm0DG10Hp9oGoNoGWboO6rQNerTV5Rj6HRBpG/S4muZBsm+1xI6k+fgd3BJrO7ZXd4x17RPnt8Ta+kvXAdKug6fHAu8GPjIhfeYK+j324zi+XT+7bevs+3P9hZY2jKMftrW9a6xtn42j717Ssr22YzSO43cg5ZF/H45yuTmUWbC/TRnUeVWH2Bktye9FND/qcB3NyW/T9/2p4CXKUiUWmLG6z9hi4n0aVxur96xi/QSwdda+5ThO9Dtb+Thi9wauzMx1DbG1mXlNU1tGrWZgY24ymsaBjapc20BDr5ZSl7ZBj2EHRBbYzr9SJpCpe3TLOylJydPYcMKZ0yLifi2xxuPX9dh21XefWGCAtFNskdudlD6zPz0e+3Ec33H1mb6OUbW+ZemHo9Zn383Mfboeo1Efv3nrbZwFe5hYROxAeazY3KOj4Jbkd9/MPK9h+68Ejs/6R0e9KjM/OHSjJoQJbk8iYhPgxQw5+1m03OS9QOyozNy1Q+wblAmqdqNcnpDAZVU998+G2RCXsL22GaR/Rnme2LB1aVtn1xmre41V8bWUeyp2Y3THYpL2zRrKvba3p4yMBuWekaspM0H/qMe6zPqM1Y3J73JpG9joa9BjKeukZdAjonkG4pbt9TroMfC+rQAyc4MrRtpifep7IKzL9ujYJ7r0l2qby9Jnun52J+Ez3/dxGtcxotwvO9I29B2ra9tS90tb342OM4CPI9ZSx84zys/F2hLjYdY57bxEuT//Qfni/mY2vAH8iCjXyA8K4ClzXzBqYk+Pcu9rXWx1RDy4Q+xRlEtVdpkboapGrvYAjqpGe4Zd58OjfhbpALZvid2LMhJVV5djI6JuCvaF1rljRLy2IbZNz7HNG2Jz8TvS3P62Y9F1f49j32zZtj3g+Zn5g/UC5SHoX4qI94y4Ll1jWwMPy4bZyiPi0w3lVkXEB3qMbVGzfM43ImJSBktqBzaiXKr478Crq9jNgwlVrHHQAziNcqndULG2ei6wznNpGPSIiMYZiCOicUCEMvts0/ZGHouIR1MmH3kM5XK7iIjbUS5P/gDwdw2xfQc/C/PW2bVPLLa/jL1PUO4NvKDD9rr2ia79BfrvM7sB+1L/2X0/5VasYT/XvX7m6f84jeMYfWcMbeg71us+i4h9KE/7mD/L96OBt0e5XPpxPcYaZxUHPtnSvkXFBhPbBZLYxnVOe/Jrgtufh+SGMyHO3QCelPtb627yvhPtN4C/eyA2Z5uOsVtl5jvnL6iSq3dGxP4d17kF5VK3uhG727XENlmgLl3W+Yc0T7azWc+xjYC3A//WFO94LLZgcvbN5i3b22gwuQXIzBMiYrsx1KVrDJon3NmC5gmaXtlz7CUtye929DtY0jqQQvPAxrerOo5y0KN1kGUMgx5tMxD3PSCyRc3yOV+jTADzvKwuT4yIjSn3bH2d8sW0Nhblvu267XXtE137yzgGwrYFduuwva59oi321ShXVdVZjj5zBGXW+GE/u11j4zi+4zhOfX+ux9GGvmPj2Gdb1Cyf8690mwF8HLG1EfHshjbcKSK+2CG2dc3yOUdWCfew5doS6onnJco9iYgTgPdQfwP4wZTH09Td5P17ymNtmmL3y8w1I4xdS3kEzcFzlwhFuXRoT+D/Ag/qsM7rgQdn/QzSbbG2uvwT8IgO67yuKlc32U7fsbWUiR5e1RDveiy67u9xtLFte7+mjEQfwvoT2bwQeDDw1Ak5TldTZiWvm3DndsALsn6Cpt8Bj+0xdhPNj+r6ZDY/PiEpXzLrBkt2ofx9qvuPYs+OsT2y5vEQVV3aHoNwE+WRRsM+Tu3NC9SzrVxT7I00P6brN8BtGmJdH9/WNfZR4OM1y4NydmzjmthCxyEZfZ9oi7X1l6X0icZj27Jf2rbXtU+0xU4DVlFma5/mPtM1Npbjy+iPU9+f6wPH0Ia+Y+PYZ619l+ZHS66jJL99xa6k26Mc22Jfo5wtrmv7U6vXsOUek5l/WBObCia4PYkNbwCfG236FmWGwSOz/ibvI4C3NcQ+CbwrM8+sib0TOLBD7HnA/6HMzruqWnwJ8EXgdOD7Hda5H3BoQ5L+EuDohtijKfcv1NXlOMrMr8Ou81nAcZl5eU3sEcBZPcZWUfrAFQ3xnSj3bQ97LLru73Hsm6cBpzRsbzXlbMnTGZjIhvJ4gab9spRjsSbrJ1pqi63ilhmrB+t5CXBt1kzQFOXWgj5j3wL+uSH5vYYq0etpsKTrwMZdKGfLRznoMY5BnatpHvS4lPJ5nYQBkbZBjwMoI/QHD9RzD8qXoS83xHanPN95lH1ikgbCum7varr1ibbYEZRZiJs+1333mY9SvrMM+9ntGhvH8b2a0R+nvj/XV1KerjHKNvQdG8c+a+u7H6LcllM3y/e3KN/N+4r9Fnh1Zn67pg1XAs/qEPs95RakuiT268CTOpQ7PDNXDZaZFia4yyCGuAFckhZjgeR3S8q9c30NlrTFGgc2MvOrEbFrXYzugx5tgyxdB3UaBz2yzDJ834Y29D0g0jbocR7wroZ6HgK8oCF2BnD2iPtEp/7CeAbCVlESq6btNc1G3qlPLCI2SQNl51HOhA312e0ao31/j3zAsutxoufPdRVvm+V84mNj2mcL9d0H02EG8HHEBuu3VBFxFOWEV10S+53MfNQoy00DE9weRZn5bvCP45GZeUZD7IuZeXpbrGVbL8rMg7rEKM/M3K2mnl+LiCd2ibXU842Z+damGPCDUa+zJdZ1xuqRx+biwOcY7f5+M3Ax/c3m/XHKPeR9ba8tdhBlRHk3Npxo6cOUL251sXHMHt5rTCvTQl+SpUH2mekSHWdAn8WYfVfzmeD2JMoMbs+lXKoxfxbl3SkJx3YdYodl5v4N2+v6OJFfAcdTRvDnb++FVT0u7hBbk5mvHnFduq7zQsol2BuEgJ8AdTN5jiv244a6zMXPB77LaPf3b6syB7PhbN53pFwSPWw72mJrq231tb222EXAmyiX6Q5OtPSPlMm+6mK7USZ3qlvn1ygjtJMQ+3JmblcTmxulfT/9DZZ8irLvJmFgo+9BlokZ2FggNvfout0YOO7ccvzaYsMe966x5RoIm7vaYSIHwsa13gVitwf2o33fjCO2G8MNSk7McRpT7NuUPvoYqlnOKZf81s6APuGxx1KeMDKK2M0zvLclxjX7cxyzv09MrE3XctPABLcnEXEWcP/B/6jjlhvx/7BD7NeUZ6FtsDng/pTZVoeOZc2EHhERwHVZMxnEImI3UO45qNve5lU7amOZucHEN0tdJ+X+m/nrnZuVeoeeY3cGNqbcS1NbtqX9nfd33TqrsjmGNta2YZzb61iXhSbV+fbAOufsMkGxPwEeUbM8KPcUHkd/gyVPBr7aEOt7YGMcgyynAXVfPqdp0OMXlC/8wx6/vmOTMhC2J/AP9DsQ9mXgKTWxpa63a59ZR5m9f5h9M47YQoOSszBg2Rb7PuUWgv/KDWc5/zhlBvSVGHs9cBb1ye+3KRM/1e3PA4EXTXnso5m5bU2MKI89+/sO5aY6+TXB7UlEnAE8MTMvGFh+N8oHcqcOsXOA1ZRJq9YLV7GHdIitoUzOcOLA9h5G+YL8qA6x44G7Zs2D2yPiBuDODbHrgT8d8ToXmpW6z9haykyCj22It7W/6/6+Dng+o5/NuynW9/baYtfSPCv16ymTwI1y9vC+Y22J+J93HLjqOliy0IDBpAxsLKUukzKw0RZrG/T4QTbPrD2OGXG7xiapv/Q9EPZwYFPK33v7zGhi0zJg2Rar/XsOE7ev+479jvJ/dl3y+xnKgGxd0rMn/c4MP47Y7sDzapYH8F8dyzUmv1MhM3318KLMBnw2cBRl9soDKCN0Z1MeedMl9jXgzxq2d07H2FGU+15Po8z+djRlEpoTKB+CLrFPUJ7ZVre977XEPjWGdR4NPLAhdkTPsVdRRpKb4u8cw/7+CHA4ZSTzrOp1WbXsjWNo45sGtrem+nlc22uLzSWxZ1Cm6b+y2mfvpDxrsym2J+VZfk3HaFJiFwD3aohdDzy0ZvnDgN+1xK4HVjWs8zrKF4eN5i3bCPgryhUUTbFrge0b1vn7McTa6rmUujTt60mKJbecvRh83dTx+PUd67u/XEv5W7Fq3rJVwD7AFS2x34zh+K2lXG01KX3mxo77ZpJi4zhOfceuoVyG/ceUS/rvVP38YeDnKzj267r9Ne9z/YCG2PUzEEvKd+aDal43dizXuD+n4VU7EqfRy3Kf206UL43z72k6MTNvjIh3dIm1bO8eHWO7AnOX89y8vawu8wEO7Rhr2l7dSPFcbM8F6tJlnU9oiT2n59gHm2JVfB/ofCya1vnyuZ9jiNm8l9DGt/S8vbbYu6of92l4yz4tsaZ1Nr6/7xjlWZm1o/qUGZQ/FBG35ZZLQO9KuS/qJS2xQ4C7Ue73GnQg8GzgwxFxVbVsC8oX4V0pAzh1sbcDW1K+qAz6whhibx+oZ3DLI9q61uUQmvf1eyco9nPgpVl/xv8XNO+XweO3nLG++8sbga2B4yLiDtWySykzv66mPIakLrY3oz9+rwI2GcN6u/aZC7ll36yqFs/Nxj5/30xybBzHqe/YCykzi7+FW/7/vxD4EiWxf8EKjR0VER+m/vFm3wN+VbczKfdrT3vsDODdWf+otSd3LPe4hjJTwUuUe1Rd8jeYqP4wM3OSYi31v09mntFnjHJv4JMG6vn1zLw6yoQXQ8da2vf4zPzGJMTm4sAPm9oxLe2n/Efz9IG6LGn28K6xlrZ3nnV8GmLz3tM4IDLsYMnAehsHL4YZ2Bi3SapLHyLi2cBPs/4RULtl5n9XP3c6fn3HNH6L7TPSpIkyN82LWf///7nk95OZWfd83JkQEY8ELsiGR61l5kmjLDcNTHB7EhFPoFxCsYbyhRvKJBr3pFxS+pIJib0iM49uaEPXmZm7xq6gTBRw9EA9Hw8cAzyuQ+wtmXnIhLSvMVbFZ6H9V3HLA+lHNXv4JM06Pi2xFwGfZ8oHSyZpkKVuW9X2JmZgY6FBj4b2te2XSYqt6IGwZdxm44zrxvqL0SC6PyZxpmNtJqmek9T2aWeC25OIOB3YNaspzOct35FyicC9JyR2MvDpuiYAewEf6zH2CmDrwS/SUR6efSlwhw6xtZTL3uq2tytlJs++Yo8BvlkTm4s/Bdhqytv/FODWOdrZwydq1vEJiu2Umbeuic3KYMkkDbLMwmDJEcBOTMbg0yTFpuLYLlN9jqZMPDfsbOzGJuPRiys51pYcTlI9J6ntU538muD2JCLWAPfNzBsGls99Kb/NhMSupdy7Uncpx0E9xw6kJHi/HKjn7SkTFW3bIXYl8DTKPlgvTEn8/qLH2OGUGTKf3xA/hpLgT3P7j6HManzBQF3uRvfZwydp1vFJiq2hTIw1aC4xnvbBkkkaZJmWwZK2QY/fU/4fmITBp0mKTdKx3amhLmPdZlufycxb1SwPuj9G0NjwsaU8JnGWY5tl8yzfN7Hhd5Tlqucktb2tXOsA26SrbZTG4kDgxIg4jPVvft+d8iVxUmLnAadm5vcGGxARH+059m7gR9Wo8Vw9t6ecPTq4Y+xnwDWZeVzN9q7uOXYm5T+ppvg5M9D+04FvVgM88+tyT+CtPceOoTza5pSaep4/A7EbKKP7dcnv2dQ/IuCm6t8usc0oV1/UDWw8ZYJiu1Jm2rxgILYdpW1dYhvTvK/PmaDYmoj4CRuKqg2j3i+zEJukY/s9yuy/T+xxm619JiIemgOPpwMeCtxorL8YZYblpsckrtTYTRFRN5lSVK9Jqecktb2t3GY1y6eGCW5PMvMdEXEk5ezZn1SLLwKel5mnRcT9JiFGmWnw2oZm3LnPWGZuG+VM0RO55R6UY4H9MvOqrrGGepCZW/Uce1RTrIrfewba/0dRnns70tnDu8Za6tl11vGJiVGe89eU/J7I9A+WdI2NY5BlWgZL2gY9Tm5pX9+DT5MUm6RjeyzlMV6T0mdOpNts7MZGG2ub3f6HKzj2G5oTuV9OUD0nqe1t5dbWvH9qeInyMoiIrQAy80pji4utZFEec3Bzsjb/D9G0xBratXlm1l02Y2zEsSo+OOgxN1lU3YDIomJN25o0CwyydIr1V/vuIuKTwEGZeXxN7DOU2yNGul9mIbbwnp1dC/WZzPzr6Dgbu7HRxrS+iPhXykRxP6yJvTPbH7M31bq2fZb3mQluTyJie+BdlImFfkkZDb0d5XLhDwB/N2Gxx1ImpVnO2L45MBHWvP3508z8o1mNVfE1lAfW354yghuUSSauBt4PvKZD7N+BV1ex+ZMGjSv2isz8UUP7JmkShlmPbZ6Zv5mkQQ8HWZY3VsWDCXk8nbHmx/YtxzaHFcvwGEFjxhYbazNJ9Zyktk87L1Huz+GUxON5WY0QR8TGwF8CX6dcjmJsIBYR+9XsywC2j4hnTnnsjg2xufiOwPMz8wfrBSIeDnwb2GUKYl+KiPc0tG+biHitsZHFNq9ZPmdNRFzAwKBHlEt730/NgMgiYrUDGxMYaxxkAU6jXJq64mIR8TTgvdQ8Li4iGh8lZ6zX2Cuq32sfMTjGbX6D4ZPfo2nuh8aMLWtsgURuYuo5jljXtk978muC259tMvPw+QuqhO6wiDjEWG3ss5T7hOv+U73dDMT+gDLwcWhDfKPBpBEgM0+IiI2nJLYdsCVl1sdBmxkbbawl+d0W2G1CBj0cZOkv1jbo8TnKvVfnr1do8h5dt5JjczODP67HbR4HXE998nsq8HM2FMCqiPiAMWPLGNuiZvmcEyPioAmp5yS1va1cW0I98bxEuSdRZiy+kjJpy/yZi/cAngp82dgGsd2BR2XmqTX783rgwVMeWwtcBuzREP818B3KhBLz980LKV84LpyC2IOBp2bmyTXtuw54hLGRxRL4V+qT3zdm5sY1y4mI67PmcRQzFLsJeBv1++XNNO+zWYjtC/xTzfKg3DJz65yMx9MZq4+dRhn87PsRgzs1JL9nAy9nMh4jaMzYoI8CH69ZHsDewEsnpJ6T1Pa2cntk5u1qYlPBBLcn1X8cLwaezvqX/XyRkhi8wNgGsTOAszNzgxHjiHgJcPSUx1ZTzshd0BLflpp9k5lfjYhdJz1GeRzFFZl5eU37HgGcZWxksROBlzUkv7MwWOIgy2gHPfal/I09jA0fF3cp5fE0xpY3dkT1+3N63OZdgTs2JL9XU84m1z3W73fAY40ZW8bYTTQngB+nXOUzCfWcpLa3lXtPZm5Ts3wqmOBK0gyIiHvTPJiwipLoLfugxzINslyZmetq9ssjgDUzHGsb9FgLPIH6/XlaRNzX2PLHqmPV2zarZU0J9ZeA/TPzGgZEeQrCtcaMLWPsW8A/NyRyF1CuhJiEek5S29vKnZeZOw4unxYmuD2JiE0oZ3B3Y/3/UI4EPkW5JNdYfewZwJ1mMPZJyuVnL26KZ+bvqRERB2TmXsaMLSamlaka9GhK7lflArNMa2VaKOGWJlFbAjjrurZ9pvdZZvrq4QV8FvgI8HDK5XR3qX7+CHCBsRUZO5z2fvEFYKua19aULxzGjM2PXdjy9+cgYH/gdMpcAFdUP+9PmURiJcTOWIGxLTr+f3WUscmOLdc2J6UuxowNE2t7TVI9J6nt0/7yDG5PIuKszNypITZJE7IY6y92FkBLv0jgPCDmLc7q9x2MGRuI3Rm4IxsKSmL8JuDgzLwEICLuCOwJ/APwbyswtgfwjzMeeyLwQ8oVMneg9JXLKFeIfJUyodCgAL5WlTW2vLEvA0+piY11m5m5Xe0GI/4HeFWPdTFmbJjYSu67XdveVu6ozNy1LjYNTHB7EhEnAO8BPpeZN1XLNqI87/UTwN8YW3Gx11K+cDb1i4Mps1n+nAER8XvgHsaMzYu1DohkZgyWqcpN0qCPsdHGfgu8lfrkd3/K45Xq+sUuxiYi9nBgU8qje/ra5p8Aj6hZHsBJY9ieMWOjiq3kvtu17W3lGpPfqTDO08O+bnlRzrwcThk9P6t6XVYt+zNjKzK2I+394o3AAxv60xHGjA3ELgO2b4hdC7weWDVv2SpgH8olrcZmM/bbuv5Qvec6ynNw62K/NzYRsbXAqT1vM4FvUb4QD75umqB9Y8yYfXfpbW8r97u6MtPy2gT1IjPPj4g3A//LwGRKmXl6RFxhbMXFzgNYoF/cJyL2YcPJPp5jzNj8GPAWYEtgg7O7lMGSrYHjIuIO1bJLq3IPoTzb0tjsxX4QEa+nnMG9FCDKjNp7AmcCG1HvvcYmIvYqYJOet/lz4KWZuWYwEBGX91wXY8aGia3kvtu17W3l1ta8f2qY4Pak+kK6O2Xq/R9Ui+8CfDYiLqbcO2dsZcUOo4yeNfWLX1BmVj6Mch+dMWONsWrZdQ2J8buq3/dhQES8KDP3MTaTsVdSJqGqS37/PDOvGixTOSMzzzS27LEtM/Oghti4tvllmr8Mv2SC9o0xY4NWct/t2va2cnX37U6PYU/5+ur2olx6equa5ZsC1xtbkbE19gtjI4xdDpwC7As8v3rtO7dssMy8sj83tiJjL5qguhgbMmafMWbMvjvmtjeWm4aXZ3D7cxPlzMsFA8u3o5zFM7byYjdNWH2MTXfsdsBDc+DZyRHxXuDXEfHXbCiAu0TET4zNZGxVzfI5H4uIv5+Qehqrj61qiI11mw3bA/uMscmOreS+27XtbeXeQnnE4FRyFuWeRMSTgA9RztrNXde+PXBPSgd6kbEVF9u7+t1+YWwUsVsDf5aZ6yW/EXE34BxgNXAV64sq9hBjMxlbQ3km7qAAHgA8aELqaaw+9j1gY8qjQewzxozZd0fd9rZyO2XmrWtiU8EzuD3JzK9FxE7Aw1j//rgTM/PGiHiHsZUXA7BfGBtFDHg88M2IqEt+jwE2z8xTGBAR5xub2dgNwAup/2Lzswmqp7H62LHA73repn3G2LTGVnLf7dr2tnLfG3z/NPEMriTNiCjPUG4cTNHKEhGfBA7KzONrYp/JzLrL1rWC2Wc0rVZy3+3a9lneZya4kiRJkqSZ0DQ1tCRJkiRJU8UEV5IkSZI0E0xwJUkaQkS8OSIyIhonaoyIXar37DJv2Wsi4pkdtrdztc2thiizwfYlSVoJTHAlSRq9HwF/Uv075zXA0AkusDPwJmDRCW7D9iVJmnk+JkiSpBHLzF8BJ/S93YjYmDKB5LJsX5Kk5eYZXEmSurlvRHw7Iq6JiIsj4q3Vo5o2uES4eobh3YDnVcszIj5VxXaKiC9ExGURcW1E/Dwi/jMiNomIPYGDqu2tmVd2h6psRsTbImLfiDgPuB74o4ZLpI+NiOMj4nER8aOq3qdGxDMGGxYRz42IM6r6/DQinlaVP3beezaPiA9W9b2uqv8xEXGfke5lSZKG4BlcSZK6+W/gQOAdwBOB/wvcBLy55r3PAL4K/HhefF3171eAq4CXA5dTnmP8ZMog9FeAfwX+GfhL4MKqzMXz1r0ncC7wD8BvgV8At2+o8z2Af6/qfDnwOuA/I+I+mXk2QEQ8HjgU+CLwWmBb4P3AHwBnzVvX+4CnAW8A1gBbA38KbNGwbUmSxs4EV5Kkbj6emftXPx8dEbcDXhcR7x98Y2b+b0RcB1yemTdfOhwR2wD3BJ6emV+cV+Qz1b/rIuKc6udT5pLQAQE8ITN/N2+9922o8zbAozJzTfW+H1GS5ecAb6/e8xbgNOAZmZnV+04FTmL9BPdPgEMz85Pzln2hYbuSJPXCS5QlSermiIHfDwM2Bx4wxDquoJx93T8i/jYi7tWhHl+bn9wuYM1ccguQmZcBlwHbw8338K4GPjeX3FbvOxk4b2BdJwJ7RsQbImJ1VVaSpGVlgitJUjeXNvx+58WuoEoiH085O/oO4KyIODciXj5EPS5e+C03u7Jm2XWUy4+hnOG9FSXpHTTY3lcBHwP+hpLsXhYR74uI2wxRH0mSRsoEV5KkblY1/H7RMCvJzHMz84WUe10fBHwL+HBE7LrYVQyzvQVcDvweuENNbL32ZuZvMnO/zLwnsAPlEue9KY80kiRpWZjgSpLUzXMGft8d+A3w04b3Xwds1rSyLE6hTOwEt1zqfF31b2PZUcnMGylnk58VETG3PCIeAuzYUu6CzHwPpe3DXKItSdJIOcmUJEnd/G31WKATKbMovwR4c2b+cl5uON9pwCMj4qnAJZSzpbejzGp8OHA2sDFlVuQbKGdy58oBvDIiDqacYf1JZl4/jkZRzsAeDXwhIg6gXLb85qrON829KSK+T5lp+aeUxP7PgQcCB4+pXpIkLcgzuJIkdfN0yv2zXwSeT3mcz7+0vH8/4EzK5FQnckvS+HPKWdsvAp8F7gQ8tZrYicyce7TQXwDHV2XvNOrGzMnMbwDPA+5LmRV5H8rjhC4Bfjnvrd+hnMU+lPI4o2cDf5+Z/z6uukmStJCYN0miJEnSBiLiLpQzzG/LzLYkXpKkZWWCK0mSbhYRmwHvBY6hXEZ9d+D1lEmm7p+Zw8zaLElSr7wHV5IkzXcjcEfgQ8DWwG+B7wJ/aXIrSZp0nsGVJEmSJM0EJ5mSJEmSJM0EE1xJkiRJ0kwwwZUkSZIkzQQTXEmSJEnSTDDBlSRJkiTNhP8PwUGQh0BP1MgAAAAASUVORK5CYII=", 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\n", 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", 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zzwQ+HxHXt35tQ2Az4JPjb54kSZIkSYuzUJGpnwBfaX4+ADgNuLL1O9cBZwEfGm/TJEmSJElavKED3Mw8GjgaICIA3pCZP51AuyRJkiRJ6qXPNEEHLWVDJEmSJElaE4se4AJExF2ApwPbUSonz5eZefC4GiZJkiRJUh+LHuBGxD7ApymVl6+gPHs7X1eFZUmSJEmSllyfK7j/BHwN2C8z24WmJEmSJElaVn0GuHcBXubgVpIkSZI0jdbp8bs/AjZfqoZIkiRJkrQm+gxwXw68qik0JUmSJEnSVOlzi/LrKFdwz46I84BftdZnZj58XA2TJEmSJKmPPgPcm4BzlqohkiRJkiStiUUPcDNzjyVshyRJkiRJa6TPM7iSJEmSJE2tRV/BjYiHLfQ7mfmNNWuOJEmSJEmj6fMM7teAXOB31h29KZIkSZIkja7PAPcRlWWbA08EHg78zVhaJEmSJEnSCPoUmfp6x6rPRsS/AX8GHDuWVkmSJEmS1NO4ikx9EXj6mF5LkiRJkqTexjXA3QW4eUyvJUmSJElSb32qKO9fWbw+cC/gYOCz42qUJEmSJEl99SkydWTH8uuATwEvWpOGRMTtgQ9RBswJ/CVwTvPaOwAXAE/PzKvWZDuSJEmSpNnUZ4C7Y2XZHzLz8jG15Z3AcZn51IhYH7gN8CrgK5n55oh4JfBK4BVj2p4kSZIkaYb0qaJ84VI1IiI2AR4GHNhs63rg+ojYG9ij+bWjKHPxOsCVJEmSJA3ocwUXgIiYm/d2M+BXwNcy84tr2I4dgSuBIyLiT4DTKbc8b5WZP29+5zJgq442HQIcArDddtutYVMkSZIkSSvRoqsoR8TtIuLrwDGUwefjm/8fExFfi4iN1qAd6wH3Bd6bmfcBfke5HfkWmZmUZ3MHZOYHMnO3zNxtyy23XINmSJIkSZJWqj7TBL2RMgh9NrBhZm4NbAjs3yx/4xq04xLgksw8pfn3Z5rXvDwitgZo/n/FGmxDkiRJkjTD+gxwnwK8OjM/npk3AWTmTZn5ceAfm/UjyczLgIsjYpdm0aOAsyhXiw9olh0AHD3qNiRJkiRJs63PM7ibUwadNWc169fEC4GPNxWUfwIcRBmAfzoiDgYuBJ6+htuQJEmSJM2oPgPcnwJPBE6orHt8s35kmfldYLfKqketyetKkiRJktYOfQa47wfe3hST+jjwc+COwL7Ac4CXjr95kiRJkiQtTp95cP8tIrakDGQPbBYHcD3w5sx85/ibJ0mSJEnS4vSaBzczXxUR/wrszqp5cE/OzKuWonGSJEmSJC3Woge4EfEKYJvMfCFwbGvdu4CLM/Nfx9w+SZIkSZIWpc80QQcB3+9Y971mvSRJkiRJy6LPAHc74LyOdT8Gtl/z5kiSJEmSNJo+A9zfA3fuWLcNcN2aN0eSJEmSpNH0GeB+E/j7iNhg/sLm3y9r1kuSJEmStCz6VFF+HfBt4NyI+BhwKeWK7rOAzVk1dZAkSZIkSRPXZx7c70XEI4C3Aa+gXP29GfgW8JTM/N7SNFGSJEmSpIX1nQf3O8DDImJDYFPgqsy8dklaJkmSJElSD70GuHOaQa0DW0mSJEnS1OhTZEqSJEmSpKnlAFeSJEmSNBMc4EqSJEmSZoIDXEmSJEnSTHCAK0mSJEmaCQ5wJUmSJEkzwQGuJEmSJGkmOMCVJEmSJM0EB7iSJEmSpJngAFeSJEmSNBMc4EqSJEmSZsJ6y90AaUlFDC7LnHw7JEmSJC05r+BKkiRJkmaCA1xJkiRJ0kxwgCtJkiRJmgk+g6tFidqzrED6PKskSZKkKeEVXEmSJEnSTHCAK0mSJEmaCQ5wJUmSJEkzwQGuJEmSJGkmTNUANyLWjYj/FxFfaP69Y0ScEhHnR8SnImL95W6jJEmSJGk6TdUAF3gRcPa8f78F+LfM3Am4Cjh4WVolSZIkSZp6UzPAjYhtgCcAH2r+HcAjgc80v3IUsM+yNE6SJEmSNPWmZoALHAa8HLi5+ffmwNWZeWPz70uAO9cCI+KQiDgtIk678sorl7yhkiRJkqTpMxUD3Ih4InBFZp4+SnxmfiAzd8vM3bbccssxt06SJEmStBKst9wNaDwEeFJEPB64NbAx8E7g9hGxXnMVdxvg0mVsoyRJkiRpik3FFdzMPDQzt8nMHYB9gZMycz/gq8BTm187ADh6mZooSZIkSZpyUzHAHeIVwEsj4nzKM7mHL3N7JEmSJElTalpuUb5FZn4N+Frz80+AByxneyRJkiRJK8O0X8GVJEmSJGlRHOBKkiRJkmaCA1xJkiRJ0kxwgCtJkiRJmgkOcCVJkiRJM8EBriRJkiRpJjjAlSRJkiTNBAe4kiRJkqSZ4ABXkiRJkjQT1lvuBkiSJEkARAwuy5x8OyStWF7BlSRJkiTNBAe4kiRJkqSZ4ABXkiRJkjQTHOBKkiRJkmaCA1xJkiRJ0kywirIkSZIkrXRWIQe8gitJkiRJmhEOcCVJkiRJM8EBriRJkiRpJjjAlSRJkiTNBAe4kiRJkqSZ4ABXkiRJkjQTHOBKkiRJkmaC8+BKkqSxispcjLkWzsUoSZo8r+BKkiRJkmaCV3AlScvKq32SJGlcvIIrSZIkSZoJDnAlSZIkSTPBAa4kSZIkaSY4wJUkSZIkzQSLTElShYWPJEmSVh6v4EqSJEmSZoIDXEmSJEnSTJiKAW5EbBsRX42IsyLihxHxomb5ZhFxQkSc1/x/0+VuqyRJkiRpOk3FABe4EXhZZt4D2B14QUTcA3gl8JXM3Bn4SvNvSZIkSZIGTMUANzN/nplnND9fA5wN3BnYGziq+bWjgH2WpYGSJEmSpKk3FQPc+SJiB+A+wCnAVpn582bVZcBWHTGHRMRpEXHalVdeOZmGSmsoIgb+kyRJkjS6qRrgRsRGwH8DL87M38xfl2V+juocHZn5gczcLTN323LLLSfQUkmSJEnStJmaAW5E3IoyuP14Zn62WXx5RGzdrN8auGK52idJkiRJmm5TMcCNcm/m4cDZmfmOeauOAQ5ofj4AOHrSbZMkSZIkrQzrLXcDGg8Bng38ICK+2yx7FfBm4NMRcTBwIfD05WmeJEmSJGnaTcUANzO/BXRV2HnUJNsiSZIkSVqZpuIWZUmSJEmS1pQDXEmSJEnSTHCAK0mSJEmaCQ5wJUmSJEkzYSqKTEmSpPEps++tLjOXoSVaaTx3JK10XsGVJEmSJM0EB7iSJEmSpJngAFeSJEmSNBMc4EqSJEmSZoIDXEmSJEnSTLCKsjTjrIgpSZKktYVXcCVJkiRJM8EruJIkrSW8o0OSNOu8gitJkiRJmglewZUkScvOq8uSpHHwCq4kSZIkaSY4wJUkSZIkzQRvUZYkqYO3zUqStLJ4BVeSJEmSNBO8giuNgVd5JEmSpOXnFVxJkiRJ0kzwCq4kTaHaXQHgnQHSSuGdPVqI54i0NLyCK0mSJEmaCV7BlTQWZqIlSVp7+T1gvOzP0XkFV5IkSZI0E7yCKy0hn6OUJI2DV3Om26SOj+eBtDCv4EqSJEmSZoJXcCUtm65MtFe+Bd4BoTXjla7l5zGYErXP0ik5Dst+jkxx3wwzzn5b9mOwBLyCK0mSJEmaCV7BXQazmCmp8erL+K0t5840G/d5vdRZ2DV5vZXIPtBK5mf8dPP4TIflft7Z82D6eQVXkiRJkjQTvIK7QoySLTLDJE3WNL/nprlto3B/pGK5r2ZNsg3jtBLbPA3styWwQp8DnmYr4gpuRDwuIs6JiPMj4pXL3R5JkiRJ0vSZ+iu4EbEu8B/AnsAlwKkRcUxmnrW8LRu/SV2lHfaM2nK2YaHqueOsuDup7UzKNPfBpJ5ZneZj6rkzHe+flfgZMu7P62FGed5sGv9mrMn2J/XM3XJfdZ2Gq3CTOg+W+w645X6PjBoza33ge27tuvq+Eq7gPgA4PzN/kpnXA58E9l7mNkmSJEmSpszUX8EF7gxcPO/flwAPnP8LEXEIcEjzz99GxDkTatua2gL4BVSzKlsAv+haPu6YyrqZjak86zA1bRsWwxS3bQXF3LJu1mJ6nNcrYjvGGLOSY7reJ9PQtpUY4+eO5+gkY6bhfBvn8RnT/kyj7TvXZOZU/wc8FfjQvH8/G3j3crdrTPt2Wt91xhhjzOgx09AGY4wxxhhjjDHGmJUUs9L+Wwm3KF8KbDvv39s0yyRJkiRJusVKGOCeCuwcETtGxPrAvsAxy9wmSZIkSdKUmfpncDPzxoj4G+DLwLrAhzPzh8vcrHH5wAjrjDHGmNFjpqENxhhjjDHGGGOMMSspZkWJ5n5rSZIkSZJWtJVwi7IkSZIkSQtygCtJkiRJmgkOcCVJkiRJM8EBriRJkiRpJkx9FeVZExFbAXdu/nlpZl4+4uvcPTN/1PV6fbcz93q15cDPgcfNfz3gy5l5dddrjRIzbH/GuJ2DMvOIYW3oGbMbcNfFbn/Y60XEQcDPgH1ar3d0Zh43rM09+23Y/nS2bUjM24Hb1NocEZtQOT7AA/vs50JtY4R+Y4Rzp2s7wP/1ea1R9wfIPvspSZK0trGK8oRExL2B9wGbUL6UAmwDXA08PzPP6Ij7QWb+UWX5z4ELK693ffPzrXpu56LM3K6y/JdN7PGt19sTeH1mfmRMMV3709nuEbdT3c9h64Ys3x84HPjQYre/wOv9BvgW8BHgknmvtz9wXma+qBIzSr+Nsw8OA54HHFRp87rATgwen32bNr9psfu5QBtG6bdRzp2u7RwKbA98YrGvNeL+vKn5+dDF7mfzeh/IzEM61o0tQTPMuBNBoyRb+rS3ea1qwmuBtk0q5iDgs4wvuTdKsmWUpM6wZNhja9sZZ/Jmgb4eZ5J3rH09JFE3bDvPB35Za3NX4nHE/en9nuu7/WFtWIP3zzj7epTPqrG1bZhhiXvG+xkyynkw1uPTN2aY5U5Aj7o/k/gcXSkc4E5IRHwXeF5mntJavjvwSeCllbCDgUdQBlCrhQHPBx5ceb1zKcd158p2Pk/5Et72cODuwPs7trN5+wMvIt4PPB346BhjavvzaUoftNs9bDs/pAyszqnE7Aq8h0F/AWwKtOdYHhbzbGDdzNy4tf1Ngcsq2wfYGdgAOLOynXtm5sBjAxHxLsogpj1QGtZv51M+4M6rbaey/QXb1hGzC0BmbtDafgDXAXeoHJ/zgZsz826VmD8wuX7re+50beccYL3MvGtr+bjPg673dgDnA/evbCeA72XmNpXXG3eCpisZN+7tjJJsGZYA6Gr32BJBSxAzqeTe2JI6CxyfrSmDyD5JqmGJm3Ee0+Xu62F9MEry9UTg0ePYn1Hec03b3g18fLHbX6ANy9rXU9K2Ye+FJT+v1+Czd5x9MKl+m0gCesT9+QFwcc+YI4DLKYPiO1AG6VdQBuxvHpZ0mnYOcCckIs5rfzGdty6Boygn1nzPAm6ifHC0fTgz161th3Jcd6qsuxn4K8rAY773ATcCf1PbDrBZZv669VrXAL8HXj6umI79uQa4Fvj7Htu5ool5WPvlgJ9Q74N3ATcD9+4R8xbgVpm5eWv7m1Ay57sBV7ViTqMc5/ZgJCiD0Qdn5qmt1/td8zr/wKCufru8ae9DK9v5MXC/nm3rijkO2CIzt2xt/wHANykD3PbxORPYuP3Ho4n532b7S95v9D93urZzEXBNZt6ztXzc50H1vd302ynAT5v4Odn8ezvgPxg0SoJmE+BOwDMq7f4og8m4Ubcz7mTLpdQ/3xZKIrYTXlD2//YMJr3GHbNQH2zWN0FTea0FtzPGpM7QZFhmrl/ZzmbAqdTfJ+cAtS+nw47pIQwmcmG0JO+k+vr7lL7rs52uvt6U8mW2lngcZX+GHdOupNspwG0y886tmGHnztjfP2Ps61E+q0Zp27D3wgVA7crqsMT9OD9Dhp0H40xaDz0+I/Tbz4Ha1f9JJaC/2Wyr3YZR9+eyzLxVz5hLgdcCR2XmZc3v3xE4AHhUZj6m0u4VwWdwJ+fYiPgiJbNycbNsW0pm5UrgbZm52pspIrannORHtV8sIt7V8XrrldXxjMp2fgacmZnfbr3WAcDdO7bzNuCMiDh+3uttR7n99INjjOnan+uAb/Tczq2b7VxYibmiow8eBuzVMwbgQxHx3tb29wS+DWyUmd9txRwDbNexnROBd0fE7ViVfduWknz4xxH67dSO7VwwQtu6YvYDjo+Is1pt/jXwRurHZxPg+o6YL47QtlH6bZRzp2s71wMbT+A8uLasrvbbxcAemXlR5fVupvyhbidonk55lKHtZsp7dX8GB+bnAzcAf1aJ23CM2zkNuKaynblkSy3mOGCLynbuT7lC+CQGk4iPoSQRT6/ERcd2fgj8dgIxw/rgfAb3BWBLyiChT78N2855EXH/drKFcqyvqWx/2DEddnxu6tjOlQwen7nEzeb0P6YvoH6OPpfyPq7FzG2zbVJ9fWfgop7b+SrlGLXd3Px/XPsz7JjehbK/7aTbth3bH3bujPv9M86+HuWzapS2DXsvbET9vD4A+E3Hdsb5GTLsPFinI2bcx2eUfrvVGNsWrH6uzxn2Xtiect6Pa3/WHSFmg8x8y/xfbga6b4mIv6zsz4rhFdwJioi9gL1Z/d74YyhvpAvbX06brMuumfm/PV8vO5afDPwhM39f2c7A8nnrNwUe23q9U4DLxxxT25+TgC/03M6XM7P9gbWofe0b03f7i9zeHVuvd/2wNnedB5n5pVHb0Fe7zfMygZ390xUzrjawcL+NdOxq7Z7EedDKrra3/wLgW5n5vcrrnAsc2JHY+lDzX3tgfinwqsz8VivmdEqW+AmV7fwBeOSYtnM4JQGwZ2U7PwYOqMTcl3K73RUMJgA2AZ5WSSKeREkiblXZzjWUpFd7OycBN9Yy22OOGdYH/0e5naydoPkL4N8zc+CuhSH9Nmw7xwKbAe1ky60oVzi+wOKP6bDj8+/A31a2swPw1Mz8YqVt1wP37XlMr6VclWifoydRkrx3qsRcSRkkLFdfbw4cmpmH99jOAZTbgD/G4PE5CXjkmPZn2DG9E/Cnle81B1Bun/5gpW1d58643z/j7OtRPqtGadsOdL8Xuj57D6f0Te28HudnyLDz4DLKBZylPj6j9NtvgceNqW030AwyK33Q9V44HHhix2fVKPtzGaW+Sd9z5zWUK7jzi9QeCOyZmY9ux6wUDnC1mmYwR2b+arnbsljRr4LwRpn5256vPzSmz/bnXo9yq8/8wdB3csibMYYU0uhr2P50rWva/DvgASyy3TFiZeyu/RzWtiH7M9eG6nk9rnMnSiGaq2qvFRFBpd+anxfdn81rjVKsaWwJmoh4KJVkXLPukcDJk0oEdelIAFTbvVASseP1x5okG9UU9OnYEkRd64Cn0J24OQx4R59j2nUcRkzyTqyvR3ydYcnFse7PCEm3l1OuxC1ZYnrUdo/z9ddEz/fCKyiD0l79M4nzYJTXWZPX69lvL8zMfx9n20Z4Lwxtwyj70zPm5ZTEwd6UhAeUxxiOAd6yksYCbQ5wp0BEfJBy+8KTKZkeWFWV7fDMvKEScwLldoO9ga1Y9WD4XKW0x7WWH03HA+MRsR3l6u6tKEUHAtiYkul9ZWZe0NHurkIex2bmXj1juvZnWLu/RbklexNKtipYswrC1XZ3xUSpjP01ynMci93+Yyi34Z7I6oUddmpiju9oW1cbRum3UQpfXEG5inHeYtsdq1d4Hsfx6Vt0aDvgbMpz31ez+nn9EUrWco3b1pwHp1Ce1W6/1kcoBeTa/Tb3Hvg+i+/PYYVjhhVn2TMzT6ita9b3TtAslCQaR6JsKZIto7alq2199nNNYha7fJTXmlvHCEk3OpI6fdsQI1QqXipLee4OW7eYRF3f7QC3peP49E3usYLfc4td3qzr7Gv690FnTPNz3wTngn262P6Z15/Ldh6swefOJn1j+p6LI7ZtlAR0NQE+6v7ECBXKZ5XP4E7I3IdObRWwH+V5wdexeuWzlwFPjohDKzEPowySHtHKHH2j+Z2HtZYfSHkO+AWVNhxJeV5ni8y8qYlZF3ga8OXK9qE8U7BNlFtT2m3bPSL+vGdM1/68qqPdQZlL9U9zsILwO4HPRylb347ZpLJ9KAW9HhIRL+0R8wnKbVO7tra/e8f2AV4B/K49kI6IHYHTI+JjlZg7AJv17LcPAadEqVzdjtmisp9QCq3U1s097/aAdrIjIo4EPllpdzTt3qdyfD5NOa9qlbHvMELb7hClYnLb05r1d6yc1/9FuaWrz7nTtZ1n0n0efI1y6+MFrXXnA/Q8D54NXJuZf92K2ZQywK4OcCmD4kUnaCLiaroH+esBF0XEdxhMxh1Pee76UTQJhYgYmijrSng1zqq1m5JIqCZbIqIrSXR8RPwL/ZKIXQmv7YCzI+KWxMki9rN3TKOrD7qWD+vTasywpFtXf3YldYadOwu0+38ZrOj6COCNEdFV0fWxlFvvxnVMV0vyLub49O3rBdYNizm+T8wCx+cw4MW0knsLHLuR3nNd+9OVdOvqz0m+f4a0e5Q+6IrpTHAOea3OtkXEX1CeR1/0Zy/lGc+BBPSkzoMRP3d6xyzQhq7zcJS21RLQC32GPYZSHGxc58FbgKfS73O0MwEeI0wJOE0c4E7OlZSrWe2HzAPYsP2lFbikGSReB9QGSrfKyoPhUSoyzz0kzryf3xwRbwLe1moDlKtGMTcIaGJuogxcPkG9kMcBlIfja227/QgxXfvzAkoVvlrMuu0BSuN5lKt2t6us25h6HzysaVufmG0ry8jMkyNia0r1whtbq7vec5dS+q1WKOKI5nX69NujKUU5avuzYUfbHk8pRFCLgVXJl/meQil+VC3O0nF89hoS8zcjtG2u2mGtuNHvO87rj4xw7gzbzrXtX27Og3Wo99vce79t2HkwrFjTnaIUrmoLYMsxJmieRZnr8HWsnow7APhnShGf/VoJhbdST5QFsOOEki1bUyqkt9s9LIlYS3hBeVY0qSdOvtaRBBklZlgfbNqRRHwS9T4dltgaJek2LKnTde48mXqSKCjn/F3bVxkWSN58tln+OhZ/THfreC8cSf8k7yh9PUqi7j7UE3/DtvMyyi29tePzVUpBuj7JvVHec7evtIvmvH1/RLRnhxj2eTDu988ofd3VB/8MfLQZXCw2ZpQEZ2efUr4j7M/gZ+8R1PsnKMWkagnocZ8HXUnrUT53nkU5r8fVb5+OiH8aU9tGSUC/E8Z6HrwU2LLn52g1Ad54PfXq3CuCA9zJ+QmluEXt2bXrIuJpwH9n5s3NsnUoX3Z/lZmPqMT8Icq98+0Hw6P8GFvl4APjv6PMxXte67U+CTwhIh7I6pV4D6Dcglar8Hx/ytQJtbZdP0JM1/5cCfy4I+aaqFcQvhY4ITNfX4l5dUcffBu4S8+YdwHPjXrF6suB/8nM01sx1wOvj/LMzPyYfSnTvNSqNf89sEnPfruMUtCrtj+v6mjbY4f0wUuAU5tzZX67oZy3R1ViRqmM/f4R2nYo9X7bC3h8x3l94QjnTtd27gc8p+M8OJ16v922ie1zHkB31e6gTHHSvu0ugCcyvgTNdpQvGCfPW3YJcHJEHJyZn2q91k0R8beUfm1XioTSD5NItqwzQhLxdh1tux1lcFdLnPznGGOG9cFG1JOIc1PL9UlsjZJ0G5bU6Tp3HkB3dWOoV3T9OPXkzaiJ4S0YX5J3lL4eJVH3V83r9NnOrSvtnTs+oySGof977pDKcYPyeXQT/T4Pxv3+GaWvod4Hf0f/fhslwfkiYIOO98KtOj57n0753ldrW0zoPOhKWo/6uXNzz5hh/Xb7MbetbwK6+jd4ge0M25916f85uk2U6ZfagvLY24rlM7gTEsMfMn8tcA9KZcOrWPXGO5cyzclJlZj9gD9m8MHwLzfxj2XVyXkZ5YHxs4H/y8xzWq+1PmUe2B1Ydd/+JcDnKfNznd8emEfEU4HMzP+utO1Q4OM9Y+bvz/x2nwO8MTO/U4nZh/Lmb1cQPh34XGb+ohJzMOU4tPtgF0pm8qOLjWnWvYHS/+2K1T+mJCeurMT8KfCnlZjLqBdAeSrwg47td50H36IUCDi/EvNg4Nx2/zR9sE5mnl2J2YpS0a/d118Hvtdu87y4XpWxmzb8sqNt62bmWR0xF1f6bX3KnJi1iuKHU95vfc6d6naG7OcxmfmliNi1ow1Qvjgv6jxotrMplaIgwH8Cb83Mr1ZifgfcuytBQ0l+tQfm96VUd2wnGk6mDAbu0ErGPY0yR+WnKHN6z3+9zwNnZObjKm27jjLnb3s7cwmnO1ZirqYkAdpJg1cDH6sMeohSKfLZDCYRL6C8T+/do22fBJ4APJrBxMnTgceOKWZYH3RVEB4W07U/h1Iy9f/IYLJlY+DZlWTLu4DnAAex+HPnJLqrG3dVKn4u5SrH0e2Q5nf3o98xvQG4R1eSl/rx2ZfyyM84+npYTFcl3G8D22dr3tgFtjPsvb0N5e97O7n3PuD4zHxGZTtX0/89dxPl862ddPsAJdG9RY/9Gff7Z5S+7uqDfwSOyMwX9oh5UfPzO1n8e+4qyved9vEJSu2VDzP42fs/lGRprRrwNZRH2pb6PPgD8JAxfe4cSrlj4zU9Yob12/HAg8bUtmGzBdyRcp623wv7UT5DXz2m/Tka+AX9Pke/AtyPwamSAvh2VipwrxQOcKdMRGwOkJm/XO62SFp6MaQoyLB1PV5/WIJkWIKmlmjYgfIM30NYPRl3EuXLwKNYfTB/CeV5t/d2JFueAnx9qZMtTbvfQr8k4vOBT1fatj7lGcZHMthv36BMozSOmGFJnScB360kEXcBtsnMr1RiHgycN46kWxNTTerQkdxrzuWNs/t51lry5mBKgrOWuDmFMpjtc0zfAnx4TEnehfq6K4nYN1FXTfoN206z7pmU23RrSbfasTudcvdM7fwY5T13LJWkW5SK5m/LzAdWYro+D8b9/hmlr7v64PuUOef79FvvBGdXfzbrvklJcra3czLwgcz8WUfb7luJGfd5sAtjSvY3MfdgfP12KvD4MbZtlAT0acBnxrQ/36Acmz6fo5dRphD6VmXdf2bmX7SXrxQOcKdAROxJyba0PzSOzu5KaZ0Pf0d5duI2ldc6LsqtnvvU1nW81msy8w191i3Qts4Y4Ge1tlFuuVh0m5vX+0BmHrLY5cPaPeS1NqF8cN2FwQrG7wGe37T7Dq11XdWNh1Wf7iqOMqyve+3PAvs6LKarbaNUeO56rU0of0RvzeL7cz3gu5T3VvucqhahGbavC5wHZ1Dm3e1buXw9yjx5waoKz++iPHN2S8EQFqhqHsOLNS2JlZqMW6ntVjePqVaqpU5wzqpZ67dx7k/0nBlhVjnAnQLNLQcXUm77mF8sY1/gk5n55kpM15Qlh1GeoTio9Vr7Uwqt/JxyS0p73XmZ+SJaurazQBtGifkN5bbadtve1Px8aKXNF1GqLLdtSsnqtr/wByXLuE1l+5tRMrF/3CPmy5TnynbNwYrVfwf8K+XZ2PnrDm1ialWhj6Nk3gY2Rbmld+tKG7r6c9j+fJ/BvoHh/XYWpThU266UK3rtdgfwbVZNIN6ujH1/6n3wRcrt+m2fodxGs0vrtQ6gJBFq1cHfCDyUMlBsF0S6IyWr2TZKH/wHZTqBu1XOg7072nYk5VakWlGbD1Ju//xMDhZreiLlHGq37X2ZuWVlO0MHvyMmQT5FGdAvNhm3HqUg0PosstrtsDaMmGw5FngJY0giNvvzVcrtZotKnIwYMyyp86+UaplPZvEVhI+gPL6wT+X1Rkk4dSV1xp3cG3b+3p2ex5SOROqYk7zDkmG9EnVNXNdxWGg7G9B9fNqJx7n932tMbes8pl2G9Oe43z/j7uszgBsWu50lSHCuR/l7tg+L75+5z4OlPg/mEt379Oifhaqdj6vfNqH8PV3jti3Q7mHVwd9KuTNhHPtzb8ot5ptQmf6Qck4seuAbI0xHN00c4E5I1B8wh3ICPgHYoP0hFBE/AO5GeQ61HXNPygPobbsAZOYGrdcK4LrMXL/Stt9Qijv8rrKdjYBrKtu5LbAO8INKzL1GiLlnZq5Tadu5lPN058r+3Ex5HmT+Q/oJ7Nj8fEFreQDbU++3uQ+fPjHVvm7ad31HX99EqQo98EwxsAflj3i76MD9KVfk220Ydh4M258d6N9vO3S0bQ/KcfhGpQ0P7zimC/VBrW3bAHT0aXa07YGUc2fDjphx9cFC2+kb03Xu3EAp5DHwDDslGbZfZXnn4HfEJMiLKF8IXsPik3GfoAzM92RpEw3Dki1fpRT665NEvITBvoGSgHgs5XnAxe7PKDHDkjovpjxfd1Tr9Q6h9F379YLy5ea1jCfpNiypM0py78SOPhh2/v6c8gW9zzHtSqT2TvKO+P4ZJVG3CaXieTvxN8p2DqT7+Mx9hrenGBzWtmHvuWpStnndri/843zPjbuv+yY4h23nSPonOJ8GvDgzd2+/WJRnYz/BeD4Pxn0edCW6R/ncOZLx9ts1lOr/42jbI4F/oN7XXZ9h/0cZcO44pv25lnqF9P2B91IuCs2fjuhqhkzrNuxi1UrgAHdCmqu0z6Je5fREyhQJF7ZirqQMOh9eifkx9QfDj6O8+Vd7M0XEAyjZzIdl5qmtdZcBV2VrSoFm3Y3AnduZnoi4nPLFv/0cTQDnjxBzHqW4RLtt51HO050q+/NNYOccfDbqPOC2WXk4vhlc1frta8Btsl4ApSvmo8CfUP6wtStWv5zyzF+7uvH3KFWhH1LZTlcBlGH91nUeDNufGyjnW59+62rbmZSCIbWry39g1R+2Pn1Qa9vxlD84u1T6+h+B+1TaNqwg0lGU4zaOPhilbcOKpjwR+AL9ijVl8/u1D/T9gR9Wlo+SBNmWUihutQF4lGfkrmUwSQYlSXZzZg5UphxzomEPhiRb6J9EvFdH26p9sMD+jBIzLKkzLIF2E6u+xMy9VgA7ZGa7z0ZNuo2SoFloO7Xzd3vgQQwmb4JS0GzDvonhjqTbKEneuSqzF8xbttD7Z5RE3R5N7PzvB2uyna7jc04Ts0vPtnW95x5MmU6q7WOUq6q170IbdezPuN8/o/b1ciY4/xx4N6Uq8WqrgE91fL6O8nkw7vNg1ET3cvdb37Y9nNIHA4VK6U5Av5tSAbs2+B3n/nyX8j1t29by3SkJ87dX2hbAP2TmZpV1K4LTBE3OyZQ5Ob/eXhERZwNfab5cz698tgHlNokLKzEXABtl5ndby/cDjo+Is1iVyduWcvvDc4B3R8TtWutuZNWtwG3foXzJaN/K8AXKF8Za2340QsyJHW27tqyu7s+/UbKTF7Ve7jCaP24d+1Prt7dRBhx9YvYG/hf4ekTcoVl8OaVAwG6UKQfa606m3Dpb8w7KFe62L1CKnPQ5D4btz+fo328f6Wjb61hVvbntYEo2vk8ffLijbc8APl15rWMoH/S1tu1L+TJ1eZNgClYVRHpjx3YOo38fPINSlfnrzcAWVlUu72rb/pRbkF7PYFGbl1Mq/rbXfZSSha35EZWpueCW7O3+dCdBdqzE3EDJBLcTAD+i3CbVtjXlj/vOlcTWyZSJ69epJBqu69jOXKKhq221abvmki2P6Ii5E6t/cYVy9efnDE5ZMpeoq7VtlP0ZJeZ44AFRn/Lt2qhPLXcFpeDOfSp9MMpUbF19Paxt14ywna6p5W6g3AbdPj5QjlHfY3peRNy/nUilTIty6ZAkb+28Xugc7XtMu6bxG/d2uo5PlB97tW3Yey6pT690M+W70MDnyJD9Gff7Z9S+7vteGGVqxssi4j0MJjj/i/L3qvpeGOPnwbjPg3F+7oy73xhj204H7pSZB1ViDqT+XvgDZa7mce3PzVGf/nBnKvPZ5vBp3aD+3WXF8ArulGg+jB7A6s9PnJrz5n3r+Xp3ZPV77S9bzLrl1tW2aW6zpl/MeBGaKJVJL2x/yWvWHQ38a7aqJEaZuuwJmfn4SsyngX/J1rRmEfE4ykD7NFZPxu1ESQQdlq0pvaJUMD6WMgfp3CD79pQs+A8pzz+2t/MCSoXa9vPGRMThlCqS7Uq4T6VcrX9PJeb1lAx6O4n4YOD1mTmQwY5SYfM5lbbtQHmmeNse+9OOmZ9s6YrZlJLU2ZbVpwA7hlLc7lUMVhC+CHh1Zh5b2Z+XA5szOKXYOXRPxdZVdXhTSlLnHgxOR/c+SnJvbxY/5duh1KeWO51yrD9VibmSkujsc0yPpVSBbSdSbwe8KjM/Uon5NuWWwPZ5/QLgfpn5l5WYrvfPsGN6NvVp/F5Aucpz2Ji2M//4zF/3Zcp59FgWP8XgsPfc+cA+laTFPwPPzfrdRccDfz+B988ofd31uTPsvdC1nWFVuz9CSXDu3Vp3T2DvzPx/lbb9jHJH2zg+D+afB4vtt2HnwX70nwKy63Nn3P12CWWO2HG07aGUu7g+VIk5G3ha5b2wfrO97zBYRbm2P5dSzrOu/bmYclt6O2ZrygXN9sC3c1q3udfL1lXflcQB7pSLjoe8I2IjSjatPSj+TnYc1Ii4e3YX36iuGzGm88H0KEVBNqm1OcoD/49rrfsyJes1sDw7ih0029kzM09Y7PJh7Y5S5fpE+vX1KNWNR4kZ5TwY1gd/CWzZeq1jsjJdyxLtz0HA/1GfU7bahgW2cyjlisH81zo6M38U9QI1x1DOt0Vvv9nOclcu74wZtzVJxi1nomHcScTmNXvvz7j7YJaTNwskbnajFPfpfUxNmC69hY5dZp424uvO7Pk+zGL7c23tny5LdR4uVxtGfa0YPq1b59RYuYIrMDvAnQIxvEpkV4GLK4DfULLX8x8a34ny0Pjxi32tBbYztpiIeAylSu6JlTZ/FngKZYLq+eue3Pz82dbyPSlZ+oGM+xLszzT39djaFhGvAP6JHgWElmB/rmJ8FcWH7c/PKVnN9nb+tvn5XT22fxjTXbl82IC5M6kzJAmyJ+WcW3SSqisZtdB2RkhSDUucHEklEdT8vOgE0Rq0rZY8GppsGZbUoZ4IOrr5uW+CZpQkVe+kDqNN+Tbs/A3Gd0zHmeQd5bweJSHYNcXgqAnBSR3TXp8TTcxY3z89k6JTneBs1tX6YJKfB8M+e3tPATnBfut9Xo+rbTG8+vWRrCrs1V7XOftAlxgy+8CscoA7IVEeDK+uotxj/5rKuodTBnKvrsS8lVKM54LWdo6k3Ov/sUrMIcD7O7Zz98q6YTH3oVT3bU/TE8C/AB+oxDyLcvvP7VYLiNiR8gzhVu0/cNFdZGpTyh/2kyrbeQDldsgvVdr2GAanWYHyIbMzq0rkz495AnCXSl+fTSmGc24l5p7UqxvvTHm2uk9F5C2b/15eiek6D06kFGT4aiVmLwb7BsrtTZGZt2291vqUIivtoi2j7s9CMUtdUXx9SoGT21a201W1e1gfTKpy+dz7pr0uKMV2qjUVpiBJ1ZmMmoJE0Fxi8fuV/akmiEZp24jJlmFJla5EUO8EzYj7cxj9kzpzdR4OrcRUkz0LtOEo4CEs0zGdkphxJgQPYwLHNEpNgMMoRcIW+zkx7vfP2JK8U5LgPJ5yO/GyfB4sEPMb+k8BOal++z/KLd3L0rYoMwzclXKc2tXBH0/5nnZUa92BlBovv2BwirjPUn+WNuie7nLYtHsrelDsAHdCojyY/nHqVU4PpJQqb5+Yr6ZUwKsVgHoNZSCwWkyUsufXAn9fiTmC8uzNda3l72u23a7KNizmg03MWyoxr6N84Ldj3gKsl5lbtNo8N+DYMjN/3Vp3PuU8vWtr+SbAryiD+fYVqC80bduntTwoA75aX7+S0tdPqMScCNy60teXN/v40EpMV3Xj0yjnwP17xJxLKbTyNgZ1nQdXUc6DdhXLoCQF/ozBfvsoZaDUrsC9fdO23ca0P8NizgN2yvFUFD+JUoBrtWe9mv05l1JFub2drvNtWB9MqnL5RcC6mXnnSszNDB5PKP1zG+qfB8OSOl1JkEc0bbhNa/vDklTHUt4j7WTUsO0MS1I9gdESJ7Uk1fkAlQTajpQBUjtBtFDbRkkedSVbFkrq1BJBoyRoRklSjZLUGTbl243UK3B3Jm+av6k79zympzOY/IXRkrxPpDzH1n6+cdh5sAclgdWnr3egJMO+WIl5Ass7xeCwY3pue3mz7hxKcbt2RddhSeuleP/0TYoud4JzoffCbZbx82BoTPafAnJi/ZaZt5pA27q2fy7lu2Vt8NtVRfkTlM+XxzE4KH4epap7zAtJYF3KwPyOlbZ9n/pUY52D4pXCKsqT8326q5z+BfA/2XrIO8ptEHfJzNdXYl4CnBqlqtz8h8ahVNI7qhLzPuDMzPx2a/kBwN17xjwP2L6jbYd2xNwJeF2TJZ3f5n0p2aczmkzk/IIhtyuh8d7W8j0pxSUGKlNHxLco1exWW96s+w31vt6dUjCkFnMx9b5eD/hce5DUxFxAvbrxMcB2PWNOB37d8zxYF/hix/5cTb3fDgE+1wxI2gWEThzj/gyL+Rbjqyj+AuC/OvbnDR3buU0T26cP9mMylcs/0rSl5rdUKr027UvqVRLvQkmctIvxzH1xfj+Dg+b7AgNfCCgZ5KCewNud0hd9tvMFypeVWszjqVeFPq2J6aqIfAmDktW/DMy5lHIu9G1b1/7cGxiY2oLypSMZoRpwR8w61Pdna8pnQt9++3FHzHGUQX7b/YGbol6pODradn9KcrFWqfg3wDrN/9uvtR79j+ntKV/C28nX51LeCwOFVihzYdZiDqZUX+5zHvwppS/79PX3KfNV1razF/3PnUkd07tUjhs004ZVlt9MeY9M4v3T1QcnNe2rbafr/TNKv41StXuh98Jyfh4Mi+mqXD7082BS/Tahtt3c8V64bbO+Wh086pWxHw6ck5knz3udS4CTI+Jg6hXFb6K8T+Z/vs19Tt6Jcvzag+Kge4aMFcEB7uS8mHKLXM1fMPjBBOWWl64y3btQqkHuTZknEMof8MdQ5hmtuTOlLHnbUzuWD4s5iHIFteZPWDU4uEVmvilKRdcntdq8X2ae1WRwH8uq5w2+xqrbiQeWZ2b7Q3ZuO3t1tAvK1ZeBdmfmXrFqipf2uh0j4h6Vdj80M8/qiLlrx/LahPRDYxje17tQKiG227b7kLZt1rH8uIi4LT2KtoyyPwvEPCJ6FgMasp3jolzpr75WRLypto7y4d5n+2dQSv13Fa75+JB1tdd7cMfy9qMK872L+qAYyvNPfZM6V1NPgvwH8A89k1QbAO/vuZ1hSaor6Z84OZN6ImjuC0Ztf84foW1d+zMsedSVbBmW1OlKBI2SoBklSTVKUufaElqN+Qj18/dqyhQeL6y07Wr6H9OfMr4k777AvXueB5+jvOf69PW3KY/21LZzNss7xeCwY3oFpX/aA44DgMN7Jq3H/f7p6oNhSdHlTnBeTfd74cqOPpjU58GwmFGmgJxUv102obZVE9BRqoOfxaqpDGFVRf69gBcC72mtWwf4SMeg+FLq0x/+BPho1p8DvoHKoLhZN/A9fiXxFmUti4jYDCAzuwZuU2mx7Y7hlaQ7K2P3jRmlbfN+fytWH3R1Vsub1P4sRQzltqqBYjMR/QvUdMVkxwdpTK5y+bCYXYBfZeaVlXW9qyTOS/a0i5bUklRzxWOqyahJ6mp383N1f8a8/c7ETd+kzrDXo2eCZk0MS9x0reuT7Ikypcwx2T1dx1H0OKaUKTn+kJm/b73WZrXlC62bBqOcOwu83liO6QLH7jDKfOiL/pwY9/tnlO0sENe733q2a6H3wqG1djPBz4NhRvk8mES/ZeYrlrpti2xDZ/Xr+euaQfFbWDUlFKwaFL8yM39aiX8B8K1sTaXVrKtONdase2Fm/nvXfk07B7gTEquqpT2ZwQfDOyuiRcdD3l3Lm3XHZuUqZtfyUWKiXBk7A7iBchtDUjK2R1OypldXYraj/FFbj5IJC2Bjym1Br8zWs1Tz4qpVpruWLxDzQ8qcafv0aPePgO9SPlAW1e4Yb2GfTSgZuZ9X2vwxyrPafdp2HvBLynRN84t8XE0pwnLGUu7PpGJieEGkD1EysX0K1HTF7MTyV9MeFtM5+F0Tk0hS9UnCNL+/YBKkq93jTBBNInlEx/Rgzc+9EjSjxCx3Uqf1O72P6ThjhrRrbOcB5cr0WN4LLPMUg836ZX3/jDMpOg3vhXG+t8cdU2vvsP2ZcL9VK3ozZHrKUWJq2x4mFlGJfdiguM/rzSpvUZ6cj1IGEK9j8MHwT0e5d75tU+AJc39U5wlg7yi3TrXtCuwWEfetxNSWjxrzH5TKvndrZbsOAI5tMkZtR1Keo9liLnsYEetSbq34cpRnd9t2B7aLwSrU0bF8oZidKVmvPVrtfivwtYho38IRlCp3r6XcSj2/3Uc0Me+qxGwRES+ttO3hHeuGxRxCee6x3eYDKNUJn1dp21up92lQKj8/KzNPWW1FxDuBz0eZ8mAp92dSMa8AftdO0MSqgki7tBMAsapATZ+YI4FPRkS7eE0AW1XOj7n9qa0bNeb2ld+fc0JEfJx+SZ2uBNF2lMH/9TQJlYgYe5IqIu4D/C/l0Y1bkglRbv2sJmEaZ1F/TvmciPgm8xJBTbtPpvTfAxe7Pwu07Z3Ai2gljxZod1ebh637CT2rQkdE76TOkJidIqKrIvHxQ/ana13vmIh4BOWzr88xfRelYumjKH+P2zG7t5YPi+mdRBzlPGjOt5Mpx3wc74Wuc2cixzQi7k35LrAB5btQMPn3T1dfdyZFp/y9cDDlCu5yfR4Mi+msXD5kXyfVb6+gfL+aX/n/EZT3PKw+PeUjgDdGueX60T1jXk/5e9tn8Hv4kP05nHJL+GoD2wUGsdXXW8xAuuP1pp4D3Mm5X2berbVs7sHwpDy32n7Ie8fm59qD4XegVNVtPwi/B6VYQ3uQAqUQwrhi/gSInHdbRvPzWyLizR0xOzUxN82LuYkyOPgE5ZaydrbvAEoBknbxAihXKvvGrJeZq1V+znI71TMpRQJqMetm5qdaMTdFxNMpmfDbVWI2pF7Y5/FN2/rEbAFc39HX/9TRtr+lPEdS25912oPbxvMofbDU+zOpmK7Pt7mCSH0L1HTFPIXS130K1IxS1GZYzHM6BsVBKSjSN6nTlTx6E+UZoK2XOEn1dkq1ytUKdkR5briWhIHhSZA7AJ9jMBF0drO+z/4Ma9tXKf281MmjzYEHjClBM81JnWHJm+MoBW96HVPKF/SumDv2iJlUEvFllFukx/Ve6Dp3jmS8x/T2ld+HMri9Y2auVtF1wu+fcSZFj2T53wvvY3hF8aX+PBgWc3qlb2D8Sd5R+u2fKbN3XN1q96Mo31X/urV8U8ozvnfoGXMW5SJJe1D84ShVxdvfKwK4U5Rnm2v7s3nH/hzdDMD7xCw4kO5YN/Uc4E7Or6JeEe1plC+ytcpn51HK3+/YfrEoD4Y/LzPPay0/E9g8Mx+xxDHHAw+Iec/wRbmt6EDKoK8W80nKFekHsnrxjwMoX8AHqkxHxB9TCrocVGnbfiPEPDMiXk4pOjC/3ZcDF3fEPCMi3kN53mt+u6+iFB95fSXmVfSvjN0V8xC6+/pXQ9p2Rsf+PDUivkgp7DI/5lrghAnsz6RirgdeH/ViMyfRv0BNVwxMpnL5sJgjqA+KARghqdOVPLojk0lSbUalwF5mnhwRW9M/CbJORyIo5u3DYvdnWNvWnVDyCMaXoJnmpM5+wK06vtCuN8Ix/cgYYyaVRLw1lerka/BegMkc066k2w4s//tnnEnRaXgvrNPRtkl9HkyqcvlS9FvtFuqgvj83N//vG7M5sGtlULwvsBXwksprPYF6RfE3AutWBr9BdxXyYTGjDKRXBJ/BnZAYfDB8Lqt0EqVy4NHZesg7ym2+22TmwFWEiDgceGtmntNa/lRKduk9lZi3AB8eU8ymlOzOPShvUCgFPI6hZMP/rxKzPuU2jh1YdZvGJZRnYn8EnF8Z5D8U2Dgzv1hp23OA43vGPIJym8jerXafQbld8weVmN2B+zQx828vORn4QGb+rBLzYMo8gL9oLd+F8uXn7B4xm1IqRe7JYF+/A/jzVtsuoWQM35uZ51e2sxvl9vL2/pxOmfboF5WYce7PQjHnZasgUhOzblYK/3TFNOv+lDI1R60g0q6VPjiG8sdrYPmQmK9T5otbdIGaruVrEHMS8Or2oLhZ93vKoxHtpM7plKTOgyox1wP3rSSPPkn5w/toBpNU+1Lm/G3HnE5JOG3dYzvvonyZObC1nf0pUxU9sZLQ+DYlCbLalaF5fXAkg4mgj1E+i/frsT/D2rYN5f3XTh69j/JZ9YxK264DHtxzf66mVARuJ1te1Pz8TgYTNJdTPj/GEfNq4GPtqxVN264FHlVJ0JxESdDcqUfMNZQvrC9rxwAfoPwN6nNMn0iZ5mkcMZ+nJBEfV9mfa4BvMJ7zYNzvhaupnzvjPqY3A39FfcCxB/Dsyv5M6v3TFXMo8HrgH1lZ74X3UOZ2X67Pg2ExGwPPHlMfjLvf3k15XKdd+f/JlM+D/24t35Pynf2RPWNuS7ny/etW206gDHwH5pqNiF8BT8nMr7aWX0X53tF+DDAod5s8rmfMSZQkXXtQHMCnMrM6u8hK4AB3GUSPB8MlaSELDH43BV5Jv6ROV/JofcoXwPswmBgYW5KqWfe3lARaezs/Bn7ZM3GyDeWPeDs5MdemJyx2f4a1LTO/FBF7VbZzOuUug1oSZpSkzlasmiZuTRM005zUGZa4+SnlNuE+x/QjlIHVOGJ+yASSiM26Z1Juz6y9FwYqpI947oz7mA47dpcB/9Penwm+f8aZFJ2W98IT6FklPkaoLN83hjFWLl+ifrsvlcr/zc/VGQGiY7aArpimX17D4KB4T+CfMvPIdtu6RJny6a3tQWyz7huZ+bCeMdWB9LDXWykc4E5QRNydwQ/HozPzRx3rhn3xGPgSN287B2XmEYtdvgYxb6fMs9ben+Oi3FK6T21dx2u9JitzdA1bN2oMcErPtr2OUsF4IIaOCtgxvMr1KJWxv0QparOoNjcxo/TbKG0bZ8wRlCzxPqxeEGluP/disFDSe4DnV2KOpruI0tgqik97jLRSDftCq+nmsRuvxfZnLHNF8WmLmZbzsGtQnFMwhd6scoA7IVGe6Xsm5daO+VWU96UMnraurPvb5ud3VWI+mZlv7tjWJKZgOYzyXMxBrbbt3+zLzymZ7/a68zLzRbSMs20LxPyGUnm4T9t+1/z+Ua2YQyjP8bQrYAelmuAfVZq2KeXWtfa6YTFvpNwqV+vrapubdnf1wSXAH4+pbeOOuZRSjOGoXL0g0jea33lYDlaS/nvgXysxh1KmLajdlnMc5Y9N267AYZV10x7zhazcAgy3ZG8PYwwJpyjTnX2WUtW7new5knI8nszqU6F9nlWJuvYUaV0x404efRA4jcE+mGtb+8rDWNsWZVqJr1Oep1xsgmZYUqeauGm2NbXJljHHzE29tw/9j+k4Y2rn9dim/pt37mxAuQNjRZ87zf4cyqo7Smr70143SoJzbUqKbke5m+GRrD5d4EBFcVZNI/guynfMScXcUoW8FbN7a/m4Y06iqXY+bFBc6dNxTk/ZGdN3+0vxerPKAe6ERMS5wD3bf/Si3PL3W0oxqfa6cynHaOdKzDXAas+4Nnam/CE8s7U8KHO7tZePGrMLQGZu0GpbUCqMrt8OaAaXt6UUoWpvZyPKPrXNFYhorxs5JjMHCgEs1LaOmJsoRSwunbd4rtjCDpTnnKK1bsfm5wt6xGwLZLtPm76+sdJmWLjfxtW2scd09PU5AJm5S2Xd9R3n203AH1g1R998e1Aqdra3tQelKMQ3GDTNMQ8CHlxZHs3vf50xJJyiFF56IuXWqvZ0Z48HvsRgIuijTTue1SNm3Mmji5ttLFfbPgPcj/Ic1mITNMOSOvswmLiB6Ui2jDOmM3kTET+jDEjGcUzHHXNHBs8PGC0h2HXuHAj8HdN77nQdty8D28NAVfdR92eU989KTIoO69OrKM87fybr1cHvnoPVwT9IqQ4+6zF/RXmsJhkc+H4VGLhNnTKAnruA0z4GH64sXyjmfZm5ZTsgygwC/0a9yFTXdoa93oWV11ooZqwD6WniAHdCIuJHwGMz88LW8u0pxQHuVll3PuUY3bUS82NgN0rBqvlOo7yR799uQhNzvzHFHEeZz3a1N0xEPIDyZfphmXlqa91lwFXZmu6gWXcjcOdsTegeERdRnqO585hirgce0rNt11G+xLQrYF8KXJaZ96nE3ADcNbsrY9eKJHTFfB/YKlsP+zd9/S1g2559MM62jTvmD5RnVdoFkb5JOR//NAcrSb+cUsCtHfM94MeZ+ZCOtt0juyuK175ETHNMUh8UAzw8M9epxIyScNoIuDkzB6qNDkk0nAuQg9OkLZScGGvyqCNxMqm2bdNsp/Z6XdsZltTpOt57sPzJlnHGbEQZ9LWTNwGc0nEejnJMxx2TjC8huFLPna6k22eBDTo+x0bZn3HGTHNSdJT3wnkA7YskzbquPpi1mP+jvLe2qQyK/5OS+G0PhA6gfMbXpjc6kJLs6hOzL6W4WtunKRcpPtFjO9tT3lvt1wtKMqxvTO+B9ErhNEGT82LgK80bdP5D5jtRKuTW1t0GIMothu2YEylXFr87fyNRyn1v1x4sN+suGGPMfsDxEXEWq7LX21JuT3kO8O6IuF1r3Y2UeTRrvkN5E17eWv4RuufhGiXmP0do24eBpwLvaTKlUCpgX0ypoFjzOUq2vl2g5jCaLyw9Yg4EPtvR1x+hfx98dYxtG3fMayil6b8eEXdoll1OuYISleXHUBI9f1VZdzLl9u6ad1CZqoJSbfgOleXTHnMRlam5oPzhj4j7t5M6lGk3Lh2ScNq5kjg5GdgpItbJynRnUZ8KLeZ+7hFzBcOTR32nVVvutg2bVu2aqE9dFuXHXlOxTWqauEnF3ATcQH2O9nXGeEzHHkP/c3TWzp0E3jbXT/NsDNxujPszzpgrKUnRlfZeIOrTBTarqlMzXraWxOxKKWR401xf5aopwI5kMtNTHkh9yrmrgQ17bucGylRJf9aOoSRN+sZs3NE2KI/UrFhewZ2g5o/fA1j9OZ5Ts8ypV11HOemqMRNr+BBRbuu5pW3Z3O6z0LrlNmrbYhkrYE9zf2p5RZnq6wfZmpqrWfdS4BmU29bnJ0huB7wqMz9Sifk28OLM/E5r+Q7AscAWrLqr4/aUpMm/Ay9k1VRoc+tOoXzZfGCPmIsplS+PrbTt08C/ZL9p1V5LqXo8t51otjP/2bH5y0+qtG1u3UUjtG1TSrZ+W1YlKeYSNO+jJGj2bq37crPNx1ZizqY+FdukpombVMyZwH9k5nsrMT+j3NkxjmM67pgf0n/qv1k7dy4EHl0ZqG1KKZj4MwanvZu/P/PXzd+fpYw5B3hj+3NvDfpgUu+FiynJ3L1ZfbrALzU/P6G1/PPUq4PPYsyWlLsUj2BwUHx34MBKUql35f8FYs4GnlYZeD6UMhVP7W62ru2cTqmI/KlKzOXA/XvGVKfqa9ZdnJnbtpevFA5wJygigsHB6ncyM7vWNT9PZUx2nDwRcffM/FGfdZOMoRTAelxrf75MSSYMLM+OghzN6+2ZmScsdvkaxOxNyaYNtC1K0Y6p3B/KH5T5f3BWQnXwmYqZ9ztjTZAMS/Z0rRslZtymuW1a3QKJm30y83+an8d2TMcds7Za7LHT4tifo4lSs+ZgBge+n6cUg7tuAm14KHBhe+DZrNstM09bytdaIGbYVH292jZtHOBOSEQ8hlK57zxWPbu1DeV24w9Rbuttr5t7uPv7UxizE/D8zDy+sq+Tqog8SswvKbeFHN/anyc3P3+2tXxP4PW1q1yT2p+I2B84nHIs2m07EXj0lO7PVZRM/YqpDj6jMQdRzoNeSZCuxElXgmQJkjqTSLYc3fw8sDy7p2/rijkmM8/uiOlM3kxDEmQlxjTr+hyfUY7puGOGJvfWonOncxrBrnWUflurY3LItIBdYszTLM5SzDCTalvf7S/F680qB7gTEuUWhb0y84LW8h2BH1EqJLbXnQ+QmTtNYcyRlPv22w/UB6XK6PsZ9HDKLSHtdZOMeT7l2ZerV1tRno2KSh9sSvlifFJlOw+g3Kr5pdbyoExN0F4+aswjm7bdttK2yym3Ok3j/jyBUkzkhlYbprk6+EqNuVu2KprfsnK0pE5X4qQzQTIlg/lxJltGmb6tK6YzeTPl/TbNMZ8G7sbyHVPPnRFiokwx+EjKLbXz93N/uqcYnKuPcehaHLM/5dnuy+gx8J3W82BKYoYNIifVtq5B8aT2Z6wD6WniAHdCmgHHrpl5Y2v53DRBt6ms6xqkTEPMNcC1lHL7bUdQnm9p3/rxPkoxp79ZxpgPA5tl5q/nL4zuitWbAL+iDOZ/23qtLzTb2ae1PCgDyD8bU8zHKAPF1QoMNW27EthySvfnREoV5dWKlw1p2/Ysf3XwlRpzHuXZura5AfNmPZMgXYmTY4GHMpggGXdSZxqSLb+l//RtXTE/oAzG2smbaU+cLHdMZ/ImSuGU2yzjMfXcGSHp1vTBrdtftiOGTjHY1W9rU8xhwHMpt9u2B76PYvB7ECzBNIsrMCYohZzWG1gRcTOD32mWom1d2/9N83rtNox7f4bF9B5IrxQDO6sl82Hg1Ij4JKs/6L4v5ctibd1tASLiFVMYA6Wq5FHtHY2I9wFnZua3W8sPoMxftpwxbwPOiFKZcn5l6tuV1fHe1vI9KQVDfp+ZX2+91rcoVfNWW96su3qMMa8G3tfRtqOmeH/OZuVVB1+pMTdSvui0B8UBnE+9QmI0/7Xd3Py/FrM7pXp3u5Ln3CDy/dSTINcsc8xewJ0oV3Hnu6UabsvWlP0fV8wdKVdl2lUs5xIatWN3GmV/1uaY4yjVkr9fiVmX5T2mnjvDY86rHDco3x/WrSy/P3BT1Cu+d31WrU0xfw78JDM/udoLRXyKUiG3VvX+IoZPs7g2xPyG8hnym3ZI898k2nZzZfuwatq9jZd4f4bFbNTRtgA2rCxfMRzgTkhmvikijqZcOXtQs/hSYL/MPCsi7lFZ9+jm52mMeQxlntGaO1Pmkmt7asfyicVk5pbNFarHsuo2n69RbhWitjwz23/U515rr452kZmbjTHmqGZwU23bFO/PH8WYqoNn5sFDtnPXjuVrTQxl+quBQTFARJxK/yRIV+JkA+D9E0jqTEOy5Q1jjNkAePMKTJwsd8xmwAnAX7dDgNNZ3mPquTNa0u1ewOei3xSD15aXXKtjNgf+gUH3p/Tx9iz9NIsrMeZqynRQL2wHRMSvJ9S231IfeP4z5ap8n+1cTf/9GRZTHUg36y5uL1tJvEV5GTR/tMnMXy12nTHjj1lposzTN78K7uULrZuGmI592SgzB26l6VpuzOgxzfp2EmSuyBS15R2Jk1vWdW1nmo2SbBlnTFfyRt0i4nDgiMz8VmXdfwLPYhmP6aRiVuK5s4hj91J6TjHYtXxtiKFc3X8vg9O9/Rp4QWaejgY0g8hjsmPqp8x8xUpqwyivtUBMdUrAUdo2bRzgTkhEbAe8lVJc4deUDPTGlFuA30UpPtFeN3+evWmN2Z2SHarFPKq1bhpiXpmtglnzjtEPMvOPFrt8UjERcW/g25Rb1y5p9mebZt8OA14MbNJad30TfqtKzDuBFzUx8wsIzY+Zv3zUmOdn5hkdfbDcxSXWppiNMvO305wEMdliTN+YZl2wwqbXW5ti6CmmfIrBaYih/G2tDoqXu20rLWaYSbWt7/aX4vVmlbcoT86nKIOR/eaysRGxLvA0ypWU51TWnU0ZmGw9xTF3XGkxETF3++58uwPbRcSft5ZHx/JJxrydUnhi19UCInYHvgrskZmntNZ1FbGYZMznI6L9PCSUKtdbRMRLW8ujY7kxC8dsVPn9OedFxIW0kiBRJniHVhIkyi3Ah1FJnLRibkloNDHVJMiUxHQmW4CzqN9a1rXcmCmIiYgnAe+gx7R3EdF7qjxjRo55PuX28j6D3+PpPg+61q1VMU0ic7VB7QKDl6nen+WMmYZ+G9KGiezPuAfS08QB7uRskZmfmr+gGXx9MiI+UlvXZEznfs+Y8cR8gvI8cfsP7AHATQwWy4By9Xc5YzajFCdZTWaeHBHrtgedjWoRiwnHbA1sSqmyPN/jKX1wu3YcpaiBMSPEdAyKA9gS2GdKkyAmW4wZFjMsefPflGfHLlgtYNW0d3u1lu/IwlPlGTO+mK9T7vBpD4p3j4ivARexugC2ioh3MejhHevWppgAbl9ZDqUQaG3O4Wnen7Wp36rbb373oEobxr0/w2JGGbCvCA5wJ+f0iHgPpXjL/GrEBwCXdawDiIh4oDFji7kKeFtmrjatQUT8MaVAzUG0RMR+yxxzDfDciHhGa3/2B86OiC9SChzMX7de0wfLGXM58D/ZejYoyiT2d8nM11f64FXGjBTzOuqDYiiDvmlNgphsMWZYzIOoJ2+CkvS7hEFJ5TygDLLCmInF3JEyVdAF81dExG8pj0N9rhL3AsqUQ+0pb55LGSzXnjNdW2L2A27VMYDaaJnbNs0x09Bvz+kYrP4V5TNxqfdnWMwoA+kVwWdwJyTKPHcHA3uz+u06x1AGDc+urPti8/MTjBlbzI+A8zNztexxRDwU2Dgzv0hLRDyHcmvQssQ06/4WuEd7fzLzSxGxV6UPjqF8+RhYPqkYytQSv8zMX7T2ZRdgncwcmLc1Ih4MnGtM75hTgb9qD4qbddcA32AwOfFmyh+x9vRg+1OutFwyQzH3BZ5YSRp8m5I0uGOl364DHmzMssb8gfJl7R3tGOCVlM/z9hR2L2p+fmdr+b6UpNtWxkwkZlvKY0KrJS0i4quUK+/b0BIR1wKPysGp/06iTP13p7U45hrKe+Fl7Rjgg5S7WlbS/qxN/XYzZTDbHni+gnLB4/ZLvD/DYo7oaBvA2zNzi8ryFcEBriStcM3gdyCZ0KzbijLAm7okyISTLb/KzCsr/bZuZp5V6bcHA+cZs6wx3wZem5knVGIupkxX1+fcOSsidjVm6WOaZU9ncFC8H/CZzHwDLVFmPfhDZv5+McvXspiTgFe3By/NuguBXVfY/qxN/VZtQxNzRmbusMT7MyymOpBu1v00M3dsL18pHOBOSESsR7mCuw+r/yE4GjiScvtse93nKX88nmTM2GOeTCm7347Zu7V8GmKOBg7PzBtoiYgPZOYh7eXD1hmzdsVIK1UzKB5ITDTrtsoFKmRreXUNimtJDg03bAClbtPQb+NswyivNcqAfRY4wJ2QKMWNrqY8Lzr3LMs2lAHN44EvVdZ9FAjKXH/GrL0xh1CeTzuY1QWleuUfMWhTym2p7XXGzGbM97Jyyx9AlKITl1O+aG5FSbBcARzX/MrjWsuPBt4DPH8GY/YB7lCJ2au13JjpiDkaeHNmXk0PEXFstgofDVtuzGRjukxD21ZizDDL3bZpjhlmJb4XRn29WWWRqcm5X2berbXsEuDkiLg+M/+6vS4iEsjMPNmYtTrmfxksRJCUgc2dgNOan+evm7utxJi1I+YOTSa2LYBnAq8FHpHNnIkRcUfKYBngYa3lBwJnAP86gzF7LDLmAGOmIuZ5wP+L8oxue+D7JaA2P+6uwG4Rcd/W8uhYbszSxNy78vs0v/vvwAuXsW0rMWZYn/4vg/05ybZNc8w09FvX9kd5L4yyP8NixjqQniZewZ2QiDiZMp/pf2fmzc2ydShzs34I+MvKunOa8F2MWatjLgUuy8z70BIRNwB3zcFiVucBt816wQNjZi8mgZ8yOCgOYIfMjErMOQCZuUtl3fWZub4xxixzzJcpz4//UWVQ/GbKlFHtc3sP4GZWDajb64yZTMyDgAdXlp9KqZY98MzfBNu2EmM2oty90+7ToCREV9r+TCpmGvptnO+FUfZnWMxxwGMr2w/gC5m5dWXdiuAV3MnZF3gL8B8RcXWz7PaUk3EvStalve4Uykl2mTFrdczFwKup+xzl1tWLWssPo9zebMzaEXMl5epXO4aI+ENEvBw4KptnFqMUnory46pnGZvlBwLXGGPMFMTcl3L7/WU0mp/fEhFvAJ6XmecxT0ScCWyemY+gpUkeGTOZmATexuCX7WuB9Vbg/ix3zE3ADZQLJW25Avdnbeq3cb4XRtmfYTFbdLQNVvg0QQ5wJyQzL4gyV+X/o1X4KDPPjohf1tY1P+9tzFofkxHxCgaLdTw9Iu5eW2fM2hMDvJ76oBjgNcDmwNcj4g7Nssspt3hSWX4McD/gr40xZpljfgt8M+qD4nMoc+G2vY5yO3PNO4yZWMxF1L9sP5VyW+Zytm0lxpwN/Edmvre9IiJ+scxtm+aYaei3cb4XRtmfYTHVgXSz7uL2spXEAe6ENF9K96WUzD+lWbwN8ImI+DllUvT2uq9QMjLvMmatj6GJ+U4r5meUZzY/2VpnzFoU0/zudR0D5rc2/34FLRFxUGa+uGP5K4wxZjljImJT4OPAMyuD4odn5lXt18rMz0TEQe3ljR9l5jnGTCTmC1S+bDcxN1Z+f5JtW3ExlAHUzh0xz1lp+7OW9dvY3guMsD8LxHQNpKH+PO/KkZn+N4H/gHOBW1WWrw9c37HuXMrcgMYYY4wxw2J+AXwXeCWlQvezmp+/C7yyHTMv9qI+y40xZopiDprithkzPKZ67KakbSsxxvfCyu23cb4XRtmfYTGd61bCf17BnZybKVdlLmwt35pyRa+2bh3q98UbY4wxxsyP2Ri4f7bmSo6Id1Ceb/yLStzOwAYR8f3W8gC2qSw3xphJxwRlyqea90fES5axbcYMj6ket+Z37145dtO+P8sd43th5fbbON8Lo+zPsJjXA0eMsG7qWUV5QiLiccC7gfMoRYMAtgN2opxAB1XW/XHz8/eMMcYYY4bEbAD8aWauNiiOiO2BHwO7AVexutMog+n7t5ZHE3M/Y4xZ5pjjKOf32ZWYewH3WWH7szbFnMfgcYMynUoyeMvktO/Pcsf4Xli5/TbO98Io+zMs5p7AmZW2BXC3zNygsm5F8AruhGTmcRFxN+ABrP6M3KmZeVNEvKm2jnLyG2OMMcZ0xgB7Al+JMpVQe/B7IrBRZn6XeSLiGGC79qC4WXeBMcZMQcxmwAmUAlSrrQJ+uAL3Z22KuRHYn8Ev228FHrUC92e5Y3wvrNx+G+d7YZT9GRbz4462BfXpi1YMr+BK0gyIMmdydcC8fK2SRhcRhwNHZOa3Kuv+MzNrt95rCnjsxsv+HM009Ns42zDKay0Q82PggFk8rxzgSpIkSZJmwjrL3QBJkiRJksbBAa4kSZIkaSY4wJUkqYeIeF1EZER0FmqMiD2a39lj3rIXR8Sfj7C9ezfb3KxHzMD2JUlaGzjAlSRp/M4AHtT8f86Lgd4DXODewGuBRQ9wO7YvSdLMc5ogSZLGLDN/A5w86e1GxLqUApLLsn1JkpabV3AlSRrNrhHx1Yj4fUT8PCLe0EzXNHCLcDOv4fbAfs3yjIgjm3V3i4jPRcQVEfGHiLgoIv4rItaLiAOBI5rtnTcvdocmNiPiXyLilRHxU+B64I86bpH+WkR8KyIeHRFnNO0+MyKe3N6xiHhmRPyoac8PIuJJTfzX5v3ORhHx7017r2vaf2JE3H2svSxJUg9ewZUkaTT/A3wYeBPwWOAfgZuB11V+98nAl4DvzVt/ZfP/LwJXAX8N/IIyl/HjKUnoLwL/DLwaeBpwSRPz83mvfSDwE+DvgN8BPwM26WjzXYF3Nm3+BfAy4L8i4u6ZeT5AROwJfBw4BngpsCVwGHBr4Nx5r/VvwJOAVwHnAZsDDwFu37FtSZKWnANcSZJG88HMfHPz8/ERsTHwsog4rP2Lmfn/IuI64BeZecutwxGxBbATsHdmHjMv5D+b/18ZET9ufv7u3CC0JYDHZOa181531442bwE8LDPPa37vDMpg+enAG5vfeT1wFvDkzMzm984ETmP1Ae6DgI9n5uHzln2uY7uSJE2EtyhLkjSaT7f+/UlgI+BePV7jl5Srr2+OiOdGxM4jtOO4+YPbBZw3N7gFyMwrgCuA7eCWZ3h3A/57bnDb/N7pwE9br3UqcGBEvCoidmtiJUlaVg5wJUkazeUd/77zYl+gGUTuSbk6+ibg3Ij4SUT8dY92/HzhX7nFryrLrqPcfgzlCu+tKIPetvb+vhB4P/CXlMHuFRHxbxFxmx7tkSRprBzgSpI0mq06/n1pnxfJzJ9k5v6UZ13vA5wEvCci9lrsS/TZ3gJ+AdwA3KGybrX9zczfZuahmbkTsAPlFue/oUxpJEnSsnCAK0nSaJ7e+ve+wG+BH3T8/nXAhl0vlsV3KYWdYNWtztc1/++MHZfMvIlyNfkpERFzyyPifsCOQ+IuzMy3U/a9zy3akiSNlUWmJEkazXObaYFOpVRRfg7wusz89byx4XxnAQ+NiCcCl1Gulm5MqWr8KeB8YF1KVeQbKVdy5+IAXhARR1GusH4/M69fip2iXIE9HvhcRHyActvy65o23zz3SxHxf5RKyz+gDOwfDvwJcNQStUuSpAV5BVeSpNHsTXl+9hjgWZTpfP5pyO8fCpxDKU51KqsGjRdRrtoeA3wCuBPwxKawE5k5N7XQnwHfamLvNO6dmZOZJwD7AbtSqiK/gjKd0GXAr+f96jcoV7E/TpnO6KnASzLznUvVNkmSFhLziiRKkiQNiIhtKFeY/yUzhw3iJUlaVg5wJUnSLSJiQ+AdwImU26jvArycUmTqnpnZp2qzJEkT5TO4kiRpvpuAOwLvBjYHfgd8E3iag1tJ0rTzCq4kSZIkaSZYZEqSJEmSNBMc4EqSJEmSZoIDXEmSJEnSTHCAK0mSJEmaCQ5wJUmSJEkz4f8DvcjFMCCKcFEAAAAASUVORK5CYII=\n", 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", 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\n", 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", 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\n", 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", 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\n", 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ERERERGQi6DZBIiIys3S7FhERERmQykdwzWxjM9sq8/9lzOxjZvYTM3vbYNITERERERERqabOKcqfA/bI/P8jwCHA2sCnzOytTSYmIiIiIiIiUkedCe4zgV8BmNliwL7Aoe7+LODDwEHNpyciIiIiIiJSTZ0J7krA39O/twBWAb6f/n8+8OTm0hIRERERERGpp84E9w5gw/TvFwF/dPdb0v+XBx5pMjERERERERGROupUUT4V+JiZPQ3YD/jfzGNPB25sMC8RERERERGRWupMcA8DlgZ2Jia7H8k89nLg7AbzEhEREREREaml8gTX3f8FHFjy2HMay0hERERERESkD3Xug3ujmT2z5LGnmZlOURYREREREZGhqVNkajawVMljSwNPmnY2IiIiIiIiIn2qM8EF8JL4XOCe6aUiIiIiIiIi0r+u1+Ca2buAd6X/OvATM3so12wZYFXg282nJyIiIiIiIlJNryJTNwLnpH/PAxYAC3NtHgSuBr7SbGoiIiIiIiIi1XWd4Lr7KcApAGYG8EF3/9MM5CUiIiIiIiJSS53bBO0/yEREREREREREpqPyBBfAzJ4M7AmsT1ROznJ3P6CpxERERERERETqqDzBNbNXAN8lKi/fSVx7m1VWYVlERERERERk4Oocwf0QcD6wt7vnC02JiIiIiIiIDFWdCe6TgUM0uRUREREREZFRVGeC+wdgtUElIiJjIKqpt3NdnSAiIiIio2GxGm3fAxyRCk2JiIiIiIiIjJQ6R3CPIo7gXmNm1wN35R53d9+uqcRERERERERE6qgzwX0UuHZQiZjZysBXgKcRFZlfn57vO8Bs4M/Anu5+96ByEBERERERkfFVeYLr7tsPMA+ATwNnuPseZrYksCxwBHCOu3/czA4DDgMOHXAeIiIiIiIiMobqXIM7MGa2ErAtcDyAuz/k7vcAuwEnpWYnAa8YRn4iIiIiIiIy+iofwTWzbXu1cfdf9JnHBsBC4AQzeyZwKfAOYE13/2tqczuwZkluBwEHAay//vp9piAiIiIiIiLjrM41uOcT18Z2s/g08tgSeLu7X2JmnyZOR36cu7uZFT6/ux8HHAcwd+5c3bNERERERERkEVRngrtDQWw14KXAdsDbppHHrcCt7n5J+v/3iQnuHWb2RHf/q5k9EbhzGs8hIiIiIiIiE6xOkakLSh76oZl9CngZcHo/Sbj77WZ2i5nNcfdrgRcAV6c/84CPp79P6ad/ERERERERmXx1juB281Pg28BbptHH24GTUwXlG4H9iSJY3zWzA4CbgD2nm6iIiIiIiIhMpqYmuHOAx6bTgbv/Dphb8NALptOviIiIiIiILBrqVFHetyC8JPA04ADgh00lJSIiIiIiIlJXnSO4J5bEHwS+Q9zWR0RERERERGQo6kxwNyiIPeDudzSVjIiIiIiIiEi/6lRRvmmQiYiIiIiIiIhMR+0iU2bWuu/tqsBdwPnu/tOmExMRERERERGpo06RqRWA04DnA48AfwdWA95tZr8EXuru/xxIliIiIiIiIiI9LFaj7UeBLYF9gGXc/YnAMsC+Kf7R5tMTERERERERqabOBPdVwJHufrK7Pwrg7o+6+8nA+9LjIiIiIiIiIkNRZ4K7GnB1yWNXp8dFREREREREhqLOBPdPwEtLHntxelxERERERERkKOpUUf5f4FgzWx44GfgrsBawF/AG4N3NpyciIiIiIiJSTZ374H7KzNYgJrL7pbABDwEfd/dPN5+eiIiIiIiISDW17oPr7keY2X8D2zB1H9yL3f3uQSQnIiIiIiIiUlWd++AeCqzr7m8HTs899hngFnf/74bzExEREREREamkTpGp/YErSh67PD0uIiIiIiIiMhR1JrjrA9eXPPZH4EnTT0dERERERESkP3UmuPcD65Q8ti7w4PTTEREREREREelPnQnuL4H/Z2ZLZYPp/4ekx0VERERERESGok4V5aOAi4DrzOwbwG3EEd3XAasxdesgERERERERkRlX5z64l5vZDsAngUOJo7+PARcCr3L3yweTooiIiIiIiEhvde+D+xtgWzNbBlgFuNvd/z2QzERERERERERqqDXBbUmTWk1sRUREREREZGTUKTIlIiIiIiIiMrI0wRUREREREZGJoAmuiIiIiIiITARNcEVERERERGQiaIIrIiIiIiIiE0ETXBEREREREZkImuCKiIiIiIjIRNAEV0RERERERCbCrGEnICIiMiPM2v/vPpw8REREZGB0BFdEREREREQmgia4IiIiIiIiMhE0wRUREREREZGJoAmuiIiIiIiITARNcEVERERERGQijNQE18wWN7P/M7PT0v83MLNLzOwGM/uOmS057BxFRERERERkNI3UBBd4B3BN5v/HAJ9y9w2Bu4EDhpKViIiIiIiIjLyRmeCa2brAS4CvpP8bsCPw/dTkJOAVQ0lORERERERERt7ITHCB+cB7gMfS/1cD7nH3R9L/bwXWKfpBMzvIzBaY2YKFCxcOPFEREREREREZPSMxwTWzlwJ3uvul/fy8ux/n7nPdfe4aa6zRcHYiIiIiIiIyDmYNO4HkucDLzezFwNLAisCngZXNbFY6irsucNsQc5SGxNnn7dx9CJmIiIiIiMgkGYkjuO5+uLuv6+6zgb2Ac919b+A8YI/UbB5wypBSFBERERERkRE3EhPcLg4F3m1mNxDX5B4/5HxERERERERkRI3KKcqPc/fzgfPTv28EthpmPiIiIiIiIjIeRv0IroiIiIiIiEglmuCKiIiIiIjIRNAEV0RERERERCaCJrgiIiIiIiIyETTBFRERERERkYmgCa6IiIiIiIhMBE1wRUREREREZCJogisiIiIiIiITQRNcERERERERmQia4IqIiIiIiMhE0ARXREREREREJoImuCIiIiIiIjIRNMEVERERERGRiaAJroiIiIiIiEwETXBFRERERERkImiCKyIiIiIiIhNBE1wRERERERGZCJrgioiIiIiIyETQBFdEREREREQmgia4IiIiIiIiMhE0wRUREREREZGJoAmuiIiIiIiITARNcEVERERERGQiaIIrIiIiIiIiE0ETXBEREREREZkImuCKiIiIiIjIRNAEV0RERERERCbCrGEnIDLRzNr/7z6cPEREREREFgE6gisiIiIiIiITQRNcERERERERmQia4IqIiIiIiMhE0ARXREREREREJoImuCIiIiIiIjIRNMEVERERERGRiaAJroiIiIiIiEyEkZjgmtl6ZnaemV1tZr83s3ek+KpmdraZXZ/+XmXYuYqIiIiIiMhoGokJLvAIcIi7bwZsA7zVzDYDDgPOcfeNgHPS/0VEREREREQ6jMQE193/6u6XpX/fB1wDrAPsBpyUmp0EvGIoCYqIiIiIiMjIG4kJbpaZzQa2AC4B1nT3v6aHbgfWHFZeIiIiIiIiMtpGaoJrZssDPwDe6e73Zh9zdwe85OcOMrMFZrZg4cKFM5CpiIiIiIiIjJqRmeCa2RLE5PZkd/9hCt9hZk9Mjz8RuLPoZ939OHef6+5z11hjjZlJWEREREREREbKSExwzcyA44Fr3P1/Mg+dCsxL/54HnDLTuYmIiIiIiMwIs/Y/UtusYSeQPBfYB7jSzH6XYkcAHwe+a2YHADcBew4nPRERERERERl1IzHBdfcLgbIlihfMZC4iIiIiIlKP5Y42RvmcEZM/ItpHjvnXGd2M4GtdhI3EKcoiIiIiIiIi06UJroiIiIiIiEwETXBFRERERERkImiCKyIiIiIiIhNBE1wRERERERGZCJrgioiIiIiIyEQYidsEiYy7sSiNLyIiIiIy4XQEV0RERERERCaCjuCKiIiIiEg1ubPW0FlrMmJ0BFdEREREREQmgo7gLiLy14iCrhMVERERERl1+h5fj47gioiIiIiIyETQBFdEREREREQmgia4IiIiIiIiMhE0wRUREREREZGJoCJTizhdtC4iIiIio0DfS6UJOoIrIiIiIiIiE0FHcEVERCrS0QUREZk0+d9t4/57TUdwRUREREREZCLoCK6IiIiIDITOehCRmaYjuCIiIiIiIjIRdARXFmlaWZ686y5ERESkOn0XkkmjI7giIiIiIiIyETTBFRERERERkYmgU5RFZJGmU7OKabuIiMy8UfrsHaVcpJjGqJiO4IqIiIiIiMhE0BFcEREZGBUxExERkZmkI7giIiIiIiIyEXQEV0RkEabrd0SaobMVmjGu21GfpaOlzn6ksZs8OoIrIiIiIiIiE0FHcGVsjesqb1PKVhwX9e0yaKO0fUcpFxkcHV0Yjqa2u44kyajQ/jVa9Dt8cHQEV0RERERERCaCjuCKSAet8tazKG2vRem11jGMo33jTPuRjCvtu4uOumOtfWN06AiuiIiIiIiITARNcEVERERERGQi6BTlIVhUTkGbRE2cfqJTWAZrHLbvdE97GrXXA+ORYx2DOuW41c8gt1fV5+wnl2G9v0Zp/xrlXKD7mI5y7v3sd+Pweb+o09iN9vsOJnO7j8URXDPbxcyuNbMbzOywYecjIiIiIiIio2fkj+Ca2eLA54GdgFuB35rZqe5+9XAza17RCs90V+Jb8SZyaSrHqs9Z1s9MrPIOY4VrWGPahKaOGA1j/6ravsmjd021r9PHOKzaNvV5Nw5HJJsw6NzH4bO3zmdGU8/ZhFHKcVDvuyZzrKupz/tB/h4YZI6jNBZNGYf9TkbHOBzB3Qq4wd1vdPeHgG8Duw05JxERERERERkxI38EF1gHuCXz/1uBrbMNzOwg4KD033+a2bUzlNt0rQ78rWD1aZDx1YG/QcdqWNf4IpDLjOTICOXSVJzRzbGvXMYhx1HKpakcK743Run9OCM5TlAuI5njCH9+lcVHKceJ+Iwt2gc0pjXHdIy31zh8L2sqxzqfdwV9j6InlT7i7iP9B9gD+Erm//sAnxt2Xg29tgUzHR/Gc45DLuOQ4yjlMg45jlIu45DjKOWiHCcvl3HIcZRyGYccRykX5Th5uYxDjqOUy6BzHLc/43CK8m3Aepn/r5tiIiIiIiIiIo8bhwnub4GNzGwDM1sS2As4dcg5iYiIiIiIyIiZNewEenH3R8zsbcCZwOLAV93990NOqynHDSE+jOcch1zK4splfHMcpVzK4spFOS4quZTFlcv45jhKuZTFRymXsrhyGd8cRymXsnhTfY8VS+dbi4iIiIiIiIy1cThFWURERERERKQnTXBFRERERERkImiCKyIiIiIiIhNBE1wRERERERGZCCNfRXlRYWZrAuuk/97m7nc01Uedvs1sE+DuOu3d/Q+9Yik+F3hKtm/gTHe/p85rMrOVgF3y/QBLVW3r7vfU3C6FuQNe1n9JP/u7+wm52FuAv0+njx7xyv136eNYYNlcH6e4+xklOS7v7v8siNfeB2rkOO3XX7f/su0C/Jpp7hc94pXHo+z1A1sDrxhg7kU53krcy7zSfiQiIiJSl6ooD5mZbQ5cBNxEfNmD+AJ4D/AWd78s1/5Kd396xT4eSv9eomLfmwOXADdWaZ9+5mZ3X79CbF/geOArub53Ao52969VfE2ziInsaZn4M4GXAXcBV/douy7wEuBB4OGK26Us993Tv39Y5TUVbZu626Woj27xPrZ7UR/zgTcC+xMTlFYf+wLXu/s7BpFL+pmi/X1gr79O/122y+HAk4BvNf2cPZ63Yzy6vP69iPfWxwaRe0mORwBzgUuBj3TLO9dX2YLZc4F/p//2XNAq6XuQix8DW7Roqp8uCzRO8eJHrXjdhYuSBdNai4tl8T7GtCiXumM66LGuvOg24P1oZ+BQ4J/ZtvSxX9RdkJ/uYvowxi59Tn0cuL5Xfn0+Z633dZ33aZdtPuMLz136rvt7YKTeAyU51t0Hdm7ieceZJrgzxMxeWfLQscDK7r5Kru3GwJuAd2fabsPUl8aufaR+riPGeKNc/LvADsSX2KzXAMu6+3IV2m8LGLAJ8L+Z+HYFMYB9gMXdfcVc3/sAxwBvq/iabgLuzU56zOx3wH8BX3D3jbu1TfErgZUKvpSXbZey3K8ntu+GufjvgQ2BazPh1hgsBVyVic8BcPelKvTR6iffR7d4R/9mdgVxT+miHCv1kfp5NzEh+liu/e7As+m8l1qdfeB/0t/rMrVo0S3HurnX2b51x+5aYJa7P2Uaz1nrNaUxbT12bbe2qf0NwGPZ90vDuRfleF2KX5f9TDIzy8dyz51fWNgc+BLwLOCCFO62oFW2mDPoxaWBLFo01U+Xtq338+HTjHfLvWPhqug19bG4OO1Fx27xOm279DGfBsa6KD7E/WhjYCviC3Wrba39Ir2vzyQWqqezwD7tRd0ufc9nmts35fcB4AnE771e+dV6zj7e191yP87dD+r2evqJN/XZ26XvzwEnT7Pv+czweyD1VbTNK+eSaf9K4D0Vcl+J+D26NLFPOnAnMTn/eNmiwDjQBHeGmNnDxBsuv8FfCSzm7isUtH0l8INM23nAo8A3evWR+imbgN1HHPn4f7l+jiG+3K5eof2XgG8De9A+MfkS8AidE9ZjgCXcfbVc3w+nvn+Qa1/2mq5LOT459zrnAgtyX5w72qb4H4kv9/mJf7ftUpT7DcT2zU8G7kz9bJsJLyDG7wTiQ7DlPGAZd39ihT5a/TgxgawS7+jfzO4AXgV8Hdi+Qh9nAKu7+xq5HB8E/gF8Ptf+CGI/fXMuXmcf2As4C3gB8NQKOVZ+/ek562zfsrEr2y43A/e5+1Nz8abGtON505i+G3gfsHOmbdnrvwpYsegLQ0O5F+V4BbFw9V+5BaqtgJ8B36RT0YLZa9Lr2iW7WNJlQatscj7IxY+mFmKuoHPRolY/fSx+lC2M1o2/kviimf9dcACxkPiVTKxswbTu4mKdRUeAtYGVgS9kYq9Nf68C/D4Trzumgxzruotu096PuvQzB7gO2LhgQatov1gV+C2dnxkXAGu6+xNy7csWnisvpvexqDvIsdsY+COwYW571f2cKnvOuu/rovH4GPF+fA3xe6/ltXS+L8peZ7d4nc/esn19FnGUvu13G3EW4rLuvk422MQibepnkO+BX6Z88kfIy3Ip6+c3wNLuvm6uLQXtv08sFs9x99tT27WI7zwvcPcXMaZ0De7MuQL4pLu3vYnSpOpAM3s1cEsK3wJsAHzN3d+WafsMYG13379CH+sR42sF8QeBX7j7Sbl+ngW8oUp7M5sHfBnYviC+SUHfAF8xsy9m+l4feAw4xt0/kmtf9pruBzbI9fMA8BfgJDN7To+26wOrATfV2C5lua8QD3fElwa+7O43Zfo4FbgXOCcXPxr4XJU+Mv2sXyNe1P9dwHeA9xbkWNTH3sBZZnY1U6uB6xGnwL/L3U/Otd8OeOp09gEze4z4xbplxRzrvP5a27fL2HXbLisOcEyLnncpYmFh74r710rAQwPMvSjHFYgJz0IzOyvznP8AliG+YDxIuwNTTpdmYnsCPwV2zLV9EFiOTmsAtxOXMWSdl563avsFwH25eHbxIx/Pt+0WPwNYnU7rADdPs581mVr8eFmPthBfbK2B+HdLcnwRsQCWHdMDiQXTJ9M51ks0kEvZmP6eOKUw+5zzgE8C72J6YzrIsS7b7wa5H5X1cwbwXmJxO6tsLBbSOf5OfBYUHXHZlVhcuzQXL/psgOJ9Zk2mFnWH/T49j1jAPTPXtmwfrfucdd/XRePxJOJ7wjJ0vjfuLXjOutuxzmdv2b5+I7G/VN2P6nyuw3DeA08ivg9VzaWsn3ULnnch8frXyLRvtaU1uc38+xgze33Bc44NHcGdIWb2fOAmd7+54LGDgc2YOlf+UeBHBROE5xNHXX5aoY/bgFOJHXi3XPxc4DR3v7+gn12rtE+rQQ/k+yiLp8dWIY4uZfv+B/D7itul9Zp+XdDPo8TRviptzyRO966zXYpyb/2C6oi7+935PsqU9V2nj5noP63qZftYCfi7u/8t125VYj/9c8Vcuu0Dc919QZ08Kz5nk9u3bbu4++2Dfs6y5y1pV5rLoHMv6b8odi5wpLtflPv5c4kFs7Uzsc8QK/fPZup0sPWI08A2II4EZyfnrwU+6+7vzfU9j5hwf6Ni++OJyfxOudgJxCmUr+3Wtkd8S+KMhTtpX3BYDTjc3Y+fRj9bEWO4r7tf2qPtekRtAiOOeE0nvh7wenf/bi7Hc4kFsDVzsSOBk919g0x8HnGk9yu0j9Hu6Tl/UDFeNqbnAo9kj1JkxvTruVzqjunAxrrLfjfI/aiwn9T2i8TR1Esybcv2i9nAHvnvMel9fSCwH+0Lz58Hznf3PXLtOz4bUrxon9kHWJFY1D2xwusf5NjNA95PTGRaR0e77aN1n7Pu+3o2ufHIvB+/4+7r5V7PrgXbvO52rPzZ22Vfv5743vCKgr6PJw7A9PW5nuLDeA8cD7w0+9nYI5eyfrYkvvv+MdN+e2IR+cDc74GziN8Rc7y9MO1+wE7u/kLGlCa4MjBpooO73zXsXOqqk7vVK4rRUV24KNbj+coqFJfGiSNbPXNMbZ9K++TmN17yQWElBScGqcfrLMy9gTEy4pS41mk+jW6Xfl5T1eetM/6tPqhXSb1yjhbX+7yeqdXl1kLRYtRYMCtZiCtd0CqbnM/EQkQdVRctmuynrO1048Spdh2LumlMN3X3X+VidRZGSxcXy+JFY9rteQetqbEeZN+D2I+II6kXuvvlBX18gfgs6Lnw3M8+M8hFzZo/Xzu/us85nfFobVvgAHf/bJ3XVtV0x8jM3kr5fvQe4ijrQBZp67at+R54e9k2r9sP8L1M+52A0/Nt0zh8l5gwty4PuIP4fXrMOH5/b9EEdwSY2fvd/YMF8baLzc1sFlE8Y0ni2iGYqtR2vLs/nPv51sXjSxGn6PS8eNzMTnf3XXvlUtbezNYHLiZOEbqH+CK7IvFL6jDPHdVLr+km4Mrcazoz/f9lvXJPr/Nw4J3EacldX2dqfz1RwS/b93np/9tWzH1zpio935raN1J1OsUrFWLp0ffmTFXG7pqjmb2IOPXz57QXZ9gwtT2LnC65VNpfUqxsHyjbr4teZ1numwH/Sv/va4xS318gflG0Tsfud7tUHtMmxqPO+PfZvnKOVrO4SsFra3zBbJCLS4NctKjbT1qg+U+iPsJ0nrOxW8CVaWKcp7ugVbftTIz1oBbd+smROHunyn7X2H7RzXT2mX7GtCReeexSfAPielbocx+tMxapfe3xqLptB7nwXHdfTz9T6zOgLMeS9gN7D6TXulWdXEr2u5WoUUl6EmmCO2Tpw+MK4Bn5h4DLvf0i8W8BLyW+EGYrox1EfFAekOuj7OLxI4jT+96aa78SUchhs1z8mcT1UdmJySYpx/m0F7U5kVgJWt3dH03PuTjwiZT74bm+30UUiNoh95q+SxxF2jmX+38DTweyCwLvI0692NVTgawubSFOD9qQKPKQ7fsS4ov8lhVzP5biSs+fJopvHZsJb5f+3ok49aflGcQq3Lxc3y9Pfbw/F9+uoI9u8UOA5d19pUx+7yau9cjneCiwlLuvnHs9J6Z88sXNliauJZqdixftLxDXTL6Xzv20aB/4fym2LHHdVK/XWZb71USRpQ1y8aIxKuv/UOJ0p7e4e+sXbLft8pT0Wl6Xi9cd0zrjsTnxfnw28f5u6Rj/1EfdSupl26tOjvsQ7+vXeHsBmFWASzxX0Tk9VrRgthJxPdGSxBeeKgt3dReLmlhcGsiiRd1+Gl6gqVsR9RdEEZiuC1dpnD9BFBe8iR6Li+lnet0yr+9FxxQvWoyrs12aXDAcyKJb3Rz7WNAq2y+KqgWvRBSMeiYVFuRLPhv62WemXRW4Ttzaq8CfT5/7aB9jV3k8Mu/HXYjP2irbdiALz3X39bR9W7/XGl2krdu+j/fAi4jTsa+okkv6mbLP5I8T750qz7uTu59d0Hfh7ZPGhYpMzRAzu7fkoRXS3/kL/NOPtf3c8kT134szsVvN7FeUX2jecfG4xakdD9D5ZXV7ii/a3yD9fWyu7T3Eh142viGxcPJo5jkftbie9t90Xjz/DOIaqPxr+jcxMcnnvhdxlDbbz5OB3xFHqnu1pfV6Cvp+iKg8VzX3VYnTqfLemJ53hUzsxcQXL8vF9yHGLt/361J8hVz8xTXjS9NZcOGjxOR/6Vz7ss+DV1Fc5OMEqu8vEBO5ogIKHfuAmb0x5fhmOrdj0essy32J1D6vaIzK+p+VYvmxLtsubyGOlE13TOuMx5uA0wv6KRp/KC/csidT95bNKttedXLcE/i/9FjWY8DSFtcN5Z1IFCPJLpidSZx9McdTtdzMgtb5ZpZf0Ho5UWzu3bn4M4BVrPMWbmXttwNWz8VbC1dF8XwMYkHgXwUTpxOBb5tZfrFkC+AJ0+zndcTpZq909zdUeM7tgDUtromsEt8H+Le7t1VNN7PvE9fE7kL74uUhwO5mll0wPJG4dno7TxVHM4uLZ+baQtRQWL9g7I4FHnT3TXO5fBr4icW9JLPWBVbK7Xt7E5/Tz604poMe66L+s4tuVca0if0IYl96wNvP2voMUbwm375sv1iFWEzOn7HxXeLI1aYFC/Knp+8tWSfS+dlQts+8jBjT/Pu67pjuTmzH/HugztgdQhx82Njbr80t20fLnrNjLFI/J1I8dnXG4zvEwYvn5N6PJxCfsUWfDXXeA90WnvPboGxf34Covt/2Xif2i1Xcfa0KfXfL8URm/j3waYAauRhRBT7vvcCjNZ73eOI65byjaa+iPVY0wZ059wDPzp8mYXGh/HLeXsziZuJIzAJvv8D/YmBDM1vM3R9LscWIVc7b3X2LXN9nAVuZ2ZrefvH4QuCP7r5Dr1xS/CpgtWz7FNsdODcX/zbwEjPbmvZCEXcDl3lnBehNS17TA8SXz3zudwC3ZPsxs3WAa4jqfnRrm2lftF3+BaxWI/eySs//Bs5296MzbXcmKpn+OBd/OcWVsecAT862zfRTJ75aQY43EQsU38rl8hBwtJkdmns9AD/wzqJnRxJH73ruLyl+aclr7dgHgMuI8fhjwXYsep1lua8E3FVljMr6T33vmf792kwfZdvlYOJ2F9Md08rjYbEg8EHgGbnci8Z/PepXUi/bXnVyhDh6vaSZtY4yr0+sKq9LVK412nUsmAGz3X3n9HkF9FzQKltYaGJxqWzhqolFC4iFi0em2c+exKUqL634nAdSXJ22LF5W6XhzYGHB4uUrif0vvzC6M5kvaz0WF+dRPHZ1Fh0hLmt5NJfLtsS+vAzVxnTQY93EolsT+xHEWD+Wi+1P3G9zV6pVwD4ZWNuiAnvWc4n3etUF+TqL6fsQZ3ktwfTGdCs6DyZAvbFbmqhQnB+7sn207DmLxgK6j13V8Xg68Tm4WiuQtu2exO/l6b4H6iw8l+3rnyEWufL70YZ0/h4p67tbjsN4D6xXkntZLu8AliroZ518P6nNrILn3R5Y3qZuJ/f4jxBnUowtTXBnzteII7P56wDmE6eqFLX9RC6+F3GU5g4zu5up1Zub6Ty9EeDVxKroBWaWvXj8YuIoXt58it+MRxGnDeVjiwFvz8X3JT54jmbq3P9bidNMv1jQ917AcQWv6ZfExD2f+wXEqRdZrwYOAx42s7t6tG21n5/6br2BbwdOS39Xyt3dD7a4F+4OtF/n8B7gR7nm+xNHnebm4u9k6lSdfPt1S+JFX+AK4ynHX+dyvAw4xd2/nWv7MTP7JfA84D8yr+dFwOUFzzkfuLAgfhSd+wvEa51TEC/aB1YjxnCvXNuy11mW+/bEeylfkKhojFr9L17Q94+JIztVtss7iUWEor7LxnTxfLDmeOxPVLRuW2woGf/biKPMpxU858FmdjrF2+vHJTleSHw57Zqju5+UfrFmC4ucT5z6/0vgje5+ffZnShbM7jGzi4CrM+26LWiVLSxMe3Gpy8LVtBctUvs3Ak+a5sLC2sDBwGIVF2jmUXyrt7I4FN/+a13g+ILFy9uAuwoWRu8CNkv5tnIsW1x8BvVumVe2QPOfdC7eXkT8Xqs6poMe6yYW3aa9H6X2awNH5drfQVxmMr9gQatov9iZOFJ1Cu2eCGxi1RfkKy+mp/d0E2O6HcW3wKszdqsRk9ZHrP22hmX7aNlzFo1Ft7GD6uOxHDGpfKDg/XhVA++BygvPXfb1XYlLYPJHGB8GXjqIRdrUfpDvgduB/WvkMp8Yk/ziz++B/5d73p2Jz9jPErcdanle6iO/WGjE4u3Y0jW4Yyp9SOLufx92Lk2ZxNck9WgfGF9WUoykLJ5rswdwpbtfm4svSSyYzWZqUnw7U/cMfEImdhlxrd6VuT7mAOu6+zm5+POJU9Py12SVtZ8DLObu1+RifwcW98zZOSm+uLtfTY6ZPY/4UpFdQLiAqLmQrxQ7h4JbcfXRz6Z0LlqUtS2rXF23au3VxKlyOxJfoFqLl9cB73P3czM/vyRxbf6BTC2y3pr6+KK735B7vudT75Z5lxK33svf0mwP4C+euT1VZkyf5+4/zsXbxj8TH9hYl/WfxnRvYrLQV991c0ztNyNO42+1v4tYMC26drRovzgA+Ki7n1fQ9gpiwTe78Hxtav+bXPuiz4bCfSYzprM9c9u5umNqJbfA62PsdiUWb7NFky4lJuALqzxneiw/Fr3GrtJ4ZN6PH2HqGs7biIMjx7n7X6q8zvRY0f51KsULz2XbYDPiNj/Zff3FwAfy+1FqfyXwqyp9d8lxxt8Dqf0C4hT2Kp8ZpwOfKOnnIuL90epnH+Jz9ye5dscDz3L3zQv6+KZnbs00bjTBHQFWfGuPlYhCO60vhbcRlYXXovNDobUKlI+fmv/gzvRfePG4xXUrj1XpJ63aHQr8M9P2FHc/I982tS+rFv2Z9LNtrym/TSrk3tF/l7ZfI36R5p+zI3erWb06/UxZFeGOeJe2lapr9+i7qJL234j9ajXi5uHTqa5dFu8oWpC241eIfTi/HS8EXkKFfaDO6++RY7fttTQxeepV6KTudinaR2s9Z53nLRn/XhXGLyNORc22b70vds3leF5q93zifsZGXJd/cfr3NlSrSF5Y1EkmgxauJk/RwlWVxSxp3nQWF8fJqL2eQb4HmhhTq1FJehJpgjsCrLPKXtntNP6TmEx+kfaiHQenf38mF98L+La7d5ymm3/OFDsU+BBxnVzXftKpERsTp9q8ItN2X+B6d39H08/ZrZ8u/RfF5hPXY+yfe87C3K1+9epViOqh2S/sKxNf8vNxI1ctOz1nWXXtor67xYsqaZ9HXNO0jLtvn2JrEaeKbkVnde1N6ayW3cr9NHd/IvkHird72Xb8FFGV+5hMfCOiouoPSYUXerzOssrNZZXB62yvnVLe+W1Ta7t0GdOyaud1xmPF9Lz511q3kvrnidslbJxr/4v0+LZerfL4NSmfTTLxPYjTmPMVyQ34kruvQYGiRaq0uPYh4ggPxILIT4jTuXan2u2mZhET9H9VaZ9+ptLiyiAXLSrkUrmfPp6zbvw6ooBJ34uXJYtCTS46/oy4pKDKYue0F8XSz9QZo0EvutW5pVurcvEspha0VibeQ8sTC6ddF7NSP2VVyncmvk/kF/C9KN7HYnrRvjSwMS3pp7XAvCqwBr0XEcvGuWgsai8upr7axiO9vw6geCy63ZKy6j5aZyH1LuK04zWqvB6bum3kbhX67mdMB/YesKnq1TsyvQXjzYlK3SvRWUn6SDIHzrpNeq3mbfRGjSa4M8Q6q861vJT4UnZ6JrYjcU3atp65XYfF9Z6Pee52GulLhHmqdpeJX0lMQttO+yMmD0sRRRey5gC4+1IV+plDnGq2cba9RdXn5ei8rrR1Yf99uXirMnTb9eBdcn8qcQ1mtp/WNlqMuJdqt7bdnrMs97L2jxLXqdyWCbeqCAP8OROfTRShmJWJe4pbQY6t7ZXto6zvbvFWJe0lM3lf6+5zWn/nXs8DQNupYMR1rI8xNclpeToxUcyfJrg9sc2q7l/XpfjGmdijxJfO9Zia9PZ6/U78ks3n4rRfE1t3ez2a+t2GmNBl+y7aLs8nrqmtOqYdz5l53qrjsX1quxRTr7WfvrcmPkuWybW/NvUzJxe/nnhvFMXJfiaZ2cNEcY3diclJ1h7uni/+0fq5/ALgfIoX1/6H+PJ4ENUWor5MLBK8sGL7OgtXTSxaQL1bt5X1s0nKMR8ve8668R0pvv3X7sRtp45keguvdRbL6owdRC2KebQvds5JOf6R9ltu1VkUa3Ksm1h0a2I/guLbAP6a+BzZ2t23SbFut9crXNBK7+vX0/n+/Vj69+FUWJBOfeU/M1qnAucXGJsa0zpjdx5xPeiyuQXmskXEsuc8keJbMhYtLtYaj/T+uod4f22fwt3eX3W3V52F1AXE98A13P1Fmdfzn8A7W/tcJvcziTHeouIibd336YkM7j3wa2JBYYOKY1q2DX5H3B5ww0xsC6KezIZM1U4pvX1S+pnCg0njQhPcGWJR/OIQonpk1heIic/LM7FvEFX5vururWtRWhOBJbyzau0NxFg+JRdfSEzWtss95wLiy++zc/FziTfFmtlgST9nEKcnf8nd1860vR242ztv03AzcY3GOrn4H4jrS9bOxctyvyj1s1am7R3Eh9DPmCp0U9g2tb+CqHKbf51luV9MfCg8wTuLpRRVr25Vo147F3sB8Ctvr4xdtl06+ugzfhbx4T3Hpwp3nE9UE1ze3bdNsTWJokB/dPfn5vpoVUXOH5G8m9h396Ddj4j9a8tc/MfEdTer57bjLcCd2e2YXs++wIm5SXjZ6yzLsYntdQ1xdHBLd39hheesO6ZFz1lrPLrsX3X7Lmv/S+IX7PNy8TOJU933oL0oxjdS+70z8XPS8y7m7nvmnvchoOioXtmC1v3EGQiPLzp1WegrW4haj7hdWNHkf7oLV00sWrTiZQs0ZQs6RYsf9xAr/vl42XPWiW+X4l/PxV9JjHXbwkXJ4uVT09/5sa67MFpn7Ar3gT4W1wY91k0supX1XWc/goIFMDO73t03av2diT9MTOR+kOvjScTv6b1z8c8R32/yX/rL3td1FtNXSK/TaH+fNjmmVceubIG5bBGx7mJkx+JiitcZj88RRbnyE9+y99fAFlIz+1d+e70y5fm2XN+fBZYs2I/qbt/tGZ33QNmYlm2Djn7SpPeNwMm5ie+nKb7HvQHvdfdVGVOqojxzfktUn2urSmZxa4vN3f2CTOxI4nTjR639dhrLA4tbXFierci2bPq5fHwp4tSL7IcuFpVM1y+IvxX4XpV+zGxv4lTp5dKXYogvBI8wteKa9TWK77P1TuBbNXI/gc5KxKcR2+bruRyL2gLsB5xmcS+21heYbrnnq1dDHLm5heLq1fPprJY7n1j5zFfG/hpxWk9RH/nq2mV9d4u/ms5K2guJoi+rWnvV6bLq2kcxVcwn62JiQtX25cjMfkQULciP3e7EF6n8drwGeFJuH3iYuBdf/tS5std5FMWVm+dTXBm8rJ+i7XU/MXZ75toeRfF2+RpxelHRcxaNadFz1h2P+SnHfFXzun2/mjil9AJrrzD+M+IXXj5eVnm8dcZKNv4P4gyLTxY8733EgsbduXjRgtYVxBGE/FHgB4EVrfpt1C6m3m3XWgsUG+RirYWFbPws6t2irezWWh3P2aN9nVu6Ve6jR/xSiisa/wcxqc5bC/gr7VU7LyJOKTw1tzBYtlg07bFL8SvovBXGjURl7I96+2mbZX0Meqw7+ifOnlpATPKLFt0a349SvKhy8Z9S+5usWgXsh4lTU/NVW5cmcy/77I+kP3n3E6dY9lxM7/I+bWpM64zdXy1Oi88WpVuz9TprPGdZFen0cOXbHRaNxyxi0rR0pl2391fd7VXWvmMbAFdZHNn8R27/+h5xB5H8fgSwQpW++3yfDvI98ABxP+GqY1q2De4BNrb2StJrEpcC5k/rfyPFt0+C4jt2jA0dwZ0h1qUCZUn7VeisvHYm8SVxq1z8t8QqWkfc2+8fWeV5F6vTj8UpHtmL2G8vatfkczaln9xNxVIakd+Ow9oHZDRYVHI8wd0vzMU/DMx1910ysS2JxbWnENdxQfyyf4D4srIFUxPllUkLUe6evQwEM5tNTJLXq9j+rUR15cNzsQuJ094+m4mvQiwsrMfUYsQdlFeE3YM4Q+QLBc+5hLvPL2j/THd/X69+UuxKYFNvrwpc9px1488njvh/JRffhTgN/CraFy+fAxzt7sdm2n6YqKz6Ts9U7Uzx9d1931zfs4lFx9Xpc+xSfEtiH7ifqcXOZxBf5Pd390sr9DHosS7q//70+g/19gI3A9uPUnxJOisX30ZaMGXqeuhuFbAvJSq/ficX3xL4NXFqeHbh+WFicrJ4Lr4CcIS7fy3XT8c+k3mfvtbdD83FmxjTOmP3+AIzcV0pxCLhmel17sz0qkj/LP37Jbl45fFI769jiCN7f0x5rUy6JWXB+6vu9lqFWEjdLPday7bBzcSku9X3rcRZH7u5+/8V9H0T8JeKfdd9nw7yPbBkyuk3ub5bl4G9JBffrGgbpL4WEkeOW+1nE0eTj6V9kvwl4Cx3f3VBH7d45oywcaMJ7hBYxSpoVqMCmpVcDN5knPhAyb65fuMFO5AVVIXuFSeuYWnrO/07P+kpe04jrkd4pFfb1H4u8QU5v4DgwC75uBcUIEj9dFQLrhs3s92IldLpPufriV+Y2X5qVdIuivWKE79Qp7uf7gTcS7WxrrvNy3Kfdj9d+n4LcVuK6Y5p5fFI76MP0l54qZ9K6scSZ4Vkcz+FwRZ6KWzbTbcFqroLUVq4GoyZWLhqYuyaWKiVatKCyE3ufnPBY3OJiUHHWGiMBqPCePwJRu+zsVfenrkd1KhpKve6/Vjcnmo3KtxCLbXPHkkfO5rgzhCbqo72AnpUQbOpCmibM3XtW18XgzcRN7MXEStIP2fq2ot1ietS3+LuZ/Xqo4++W6eGXdHrOVMfXyDetCdXyG9fYvXwK7m+d0///mEuvhNxxKFtpbjuay2Kd8ml1nNaQ9Woa47dFsSprjfmcr+HGvvpIPevQcdLYo2MaZ14Gv/XEKu0retx+hn/+RRXGJ92oZcez1tazKJoUcSiUubr4fFTFwe+EFUWTwsLhxCr461c+llYqBtvYlGk7nMWLn50W+QgFg6ns3hZd8G07phWXmBsYhGxz3gTi26HU/EWgKl97bGuunDVJb4SBQvMlCw8140PeEwrb18rucUiJYuIZfEmFhfL4ulzLT8ZOiX9Ox8fykJql9ezc1EfZX3XjQ/qPWDl1at/knJ5eUGOhVXjy1hJ1fRJpAnuDLG4hmA+8H3vXdnuWOB/gUO8/SLxsovBtyO+POevB20qfiiwlLuvnMnlM8QE/eVEQZlsH5uk/PN9F8VfR5zaky9EcgOAZy6GT/ETC57zdcTpba909xV7tIW44fXi2bap/fXEeyL/nKcTlXHPzfWzFXGK3M8qxLdKf+fjO6bnXG6az1nWT1FBl1ahgqVor3RcVl27W3xx7ywsUbafHpB+Jv/LYYfUz7KZPk4lfvltTdzKpaXs9W9PceXm2UQhknyl5zr91N1ec6CwWnTZmJblXmc85lBc1bypSupNFXqhIG7kCkblnqPqbdQGtoDQJZdGFhaGFa/Zdj71bq827cXLpl5nl9dUeTGqqUXEGXhN014ArTvWdXLp0nZf4jvSd6i28Fw3PrAxrdOPlVeBL1tEHOjiYlE887l2J1FTovWcB6d/f6bX6+z2nE0tpHbp+8V0jkXd7dvIdq/5HvgWcWbhwbnn/Drxe/J1ufh+RJ2Zv9F+u7Szie+9rTMaH38Kim9LWen2d+NGE9wZYrnqapn4w3RWWXsl8eHcdusMM3uAuPbm07lujiQq2+ULJDUVP5iYgKycyeU+4D1EwZv3ZNp+iXhT5au6lcWPAWa5++rZYJfJ5n3E9vp/uT4OB/47209J21b7Jdx9tVzfZdWo7ya2+2tpd1p6Ta+oED8N+AixrXbPxL9BLB60FQ3q4zm/TkwS8tX3FtJZjXoBcWuME5iaeLfiRdW1y+Lnp9zzVYHL9tPDiP3rJbn4ycR4PF7sJb3+/YjbCeydaVv2+ssqN1+R2r8yF6/TT93tdR4xFkVVp4vGtCz3OuNxTsrxe95epKdo/Lv1fQZR5bqoimPR+7Fy1fQUezawwHPX9aRt8yE67Z5+5rhMbB/ieq/X5Ba0mliIqhvfkXgf7ODtt3Sru7BQFp9N8QLN9lRfFGlqQats8aNskWO5aN5R6bhj8bKPBdOX0nl7Pag/ph0Lg2lxbQk696U6i4gw2LFuatGtLPc6Y122cFVWAbtwQcuiyu2yBZ8NZZ89leMzMKZ1tm/ZYmTZImITi4vd4h3jkZ7zqUTxpfVz8UHekrKj//Q6ofO1lu1H1wFLF0we627fOtu9qfdAt9zbbqWY4t8ibvG1C+0T34uI7xoLc89pwBOJQn8tq1B8q6zCyfA4KVwxl4G41My+AJxEj8p2aWL2FOBBM3tOpu2/gbPd/ehsxxanYzx5gPGHgKPTql4r9zuAdwHz3f2kTNt5xL26Tsr1URZfGzgq1/d6pHvbFsQBfpB7zrWJSfhiZvbabm1Te4CvmNkXaS9+skI83BFfCvhf76wWfCFRPbRnPMUuISppZ+NHAl9q4DkPAn5k1Spgn0pc73pOQbyounZZ/CfAq6y9Ul+3/XQborpyPvfPA+8t2L8+CXy+YDsWvf6yys0XEZPnqtuxo58+ttfRwOdqjGlZ7pXHw8zeRny2YGatyWDh+Pfoe2/gLOusMP7veLhy5fGiqulfI26P8M2C9isQv2jzK85bEYsil2ZiewL/B7wq13Yboghf/syB04gvGIOIP5NYXMovFBRVCoZYWLivRvwK4pT/fC7PI353VOknu0CTj9fJ5QxikphXVs32eoorxDt0VMXdn1j825X2sT6QqDZ6aa79Ael5pzumW9BZuff5RPX2p+fabw4sQ6dhjHXdMT2XmCRXzb3yWJctXBUtcqX4vcTv6ntptzxxim9eWRXlOvFBj2md7XsGcd/oL1XIu1u8chXpHvGi8ViO2Ofy79/FSnKpu73K9q+i13oPMXb/7e1Vzcv2o+WIz5gqffcTH+R7oPW9N18d3kri2wHXuvvFmT5uNbM/pud9cuY5HyWKby3G1OepM3WrrOxnbOszuuguEWNDR3BniEV1tAPovHbh9xRXWdsVeAOwZKZt4cXgZjaHuN/gNQ3EF3f3qwvyfx7xi7aV+13EtQiX5doVVosui6fHNqXgmg7iTZaPX0CsKuX734zO6xMK26b2q1BcpZqiuLvfzYCU5VL3OW0IlYitvGjBj919YUH7wqIFJeN3atG+OA6aGtOaz9nY+FtJQZey+HSlhYi3e6ZqbYqfCzw1d3R/HnH62RpA65S49YlTxz7j7ofl+jidWMx4ZtNxi0rBnyOqZ34/k0tHpeDU/nhiYWGnivHTiQWaFxa0f5a7b96rnxQ7gTj997Xd2vaIbwmcRZy2mF3kWIHiaraHE7eB+x/aF8Bap/Z9OhP/IDG5OcHdP5bp41xiYTR/lsjpxO318mdJ1B3TecRE48RMLm8iKva+191PzLTdhTjb4nx6VIVO7Qc21n2M6S7ELUUurJh75bG2qQrYr/L2CsUfprgC9s3Ed4e35+LziNPFv5zLcXfiy/YPphEf9JhW3r42VQV+E6buYbwe5dWiy+Jl77uy7V55PDKfa+swdX/r9YkK4xD3M5/O9irbv4pe65bE5Hk/b69qXrYf1a3GPe3t3uB7YDZReflftFeHvyTlsnUuvhhxuc4XcxPfLwNbeedtzl5AVF3+bC6+XP4zNj2mKsoio8JKKlSXxceVDajCdp223eL9qjpOTbz+PnKrtb2I1diOHAc4dkas6K7a6psuBX3K4l7yS8H6qI5eNZ6+UF2aXxRJ+8OKninCl+IzvoBQZhgLS8NUZ5GjbOEq/Tsbr7Vg2qQ6+9I4j3U/uQ9iQas1GfDcbVnSY/OJwoWVFp7rxgc5pnX7qbuIOMDFxcLxSK/nJODH2ddDQ7ekTM/R92vtsR8dA3yqTt8zud175e7uh1pJdfhs3KZu57Qj7RPf84jitX/K/NxbgQvd/fJcf2+l4FZZ6bG3ZyfD40YT3Blivauj7Ub7ReKF1dGs5sXgdeIWFQwvIE5LeULK686Uy8e9uALh6e6+a6/YIOM2VaF6d+J+aUZcz3Vx+vc29Khcnenryuyq10zEa7bdAvgVcapJtoDGPTRQYbtm25WI7d2631xrf2kVkdqVCvtRGr8riFMR/0GXcWr49dcdu6oFXTYnVlxvJFZ+LeX4UGqyRAO554uClFUSb72OfEGfsnhhoZ86ufQTL2s7iqouUDS1WFR3gaapBS0q3hYute+6mFFn4XEYi5RFYzqohahecUoWxqo+b90xTf8eyEJX3cWv9Fjhdm8iPugxZZrbd5CLiHXjacG08sJo3fiA96+BVuMuif8a+I+qfRR9n+7GelSHL5sQV+1nEuka3JnzdeKL7NH0ro62GVGVbmczOyDTxyrAS1q/6AcQ/z5xPv6c3OrV4cDpaaWnZZOU91yLU0JaNi2IDTp+InFd33aeCgJYVKi+JuW4lndWrj7T4hS6rG2A9c0sX5CoifjWKZd8vG7fxwIPeud1N58GfmJRej9rO2B1M3t3LkZJPB/rFj+ImKztkNtffpEe3zYX/wRwvpl9MNfPx4hTbZ6YGac9iF8W+XGq+/qfAaxSY/u+HNhgmtvrEOKoUz7HsqIVdcauLH4ocVrSW9z9DZm+WwV98otQZfETgW+bWbbQT+s517QoBtRvfFviPZCPG7CymX2cWACczuLaIBeiChdXzOweihcorqbzOuTG4mW51+ynI2ZdbttlZoWLH8Tphvl+1idqT/yLtHBlZtmFx60z8ZWJ0/KWJ47m5ts+vkiZ4o0sUlosRl1EjGlrMeopZrY6UZn0j63X32WcoYExtZLbrvXxvHXGtHShq85Yd4lXbpvG4kTimujHFwYtaoBA/K7pNz7QMW1w+9bZtgOL29SC6V+IglitvOsumM74/mVRjfvDxHi0nnMHovIztFfXbir+RqI43i+YuutDtz4+amafID7Tqk58j8+/1mw8P7HtMpHt6KfX5Lkkn5GnCe7MeZbnKqARF4M7UWny8YvEzewm4hfuelS7GLyp+LpEMo+fguHut1sUsHmA9uIM2xNvzhUL4o/RWShjkPENidORVs7k/WhagcQzp86k+MFE0Zx84YN5REGbQcTnEb9Yly2I1+l7VWIymPdGovjBCrn4i1M/K+RiFxG/+PPxfNtu8dWBhwr2F2/9Oxd/Tcox/5rWIiZ+2VOcvkUciVwr177u69+Hetv3dUx/ey1NvSIXdcauLD4rxfLbpqigT7f4q4j3xqW5eFmxnzrxA4FvA08uaP9G4hSr7Sssru2Qct9mmotF015cSosMT6JzgaLuYlFZvGyBpmghpqyfugs0hwL/amDx4z+JccouXGUXHrPxXxNHe7Z2920K2g5qkbJoTH9HVPR+h2euh21oIapbvGxhrOh5mxrTJha6yhauyvYLI/O7OuNEYpzXamvcQPXbGRjTyts3bY/b6dy+TSwi9hMvGo9PAy8EfuHu23Z7PX3GT2T6+1fZfvReYvzfnHvOFwwqbmb7EN9Dz3b3D1fo403AZ4nJZnbi+1WLauK30m4rYr87NRc3YDXywWj3QjP7eZX29Jg8F8THgia4M+cuM/tPoqpv9mLwjupoxArup4B57r51qwObuhh8g2zHDcbPArayTCEgi1N6FhLl4nfItL2KOCX43IL4atnYoONm9m1i1X8zi4rKMFVF2cxsa3pUrk4Nn0EUImk8nmLzgNML4nX6vg840KpXLu6ojJ1i7yYKQeXjdaprP5fi/cXinx3xO4BbCl7TMsQZBdlxuoGYKJ7pnRXG67z+l5dsx7LtO6eB7bVaSY6z0nbpe+zK4hZHLfZM/85WEi+rRl65SnlqN4961dE74in2ZWISm2//ZXc/Jhvrsri2HbEvrcD0FouaWFz6KPDfxL46ncWisnjZAk3RQkxZP3UXaMq+F9Rd/NgTuL9ggbFj4ZG4NdU70u+mrm0bXqQsGtPl3P2rZnZELt7EQlS3eNnCWNHzNjWmTSx0lS1cle0XewNLFEy0ZlO8eFm2MFgnPugxrbN996dexfBBx4vG44nAf9E5gay7YDrI/atsP3oCURgqr4n9qCxuxIGXonhRH4cQ34XyE9+9iMu93pVrX1YF/qPA4gUT313oPBD00fR3vn2tyfM40TW4M8Q6LwZvrT5lT9dqxdciToV7s3deJN5xMXiD8VWI+0uux1R58DuIe5t91DMXxFucQnolsKm7/zgXf4K7fyHX98DiNlWh+kDitCSIFbDWPQ9fwtRpILcSpxgVVa5+PlHQ5qdNx1PsppT7gn77To8dTJzGnq9c/COvUGE7xf5OVMy+o1vbHvFViOqnOxEfyhAr02cS+/HOufhlxOmmV+b6WZI4dX+LzGu6nyhG80l3f3Aar//5xMp6vnhN2XafQ7w3zul3e6XHXkNMxKpUBr+UgqrTVlLVvEt8U+KX/koVnrMsfgHFVcoLi/3UiZe1TY+dRZzed1JuUeRyYnHtuZm2lzK1WLReLr62d1bWbSr+GeIzZj+mFgVOIE6lvtzd35ZpexGxCJE/GlU3XpZL5X5S7O3EPrZehT4OJ96P76N98eNI4BsFX8rOpbjS8beJCcFOuX6+QXw+7J2Jn0jUoLidqIbdre16RO2Ky9x9l4rbq86Yvg94FvF51bqcYj2i2vJZ7v7qXB9NjXVRLoXP2+CYviP9+9P0OdYpdiRwcnbRvMt+cR/wIPElP2tv4gytfXK5fJzYB/KLcXXigx7TOtv3g9SrGD7oeNF4vJSpe0cf2OP19BNvYv8q24+eS3wfPA76rrpdN/5C4lZUvyR+j/Xq4/XE2QRfzL3+s4nv1Ovm4mVV4O8mfm9nz3CCOMNlI3dfNdf2dcRE9+BM29OI2/O9IteHAd/xzB0Mxo0muENgFaqjDSMvEZFhSIslhxET7uyiSNHiWmuxaMvc4trAFqgyj+UXV/5JVP/9dq5dU7duK1ug6ViIKesnxe5K8fwCTdXbwvWz+FF2a7zWds0uPP4l5bgq7cUWi9reRvnt9ZpaMLwt/b+vRcR+4umxooWxjgWwBsd02gtdfSx+nQsc6e4XFeR4O1G1t0qOdeNlYzrtxcX0WNXtexcN3GKxwXjheFhUQL+QqaKFrdcD1Sqjd4s3sX91249uIhY4BlKNuyR+MXEpRJW2qxBHyM+ifeK7E/Ahz9zKqps08f2Eu59X8Fj+9PLCtmWT56I+xo0muDPIzDah80P3lPTvfPwK4vqrQXzQl8aLfumm3Pd39xOqxOu0TfFjiWtT89vF6aw6XRp391b13mzf73f3fFGjkYlbVNf+IXG/4+wXu1rVtVNfTVTSrtvHz4jJxnTGqLUNlsi0/wtx1HR14nSpbN+VX3/q+zyigE3j27ckthLxC3wpiqtL71ISz1edrhs/heIq1TNa1byfeFlbEZksZRMWGY5e42E1q5oPMp6Njft+ZCN0q7tJpQnuDLG41u01xLUE2SrKrVMFPpOJt07VOZ/2W34UtW0yvhfwbXf/eEH+g7rVzHziOpj9c7m0Tt05vGJ8X+B6d2+dElM7l2HEzexbxOlAO9G7uva6ROXiVYgjI1mrEBX8nl4hvnLqOx+v0wfEqS7zqD52ZWNUtA2+ytQpi/tl+qj7+r9M/BJ5If1v35Wpt72+T5wOl69G/ov0eL66dBPxnZg6rSx7utKmwPy0DRhyvFV5PR834jSp/ZjBBa1BLy41tOBUtkBTOZe04HI48Cbi1Mi+FkpSX3UWLWYBvyOOUGTHrrV9s0d1Wgtaq1E8FvkjQGVjVDc+42Pape9eC2PZcfobcSRwNWIRcEbGtG68ZtvWfto6i6PqwmCVeGt7rQqsUdB2IIuLfWyDkVh0tKlbL+5I+637OiqgTzP+eHX0LvGVyVVYJ3cbwbJJcsHrHOnbQDaZe91+JpEmuDPEorLfU/O/SK244t91wObEKRz5+LSqCfaIXwlsTJwWmLUR8Uv3qlyMkng+1i0+B8Ddl+o3dzO7N/1zOeKDsKVVCOK+3HMOI54tSpGNLw885u5tRSrS68RzlbfN7FGiyMVtmbAzVRn7zxXis4lrLmZl4nX7cOJaGnf3JQtyLxq7e+kcIyjYBmZ2nbtv3Po7E6/7+rvlWHX7zqbe9mpVI88/57UpPqfpeMr7AuKLwSWZ5tsTk5pf0G4Y8e2Zqryeb/984nStrzFDC1oNLS6tTDOLRXUXaOos9HyPONXwQE/XtvVYQDmczoUSqL/I8VFiXF9A7+1btqBVNhZNxYcxpk0sjJ1HFOlZ1t2379G2yTGts3BV1ocBp3nn9dBnEhXJ85XUm1gAPI8oVrdMxe1VN15n+9bdXoOOd4yHRVXz+cB+rYmvtVc138SLK6MPIv5riiusv4m47tlpnyRfR7zf2047J343tg6mNB3fOj1/Pl7WhwFfcvc12oJR5f1TdBaZaqqfsvaNTJ5Hjaooz5zHiF/gN+Xii0FHlbXHiAnuYxXaNhlfC/grnZUmFxCTspflYvOIIiv5eL5tt/gZxAp0npXkWBS/h/ii8t/ZN6OZ3UxcL7NOtvEw4in2bGCBtxcFuZi4J9zjVbStpLp2it8J3O7uW+Ses3LF7BR7AfCrgnidqttXMHW9ZNtDFI/d/cBt3nkbjI5tQFQd/xRReK3Vrp/XP+3t28f2KqtGbvHP5uPEL/UFxEJB9jYYM17VvCxuJZXX02MPu/uLybG4Z7J55hrXzILWzmaW/YW/Qu7xXvHWwsrFmVjhrdtS/FfE4selmfhsphY/WvHs4ke2bT/x1gJNlVzK+lmHqED6+Oesd7+dV1HlaphatKga35oYu57b18xmZxa0bu3WtuH4MMa0LF52m76OcSKumZvTWuzq1rbhMS2Kb0+9WwYuT9z6astcfA6wVJXXXzdO/e1VN15n+27P6NxiEYrHYx3iTgabtwLevar5wOKUV1h/HfAwUYsgO0l+kNiXTsu9znmMzm0gnwQsb523Lfsu8dlTNZc6/Twp/Z1vvw3Ft1AzYk4wtjTBnTnvBM5Jb9LsReXLAlhc6N2K/5M4PflyMzuuR9sm40sRp9m0TcItyoevn42n2L3AOQXx9av0keJ7A2eZ2dVMrayvR6xOW8X4ssStOvaj3dcovofXMOJfIz5gvpmL7wWcDtxhUeUO4gjCJcQHTD5+C1FRMG8+6QtShfh84ijCJ6bRB8T2/mGNsXuEqSNyWUXbYFViQWg5S0db6e/170Wchtrq22ivXp6P31zQ/3zqba9XE79gLjCzbDXyn6XnGUT8/pTjnrlcjmKqIvqw40cRi2tvL2h/m5k9291/m4sPckFrGIsfdeNlOVZe6LGpCtXvyMS6LaAsJHdbuPRY3UWOytuX8gWtsrFoKj6MMZ32whjwV4v6B9kCUzMxppUXrrr08SgxMclPtFYEVqjy+uvG+9hedeOVt28f22vQ8aLxWIqo1bKKVbv14iDjf0q535TLZVOiAnZ+knw5MSnOLn5io3UbyIeJ2yHlJ6z3EGcZVM2lcj+p7cnEwmOVSTjErcvGlk5RnkHpF+pWtF9L9Fti9TYfXwDMrdi2sXhu5WzGWJzm83gu3n76T+X4OLMxra7d5BgVvdamXv+4bt9FgcXRgy8SR1uziyIPE18qF8/EtyTONNnP3S/N9PFhYhFt31zfZfHZxMLK6kxNrFZmanFp61z8FqJq5+mZPt5KnP67rbt/Nhdv4tZts4kFmvVov73czflcyvqxqQrV+zF1G7U7aL+dV3YBpaNydeqn7i3dZhO3+ZlD71vjrUpcurAccS1ut7ZNxju24wyMaVl8FYpv01c0TgvT61iVqSPzMzGmRbfpq3vLwKuAz3vnLVJWIc5w+wvVbjtXJ35nZnut0Wcf3eKVt28f22vQ8Y7xsKkK6P9DHMmF7rdeHGT8NqbGbu1M2zWI78kn0D5JPgxYwt1fmnudz2d0bgN5KVHR+DsFfX/HO2/lNO1+bOr2ekvncryUgluopcdu8cwZh+NGE9wZZGZG56Sy9YFYFK/TdljxaffhJTuhmW3i7n+YTryJPhrMZSXi/metI1Ktqnn35H8+td/J3c+eyXgffexGrPK1VQIkFlB2qRqvsw36yPH1xC/DfPEiqF7V/Erimrls7FQGWI28qfgwnrPfHIe1oDUOix+jlEtddbZv3bFoKi4zozXBc/d8rQ/M7BXZSZ8M3riOhxXfhuxWoqDc8e7+4LBy66U1IXb3mwsem5udgDbVT1nbsslz3VxGkSa4M8TMXgR8AbieqQI26zJVaOKKTPyZ6c/vUrxb22HFm8pxQ+At7n4WOdZA5eIm+mgibmb7Ah8gVtWPSeF1iQI3R7v714adYx9t9wWOB75C+5junv79w4rxWtugZo6HAh8C3k//Fcb3Jq5fOp9qVc33ooFq5E3FR+U90CtH4jOi6qLIr4H/qNi2kYWVuvEGF6LqLNDUWnSZgcWMw4nr/qrkfgWdt8aruxDVVLzWglZT8SYWxkZx4apGHztTo5J6zfitxOfzIPoe51wKK9KXsRG5xWK3tqOUe90+msq9bj+TSBPcGWJm1wC7uvufc/EbANx9w1zbA4Eve6YgT1HbYcUbzPFE4hYQ36DddkS1wf+tEN8WovJeLl6nj0HH9yFOP3uNu6/YClpcB/18ouR91lbEqWc/G0B8q/R3UbxO3zsSnyHLZYMW15hZwViXxYu2waBzrFOlu25V8zrVyJuKN1XVfBg5tt67NxOVlHstiuxI7C+/IG6h061tYwsrdeMN9dHEAk3posuAFy3q5F53EWmQ8WHl0sjC2KgtXNVoO594b3+U6d0ysCh+BHHJ16XARxrue5xzaVWkf5A41brnxHdM9q+yyeAo5Tjo3Cv309TkedRogjtD0pf7Td39kYJ425f+FHsGcdpIPl5n4jCweIM53kcUJfp/tPsSUZTobRXiXyLuL7xHQbxqH4OOH0MU2fmwu7eumcKiwNH9wGtzfZyW+njFAOKnEb9Y38PUl/9++v4GUfWyrZhQWswwd39KxXjRNmgqx68TxRbWyAbr5GhmfyBOLT/B22/NU9bHQuJ6wu1yuSwgVtCfPYD4Aqaqmm/Vo+2w4mU5GnGd1+qeO6Ja8tlzLfAi4Gxvv4VUEwsrMJzFokEu0FxBbOP8osugFznmAJVuAdfHItLA4kPMZRi36RvG4poBG5fsF0sXfPluakznANeNyP41Ermk+HziYMUBTE18zyKu13+MmPy2rJD+HoVbLBrxu31WtgOLavnLE8Vah51jWR9N5V63n7L2tSbP42JW7ybSkK8CvzWzb9N+QfxyQGu1uxW/gSi0cYGZvbZH22HFm8oR4AfuflJ2Y5nZPOJeaD3jKfZl4v55+XilPgYdt6h+/35gSTM7IoXXJ74A/K+7X5Dr40Liwv/G4yl2CXBVQbxO30cCXzKzL9JejXuFeLhyvGMbNJjjQcCPbHoVxv9JvarmS1GxGnlTcWuuqvmM55geu5+YEOdZ+pOPPVYSz8e6xbcB/kFnNdfTiC8SVeLZhZh8vGof3eKbA8sU5P54ZeAK8TWJAlOfY3q3dKsbP5f4klUlx8cYzq3xRimXYdymr4l43VzOABZLCy9Z6xHF5PLqvq+L4g8QRyofGEDf45wLwCuBG739Vmx/IxYnL8xNwutWqp923MpvsXgvsR/dm3s9yxO3f1sxGxxSjmV9NJV75X5SGy9ov0Lm8bbuKf7dMzZ0BHcGmdlmxOm4+etxKIj/HnhqxbbDijeR4wXESvn9ZJjZqsADVeJ12g45vgpRfTF/HeDdjKmy15T+XTk+yG1g9aqXl8UXUKOquQ+pGvm4SotC7yeOHGQXC3YnftH+IBN/IXHk9ZfE7W+6te0W3xf4jLsflsvldGKx5Jm94in2CeJU52376aNHfBfgR8QCSzb3Z6R/X14h3rq/8Bs8c8qhmR1PLELslHvOpuK7AN8jKhL3ynHLFL8c+L8+X2dT8WHl8hxiP2pb5Cjavil2AlG/4rXd2g463kcudxCn576Zdk8j9vUb6F1JvW58I+IMiYVMVQVuqu9xzmU9Yt97k7t/PcWwqDx/A3CIt9+K7cPUq1Q/7XiKnQq8yt0PzcRvJk6jfntBHwe6+5oF8ZnOsayPpnKv3E9mEv7ugu3YMXlOj6mKstSTJkC4+1294nXaDiuuXOrHJ4nFfQAfn+B5+/0BpxVvqu+SvJd39/wpPLXiTfQx6Pgo5dItR+KUuKqLIhcTR2BHdmGlKQ0t0Axl0aVm7rUWkQYcH0oui8LCWGtC7O4XFjz2TeDdNHDLwKJ4E31MWi7ErXe+SOct2v4BvNUzt2IbJa1JpeduzZQeOyY7iRs1TeVep5+ytmWT57q5jCKdojxDzGx9YqV/R+KDw8xsRdrvz9eKr8zUPQHv7tF2WPGmc9yGuDl1P/FWLssDdzXcd5Pxc4HDvLPQ2JXZVdJhxmu23Ry4iLj/263pNa9rZg+lJkv0Eb8t/fspZrY68DfgjylW1rZb/B7iyMJl+dcEXE2sXk8n3kQfg46PUi6lObr7+mZ2Hu1fyu4GKIjfYWb/rti2V3xkFmKK4u7+GPGZ0iYtFNSNz+jiB3EavzN1+rkzdepvPv5ojbaDjg8lF7OxvpVg1bZv8JIjKz51BLjtNmA2ddu9acWBlfMTtqb6HuNcbrc4g+YeKtyKzUbk1ovufqSZbVKUY9mkbBi5F8Wayr1OP+5+ZFE/rXidXMaFJrgz5zvAfGDv1iqtmS0OXEN80X9iJv5r4pfD1u6+TY+2w4o3neNa04j3ymU6fTcV34O4rcmZFrfOaNkGWN/MXkm7Qca3Tvnl43X7PhZ40DNVtNNrbaIQx++ICqzvcPcXTqPvTwM/MbP8tY3bAaub2bsrxFvFooriVfsYdHycczRgJTO7GFiJ3osi2cWPG3q0rbuwUjfe1ELMxC3QWNwa76fEaeSPvyZG/9Z4o5TLOORYN5cNzewtxEJ324S4bOJLXLpQtN81ER9k32OTi0cxobIJ8UjkWBAvbFs2AR2lHMviTeVep5+6E/lxoQnuzFnd3b+TDbj7o2nFFm8/NWl1d3+HRfXPrm2HFR+lHEcply45fou45cRatBfdmEccLcgX4hhkfB7xZXzZaeayKlEwJa+J4hfLuftXbaogV799v5Go0LxCLv5i4jVVib+YOFJtBfGqfQw6Ps45Qpx98Q53vyQbrLD4sVO3toOON7gQM4kLNIcC/3L3XXOv6QaAbNzitnM7Erede0O3toOOj1Iu45BjH7nsQxRVO4f2ie82ZnY+cbuwrO2ANc3sM9OIb0t87uTjTfQ9zrmQclm5LTDVZn8zO2HIOZZtr468M7nn8x5WjpW3eZ+51+2nsD31J/JjQRPcmXOpmX0BOInOKsJmZltn4n8ys6uAm8xs7R5thxUfpRxHKZey+A3A0sR1f/sz1egZRHGZx2ODjqfYPOD0aeZyH3Cgmb069/pnpdc/nfgNZnYncJmZPWcaff+buJ3M0bncdwaeXCWeYu8GflwQr9THoOPjnGN67Mj85Lb1EINb/BilhZhJXKAp+37hdL7WWUThoSUqtB10fJRyGYcc6+ZyBPCXgonvP4mzi36Ua38g8FB6jn7jBxK3EnxyQXy6fY9zLhD3fV4iN3l6E1EcbhbDz7FsexXl3cr90RHJsc427yf3Ov3snf7Ot681eR4nKjI1Q8xsSeI+Y7vRflrOT9O/X5KJ/4W4lnRVogBAt7bDio9SjqOUS1n8fqLK3ifd/fH7ypnZ84EV3b31MwOPp9hNwBPcfUG/fafHDgY2y73+U4kvN/l9vW78tvT/6fRxKfAjd/9bLu85wGLufk2veIr9nag0eEc/fQw6Ps45pse+TLx3v0b7AsXHiV+0h2bi7wOeBVwGfLBH20HHB53Ll4jTB1+d214XEQsIa/WKp9jbiYWF9frpo8/44cDRaRtlX9M70r8/nYnvQ3zRuoC4d3W3toOOj1Iu45Bj3VyOBj7k7h8hw+Ia+Y3cfd1c/Fzitntr9xtPsSOBk919gyb7HudcUvw+4l63h2TChxJV59/s7qsPM8cu26so71bua7v7yiOQY51t3k/ulftJbQ8hbmv3X5m2XwIeAd5Gp2Oz4z9uNMEVEZGhM7NdmdnFj1FaiOm2QPNjd1+Y21ZziIWCq3vFU+wuYsEhv/hRqY9+4umx5wHPq/haryJuFTMKYzpKuYxDjnVyWRPYnjjilZ347g18391bC0UAWAO36Wuij0nMJcXPBY5094vybYHf5yZso7S9OvLOtL/M3WePQI6Dzr1yP13aFk6e02N/yo7/uNEEd4aY2SziCO4raP+w/wnxS+DlmfhfiCMdq9F+RLKo7bDio5TjKOXSK8fVgScWtN2tJPdBxJvK5RTgeHd/mBwzO87dDxpEfJB9j3Muk5ijiEweM9uUgslz0SKJDFbZ5GnUjWve0FzudfqpOwmfCO6uPzPwhygy9EWiIu266c82wPXE9ZnZ+FnEiujPK7QdVnyUchylXMYhx6Zy+SpxvdSquT9PIb6wTCf+5JJ4E32Pcy6TmONqKfZxogL5XcQCzDXAp9KfbPxa4Nfp715tBx2fqVz+MI14K8frBtB3t/jHiVuhFP0+PL1qvE7bQcdHKZdxyLFuH12+P41MjotKLsMYi6a21yjlPqz3QN1+JvGPikzNnGe5+8a52K1m5oB75r6FZjbb3Tc2s+vc/dZubYcVH6UcRymXccixwVx+RWchAwdap7RMJz6buC4kW+Siqb7HOZdJzNGIMwnuBnbwdP9FM1sL+EVqt20mfh5x+tyD7j6nR9tBx0cpl245/jvluPEM5nI4cLqZvZV2mwJzzWzLTGwTYj/Ix4vaDjo+SrmMQ451czFgc/LBaPdZ4nrxYec4Sttr0PGO8ci0yY/HKG2vYexHdXMc9Hugbj9l7U/3XNG3bvFxoVOUZ4jFPR6PBX7g7o+l2GLE6jrAnEz8YmLF/TnuvnWPtsOKj1KOo5TLOOTYVC63Abe7+xZkWNyuaTnvLIhQOZ5iLwB+5e2Fcabd9zjnMok5pscedvclyDGzawFak8dWzN3ntP7u1nbQ8VHKZQRzfJSY/P+GdtsDjzE1MW7F7gFWLIjn2w46Pkq5jEOOdXNZnrhH7nNy8d8SC2AX5eLDyHGQ8VHKBYrH47dE4bxn0D4ew8hxe4q31zD2o7o5lvXRVO51+lk+/Z1vvykwH9g514cBp7n7ExlTOoI7c/YCjgE+b2b3pNjKwCXEjnR7Jr4qca3jshb3R+zWdljxUcpxlHIZhxybyuUWonJg3nziNObpxOcDqwCfGEDf45zLJOYIcVuo9wAneSqEZGZrEvudmdmaPlUg6a9m9jMgWzCpsO2g46OUywjmuBD4o7vvkB1oi1u6rZaNp9juwLkF8dWq9NFUfJRyGYcc+8jlUeBhYsE/69/ArBHJcZS216DjRePxb+LsrMeGnWOX7TXj+1EfOQ76PVC5n9T2gvTfbPvticlzvg8Y89sEaYI7Q9z9z2Z2FPB/tBdWOCX9e7dc/EpipaVK22HFRynHUcplHHJsKhc3s0Nz8VMbiq9DTMI/M4C+xzmXSczxOcBhwAVm9oT02B3Az9K/s/GFxOnMq5rZXT3aDjo+SrmMWo4XE7ekyDsKeEJBbDE6T6krajvo+CjlMg451s3lGuDz7v7FbNDM9iBOqxyFHAcZH6VcoGA80lhcSdTpGHaOR1G8vYaxH9XNsayPpnKv0881xH3Vn+ruP860LZw8p8duycfGyWLDTmBRkb7ofZO45uyS9AfgHOJDJBvfDvhe+rtX22HFRynHUcplHHJsMpdziSM4v0l/rKH4dsR9+LYfQN/jnMsk5vgt4I3ufqi7b+Luq6Y/m7r7O4HLc/E57r4N8LEKbQcdH6VcRi3HVxC3jmnj7t8njjC0xdz9WuIIf9e2g46PUi7jkGPdXIgv6yuX9P3mUchxlLbXoOMUjEcmx7fn4zOdY9n2Kso708dA9qO6OQ76PVCzn6OIOV/RdvxQQd/QOWEfLz4Cla4WhT9EBcslSuLXF8SWLYlfX6WPQcdHKcdRymUcchylXMYhx1HKZUJzXLKoj8zjN1eN12k76Pgo5aIcJy+Xccixjz72H4McF4lcysZjlHIcpf2owTFtKvfK/XRpWxgflz86RXnmPEZc93hTLr4YcRQj33bz9HevtsOKj1KOo5TLOOQ4SrmMQ46jlMsk5vhEYD0zu6Kgn42ApXKPbZT+LornY4OOj1IuynHychmHHOvmYsCa5IPRbhMze9cI5DhK22vQ8Y7xyLTJj8coba9h7Ed1cxz0e6BuP4XtgaOBE2rEx4KqKM8QM9sF+BxxT9HWee3rE1XqAC7PxLdM8cuJa3a7tR1WfJRyHKVcxiHHUcplHHIcpVwmMccNiXvh7kBcK5q1gDg1/tm52DziF+9WPdoOOj5KuSjHyctlHHKsm8sZxHv+mlx809R+o1x8Ud9eg44XjcemwJ+Jz+fs7S1HaXsNYz+qm+Og3wN1+jkj/Z1vvxGwFHBVrg8DNnb3pRhTOoI7Q9z9DDPbmNj5s0VXfkvsiNn4V4kdem6FtsOKj1KOo5TLOOQ4SrmMQ46jlMuk5ngcsLy7/44MMzsVWN/db8rF7gXOKYi3tR10fJRyUY6Tl8s45NhHLqsCZ9N5jeAngBeMSI6jtL0GHS8aj2OA7wOfGXaOXbbXjO9HfeQ46PdA5X5S252Ja27fkWm7ALgPeFmuD6PzdkVjRUdwRURERGTgzOx44AR3v7DgsW+6+2uHkNYia1zHY1zzhuZyr9NPWdsUX9/dd5pOLqNIE1wRERERERGZCIsNOwERERERERGRJmiCKyIiIiIiIhNBE1wREZEazOwoM3MzKy3UaGbbpzbbZ2LvNLNX9vF8m6fnXLXGz3Q8v4iIyKJAE1wREZHmXQb8R/q75Z1A7Qkucf/gDwCVJ7glzy8iIjLxdJsgERGRhrn7vcDFM/28ZrY4UUByKM8vIiIybDqCKyIi0p9Nzew8M7vfzP5qZh80s8Wg8xRhM/sz8CRg7xR3MzsxPbaxmf3IzO40swfM7GYz+56ZzTKz/YAT0vNdn/nZ2eln3cw+YmaHmdmfgIeAp5ecIn2+mV1oZi80s8tS3leZ2e75F2ZmrzGzP6R8rjSzl6efPz/TZnkz+2zK98GU/8/NbJNGt7KIiEgNOoIrIiLSnx8DXwU+BuwMvA94DDiqoO3uwM+AyzOPL0x//xS4G3gz8DdgHeDFxCL0T4EPA0cC/wncmn7mr5m+9wNuBP4L+BfwF2ClkpyfAnw65fw34BDge2a2ibvfAGBmOwEnA6cC7wbWAOYDSwPXZfr6FPBy4AjgemA14LnAyiXPLSIiMnCa4IqIiPTny+7+8fTvs8xsReAQM5ufb+ju/2dmDwJ/c/fHTx02s9WBDYHd3P3UzI98M/290Mz+mP79u9YkNMeAF7n7vzP9blqS8+rAtu5+fWp3GTFZ3hP4aGpzNHA1sLu7e2p3FbCA9gnufwAnu/vxmdiPSp5XRERkRugUZRERkf58N/f/bwPLA0+r0cffiaOvHzezA81soz7yOCM7ue3h+tbkFsDd7wTuBNaHx6/hnQv8oDW5Te0uBf6U6+u3wH5mdoSZzU0/KyIiMlSa4IqIiPTnjpL/r1O1gzSJ3Ik4Ovox4Dozu9HM3lwjj7/2bvK4uwpiDxKnH0Mc4V2CmPTm5V/v24H/BV5PTHbvNLNPmdmyNfIRERFplCa4IiIi/Vmz5P+31enE3W90932Ja123AM4FvmBmu1btos7z9fA34GHgCQWPtb1ed/+nux/u7hsCs4lTnN9G3NJIRERkKDTBFRER6c+euf/vBfwTuLKk/YPAMmWdefgdUdgJpk51fjD9XfqzTXH3R4mjya8yM2vFzexZwAZdfu4mdz+WeO11TtEWERFplIpMiYiI9OfAdFug3xJVlN8AHOXu/8jMDbOuBp5vZi8FbieOlq5IVDX+DnADsDhRFfkR4khu6+cA3mpmJxFHWK9w94cG8aKII7BnAT8ys+OI05aPSjk/1mpkZr8mKi1fSUzstwOeCZw0oLxERER60hFcERGR/uxGXD97KvA64nY+H+rS/nDgWqI41W+ZmjTeTBy1PRX4FrA28NJU2Al3b91a6GXAheln1276xbS4+9nA3sCmRFXkQ4nbCd0O/CPT9BfEUeyTidsZ7QG8y90/PajcREREerFMkUQRERGRDma2LnGE+SPu3m0SLyIiMlSa4IqIiMjjzGwZ4H+AnxOnUT8ZeA9RZOqp7l6narOIiMiM0jW4IiIikvUosBbwOWA14F/AL4H/1ORWRERGnY7gioiIiIiIyERQkSkRERERERGZCJrgioiIiIiIyETQBFdEREREREQmgia4IiIiIiIiMhE0wRUREREREZGJ8P8BQ6XzBFhBfSoAAAAASUVORK5CYII=", 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -741,7 +741,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.5 ('pulser-dev')", "language": "python", "name": "python3" }, @@ -755,7 +755,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.5" + }, + "vscode": { + "interpreter": { + "hash": "e088768f7ff7b4294439f8ed10f7eed9e3b885124bc20d9d06cc2a37b1883330" + } } }, "nbformat": 4, From 965ce514bab912b7fca7dac43925f01ad85b6c4c Mon Sep 17 00:00:00 2001 From: HGSilveri Date: Wed, 10 Aug 2022 10:25:53 +0200 Subject: [PATCH 18/18] Bump version to 0.7.0 --- VERSION.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION.txt b/VERSION.txt index 2551af7e6..faef31a43 100644 --- a/VERSION.txt +++ b/VERSION.txt @@ -1 +1 @@ -0.7dev0 +0.7.0