diff --git a/README.md b/README.md index 1c0a56a..65a6394 100644 --- a/README.md +++ b/README.md @@ -9,10 +9,12 @@ The documentation of the base package `neurodesign` is available at [ReadTheDocs ## File description ``` -├── docs Contains the source code to generate the documentation with sphinx. -├── examples Contains scripts to perform a design optimalisation. +├── docs Contains the source code to generate the documentation with sphinx (WIP). +├── manuals Contains comprehensive markdown documentation on neurodesign-plus. └── neurodesign Folder with the source code of the python package └── media Folder contains the logo of neurodesign, which is used in the reports. +├── tests Contains scripts to test modifications to the package. +└── tutorials Contains .ipynb tutorials on neurodesign-plus, the base functions and the new functions. ``` ## Overview of Modifications @@ -27,7 +29,7 @@ The modifications focus on the `classes.py` file, specifically enhancing the `Ex ## Installation & Setup -See [link](./SETUP.md) for environment setup and installation instructions. +See [link](./manuals/SETUP.md) for environment setup and installation instructions. --- @@ -69,18 +71,21 @@ The Neurodesign Python package optimizes these experiments based on four metrics During optimization, you can assign weights to these metrics to reflect what matters most for your experiment. +**See [link](./manuals/METRICS.md) for guidelines on interpreting metrics when optimising designs.** + --- ## Modifications Documentation -See [link](./TECHNICAL_CHANGES.md) for a detailed summary of changes relative to the upstream Neurodesign package. +See [link](./manuals/TECHNICAL_CHANGES.md) for a detailed summary of changes relative to the upstream Neurodesign package. -With these new parameters and changes in the package, it is simply a matter of defining the parameters required for the specific use case and the tool will perform the optimization. +With these new parameters and changes in the package, it is simply a matter of defining the parameters required for the specific use case and the tool will perform the optimization. **Note** that precedence for order follows as (if all provided): -* Fixed ordering -* Controlled ordering -* Random ordering + +- Fixed ordering +- Controlled ordering +- Random ordering The same holds for conditional_ITI and stimuli_durations. @@ -89,25 +94,28 @@ The same holds for conditional_ITI and stimuli_durations. ## Tutorials Tutorial notebooks / scripts are under `tutorials/`. + ``` tutorials -├── base_functions Contains tutorials of the different base package functions. -├── new_functions Contains tutorials on using the modifications. -└── tutorial_neurodesign_base_overview Base tutorial of the neurodesign package. +├── base_functions Contains tutorials of the different base package functions. +├── new_functions Contains tutorials on using the modifications. +├── tutorial_1_neurodesign_base_overview Base tutorial of the neurodesign package. +└── tutorial_2_comparing_designs_across_experiments Tutorial on comparing designs across diverse experiment definitions. ``` For quick access, find the tutorials in the table below: | # | Tutorial | Link | | :---: | :--- | :---: | -| 1 | **tutorial_neurodesign_base_overview** | [View](./tutorials/tutorial_neurodesign_base_overview.ipynb) | -| 2 | **tutorial_base_compare_and_simulate** | [View](./tutorials/base_functions/tutorial_base_compare_and_simulate.ipynb) | -| 3 | **tutorial_base_comparing_designs** | [View](./tutorials/base_functions/tutorial_base_comparing_designs.ipynb) | -| 4 | **tutorial_base_discovering_best_design** | [View](./tutorials/base_functions/tutorial_base_discovering_best_design.ipynb) | -| 5 | **tutorial_base_optimizating_and_report** | [View](./tutorials/base_functions/tutorial_base_optimizating_and_report.ipynb) | -| 6 | **tutorial_new_controlled_probabalistic_ordering** | [View](./tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb) | -| 7 | **tutorial_new_fixed_order** | [View](./tutorials/new_functions/tutorial_new_fixed_order.ipynb) | -| 8 | **tutorial_new_varied_ITI** | [View](./tutorials/new_functions/tutorial_new_varied_ITI.ipynb) | -| 9 | **tutorial_new_varied_stimuli_durations** | [View](./tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb) | +| 1 | **tutorial_1_neurodesign_base_overview** | [View](./tutorials/tutorial_1_neurodesign_base_overview.ipynb) | +| 2 | **tutorial_2_comparing_designs_across_experiments** | [View](./tutorials/tutorial_2_comparing_designs_across_experiments.ipynb) | +| 3 | **tutorial_base_compare_and_simulate** | [View](./tutorials/base_functions/tutorial_base_compare_and_simulate.ipynb) | +| 4 | **tutorial_base_comparing_designs** | [View](./tutorials/base_functions/tutorial_base_comparing_designs.ipynb) | +| 5 | **tutorial_base_discovering_best_design** | [View](./tutorials/base_functions/tutorial_base_discovering_best_design.ipynb) | +| 6 | **tutorial_base_optimizating_and_report** | [View](./tutorials/base_functions/tutorial_base_optimizating_and_report.ipynb) | +| 7 | **tutorial_new_controlled_probabalistic_ordering** | [View](./tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb) | +| 8 | **tutorial_new_fixed_order** | [View](./tutorials/new_functions/tutorial_new_fixed_order.ipynb) | +| 9 | **tutorial_new_varied_ITI** | [View](./tutorials/new_functions/tutorial_new_varied_ITI.ipynb) | +| 10 | **tutorial_new_varied_stimuli_durations** | [View](./tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb) | ## Credits diff --git a/TECHNICAL_CHANGES.md b/TECHNICAL_CHANGES.md deleted file mode 100644 index ee927a5..0000000 --- a/TECHNICAL_CHANGES.md +++ /dev/null @@ -1,99 +0,0 @@ -# Technical Documentation: Refactoring Details - -This document outlines the specific internal modifications made to the `classes.py` file in the Neurodesign package. It details new class variables, added functions, and modifications to existing logic within the `Design`, `Experiment`, and `Optimisation` classes. - -## 1. Added Class Variables - -### **Experiment Class** - -* **`stimuli_durations`** `(list of int or dicts)` - * A list of `stimuli_durations` corresponding to each stimulus (length of `stimuli_durations` must be the same as `n_stimuli`). - * If not provided, `stimuli_durations` uses `stimuli_duration` (the previous class duration variable) for all stimuli. - -* **`conditional_iti`** `(dict)` - * Adds a new input of ITI that can be calculated from the chosen probability distribution of the users. This variable holds the information about the probability distribution for an ITI between two specific stimuli. - * There is also a `'default'` key used if a relationship between two specific ITI in order is not provided. - * **Example Structure:** - ```python - self.conditional_ITI = { - (0, 1): {"model": "exponential", "mean": 2, "min": 1}, - (1, 2): {"model": "fixed", "mean": 4}, - "default": {"model": "exponential", "mean": 3, "min": 1} - } - ``` - -* **`order`** `(list of ints or None)` - * Allows the user to input their own custom order for their experiment. - * If `order` is provided, `self.order_fixed` (boolean) is set to **True**. This means the order is fixed and will be used for all designs and optimization. - -* **`ITI`** `(list of floats)` - * Allows the user to input their own custom ITI. This ITI can be modified later with the `conditional_iti` or the `ITImodel` provided. - * Acts as the baseline ITI for the model. - -* **`trial_max`** `(float)` - * Refers to the max `stimuli_duration` provided in the list of `stimuli_durations`. - * Used to calculate a maximum `n_tp` (timepoints), so the XConv calculation in the design can work for all varying ITIs and stimuli durations. - -* **`all_stimuli_durations`** `(list of floats)` - * Based on the given `stimuli_durations` provided, calculates the duration of each stimulus in order. This is used in the `Design` class in all cases when `stimuli_durations` have been provided. - -#### **Sequence Generation Variables** -*The following three variables are used to generate an order with keys (sequences). They are inputs for the `sample_from_probabilities` function.* - -* **`order_keys`** `(list of integer lists)` - * The list of sequences corresponding to stimuli/stimuli ordering. -* **`order_probabilities`** `(list of floats)` - * Defines the probability of the corresponding sequences (must sum to 1). -* **`order_length`** `(int)` - * The number of times we want to select from the `order_keys` based on the `order_probabilities`. - ---- - -## 2. New Functions - -### **Experiment Class** - -* **`calculate_duration(self, ITI, dur)`** - * Generates/calculates the duration based on `stimuli_duration` and ITI. - -* **`sample_from_probabilities(self, prob, key, length)`** - * Generates an order based on a given probability distribution of certain keys (sequences of stimuli). - -* **`generate_iti(self, order, conditional_iti)`** - * Generates ITI with the first value using the `'default'` entry, then appended relative to distributions of stimuli in order. - * Ensures `n_trials` and ITI length are the same. - -* **`calculate_all_stimuli(self)`** - * Calculates the `all_stimuli_durations` based on the new order (also calculates duration). - ---- - -## 3. Modified Functions - -### **Design Class** - -* **`design_matrix`** - * Added cases for multiple stimuli durations by working with `all_stimuli_durations`. - * If only `stim_duration` is provided, it uses the base code of the package. - -* **`mutation`** - * Added a case for **fixed order**, making the mutation utilize the same order for any new design. - -* **`crossover`** - * Added a case for **fixed order**, making the offspring orders the same as the parent order. - -### **Experiment Class** - -* **`count_stim`** - * Added cases for when `stimuli_durations` (multiple stimuli) is provided. - * When there are various durations, order matters; therefore, this function now accounts for cases where a fixed order is provided or not. - -### **Optimisation Class** - -* **`add_new_designs`** - * Added handling for the case of **fixed order**, custom order probabilities/functions, and default package order generator. - * Added handling for the case of custom ITI probabilities/functions and default package ITI generator. - -* **`to_next_generation`** - * When there is a **fixed order**, mutation and crossover should not run because the focus is on optimizing the order. - * In this case, the function only uses the immigration optimization function. \ No newline at end of file diff --git a/manuals/METRICS.md b/manuals/METRICS.md new file mode 100644 index 0000000..2f4c441 --- /dev/null +++ b/manuals/METRICS.md @@ -0,0 +1,214 @@ +# Neurodesign Efficiency Metrics: A Complete Guide + +Neurodesign optimizes fMRI experimental designs by evaluating them on four efficiency metrics. This document explains what each metric measures, how they are computed, how normalization works, and — critically — when you can and cannot compare metric values across different experimental setups. + +## Overview + +Neurodesign separates the specification of an experiment from the evaluation of candidate designs. + +First, define an Experiment: the fixed scientific and acquisition assumptions that determine the “ruler” used to score designs (e.g., TR, total duration or trial budget, autocorrelation parameter rho, target probabilities, contrasts). These settings define how the convolved and deconvolved design matrices are constructed and, for Fe/Fd, fix the whitening matrix 𝑊. + +Second, generate and compare Designs: alternative realizations of event timing and ordering that share the same Experiment base. The efficiency metrics then quantify how well each Design performs relative to that base. + +A practical consequence is that raw Fe and Fd are meaningful for comparing Designs within the same Experiment, and across Experiments only when 𝑊 is identical (in practice: same duration, TR, and rho). If you change those, you changed the ruler, so the absolute values are on different scales. + +--- + +## The Four Metrics + +### Fe — Estimation Efficiency + +**What it measures:** How precisely the design allows you to estimate the shape and amplitude of each condition's hemodynamic response, even when events overlap in time. + +**Mathematical definition (A-optimality):** + +``` +Fe_raw = n_contrasts / trace( C × inv(X'WX) × C' ) +``` + +Where: + +- `X` is the deconvolved design matrix (stimuli × HRF lag bins), downsampled to TR resolution +- `W` is the whitening matrix (accounts for temporal autocorrelation and drift) +- `C` is the contrast matrix expanded to HRF resolution (`CX = kron(C, I_laghrf)`) +- `trace(C × inv(X'WX) × C')` is the sum of variances of your contrast estimates + +**Interpretation:** Fe is the average _precision_ (inverse variance) of your contrast estimates. Higher Fe means the design allows you to more precisely estimate the differences between conditions specified by your contrast matrix. If Design A has twice the Fe of Design B, it means Design A yields contrast estimates with half the variance — you'd need roughly twice as many subjects with Design B to match Design A's precision. + +**When it matters most:** Event-related designs where stimuli are closely spaced and temporal overlap is a concern. Fe rewards designs that create orthogonal (non-overlapping) patterns across conditions. + +### Fd — Detection Efficiency + +**What it measures:** How well the design supports detecting activation differences between conditions, assuming the canonical HRF shape is correct. + +**Mathematical definition (A-optimality):** + +``` +Fd_raw = n_contrasts / trace( C × inv(Z'WZ) × C' ) +``` + +Where `Z` is the _convolved_ design matrix (each column is the stimulus train convolved with the canonical HRF), as opposed to the deconvolved `X` used for Fe. + +**Interpretation:** Fd reflects the design's ability to detect that a contrast is nonzero, given that the HRF has the canonical shape. It rewards designs where the convolved regressors for different conditions are maximally distinguishable from each other and from noise. + +**When it matters most:** Blocked or mixed designs where you trust the canonical HRF and want to maximize statistical power for detecting activation. In practice, blocked designs tend to have higher Fd than rapid event-related designs. + +**Fe vs Fd:** Fe is about _estimating_ the response (flexible HRF shape), Fd is about _detecting_ it (assuming canonical shape). These are sometimes in tension — designs that are optimal for one are often suboptimal for the other, which is why you can weight them differently. + +### Fc — Confounding Efficiency + +**What it measures:** Whether the sequence of conditions is balanced in terms of transitions — i.e., that no condition systematically follows another condition more than expected by chance. + +**Computation:** Fc compares the observed transition matrix (how often condition _i_ follows condition _j_, up to 3rd order) against the expected transition matrix (based on the specified probabilities). The closer the match, the higher Fc. + +``` +Fc = 1 - |Q_observed - Q_expected| / FcMax +``` + +Where `FcMax` is calibrated from a worst-case (all-same-stimulus) null design. + +**Interpretation:** Fc = 1 means transitions perfectly match what you'd expect from random independent draws at the specified probabilities. Fc = 0 means transitions are maximally biased (e.g., stimulus 0 always follows stimulus 1). Values near 1 are desirable to avoid confounding condition effects with transition effects. + +**When it matters most:** Any design where you worry about carry-over effects, adaptation, or expectation. High Fc means the design won't accidentally create systematic patterns (like alternation or repetition) that could confound your contrasts. + +### Ff — Frequency Accuracy + +**What it measures:** Whether the realized number of trials per condition matches the prescribed probabilities. + +**Computation:** + +``` +Ff = 1 - |P_observed - P_expected| / FfMax +``` + +**Interpretation:** If you specified P = [0.3, 0.3, 0.4] and got trial counts of [6, 6, 8] out of 20 trials, that's a perfect match (Ff = 1). If you got [10, 5, 5], there's a mismatch (lower Ff). This metric ensures the optimizer doesn't accidentally over- or under-represent conditions. + +**When it matters most:** When you have specific requirements about how many trials of each type are needed — for example, to ensure enough power per condition, or when trial counts need to match a specific ratio for your analysis. + +--- + +## Normalization: Raw Values vs Calibrated Values + +### The two regimes + +Neurodesign metrics can appear in two forms: + +**Raw (uncalibrated):** `FeMax = 1`, so `Fe = Fe_raw`. This is what you get when you manually create an Experiment and Designs, then call `FeCalc()`. The values can be any positive number (typically 10–300 for Fe/Fd depending on your experiment). + +**Calibrated (normalized):** `FeMax` is set to the best Fe found during an optimization pre-run, so `Fe = Fe_raw / FeMax ∈ [0, 1]`. This is what happens inside `Optimisation.optimise()`. + +### How calibration works + +When you call `optimise()`, the optimizer: + +1. Runs a pre-optimization with `weights=[1, 0, 0, 0]` (only Fe) for `preruncycles` generations +2. Takes the best Fe found and sets `exp.FeMax = best_Fe_raw` +3. Repeats for Fd with `weights=[0, 1, 0, 0]` to calibrate `exp.FdMax` +4. Fc and Ff are calibrated during `Experiment.__init__` using a worst-case null design + +After calibration, all four metrics are on a [0, 1] scale, and the weighted sum `F = w₁·Fe + w₂·Fd + w₃·Ff + w₄·Fc` becomes meaningful. + +### When to use which + +- **Manual design comparison (same Experiment):** Use raw Fe/Fd. They are directly comparable and ratios are meaningful. A design with Fe=200 has exactly 10× the estimation precision of one with Fe=20. + +- **During optimization:** Calibrated values are used automatically. You don't need to do anything. + +- **Cross-experiment comparison:** See the next section. + +--- + +## Comparing Metrics Across Experiments + +### The fundamental rule + +**Fe and Fd values are only directly comparable when the whitening matrix `W` is identical.** + +The whitening matrix depends on three things: + +1. **`n_scans`** = ceil(duration / TR) +2. **`rho`** (autocorrelation coefficient) +3. **Drift polynomials** (derived from `n_scans`) + +Therefore: two Experiments with the **same `duration`, `TR`, and `rho`** produce **identical `W`**, and their Fe/Fd values live on the same scale. Everything else (ITI model, stim duration, number of stimuli, contrasts, probabilities) affects the _design matrix_ but not the _ruler_ used to measure it. + +### The common trap: specifying n_trials + +When you specify `n_trials` instead of `duration`, the code computes: + +``` +duration = n_trials × (trial_duration + ITImean) +``` + +If two Experiments have different `ITImean` but the same `n_trials`, they will have **different durations**, **different `n_scans`**, **different `W`**, and their Fe/Fd values are **not comparable**. This is not a bug — it's a fundamental property. A longer experiment gives you more data, which inherently changes the precision you can achieve. + +### How to compare ITI models correctly + +**Method 1: Fix duration (recommended for comparing design efficiency)** + +Specify `duration=` instead of `n_trials=` when creating both Experiments. The duration determines `n_scans` and `W`, which become identical. Differences in ITI model will change the number of trials that fit and the arrangement of events, which is exactly what you're comparing. + +```python +# CORRECT: same duration → same W → Fe is comparable +exp_A = Experiment(duration=300, ITImodel="exponential", ITImean=2.1, ...) +exp_B = Experiment(duration=300, ITImodel="uniform", ITImean=3.0, ...) +# exp_A.n_scans == exp_B.n_scans → raw Fe directly comparable +``` + +**Method 2: Fix n_trials and compare power (recommended for fixed-trial protocols)** + +If your experiment must have exactly N trials regardless of timing, then the right comparison is not Fe but _statistical power_ — the probability of detecting your effect at a given significance level and effect size. This accounts for both design efficiency and total data volume. + +```python +# ALSO VALID: same n_trials, compare power via simulation +exp_A = Experiment(n_trials=40, ITImodel="exponential", ITImean=2.1, ...) +exp_B = Experiment(n_trials=40, ITImodel="uniform", ITImean=3.0, ...) +# Fe values are NOT comparable, but power analysis gives the right answer +``` + +### Summary table + +| Scenario | Same `W`? | Raw Fe/Fd comparable? | What to do | +| ------------------------------------------------------------- | --------- | --------------------- | ----------------------------------------- | +| Same Experiment, different Design | Yes | Yes | Compare raw Fe directly | +| Different Experiment, same `duration`/`TR`/`rho` | Yes | Yes | Compare raw Fe directly | +| Different Experiment, same `n_trials` but different `ITImean` | **No** | **No** | Use `duration=` instead, or compare power | +| Different `TR` | **No** | **No** | Use power analysis | +| Different `rho` | **No** | **No** | Use power analysis | + +### Fc and Ff comparisons + +Fc and Ff depend only on the stimulus order and probabilities, not on the whitening matrix. They are always comparable across any two designs with the same number of stimuli and the same target probabilities, regardless of timing parameters. + +--- + +## Practical Recommendations + +### Choosing weights + +The weight vector `[w_Fe, w_Fd, w_Ff, w_Fc]` controls the tradeoff between metrics. Common choices: + +- **`[0.25, 0.25, 0.25, 0.25]`**: Balanced — no strong preference. Good starting point. +- **`[1, 0, 0, 0]`**: Pure estimation efficiency. Best when you want to estimate HRF shape freely (e.g., FIR models). +- **`[0, 1, 0, 0]`**: Pure detection power. Best for blocked designs or when you trust the canonical HRF. +- **`[0, 0.5, 0.25, 0.25]`**: Detection-focused with frequency and confound balance. Common for standard GLM analyses. + +### Interpreting Fe/Fd magnitudes + +Raw (uncalibrated) Fe/Fd values are hard to interpret in isolation. What matters is: + +- **Relative ranking:** Within the same Experiment, higher is always better. +- **Ratios:** Fe=200 has 2× the precision of Fe=100. This is linear. +- **Normalized values:** Fe=0.85 (calibrated) means "85% as good as the best design the optimizer found." This tells you how much room there is for improvement. + +### When Fe and Fd conflict + +For rapid event-related designs, Fe and Fd often point in opposite directions. Jittered ITIs and randomized orders help Fe (by decorrelating the design matrix) but can hurt Fd (by spreading activation over time). If you need both, use mixed weights and accept that neither will be at its theoretical maximum. + +### Design matrix sparsity warning + +When using variable ITIs (exponential model), some randomly generated designs may produce ITI sequences that cause the total experiment duration to exceed the container. These designs are automatically rejected during optimization. If you see many rejected designs (slow optimization), consider: + +1. Using a longer `duration` to give more headroom +2. Using `ITImax` to bound the tail of the exponential distribution +3. Using `trial_max` to set the container size based on the longest possible trial diff --git a/SETUP.md b/manuals/SETUP.md similarity index 100% rename from SETUP.md rename to manuals/SETUP.md diff --git a/manuals/TECHNICAL_CHANGES.md b/manuals/TECHNICAL_CHANGES.md new file mode 100644 index 0000000..5c253bb --- /dev/null +++ b/manuals/TECHNICAL_CHANGES.md @@ -0,0 +1,137 @@ +# Technical Documentation: Refactoring Details + +This document outlines the specific internal modifications made to the `classes.py` file in the Neurodesign package. It details new class variables, added functions, and modifications to existing logic within the `Design`, `Experiment`, and `Optimisation` classes. + +--- + +## Architecture Overview + +The refactored code enforces the strict separation between the **Experiment** (a fixed container defining the experimental context) and the **Design** (a specific trial sequence within that container). + +- **Experiment** stores distribution *specifications* (ITI model parameters, stimulus duration specs) and computes the whitening matrix, timepoints, and HRF components once. It does not hold per-trial timing arrays. +- **Design** stores concrete per-trial arrays (order, ITI, stimulus durations) sampled from the Experiment's specifications. All designs in an optimization population share a single Experiment object. + +This guarantees that all efficiency metrics (Fe, Fd) are computed against the same whitening matrix, making them comparable across designs. + +--- + +## 1. Added Class Variables + +### **Design Class** + +* **`all_stim_durations`** `(list of floats or None)` + * Concrete per-trial stimulus durations for this design, including `t_pre` and `t_post`. + * `None` when all stimuli share the same `stim_duration` (original package behavior). + * When set, `designmatrix()` uses these per-trial values instead of `experiment.stim_duration`. + * Sampled via `Experiment.sample_stim_durations()` and passed at construction: `Design(order=..., ITI=..., experiment=..., all_stim_durations=...)`. + +### **Experiment Class** + +* **`stimuli_durations`** `(list of int/float or dicts, or None)` + * A specification template — one entry per stimulus — defining how to sample each stimulus's duration. + * Each entry is either a scalar (fixed duration) or a dict specifying a distribution: + ```python + stimuli_durations = [ + {"model": "fixed", "mean": 1.0}, + {"model": "exponential", "mean": 2.0, "min": 1.0, "max": 5.0}, + 1.5, # scalar shorthand for fixed + ] + ``` + * Length must equal `n_stimuli`. Requires `trial_max` to be specified. + * If not provided, all stimuli use the original `stim_duration`. + +* **`conditional_ITI`** `(dict or None)` + * Specification for condition-dependent ITI distributions. Keys are `(prev_stim, curr_stim)` tuples or `"default"`. Values are dicts with `"model"`, `"mean"`, and optional `"min"`, `"max"`, `"std"`. + * **Example:** + ```python + conditional_ITI = { + (0, 1): {"model": "exponential", "mean": 2, "min": 1}, + (1, 2): {"model": "fixed", "mean": 4}, + "default": {"model": "exponential", "mean": 3, "min": 1} + } + ``` + +* **`order`** `(list of ints or None)` + * A user-provided fixed stimulus order. When provided, `order_fixed` is set to `True`. + * The order is preserved across all designs during optimization — crossover and mutation do not modify it. + +* **`trial_max`** `(float)` + * The maximum stimulus duration across all conditions. Required when `stimuli_durations` is provided. Defaults to `stim_duration` otherwise. + * Used to compute the container's `trial_duration = trial_max + t_pre + t_post`, which determines the experiment's total duration and whitening matrix dimensions. + +#### **Sequence Generation Variables** + +* **`order_keys`** `(list of integer lists)` — Sequences of stimuli used as sampling units. +* **`order_probabilities`** `(list of floats)` — Probabilities for each key (must sum to 1). +* **`order_length`** `(int)` — Number of draws from the key distribution. + +These three are inputs for `sample_from_probabilities()`. When `order_probabilities` is provided, the optimizer generates stimulus orders by sampling from these sequence distributions rather than using the original blocked/random/m-sequence generators. + +--- + +## 2. New Functions + +### **Experiment Class (all `@staticmethod`)** + +* **`sample_stim_durations(order, stimuli_durations, t_pre, t_post)`** → `list[float]` + * Samples concrete per-trial stimulus durations for a given order, using the distribution specs in `stimuli_durations`. + * Adds `t_pre + t_post` to each trial's duration. + * Supports `"fixed"`, `"exponential"`, `"uniform"`, `"gaussian"` distribution models, as well as scalar values. + +* **`generate_iti(order, conditional_iti)`** → `list[float]` + * Samples a concrete ITI array based on condition-dependent distributions. + * For the first trial (no previous stimulus), uses the `"default"` key. + * Supports `"fixed"`, `"exponential"`, `"uniform"`, `"gaussian"` distribution models. + * Returns a list of length `len(order)`. + +* **`calculate_duration(ITI, dur)`** → `float` + * Computes total duration as the sum of all ITIs and all trial durations. + +* **`sample_from_probabilities(prob, key, length)`** → `list` + * Generates a stimulus order by sampling `length` times from `key` with weights `prob`, then flattening the sampled sequences into a single order list. + +--- + +## 3. Modified Functions + +### **Design Class** + +* **`__init__(self, order, ITI, experiment, onsets=None, all_stim_durations=None)`** + * **Added:** `all_stim_durations` parameter for per-trial variable stimulus durations. + +* **`designmatrix(self)`** + * Added branches for variable stimulus durations via `self.all_stim_durations`. When set, onset computation and design matrix construction use per-trial durations instead of a single `stim_duration`. + * Returns `False` if the design's actual timing exceeds the Experiment container, allowing the optimizer to skip invalid candidates gracefully. + +* **`crossover(self, other, seed)`** + * Added fixed-order support: when `order_fixed=True`, offspring inherit the parent's order unchanged. + * Propagates `all_stim_durations` to offspring. When the order changes and `stimuli_durations` is set, durations are re-sampled to match the new order. When `order_fixed=True`, durations are inherited. + +* **`mutation(self, q, seed)`** + * Added fixed-order support: mutation is skipped when `order_fixed=True`. + * Same `all_stim_durations` propagation logic as crossover. + +### **Experiment Class** + +* **`countstim(self)`** + * Duration is computed from expected values (`n_trials × (trial_duration + ITImean)`) regardless of whether `stimuli_durations` is set, ensuring the whitening matrix dimensions are stable across all designs in a population. + +* **`max_eff(self)`** + * Extended to estimate FeMax and FdMax by sampling random designs and taking the maximum raw efficiency. This provides initial normalization for Fe and Fd even when evaluating designs manually (outside the optimizer). Respects user-provided values. + +### **Optimisation Class** + +* **`add_new_designs(self, weights, R)`** + * Added order sampling paths: fixed order → `self.exp.order`; probability-based → `Experiment.sample_from_probabilities()`; default → `generate.order()` (original behavior). + * Added ITI sampling paths: conditional → `Experiment.generate_iti()`; default → `generate.iti()` (original behavior). + * Added stimulus duration sampling: when `stimuli_durations` is set, calls `Experiment.sample_stim_durations()` per design. + * All designs share a single Experiment object, ensuring efficiency metrics remain on a comparable scale. + +* **`clear(self)`** + * Propagates `all_stim_durations` when preserving the best design across generation clears. + +* **`optimise(self)`** + * Skips Fe/Fd calibration pre-runs when the corresponding weight is zero or when the user has provided explicit FeMax/FdMax values. + +* **`to_next_generation(self, weights, seed, optimisation)`** + * When `order_fixed=True`, skips mutation and crossover entirely, using immigration only. This focuses optimization on ITI timing and other non-order parameters. diff --git a/neurodesign/classes.py b/neurodesign/classes.py index 7df43fd..929d2b9 100644 --- a/neurodesign/classes.py +++ b/neurodesign/classes.py @@ -58,7 +58,7 @@ class Design: :type onsets: list of floats """ - def __init__(self, order, ITI, experiment, onsets=None): + def __init__(self, order, ITI, experiment, onsets=None, all_stim_durations=None): self.order = order self.ITI = ITI @@ -66,11 +66,12 @@ def __init__(self, order, ITI, experiment, onsets=None): self.Fe = 0 self.Fd = 0 + # Per-design variable stimulus durations (None = use experiment.stim_duration) + self.all_stim_durations = all_stim_durations + self.experiment = experiment # assert whether design is valid - if self.experiment.ITI is not None: - self.ITI = self.experiment.ITI if len(self.ITI) != experiment.n_trials: raise ValueError("length of design (ITI's) does not comply with experiment") if len(self.order) != experiment.n_trials: @@ -124,19 +125,42 @@ def crossover(self, other, seed=1234): offspringorder1 = None offspringorder2 = None - #Making sure the order doesn't change for the crossover + # Making sure the order doesn't change for the crossover if self.experiment.order_fixed: offspringorder1 = self.order offspringorder2 = other.order - else: - offspringorder1 = list(self.order)[:changepoint] + list(other.order)[changepoint:] - offspringorder2 = list(other.order)[:changepoint] + list(self.order)[changepoint:] + elif self.experiment.order_probabilities is not None: + offspringorder1 = Experiment.sample_from_probabilities( + self.experiment.order_probabilities, self.experiment.order_keys, self.experiment.order_length) + offspringorder2 = Experiment.sample_from_probabilities( + self.experiment.order_probabilities, self.experiment.order_keys, self.experiment.order_length) + else: + offspringorder1 = list(self.order)[ + :changepoint] + list(other.order)[changepoint:] + offspringorder2 = list(other.order)[ + :changepoint] + list(self.order)[changepoint:] + + # Re-sample stim durations for new orders if variable durations are used + asd1 = self.all_stim_durations # default: inherit from parent + asd2 = other.all_stim_durations + if self.experiment.stimuli_durations is not None and not self.experiment.order_fixed: + # Order changed, so re-sample durations to match new order + asd1 = Experiment.sample_stim_durations( + offspringorder1, self.experiment.stimuli_durations, + self.experiment.t_pre, self.experiment.t_post, + ) + asd2 = Experiment.sample_stim_durations( + offspringorder2, self.experiment.stimuli_durations, + self.experiment.t_pre, self.experiment.t_post, + ) offspring1 = Design( - order=offspringorder1, ITI=self.ITI, experiment=self.experiment + order=offspringorder1, ITI=self.ITI, experiment=self.experiment, + all_stim_durations=asd1, ) offspring2 = Design( - order=offspringorder2, ITI=other.ITI, experiment=self.experiment + order=offspringorder2, ITI=other.ITI, experiment=self.experiment, + all_stim_durations=asd2, ) return [offspring1, offspring2] @@ -156,28 +180,38 @@ def mutation(self, q, seed=1234): ) mutated = copy.copy(self.order) - if not self.experiment.order_fixed: + if not self.experiment.order_fixed and self.experiment.order_probabilities is None: for mut in mut_ind: np.random.seed(seed) mut_stim = np.random.choice(self.experiment.n_stimuli, 1, replace=True)[0] mutated[mut] = mut_stim - offspring = Design(order=mutated, ITI=self.ITI, experiment=self.experiment) + # Re-sample stim durations if order changed and variable durations are used + asd = self.all_stim_durations + if self.experiment.stimuli_durations is not None and not self.experiment.order_fixed: + asd = Experiment.sample_stim_durations( + mutated, self.experiment.stimuli_durations, + self.experiment.t_pre, self.experiment.t_post, + ) - return offspring + offspring = Design( + order=mutated, ITI=self.ITI, experiment=self.experiment, + all_stim_durations=asd, + ) + return offspring def designmatrix(self): - """Expand from order of stimuli to a fMRI timeseries.""" + """Expand from order of stimuli to a fMRI timeseries. + + Returns self on success, or False if the design's actual timing + exceeds the experiment's container (e.g. from extreme ITI draws). + """ # ITIs to onsets orderli = list(self.order) ITIli = list(self.ITI) - - if self.experiment.stimuli_durations is not None and self.experiment.all_stim_durations is None: - self.experiment.calculate_all_stimuli(orderli) - if self.experiment.restnum > 0: - if self.experiment.all_stim_durations is None: + if self.all_stim_durations is None: # Old Package Code ITIli = [ y + self.experiment.trial_duration if not x == "R" else y @@ -186,42 +220,48 @@ def designmatrix(self): onsets = np.cumsum(ITIli) - self.experiment.trial_duration self.onsets = [y for x, y in zip(orderli, onsets) if not x == "R"] - else: + else: for x in np.arange(0, self.experiment.n_trials, self.experiment.restnum)[1:][ ::-1 ]: orderli.insert(x, "R") ITIli.insert(x, self.experiment.restdur) - - #Modified by Atharv Umap - #Calculate the ITI_li based on the rest numbers and varied trial duration - ITIli_new = [] - onsets = [] + + # Calculate the ITI_li based on the rest numbers and varied trial duration + # Use a separate trial counter since orderli is now expanded with "R" entries + # but all_stim_durations only has n_trials entries. + ITIli_new = [] + onsets = [] + trial_idx = 0 for i, (x, y) in enumerate(zip(orderli, ITIli)): if not x == "R": - ITIli_new.append(y+self.experiment.all_stim_durations[i]) - else: + ITIli_new.append(y + self.all_stim_durations[trial_idx]) + trial_idx += 1 + else: ITIli_new.append(y) ITIli_cumsum = np.cumsum(ITIli_new) - # Subtracts the cumulative sum by the respsective trial durations while excluding the rest trials. + # Subtracts the cumulative sum by the respective trial durations + # while excluding the rest trials. + trial_idx = 0 for i, (x, y) in enumerate(zip(orderli, ITIli_cumsum)): if not x == "R": - onsets.append(y - self.experiment.all_stim_durations[i]) + onsets.append(y - self.all_stim_durations[trial_idx]) + trial_idx += 1 self.onsets = onsets else: - if self.experiment.all_stim_durations is None: - #Old Package Code + if self.all_stim_durations is None: + # Old Package Code ITIli = np.array(self.ITI) + self.experiment.trial_duration self.onsets = np.cumsum(ITIli) - self.experiment.trial_duration - else: - #Modified by Atharv Umap - ITIli_new = [y+x for x, y in zip(self.experiment.all_stim_durations, ITIli)] + else: + # Modified by Atharv Umap + ITIli_new = [y + x for x, y in zip(self.all_stim_durations, ITIli)] ITIli_cumsum = np.cumsum(ITIli_new) - - onsets_temp = [] - for x, y in zip(self.experiment.all_stim_durations, list(ITIli_cumsum)): + + onsets_temp = [] + for x, y in zip(self.all_stim_durations, list(ITIli_cumsum)): onsets_temp.append(y - x) self.onsets = onsets_temp @@ -231,12 +271,13 @@ def designmatrix(self): self.ITI, x = _round_to_resolution(self.ITI, self.experiment.resolution) onsetX, XindStim = _round_to_resolution(stimonsets, self.experiment.resolution) - if self.experiment.all_stim_durations is None: - stim_duration_tp = int(self.experiment.stim_duration / self.experiment.resolution) + if self.all_stim_durations is None: + stim_duration_tp = int(self.experiment.stim_duration + / self.experiment.resolution) - # find indices in resolution scale of stimuli - assert np.max(XindStim) <= self.experiment.n_tp - assert np.max(XindStim) + stim_duration_tp <= self.experiment.n_tp + # Check if design fits in container — reject if not + if np.max(XindStim) + stim_duration_tp > self.experiment.n_tp: + return False # create design matrix in resolution scale (=deltasM in Kao toolbox) X_X = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) @@ -245,36 +286,30 @@ def designmatrix(self): X_X[np.array(XindStim) + dur, int(stimulus)] = [ 1 if z == stimulus else 0 for z in self.order ] - else: - stim_duration_tp = self.experiment.all_stim_durations / self.experiment.resolution - stim_duration_tp = [int(x) for x in stim_duration_tp] - - # find indices in resolution scale of stimuli - try: - assert np.max(XindStim) <= self.experiment.n_tp + else: + stim_duration_tp = np.array(self.all_stim_durations) / \ + self.experiment.resolution + stim_duration_tp = [int(x) for x in stim_duration_tp] - #Modified to add multiple different stimuli_duration - assert np.max(XindStim) + stim_duration_tp[-1] <= self.experiment.n_tp - except Exception as e: - print(XindStim) - print(stim_duration_tp[-1]) - print(self.experiment.n_tp) - assert np.max(XindStim) <= self.experiment.n_tp - assert np.max(XindStim) + stim_duration_tp[-1] <= self.experiment.n_tp + # Check if design fits in container — reject if not + max_endpoint = max(XindStim[i] + stim_duration_tp[i] + for i in range(len(self.order))) + if max_endpoint > self.experiment.n_tp: + return False X_X = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) - #indexing and traversing through the order + # indexing and traversing through the order for i, stim in enumerate(self.order): - #the current onset + # the current onset onset = XindStim[i] - #the duration between the onsets + # the duration between the onsets dur = stim_duration_tp[i] - #labeling the binary values of the indices with + # labeling the binary values of the indices with for j in range(dur): t_idx = onset + j if t_idx < self.experiment.n_tp: X_X[t_idx, stim] = 1 - + # deconvolved matrix in resolution units deconvM = np.zeros( [ @@ -306,7 +341,7 @@ def designmatrix(self): X_Z = np.zeros([self.experiment.n_tp, self.experiment.n_stimuli]) for stim in range(self.experiment.n_stimuli): X_Z[:, stim] = deconvM[ - :, (stim * self.experiment.laghrf) : ((stim + 1) * self.experiment.laghrf) + :, (stim * self.experiment.laghrf): ((stim + 1) * self.experiment.laghrf) ].dot(self.experiment.basishrf) X_Z = X_Z[idxX, :] @@ -322,8 +357,6 @@ def designmatrix(self): return self - - def FeCalc(self, Aoptimality=True): """ Compute estimation efficiency. @@ -513,9 +546,8 @@ def __init__( rho: float, stim_duration, n_stimuli: int, - stimuli_durations=None, - ITI = None, #Adding a custom ITI - conditional_ITI=None, #Adding a new input for ITI's that can be controlled by the user. + stimuli_durations=None, + conditional_ITI=None, # Specification for condition-dependent ITI distributions ITImodel=None, ITImin=None, ITImax=None, @@ -534,12 +566,12 @@ def __init__( maxrep=None, hardprob=False, confoundorder=3, - order = None, - order_probabilities = None, - order_keys = None, - order_length = None, - order_fixed = False, - trial_max = None + order=None, + order_probabilities=None, + order_keys=None, + order_length=None, + order_fixed=False, + trial_max=None ): self.TR = TR self.P = P @@ -553,51 +585,43 @@ def __init__( self.resolution = resolution self.stim_duration = stim_duration + # We will calculate all stimuli durations based on the given values + # NOTE: all_stim_durations is now stored per-Design, not per-Experiment. + # The Experiment only stores the *specification* (stimuli_durations dict/list). - #We will calculate all stimuli durations based on the given values - self.all_stim_durations = None - - #Modification - #Working with multiple stimuli durations - if stimuli_durations is not None: - assert len(stimuli_durations) == n_stimuli, "Must specify a duration for each stimulus" + # Modification + # Working wiht multiple stimuli durations + if stimuli_durations is not None: + assert len( + stimuli_durations) == n_stimuli, "Must specify a duration for each stimulus" assert trial_max is not None, "Must provide a trial_max given stimuli_durations" self.trial_max = trial_max - self.stimuli_durations = stimuli_durations + self.stimuli_durations = stimuli_durations else: self.trial_max = stim_duration self.stimuli_durations = None - + self.order_probabilities = order_probabilities self.order_keys = order_keys self.order_length = order_length self.order_fixed = order_fixed - #Adding the custom order + # Adding the custom order if order is not None: - self.order = order + self.order = order self.order_fixed = True - elif order_probabilities is not None: - order = self.sample_from_probabilities(order_probabilities, order_keys, order_length) - + else: + self.order = None + if order_probabilities is not None: + self.order = self.sample_from_probabilities( + order_probabilities, order_keys, order_length) self.maxrep = maxrep self.hardprob = hardprob self.confoundorder = confoundorder - - #Addding a conditional ITI where you can have a number of stimulus and it can have different ITI's - if conditional_ITI is not None: - self.conditional_ITI = conditional_ITI - else: - self.conditional_ITI = None - # else: - # self.conditional_ITI = { - # (0, 1): {"model": "exponential", "mean": 2, "min": 1}, - # (1, 2): {"model": "fixed", "mean": 4}, - # "default": {"model": "exponential", "mean": 3, "min": 1} - # } - + # Addding a conditional ITI where you can have a number of stimulus and it can have different ITI's + self.conditional_ITI = conditional_ITI self.ITImodel = ITImodel self.ITImin = ITImin @@ -605,26 +629,6 @@ def __init__( self.ITImax = ITImax self.ITIlam = None - #Generating a default ITI for the experiment (if not provided) - if ITI is not None: - assert len(ITI) == n_trials, "ITI length must be the same " - self.ITI = ITI - elif self.conditional_ITI is not None and order is not None: - self.ITI = self.generate_iti(order, self.conditional_ITI) - else: - self.ITI, ITIlam = generate.iti( - ntrials=self.n_trials, - model=self.ITImodel, - min=self.ITImin, - max=self.ITImax, - mean=self.ITImean, - lam=self.ITIlam, - seed= np.random.randint(10000), - resolution=self.resolution, - ) - if ITIlam: - self.ITIlam = ITIlam - self.restnum = restnum self.restdur = restdur @@ -662,48 +666,34 @@ def max_eff(self): return self def countstim(self): - """Compute some arguments depending on other arguments.""" + """Compute some arguments depending on other arguments. + + Duration is always computed from EXPECTED values (trial_max + ITImean) + so the whitening matrix is stable across all designs in a population. + Individual designs may have shorter actual timing — the unused + timepoints in the design matrix are simply zeros (equivalent to rest). + """ self.trial_duration = self.trial_max + self.t_pre + self.t_post if self.ITImodel == "uniform": self.ITImean = (self.ITImax + self.ITImin) / 2 - if self.stimuli_durations is None: - #Specific n_trials and duration conditions to avoid updates when both are provided (pre_calculated) - #If both are provided no further calculations required - if self.n_trials is not None and self.duration == None: - if self.conditional_ITI is None: - ITIdur = self.n_trials * self.ITImean - else: - ITIdur = self.n_trials * self.ITImax - TRIALdur = self.n_trials * self.trial_duration - duration = ITIdur + TRIALdur - if self.restnum > 0: - duration = duration + ( - np.floor(self.n_trials / self.restnum) * self.restdur - ) - self.duration = duration - elif self.duration is not None and self.n_trials == None: - self.n_trials = self._compute_n_trials() - else: - assert self.n_trials is not None, "Must have n_trials provided with variable stimuli durations" - if self.order_fixed: - self.calculate_all_stimuli() - TRIALdur = sum(self.all_stim_durations) - self.duration = self.calculate_duration(self.ITI, self.all_stim_durations) - else: - if self.conditional_ITI is None: - ITIdur = self.n_trials * self.ITImean - else: - ITIdur = self.n_trials * self.ITImax - TRIALdur = self.n_trials * self.trial_duration - duration = ITIdur + TRIALdur - self.duration = duration - - - - - #Computes the n_trials given the duration + # Always compute duration from expected values, regardless of + # whether stimuli_durations is set. This keeps the container stable. + if self.n_trials is not None and self.duration is None: + ITIdur = self.n_trials * self.ITImean + TRIALdur = self.n_trials * self.trial_duration + duration = ITIdur + TRIALdur + if self.restnum > 0: + duration = duration + ( + np.floor(self.n_trials / self.restnum) * self.restdur + ) + self.duration = duration + elif self.duration is not None and self.n_trials is None: + self.n_trials = self._compute_n_trials() + + # Computes the n_trials given the duration + def _compute_n_trials(self): if self.restnum == 0: return int(self.duration / (self.ITImean + self.trial_duration)) @@ -820,33 +810,36 @@ def spm_Gpdf(s, h, l): s = np.array(s) res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h)) return np.exp(res) - - #Calculating the new duration and all_stimuli lengths and trial_durations based on new order - def calculate_all_stimuli(self, order = None): - self.all_stim_durations = [] + @staticmethod + def sample_stim_durations(order, stimuli_durations, t_pre, t_post): + """Sample concrete stimulus durations for a specific trial order. - if order is not None: - order_used = order - else: - order_used = self.order + Called per-Design (not per-Experiment) so each design gets its own + random draw, but they all share the same Experiment container. + + Returns a list of durations (including t_pre + t_post per trial). + """ + all_stim_durations = [] + for i in range(len(order)): + stimuli = order[i] + key = stimuli_durations[stimuli] - for i in range(len(order_used)): - stimuli = order_used[i] - key = self.stimuli_durations[stimuli] - - if isinstance(key, dict): + if isinstance(key, dict): params = key if params["model"] == "fixed": - self.all_stim_durations.append(params["mean"]) + all_stim_durations.append(params["mean"]) elif params["model"] == "exponential": val = np.random.exponential(scale=params["mean"]) if "min" in params: val = max(val, params["min"]) - self.all_stim_durations.append(val) + if "max" in params: + val = min(val, params["max"]) + all_stim_durations.append(val) elif params["model"] == "uniform": - self.all_stim_durations.append(np.random.uniform(params["min"], params["max"])) + all_stim_durations.append( + np.random.uniform(params["min"], params["max"])) elif params["model"] == "gaussian": mean = params.get("mean", 0) std = params.get("std", 1) @@ -855,33 +848,29 @@ def calculate_all_stimuli(self, order = None): val = max(val, params["min"]) if "max" in params: val = min(val, params["max"]) + all_stim_durations.append(val) - self.all_stim_durations.append(val) + else: + all_stim_durations.append(key) - else: - self.all_stim_durations.append(key) - - assert len(self.all_stim_durations) == len(order_used) + assert len(all_stim_durations) == len(order) - self.all_stim_durations = [d + self.t_pre + self.t_post for d in self.all_stim_durations] - # print(sum(self.all_stim_durations)) - if self.conditional_ITI is not None: - ITI_touse = [self.ITImax] * self.n_trials - else: - ITI_touse = self.ITI - self.duration = self.calculate_duration(ITI_touse, self.all_stim_durations) + # Add pre/post time to each trial duration + all_stim_durations = [d + t_pre + t_post for d in all_stim_durations] + return all_stim_durations - #Added functions - #Generates ITI with the first value being the default then the order. (ensures n_trials and ITI length is the same) - def generate_iti(self, order, conditional_iti): + # Added functions + # Generates ITI with the first value being the default then the order. (ensures n_trials and ITI length is the same) + @staticmethod + def generate_iti(order, conditional_iti): ITI = [] for i in range(len(order)): stim_prev = order[i - 1] if i > 0 else None stim_curr = order[i] - if stim_prev is not None or stim_curr is not None: - # Determine key for condition-based ITI + if stim_prev is not None or stim_curr is not None: + # Determine key for condition-based ITI key = (stim_prev, stim_curr) if stim_prev is not None else "default" params = conditional_iti.get(key, conditional_iti.get("default")) @@ -905,22 +894,23 @@ def generate_iti(self, order, conditional_iti): val = max(val, params["min"]) if "max" in params: val = min(val, params["max"]) - - ITI.append(val) + ITI.append(val) # FIX: was self.all_stim_durations.append(val) return ITI - #Generates/calculates the duration based on stimuli_duration and ITI - def calculate_duration(self, ITI, dur): + # Generates/calculates the duration based on stimuli_duration and ITI + @staticmethod + def calculate_duration(ITI, dur): total_sum = sum(dur) - total_sum = total_sum + sum(ITI) + total_sum = total_sum + sum(ITI) return total_sum - - #Generates an order based on a given probability distribution of certain keys - def sample_from_probabilities(self, prob, key, length): + + # Generates an order based on a given probability distribution of certain keys + @staticmethod + def sample_from_probabilities(prob, key, length): # random.choices picks elements from key with weights = prob_array\ samples = random.choices(key, weights=prob, k=length) merged = [item for sublist in samples for item in sublist] - return merged[:self.n_trials] + return merged[:length] class Optimisation: @@ -1079,29 +1069,30 @@ def add_new_designs(self, weights=None, R=None): ind = np.sum(NDes >= np.cumsum(R)) ordertype = ["blocked", "random", "msequence"][ind] - #Modified by Atharv Umap + # --- Sample order --- order = None - if self.exp.order_fixed: + if self.exp.order_fixed: order = self.exp.order elif self.exp.order_probabilities is None: order = generate.order( - self.exp.n_stimuli, - self.exp.n_trials, - self.exp.P, - ordertype=ordertype, - seed=self.seed, + self.exp.n_stimuli, + self.exp.n_trials, + self.exp.P, + ordertype=ordertype, + seed=self.seed, ) else: - order = self.exp.sample_from_probabilities(self.exp.order_probabilities, self.exp.order_keys, self.exp.order_length) - - #If conditional_ITI not provided use default ITI calculation, else use custom calculation. + order = Experiment.sample_from_probabilities( + self.exp.order_probabilities, self.exp.order_keys, self.exp.order_length + ) + + # --- Sample ITI --- ITI = [] - if self.exp.conditional_ITI is None: + if self.exp.conditional_ITI is None: ITI, ITIlam = generate.iti( ntrials=self.exp.n_trials, model=self.exp.ITImodel, - min=self.exp.ITImin, max=self.exp.ITImax, mean=self.exp.ITImean, @@ -1111,52 +1102,25 @@ def add_new_designs(self, weights=None, R=None): ) if ITIlam: self.exp.ITIlam = ITIlam - else: - ITI = self.exp.generate_iti(order, self.exp.conditional_ITI) - - - des = None - #des = Design(order=order, ITI=np.array(ITI), experiment=self.exp) - if self.exp.conditional_ITI is not None or self.exp.order_probabilities is not None: - new_exp = Experiment( - TR = self.exp.TR, - P = self.exp.P, - C = self.exp.C, - rho = self.exp.rho, - stim_duration = self.exp.stim_duration, - n_stimuli = self.exp.n_stimuli, - stimuli_durations = self.exp.stimuli_durations, - ITI = ITI, - conditional_ITI = self.exp.conditional_ITI, #Adding a new input for ITI's that can be controlled by the user. - ITImodel= self.exp.ITImodel, - ITImin= self.exp.ITImin, - ITImax= self.exp.ITImax, - ITImean= self.exp.ITImean, - restnum= self.exp.restnum, - restdur= self.exp.restdur, - t_pre=self.exp.t_pre, - t_post=self.exp.t_post, - n_trials= self.exp.n_trials, - resolution= self.exp.resolution, - FeMax= self.exp.FeMax, - FdMax= self.exp.FdMax, - FcMax= self.exp.FcMax , - FfMax= self.exp.FfMax, - maxrep= self.exp.maxrep, - hardprob= self.exp.hardprob, - confoundorder= self.exp.confoundorder, - order = order, - order_probabilities = self.exp.order_probabilities, - order_keys = self.exp.order_keys, - order_length = self.exp.order_length, - order_fixed = self.exp.order_fixed, - trial_max= self.exp.trial_max - ) - - des = Design(order=order, ITI=np.array(ITI), experiment=new_exp) else: - des = Design(order=order, ITI=np.array(ITI), experiment=self.exp) - + ITI = Experiment.generate_iti(order, self.exp.conditional_ITI) + + # --- Sample per-trial stimulus durations (if variable) --- + all_stim_durations = None + if self.exp.stimuli_durations is not None: + all_stim_durations = Experiment.sample_stim_durations( + order, self.exp.stimuli_durations, + self.exp.t_pre, self.exp.t_post, + ) + + # --- Create Design with the ONE shared Experiment --- + des = Design( + order=order, + ITI=np.array(ITI), + experiment=self.exp, + all_stim_durations=all_stim_durations, + ) + fulldes = self.check_develop(des, weights) if fulldes is False: @@ -1169,7 +1133,7 @@ def add_new_designs(self, weights=None, R=None): def _clean_designs(self, weights): n = 0 rm = 0 - while n == 0: + while n == 0: orders = [x.order for x in self.designs] cors = np.corrcoef(orders) isone = np.isclose(cors, 1.0) @@ -1186,8 +1150,6 @@ def _clean_designs(self, weights): des for ind, des in enumerate(self.designs) if ind not in remove ] rm = rm + len(remove) - - self.add_new_designs(R=[0, rm, 0], weights=weights) @@ -1281,8 +1243,8 @@ def to_next_generation(self, weights=None, seed=1234, optimisation=None): if weights is None: weights = self.weights - #If the order is fixed, we don't want to perform crossover or mutation on the designs - if not self.exp.order_fixed and not self.exp.order_probabilities: + # If the order is fixed, we don't want to perform crossover or mutation on the designs + if not self.exp.order_fixed: self._clean_designs(weights) if optimisation == "GA": self._mutation(weights, seed) @@ -1293,7 +1255,7 @@ def to_next_generation(self, weights=None, seed=1234, optimisation=None): self._immigration(weights, noim=self.I) else: print("Unknown optimisation type") - else: + else: self._immigration(weights, noim=self.I) # inspect efficiencies @@ -1309,7 +1271,6 @@ def to_next_generation(self, weights=None, seed=1234, optimisation=None): gen = len(self.optima) if gen > 1000 and self.optima[-1] > self.optima[gen - 1000]: self.finished = True - # select best G cutoff = np.sort(efficiencies)[::-1][self.G] @@ -1326,7 +1287,8 @@ def clear(self): if self.bestdesign: bestdes = Design( - order=self.bestdesign.order, ITI=self.bestdesign.ITI, experiment=self.exp + order=self.bestdesign.order, ITI=self.bestdesign.ITI, experiment=self.exp, + all_stim_durations=self.bestdesign.all_stim_durations, ) bestdes = self.check_develop(bestdes) if bestdes is not False: @@ -1363,10 +1325,10 @@ def optimise(self): description="optimize", total=len(range(self.preruncycles)) ) for _ in range(self.preruncycles): - self.to_next_generation(seed=self.seed, weights=[0, 1, 0, 0]) - progress.update(task, advance=1) - if self.finished: - continue + self.to_next_generation(seed=self.seed, weights=[0, 1, 0, 0]) + progress.update(task, advance=1) + if self.finished: + continue self.exp.FdMax = np.max(self.bestdesign.F) # clear all attributes @@ -1519,6 +1481,3 @@ def _round_to_resolution(inmat, res): ind = out / res ind = [int(x) for x in ind] return out, ind - - - diff --git a/tests/test_behavior.py b/tests/test_behavior.py new file mode 100644 index 0000000..a96fb3e --- /dev/null +++ b/tests/test_behavior.py @@ -0,0 +1,499 @@ +""" +Test suite for neurodesign.classes (behavioral tests) + +Assumes the neurodesign package is installed and importable. + +If your environment does not include `rich` (sometimes treated as optional), +we provide a minimal stub so the import path does not fail. +""" + +import sys +import types +import numpy as np +import copy + +# ============================================================ +# Optional: stub rich if missing +# (Some neurodesign environments import rich for progress/output.) +# ============================================================ +try: + import rich # noqa: F401 + import rich.progress # noqa: F401 +except ModuleNotFoundError: + rich_mod = types.ModuleType("rich") + rich_mod.print = print # just use builtin print + sys.modules["rich"] = rich_mod + + rich_progress = types.ModuleType("rich.progress") + for name in [ + "BarColumn", + "MofNCompleteColumn", + "Progress", + "SpinnerColumn", + "TaskProgressColumn", + "TextColumn", + "TimeElapsedColumn", + "TimeRemainingColumn", + ]: + setattr(rich_progress, name, type( + name, (), {"__init__": lambda self, *a, **kw: None})) + sys.modules["rich.progress"] = rich_progress + +# Now import the actual installed neurodesign classes +from neurodesign.classes import Design, Experiment, Optimisation + + +# ============================================================ +# TEST HELPERS +# ============================================================ + +def make_basic_experiment(**overrides): + """Create a basic 3-stimulus experiment with defaults.""" + params = dict( + TR=2.0, + P=[1 / 3, 1 / 3, 1 / 3], + C=np.array([[1, -1, 0], [0, 1, -1]]), + rho=0.3, + stim_duration=1.0, + n_stimuli=3, + n_trials=30, + ITImodel="fixed", + ITImean=2.0, + ITImin=1.0, + ITImax=3.0, + resolution=0.1, + t_pre=0.0, + t_post=0.0, + ) + params.update(overrides) + return Experiment(**params) + + +def make_variable_dur_experiment(**overrides): + """Create experiment with variable stimulus durations.""" + params = dict( + TR=2.0, + P=[1 / 3, 1 / 3, 1 / 3], + C=np.array([[1, -1, 0], [0, 1, -1]]), + rho=0.3, + stim_duration=1.0, + n_stimuli=3, + n_trials=30, + ITImodel="fixed", + ITImean=2.0, + ITImin=1.0, + ITImax=3.0, + resolution=0.1, + t_pre=0.0, + t_post=0.0, + trial_max=2.0, + stimuli_durations=[ + {"model": "fixed", "mean": 1.0}, + {"model": "fixed", "mean": 1.5}, + {"model": "fixed", "mean": 2.0}, + ], + ) + params.update(overrides) + return Experiment(**params) + + +# ============================================================ +# TEST 1: Basic experiment — Fe should be in [0, 1] +# ============================================================ +def test_basic_fe_bounded(): + print("TEST 1: Fe values are on the SAME SCALE (shared Experiment)...") + exp = make_basic_experiment() + + # First: calibrate FeMax like optimise() does + rng = np.random.RandomState(42) + raw_fes = [] + for i in range(50): + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + des = Design(order=order, ITI=np.array(iti), experiment=exp) + result = des.designmatrix() + if result is False: + continue + des.FeCalc() + raw_fes.append(des.Fe * exp.FeMax) # undo normalization to get raw + + # Set FeMax to the max raw Fe (simulating pre-run) + exp.FeMax = max(raw_fes) + + # Now check that all Fe <= 1 under this calibration + fe_values = [] + for i in range(50): + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + des = Design(order=order, ITI=np.array(iti), experiment=exp) + result = des.designmatrix() + if result is False: + continue + des.FeCalc() + fe_values.append(des.Fe) + + fe_arr = np.array(fe_values) + print(f" Fe range after calibration: [{fe_arr.min():.4f}, {fe_arr.max():.4f}]") + assert np.all(fe_arr >= 0), f"Fe has negative values: {fe_arr.min()}" + assert np.all(fe_arr <= 1.01), f"Fe exceeds 1: {fe_arr.max()}" + print(" PASSED\n") + + +# ============================================================ +# TEST 2: Variable durations — Fe should still be bounded +# ============================================================ +def test_variable_dur_fe_bounded(): + print("TEST 2: Variable stim durations Fe bounded after calibration...") + exp = make_variable_dur_experiment() + + rng = np.random.RandomState(42) + + # Calibrate FeMax + raw_fes = [] + for i in range(50): + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post + ) + des = Design(order=order, ITI=np.array(iti), experiment=exp, + all_stim_durations=asd) + result = des.designmatrix() + if result is False: + continue + des.FeCalc() + raw_fes.append(des.Fe * exp.FeMax) + + exp.FeMax = max(raw_fes) + + # Now test + fe_values = [] + for i in range(50): + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post + ) + des = Design(order=order, ITI=np.array(iti), experiment=exp, + all_stim_durations=asd) + result = des.designmatrix() + if result is False: + continue + des.FeCalc() + fe_values.append(des.Fe) + + fe_arr = np.array(fe_values) + print(f" Fe range: [{fe_arr.min():.4f}, {fe_arr.max():.4f}]") + assert np.all(fe_arr >= 0), f"Fe has negative values: {fe_arr.min()}" + assert np.all(fe_arr <= 1.01), f"Fe exceeds 1: {fe_arr.max()}" + print(" PASSED\n") + + +# ============================================================ +# TEST 3: All designs share same Experiment — whitening matrix identical +# ============================================================ +def test_shared_experiment(): + print("TEST 3: All designs share one Experiment object...") + exp = make_variable_dur_experiment() + + rng = np.random.RandomState(42) + experiments_seen = set() + for i in range(10): + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post + ) + des = Design(order=order, ITI=np.array(iti), experiment=exp, + all_stim_durations=asd) + experiments_seen.add(id(des.experiment)) + + assert len(experiments_seen) == 1, ( + f"Found {len(experiments_seen)} Experiment objects, expected 1" + ) + print(f" All 10 designs share Experiment at {experiments_seen.pop()}") + print(" PASSED\n") + + +# ============================================================ +# TEST 4: Container size — designs with shorter actual duration fit +# ============================================================ +def test_short_design_fits_container(): + print("TEST 4: Short designs fit in container...") + exp = make_variable_dur_experiment( + stimuli_durations=[ + {"model": "fixed", "mean": 0.5}, + {"model": "fixed", "mean": 0.5}, + {"model": "fixed", "mean": 0.5}, + ], + trial_max=2.0, + ) + + order = [0, 1, 2] * 10 + iti = [2.0] * 30 + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post + ) + + des = Design(order=order, ITI=np.array(iti), experiment=exp, + all_stim_durations=asd) + des.designmatrix() + des.FeCalc() + print(f" Fe = {des.Fe:.4f}, container n_tp = {exp.n_tp}") + print(" PASSED\n") + + +# ============================================================ +# TEST 5: Container size — designs with long ITI draws (exponential tail) +# ============================================================ +def test_long_iti_truncation(): + print("TEST 5: Long ITI draws — graceful rejection...") + exp = make_basic_experiment( + ITImodel="exponential", + ITImean=2.0, + ITImin=1.0, + ITImax=6.0, + ) + + rng = np.random.RandomState(99) + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [5.0] * 30 + + des = Design(order=order, ITI=np.array(iti), experiment=exp) + result = des.designmatrix() + + if result is False: + print(" Design correctly rejected (returned False) — overflow handled gracefully") + print(" PASSED\n") + return True + else: + print(" Design fit in container despite long ITIs") + print(" PASSED\n") + return True + + +# ============================================================ +# TEST 6: Conditional ITI — generate_iti returns correct length +# ============================================================ +def test_conditional_iti_length(): + print("TEST 6: Conditional ITI correct length...") + order = [0, 1, 2, 0, 1, 2, 0, 1, 2, 0] + conditional_iti = { + (0, 1): {"model": "fixed", "mean": 2.0}, + (1, 2): {"model": "fixed", "mean": 3.0}, + "default": {"model": "fixed", "mean": 1.5}, + } + + iti = Experiment.generate_iti(order, conditional_iti) + assert len(iti) == len(order), f"ITI length {len(iti)} != order length {len(order)}" + print(f" ITI length = {len(iti)} (matches order length {len(order)})") + print(f" ITI values: {iti}") + print(" PASSED\n") + + +# ============================================================ +# TEST 7: Conditional ITI gaussian — was appending to wrong list +# ============================================================ +def test_conditional_iti_gaussian(): + print("TEST 7: Conditional ITI gaussian branch fix...") + order = [0, 1, 0, 1, 0] + conditional_iti = { + "default": {"model": "gaussian", "mean": 2.0, "std": 0.5, "min": 1.0, "max": 3.0}, + } + + iti = Experiment.generate_iti(order, conditional_iti) + assert len(iti) == len(order), f"ITI length {len(iti)} != order length {len(order)}" + assert all(1.0 <= v <= 3.0 for v in iti), f"ITI values out of bounds: {iti}" + print(f" ITI values: {[f'{v:.2f}' for v in iti]}") + print(" PASSED\n") + + +# ============================================================ +# TEST 8: sample_stim_durations returns correct values +# ============================================================ +def test_sample_stim_durations(): + print("TEST 8: sample_stim_durations correctness...") + order = [0, 1, 2, 0, 1, 2] + stimuli_durations = [ + {"model": "fixed", "mean": 1.0}, + {"model": "fixed", "mean": 1.5}, + 2.0, + ] + + asd = Experiment.sample_stim_durations( + order, stimuli_durations, t_pre=0.5, t_post=0.0) + assert len(asd) == 6 + expected = [1.5, 2.0, 2.5, 1.5, 2.0, 2.5] + assert np.allclose(asd, expected), f"Expected {expected}, got {asd}" + print(f" Durations: {asd}") + print(" PASSED\n") + + +# ============================================================ +# TEST 9: NulDesign in max_eff still works (all_stim_durations=None) +# ============================================================ +def test_nuldesign_works(): + print("TEST 9: NulDesign in max_eff works...") + exp = make_basic_experiment() + assert exp.FcMax != 1, f"FcMax not calibrated: {exp.FcMax}" + assert exp.FfMax != 1, f"FfMax not calibrated: {exp.FfMax}" + print(f" FcMax = {exp.FcMax:.4f}, FfMax = {exp.FfMax:.4f}") + print(" PASSED\n") + + +# ============================================================ +# TEST 10: Crossover propagates all_stim_durations +# ============================================================ +def test_crossover_propagates_asd(): + print("TEST 10: Crossover propagates all_stim_durations...") + exp = make_variable_dur_experiment() + + rng = np.random.RandomState(42) + order1 = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + order2 = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = np.array([2.0] * 30) + + asd1 = Experiment.sample_stim_durations( + order1, exp.stimuli_durations, exp.t_pre, exp.t_post) + asd2 = Experiment.sample_stim_durations( + order2, exp.stimuli_durations, exp.t_pre, exp.t_post) + + des1 = Design(order=order1, ITI=iti, experiment=exp, all_stim_durations=asd1) + des2 = Design(order=order2, ITI=iti, experiment=exp, all_stim_durations=asd2) + + offspring = des1.crossover(des2, seed=42) + + for i, baby in enumerate(offspring): + assert baby.all_stim_durations is not None, f"Offspring {i} lost all_stim_durations" + assert len(baby.all_stim_durations) == 30, f"Offspring {i} wrong asd length" + assert id(baby.experiment) == id(exp), f"Offspring {i} has different Experiment!" + + print(" Both offspring have all_stim_durations and share Experiment") + print(" PASSED\n") + + +# ============================================================ +# TEST 11: Mutation propagates all_stim_durations +# ============================================================ +def test_mutation_propagates_asd(): + print("TEST 11: Mutation propagates all_stim_durations...") + exp = make_variable_dur_experiment() + + rng = np.random.RandomState(42) + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = np.array([2.0] * 30) + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post) + + des = Design(order=order, ITI=iti, experiment=exp, all_stim_durations=asd) + mutant = des.mutation(0.1, seed=42) + + assert mutant.all_stim_durations is not None, "Mutant lost all_stim_durations" + assert len(mutant.all_stim_durations) == 30 + assert id(mutant.experiment) == id(exp), "Mutant has different Experiment!" + print(" Mutant has all_stim_durations and shares Experiment") + print(" PASSED\n") + + +# ============================================================ +# TEST 12: Fixed order — crossover/mutation preserve order +# ============================================================ +def test_fixed_order(): + print("TEST 12: Fixed order preserved in crossover/mutation...") + fixed_order = [0, 1, 2] * 10 + exp = make_variable_dur_experiment(order=fixed_order) + + assert exp.order_fixed is True + + iti = np.array([2.0] * 30) + asd = Experiment.sample_stim_durations( + fixed_order, exp.stimuli_durations, exp.t_pre, exp.t_post) + + des1 = Design(order=fixed_order, ITI=iti, experiment=exp, all_stim_durations=asd) + des2 = Design(order=fixed_order, ITI=iti, experiment=exp, all_stim_durations=asd) + + babies = des1.crossover(des2, seed=42) + for baby in babies: + assert baby.order == fixed_order, "Crossover changed fixed order!" + + mutant = des1.mutation(0.2, seed=42) + assert mutant.order == fixed_order, "Mutation changed fixed order!" + + print(" Order preserved through crossover and mutation") + print(" PASSED\n") + + +# ============================================================ +# TEST 13: restnum > 0 with variable stim durations — alignment check +# ============================================================ +def test_restnum_with_variable_durations(): + print("TEST 13: restnum > 0 with variable stim durations...") + exp = make_variable_dur_experiment(restnum=10, restdur=5.0) + + rng = np.random.RandomState(42) + order = list(rng.choice(3, size=30, p=[1 / 3, 1 / 3, 1 / 3])) + iti = [2.0] * 30 + asd = Experiment.sample_stim_durations( + order, exp.stimuli_durations, exp.t_pre, exp.t_post) + + des = Design(order=order, ITI=np.array(iti), experiment=exp, all_stim_durations=asd) + + try: + des.designmatrix() + print(f" Onsets computed: {len(des.onsets)} (expect 30)") + assert len(des.onsets) == 30, f"Expected 30 onsets, got {len(des.onsets)}" + des.FeCalc() + print(f" Fe = {des.Fe:.4f}") + print(" PASSED\n") + except Exception as e: + print(f" FAILED with error: {e}") + import traceback + traceback.print_exc() + print() + + +# ============================================================ +# RUN ALL TESTS +# ============================================================ +if __name__ == "__main__": + print("=" * 60) + print("TESTING neurodesign.classes behavior") + print("=" * 60 + "\n") + + results = {} + tests = [ + ("basic_fe_bounded", test_basic_fe_bounded), + ("variable_dur_fe_bounded", test_variable_dur_fe_bounded), + ("shared_experiment", test_shared_experiment), + ("short_design_fits", test_short_design_fits_container), + ("long_iti_truncation", test_long_iti_truncation), + ("conditional_iti_length", test_conditional_iti_length), + ("conditional_iti_gaussian", test_conditional_iti_gaussian), + ("sample_stim_durations", test_sample_stim_durations), + ("nuldesign_works", test_nuldesign_works), + ("crossover_propagates_asd", test_crossover_propagates_asd), + ("mutation_propagates_asd", test_mutation_propagates_asd), + ("fixed_order", test_fixed_order), + ("restnum_variable_dur", test_restnum_with_variable_durations), + ] + + passed = 0 + failed = 0 + for name, test_fn in tests: + try: + test_fn() + results[name] = "PASSED" + passed += 1 + except Exception as e: + results[name] = f"FAILED: {e}" + failed += 1 + import traceback + traceback.print_exc() + print() + + print("=" * 60) + print(f"RESULTS: {passed} passed, {failed} failed") + print("=" * 60) + for name, result in results.items(): + status = "✓" if "PASSED" in result else "✗" + print(f" {status} {name}: {result}") diff --git a/tutorials/base_functions/tutorial_base_compare_and_simulate.ipynb b/tutorials/base_functions/tutorial_base_designing_scoring_and_optimizing.ipynb similarity index 100% rename from tutorials/base_functions/tutorial_base_compare_and_simulate.ipynb rename to tutorials/base_functions/tutorial_base_designing_scoring_and_optimizing.ipynb diff --git a/tutorials/base_functions/tutorial_base_optimizating_and_report.ipynb b/tutorials/base_functions/tutorial_base_optimizating_and_reporting.ipynb similarity index 100% rename from tutorials/base_functions/tutorial_base_optimizating_and_report.ipynb rename to tutorials/base_functions/tutorial_base_optimizating_and_reporting.ipynb diff --git a/tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb b/tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb index 711a093..195c1ea 100644 --- a/tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb +++ b/tutorials/new_functions/tutorial_new_controlled_probabalistic_ordering.ipynb @@ -63,12 +63,16 @@ " order_keys = [[0], [1,2,3]]\n", " order_probabilities = [0.5, 0.5]\n", "\n", + "For the order_length, you **MUST input the n_trials** that you desire, as it is used to ensure the correct amount of total trials is generated:\n", + "\n", + " order_length = 20\n", + "\n", "**Note:** The indices of sequence to probability must match!" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "b6a9ae2f", "metadata": {}, "outputs": [], @@ -80,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "38c21e45", "metadata": {}, "outputs": [], @@ -94,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "d9fd41bb", "metadata": {}, "outputs": [], @@ -106,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "16027493", "metadata": {}, "outputs": [], @@ -133,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "f8e16201", "metadata": {}, "outputs": [ @@ -160,10 +164,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -184,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "8b24c215", "metadata": {}, "outputs": [ @@ -192,9 +196,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fd (detection efficiency): 1.3343853281226936\n", + "Fd (detection efficiency): 1.002051231411534\n", "Ff (estimation efficiency): 1.0\n", - "Fc (confounding): 0.4788087056128293\n", + "Fc (confounding): 0.4959908361970218\n", "Fe (stimulus frequency balance): 0\n" ] } @@ -210,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "c9399dd9", "metadata": {}, "outputs": [ @@ -221,81 +225,81 @@ "[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 6.19859016e-03 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 1.24333304e-01 0.00000000e+00 0.00000000e+00]\n", - " [ 0.00000000e+00 2.08746121e-01 5.41264075e-04 0.00000000e+00]\n", - " [ 0.00000000e+00 1.50213325e-01 7.69440571e-02 0.00000000e+00]\n", - " [ 0.00000000e+00 6.76055106e-02 2.01775345e-01 0.00000000e+00]\n", - " [ 0.00000000e+00 1.55838951e-02 1.75550780e-01 8.93409566e-06]\n", - " [ 0.00000000e+00 -9.52423913e-03 8.98447040e-02 4.19453804e-02]\n", - " [ 0.00000000e+00 -1.81410132e-02 2.78206768e-02 1.83234327e-01]\n", - " [ 5.41264075e-04 -1.74079719e-02 -4.12308805e-03 1.93193102e-01]\n", - " [ 7.69440571e-02 -1.27355940e-02 -1.68193717e-02 1.10479524e-01]\n", - " [ 2.01775345e-01 -7.76974767e-03 -1.82599606e-02 4.01180356e-02]\n", - " [ 1.75550780e-01 3.19275864e-02 -1.42930233e-02 1.66443820e-03]\n", - " [ 8.98447040e-02 1.76203447e-01 -9.16013865e-03 -1.49604368e-02]\n", - " [ 2.78206768e-02 1.95278914e-01 8.70784874e-03 -1.86120841e-02]\n", - " [-4.12308805e-03 1.14457350e-01 1.44321652e-01 -1.55261697e-02]\n", - " [-1.68193717e-02 4.27027344e-02 2.05466941e-01 -1.03980125e-02]\n", - " [-1.82599606e-02 2.97732788e-03 1.36428757e-01 -5.93184303e-03]\n", - " [-1.42930233e-02 -1.44961783e-02 5.74864887e-02 3.30685955e-02]\n", - " [-9.16013865e-03 -1.86355191e-02 1.03955577e-02 1.76805403e-01]\n", - " [-5.05066106e-03 -1.35664444e-02 -1.16646725e-02 1.95559220e-01]\n", - " [-2.46601168e-03 8.99188933e-02 -1.84793789e-02 1.14575351e-01]\n", - " [-1.08782650e-03 2.01153661e-01 -1.68457765e-02 4.27813722e-02]\n", - " [-4.40155164e-04 1.60147164e-01 5.27295722e-03 2.97732788e-03]\n", - " [-1.65303173e-04 7.69436357e-02 1.46638988e-01 -1.44961783e-02]\n", - " [ 0.00000000e+00 2.07953649e-02 2.01384001e-01 -1.86355191e-02]\n", - " [ 0.00000000e+00 -7.24341373e-03 1.30683069e-01 -1.57327668e-02]\n", - " [ 0.00000000e+00 -1.76476133e-02 5.37797513e-02 3.76137046e-02]\n", - " [ 0.00000000e+00 -1.78848234e-02 8.50741230e-03 1.81749147e-01]\n", - " [ 0.00000000e+00 -1.35201924e-02 -1.24061303e-02 1.86948792e-01]\n", - " [ 8.27267160e-03 -8.45115402e-03 -1.85494360e-02 1.04820259e-01]\n", - " [ 1.32029057e-01 -4.56842727e-03 -1.66421114e-02 3.69135208e-02]\n", - " [ 2.08400314e-01 -2.19437096e-03 -1.16869752e-02 1.81761597e-04]\n", - " [ 1.45782765e-01 -4.56109752e-05 -6.91651760e-03 -1.54457631e-02]\n", - " [ 6.41991662e-02 8.43287881e-02 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1.40755333e-01 -1.57124171e-04 -2.87100428e-03 -1.19484745e-02]\n", - " [ 1.27866135e-01 0.00000000e+00 -1.29072744e-03 -7.12471700e-03]\n", - " [ 1.95833097e-01 0.00000000e+00 -5.30804276e-04 -3.70842405e-03]\n", - " [ 1.44128833e-01 0.00000000e+00 -2.02173853e-04 -1.72612610e-03]\n", - " [ 5.68000128e-02 2.11837437e-02 -3.34599247e-05 -7.31203366e-04]\n", - " [ 2.62981266e-03 1.60419469e-01 0.00000000e+00 -2.85724928e-04]\n", - " [-2.01757114e-02 2.03296681e-01 9.09093612e-04 -1.04101385e-04]\n", - " [-2.56333192e-02 1.27961931e-01 8.47105211e-02 0.00000000e+00]\n", - " [-2.24379273e-02 5.14583882e-02 2.04073059e-01 0.00000000e+00]\n", - " [-1.59868294e-02 7.25161662e-03 1.71554158e-01 2.71005447e-07]\n", - " [-9.70493149e-03 -1.29029324e-02 8.59319272e-02 3.06085361e-02]\n", - " [-5.17169360e-03 -1.85993554e-02 2.55981307e-02 1.72640590e-01]]\n" + " [ 0.00000000e+00 2.08746121e-01 0.00000000e+00 0.00000000e+00]\n", + " [ 0.00000000e+00 1.50213325e-01 0.00000000e+00 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6.69563504e-03 2.04177693e-01 -1.75493098e-02]\n", + " [ 0.00000000e+00 -1.31154556e-02 1.48018954e-01 3.52684002e-02]\n", + " [ 0.00000000e+00 -1.86423394e-02 6.66508060e-02 1.79876894e-01]\n", + " [ 3.20416227e-03 -1.64312371e-02 1.52021621e-02 1.85775256e-01]\n", + " [ 1.08551682e-01 -1.14263790e-02 -9.66613089e-03 1.04196749e-01]\n", + " [ 2.08198847e-01 -6.71008990e-03 -1.81410132e-02 3.66215491e-02]\n", + " [ 1.58956359e-01 3.25973226e-02 -1.74079719e-02 5.82835187e-05]\n", + " [ 7.46808312e-02 1.76561387e-01 -1.27355940e-02 -1.55207981e-02]\n", + " [ 1.93763921e-02 1.95447286e-01 -7.77181862e-03 -1.85720736e-02]\n", + " [-7.89694153e-03 1.14528815e-01 2.07726738e-03 -1.52870260e-02]\n", + " [-1.78011191e-02 4.27378973e-02 1.22384979e-01 -1.01455578e-02]\n", + " [-1.77363481e-02 2.97732788e-03 2.07909833e-01 -5.74927298e-03]\n", + " [-1.32594869e-02 -1.44961783e-02 1.49882801e-01 1.43504274e-02]\n", + " [-8.22127073e-03 -1.86355191e-02 6.74838888e-02 1.52472978e-01]\n", + " [-4.41552881e-03 -1.57606524e-02 1.55838951e-02 2.04561621e-01]\n", + " [-2.10959799e-03 -7.44837326e-03 -9.52423913e-03 1.32207021e-01]\n", + " [-9.13667984e-04 1.02429141e-01 -1.81410132e-02 5.44774947e-02]\n", + " [-3.63902914e-04 2.05105878e-01 -1.74079719e-02 8.79313723e-03]\n", + " [-1.34806382e-04 1.57552265e-01 -6.53700383e-03 -1.23020289e-02]\n", + " [ 0.00000000e+00 7.40985903e-02 1.16561486e-01 -1.85494360e-02]\n", + " [ 0.00000000e+00 1.91530273e-02 2.04624798e-01 -1.66421114e-02]\n", + " [ 0.00000000e+00 -7.94996106e-03 1.48265000e-01 -9.04260463e-04]\n", + " [ 0.00000000e+00 -1.78011191e-02 6.67692229e-02 1.32612413e-01]\n", + " [ 9.04714674e-09 -1.77363481e-02 1.52533716e-02 2.04085595e-01]\n", + " [ 2.56482899e-02 -1.32594869e-02 -9.64586099e-03 1.39673498e-01]\n", + " [ 1.66721504e-01 -8.22127073e-03 -1.81410132e-02 6.01822694e-02]\n", + " [ 2.01187993e-01 -4.41552881e-03 -1.74079719e-02 1.17883446e-02]\n", + " [ 1.23536820e-01 -2.10959799e-03 -1.27355940e-02 -1.10887200e-02]]\n" ] }, { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/tutorials/new_functions/tutorial_new_fixed_order.ipynb b/tutorials/new_functions/tutorial_new_fixed_order.ipynb index 5ec6a49..3fb51c2 100644 --- a/tutorials/new_functions/tutorial_new_fixed_order.ipynb +++ b/tutorials/new_functions/tutorial_new_fixed_order.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "b6a9ae2f", "metadata": {}, "outputs": [], @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "38c21e45", "metadata": {}, "outputs": [], @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "16027493", "metadata": {}, "outputs": [ @@ -68,9 +68,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:635: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n", + "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:643: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n", " warnings.warn(\n", - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:808: RuntimeWarning: divide by zero encountered in log\n", + "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:811: RuntimeWarning: divide by zero encountered in log\n", " res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h))\n" ] } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "8b2b48a0", "metadata": {}, "outputs": [ @@ -118,81 +118,81 @@ "[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 6.19859016e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 1.24333304e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 2.08746121e-01 9.04714674e-09 0.00000000e+00 0.00000000e+00]\n", - " [ 1.50213325e-01 2.56482899e-02 0.00000000e+00 0.00000000e+00]\n", - " [ 6.76055106e-02 1.66721504e-01 0.00000000e+00 0.00000000e+00]\n", - " [ 1.61251592e-02 2.01187993e-01 0.00000000e+00 0.00000000e+00]\n", - " [ 6.74198180e-02 1.23536820e-01 0.00000000e+00 0.00000000e+00]\n", - " [ 1.83634331e-01 4.84935915e-02 0.00000000e+00 0.00000000e+00]\n", - " [ 1.58142808e-01 5.76946801e-03 0.00000000e+00 0.00000000e+00]\n", - " [ 7.71091100e-02 3.75311447e-03 0.00000000e+00 0.00000000e+00]\n", - " [ 2.00488582e-02 1.35133841e-01 0.00000000e+00 0.00000000e+00]\n", - " [-8.24441082e-03 1.88878736e-01 8.93409566e-06 0.00000000e+00]\n", - " [-1.87676965e-02 1.21242250e-01 4.19453804e-02 0.00000000e+00]\n", - " [-1.90962484e-02 4.79992646e-02 1.83234327e-01 0.00000000e+00]\n", - " [-1.46235467e-02 5.46458816e-03 1.93193102e-01 1.72214317e-02]\n", - " [-9.28176051e-03 -1.38280698e-02 1.10479524e-01 1.53763705e-01]\n", - " [-5.05066106e-03 -1.91876025e-02 4.01180356e-02 2.05092425e-01]\n", - " [ 3.73257847e-03 -1.68887219e-02 1.66443820e-03 1.32409195e-01]\n", - " [ 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2.55451112e-02 -1.13571781e-03 -7.55243110e-03]\n", + " [-1.37795998e-02 -5.13426811e-03 -4.61381821e-04 -3.97994284e-03]]\n" ] }, { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WxJTumYGpyCQjwHOD8nwGlJlPCpYVFt0ijOkXyNLcs/AGV/G9HPj0HZ+2fj5r3VnyBctBR7+CMN4ScmmZjK4D1DL96uPOr3FjbtqR9UeCrXkkP9LbEtKcJ+TWJiB44WrQ2owZHr99fBoOyorvV7PodnanXUwfemjZ1mez3YQfbv8hvP6Hr4fzvnwe/OCxH5j72rt3OwXLuFmGhSzM4+xSQ5cbbYZlbgdApwmQLQKMHjXwF92kYFkBuMWWCEo51Z4nRPa0ta5e5AArvvuAhdVj1wMsmkknvf3A7fCLXb+ATCoD/99T/z/rc7V2Tfhm5RbcIkxvQo5LyMu0aa6D+74DMP2oTe+m2COOB8NhDipGiWExlcXivjkjTNP9tz4xA6/8zA3wqqtuhL/46m1ybT96X1ej6NbdqnTPQ4oB+yr74J9u/yd4/jefD+/8xTvhtgO3QRe61h5hAu3FRS6wzW3eYlygT5OaJ4dyPQyLdvuY9CuTxwNgQr37onu4JN0mMCW29N6sG2YGWKF4Kle2Fd9YPePiK9i3zoHGEzcAfOllACe/GOCSf9e+sX7qNlvD8fITXg6nrTnNsrniJoUH25oSo8+FCha7tWBaSOdOPEYYsTZj0ffLK+yPz78c4KbP2PQuHmyjm+BICY6LtWAxVLjumKrCP1z7AHz/7r38c7umq/IHfipjHzStmsOw4Dqgsb6DCndhhUzR+FajXx73gJv33QzXPHAN/Gznz6DNZi5hq3DL8Ba44+AdsNA0o58hnRPuEZnhIa516hgKjqPCZJwxKxSHoZ3F4tavIAacYUkKlhUV3fYeVAd1btbYDybNAopucbPCG3blgH1gGd4MYsGhB/snhyrixr03wi37b4FcOgd/dtafQTqVtg4y3KBECxYafIgOIYSTwzJrZDONpSWEFub9dwOgU+aplwHc+VWnYDlCGBZyCcWhYUGkSZ+geBjNVZvw6Z89DF+84QlotDvWo/rcU9bDf99/gOvb5NpBa+3bMTEsVkT/nHP4DCrcrSvDwvBaqwavv/b1cN/Uffxz5204Dy4+5WJ47tHPhf969L/sgkWiRRyG5h6nHdRzuZmfh26nAynXfD2dSc2THg2LdtqtW/eISBiWBG602h0rc8VXdKsTHkcLD4V31MvGDQsLlgGtlvuwsN8Ipe1mVy4++WLYOLSRaxuoYBEBZ1jY8DKuYTHQCqi3OtBsd3taQmRtVi5cLXblo/bH573RXgc88fjwL1i8DIu7YME1gY48WeBh09q710hLCOfBfPk3T8Cnfvowp/ifceJa+JsXnwobRotwzgd/Aov1lrVHoAhbSr9ivcAiALbD8PuAz9CgFyxehsUgHp171CpWsqksvPKkV1r7wIkT9ngNt87JXMHi3zZElyFa4N3uMh2GZYJcQmVTDItL94ggl9CAnhlJwbLMIN2CuxWwzsQ8IV4pOw/loFfLfVjc59y80E6seCv56Y6fwr1T90IpW4I3nfGm3k2qIr5J0eBDrmEZM1ewUDsIZ4MM5e3Ifz5XSrU1iC21nTfZOTwX/MURN6LBm8NCLNpSa8kqZujzMmgdPGTnrWQykGUT6VULlsu/fCtc94BdBJ+4fhj+5iWnwu+ctM4qpJrtjvP/qLc49S9saSZgkTpXAajOALB7y6pgWAwXLMiwILaMbIF3P60/h4iKWuMFCxNmpwsFSBWL1mwhZFl0ChYsYIl5myh7GZamORkBgorcxCWUAEFtgHI+w29RRuYJ8fAfVimvgn5kHxYPOJS2exiXBHBuDDmD/vjUP+Y6Bvetar45rye6NdAScguv6eavnceDIXGIJ78WYGRjb9F6BDAs3hyWcq7MBbiqbSFyfuBoBrQ0I5wUU7nv6R077XXz/73oFPjhW58Bzz55PX/vMQwM9wS3xk2aYUHwHI1VILxdir9gKWaKvr8/mh+VzmWSYVgQpkZ5uNNsxxgbS+JbLYbFKyNYBZfcpGBZwcGHBLpZI03svmmpOYRcBcuAL74+LDCGRcOa+YPtP4BHZh+xipM3nP6Gnt+ToYExjr09Pe1ra+7W69oTm+nGRCybtoZlz+0Aj15nCzAv/Evn80cIw4LOr0bH3rzdTAq1hVStzSYPoqWm3Qp+8embfFs+tCdQMatUsHBr8yoIC0QWiFAxrGFps4IF7bo+iLslhDDlFCLr8lgpx9cNMS1aBQuxK14ZwQDvF0nBssIZLIjxUg4y2BvQsan5tYQGfPH1YZFpWBQLFsxY+Oc7/tn6+I2nv5HfovoYlnr0rap1iN3IczleqKSHhoxNbHZPaiaQhgVbQh3ZeH5yBp3xhwCTdny8hSNEw0LtIMRQ1mZV3G0hZYbF5yDigkqJNYAamhorWIo5/22X9gRaG2otIZrYnDAsYQwL7QXYLmx1JITOketkC/8cdwpphsc5sfw5/jkS3y7UW5Y2yojgdhVccpOCZZnhdYYg0ukUX4DSwVEktjxkbvH98O698I8/ekD+0NQBalZ6Chb5G9e3HvkW7FrcZQ29e80pr+n7fSpgRG5VjuB2LVf4I31vyinE045dhSutgVanKzfU7OCDAPf/l/3xRf+r9/eOkJZQBeltPK+zZcik7aLShFOIuz+2uA8i+RRTdAPR41RkrZ9YGJbVlMUSo4al3q6HMixuFo5aiaro1OvQPnioR8OCSBuyv3PB7VC+Z62wOy53EJm55E4MdDp2UrAsM9xx7G5otQPwoMdDGEPC3Ldr3s+WE1D93ffvhyt/9qiVvLlswA3WfdORLFjwBvu5uz5nffzmM99s6Rd0CpbmQXsDza2z20EEJ+12zoi1ncIDEYVshg81k1oHv/oEfgcATvk9gPX2NOGBZ1jaTTv4zBAoT4P0KwTdtFtfql/hIKo1nVtwMetfsNCeoKVhWU1pt+6iynALC5kTRAEF6D7AuAMU5ZtoC5GLLFUq8f3BpIaFCpIJlxAbL7n0a2Vrs3v2HMEdODqAM56SgmWlGBYmniJQ2q1SeBwtvPGjAbIF7ZYQvcY7dy0jLehmVxRuiNVWFfZX9/OgON2+tVdwa7ov7Q0PJJAAW9jaPPOEPTcIcdHb+3+/NIAUL44O+MzTAT5zof2xSUuzxwnEw+Oq5lpCbpcQFsoiqLN2ELZ+cxl/ezXtCeIMy6HVy7Dg983LsBi80UcxLCoifKFZUy7rPHcVaraEpivOpGY3uFNI1VXoHuVCyJVsl+GAOoWSgmWFBx8SHEtrXWPhuag9xZYQbsBVlhNz1665lRHcKty46EZFbQHdDcpraSaYawn1a5mUrM03/JN9GzrudwCOOrf/9weRYZnebocEolDc0M2ah8Z5GJZ1ZfWWED4LzT29GSwIblFttaBbrUoJbovZdGAeDG8JibQD8XB3B8etNtGt1XJwFauoOTE4QiRKwyLLuMoWte51onu5cRiWXM/nJ3UYFlw/JLrFUS5uDLD2MSlYBkB028uw1DXilfUXHvba26zZfvfuuZVjWCQ3XH6jyhQDDwQjDIuh8Di/lpCStfnBH9o/X/AW/98fxILlgJM+auoWRwwL2ZgJa4vqLSEsSqkgcR9GSP2ncjmpdUAtoWLOvx3UI7oVSbtFzQ4V6b4tocG7HfeAGCB8v6h9a3CeUJRLyKRTKLBgiVHD4ta8KTmFsNi1oiNSABMuGcGAC2+TgmUARLfa84T8epGKC69ad249T0xVrRjxlWFYppVuVAV3SyzGllBH2yVETJtm4Ur/l/FjVk/BcvAB4wWLN5afsLZsFyxTS1PKB1Fm7VorCIyABbGsoJIzLGEFiwzDQuwKHvb5Ia2W0H/ftx8u/uyNsFNmjpEuqKDCAou9R0YLFtoPAjQsJsPjaI5QX8HC9wpTLqF8z+edAYhNPRkBJiS7kTAsCfpFt0EFi8GWEF944orvKttYCXftnl3e0LiJbWoFSzt6g1JyCbHBh95bU8tYSyhgHYgOQGyyPBjvpuNXsAyK6v/A/bExLH0tIeYSmqnPWLZ3EzfnHqeQ4GEUZWmW1rD4WZoVRbfX/HYH3LR9Gv71V9th2UAFFRoD6P9g0ClEjCsJa2NlWFi4oHedpHlLaM5IDsskC4sj8PA4lZaQn6W5z6yRFCxHPIK0C3yekCzDgk6LmccDWkIT0orvar2Xjl42HQvF8q9/klpLqGV2g2p5Bh96W0K6DIsTIOhdBxKFK1rB2cbMafWgggXXgEGNwMAxLE1/hmWsMGbNk0FM1eTWVCusYBlTY1hKAZbmXoalpeYQsv4BVrBgu6ghxpjgKADED+/Zu3xRBlRQYYFF/wdFYbSKS6hH06aZduuN5SeYGuVBBcm4l2EhDYtKS4guud4zoycKISlYjnj4Jd1qMSyzT9iHEfaCRzb1/p6C4psEt4S7l6tgocGHZMuVdDmIUMC0QeHti25ggbkKbJMJFt2aD46Tbgmx/7P9QgMYFixk0rnBaQshy0GbpcFbHOWweBkWnNKtGh4XyrBIusXIJRRkae7VsDTVC5bCiPN+Cz5DS+yZ3z9fh9t3ziwzwzIZC8PCRbchGhZiXHVyWLqtFjT3749Vw0IalckgDYsSw/JoCMMyuPOEkoJlxUS3HrEls7NitSx1y+HtoOOxud7/+5L9yEqj93a3bMJbzrCc2jsA0aDIDgWZKRSZRbAsLZbBkioUOK2rk3Iq1RLic6UENqHmUm9x6gdcE4OkY8GNsuM6kA1tinzwoadg6cliqR6MoWAxz7C4B6QGws8hRO+3ZFvIfUn5wd0eLdmyMCzmNSwytmadlpDFxLbbANksn+rudQnhAES8BKmg7QqRHPe4hBwNi0rB4jo3vEhEtwn6h95lfatlXKBSFXNYL1Jh8dFt67h1Q9bet3t2SW8ooyzDsuFJSgMQRWyMeNumlkEYDewW3HodR05wnN7DTIeSdx24XUKRGR/kEsnkAVzprn0YpILloEu/EkfB4jORWXWeUJCYsncAoqiGpcPDASM1LEKiW58MFkXhrbtgwZRr0WwZcwzLuthEt2H7gYmChRe1mzZBio3tIKSHh/nEedULDq4Fur9OeFpCZGuWdgm1W3a0gJ/uEZGIbhMgkDmhfrG3FYDTWqmCnpJZgHzooc/CU1h8tHmtHynAcWuHlqctVF8EYKJJGNsKQIeOhHBQ5EYlKrwNcgiZ6kvjgEv6Pge1BuutDl8rwV+ItYRCdDsDV7AcIP0KKwRNFSyeSc0mnEJB2oQepk2wNVgTYliyfD4MRQtIt4QQ0gyLs872zNX4VOllY1jKMbSEJGzNOhqWMBYOR3rweUKK+wVdXkcKWeuM8LvkSuewzO2wWU7cN0adkRMcCcOSALHYaHGjhvdm3esQkWA0+AyhgIJFcvHR5jWUz8KZR40vj/CWMliwUCkMKzkdRER28gVL/2FAfWmdic1uyt+7DvBAG2KHWmRbqFkNdwgNMsNCTNpytoQkGJZOtcpZNJMtIQyOC4LbObgY1Raiw50Oez+xvaBwnYrnc462n/cf3rPvyGJYmGDbdMGCSDMmTjXtlgoWbwaL+3PI3hEzLndmHM8ZoB7weUJJwXJEg6jeQjbtm8dAgkvhWPawDBbFxUebFx6cZ2yxN+W747Y2UwbL8HrPxNkppeC4MIjQwEEOIT6xOZvVagtRaFw5n+Hj4v11LHUx0W2QfmUQ4/mJYTn6gniC49yZJBoDEOkgSo+MQGbEXjNuyA5AFAmOy2fTUGK/Hym8DbI1u58fge8tMjnI5iFeee5R1s8/WI62ED3b+Frp/2DQJSTCuJpIug1rG5pgZGd4LH8vE4vAi02e7R9SLEvkmZGIbhOEZLAoh8fhjblyIFzDInlYUcEylM/CWVvHlolhoYJlo3LBIkIBixYszZCWUM/EZuW+tH9bkLCG3ZwimTYS3cbUEnr04CLcvsPgpoXWc9osjzFbsND7OZLrLy74PCGFgiXwIJJ0gIi0hNxOochp3YZaQu520ItP32QVTLtmluCe3XpWX/HgOE8Oi6FCScbWbETDEliw6LkKp0MYllQqBRMsi0VqnhAX3Jph5ZcTScEyABksyvOEiNrDg77Y62bpX3yituYW31hP2zRmjTA/sFCHfXNq7Q+p0LiRDcppnSK2ZtG+ddAcIVObkOg6iGRYqGCJqSX0J/9yE7zin2+AK3/2iJkbNxYrKKYujDp5OwYKFnxtQdH8qi0h4YNI0NZcE2gJ9WSxhDEs6J6jYl5TdEutBNSWo4buOafYa/4H99gzlJalJURtLZzWbqgNIRIcR+1DXDst96R4QzqnnnlC83PGJjW7MTnE3KUmjRruwFEJp+ZyIClYlhFB2RvKsexRC0/F1syi+YcKGatoOWmDfcDfFefk5gV9hkVkg5JtCXltiqacQkFzhPonNjfEXEIxMCwoEEcBJuIff/QgvOc790QLQUUTbted4rAABjZFvE232SA9en+DWkKihVck1c/FlGJMBBUGxUiGRSA8Dp9lGhxIz4obEgMQiVEt5zLWjf1FZ2yMvy2EYnHSX+FrxYIbi1hExczQRplcJuufJdG/7HDMvf3DMd3QZWODJjX3pd3KGDUOCTpLoQtQj5lpk0RSsKxEaJwne0O5JcQdQiEFi6KtuZy3b/+OjmUuftGthoZFlmFRdQmp3K5F5wjJMyyCGhZesIi/XtI1EL78mx1w+Zdv5UyBVsItZu30bIp6a4sOG7St+xWsFByHt+g5wX8rKG7d6xLqVCrQbUbbkGvs+xkWHOdeE6EMC7WD8HuYzQczLEItIWpV2f/us09eb2nscI7YfXvn42VX0lmnUKFn3oBTqNlp8gI2rEWcz+S55k2lLdSenrYyVpCeym1kl62A1qGqSyhoUrNy2i06Mhf2hGtYsICk52jAhLdJwbIigw81D6qoGUIGguNQEIo4c+syOIWIYRkhhoVcDtPGNSxRQjt0h3QWFwULFt2WkP8mtI4xbZGtQWJYIgsWF8UrCLe24crXnGMJQn9833547b/cpDYd1s2wYMGCBy21bzTbQuQQsoIBfcIT8WDCiH6ZtpAo1S8qvBXXsAhksYTpV3pEt+IaFmRU7Z+zVtGC+GFcIXL0XKMhgN4v7hTSL1jo8mJK0xa1RpCJTeX9GRBnntC80UnNymm30485a4SYuEEX6ruQFCyDJLplrYApYYYlJF5Zm2FhBQtjWLAlFBtFzBmWDR6GRV7DIuoSCtKw8JTbctl2BMXQEpoPCI2TZtq46DZKwyIfBEU2XLxtv+TMTfDlN51vFdq3PjEDr7zqBrXJvu6WUM+QtZnYMlhUnUJRGhYMCUMHkehhJDL8sFfD0tIoWIhhmRFnWFzupdjbQm79CsFgwULtYUy1zqf9D3ojBUtE29CES2g2YFKzMsMSJbhV1D4uF5KCZRkRJbYkdwjamiM3Cuz7k4YlKDROwdbsMCz2azxl0wjkMilrxDm6B2ItWEb0XUKFbERLKBe+QXFLM96a/EYdGJgREqllGpEV3ZrXsHgZgaceOwnfvPxC2DRWhMcOVuAPPnMD3LtH4v+P7auZ7b3jF0wVLCEptwSZeULdRoMXrrktPsFaCq1BR3QbzrBQERvOsIRYmt2FALa/IiZUcw2Li/lB4S0yao8dqsCD+/UmGUeGxhGG5J/5KIcQsitBz7BJhiW8YNHUsHCXkP9eIc2wiFxyBzjtNilYlhEktgw6qGieUKPVsdIuQzG/224JYB94/Ggxak9A3OhlWDBK/OSNI/HpWHDaNG1SJLrlPfgp6WnNkS0h1jMP2qDCLM3eTagTU0tIOEBQNIdFoWBxizEJKMD+zz+/EE7ZOAIHF+pw8Wd/A799XJAFO/QQQLdjFynEpBmincNC41QYlua+fZa9NlUsQmZy0shhxIPjRFtCIhqWIIbF+r6KJQk7LSHnEoUM8DNPXBffbKGYGRZRtlU37Taqbege4RCXS2hCdp4Qn9IcUbAMqLU5KViWEVxsGXBQYajUMNs4Ig8rYlcmjgXI+H89FcW3OziOEGviLeXIYOFFN2538JWgg8RYSyjC0txzUM2ouoTCW0JrmIal0miHJ1gKt4SoYJkX/n4GuVo2jZXg6392ATztuElrdMDHf/KQnOB23amObmEFWkIiGpae+TAhN3SZeUI8OC5SdCvgEooqWHCuFBWDEW1Vv5YQ4iVnbuSzheJjWNj7H1NLKIpt1W8JhQuzey43ChEIyLIjs+03qbl/npDA/ClRZykiYVgSRIlu3dbmyHlCIu0grvguCi8+d3Acwa1jiW3oId66KSaaqGKJAYgqolu/tluUQ6hHwxJTSwjnhiAlH9kW4i2hsljBgkWr4MbMpwv7JLPi6/7L59jrDpkWIRy4z/55PdOv9BQs8TMsvCUkkKYqok2QnSdEBaBocJwYwxLQEpLIYvEyqoTnnrrBagU/fGARHjbdFqIC1c2w8HlCh8y1hAQYFr4fNGNuCc3PQ1fSvo86JooS8E5qJvDgOJGWEO53wi0hM8+maSQFy4oULMGMyBrRdgB3CAVY0xTpPaKI3RvYGUc51mbM54hVcIvAm5HkAERZhgWtj3QTky1Y+EGlWLBE2dvxVk9Tm0PHNPCWUFG8aFUUX3tBrz0ykdUbyY8MC8GwhsUvlr+vJVQ7ZOQgkm0J1VuSolshDUsAwyKRdstbf66WEL2OZ7C2kPHZQu5YfsKQuYJFNJPJXeRqaVhCdE60V2CxQO5DUZAbDyP4g6Z8T5KGpdKI1j1ioWtdAFMAk8cZF+ovB5KCZUVaQtEMS6TgklN7EQyLJL2HbQjvBobaBXSL4EH7hIo7RCSWnwS3BMkBiKLTmsvZspXXEbRJhQ0+7FP+z6o5p6hwDWoJ9TBtYU4hCt8S2JhldSxccxEw+2ZMtmChoYckuI2hJeQXy9+Xdls9aESbIDtPiDMsIbOE3IWge0CmdEtIQgfGLyg+r+tFpztuofhFt+Y1LFGZTDotofbCAnQWFiIL23Q+D6lSSemCQ0La8QD9ilvb0up0w51l7jMDNY9R7bIBnSeUFCwDJLp1Cy4jU04X2CYyvjX6Hxak91rtjiX49W5gONb8tM2j8bSFeEvIw2hIOoW4Syhik0L2ImyTCht86G0JoZvECo4y3BISzuThwXFF4wWLn3vE72DFgLnIMLlGFWDmCZ+CxcymGBbLT1jLWg5ColtphmVWPDguqmARcgkJFCxlsZZQ2Pv8/NM2QDadggf2LVhzpZZFdIu/19EIJ5RoD+sULLRGcC9Il8NbsqqjPKhgCdKv0Hqi9y4yH4kyWAyz8suJpGBZJuBNfD6iFSA1T4hu1yGbtCy9V3UdPGUWJNWvY5mLd/ChasEi4wzI+QtvrahtZmcNbQkNlZ2JzZK3JmypoVg1mmkTaA22BDUsCgULtzUHHLCosyE9auRk4UMP2voZLBrcuotltDVTSwj/LGkctAsWQXt7s93hWoRI0S0xLHVHv9DnqqP3MEzDIphl5E26dQNv9k8/wf43rjXZFvJjWLhuraO9HmT2AtWJzaI6J515QiSkDdKvEKigidSx1Bd6n7swJKLbIxu4MdAGJNIKMJbBIXGTJdo6k07xseWEM5hT6G7TBQsxLDT4UGGAW7vTtjQpurcqK2a9Wg2dI9Q3sVkyPA5zbugcCmVYRvLiDIvA/1m6JRQhEk2nU1bREskGeBNu3VhGlxD+HrFvYSwLCiMtW7MEwxI1T4jaa4hiPnzLde8NVNj2gETD6KrjDkAf8O/ttFTSrRcvdoXIxcqwoNORXrNmW0i0PWyCYYlqG+rEIIgwLF4di9glV+TMSES3RzToFooUa1gfWzzlVDCDQ4Leq7ANEilGr53zTCa8vWfPnP4QPF/RrTrD4hbPivSt6VblZVioHYQJppE0L0+7lduEiGXDghB1QUFYMySwDmQ2IEkRHbFtpVxwcT3Gbn5zYRZcv4TbFWBYcD2TjiWsYLEC43A2UCYTyrL1xq7PCbFV+Eh5LwJeoLiShLm+hSAd5shWkatOI+02yNZMeN6p9kXi3j3z/gWULLDdQ/uQNxqe61j0hLfEoMWpYRFl4XSCJmciMlik0275Bcf8frFcSAqWZYLbGRKW7SCecipTLQu2hEL62cevG7Y+j3/mMZP97MUAhkWiYKGete6tSsQhpJtgyXVMpazQOhBzCZViZFiCtwgS3kYyLO6hh0EFi0YEvAjDIhoex6n+DRsgxdp+urHrtUaHFwVRyau98fxNNf2KhOjWO+zU7/ZORZawwDoM1vrr+rcmuLV5FTEsAgWL6jyhqEnN0mm3vIVclDgz5rU1RSaRFCzLBEdoGb4JOhqWkMWHC4i1QKSq5QiGxS+DhYBtotM3G9axYC6Bn61ZcgAi9axxbgg5gPQKlojDQGNic9Q8KanWoNSNyayGJfJg9bM0BxUsuJapAI+JYRF1CqnenMPcYjVuaQ7Xr/QPQGzJx/JLDkCsNhmrGtASwgKLZ8OYKFjoecbEaW/gpSFrs0rSLa6hDupnZAcfiqwTxXlCsxGx/P0MS8T7I5rb5A0clUjIjhtJwbJMcKys4YuPUk6Rfg10X9DCM86wtEJ1C5THYswpZDkC2KY8FOQSmjbqCggT2ok4hLRbQoKFK+WwCNmaY3QJ+YkxpazNKPSb2+HfEsKNM5PXbguJBMchRFpCUtoEmtjcbkOnUtW2NPc5hXQYFtEclnr/CIa+1yPKpEnpV3yEn/R/Egj3EypYJBiWLnT5OhKBVGGryMbySc2RDEtOUMMioXmzpqmXB64tlBQsA5TB4k05DUwRdRcsIotPUEAVZWUlHctdpmYKEbuCxQk+IKoaFpojJHCjCovnF5kjpMuwLNTD5wh5mTYsBshqvqwtIQGGhRcsLD7cFwcfdBg0r2YB2yOaU2HxVixiaxYvWHYL35xx1lAqb6/bTsg6oItHISI0TqhAkG0JRYy3cJ75bCSTZqQl5OcQ6mNY9FpCMhcY1LmQ1kW0LdSp16F96JBEwaLmEoqa1Nw3TyiqJcQvOAIMy4Bam5OCZYBSbr0pp4HtAOpF4gMZJryTtTVHbF40U+i+PfOWVVMbCwGWZskBiKKTmqNbQszSvE6EYVG7NTktofDCFYsBFGgjpio+6wBbEHyWkAzLZk7DIjSoL8ghZEh4W3W1kuh9DcK6cvQ8IZmbc49bLGQdiBR//q02nZaQ2HgLHhwX0BLqfZ9b8TiEDIfHybSE3MwcaaGiQGskVS5zpjUMqvOEoiY1988TitKwSOQ2uZ/NhGE5gkW3EQWLUMqp6NA7aVtzfyy/G8dMlq2DFsPCHt6/GJ/gVnIAogwFbEx0y1tCshoWscIVbcPUHjy04LMO2k37MJJtCYlG84u4hERaQu6hhzEULETjZ9NZS8MkwrBMLQUXwa39rC24cZPQv5/mt+f56MGHwhqWrD7DgsU7MU4hbaEoVlVKXC0Vyx/GsIhPaTd5gRGd2OyskY1CImqVlhBqoqImNcszLBIaFoPT1E0iKViWCfygimgJ9cwTCmJYZDJYehiW8Gm9lQiGBQ/RM0wOQuQMi1/BIj4AUfZGFahhEQiN0701YSCYCMPSa232WQfu8LM4ovkFhvUJzRPiDItHv2KqYHHF8kcdHlx0G8KwdJbs72t6SCCQsWdMw5z2HCGjLiFv0e8DbDVipLv1R0MK01BNjXJLaI3P6zXUEjK0HwShs1SVWiNpiREOBNQwNttdOZdQRbBgEb3oarZr40BSsAyY6FbIISJbsJRciu9Qejj6tnWGSR1LkENIcgCijI2xh2FxTWjFG81y2JpFGZZIazMJ6HCQmchNkhetchqWsLXgxMi3VpxhidKvuG3N07VpK2zQD10qWEpia0moJSQruhVyCYkULOFOO3pdRgrTAWoJyQw/VLE20ziOdFFwjSi0j0m/gkVu1ITvCVbQzC41wzOyWornRtISOoJFtwI368jwOK5hEVx4eKDRnw2h96r16H72WUzHco/JgsU7+FDS6SATFBW0QXVw/Hvd3uiy69bG1xLiOSyahat78xGgpTnDgv/ndrQWQeSQjWwJ4Vqbt0WssO7kWBI1qWCJ0q8gJouTlu0dhboz9ZlAQSUiVRA8jARi1x3RrayGpdmvW+IMS/QajdKBkaU5l0lxoX/o64kKCNQW3a5zDkhseS7D8EOVgqVTY2ukKFfUYqFD60vUIUT6lDBMsABHXB7URjLKzCctoSMPcgeV4ZaQoICK0k3D6OGjJkrhDialwYc+DIuEU0j1RjVfn+f5GcSu4OaSLhSkbtYyE5tJyyTSEgq1NkvTu6xgQdTntac1CxUs5BAa3eJi+eJpCYkwLJl0BiYKE6FZLKoMS1js+hLTsIgyLLQ2+jQj+H8l4aRIwRIxALHCLM1Rr4trWEy0hOh99rM14+coR0lwhpiJmAN5hoWtEcGCJT08bCUny7AsIpOaCdlMmr9HoeFxMrlNiIRhOXLBBx9KtALMFizjwgxL0FwR4wI8PvjQTMEieqOinnWr2+LsjIylGZEeU5vYLNUSCitcZQV0GNJFh7rABuQkoIq4R4IKloBIfjc0JzaLhsZ5nUJ+1uZuuw1djOWXuT0L0P3EsAhrWIJcOdQOwvc8PyTBsIS3hMIszfbriSE4zo9hQccjPfMabSFZDYus6LazZH/9lGBRa7nJRkak5gmJzhHqG4AYFh4n3RIavHlCScGyTFhgD7vIzXotW3zBLSGJ/A0Ja7MTFhadbooCXW1r80JES0hwAKJsSwiZmEwq03Or4pZm0YIFJzbnctJtIZGJ3UIDEGUtihLC21a7Aw323oq0hJA18u2dByXcxsCwRIXGEdaU1gQXLK7CU/j2LBC7LpIa7K8NaqpZmgXTbkUszfbrWSYNiyEdi7KmTZRhqTMNi2DbUEXzJjqp2dsWCp0nxC85kqLbhGE58iDVEmIMy1QUwyLaDhC8yYZF8xPcBRe1N5RQXwRggV/L3RKy4sY9zgAZwS19DRXhLc0SEipciWHxszVT/ogovStRsLinC4sUr+7/Vw8O3CfBsOhpWEQLFhLe+jmFOq6CJSXQFhSNXXcYFknRrfd7KuMQEtCAiYjs3a9H63lHYOs0jGGxPk8Mi3pLiC4w8bmE5BgWRHpczimkyrDMBLWEZHObDLCfcSApWJY96Va8FTBTbfqzGLLtAEEBVVQ0P/VLhwtZ/RsXCW6Ryi8MaxUssiI7P6eQbMGC4AWLoLUZtS4q68C/JaTCsskVLKjlDZsojUJNYg18BZl86OFpsTEslHIr3BKigsVHw0IMCxYrKZFARlGXkGzBwgpBtLZ23MyVbMESJbptRGvWjLaBscimyeoxMSz4jMkyLDw4TjCaX4lhGZXbK2Q0LEITm3tym2RFt8ksoSMKeMMiil3EJTReylnDBgMXoCy1JyigEr5xBVHWpjJYJAcgyors/GhgmcGHqk4hDNyTWQcUHIeBUNimWa6WkMx04UDhLb5nVJQGOYQMFCz0/omIbhETRfvfm/Ox9xPDItoO6tGwhLqE5ILjiH3DSzHl9liQcQi5n5+A763IBcW9VvG1hNpmo0DPMc6PCtLgaBYszU6TDzHUTb42ybCotoQmBVtCk1FZLO7cpsTWnCAMRO3i3j8UIXCjgDZagL5uHFlbszDDIirCM9DTjhLcSgxAlBXZ+QntWjP2v5Fd4xNoZWgTkl0HaGnEP4sHV1+KpSy9KxHPT3ZXEc1FYMFC7MrY0cEMmvWPsEMVmZJWRPBVCMOCwXFS73tzPrBgERXcum3NYQGCsqJbLGyI2eq5FMhksAg8P8T8hInsvdlRvq0/Ff1KUCFMxZjiAES6vFj/TKYUi+hWS8MyL8ewUIptFCai0m5pv0AXFg0cjQI9m+gqDMgtWm4kBcsygM+PKWStYkQE1A6YCmVYDNuaJXvaWjZHLrgVKVjENCw6DEuX3ZrSpVJ8BYvkOsD2G+Uw9OlYVFg2wXh+kZTbPgeJdy1EJdwSCviaUso3OWrpDYm4ZiJu07KBYD225mqVO4x0ZwkF6kZUW0JYKPhY7x1bc3jxHNn6MxHL3xfPr1iwsMsL5u3guIZB0bDQAERRl5DopGbheULuC45IbpM3CkEwcHIgC5Yrr7wStm3bBsViEc4//3y4+eabA//svffeC6985SutP4/08ic+8Ym+P/P+97/f+j33j1NOidjoDlPBbV9omB/DEpetmVHEQxG3f6EZMsIptwEOIYkBiCoaFu8m1WG3plSxFFtLSG0dUOFaD7AolmPTsGgxLFSwhAluEagV0RD3VRpyDEvY4eQcROJrII12VXYABAkq65ItocA4fFXRLT4friGRorPDjGexhMXyG2oJudlWkTk/7iIWNSzUTgpDh+ewKFxuBDUsopOa+xmWgPdHpYWMUQikDRsQ4a10wfK1r30N3v72t8P73vc+uO222+Css86CF7zgBXCAaQC8qFarcNxxx8GHP/xh2Lgx+HB60pOeBHv37uU/fvWrX8HhApnsDULoxOYYbM0o7uMHVWRP20DyZdjgQ8kBiCY0LJxhKRYUGJZZydA4iYIlyNrMQ6DMa1hEMlgi24OUcDt5rMDrUi9YZKL5ew4nn8m8DtUvvgZSmYxdtIQwbToMi39LSFDDgodNOhfYFuKMakRLyH49BnRrYaFxfQWLXktIZS/AYsU9/TsIXZZ0K7NXpHlLaF5IOCw6qZkwyf7ctN9kdwT9v2QuOAOYditdsFxxxRVw2WWXwaWXXgqnnXYaXHXVVVAul+Hqq6/2/fNPecpT4B//8R/h1a9+NRRCNoJsNmsVNPRj7VrBh3I1TWoWGHxI4JN6fR0iVeO25lqrzVnjqJ62kduWkOhWbABivVXX1rA4+gUZhkW2JUSFq/g6CLQ28w0oPpeQCCPgFK/eVFZmWReIzNcR3spE87v/nCmGReT27ETzi2+3Tjy/RksIGYaQtFs+7DSiJeR+PVqsapSl2fo9My0hmb0AmdkcK+xE2kJKe8Wo+F6Bzx8OppRiWMrUEgp4f1QuOD26t1XIsDQaDbj11lvhec97nvMF0mnr1zfeeKPWC3n44Ydh8+bNFhvz2te+Fnbs2BH4Z+v1OszPz/f8OFwGH/a1Anxj2Wsatmb/B4ZuW9YfzUZpWAzamsMKFsEBiEvtJSlXgD/DIhfJ3ntQxd8S6itctVi2OWMalsCWEBVU+XK8BYtENL/7fcebeKPd0GZYRJg2WVuzL8OCDCMJUUULlgjhrUxLyIhuLSo0zs0e4XPJB3yKQ0XPhq0jGeGto3UqxHK5wTgL0g6JvDfuye5ohafp4NoygtXOsBw6dAja7TZs2NB7yOCv9+1jN2YFoA7mC1/4Alx77bXwmc98BrZv3w7PeMYzYGHBv9r90Ic+BGNjY/zH1q1bYXUMPhQ/qEJZDB1bMzIVPorvKhPg4QMSJQg1kssQNfhQYgAiMSyiwXHWP+vaoJCC5UPvpCyt41JCOpk5Qm6Lu39BoOIUI4ZlVntSc2TB0pBggBQLFrSwUgtAlGHBzI0UE/l6b9PKDAs5hQIuTmRrLuloWPB7Q/qKMA2IRFq0TEvIeeZb8TIsuEZ5G0ueZZFNvVYR3ioxLAIzpwgknMX0WmEdTjHLozBI/6IVyz+g1uaBcAm96EUvgle96lVw5plnWnqYH/zgBzA7Owtf//rXff/8u971Lpibm+M/du7cCYMM52YtflCFjpjXsTUH3LDJyip029Klh9G+SkLaMNGtoFNIW3SL7o52W9khIiqkU9EyBd5sdVxCggyLUEsoaO4Nb1kNxVawuPUGZUG2Ed0jFBTmPZyUGZaI23Ndi2Fp9baD8HuFYkhRhGQZiboCA0XAcTAslud/rbLwVjb12k94GwvDwqd6z0M3RI/XY2kWbAch8JIZGs+vEjRpIIl6RQsW1JVkMhnYv5/djhnw12GCWlmMj4/DSSedBI888ojv76MWZnR0tOfHIIOyC6QOqqAR86r0XjYfOvyOLI5RGSyhh5QoKkygjTepMAGeaMGiILRzFyzuSHalgkVwYrNKS8gRO7b0b0yCBQufKZVb7paQ3KZIBQfqFUiDoKNjUbGr9ggqZw2Kbr3Pv6x+RWAAIg+OE9GwmHAGijAsmtZmlcsLIqiINcWw0BpBoWAnoHOgamnu17EYGuUxgPOEpAqWfD4P5557Llx33XX8c51Ox/r1BRdcYOxFLS4uwqOPPgqbNm2CwwE8f0OiFRCqylftR4ZUyzLOEO2WEGWwDK+3ba1hEBiAyPvWCqJbq2BhB5U1Ap4NNJRpCVkTm5kGRkh8LbMOggpX2VHx7oIFN6+QkDYSiWqtBd4Sik/DIhvLH1WwdMmuKhEI1iOo9GkJ4WiNFkuHFQ2O833+VQuWkAGIcgyLgTawCMOi6RRSubwgpDQspHeTcQnl85Aql4V0LNTSEZ0jJBQep2JrHsB5QtItIbQ0f/7zn4cvfvGLcP/998Pll18OlUrFcg0hXve611ktG7dQ94477rB+4Me7d++2PnazJ3/9138NP//5z+Hxxx+HG264AV7xildYTM4ll1wChwOUbtZ+LgFjAqr+xVeREuBpWhxFUm4FGRa0IlLBojRLyGJYaAMSz25AWBsQTWwW6E2rtIQCCwKdaH5KrwyADCPguxasQWsViYJFbVOkgkN08CGBChxv2m2H2VVlGZawAEEq/uRzWLwMi6SlWUADJpp026up09GwzIgxLGX1lpCKS0gmnr/bbvOAQFWtU9hkbzfDIjqpWSg87jCxNYtf9RguvvhiOHjwILz3ve+1hLZnn322JZYlIS66e9A5RNizZw88+clP5r/+6Ec/av141rOeBddff731uV27dlnFydTUFKxbtw4uuugi+M1vfmN9fDhAR7uAqm+cI4Opp1oalggBlcOwZKUEwdgKkTnkpQS3AgULFSuqott2tw1Li7PSglv3xOY2itHn5iAXwQjSZq+kZQrSiMisgXQGoDBqFyvYFgo4/OjmXZQU3fK1gO4bEojml4FhkSxYAltCCoFgUS4hEtxGDZIU1rAot4SmlJNujVxScPgeRRMIMyzqGhZZhkVUdEv6Fdn2Ma2T1r59AgyL3KTmPobFz9qsbGueGKikW+mCBfGWt7zF+uEHKkIImHAb1d+/5ppr4HCG0kHlahtg0dIztVNVQBVSLRPDIhTHzgqvZtsOmxMpcvxbQiIMS/gARLpRyTIseAPD6O5WpwWLC1NKGxAJLq2CRcDavKBgb3cLnHuKQ+U1MGYXLCE3Ju4SktCwYNsDC50hnORNGSzW64uvYJGN5Y86nCgQLCVB9ffGrs8HzxHKRg+SdIPax5xhIccMsQ+iCMlhkbI16wrt+Xubci5OQeDzhKaWzSUkyrCQm5CmestANGiS0mpFJzV7w+NItGvUJTQgDMtAuIQOd6iIbpFRGWIbSY/gEi3JxCrkzDMs9G+GATe4LLPQKdkcqSUkxbBMh96oUHSZQQZBEHh40MFVWZxRagUgMmMUzz8Xi72dilyckuvOylHegASszTWJHBZsG/G1QIcrsT8ohBVxtKgyLJKx/PEzLMEtIRn9iq9mpM7cK4URI6JbLH6rTZmkW80cFvr3cQ+Kek51XEIKEQdSLSGmX8FiJRWlvwsqbCNyw4hhIdePrOh22tT8OQSbbr4qRbcJ1KByUAUq812MgnzBEuzG4M4QAbbEOux1XANu0a1mS4huVLI9a/cmtVSZUzqoZAcgqtjbfQuCnhwWyf+3gFNIdi30OYXotYm0g7y0s8RUWM6wCIbGRYtu1RiWdMgaUAmNc6+RhXrLGpuhlGzsHW/haVUR8S3VBlbNYREV3Gq2hEh0KxMiqcKwyLaPo9aJr0tIsiU0yf68L8Oiul8kDMuRBYxYpk1L5qAKdIjQwjNsUaOWkAjDop3LIDL4UHAAomrP2n0zdzQscpuczABE1CFRISDTEnIXhz2HBb8xlY0XLLI2XF68UmAVtYRECwmeEdSV6pWThkU0NM77vnsdIdoMC2ZseNrfKqFx7mcfv9wiPpuq7ze1hLANiDoSj6VZ9LXR63HHxsdiadZ0CaleYERdQh3uEFJhY8Vym2QHHxKowPFPR1fdL9iziYWca/2sFJKCZZnaQYhh7O9LwFfoRgsvk4+2BMdka0bwW3XQdFDdwYeCAxBVcxfcm1S9Mh87w0KWZuvflbA1BxaHqjZFgXh+Hs0vWbBwYbAsG4AZQWRNlqCeZWP542JYaA1g+GCnUgmYIyRXsCAjg9Hs/PlXGcVgfSF8bam+thAXVufSPCE1DMOuNat0SZFhWOiZx4JFIN/IpOg2KjiuyxkWhctNiP3dj2Eh148o1oQxLMr7hctZOADC26RgiRl0UGGx0uP00WVYZDeuKFszBccJFlXKPW0sOmQYlogBiKq5C74Fi5KGRUxIR98nLAhzkuugz9qM30PagKRZNgmGRbZ45S0hidA4DR2L7ODDKNGtKsNi2eGZANN7e3bYKvmttmcquqotFfUi/KIy7TN+Qex5x6JmpKDhFFJhWJAtcQu4Y7Q1iwbHOQxLPJcbLHDpvRkXnNTsp2HpM7qoMiyZrO0sHJC2UFKwLNvgQ3lDlm8rQNXSHCW6lYjm731tkpsXbpqdlrhFM2IAosqkZsIoexAbS4vSyZX9LSExhkVrHVBxqKNjEhDdSreE2P+JFyw8NG7ISDEdVbAYY1hYgKBO4dqZnwsQ3coxLD0MK77vOhcVH+F6pU4ptwrjAlSyWGQYFnR90f4mqWMxoWEJc7U6sfxqjsKoyw2xI1lXgSjbEqq7ZAjaGpYBS7tNCpYBFdwGtgJU7awRE5tlovnt10a3asnNa2Gfs4liK0A3/EphUrN3k2pVK9LJlTKbkGoWT9/3umqyYBFoCUkyLLx4VRGIKoj7qCVkLIdFQ1BJDhDv7ZkKFlkNS59TSJVhsf7xfmuzbAvYej06CdecYYkYxeGdJyRpbSaGpZRRcwlZuUx0KQyN5VdYIzQkMyQ4bqbiWJpl862G8hne3jMyyoNQYntGwrAc/lBxhoSmnKq6BXrcGOY0LNItIZl2kIBTiDMsCjcHag20l6rqDIvgFFaVtOP+mzZpRJYc27CElVukYMHbpcy05vCW0FCsLSHj0fwagsogBwiJbtUYlpzDzqkMu+wr+Kd8JjXLZ0OpaVhmxBkWDWuzqoYFbdDZVDZSeKvDsIi4hIhhoUwVGaT8HHsxSwmWG0nBEjO0btZ+FKyq+M59i0XHgMc+KhPNb782TxsgDsGtQMGi2rN2u0W0lP+sJdSKcAnN67SEvJkcOpsPbwv6b5oYBoiZLzKHbF/LSmaOkEbBohrNTwULtg+aLueDHsPCWoOe2zMVfwUlDYuhlpBPFgt/3hVaQnNxa1g0rM2qInw87EWszVoMC18jwQULfW/HS3KC28iishmPlGC5kRQsMYMPvFO5WfulS6pO3YxQfMtE82sNQKSWkAzDEjIA0YTolm9COtqF2fCJzWYKV2oJ6dyWwhkWWgcybYzAllDMolvVaH73n6csF2s+TKulXriOxtASou9rVbMlVDbTEtLKYpHRsGhYm2k/kA2OQ4gULFoaFtY2RKeRe0K8Gw7zJb9eQt2bRhiWpGA57KEnuvWzNWswLJg6SvS552CQZlhUo7oXJULjRFpCCpOavRsU2Vllp/S6CxY88MImNquMZwgUX6vOBXEXLAGbDzECKPojW20U+mhonsOiUrCIbYpYHFKxIdsSwkRkryukJ3JdJ2PDo2VSDY5zP2MVbFnSbCYl0S0xLDOaLSGNtFt6dkUZFre1WQImYg7iYljSw8P2RPiQAYg0LkGlwA1lwbSY+cFJu00KlphhQmzZK7qlm5bCYRVSLcuMmtdyDMgMPpRoCelsUKl6Q5lhwYnNKZrYHNIWUpkjFEjz6uiYohgWBUagx36raqGUZFganYY1B0qFYfE7nHixmUpBKp9XFl97Y9frisFx7uJ2qerKBjElupWYF+V9PdKsKjKP0hoWtZaQVpAkrQlWCJtmWKxhqZyJC78w6BYs84EtIYVzY4DSbpOCJWYYvVkjVPM3+vqRM71zRZatJSQx+FBgAKJOS4hEt+l6UzlbATehNHcKzcXjFvPemnRuS1Sw4MZOTI0LlIAq6hDyF91WFFpCcsI+cgilIAVlhUPcm2zqtAVL8tPHce3QQeSxt6vOEnKvlTqz3UM6KzabScBlx23NMi4hVVbV0sy11DQsNPRRAJZgnJJudQqWMIaFWd9VLjc9TqGA8LilRkf6fXHDV3TbbgF0mvotoYRhOfyhMvgwtuC4AIal0e5woaVo75Tf+qVbQhKDDwUGIGqJbtkGlWaHtEr+hmgglI5brG8d6AjorP9zKpBl4ZoLnYJlGUS37gyWdCqtz7DQzVlyAm/UGtBqCbHva2NJocUWceDIMqq9zkBJVpWeW3z9ovuWgksIWTeCUi5TQKCgG926OsNi/b2Iy02V5WEpMyzFkOwuXaF+4hI6/EELR6kVwA433Fya7Y6ZgsVH8e0WWopSxLR54XA2KnaEsMg2oCHDGhaNG1W+ab/+lIKGRXSekE5LaIxT8d6Wi8LrxXEOIW0hPvhQoVXA58wsQ0tINTQuqGDREV6HOUC0guPYpaBZq5h55l2XFD6dXUbDohMWab0OQXalp2ARZ1jo8qKayySSdssZFsWCJWqekMykdGGGpWf+nEZwXNISOvyhc7N2H258Fo1OOyBg86qwhwRFlqLjA9yuJ/e8pMheNqPyoTgq8ZqDByASBayiYcEiJ5/OQ4F9a9UZlui0W6clpM6w4PfZmtzLXUKqN+7ggkU2NM67Tm0LbkUvh0VgfoxqaFzQbdphWFTXAGsJeaj+JQM5LK16Rf2wsf7x/ksKiezltEqKOSwk9hUJjfNzCQnOE6K9APNUcphRFIdLSJNhiZonJDsWIzQdmeBmZBXanYno9giCjujWd36Hjq05YPMiZboMPYzzcOjPC9scrZC3rnzBFTIAkRgWFRsjbVJ59q3VvjUJtITUmDb772CtYh00Oi6hiHh+FdGftU7d8fwNlaRbtilir11gfgwxLLIOofgYlrHYkm47dQ1Ls/v9xssOWzvLmnSrwrCU1zrrQXDoHu0FKuyK6MRmYwxLgOhWheGMZFhiuOSuFJKCZdlEtwpiOT/BpY6tOYhhYbH8Q4KCW2URHhVbCJkCI2QAIiXdqjAsvGDhDEtJryUUUrAQQ0btHeXJvfh1OMNSjI9hkdwwe2PkFRgg/LM4gVywLaTLsASJbtUZFmZvr1ah22iYEd2ytdLVcYUhrJlZpFuaVbY1u3NYwjKHtEPjaG3TUEvBtpCOnm05clhELjc6Ba5vyCRCd/3QJReZU1fQ4kogKVhiBGo7FpkaXyWHxf33jFhaA2KWeVUvSUNKx/PTa8eDCaeAiiJkAKLOLCFqDRSIYdEVXAZoWKzMEA3xdWBBoMyyCTAsimvBKl5VWkJIVUtQz9oMC0s57msJKTIs6ZERTre76X4TDEuh29BjWHx0S1UlWzMTAbc71oC9WBkWBJ8nJFiwaDgGRUW3OjksPfb3uXhaQmO+41wMMbIDwLIkBUuMWHSp6VULlj5rsza9R4fCXJ+VFYdnyb02yXh+rYmz/gMQiWGRHXbmvlVRwaLMsHD9gv+tCTVCpEtWaQn1fa+NCa99GBbFA7anYFFxCUkKb00xLLwlxHJYVIXXqXTasTa7bs80S6igULAUsmnIZ9JQhIbe++3DrFbZRUqmJYT7A5utJ9cWUmFYFJxCy8GwdGrqYzysvxeQiBxrS6ip2VLEeWWFsYFwCiUFS4wg5gHp4EJWk+Lrs7QWjbWEVBkWX/oxDFrx4v5OIdVx8oTRzBBk2WVRXUgXPoWVvj+5TEqpNdD3vdYtWrmOyYzotm9AoyoLKDFkTTWWP9jWXNdiWILofp0gMMyDwe9rKVXXL1g82jWVlpD9ehSyWJQZFrnwOB3HoHtNEHvnB75OYmoJqT5/BHp/8JLUImcp3y/U1zaf2LzCwtukYIkR9FCrtgF80yW1c1j6aXdiWERD49RbQjoMS0DBQrcqxU1qHJziCUPDVJCOUP5zp1gxpxRKZv1ddwaGbtEaEs+vumHytYDTZlWmNcsyLGRrlv03okS3CuGBYYeRjoaF1ozDsCjekH0uKrITubWmtMvG8vcxLP3uQD/oOAa9uqYgjY47YFDLURiwV9QU3xeC24XYN91di6GTG50RF5KCZUAHHwYyLDEovlVCpKzXJnvb4jdvhUMmwNqsM0sIMQb23+umQCmSvaclFHBronWg2hbsC+pTEbW6EZbDotkSWqxWXHNv4m8JkRbFWHBcUe2wCxqAqBLE1/M6SzkogXmGhSfdKourW/ItIVmGhZxClQNCf1zXMUgaFhz5QMytF/oBg2ItIRUbPAJjKai133fJVdW8DVDabVKwDOjgw+DBd4ZszbhRY2SzgYJFePMywbB4JjbrMiyjHfvvtXMZZfaDH1RBDAsxbTqFq/tmq0vxhhQsNdW1QDHyFVf/P86CxVRwXNM8w9JhhxFS8s22fVMvKreEsSVkQMPiYtXQDECiWflnXiGLhd5PeoalRbf9Cdd+0JkrRoVOJpUJ1bHoMyzOzKmuJ6LBxCwhXx2LkZbQYFibk4JlQDNYAilYXVuzW/FNjgHFlhDd+pdHdNvfEkLalmtYFDep0a7995p59UeBhHSWpbXZDG0JqcJtKTWXxWNQdFu2X1+NBvXJOsEUCxYqPFRv09hGaLabZhgWHrtuF641l5NG9cY8aophcSVc03ssm3SrPE+Ii24lguMUbvW6lxe8sJDrzK9gsZ7tVkuPYWF7BYbhdRYWQlpCBi65Na+zVKOlmDAshz90M1h8iwJdWzMeIrTJs4NBlWGR7mdTIJiS6LZ/ACJRwDotoeGO3QZq5NTYlZ5NKIBlMdMScn2vTRWtIdH8sgcsrYXG0oLGVGHxW5wuw+L+e8iymGBYvA4QOnzI8aOvYTFB6c/xCwoSirKvS3roKbr4yOYu2xKSHNega2v2s7u70ak7+40qw4JtZ5zw7tcWaroYOR2GZTQou0vj+zIo84SSgmVZGBYTLSFDGhafeUJVCo6TvW1Ja1hMMCz+BYuqS2i4k9MuWFKZjJ3D4bpdm2baemzNPDjOfMHiiDHVbt5NipFXKliWz9acTWd50YJfy4yGpVd8TQJmLArS5AeWRK9LyIzolp73skIbtEf8LQJ6XrHV4mZ3YzgkdW3NUWm3tEaw0lPVu/VqnbxjHJwCt6jB+I56dUYmGJZEdHv4w7lZmxDd4ojwjlOwZA1mMii2AVba1qw7O8T6sm37/1zPSCR3ho6Nnwth2gwwLJbo1mA0v8cN4YhE02pzb2hQX355GBbV4Div8FY3EMwvdr3e0hPc0vteMsqwzCpZmpWntHNL84T8HBvJQ1I34iAqPM69RlT1bqFjHNj7gqMuMH9noDQsxaQldNhDZ/Chr63ZNY3U5OKjEKmhgmpLaBlEtz4uId3cBevLtuxHYCnr3G5UkA4Yfqc7qTnU1qzLsHRcehhDLaHOMjAsqF3SzWHps7FSIJgi1e92gFAeD4XGqQpu6X03bWteasqHxoUGk8URGodwJx/7CFSDGBbVEMmo8LguCxdUzWCJuty49WM6BdFYX0vIgEsoEd0e/jDSCujRLrjHhJtYfL0alpJsG0A66ZbyOTQYFtcARF1XAKLYth+BaqYDHbLj6rQDfFtC6pOafW+2ui0hzC5hbghvW8gZiqe2FrRYNMGCBZk1eq+0GBaXXqHDAsFUxzP43Zx1Y9bpfS+adAnVZvnsMKVxAbK6NdXQOPfFCt/rkPRZU8MPo8Lj+BrRLVi4OHvOqKU50MllIoclYVgOfxgRW7INAm9r9Rp7iLD9IevACE29VIvm50LLVqdHYBgInfwQnwGIJkR2FMvfyDnpqXrWZr+WkL6tuUfgrNsSsub2+DuFVGffUGFd6tY0WkKsYMH3gY1c8AMdJmhB1dEruOn/jgGGxRsgSN9LVcFtv0vIgMsDNSwNNc2aUg6LDsOCLDJdzAR0LNQijk/DYoZhSUdoWGTbsdEtIc1hqYiEYTn84RQC6sXFSCHLW7+VigbdHqZhUUw3Hcpn5WaL6FT6PgMQaY6QzgaVYe9RIxs+Q0S4HeDTEqKWmYmWEA7T1J7eGyK8VZ1lgrdCPJidzBCFNWrNK0lFboxccJsf1qLO3fR/1wjD4riEsG21ZODG3KNh0XJ5OMVgrb6k3BKSzmHRYVgkhbcmWsRhLSFjDMtouIalnNO4jIKPtlA3aFJybEacSAqWGKFqF3YD3QXD7CZUXZzXr5R9xGz8xiVZWOFrk6KIicFQfXA8AxBNiOw6S/bXqOf8b1W6tybEIvve0PuoAmLpMt0WpJBlMhgkRrAOWY02xpibDVCJzMepwgIHFBfcauhXvLdpEwwLtYSg3YZOpcpzWPRCwAy5hFwOnVZlVvl1qWtYJDNYFIS3Jl1C/gWLIQ0LtQ7nA1pCGueFr/XchK0ZW/LHPwfg5BcBdPT0fjrQK+UShEJHje9dgNheqlAol85B5aMVcILjFG5cxRzMVptiG5huLxUfmtkdXHhrYoPq1p2CRYthCbg19epC1DciHJ6J82iyTXbb1o7a7mdYSCSqU7CUq5ohZ7g2cV2G9Mp1Lc1xMSyWeySXswLGUFBJN2bVOUL0fKUZw9LNFYl/Upy4OwpQn4dOdVbreafDEAvcSIZLNZZfwepuJIclTHTLGRb1NRLGxjqiWz0eYdRrhtDVvCGKowB/8i1YaSQMS4zQKQT8NokazmnRPai86ns22dP6tIZrQKinbaJgQVQPGdugiGFpaDIsvB3g0xIi27iJdcDbA3h0aTBLfgWLOwdCVZDpsAFDsVHPuqFxfvH8ZFnVYVjw8E67BJU1E7ZmF2tVZanMymDf2w5jK5RszeQG6zr7hlBLSEXDIin2NMKw5EQYlpIZhmXW3yWkk3Ib6hLSvegOAJKCJUao6gGC+sY89lx34bkOBZx3gqJZxJDCgyLV09YNMKJbGjvMTGxQtAlpa1jIqujDsPCgLk2mDQ+LgtsxoqHf8Ivnpw0zn01bWRB6LaFybDdq3Vj+PtFtfd6gZdVxi3ENi4atuZBJ8SJ1oa2ugbJQsl9bqjbDg+OkX082zTNChHRrtL5ovcXIsMStYemyy026pCu69Z/uvmTovBhztYSsqdMmbM0DgqRgiQk6A8aCGJYGhXIZbAnR7d/6tAZFLNcSMnOYmbAx0iZUz6W0Cpb0mP8mZBWEbbYOdAtXtLiaEGB6w+MYlhgjqLphWi0h3bk3IgVLwyzDUl2a5wF6+oJKR3jLc1g0nv9UpwnplP3a5tuaHXxWNKRYEaGyLyGLJJVwzd4rLpiPUXRLLiETtmY/trXD2sepgiENi+dyQxcGU7bmVqdrX5oThiWBaDtI1T7oBm0QzRoNvTMlup3hVT3eqFXsl1KzRbQZlt7DjAdFadwcaBOqazMs/gWLbkHo/V4bmSsT1BJq6BXY9mRhzZaQQMFiIjTOfTjVSMxuWFBJLSEdhsUd7DfX1C1Y7NeWYbEAqoxfT5hlFBoa2UuSdlq6wOgEx4Ul3ZpiWILaxya0bnThyGVSrunuScGSIAK0+FQGjAXPaTFgZ3UfCq0aLLE2k8pcEeu1ydy2dCt9b8GiOanZvQmhhsWErTloE0qbWAdWyyVGhkVztP1ytYRQc6IbGucuWOpV9p5lMpZo1pSg0rE1a7zv7JlpdDMwFxxNI3X4Z5vzmoWpRMK14UtKGHiQpAGGpdlp9swqM8qwuKe7NxwRvYmgQc6CuZlv3WGpA4SkYInbIaQZs+y+0bR57LnmwiuM8KTT2rztuClLxvIriW7ptmWqYDGiYWEFS1ZTdOvehHAMfZ+1Pau/DnByr07OSYSGhVhBVUp6tKclVF41DEuDFe267Ir1NVyCSj5LSIfiZwVLDfLi2ScR73mOrXPV19U3kFUo3VpXhB3OsGDyMdewaOwH5VwZ0in7WPReYIxpWHBQKtsL3BccE7k9fcLbxRoeHPYnEw1LgiCoxt37garldsMQtedKOm1VprWU6RQZH8mwWOKvqtEcFhMiOxJb6tqaaVqzdxOiIkD31kSFq9MS0m0L9hcslMyqfPPGgkU3M0Qgd4PeJ22GhTlCTAw+9GsNGjmA2DNTg4L4wMGI97zQmtd65qWyWPglRXM9RLiE3GyIzn6AxQoVwt4LjCmGpWe6u7tgMeQmRIxQ0CQ5S03sGQOApGCJO+VWkbnw2yC69PCbqJTZRtBcnNI+pBCRt792047VN8KwzPaK7DRaQp163UjB0rMJucLjTPWljaaeBmhYVBOPfTU2+cFnWKjgofEMJhgW06JbYliWusiwCMbhR7AVxfaCFqvK51qJPPOdZuzrwc226u4HYU4hUwxLkLXZlEvIfWZUFl3/h4RhSRC3pdldFHRNqr3ZRqATIuV+bZGbl3sqsGGXkAmGpZFLabWEgqawUlaFbrZCv61ZtyUUnMNS1HIJ1TRFt+PCBQvS9zrIprNQzpah0DLjEHIPtsM1wL+fOtol9twsGWFY7OenTAWLdksoooBquG/3uutBjGHB9xR/xFGwcCZOk2EJmj2mqyHzKypJowhYxGGS9CrH6v8fHMax/N7FlzKp9qYQqeqMZktIkB6mYgu1M5mcXsGCG0m7aWSWkFvDosOwINKuWTJeq7AphsVYS8hdsLDp17pskMUApeK3NVdbVSO2Zjqc8gYZFveIBj5IMj8oGhb7PS93Fo0888KXFCwgsnmlf0t0ICaxrToOoUiGpWaSYelPu+UMS94cw7JUMZTdNSBICpaYsNSklpCZmzUiTQ+sQYYlVSMNS8yiW7f4TlV8WuwdjrfUXtJPumWbkG5LKMjabLJw7XHh6NK7VLB0OzwrQ5eSHis7LauutmYBCyn/JNUqW0s6dvaegoUt3ZRJqt9Kuu0YsDVTS6jAp7/rXlKGu4tGhPaRlxSuX9EoLAUHYprIZIpKuzWpdXKLs70RCCZbQvVaUrAkEEClbr4llGEHtEkNS5qJ2VSrenfSbQfzuoNgop2F81D40L5pIwwL3ZowhwUTVNsag70c/UJ/wWJmHaDotmlmA8IiL5PvaQvp2irHilnuEqqlFN8Tnoja7Zsk7b1NYztHF5i7wRkWE1S/K0CQZgmZYFiWTDAsrL0y0mUtNd1nPpJVrejpVxDYxvCx4HthwjEYFR7HGRbD4myCkfXiOTPqS4ayuwYEScESE8yKLe0NIt811A5wFywsRGpIkx5GE9CiKyyvD6b0N66Wge605m6nA10musUcFnfsu6kprKZFt46t2YBTzGNt1u2hD6WdVFblGHlsHZD7J6AtRAyLroaFDqeCSYaF65jmod5oGshhIQ1LXiw6IAzs/R5NVSENHSjndFtCrXgdQhJtQhNzxYQ1LCYKFp+0W1M5LG6GpcnT0fWflUFAUrCsAlvzUD5rBY85+gUDi4/dtnKUeqn4kKBAkwLR5qrN6NuWwc1L91ZFNyb3QDMT4XEdP4bFkOiW1kBH0wnhJ7zVbQmlXE6N2ZZGAFuItRkZMDqcTDAsPRoWg2JKRIrpB3DS9kBoWEjAiv9vqGqwqrnlY1gEhbcmGRZKu6URELEwLFzD4uPSMyK6zVk/tygd/TCwNCOSgiVuW7OBajmdTsGIW9Bogt5jh0K+SQWL+oHKdSxhG2qMDIvqrYpuTNbXKAUPPdMZalZlWiYj2QrWLCFmw4YYChbdGx5zhdS7OZir2/oNvYyY/hs1veeDyrCk8nlIle3XlWUFixHRbdeASyiTgw77nq3NVK0hlyoQHseRMCxC4mxCLQYNS5unoycMS4IQmBRbGtcvuDaBIg+RUn+dQjZH3dA4n81LV8NCN6ZUoQDDxeAZIvJWRVfBwrRMJgrXXCYNw5kWbxFow6MN0HYp9LQvNA7XkHRTagdlUhnIp/OGGJauMYbFTffnmKXURHCcrWFp2dN3NdBh7MG6HLtAaLSoF+ota8hr7M+8a7p8nLH8fRoWNsLAW7CkY2gJ4ftqMjhulNLR6T1INCwJwmCyFeCEctXjK1g0Au6EXAMDqGFxb0BhU1rlW0Jzsa2D4XSTB4nFxrDk9BiWKhTEUlAVbtRkacZ2kO6oAy66ZXW2Cbuqu3AtslEaJqL5MYcFiwNaT6po5e33fH1WvWBBtpewGKZjoRwW1Vh+ibRburzEZWvGgoJfcGIQ3eJEdyr+tIIGPXtyh6ejJwVLAiFbsyGGxaTg0nVr4SFSOgyLSPIlL1g0b1ssnr9bmdKe1txhoXG4AY0W7ENmjml6tGjenrhtcy0hxHDG/h5Xu3pD+vxC2rQFwi4LbmwFC1maDdk0rZYQe6kppmMyVbCMsJaICdFtI2UX5bo6lkbOfm1rsk5rTRbYSqIiLPyZX/6WkEmGpadgwflgLK8oXSoZCxgkhqXGJqWb1rBkqIWatIQSLJetuT+W3RzDMtRdtB0DGgyAkAjP8ObVXJqGLlpfNaK43SK68YJ9eM/Wo8fYq+SwmFD+I8qMYVlUdeG4UZrsOQjotSq3MNh7jAyLlqNFkGExAXcOS7poQBfkOoyGWQFnIoely555XadQnRUskxlX8nTcrOoyiG7J6m7S1txTsLgF+oWCOTfZ3FxPOyibTlmtX12MsveHGzWSllCCMDi3VTOtgN7BdyYKFscxMAoVLQZASIRnuCW0tGQH3ulsUlxEVyrBmBVQpcewUEvIbVU0rWWionWhbWBdeYZJaov+WAsA2xdxMywmBLdxMSxpF8OCbASK5pXBWMQUO/R1GZZaxj6MJ1N6BYtQFgu1hHQTiQUYFhNjOsIKlg6bIwSZDEBO/7JAAn1kbnA8iMkhqYgMGjUKWVfydMKwJAiBSXcIYtRafAYLFozHZw/mWKqix7CIxPPzzctMwVJn/WwUX6rODjHPsNgHFW5A3UbDeA4LgtaAkYKFMyx2weKI/rKD0RJihZQfw2Ii5TY2hmWMJco2l/TmCCFYgZZiz42uU2iJFSzjadecn7ji+Xm69TK0hGIIjsMiqNFmyc1sUjOyKya0U+mhMkA260z2NugQ6o1BIKNGwrAkCIHpm/VEwWUTNUXvMZZlHBbNMCxhAjxTGha2edVYYYHtINUNhG5NKUMFC01rdreF+M1JMaTLiwJjWOaaOeMMi9O+UtwWWO6G1RKqxcywGGoJjeacpFvTGpbhxpL+jZk9N5nCkBGGpZoe5qyqDoScgaZszTzgcHZZNCw4BTzFxgEQy8L3CgP6FevrpFI9ydhG5k6F5DYlDEuCUJCd1VRLaCLvcgeYmgtBBUuqopx066aHl8clZB+ytfq8/qTm2hJnWEy0hFKZTJ/wlooAU+JrSjuea2ViY1iUHU3sgNJuCVEh5Wr79WlYDLaE8mxac9fQs0qtwZFmVc/SjCAbd8GMhmWRFSwjmgWLmIbFsEtIgGEx4RJKp9JW0eIuWNwMiyk41uZZo6FxbjNE0WR21wAgKVhigume5ETO/not0Jh27EG3aG8EY7Co9TrHVkB0W2dfT29Sc50HhplgWBDOrWkuFqYt17Ff82wjY5BhmbEslQ02rE9503S1hLRaF1RI+bSETM4RQgznh7mGBedJmTyIhhtV/QOIfU/zRfvQ1yoE8QDuDvUMQIzVGWgsOM4lug3IoTE5/BBBrkHaD0wzLN4xDiYnNfdGYRiUEQwAkoIlJpgMAUKMZu2vV2f2RhNoM6rV1rDo2Jolkm51+9ksO6TGhIxmGJYSL1h0GBbvJoRwNiIzp2G2Y2+c0yYKFrq5NhZgiX0v9AoWV0tIi2FZ47QAmJU0LtEt6p+KLXsbXMpqpPP6CCpHmktQMFSwFMp2u3Gmyg4gRcyBXbAMdTQLFplLiikNS7cNUF+I3SWE8DKubjbWFNIua3NcGpZSUrAkiALeVJtt+yYwZCo4LmdvDHUTCaeeECnUsOg8KKNC9LCh21Yma42crzHdiqqluVfDUujZoHTSRNPkFJqfh1a7YwVCIcomNqJuF7KMYTlkomDBgjVlbwG1+UPOp1VzQ3hLKG9GdNvt9OkWTDMsiELLXksVlnFjjGFpIsNiRnRbLtstipmKXsEyywqWEstfUoXQAERTLiE8bOliEtAWIobFlBh7ojDRy7AQG2uwYOExCC4Ni6kLbh/DkrSEEgSBbtUmKb4RFsleNZFwylDPjvG5IjrWS6cltAzTmhGlcV6w6DAsHR+GpdVtQYV675qbUJVtQsbWgWu44FRN36kA6TQXNDbnp6yfsXBVdkFQjHy3CJVGG5qsWFOb2Dzie0CZ1rAgCkzDUmHPmCkNC4pu9TUsrEAbJoZFr6iaadvft2JrYRk0LIYYFgHhLY/mNzEU1MWwUMESB8PijPKY089ACsru4s7SRHSbIMLSjCFAqgPGgiPZc9rzRLwhUmsyZiyOSGuSDiKYYTETescLFg0KuMtuTRjJjoUPfS0T1mbchKhwxVqQJlprgYo+ZFhqhh5dpmNpLh4yMKjPCY5DLITdviNfl7+1mSfdGrpJIxh5CYu0uWuC1kCp3YChtMazis85e89HhkeNtISmWcGSx4LF024zn8NicPBehPDW5PBDhFfTFgvDMhZvS2isJ7vrCGZYrrzySti2bRsUi0U4//zz4eabbw78s/feey+88pWvtP483tw+8YlPaH/NQYdpoSViyDVDptY002tfytqb4Hi6qj1FmC7lgToWUy0hRHmSa1h0RHbEsJCd1UQ8vzNPaN61DvD7Y4ARYYdXo5uBmZqZopUOgnbFYViUwQ6oDism9NpC/k4h00m3WPzn2PO0yJ4xXaC9vcve7zHWwlKCNR/Hfp9HRs0ULFMd+/uG6daoXYpXt1YxWLCED0DkOSymChbG6MTKsLj2iloMZ8ZYOcdjEIyko6/GguVrX/savP3tb4f3ve99cNttt8FZZ50FL3jBC+DAgQO+f75arcJxxx0HH/7wh2Hjxo1GvuaRZmlG0MJDy6huFgOhmrZp5gnQE+BhO2m4EGFtNtoSmoA6017o2Bi7TMNCgWFGslhc8fyVutnwQGoJ1SAfzmYpFAa8YDHAsKTYAWXE2ly1X1dcs4S6dWb7xNeb0iguPPb2VtH+HoxSoa4C198dYxk/MxW9Z3+umYE6zaEKibo3msOSN8mw+L9m3cntXnhF+A7DYs70kHYxLLwllI+rJVSCI7JgueKKK+Cyyy6DSy+9FE477TS46qqroFwuw9VXX+3755/ylKfAP/7jP8KrX/1qKAR42GW/5mqxNJusllPswMfNRtfa6M1kGNUsWISszSYZFldLSI9h6Z2+ajLt1k3zGlsH7HtIwusFE4WrJ/NEb7IwK1go5CwGa7NphoUGYCLmQX0goBcNJpKleUJKoL+bzsHkiP3/Xay3tApVLKLJKURTumPRsGC7idglXdGtQEtoqb1k1NZMe8FMbaZP72YK7tlj8bSEcke2rbnRaMCtt94Kz3ve85wvkE5bv77xxhuVXoDK16zX6zA/P9/zY5BAYkuTnnp6+C2GxVDBspCyb20jmpkMQq4BwwyLGQ0LY1hYtoJXaKc1T8glpDNlaYam/XrrTCNipHBlhUGqOqO/ZtmNmlJZ4wiPM510S2sAnc3zbT0tlxv1ErMPNwwULLmy9XyRLn52Sb0thAfjHMtiCUuO1datuVthyyi6NREc53d5ITbWJMPi3iuMX26AbM31I7dgOXToELTbbdiwYUPP5/HX+/btU3oBKl/zQx/6EIyNjfEfW7duhUECiS2HDLaEaPPCdoCpltA8jDiZDJpC3tAbV7sFwGZymGJY6kZcQv4Mi46GhZJusS+9ZJppY4dAM12ItpRKilvTNRMMi33g50yEnEUxLIZcD7QG6rneYXe6qLHvwXDdQEsoV7LaruPlvHZbCItozrBotISGWXBcINNH7SDD0+X9GJZ2pw3NTjMWhoWLbinp1iTDwlpCnVlHoG+UYSli0q39fekmtuaVw7ve9S6Ym5vjP3bu3AmDBNIuGGVYeIpoXjuemzDbtTf9HA7I0um1R7kG3LctIwXLJCylDeSweIR0RlpCbPAd0rzGxdeMYeEFi0GGJcv+z1qvla3RXMluh2gV1gEMi+kclm5MBcsSY5lKdQ3WxsNKjpdz2sLbKraEDDAsNA04sHB2C27RPh+j6JYyWExqWCZYCjjuBSjM5nq3ksHgOGofLyzAUqNp3tacdWIVKh0z6egrDSkKYO3atZDJZGD//v09n8dfBwlq4/iaqIUJ0sMMAuKg99wMS8MUw9LOW46TfKptbwQaMz9CJzbzPn4KwMQNyBDD4ohuizG0hJyCxditiRWVbc6wmNOwZOszxlxCdiprwzjDggfHEuWSGGZYGlmA+Ya51nK1YL++Uk2nYOnVfU2W8/AYVJTD4/D7h+3quaw+w0IthwUsgPzeZ5OW5gjRLVmaTbqEaC9odVoWq0cMS6pg3taMep/uQsW4UaNI7SBr9lgW7GvE6oZU6ZvP5+Hcc8+F6667jn+u0+lYv77ggguUXkAcX3Ol4bazGgN3iJjTsFRw86JlHDJYTG5is9/m5bptmbD3GtKw8E2I0bwmWkIkuu0uLUGtWovFJdRmFLsRpo0VBvnGnJ5LwcoMYfOdiGHR0rD0twCQ9sdgvzgYlgYyLE1zDMsi023kl0wyLHmt8Lh6q2O9TSYYlsh4fpOhcd55QgH6lXw6bw0uNPLPZUucvUXhbSwMS6Hg5LpU5vUmpfsgxb4veCmdrxuKQVhhSJ+oaD9+/etfD+eddx489alPtXJVKpWK5fBBvO51r4MtW7ZYOhMS1d5333384927d8Mdd9wBw8PDcMIJJwh9zdWGOHJYHIYlB1UT2gWmtZntDsO61JyR21bw5mVQcOstWEwwLGwTMmJrRvspvrZuFxqzc4ZFt0s9/WiTDEuxyVpCWoMP7U2xNIQMy7ReQeXDsJDg1vptQ7kSbobFZEtoka11rYKFu2zsrzU5pNcSon1pHlgRofvMhw1ANBXLT2AtGr8iixgWU/oVAu4H+6v7rQvMcAwMC11wWrUapBfR+DBptCUE/MzQnJ4+QJDeSS+++GI4ePAgvPe977VEsWeffTZce+21XDS7Y8cOy+VD2LNnDzz5yU/mv/7oRz9q/XjWs54F119/vdDXXG3APnFsBYulYTHEsNTdAjxDDIvfIeVyOxgBtoRIw5JWH1XgFd16B56pIJVOW0ULDj+kic1DxjQs7PtIBYtBDUuxhTe8rrruymXfHWKZIaZdQiS4RVYtk84MtIZlnhUZuarG1/Q8NxNcdNvQ0tZVWZyBjq05MoslNoZlJnaHkLdgmanPwFAMDAu1hVoHDkC2smA9i2ZZ+SXHqHGkFiyIt7zlLdYPP1ARQsD0WpEo+bCvudoQt63ZVLW81GzBbNdMwcJFt363LZOx/J5ZQiXFeHFbSOcvujUxsRkLFntic9G4S8hIMJunMMhAG0ZAY/YNiSwzBRhlm7oRDQseRqiHyJdjieWnAZiNbMqaIYWaBZzgrIs59hqz1UUDBQtbn6xgmVZkWEhbV8uM2mSYZkso1BnobgObAJ8svgjQbgJkcn2iW1P6FYKbcd1ci4dhoWGp2coiQMmsSwiYSL9mMLtrpbEqXUKDjtVia7YZFrMallDRranbViYHNXaoFFjCpSy6DWfT92pYFpuL3Cap9PLI2rywEEsOSzrPNCwmWoPWJFz2/08tqBdXrlTTUD2TKAojAFQ4MJYljsGH3bqjYUHoDL50Y45pqzKLC+ZEt6wlNKuoYaGWUC07YqglFPI+mwyKRBSZQNXndZNzzHhLiGW/WBPca3ExLPa/kV+yC1uzBUvVlY5uRkaw0kgKliPY1kwaFiMCvKKAAM/gQVPL2IdZsaXomHAlnFI0/0h+BFLoZNLNYmG3pvTCXCxJtxkqWEzdmhjLgiMalDdM/h4PiU3yjQIyaB4dCxUscTAsLfYemXIKTbOCJaVVsPiLbqcVW0LUqm7lRw2JbkOiDEzG8iOwBUhFi+dyRbH8cbSESHTrtI/N/ht0uSkwrZNZVr7GL7kJw5JgRWzNGMtubJZQ05XJsBwMi8G0xTrTSRWbagwLbUCQy0Eqa2+8qIswMgCRRW6nkOY1uQmxDSjDQslMrQMqDCZSixoaFueAcguwO52uMR0LT7mNgWGBAht3YEjHMp12Chbl6ep9DIv9Gmc1RbfNPB38ZlpC4TkshkS3IWm3cYpueRYLMSwGk27d1uZhancaPTOqh52GJSlYVotLiGtYzC0+HNI4a0h0yw+pWqt/g+abl7mCpcbc0QXFWS00Q8Y7fXWMbeYm5gllWMFiOjguxzI+zDEstj5gHBbUGRaeu1HiBxnWKhWW9qsED8NiOjTOzbAAy3UyVbBMpdjB1mz2sHlS8BT6Eyw4TplhYReptpth0Ui4Ds1eMs2whKTd8knNhkLjCG5NW2wMC2Njh9kIh2I2HYOGJZ8ULAmWOYfF7RLyKwoUXydvCekWLGzzane6UGH//9hcQtatwUbJHQGuMKXXW7AYSbsdt4ueHBNcmhbdFjjD0ho8hiU3ZAl382zjNekUMj340M2w0IwYEwULskpzkIMWywTBEEEleMTq5BLC973Vlheb06iILlvj0GlpJVwL5bDklqFgYQyLadEtuQZnatOxMSyUdjvSrEI+k4ZsJh2LhmUuKVgSLOe0ZqqWcfFhUUBFkQ7w9uuIbvXo4WIubT1wvhuY4ZYQFmt1lvlRqC9oMSwpNviQYMLaTJuQU7CYzWEplEwzLE7BorxmPTZWR9PU0j+g2GDGOFpCxLDQAEwTBQsGtKEGh7JYyN6u+szToU/MFWJW4b1HkT0iUxx2CZp1BiAKOAM10rNFw+PiYlgonr9Scf498wyL/X8ablTNtoN6NCw5c+3jFUZSsMQAZ0qv+ZZQi+WO6C5AK+YcB6FxDYtewZJKpYKtzYZvW2g9bfOCRc02ym9MnhEPRhgWpmEpsFh2cy0hRhuXh/nBWGM0vwmGBVtCyrZmbmNlk6/ZWtBjWNYsG8OSLZaNiW5Jw7bAijf1gqW30MfbNxUtKjoWR1uXjZx+LIKxckhRajqaX4BhiUvDUllw/j3zGhZiWJbMOoQ8rHzCsCRYEVtzjg1V012AjXYHWp2uMQ2LmyKe89ouDbeE3LNDSrV5rZt1EMNiYp5QscYOWMOi20JpiE84MJl2a7WEDLiEEEacQmWPhsXwHCH3Osgy1got7bqgIrKiXbCw76nLFUU6FpV4fmJ+rYtUSNS9ijOwX7cWg4YlSHQbE8NCe0GtMtcn0Dcuuo2DYWk62V2mnKUrjaRgMQxrwJjplpA1p8VefFnSL2guQCqquIYFqXAMZDKSy9CKNTiONqhUtws5xQ2XbtZBGhYT84RoUq+5HBb7+5jOlZ1JuQbnCY0DtoQUX6snaydU3yD5urwMi1FbM5vYnWWslYmWEDEZNADRDhBUgE8rdYI5hVSEt9QSGipkjDAs9B7j5cdqg8UZzR/CsMQVHDdRoHEA/no3E6C9IhaGpcVSnBNbc4Ig4INLTk5zdlZ8YFgLhKhrzQVIwthahoVIWb/QS3gNvFXHxLAUu11IKW64DsMSILrV2MjTrCVEyn/l+TyBmoaSy5WlvxE12f95IqXjEqr4x8grWnD9GBauYTHZEmKHUcFgwUIMS41dLtpzuqJb5/9L31ellpDbDGCAYcGRExk2IqNft7b6XUJDuSHIprKQZ3cCEmabRJoxLOVWHYYyhgcUNpnotpu3iuiGt6hchUgKFsNwi2GNiS1pCJprY9U9qMgxUCzkABj1acza7N28XJZXE6CgKCxYVF8z3azTnqjtsaK5lpDxbAXXMLzQoD5J1HNUsCxCUXVarOdwXaPBBEQxLEZbQkzLlB8aNaZhoYKlzgsWMxoWd8EyXWmqjwzJuULYNApzS7cWJLyNRcMSILqNySWE/z9Mu82z/1rasOAWkWFztxATHbVMqUhbM5jRPQ4CkoLFMKgdVMim+e1DG7RxpTIwVDaTckr0sHX7DxksJoNAoaVh0a2bYXFP85VB0Lh4ky2hYrsJ+U7LWgumhx+GhnZJopYb4y0hcnopvzZ2o54ctjfJQ4vmGRaTLSESXxfLowYZFvsmWy/Zl4v2vMmCRV102zOUlbeEzAxA7H/mKzG4hJaXYaH9IN/qxiK4RaAmps30U2MubZ7ZC07R+ulwyGJJCpbVEBrnaqkE6kRUXydqIQI2AmOzRQzbmmmDKmDvDTfcjrxTpsPzN0rGXUI4rZlUseuhYd3UzLeEDLhwGCoZxgilapBS1TF5NAsOw1LXZ1jYexwnw1IaHjenYaFEWcaGdnRFt+6WkAZz1fvM67eEQu3rh4FLiIS3hWY8lmZCc8hmWcZdTLrZC07Z+ulw0LEkBctqCI1jBzRWyqHzOyQnNfPCim8EszHdtmJkWFDbo3BL5AxLkOi2MacczpdKp6E7ZB9Wa8AgzRtTS6iSGoZ2N9XTfpGGR1i9Zsg+PKa0WkJsXeJ7vDQbS9ItMSxDwxPmGJZWb8GirGHhz31/S0jJJUS25pwZ0S1iuaIMLLhfs+vZ5AyL4ZYQCW9Jw+KNQDCFZmmIC2/NfuEl66dMwV4/ScGSYJlC46hSdh1UNUMtoR6Lo5l5Qn23LcMMC9ewsEGFKq/bidr2aFiYngezXuhGr4Iu602v6RiieZH5wGRSRLZoVHS71MJkVkbdK7bYglpCUzotIRxwyfVV0/EEx1HBMjJhvCXUGR5RT7q1nIH9hz5NbFYRM5NurfeZNzRPyH0Y4msnxi0O0S0+B43F2Icf9jEsnggEUyCt03BMBUuavQeHw8TmpGAxjKVYW0KOO0S3Wu51DCxTS8jQ5rXUZomvGmmdXRLdejQsqI8oZArabaE20bymhHTuzayHYdHfhHAtzHRH9BgWT0toLWdY6kbmHEF1Kp7gOJZ4PDw8yXNY2gotRj9bc2eYGJY5dXbFpUFwT2xWKVj4JQVbQqYYFr95Qu0GQLdtnmHBC08m3/fMx9kSsjUsEJuGBbHEtE5DimNGAsEYyRxzliYMS4JAu7DZqZuOeIqr8jUPKhpK19sSMsWwxEsP8xtVOqd8yHaYndXLsJgagNhi7YBxU0I69wFmMSwhsegKB+wsjWiommkJEcOCbAOxjroDEHlLyNA66rbb0G3a37+RkbX887rhcXVWsHSHR9U1LO4C1SUyponNMwqttp4p8qY0LH7ibypeTYtuUQvms1fFKbrFeH7H1hwPw7LE8nrKLLfJGJrMAWcoCmMQkBQshuHQriY1LC7RraFWQI84mDYBU/3swILFkOiWblSMCVEptDjD4rMJkbV5TsNBQX3pMVNCOldbEDdukxoWLChm+BBMVYald3YMZnSQO2rKgFOoXT1kXMNC+hVEfmiEu49020LEXqZGRnlLqNuRzMCgZwYZBWyNMYwzlxDelnGmmFLSbY+tWdMlxC9Qzf7XjheKjDP/yAj8CpaYbM3ellAcwXGIKksvL7NRHsbQtJ+XPM6OSgqWBMvuEsKbtSENi6O1cdHDhhiWHuoRN2ouHjQkuuU3KvWChQfH+dC8JpxCdVawUHicSZYNYdTWbDEsI4YYFvs9RmcUOYX0hLd2wVKrHHA+ZcjW3GETuxGpQgFGciNGChYS3aaYvR01HZ1FSdYmQPc1XrK/p1iryBxAWNyQtmbIYEvI95nnxavBdhDB53XHyrBYottuIBtrAjQkM79kuGBpsdljbC9KclgSLLutmbJOdFtCvgyLIQ0LtsVabXajdDMMpkS3PIq7pCG6DWZYTBQslHJqTPnvYtmMRd/3aFg0GRYfFm3NcEHf2swYlmr1oPVzOpV2mDVD+hUsVtDZNZI3U7AsNey1ny+X+CEnLbwNaKPms2k+lkFGx0LtoL6WEB72ZJdXgK/N2jNXyigOQ4ZlgT0zBdMFS7N3WGqiYUkQzlzEoWFhB9VCrQkdSUrYjWrdfMEywujhnpt/QC/eSA4LbeZKLaG6r+jW7RTSCY/jfWlTQjo6VNimbMreTnZXXrBUFdYAsmg8in2oT2+hFR7HGJZqdYq3g0zl2hDDQkWFqYKFGJZiLs1DBNuzkmspxFk3ruAUon0Jv3VWqw7/r6m0Nsvia1+PI5af4NHeoJsPf8QbHBdfND9inu1juar+4E2/+XNlFrFwOAxATAoWw6jGIbr1yd/AWoWEsyrgmQw9LiE9ejibScNwwXOQ8omzRYC0meXGb1T5YW2GJRUTw0KTekt1UwVLL4Phbg2q5sUQag1XS0iFYelxtDiHlJF4fmJYatPGHUIdxrCkPQWLbjw/fj9JK0LTeDuyabchQvVJcgpJxPNzV2AuYxd8+CySjkXjuV/L7ev1eEPjCJ7LFbGtcbmELNEt+zZ3C8yhZBizjDHMmCxY2vjMdXsKloRhSRC4MaDo0Bj47boExVzGooV19Qs9Md3uHBbNw69vtojhwYc9Peu8uu7CCY6LR8OyyP6/xSVDm5AnRIyYtmbb0SaoYqmHYVEoWOhw9RYsfoeZ4gG1xMShJjNYSHRLN2fzDEsG0myulLS12ZVq7IWKtbnH0kwwoGOhth8G2TWpDRxHLH9AwUJCbISpVqEbuCaKbCurZeIZHjjD8mMyi/pzrPyeyWGWCZUULAlCGBaTLSG6sRR7sw8U0i5Do/kxO6Gut1n35cTEkHjpMCyjBoLjSrG0hHhf2pTy3yW87puUa8Axxm3NKgwL2Vg9LNqkibRbYlgY62FyjhCtAdIx8YKlqVmwsALSYljY5G7ptFs3M+kBtzZLaVh8Ai0NMCzjpRzQyDRutY6TYfEUWcSwYLGC+ibTwK9Z7th7+VI2poIlbb/HqUX90MK+gjeVgdEyBcclBUuC5Ui69bhsTGRw8NeJFkc8XGljNDax2aNhMSS4deewFPiGq6Jh8R9+aIphmWfFZa5aiYVh6ZmUqz2mwRUcp8Sw+LNoDsNiQMPCCuk4GBZqCY2yAtiUrbmAGhbWEpIX3QYzk2Rtlonn5xcpfN4JxKxqWJvT6RQvTLlWiTMs8beE4ozlJ5Tb9nNWzcSjATmUtr9/qUaDF9HaaDpFozsfS0f3OAhICpZVZmtGmMjgcBgW9jrjsjny21bJOMNSKqqJhVHzERTNb6pgmWU0b9ZUX9qzBhAmU48dl5BCWzCgBWBUwxJDyq13DZhuCdkMC7WEJNdSSKHvaFjkW0KWpZlg6JnnOhZyg/FnPs6W0Kx/JlMMKLbsY3IxHQ9DMd3NQpuxQ8pzp0Lnz5nRPQ4CkoJlNQw/9Ny2fNMldV+nYWtzrC0hcgm5A+9kgrkw3bTdDrQqmmgJzaTsr5tu1KHT0DiwQ27cpjJ5epJusS0oe+MOKEodW7MBhoVFvcfJsJizNTsalsw4iW7N2JoR46ZaQobSbvuYNMNBkT3wvGbaC0y2Cr0otuye10LawHPsATIetVYXFhkjKy3OFih43brH1a5jSQqW1TT8kGtY9FsB9Dq5ONhQwULD2Xj2RgwtIa5hKa2xP9HtAEgUF27aNR3CsGBEe7Oj9j2eSWehw4YzKkWzh9yYCI61uaV9wNYhDy0aHierYwlqCXFbc13dyYSsTaYAVaaNiUPDYpphqbc6jug2BluzikvItyVkiGEhazO+zxYayye65YNQY7A0E8jWPA+G2jU+bNwCe3aU5k5FJWOHDaZdZUgKllVlay57Uk6b5l6noYJl3Yi3n22eYeGbFB4wRDtLvG5esGRQv9MfHY5ahhQrNubrahRtpdmFCisulKb1BoowS7EwLIgWK9Sk10BAS4jEoXiA03qTBlpwy5NQZcpOo4MPY2ZYekS3ygxLf8EywTUsDXn3orslZIhhWTvsEVfH8Mz3FVn4HrWbziDUGCzNhBwTUc+lzBcs9L4s5kvm9gqfCw5dchOGJUGArdlkS8gbGqZXLWNMN90Ch3hLyOzmxW9bhic1901nVSi03AeVXwhZJp3hh5dqWwjXAVmbjfSlfWyupuYJ0ZptFWgy8oyRlhCyjBieZkJ4u8TeJ5MtIS/DYkp02xMcx1pC8rbmYNEtpcuq2Jp7LlKmGBavfT3WaH4mtEfU5pxBqMQOxoAMSy6eBcPTlF2XhQor9qWZuCB4ikYTl9xBQFKwrArRbe/i8x3pLgH39FzTDAsVLAcXqGCJQXTrnh2i8LqdOULBNDLpWFSFt7gOFvityURLqL9F4LjFNFtCNF2Yi5hlW0L+N2p7nhDdvvXi+aklFCfDMpwbNlOwNF0aFmoJya4Bn/ebMMFzWMRDA6ukYelpCZkZgOiEx3lcQnGIbnEQJHs28XLVc3mJCRm2X06D4eh812WhxiYqG9krQi65CcOSoEdARZu/2ZaQh97TtDVTUYUsO03U7QmPM9IS8mpYDLaE+CyhohIz5ExqDi5YdJ1CNsNSUhNcCrqE3HZFE5tmt0wMi2LB4qNZMGNtnoiHYaHClVnbiVVD7VIHdVEKwAKCcliK7qRb6RyWkGh+1hJCplS0WOVJt3GIbvs0LDEyLIiSE2dAwXFxaVi67TakWbE31TWYRMtA50WdzR4zsleEMSxJwZLAb8DYUBwuIaZf0G0FUMEylM86LRHDDAs6Q3BDjZ1hYbZXOQ0LmyHjk8FiwimEiZ+NdocXLGZaQn4MiyFbM1u3qfIaNYYlJChs0pC1ORaGpe6vYelC1ypaVECtVrq0pKlgqVSgi+40AwULFkJUeMwKtoXiSrp1F6WObs38JaUHrr2q5/ISA7quid5THYNJtJ5Css4GFBprCbW8GpakYEkQUAhgDUC9eyPwbF66tuYKi+X372frPTB4QCFzg7WK1QYwLMBD106r23IxLPKsgMOwlGJhWGgdLOYNKv89wXGmRLfICFDBkh6aVGRYqAVQCr5967SESpNQZYV1yWDh620N5jN5flNXbQvRAYQoZtOQYbHoiPaCxNeMeG6oLSRaCMZpa3ZEt8wNFmc0v6fQ6rm8xAC3o/Bgx1Ax4TPTrUUFizGGZcnXJZS0hBL4ugNMTZSNw9ZMB1SvY8AMw5JxJ18uNIzbmklkx8OiNDQscbWEaB3QAEQjfWnf4Dh9WzMyAiSDyAwpMixcWB3cEprWaQnF5RLyMCwmnEIkuM1lUtYw0FQ2C+lhhdtzxHMzweIDZgXTbkNtzVhgtNUPMnqPsRVm/TtxRvMjXM983BoW0jk1MgDzzUU+GdoUaFBmkwoW07bmnMfWrKl3W2kkBYtBUIqgUcEtniYeW7PDsDT1GJaemG4zBYtbhGf1tA23hGiD0ipY+KTmeFpCJGrmfem4WkIGGBY3I5AbXqPGsIQcUEbSbi2GxXxLyE98zSc2K9rZeWhc1nm2SHgrFQoW8dzIMiy8DdzTEnI5bjRYFgyfpL3E0ipxTdMyFCzLxLA0cnarUHeSd9DlsT00YlZ02/JcctnlJmFYEsSbcotjwkkA6InmX6y3lGZDOJkMfgWLHj3sFt5aTiHD/Wz3BmWxWEq25nrgHCGTLaFGecgczevXEjIgpCNKOp9JQ2ZorSLDUonUsBzS1rCYbwl5XUKIyaLdFpuqTSl9TRLcFlyXgTRZm2XWQcRz4ziFRAsWnzZwOuM4bkzpWLD114jRJeRpZcWuYWFrpJlLaY/r8APPJ2KtQyOXG5/1k4huE4gp8U0tPJ/hh0i+LDC2RHuiNG0CePi42i4qWOfOYjFcsPRtUCy6XY1hiUfDshRHX9rTk+5lWFrKSbJOjHzapQeSDY4LztrhLSFtDUsMDIvPPKl1pXXWzwerB5W+puMSdLZWZ2KzwZZQWa0l1GNrNmhtpjEMy82wxO0SojXSYnu6zriOMAt8d5hmTsVkay4mGpYEAS2hWCzNuGFn7EVXyDqBXCoVc18sv/VFceNKmRHhua3NhkW3fcPO+OYlI7qN1rDotYR6aV4j80E8Oib3rQndWKpJsrRhWqwgd1yZbAm5DjJFdNHWHIOGhQpX9zpYW7ZZpkNLh5S+Zr0Z3BKScotFiW6p1SbIsCwFsb/cIqz5zNPrWaj2TZePU3TLU69jYlg6S/Ya6bDv20xNv2Xu976keF7PvPoYi5AcH10ZwaAgKVgGnmFxbVwuIa9Oxew7PgBto4ayWEjDYrWEDE9r7ht2FlNwnJ7o1i4IO8OsL22C5vXomBBYtKK4Uy9E0LUW6HvZWARoNYy2hDC2XXUjbhZGoEU5LOn+UQqq4K1BP4Zl6aDepGbXs0VZLFITmz035KCWkLitmenr3G1goxOb7cJ0zs0mLqPoNm5bc7eQi4VhoZYs2d9xKCta4OMS3c4lDEuCeCc1+29cOvqFStD4APf0Y1PzhEy3hNiNqp9hmRGe2CzCsLgLFtmDljIvgAoWIy2h/nWAGh5dqpdaGJjtYR1erPUixbKEBIVRS6jR6liaKxVUc/bXQJSaeu1K35ZQySmm15bWahUsSyzGvYdhGSPRreA6wHXsU6D6MizCtuaAy5ShSwq9z/OcTUzFM615uUW3jGGBgr3fzNTjYVgKQyVI5fPmhqU2Pdld7LxAjVWdFdWrEUnBsmomNZf8J+EqiBnn2K2MtDB9ty1D4XFxuIT6hp3RhovCZEErqnNQRbeE0MZYbVXVbk2jLIisVoNOo2HIKVYymiTbwwoiy0ZrQMYpxNfoUKiDRNUptITCc3zPOx3IamotfAtXdhi5C5apJVXRLSsAXXsA3Z6FmTZqqRjSsGCQYbNtF93lXMAzry26tb+HlcV5X0Y4LtFt7AwLhUyyy41p0S2tF3xG0qywNap5y9nrZ6SAIaGw6ic2JwXLoM8RCjioNozaD9CBefkJovvn6z1fgyOOeUIx5bDwYWf4dak9JPi6RYLjsOWUT+eVaGBqCWWQYWG7hNatKeQAWz9iv4f7FdaBO1CMW9xVdCwhwXG+SaiSoIKxjIWbrL5GkmHRbQlxxiqrIbrtEdrr25rd+qY+fZ2x8Dj79VQr8/EKbr1Jt6RhiU10y/YKtkaMt4RcBgindWh+9lg6nbKKltXeFkoKloFvCfUHhiE2jBaUD6r9C7Wew850wUItoelqHbpxiW7dQVGS8fyOhiU4bArbLao6Fr4JFXOQ5oJLjU3IfYC5XEKI9bQO2Huq3MKggoVcVzIMS8TsGN0slipbQ2W08MtmxATAislvtfoYlnXldfxgajBmR3XwobclJHxzpmcG255oPQ5pCSHDEtWyJOYX9U55VyFl1CXExNX1ymK8+hX3PtVpQo0Vs/EFx9kFUZYNJzQuunUxLE5hG09u01h59Qtvk4LFIJZibQn1bgDEjhBbIoMDnGHxPOSGsljw9oemjny3CSnoGr1x+fasJeP5OzzhNJz1GS2MKhUsvM3SM613Xp9hSWftabW+TFvdTBtTlmHp0Vv4524484QUXyM7lErY9jPEsHRcM2LcDMtofpQzaypOITfFryy6FWAlJxnDgjOrSJMmlXIb0zyhxtJCvLH8tBcy8XX8tmb762dZplJctma0wSuJsyM1b/0xCAnDkiDYfWN4iJWXxZBlWDBo7gC7jfe3hMaNxvMXoRHIDKjCNyhKkhnqUjR/iIYFQQyL7Cblbg0aoXl9MlgI69k6oPdU+4CVZVioWAljWLimaYAYFhJTplJc7Gj/MqUlvHUmNTtbK7FswqFgAqwk7jE0aX0mgrlygiJ9mF9DlxQqWJr1YMeYMbgCI+O2NZPOqcAyleIKjivlsq5EZJOuwiL/1OEQHpcULAYRGM6kg4DNi9+sUSciAUzGJAEeFT1xxPPj1y4Be22ZfB8zYJZhkSu0/ALD/KDdEsob2oRCbty6DIsTdKbIsFA7KKQo1W4JcYala5xhwTXgnfulk8XCNSw9tuZxyZaQmO6LmKuotFvfYaeGGRZifErdevwaFusfsl93jS4wsTEs9tfPl9XYVuH5c3i5oUTkuEd5LCUFSwI3ve53kzFsa3ZaQnI3a2ohoUgul/G8/YbnCZVTbPMyaG/01bBI3hLpdh1ma3Y7heQLFhbMV3Ap/3U2oQCWzc2wqGpY+lhB2bRbKqixWEGXkQ8cJ5NeS6iM7SfDDIvfGlhbZAVLVb0l5GdrxkwP9/RfWaG9F+M8nr8p5FrzbVXzYl+v1YGDHtG5VOLPfIwtIURpAvB/3YZOzAyL/V6Uhh22tUOjUky7hEzo3RDWxOx+VvZwGICYFCyD7hIKsAXTQYX/pky+RaDg1mAOC8Xzl6glZJAe9mdYJEW3rtt1nC2hHiGdFsPiKgo8cGuZVILZ+lpCsgyLQAw7Te/G8DitlpDFsMwYvTn7rQES3uq0hNxsRnpoiBdzQoWrMMOSE2oJ0aTsERZ+FgfDQq2/MtSWiWGZgJqLGYst6Zatk/IQe467beVJ3sKiW92WULsJ0G37zB5LXEIJYrc19w+9o3402dRkWBayQfcJbg3msFBLqAjmGZZwDcu03O3aJbY02RJyx6A7olsdDYv/GnC39TCYTSVfoS+dWVbDEhLLbyorJhaGxSeWn0AaFh3RrdvWnEqnXfH8s8YKFodhCf++3r3bXnunbrJfgy/DgtOpO21tVpW3gePUsCCK41Bn4xpSkOJCadOgdZIbGubp2iaFt1XflpBm8djyt8UnGpYEAY4Lky0hul37tAMUrM2BGSzGW0IFKKXMMyzkCuBJtwqvm2j52FpCLNvEFt1KCi4lWwRon6WNSKUtRBtmUZlhiRZZ6mpY6D03mcMSyrBoZLH42ZoR1BoU0jIJRgGQbiSKYbljp71+z9rKLMx+tmZDAxB5GzhOlxAC50sxhgUvL14dkinwdVIowkRhwriOxa0h43o3XQ1Lk/aBlK0fZKB9QnRg5iAiKVgGfpZQ8CAxFcElFTfrQwuWWeGY+yCsHYnntkUMC58lpFGwCLeEJDdy962J96W1WkLhc2V0MnnclLQSwxIyqdnrEsKCRaVtFYdLyG/wYV9LSGFic5+I2Su8FdEnCDIslHYbpmFB5u2+PfbaO3srY1N6XljO0ZvozhMaykM5BlbVF6VxqLMxEj2XF8Nw2Nii8gUmCDi0FN8f3hIyFRzX9J8/t07TUTgISAoWgwgVtxmc0tsnuFRiWArB9DBmpyBFrIF1w0WXhiUG0a0iw9LFsDAMDRNgWMaLui0hwxqWgMJPxynkTGv2MiwztngvCg1xhgUzQxYU5glxhgWLaGRYDEyzJbuqX3jgmtIa5Xh++n7iRHU3pCY2CzIsFB4X1hJ6YN+89X0fL+fg6MmAr2co7RYLU+eSMrRsGpa49Cs9l5tCQWsgalhxS89f2pSGpeUv0qd9Yt9cUrAkwIKl7thZjSFkCJpKeBzPYPET3aLzhv4d3Xj+kTwUuWMgBtGtYg4LUbzewDA/0I1KL4eFWgEGovl9itaeTB6dlpBXw4KiPRFmSeBwxfYIFUQkAFUW3XZaAHV90aPTFiwFtoSmalPQltR1LPmIbhFSWiZhhiW6YLlzl/3vnXnUeHDbxGB4nNMSWgbRLdOwxGVpRnQpZLJU4hcYU2m3dLFBYKYO3ysWFqDb1tATNf2fyY1jThQG5nGtRiQFiyG02h3rJoMYiqMl5KthKUofVMTG+GpYDM8TInq4Y/AG5Ds7RIIVIBGdNzDMD3SjWmguWEMQVUS3vCU0G09wnC7D0tcSwqKIF63TRlpCPcJbhbRbnsOSYhcBAzqWsPDAyeKkJeRER4jsdN66j+gW4QgqZYrAkhjDUgluCd3J9CtnH+WjXzHNsAw5z/xyiG6XhWGhdVIsKrsGRRx6WExSURtXC3ndcMFKIG91unBIMXV6pZEULIbbQcaTbkNusNTWER2AiD1TayBhUEvIYMGCt78yE93WU4XlyWERuH279StRQj2MaSeIblJNV+FqMyySoWEKN+4NGr1pX90V17HMSLSEwlsA3Nqsw7CQkNOAjoXGM6CY0otsOmsVLSpOoSANi1TarU+seriGJYRh4YJbH/2Kossu3CVEtub4W0L1VPwMC18nRfOiW6+rNJXLQbpc1g+abPnLCDArh7Oxc0nBckSDNn6Mpc97A9l0ENIOkE27xeAuZAKxyiYhZFxZLFY8f95mJardQrw5LNbE5qJQoUXahSj9Ch1cI/kR6+O5hljB4p2M2xMa5ppfY2ID6mPaTDAsiLLEASbIBqAgUzWLhTMsuZFlYVh0hLdBLqF4RLfhLSHMZ3rk4CJvCQViZKP988I+MOYSipthcbmE4hp82LNOiuZFt0s+ayUtw8RJzp9DbGR7xd45l/V5FSEpWAyB4q8xlt+oxS4sNIzpULDNI+K+cFJuC1ZB4QuyORqwNk/k7Aey0vEJrNJkWPpoYEFmiE9qjpgjRBjLy+lYvIVrb2iY4iYUcePmTJsGw9JzwMo4hQSC43oHIGowLMVRuRReRYZFJ4uFB8d5CxYZt5ik6Bb/TbcegnD3rjmrQ7plvNQ/hsON0c32z/N7QAfY9iPRbSMdH+thoTTONSyldHwFiztk0rjo1mf2nJGJzc1gGYFqQvqgIClYDIHTewWD7aCI0DDKYcENSyRuOVK/YjiLZTxnv6bFTj5eDYvE63YCw8ScS3yTEmSceBYPK1yt0LCRET2aN8Qp5k4tlk27xRZhndkqe1pCMlksPDguvAXgDEBU17CUGSW/HAyLSsGC33u6NRdcww8RxLSZFN2iVi6XSQWyLHfuCslfcWN0i/3z3C7QAQZZUht4tmXukhIYHEcMS8rwnhvgKFR1DQZhyZXXRDATNFkNLHg3MeHtvqRgObJBG5XR0LiIzcsdGiaiYzkQpV8xOL0VMZKxH/a51nIwLGLx/I6GRexWNsYYJ9FNym9id5ryFVQLlhCnmDftViZ2m9oX3terxLBEHK464XHc1iybEaPBsKiEx1Hx598SstdAR0R8LciwYEFMbSG/7yvpV0LbQQYZFnw9Q6wlNNOMuWDJZGGJ7QFFDEiLAT2OQjfDYmCMAWKpQZO93XOnDLSEWiEyAipYEg3LkQ33/BijiBiE5mSx1PVC42IsWGZbZoo4tJg2O80AhoWcDtPKdlY/yDoDnMLVWQfZCft72jooH/UuEhyHGx7mbMhO73bnQLiH9ckxLBUhkaVqSwgHzTkFy5qBZlh6CkBv0q1MxoYgw4KggsUvvfQuZmk+K7Jg2eIULJoZN6RhmW4avrj5oM7akEUDuTyhjkKfHBaVAEQR/ZhUIrKCjIA0LPvmEw3LEY1q3ZnQaxSRKafiPcnQDJa+g1+/JcRvW42s0ZRbX6GdcEtIXHSLkO1bO8p/5/+c3bTJ+rm1by8oQWB6r0qIoKNfSUParWmSYljC2R+vrfmQpEuIRNaI8tB68dclHAhmTnRL+hXUL3knoXNb8/x89GEnU7CwAYjTnpYQPuu7Z5esoNMzwizNboYFxyzoxPN3u1Ds2s/owXr8BUuNMQjFdjzTh72OQtoL8NJEbUodLPFRLj4Mi1YMQi1w/Wxc5eFxScFiCE4rwHRLKJwe5vOEBASXoSm3MWhYaJbQobqZZUY3bd84bkFmqMOitqNC4wiyzgAqXN2bUI4VLM29ii4MgQNMJUTQ1yEky7AIt4Qonl+OiqaDIZ1KQ4EVEUYYllqES0ihJRT4/XRpE6Ddhk6FsVKaLaFehqW3YLlrp33gnbBuGIbZkNRA4HtHRapOW6hVhzTYRduBWjy6EjdqjNUr8Nk5ZuG93OA4EBqyaELH4rdejCRjt4L3CwqPU3EUDgKUTpIrr7wStm3bBsViEc4//3y4+eabQ//8N77xDTjllFOsP3/GGWfAD37wg57ff8Mb3mALFF0/XvjCF8KqjOU32RLqGRNe1A4NW27RLd22DtUzRhkWLFbwANNiWAqFWFpCfhqW3CbbNtrcp1mwhORykPBWxinkxwZJMyyCLSFiWGTnCXGHULYMqSHWEqrKR+bLzpNyt4REX69jae7fVq1bes5mQzpR+gQphsW/1eYIbiPaQX1tod2gDCq0cK9ZikdX4kYtb39/SiT8NgzHUWj/OxbLYlB4yzUsPQyLCdHtUmTBgpb3hVrz8C9Yvva1r8Hb3/52eN/73ge33XYbnHXWWfCCF7wADhw44Pvnb7jhBrjkkkvgTW96E9x+++3w8pe/3Ppxzz339Pw5LFD27t3Lf/z7v/87rCb43ay14doAAufISISGUVVNrEycOSyIfNd+TQdq6fjmCPml3YpMXy3F1BLy07BstAuW1t69sUTzu99TmbRbd0tI5Xsp0xIiDUuz3RVytPVlsGCxJhNoF4Go1iAVLFgkLzbtLBPVDBaElWQ6bq+l1sysdgvQGx7n1bBQJL94wbJZv2BhxWu9m4VDVb3hqSKos7ZwwcCohtBYftflxqTwlk91dzMsbI20o9aI0AWnf23j5WSkmF211mbpk+SKK66Ayy67DC699FI47bTT4KqrroJyuQxXX32175//5Cc/aRUj73jHO+DUU0+FD37wg3DOOefApz/96Z4/VygUYOPGjfzHBBMqHtG25oAx4SqtAExgpVj0UIaF5ooYYFiyrMA4sJS2/v1Y5gh5C60IVkDW1izbEnL60tn+lpAywxLdIlBJu+XR4N4iW/B7KZPDgoc4tSYwwFCaYcH/u0yrSpNhwTU2woLqRNtCfkFgbvDCdf8+46JbN8OCjBBPuI3SrxDGXMJbVbD3agkKSvZ1WSxl7PVUrGkOCxRkWBAms1hqPmxsdsMG6+eW6l4hcInYtIqdQlIFS6PRgFtvvRWe97znOV8gnbZ+feONN/r+Hfy8+88jkJHx/vnrr78e1q9fDyeffDJcfvnlMDUVTPvW63WYn5/v+XFY2prd+oCAMDon5TT8oMINBJntLCbQsk3OF3RYYXFAC18RaVawVKGoZGcNagn5RnFLBscFaRdiaQnRQXXwIHRZroNJ4bWqhoUX2TnPmqXCAEWYLPdGZ1qzjlOIZ7Bky8573FgEaOmtJxHx9doyawtVxZxCdSa69WsJ9RSuu0OKApxIzRk1cQ2LO4fliamqZW/H4MJTNjrjJZaLYalaBYv+8x6Fetp+xopVMzZjKYbFoIbFXeDmNtvvQ3P/fvUBiK1wRpZPbT7cGZZDhw5Bu92GDawKJOCv9wVUhPj5qD+PDMyXvvQluO666+Af/uEf4Oc//zm86EUvsv4tP3zoQx+CsbEx/mPr1q2w0qDAMKO2Zr7wwsSWTisgrNfO20EjhV5HiBeFEQAKYtJkWVKs4FmCPJ9hZEJ0G8qwRLaElkLdIV7IWhn5bB43zbtmDQDqFzodaAW0TnXXARdfy7iEaMP0MizIKpFGKIplEWwJ9Q5AlChYmq6WELJ/9Lo0WZYohkVFeBsmuu0VX4e0Bl3CchGGhYpAd0uI9CunbR6FvGcIY3R4nL6GZalbkGLRVFFj21gRNU2SU7VF4JeKbbJg8c4SQmTXrgXIZgFaLeuCE4fmbSN3Cq0+a/NAuIRe/epXw0tf+lJLkIv6lu9973vw29/+1mJd/PCud70L5ubm+I+dO3fCSqNab5u3NQuILXloWLvjm8UglcGCQCbHhPAWD3d248IN7KCBDSww5Rbhfs0hhUWXaVhkGRa0MrpdStGtQYe1wLTbHCvaldpCPFehKCC6FU+7dQ5YzzaAowREBuJZbMCS8LA7Co+TGYDIGRYsiNyvS9Pa3BFhWCSzWMI0LIjcZoGCxc1qRgw/RFD+jpu1upM5hM4W1a+YCo9j4ldkWPD1dHBwWYyoMUdSEYuVxf3Gvz5dbtKuyw1vERvQsLinNRNSmYyzV+xR1Lw1w1uKJLw97BmWtWvXQiaTgf37excH/hp1J37Az8v8ecRxxx1n/VuPPPKI7++j3mV0dLTnx2FpaxboZReyGS68C7M2UxJuqKWZMMwYsXnFB8bjcKpBHg4ZYFh8JzUTSJCJwXL1+WiGRVDDgjf7XDonfKuq+GQruNtC0tZmzJjAKdTWFxEoXCXSbv30NlJOoR5RuDgbIGNt5qFx2BJyvy4NhgULuq4AwyJbsERqWDjDskesOGUzqMQYlkYfw3KmqH4FMXqUAQ0LtYSK0OqguDpeF8p8wxbbDmFhpMMMRVxu3AzLRNHcxOagyd4OE6f4XrQEC5bDXcOSz+fh3HPPtVo3hE6nY/36ggsu8P07+Hn3n0f85Cc/CfzziF27dlkalk3sjTtibc0RU3pl9AtOBosAszDOWmxzO0AZrsNsyVBP23dSMwFFnyQYDtm8ohJOvXAHRonoWHhLyLMJZTcrhscJtgjcabeiOha/aHAOEYGru2ARYAOceULyLSGLYXG/Lg2GxdIRITtkrYOSsZYQBccFMiybbBajFXZzlhDcIsaZhqXSaEO91bbE7ffslnQIIUbZXotFgKqIlTEsTTb4ME4dCxadB6p2e3U9FvVzO+NjWFyXmzhaQt4WIt8r9sbEsIyu3gGI0i0htDR//vOfhy9+8Ytw//33WwLZSqViuYYQr3vd66yWDeGtb30rXHvttfCxj30MHnjgAXj/+98Pt9xyC7zlLW+xfn9xcdFyEP3mN7+Bxx9/3CpuXvayl8EJJ5xgiXNXC/xSC7UhqA+gNk/YPCGhDBbCGCtYZndqv/YOZKAJGSMaFi66DWqN8EJrp3LCqa5TKGgTym1UDI9zh2KFtITcCcaiTiG/MQJKDAu1ayKwRld0a4phYWsgKo9HVnTrUPwBols6iA4ehE6joR0ahxgtZvnkdWwJP7R/wZpphNbVY9dEt+k4sKVHBb8qy8JeeztjH5Rx6lhm6jN8TMf6VltPLBzFsLjmjsmK8GWnNbsLW+2WUDZouru9T+xdhWm30gXLxRdfDB/96Efhve99L5x99tlwxx13WAUJCWt37Nhh5agQLrzwQvjqV78Kn/vc56zMlm9+85vw7W9/G04//XTr97HFdNddd1kalpNOOsnKa0EW55e//KXV+lktqNT7tQvaEHCH9FpaQxgW9nsU4R4KgYNfdPNqWWxIyojNMZRhQYwdbf88G8wMdVnSrSjD4i5Y5BiW3nWgHB7nngsS4BTrF96KMiwhQnERhoVPahZjAxzRraKt2RDDQmJKyGRsMbQxhiW8JZSZnLRm0iBanja57DPfOwDR0bGQfgXbQaHi+tDwOMWpzey96rL3SkZcLQtiVybTBbBWVQwtoTCGBQumODQswuJsDZcQ2ZrxOTQRN7GcUDpdkR0hhsQLP6Hsq171KuuHH0qlEvzoRz+C1Y7Q26oqBG9bIvOEHA2LSEso+uAXZlhYlW+kYAnTsLhfdxjDUqdbk3jBIkMD8zAob0tINTxOIDTOK7wVpXqnmUibgqR6ICJu5etT7CY/yeL5ZUS3pGGxXELu16XDsLjsqnjgmypYFlggXlC0Af5beBg1Hn/cuj3n/dyNkgwLWZux/YLWZid/RaId5BbeHrhXnWFhBWyKCbDjZFj2V+yCb0OO6RdjaAnFzbAQG+stcHNbiGHZEwszPzmUtyzvaNTAS+6WcbELxyBgIFxChwPitTVHaViiLa1yLaGjjbWE6LZloiUUybAQMyTEsJTiKVgCaV7FW5OAU8y7DkS/1w/ts0WLJ6wf7v9NkbRbwdC4PpeQgq2Zt4Q4wzJjNBAsrCW00FjoGcIYOVw0RNjuOIX2GNGw9GSxVJrykfwmw+OY6DZdGIpdw7K/ygoWJoyOoyXky7CwthkW0iJrQuWSq82wNIOTbqlwJjZ2tVmbk4LFsK05FoYl4rBywuP8DyoU482w27SQS4gO/oW96gFd7LWn8+YYlkgNi4D2xtGwFGJtCQ15W0KMYWnPzPDXIASJA0xmYjO6iR49aEfOn7TBTnSV1rDwllBZqiU0IzFPqMfWbEzDQnbV8DWASbc07G6qFj2/SETYzp1Ce8wVLCS23jO7ZGlY1BkWzXlCbD1ki3YBHGfaLRUs64eZHTsOlxAJ9F0My3BuGLKprBHhbVDSdJbp3Trz89BeFBsL0esqbEauISeLZXU5hZKCxQBw8yWX0FAcGpaIzYsOqiDRLc2XwRCpsVJwz55jaB2rzrvqmxcrWDLstoUFk26/lNoDvrOEJEW3cTEsFTZTyrsJpcfG+I1eKnZbYq4MH4QpwLBsP1SxrKcYl3/UREnRJSSecuu24FqW16WWmujWhIaF21XDv6d4E13HJkQfrEa3hRwWM4RhIadQ0O1ZoSVE39dfPHwQ0OGL/z5ZV6Uwqnn4s9eeL1FLqBF/S2hsm/2JyoHoVGZJOO3jUs+aGC2MareFcC/EuVp+rHxmeMjaL9RayEvOx2EFyyrNYkkKFgPAXmCbhST1zWXRgeBh5T6o/MKa3FR1WM+eA//M2FF6vWEqWPJl7mLQ3cCIYeF6Bi/Gj7F/xhCpgJHzIpHsOgVLEM1L+gVp4a2ECFN0TAPigX22dfWkDcP+a0LIJUShcWKHK2YGjbCC/pCg8LZPdGuSYRFYA6JZLJbNlqdJB39dvgaCHCBKDItdsNy0fVqdXTERHseCIgulEWlxtarodsP4NoeBNtwWChLoTxQmtIW3tE8EnRnaLeSIPWO1WpuTgsUAqA1gPIdFcPOi0DC8uU67AqT6qOqQjdS4tZm99lS+zLULuhRxhd3oAwsWFGSSAHSu3+nQ7XSgS7cmCYZFtCXkvjX5tQaVwuPc86Qi4DBt0Wm31Do4OWjWjJRLSIINGJazNveJbk0yLAIFi6jwFi3FeHGJmoYemXarxLDkeJtPWb9iIjyOxigMsYJlGTQs68sbHO2N4bYQZ1g8EQiyA1HDBh/iXQ4FsNKFrYjmLeRySgzLarM2JwWLAWBoE7Vcsj6LTxkR4ilCLpOGtewgoFuesuDWlFPIVWxRQaUbz7+vYh/0G3CT8gM+oCGhd6L5G6oMCwlug25NWWZtlgqPE5gjRKCDEg/OqLTbB5ng9uQNPoLbHiZjhoes6baEeuP565oalvARDKYZlqiWEKVMY3sGmaQguG/OvkWlBsNCUGdYWHgcFuZ1e31IgRWw5eHR2DUsnGEZ2uCag6Rox5ZlWFja7VxNvSXknjvlx3AqMywtUaMGY1iSguXIQyyhcZKD5bil1Sc0jA8+FBHcmspicd0U17KEU12n0J6KffPbTEI7yULLLXaNw9ZMTBu2wHxvTSrhcYJFKwIPStG02wf2CTIs3Y59gIUxLIItoR5rsyDDEugSwrEPigeGFMPCNCxRLSH3cNEwkOgWD8P27KxyurUb3unrZ8hE8nsHnzL2QGksBytgh0fsNTVfa3HWxyQWG4uw2Fx0Li/EBqvmx0gyLCayWKJGuUS6yYIgyNBRFkuiYTkCwQfemWwHSWZwOFOba3oZLBIhbKLFFhUsOjcu3KSoJRNasIS0svj8GMzfEEhmJawpreH21rABiGRtLwfemig8Lh6Gxd32C+tNL9ZbsGvG/n+cvNHHIYTArBtiM3bfaqx9QUygaLugT3SLr4vafoo6ljg0LHRTjXrGkNnL4ETeIEGlxCWFMMFaQojj1g6JCesjdSwKhz8rYIeGRiHLdGsyqcay7Ao6doZwLcTUEgpiWGRcg9FzhIJSkRWzWJqCYaPkEpqvCTv2BgFJwWIAQdkby2VrjponRKyLUMqtcYbFaQkdWmhosyu4WViblMLrFpnQ64fR/KhlcUXsXtgtMKk5YPgdY1iklP+SLQJi0cKcQqRfwfeFHCa+OOMP7Z9v+TfjjhaRg6zZbkKLDX7kLSEDWSwOw1IwV7BIDBcNpfs1cli09CsmhLcUZVAY4u9zHG0hnsFCreGYWkJBDIsJ0S1pWIJyu4iJC507paF5o/MCGTCKvFgNSAoWA6CbtVFLs4StOSqDQ2rwoZepwFtLkIYhDC5BJt2qdTQsexZZO2gohF2JagmxG5NMO8j686kUHDViCxJ3Le6KLliCaN5NGqJbgaJVNO2W9CunBLErhPPeyP7CD/0PMIWWkDMAsS7MrvQJrTXTbv0CwXRFt3QpEHnGeMGye4+xpFvCWartIBPhccwlhHOJ6H2OI57fEdyutz9BjsZlcgmZEN1Gt4QYw7J/P3TbjjbOFCOLekval/etIh1LUrAYQNDAO21IZHCEhcfJ3P44RjYBpDJ2CNGi5PybANHtoQX9gmXLMNtQFVJ6VSzNBCpYdi7sVE47JpdQZ3FRPBBqgc2cGWKJngbSbh3BbUTBsv5UgGOebutFbv2ikcNVZgAi6VcwvC2bdm3swxu03CwUCCbCsJCGZbo2De1O8MHh6MSKy86wYAuIxgbpMywa4XE9ujXGsBhIuA4V3CJ4BIO5ggXbJEGDUrmmrTZrQHTrfwRnsW2YzQK029awzDg0bxtWobU5KVgMwLlZxyW6lQkNq/UdojTjRGQz5chknduWio7FtXmtM6Bh4QxLmH6lJ6V3D0C7l+rkG5CEpZmwdWRrZMGyFLEO0kMKgVCkJSDGy8AmxAuWKIbFzbLc9sW+72fcLaE+hxBhgoWFzTwOKujQLCEBhgXp/3QqDZ1uxypagsB1YgJt11Brs8L3FIcc/vHTjoHnnrIeztiiybCohsdhwmq74TAsfAxDPb7QOG9LCDUlNTtfSBvNJmeWvQwLMTvE9Oi6hPyQymQgxwYKS1mbm+IaKMpiWU3W5qRgMYAqSzcNagUoQ2GOjNfWTL/GB4NCu4ShM1PIVWytNWBrFnIIIYbWY7yu7W7x3MBNMCy7FnZpaZl4FotoeBwdHFQ8asbz483xwf0SBcupvw+AM3VwTAO2hrRbQqRtEGdYuOC2r2DZDlqR6wITuzPpDEwWJyN1LDJtVx7P7+cAUWBYEB942enwr294in6sgqqGhQotRK7stIQWl6ElVBgGYDN+TLWF3I5C735BewGuB1qjsgia6u7PxO2Jx6gxtvqcQknBYgAUyz8IDAsWBZS6620HCaXcuhGSaaJia57ViOffvbhbTMOC7h+iiD3MEB96p1CwiDAsIuvACY8TuDWhep82YPo/RYBYtCDRLa4PZDdwKZy4XqBgQVfOOX9if3zLv2pNa0asYbZmnCzsl8rsBjmy+hiWyWPtn6e3azEsXqpfVceCzxsV40Ials1bggWVigWLMfDwOMmDn9ZCKm2tGZnCVLUltHHIfpbiaAvxggX3k1yuT8NCOpaw/UCEYfFOanYju1lFpF8VZlg2rcIslqRgMYCoVoAyJCytSMFiHxs3TzcNSweXVDvIRNqtK7Z9vJTTjuffu7hXjGEJcQrJ2Fm9OGr4KF44BWkZnDye4FuTEx4nwLBUp9gaSAGMCPy/PUybn13xoX22dmbbmiFxV9u5l9qv4bHrAaYeVZ7W7G4J4TqNCrfrszQTJo5dNoZFxCmEzxv+f3CJk3ZDpCWE2oROo6ElsjYOYlhQn0EiWhHQn8XiNZWCtTxvpx4/w9LjFNKYMB/Axvpd9I4eOVqvYOFsbDpy7pSUtbkpPsojYViOUEQpvpUhEcuOVDAxGe62kFLKrQlrs+umiD127hRSEOEh7UoWQqGCJaDQcobeyX8v8DaHU1qbnWbgTVtEy8TD40T60vR9R5FpNvogRJDAGdNukdEKmyEkjIljAE58vv3xLVf7OMHED1d0J4wUs0IOEqLbS96vTy0hDI7DxNu4GZaIAYj0vOHzJ9KSyUxM8Gnhrf37lZ2BsaA4CpAfkW8LefYqYlhMt4Qa7QbXEvUkXht2CnG9W8DlhhjXHQtqOVXOzDGBlpCMhqUlztA5E5uTguXItDWbZFhQ4MgyKESq5SDBJTEsImJAswxLb2y7TngcCW5H8iPWj0jQEERPS8hhWOQPA3SpULEUdKsSKlh4eNw+4/oVSrudYGm3fm2hB6MSbqPEt3d8xSlG+Xss3hJyr4WoeP5AhgUZneGNym0hVYYlqFCVvRT0DML0HkYKolvj4DoWicPfo2dyNCz1WNpB6Bwjt44Fw+FxUXq3o0dthmXHvGLB0ohuCeW2MIYlptymTQnDcmQiluA499RN0dAwLrg0xbC4Mk1k0xA9Dw6P51cpWCqCluYI7Y2jYVEo3gSEt9zWHNYSkgmPowOD6G5BhGWxUGhcZAaLFyf+rl3AIqNx77eVpjXLOoX6JjUbEt4G2VWjNCxBLSFHcCu+rgKj11daw6KaxeIpXvnA00rDaJKqux3U06oh7Y2hllCU3k27JRThElKeJ9SUMGqwggVbs+4BvoOMpGAxgKjAMC39igTD4mSxOH+XPpaaI+SlWZFmRD2FDDw3RT4AUaElJBwaF9kSUmdYRIS3sgxL5EZOm6+gpZlA77W3YEGR60P7F8UdQm6kMwDnvt4R36KOh2us1AqWyJYQY1h8p3NrCG/57dmQhsV5xsQvBdwp5NYn4Pe0XT8sGBa6oGCSKo6CiC2DJaaWUJdb32NqCQnsFXS56czPi+c2NcULXnSN0r+/WliWpGAZVNGt4JhwX8GlK4uF+utKDAs6RIh6l81iCWBYdFpCQvoVNzOEUd2ulN4u07CIHlSyBYvYJmR/P7v1uv/wO82WUG8mT+/3esd01brZoY7kmEmFA/HJrwPAALddvwXYebPzecnDVXSeUGBLSFN46+gTSkYLFprjJAISVPYwbQqsaizg4XEqGpYyZ5upRW7SKdSXweLXEjLA6HCGJSCziVpCOEG+5r5cGmRYMsMKuU0t8eGZyFCtNh1LUrAYQCWOac0K1DA/qEy1hFSFt+4QKa5hUbc5ckuzaMESkNJLDIvoQRXkFApuCUVvQul8Pnz4nRs0G0XQ0uxtDXoHYVL+yonrh9XyOkY22LksiBs/rXy4Oi2hunpLiBiWmSdAXZ9QkBbd+rFiKknSznC7gIJFkFWNBSrhca5YfkIcOpa+OUIEy0WHNsk6QCV87pMUw8LE0X6Bgjh80b0/KWlYIs4M6bZQk1xCYs/kxrHVlXabFCwGIBICJA0JtTeBNkyaa4JUbIW9NqnBh7rCW/dEY/b6nZZQLX6GBVN66Zboet1cbKmpYdm5uFNrHQiHx3ENy1FGGBaphNso8e0D37d/xmJCMt+HslhQ3yCUwxLGsEwvH8PS6DRgvjFvZFaXb9qt29IsMU18EBkWRBxZLLxg8baE0EXHRzbsip1hQXaCt4UUhLeU2RQ1zkXaKdSUOzc4w5IULEcOYhXdShQsjtiy3lM1Y69SeTCjCsPCb4opflN04vkb8Ytue9pCO4WtilGgDQrHyvsdXNVmK3Rac/8QxL3hLBWmyyq1hPw1LMJDD8Ow7RkAa0/C8k9Za0EH2XRUSygo6dYtusWiriV+g0eGRJZhKWQK1sTuoLYQtWBldGLumzNnbfgzv4Lsiuo8IZ/U4zUxZLH4ZrDE4BSKYlh6nEIKOhaa1hzFyjsFi2DxKHnR5VksSUvoyCtYhiIOKilIqL0JdMNDnUir3dET3ApMPxa6bbHbN8Xzy2pY8JZNuQvCDIu70Jp9wkdsqdYSwtYExbT7tYW46Dbi1iTkFMJiBccLpHP2uAEJrPMUrv0ZLBoFC76fxLIoOIRkDrLApFsaBplHSr4r1RZC7RBBpjUYZG3G5GYqwmUYFq5lWlpytEz8sFlBwa27JYTTsN1tqjD4WNxFtUpKoltvS6gnPM4kwxL8nuo4hUSSbiPnThm46HJrc1KwHDkgO2s5l11RhgWthJgoixc23ES1BLc684R8AsWIYcEwM3QOyCbcYr+YbrmqrSxdhiVKeFutS7aE9u4TaAdtlm4PuCc20+291mzD41P2+3KKbAaLF2dd4hTSkhksUrbmMNEtFk4KwtvOknMAy7QGg6zN5HrLZVIwWRYL97P+7UKhX8s0CJZmRHHMeV9F20I+FncnPM4Mw4IJ0xTe51uwcKeQgYKFHIUh1netlpCA3q1H6yQ6T6gpd9GlsyFpCR0hwAOBK75NtoQkhlgRMFGWCgNkV7QFt6rzhHwmho6VcpCleH4JilhacBvSynJszerfD65j8StYeA6LYEsoTMPCBbdyluagtNtHDy5a8fH4PsiIQ31RGgc445XKhyvdvLFgCZsnFJh0S5jcJq1j4QxLLgeprPgFYy0OgMSCpdpbsHAWc6RoPX8y6BNUSiRbxwosBmWtzTyavyytVRLFVG0K2t22NT17TWlN/x8wOE+IHIVhDIvIfLEg4AVCpCVE9nffuVN+kGwrkoYlEd0eIag1O9xFF5utWVG/QC0BrZYQHZg1idHtPhsvbuZ045LJYpEW3Ia0shzRrT7D4tcScuK2DbSEeMEip1/xpt2SAJsLbjeMyA/B9MOFf2lb3k9+kRLDgsUr1iphN7tQhgXBGZbHpRmWMG2CzABEnWeMFyy79wQW+qsmPI7PlRqKjWGhdhC25zB5OtaWkADDQhoW1Ng1MZk8hksuZ1j274duWyDcTbKtSC4hFOi7h+YOKpKCxZClWYTek4IiPczD4xbq/LCSyYfoA45uL03ICW8DXvs6BR2LkuDW2xJiFSWfIaNoaw4rWFDL0Gx3xYR01Jc+cCB4E1K0NAdZ3MnSrOUQcmPdyQB/9QDAs94p/VfRUn3MGntDfeTAYjTDElS0K6TdEsMiO08qSMNyQOMZc+j+AWsJqQhveRu47DOCoRFvBksM4XEiDAsWscVMETrdDt+nRIDMJxUHURqWLLYNkQlst61hmSaHH9J7hDICfD0qGVnLjaRg0QSfupnLSFPC4V942ikYVPQL8zWew6HVElKxNgfMQ+HhcQuN+FJuQ1J6ZWfIhGWxeGlg6kmL3Jqy69YBZDIArRa0Dk0ZjeX3FodE9RqxNHuhwdScsN5e1w+HFSzEsATdFhXSbjnDIlm0BmlYVDJYAgWVgzBHiMBbQqIMS39LSCcsUiqDxfvMo2AdXXYaEEnFtqzNo/I6llrD0fBFXW5SmQzkNmwQszZ35JOSsVihyIvVILxNChZNiMSxK2HvXfbP658k9dfopodUtcqMEyNOoYAZMyrzhJRbQpjSiwFyLqeQSdHtvuq+HhqYCldsdeQjQtlwE8qut50/rX1742VYWPvNiKXZIE5cPxLKsOCtNTSHxdsSciUaizAsslk8nGGpBrWE5NcUj+cnQeVAMSyS4XF+tmbWEpqpNi3XYmwZLAR006GrDt11FAmgy7BErBNyCslYm6kdhHtFTiDA0dE67Qn/g3VXy14hcHRvUrAc/hAVWkpjz+32z5ufLPXX3KpvI6LbnkwT0YLFXzyoMk9IWXTrYYas/A1+u1b/fuDB5UcDu9eBiEbEcQrFVbA4abc43Iw2oxN1LM0GQQzLowEFizvuPJBhwfcXtQx4qxQ8oIhhkW0Lkuh2aqmXEdN5xng8P92c3cFxq60l5MMOTZTznISbrjbizWBBoJtudJORtpDo3DEVazPfKwQlBNnNglks++91nguJuIHVJLxNChZTGSwmU24X9gEs7AFIpQE2niH1V0n8hzfXOrMPU6EwMC0hQYYFDy10BihpWDxOoW7D2TB1NCxYjPg5hWSZNic8bp//bZVagootIXeIIE1o3jxWtFxCgwCnJWS/tqB2UApSVoEYmGhMa1NQx8KzeBRFtwvNhZ5iyokOUG8JoTahg+tzoBgWSdGtTzQ/thvI6m1CxxKawUKg9aApvKX2cRTDotISknWV+s6dCmPlN54JMiDh7WqwNicFyyCm3O65w/557ckKGhZ78e2etTe/8XIuUthlPO02YOMlO6sow7K3Yj+gQ7khuQwWn1YWsSs60fwEKljcwlvZid3cKeTXEqLbYX7EzsRQgHsQ5gNx6Fc0cfy6Yev2je0CPxfJEltDyK6EMlZceCvmFOpwMaVcUYA5QFQ4uYW3+zQYlszEBKRY4dTav3/AChbGaKKNm4ScCpcUxynUiF90a9Ap1OGtQ/MMS022YPGbO+WHvXfaP286C2TA5wklLaEjKDTOaMGi1g7ymxmk5RAyzLDIuoRIv7JpaJOaFdf1ukm/Ipu/IZq/IEvzhobHuS3NisJW7habr8ODlHA7QAULbtZHTZQChbeRlmZF4W2XU/1yRSuuP+/UZjx4sN2m+pzh1+yZFTNItmZ0BlJrCtleBQ2Lm+nbPct+XxHY0hVjWBTGCvj9e7x9LKZh2bW4C1qdltHQOOm0272KBUuiYTlyEMvgQ42CBfvGmLpJ0Mpg8TIVlQNiUd1BtmbJeUKkX1FqB3nmCTnuEP0Czm9qs7MOJDchv/A4Tf2Ku3BFNmvQBLeEE9YNBwpvIx1CBMm0Wx65rtAWdE9tdreDirk0jJbUnn/nMNozWAxLT3hcRMGCsQH8ktKbfHziBvs9fmh/sBtMBDi7q9ZmIX1hoyoMhceJDshEAXA+nbeKlX2ViGGmXmepcEtIoGBpVAEOPajHsCQtocMfNA3ZGMOCD79GwYLWarrVINwfa922rLktglRrgOiWNCx4K6232sItISXBbU9LaKfLHaL//eAMi2tqs2xrMDQ8TtPS7E27vXPXnPXxyRs0I/kNgwTAvgVL2OBDDYal/sgjPQyXDLwMC885Gi0qh/FlOd2/Z7BszTLhcVah1fVlWDCoEEE6KlVQMTBeGLeGUQaCJpvLDGv1oD03Z7fo8P3ZED7HC1N3qUUs6hTiGpac3F7RmZ+H9uJisOAW3VFYzI3IrW33xGY+iHNAkRQsmlgy3RLCzQGZjFQGYOPpSl/CzapoW5oRuBnz9orAQ8lvir23LdTTEPtDt9NYGRa6bdXnoDN7UEm7EBUeRw84jYuXFd22Dh3qEQSbYlgw7ZZm9uDsJhRAHr9efu7PSjMsgaFxigxL9be/tX4uP+U8/YKF9CsalwK6PVuF6yAxLDJOISq0fIotakOSjkoVQu0gQy2h6q23WRfH/DHHQI7FDwi1hXzSr0M1LIIFS2Z4CNJjtpYtUHi79w6HXZEsnolhwUvXQl0vvyZuJAXLoIluiV1Zf5ryxuXeQLUtzSrC24CNF2+hx60Nd4cEaViUgI6Fsj1zpDu1U8kd4gdkfNC9gjkh5GKiwnVIsDWYmZyEVD5vbYzNAweNFyxePdOxa4esImaQcMKG4LUQOUeIMHGM/fPSDMASm3ocAGy/NXfutOyvpXPOkX693nh+E6MvyAFia1gGZJYQQbQlxO3YRYB07xqjyeDYmowadqmVwUKgZwbDIkUnTXtQveUW6+fyU58i9Oc5wyLoFFI5Mxyt0x6j+hUEmjLIPTjo4XFJwTJotmbeDjpb+UtsMM2wyApvfQahEU7dZG9g9+8VL1iUGRbX6+5M7TbGsOQzedg4tLHnVlWpy21CWLxlWVui5Q2EMtAS8oaZDZJDyGttxoN/vtZUE90WRgCG1gk5haq/tQ+i4mmnQWZYzn3nF89vIkm6R1A5cAyLYHicTyw/YbiQ5eJqnbZQZAYLoTjuMLuKOhaHhRMrWGimUFwtISEdy171gsX6a2RtTgqWwxvGg+MMFCzug0olgTMuhgVx6iZbR3Hf3vBBio12gx8MyhoWl46lO7vXmIYF4c1iER186OsUcgtvscUUA8NyyoAExrkxWnQmR3vbQsIaFom2ED+IzpNvB7lFtzSxWSeW3+8g6vKDf7W1hPozWEzrWIRbQlb7ml63vLW5vViB2n33yRUsktbmmhbDsrf/N1t1gAP3axUs7sDRQUZSsAxSND8eVtSLVBDc+h1UxlpCMgxLiHiQCpb7IwoWEtyihgGFdroFS3t6n1AQlOoQRKdwFWfafMPjsLVB3z9NhsV9kA6SpdmPZekrWERdQhLCW16wCFL9QS0hXJuoXXJGX6g/Y8SyoY22XRkgW7NMeFxEoXWyAR2LUAaLAafQ0u23WYMGc1u28CJBpmDBBOxYWkJbPIMy3cBipdO02SUyGkiCC28ThuXwBlnUhky0hJC9wN4rzsPYoCa49W6gZCXWxvgxCqLbYIZl+6EKP+RDI/mHNis7MNyFVvU+e55Q4cQTwQS8WSwqhatveBzdZjEKPqdXbLrXwaBZmqNmCkXOEZJkWDBNtrF9u3UDL597rtJrPXbsWMims5bFFosWcgnpOPFQU5XBibz4Gmdqg8mwoAmgFaI/iXA3UcHykE7BEjX40FB4XPVmuXYQYtPwJsimslBv1zkTFF9LaE94O0hxr1wtabdJwaKJismWELWDNpxmD+9TxLY1NjW7ZbwE+WzabEsIQ6RcQ/9kCxa026K9GckkygcxOvTQi/GjLbdf5aEZ65cjz342mIB3arNsDkvvJuRiWAy1g9xMG76mrRMDcmv34PgghoVaQiJsA6XdhjAsJKQsnHwyZJjjQkW7dOK4XfDeN3WfViy/b5LpnNyk3dhRngQgC3FYeJxPLL+f8PbB/QvKtllh0a372VFoCcnqVxBYxNI+JdIWUilYaFBmy68ltO8urXbQakq7TQoWTagcVIHQyF9x4+g1Zbjqj8+1fhgD+vsz6GrpiLsGAjZeEeGtuYJlKywdykO71rUOqtLZ6tog35bQIrWEVG5NPhoWgwXL6VvGrMnRTz9hrZXPM4g4MWCmkLDo1t0SmrFZNFMHkR9OW3Oa9fMdB+6BRWYB1W278sJ1rjVYDItoeFzE845jGHAy8UKtpZSmisXrQmNBTHSr0RLqVKuwdM89Sm1DmZlCSpcbKmr374duu21UcOvNYhlkJAXLINmaDRUsiBeevhHOOErtJhk4CZVvBDvFGJaAiaGnCehYaBKylkMIMbYVFvbYD+PQMy7UjuX3im4xkwM3VN4aLIh/fd/wOIMFy1ETZbj5/zwXPvNaeQvvcmtYds0s8e+hd5aQcEsIb9QBrQud/BW/guWuA/bBNlLISr3noQULkUyDwrD0sBV7pGP5Ccjyoq2eWBZZUJsFi1ec6RRXS2jpjjsAWi1LV5Q7Su75Ix2LiFOIGBaZGW9ZbBtmcTJ522pvcrRbAPvstQibdJyliYbliIAxW7Nmwu2yQER42+kAMP1BMMMiULBQBsuwYgYLoTQOi3vs1zHyVHVdkBdjhTEYweGEjGVRaQ0Sw9KeneWjA0xZmgnj5TxkM4P7mK8ZysNEOWct/0cPLqoxLMPrbSsrsn8+GqvWzAzUH35EyyHkLVgenn3ASnc1MfqCW5urmcFiWBCcYdkd7RLyBEW6cZKGjsXdDhLSs/Eia7e9r8rmrzzlKdK6ORmnkGw0PyKVyUBuw4b+LJaph+39FpPIJ48DVZCtearSEEohXykM7k52pA0/RMFgbc7uGa87FQYSItZmKlZCNl4qWNA10Ol0w1Nuh/QO7vr27dCYTwOkuzB0si1uNAW3U4jTvBK3pvTICKTL5V4dC9HYBhiW1QA8GIhl6SlYmoJJt/YXcU1t3h54EOVPOB6yk5Nar/fEiRMtgeViaw5S2TkjLjzSJ/CCBQPYBgUiWSwRDIvbVq/DsAi1g9zFfmPR3lOlBbfyRS1lscSlYQm0Nu9l7aCNZ9gsuCIwhZz0jiIp5CuFpGAZlJYQsSsYx5+1I9UHDtwpFKwV6EmXDDhsjls3ZGkrUAOArYC+L9Fu8gFzuhqWxZ9db/08tK4BmYYnUdagU8hxCYkzbVZ43CaPU8hgS2i14ATmFHrYNSBPytYcIbw1pV9B4BybEyZOsD7OlHYZKVgo7bZVydjPjI4rbiWyWARmIBHDEia0N+IQosKpNCnVFurU67B0113K64T2AtSwRAmLVRgWRNY9KNOgfoX2otWgY0kKFg20O12otzpmpjUPejtItCXUE9Ptv7xymTSf4uoXIIeDzrrQhWKmCJNFvRvx4s9+Zv08vKWmNRAtamqzaoAgD49DhqXTdtwYhlpCqwF+WSxSLaEe4a1fwWIzLEMGChbEqZM2A5ou7jbaEmrVMtBJD1A7CCEiuo1wCbnD4x4+sGjtmyqDD4ULFoWZQkt33mnN9MqsWwv5baz4lQBq7XBcB65bGtdhnGHZvLlf82aoYEFsHrcLlscPsfdzAJEULBpw54hot4T26AfGDURLSDBePEzHsruy25nZo3HbRG1I9bbbrI+HN9fEMmQUpzarJN0istwptBdgcT9Ap2UPvpScuHo4FCxup5DwLCECbwk93jd5t/7AA8YYFreOJVPcozX4kJCZmIBUwWZVW41BK1gEwuMEGJajJ8tQzKWtQZxPTFXiSbn1u1wJMizEwmFRq7LnoOWdZp5FtYVUGZace+4U6QX36luaCWcdZQd03r4zfCbXSiIpWDRACw8dowWdvBNceKuhYHFvAviaFTevKKeQKcHt4i9/aanqC0dvgPxwWyylVyWef34nNNtdpYKFT+tFazPpBPBW6xkidziDrM1PTFWtA02LYfG0hPjk3W3bILuOzRwyVLBYDIsrVVoVeEDm1k1YHzdrhoIeTRcsWEzXFyIYluD3Cm31PI9Fsi0klcGi6BQiFq6kIcoWsTajZo8E+uVcVn3uFLGJaPdGNnvtyaCLc46x1+BtT9iZVYOIpGDRgFu3oJXGOv0oW3glIwsvNuBBmkoDtBt2+mUow1IWY1j2BRcsuoJb3g666Hz7E4ZbQsSw2BZstdZgjlmbrZYQvb4jSL9CDoWhfAZana51+0YNUwuZJikNC7WEHu9xhpjUrxBOmjgJoJuGdHYR8sXwEROiyA3b+0czNWDM2tBagLUnWY4ouO1LEc98cEvIGyAXq+hWsiWErSDL0qzZNhSxNv/0gQNQa3ZgpJiVbifmvAMQaYzLhicBZPTjGs45eoK/P95hpIOCpGAZhDlCpF/ZdKaRhRcbMjmAEdbTDmIrOMNSEmJYdk4v9T0cJkLjcBNa/MUvrY9HfvfF9icX9tqDwgwBKWpMucTDFR0jGI4lmyzshMftNW5pXo1OIdSxELsi7BIi9g+LaXSpLezrt6oqzg8KEt52GvZtf7YdPr9IFNn0tPVzMyevn4gVeBG78H/aH994pX/ODT3zIQyLezyEzBBELF6nlqY0WkLRBcvSPfdCt1azWnP5E2xBtQq4tXk++GL0uV8+Zv38mvOPlsphcec2debnob24aLQdRCnk2LrDev/OAW0LJQXLIFiaV4PglkDDtYKcQiGj5t0YK+dgM/P+P+BJvOWWZo3QuOqtt0JncREya9ZA8SkXOY4lhfkiQcikM/w1pvNTSk6x3NH297PxxA7oHHziiGRY3BH9KMqkOUL5dB5yOFdLBOiso+8bE972TN7VzF9xY36pBe0lu5jes2Tnu2hh5nHIpe0J0K3mAM58OvNigOENdkF9z38Et4QinnmVlhBObEcBPl4MJoo2AyAEKvoFdGvuKd46TDlvCQUwLFgE3Lx92rrYXHohYwQlkBkegjQbK2FlsRgU3BLOZW2hWwe0LZQULEYszUeAQ0hUeCsoug0T3lLKrY6GZeGnrB30O8+yQpeEBMMaOpZ0flqpcMWpsNn16wGaTVi696EjtmBxD0GUmiMU1BZyT9496ijhybsiwKGH7Zr9Hj00c7/+F3zwh5Ar23tJ84DNtAwUcK7Z0y63P/71J/vD2AR1azQE8fGpKtSYSF1GcJtGBk0U609BeghgbgfAgq2BCYKptiFvCQVYmz/P2JWXnr2Zz+6RRY7PFIqnYCEdS1KwHIYw0hJCKyun9szMuVlRa7Pg5hVUsDQ7Tb5JqTIsuFmQfoUPO+TMkGGn0LD9/UjlsGCRL1zxRld+6lOtj6sPHXmWZoJfS0i4HRQgvFWZvCuC/fM1aC9t4UMQVQf6cTz4A8gN2XtJfbt9qA0czr0UAJOdD94P8PBPpIPjEChQHivlLFuzOyQwDPuqCpZmRGnCmXj/xK8D/1i31YIl5iTUbRvS5WWhuQBz9d7Aup3TVfjB3bb25LJnqCfS5rbY665+3+0AS9MA6SzAelsEbgLnHG07he7YMSttP18OJAWLBg6y8fJaBcuhh+1oaxSsrbUnwQ40iKnw2EdNMSz7K/uh0+1YOoE1xTVKL7HxyCPQ3LULUvk8DF14oXiGjBbDMiWdq0CgA7X6+MIRzLA4abeLrMUg7BDqY1i2xya4pXkrnfpGgG7KytygAlsJSzMAj/8aSpNNgEzGmsbb2GWubWkMpXGA897gsCyS0fxUnJ8sqWPB/UBacEvY9vTIggVbhjj0EFsthZNQXKwOLLDpdXrbQlf/ejvg+f+ME9fyfU8F5XNsFr5646/sT6w/1WbADAHzclAAv1Bv9Q0kHQQkBYsiHjmwAB+59kHr47O32lWpnuD2rNVhZaW020evA7jqGQA3f97edCVFt+6pzahKp2qeW5qHNin3k6kdVH7a+Tz6nhdaMWWxpC2GRa9gWTqQsgi3I7Fg2TpZtgTLGMS4c3ZWsSXkpN3qTN6NwoGFOkA3D8Nph2VRxsP/DdBtQ3rzyVA6y6b2q7/5DQwkzr8cFzrAE78C2GWLmWUYFneAHI7liC2DhXAMK1geDy5YeFF77rmQ0oi2D5spNFdtwtd+a//6zc9UZ1cQ5fOfZv1cvftha3SWyXYQAueOnc1YltueGDzhbVKwKGC60oA3fuEWqwp96rZJ+J/POfHI0K8gjrkQ4KxLADJ5gH13AfzgrwE+dgrAf1wGsP0XQqmX/EutGbJYCbT5bWfpiiS41XEI8XbQc57jfJLs4nts+td4wZKfgrLi1N78sdsgMzkO3U4KanMjNp19hCGTTsFxbKLv9ulpNYbFlXbLJ+9u2sRpdJMtIcSGgu0ouX9aQ8fy4A/sn09+EQw9zbbfV35zEwwk0Cp85h/1siztJkCHufwECkzZIYhKGSzeggXbWBVb1OyF6bYhzRRyMyxfufkJSz6ALqmLTtCbZ1Y89RRIj45Cp9aE2kwuFhnBuUcPro4lKVgkgcFWf/blW2HHdNWygF31J+dKW1lXdcGC9OMrrgL4qwcBXvhhgPVPwkxxgLu/DvDF33c2MgGGBQ8pooipLbS3slerYGlNTVkx24jh3/kd5ze2XWTbXg89JGR1FAXpbFKZGuSy/XORhHUspx9vfVyZnRysWTLLrGNJZWfh+zv+n/XrSZoHI9sSqk5BhVHmOMhOKyMppGDZNnKSHsOCFuFH/tv++OSX8Ntz5abf6Oti4gJZnO//L4ApzI9ypdYKXFIca/NifBkshKE1jr7Dpy3UbbctN6FJFxlPv2bWZpx8/IVfP87ZFd21mMpkOGNY2V8wzrD0BMjtSAqWVQ3cRP7Pt+62rGkjhSz86+vPg8khjUGF7ZbNUqymgoVQnrSdA5f/GuCynwKc+wZblNdl6v+CmD3Tq2PRtTQvXv9zy8VQOO1UPqeH9+A3n2N//JjNwJgAti0KqXHuFFL+OifYG3J1/wDn8MSMyfFDUN52JUw3d8L60nr4szP/TO4LFEcByrbuaemm38SiX0HsZ9NsT2WJt8oFC7ZW6vMAQ+sBtpwLpbPPglShAO2Dh6Dx2ICKb1EzcdIL7SC5Gz7ltIBxnASyrhE4ibnBds8uwYJAOBlpWJRaQhFtofqDD1rRB+mhIYu5MAE+BJExLN+9Y4/VQsTBgr93pt4gV8LQ2fa6q2LBgqFxhvHkrXbBgqz31OJgTW5OChYJfO4Xj8E3bt1lRfH/02ueDCeyfqwyDj5gsxOFUYBJvd7migFvDFvOBfj9TwL89YMAL/8MwHlvsttGAjiN6VioYOGhcUNqD/fi9eQOcrWDCMczx9Cj5goWy9VSsQuWU7b6hGoJYmirbXNc2lO3Qu+ONPx696/h+1PvhnRuAfLtzfCVl3wFjhtXeCYmjrV0QEv3P2w8f4VwgDEsZ294kmW1xawQmi4uhQeoHfRCa1BoulCAEhNVVgZVx4J4+lvtn+/4d0d8j+yKAHuA+Us0FThKeIviey0NS4TwlvQrpXPPgVTWzEXBrWHBCy5ZmS99+jY9Jt6FoW02k1WdKkAHBHOKJIDvEYngb9sxWDoWpe/glVdeCdu2bYNisQjnn38+3HzzzaF//hvf+Aaccsop1p8/44wz4Ac/YA8qA76x733ve2HTpk1QKpXgec97Hjz8sL3hDAp+ct9++PC19hC19/7eafA7JytQlKGC28OgdsRN6+zXAPzeFc6UV2GGZUE75RZHxC/++gbr42GyM7txHPvcY9cHz0KSAK7bv/2ve6HTsG8koyPqMe35oUXIFNrQbXa4WPRIwbce/hb8xXV/AY3OErQqx0N95+XqB9TENliaykO32VKevBsGnAVjiW7xcJoYg+PGjlNjWbDl8+AP7Y9PfrFzGJGoclB1LIijLwA46ikA7TrAL6+wPychkKY28IP7wttC07VpaHVb1hTkteW1egzL/nsBqr0MaCUGFxkxLPjar71vu9X6QtfNq5/KYhUMIJ/bb+8VLYAaa3+bxrkD2haSPiW/9rWvwdvf/nZ43/veB7fddhucddZZ8IIXvAAOHPC39t1www1wySWXwJve9Ca4/fbb4eUvf7n14x7XpvyRj3wEPvWpT8FVV10FN910EwwNDVlfs1azbzIrjfv2zMNbr7nd2mP++GlHw+svNLQJrjb9Sgw4hRUs++ZrcGChykV2KgVL9aaboFutWkFsxSf5ZBPgJovWy+ohgP36RcGP7t0Pv3z4EKTa9mb62Jw6jZ+a3w3ldY0eIeDhDiz4rrzjSnjvDe+FdrcNLzn296Cx642wuJTjbRdpTB4L1QN5rcm7YZiuNqyZR/hl1w4X4NTJU9UKln13A8zvshOYj3O0Vlx4e/PN0DVQVMcC/M8Ty/LIT4QdQgRRazPtBWtLa8UTj70YXu/MQnrCvswg8Hu7xAYe6swP6vvn8sMwWbS1V5+70X6OsVjB/BlTSO27C4Y21GMVaA9qgJx0wXLFFVfAZZddBpdeeimcdtppVpFRLpfh6quv9v3zn/zkJ+GFL3whvOMd74BTTz0VPvjBD8I555wDn/70p/mm9YlPfALe/e53w8te9jI488wz4Utf+hLs2bMHvv3tb8NK48BCDf7HF39rqbxR4f2+33+SuU0wKVhguJC1xMuI3zzxmHVw4eaEm5QsFmjY4bOf7f8eYXw7im8N6FhwUvcHv2cfUi850b7F/Wr3r/jQPmnM7YbyelawsJvf4QycEfPuX78brrrzKuvXbz7zzfChZ/xfOGaNXcAqZ0BMYMFSiFG/Yl+isFjJZdJ8crN0wULuoOOf0yNQL55+uqWp6MzNQf0Bm9EdSCArtMY1dycig8Uvov8Bn8GnbhyoaAhu3aBn3tUWqj/8CLTn5iBVLkPxSWZ1INQWunv/Y5axANtBRrH3TihvaHCBdhygQYg4TqDZHpzCWapx12g04NZbb4V3vetd/HPpdNpq4dx4442+fwc/j4yMG8ieUDGyfft22Ldvn/U1CGNjY1arCf/uq1/96r6vWa/XrR+E+XkzE1O9mJ+dgu3PuQg+Tp/4McBt/2z6X9kM8F/vBgD8cWTiI13r/gOpnwB8rtuFTLoLj1z1TOmvgxsQYvjZLneQn47l4R/ZOha6JSrgMz9/1BIObhkvwfue/zy48VtXwGx9Fu44cAect1FBN4EMy3p7TVdvvx26zSakcub70yaBaZ4ztRmYb8zbP+rzPR8vNheh3q73/Gi0G1Br1azvFTrCct00vPfUt8ELy0+3GLIXH7wHnnhsN9Q+fxvsX1uEbr0OnXoNuvWGNaCOf1yvWyml+APF69gCsn5dq0BrKh9bwXKAMT8b2KRd7YLl5Bf1fBq1FPi6F6+/Hio3/gaKp5lLMTUF1JZM12fg4JP/CA7++go4mMlApdCBpTs/a82BqrVr1s9LzSX+a0ywxiIVf16o12Do+Hm4N92BZ389A+12C4r1DgxVO1CutGGk0oWhpQ6UFptwSbUNxxdnYO/N77PXQqMO3VqdrQtb79VF8wK+/+225+O23XpDnWBtA8B3vwPwQXsgKv59RPnss40/Z2htvuPgHZAu7oEXHvdCOGpC0p4fhuq0lSU1tMHOfFq68y47+I7ypgwBIwbGyzmYrTatDsNZOlljK1WwHDp0CNrtNmzY0Ntfxl8/EHAbwGLE78/j5+n36XNBf8aLD33oQ/C3f/u3EDeemK7AqDM4NkYMqIVxxdCG9qI9oVUW2A4aepqtA/AF6Vh23AjQrAHk5Gd67JiqwlU/f9T6+P+85FQYKRbgd7b+Dnz30e/CT3f+VL5gqc1ZbpHCGEBmbMwqvDCBk4LEVhJ4yKCAcPv8dnh87nF4fP5x/jMWHaIYrnbhmANd2HYAYNv+Lhx9sAvr5gCGLcLiI0Azj19Cf+EuAHXPVQryoy3Ib7QHxcWSwTJir5tTJk+xNBYHlg7AoaVDYswgDuC05sCkmOOmFxh4aBUsN/0G1rzpjbBSQMHrXQfvsn48Mf+EJS7Gz+H0ZNSWWNhI7McCwB02ax6EdKcLm6YBjj7QhYsOduHoA2Ctg8kFgGzoJX4HzIJu4CMe8F3L8u7GyO8+H0zj1PHz4LvwXciO3Av/4yL5IYeh2Ge7SnNHbYXc5rXWEMTqbbfD8EVMq2MI6XTKYll++sABS8eCBUu707badDoZWbpYlR5KZHjcrA0yLFu3siRTgzht60b47UfeBov1Fhxtskp2Y2SDkB3wcANu8uwDa27FP1//iHUT+duXnqGVcpvbvAXSxZAiZN3JACObABb22kULOYck8MHv32fl8Tz9hDXwotNt6/Rztj7HLlh2/BTecd475F4/y4VJlSegdN55sHjddZaGYSUKFmzRPjz7sPX/wB8PzTxktemCMJwbhtH8KIwWRu2frR8jcPQTNdhy9z4Y2zEN5ccPQH4qpMWTTkNmfBwykxMwmxuCW+cAimsn4QXnHQepQh7ShaJl900XC5DKF6yP8fPIRqSyOUjl8Gf7B+Cvv30Z5Gv3QuqhHwKc8zqj3x/S1qxnThe0tR87dqylX0KW5ZlHCTCDJLbd+lSA4XV9v00Fd/WWW5eNacPC9IHpB+DOg3fyH5SJFPT8rimtgXXtLqyd3QXDY0dDedszrXj6YrZo/TzUzMC6W7bD2F2PQ/HxfZDbsR9SzZCWabEAMDEGqbExSI2PQnp8DLLj4zA0utZeB8Wi/f4XC5ajKoXrIp+z10AWbdUZz8dZx7n0lT+yB5+++B95iwhZifxRRxkrZH/+4EH4+UMH4RePpKF7dBYyhYNQGkb3mEF2Yq8tsk1tPhvKTzsK5v7zP6F602+MFywkvP3pQzvh2u0/hke6O+AXu35hPd/fe8X3jGvDYilY1q5dC5lMBvbv751+ib/e6M68cAE/H/bn6Wf8HLqE3H/m7LP9U/wKhYL1I25kcnl42kv/NPZ/50hH8dgqvOOmn8GuZgqOOvOZxux/vsAHDVmWO79q61gkC5brHzxgOcZwRPz7XXqmCzZfYM0/whwZPORPnmTJuiLAGzdi7CgYeupTrILF0rFcdhksB/DmhAeUVaTs/GlPrDglzm4b2wbbRrdZPx87eqz1M/bq3fH5jV27Ye6734G573wHmk/034hzW7dC8ZSToXDyKVbuRf6YYyCzZg1kRkftidoAcM/uOfi///QrK9/ode9UvP1OvwLgp/fa4WaGCxYUh7tbQtQWUipYXO4gN3CmDRZw7dlZWLr7Hj4/Jo7iFN/3r9z/FfjZzp9ZLTs30LJ94viJcOa6M+GkiZMsLQn+WFdaZxUrWRy8h8Lg+75lu3FGNlptucoNN8Dcd/8LFq67DrpLvWGKqBkpnHgC/Da1Bn7VHoNnvPACePmLzoPMxASkS5LDLmVw7jMAbvsSQPdRgJP0WSvUdaAg9foHD1p7Qu+ogSyM1U+BTuke+MkTP4ETJzRnxGFba+fNALd9EeDeb9mf23QmDBVPtAoW08LbvYt74fpd18Mv5n8CwyfeBve123Dfo047EOdnqWgMl71gyefzcO6558J1111nOX0QnU7H+vVb3vIW379zwQUXWL//tre9jX/uJz/5ifV5xLHHHmsVLfhnqEBBxgTdQpdfzkaaJziscdRECUaKWViotazhdzrDwYRwPCtYHv0pwPM/IPzXMLXyb//L1iq84cJtPTk8eHBj0XL9zuutQ1+qYEG3CGL0KCifzeYK3Xqbtfmbyofww8MzD/PDCm2YhHw6b/1fnnv0c62f0WIcdKNqLy7Cwo9+BHPf/k6PWBgPppHnPhdKTz4biqecYh/Cw3a2QxiOWzfEx19gaNWaYYWLyakvBfjp39n2dWy3FceMZ7BsYAwLFSzfe+x7YjqW2rw9wiKkYMGZNuXzz7e+r3h7Nl2woI7o2sevtd5792seL4xbxclZ686yfpy+9nQYihLTptPQfdIfQO3uu2Huu/8K8z/8IbSnnLZL7pijYfSFL4LSGadD4eSTrTEJ+P/71k8egu9e9zCUxrbCqzYvQ4thGytYQgYhiqDWbMMXb3jcagnPVJ3gO3w8zjxqHJ510jr4nZPXwc5GG9796/8DP378x/DnZ/+52j9WmQK46xr7dWNmFwHTe0//Qyg37DVYu/deaM/PW4W/KlBbhPEC33joGxbTRsBw8E5jDbzq1BfCS054Ljx5/ZPtQnWFIP0vYyvm9a9/PZx33nnw1Kc+1XL4VCoVyzWEeN3rXgdbtmyxdCaIt771rfCsZz0LPvaxj8FLXvISuOaaa+CWW26Bz33uc9bv40aIxczf/d3fwYknnmgVMO95z3tg8+bNvChKcHgD18CpG0fh5senrQC52AsWspGitXTxoC8t74erf/W4lf6IDpG3Pq//1oRtISxYfrbjZ3D5WRLFNo0KGDvK2tTTIyPQWViA2v0PWBu9aWBxcuXtV8I3H/6mdWNCjORG4Jlbn2n9Hy7aclHk4EHsnR/85Cdh/kc/tsSwFnDEwNPOh7GXvQxGn/98y+0ii3I+axWwu2aWrFA+pYIF235oZcUxDA//BOCMPwRT2L/gz7AghAoWHBqKs3fQYbMueDow2puxYMHb81pDFzcMt/v6Q1+Hrz/4dV6gYnH6kuNeAq8+5dWWRVuW6kcW5cBHPwaN7aRCAshMTsLoi18MY7//e1A880zfr0lDEHHw6bKA8liwpYJFI6YiS6DV7lihoZ/474d4WxBZwGeeuNbK5MIpzO61ekLjdyB7YxYenXsUHp19FI4ft0dvCLEpj/8S4JZ/A3jgewBtFiKJ9vfT/wDgnNfbrcRUyoqMyx97rPW9x8sCXhBUWDbcsz5+28dh+9x2zqydve5sS5d3zc+H4cGdJTjv/HPgKRtXTruiXLBcfPHFcPDgQSvoDUWxyIpce+21XDS7Y8cOyzlEuPDCC+GrX/2qZVv+m7/5G6soQYfQ6ac7G/E73/lOq+h585vfDLOzs3DRRRdZXxOD5hIcGcDJzVSwxA7MZthwup3Fsv3nQgfavrka/NNP7TDDv3nxKTBS7NcVPGvrs6yHHYfhYQCesDiNt4S22LNCzj3XEl3iJmSyYMGbNd6qP3fX5ywHDwJZlD86+Y/gKRueArlMtFYCnRczX/13OPDxj1uZN8A2zbGXv9w6oHIGbss4U8gqWA4uwvnH2VH70jj19wF++TGA+79rtmAhDQsT3bqFtyhIREEqtkui02173UFe0Fyhpdtvh06tFq7LEihUrrj1Crh2+7VcLIutnUtOuQReeeIrYaIoP2yzuXcv7Pv7v4fF/77O+nWqVLIOzLGX/j4MXXBBpO7GncWCYXwo8ox9cCNO88Zk3p03AZwo1m7E1/bDe/bBx378IDzGBrSiM/B/Pf8keMWTt1i2ZT+g1uOCTRfAL3f/0moLCRUsOKDx+28HuO87zudwuCG2NXENF/uZQrwgYMGCha1swXLvoXvho7d8FG7Zb+fRTBQm4E/P+lN48bEv5mvi8UfvgQd3PmFNbjY1WkAHStwOtn+CWkDXX3993+de9apXWT+CgBX4Bz7wAetHgiMT3sTb2IEsCxYsaG+OONDwFvJ337/PyuJBIRpuVH7AwCikTG/df6vVZnntqa8Vey3zxLDYwnGytWLBsuaNNnOpA3z9/73jv+GKW66AXYt2cYS36Xc85R3wlI3i1t/6ww/D3ne/hw+XLJ1zDmx45zugeNZZRkV4J6wbtrQBDwsOyPPFKb9nFywP/zdAc0loGKfILfsQm63ibglh2+SY0WMs5xSyLM846hn+XwAnG6OlHnEy90MFTvBGx1vrwAFr8nSo8y0EP9/5c3jPr98DM3U7AAzX52tOfY1VqKqEsVkF61e+Agc/8UnLTgvZLKx54xthzZvfDJlhcUZt25oy5DNp65nCeICtLIspVhxzkV2wIIMhULD88uGD8JFrH4S7d89xRuUtzz4BXvu0o6GAwt4I/O623+UFy5+dFTEXC4uU773dDrXElgsWKTifLWK44dD5T4PZf78GqhKjHPAy9cnbPgk/2G4Xz6i9+5PT/gTeePobYQTnwXkC5L544xNw64Ak3q5Kl1CCww/uIYh4wMauQkcdy42ftoW3SMMG/HvYs/4/37oHvnfXXuuP/O1Lw4MDsaWCBct1O64TL1jQvYAYtQshmsaKk2TxgCBBqgrwAP3Ibz9ivSYEiuXees5b4aXHv9Rig0TQaTRg6rOfg0PYxm02rVbP+r/+Kxi/+GJLjxBX+vGvHzmkvhYwjHH0KFsfhEXpKf56ERngwYpLBQXXazxDT7EtFFmw7PiNranBKdRI60dN8H7a+TD/3f+y5grJFiyYdfOxWz4G1zx4jfXrkydOhvdf+H5Ll6KKpXvvhX3vfZ+lmUCUnvxk2Pi374fiScGtrSBkM2k4fv2w9bw/uG9heQoWnCt0x5d9ByG6gUMZ3/71Oy1xPQKj9S975nHwP55xnBV0KYpnb302ZFNZS4SPUQAoVPfNVfnBOwDu+ab96/VPAnjFZ4SnMJfPfyq/TLQOHYLs2mAxLGbifObOz8BX7vsKNDp2qwn3gf/55P8JG4c2hgbI3bt7ztoLizn1vcgEDoMBNgkOByBFjOzqVKUBB9msllhx9IW2nRzZjUMPB1oVX/2538B/3GYPvPzAS58Ep28JF3A+52h76CIWCLM1gZwSdFnM73Foa3RNnXqqnXY6P29NlFXFNQ9cA5d8/xLrteAtCtNkv/+K78PLT3i5cLGCGQ/bX/EHcOjKK61iBVOEj/v+92DikktiKVYQv/ukDVDKZeDhA4twi2o0OBY52BZCoFtIE6hdev3VNztr1dMKENKxUFgcZq+kozd+PlfoRrk0Uzwg8X2nYgVvz199yVeVi5VOpQL7P/wP8Pir/sgqVlBjtfH974djvvJlpWKFcMrGFdKxYMJ4fTGwKH3VVTdaxQoyQG98+rHwi3c+G972vJOkihXEWGEMzt9kj1pAlqUPD3wf4Mrz7WIFp10/8x0Ab75euFhBZCcmoHCKPWm6GjLTD9uCl157KfzbPf9mFStP3fhU+NrvfQ3+/qK/DyxWEKgnWz9SsMZR3LXLZppWEknBkmAggJX7sWttSvm+5dCx4OyTo58WGNN/+44Z+P1/+hXcsXPWmgPypTeeD39yQXTE9lEjR1m3WRSz/nzXz6NfBxZMKKzDAgLzYVjaKU6QVY3pR5sysip/f9PfW6/j+cc8H/7r5f9l3aSixLQEZHbwkHrita+FxqOPWvbjLR+/Ao765yshFxBhYAqjxRy89Cy7X/6V3zyh/oWoYMFCAdsxirh5+zS84p9/DY9PVa0N/BMX98ct8IJlOqBg2XMHwJ3/bn8syPbQXCEcholurCggG4UapUu+dwk8MvsIrCmugc8+77Pwzqe8E/KKWU+Nxx+Hx17xBzD9hS9YxfXIi15oF6yv1mfXKKIfGZZlwcQxAGNH4+K2dSweYAz9yz79a8uivG6kAN/4swvgvb9/mprw29UW6itYlmYA/vNPAa55DQCOH1h3CsD/+G+A57zbHh8iiSHGvgXZm9H1gwXsvVP3WkXUp579KfiX3/0XvmajmL5zB2iuUFKwJBi4ttCyFCzu1FtsGbjwzVt3wcWf/Y01lfekDcPw3bc8HS46UTx3gFgWzDUJxeIBgGsusT9edyoG//Dfolh5migrimqzCm+7/m3w/+77f9av//LJfwkfe9bHYNOwk3Ek0gLa/fa/sg+pbhfG/uAP4Pjvfw9GX/SiZQuMQp0A4gd377MszkrAghSn/CLTpWhn/fbtu+GP/+UmK6Ic0z6/9edP77GzE2gI4r7Kvh6LuIXHfwXwhd+zDypsVZ1oH2JRQAtw7uijAdptqN5iCyODgGJfnHj94Zs/bN2gMQ/mP176H3DhlgtBFZgB8/hrXgvNHTsgu2kTbP3sVXDUxz8OufUGJtVbTNWw0BBE420hek9c+OHde+Hiz91oaZSQ+fn2XzzdSBw9toUyqYwlxN85vxNg+jGAzz7TtivjJeWi/wXw5p8DbLEvKCoo08BMn7lC6AB63Q9fZwnCMUfpqy/+Kjz76IBZa6tgcnNSsCQ4coW3OHiONq920xJV4kDDv/7GndBod+D5p22A//zzp8Mxa+TsuVSw3LDnBqtv7IvZnQBXv9C2Vg+tB/iDz/b8Nk2QxYmyolN7MTb9Dde+wdqk0K76j8/8R7jszMukNqf2YgV2vvlPLUst5HIWq7L5//69FWS2nMBMizO2jFnvwzdv7Q2yEwa2XYjNkGwLIVvxqesehrd97Q7rNWCi8TWXPc26eQdN6cUDwfqnpu7vDYn78isBGgu26PN13wXIit/Yh863D6NqSDgY6iMu/t7FlsATW39/c/7fwKef8+lwt1IEFn/9a9jx+tdDe3oaCqedCsd+4+sw/KxngUmcspEGXS5arZhlbQuxAhbfZ0zZvvwrt0Gt2YFnn7wOvnn5hZYTyATQbUPC9h/f+xWAq19kzQLCIZ3wpp8APO/9SuNB3Cifd56V7IthjRg3QP+vL977RfjLn/6ltQdha+rLL/6yNedIFk9mOpbbnpixvu5KIilYEgwMnsxuND+9fz/MVhVv1TLYeCZAeY11mCw8eiNc+oXfwr/+ys4i+Mvnngif/eNzpfvWCGwJbR7abA19u3GPz1DQgw8BXP0CgOlHbYr6jdcCbDyj54/gBFkMX8O5QjhZNgoPTj8Ir/3Ba62bHNoT//UF/wovPLZ/Tk0YWtPT1iGFjgP8t4/+7FUWq7JSeO359ub61Zt2WPZSJWCIHOL+79l6IQHg2IW/+sadcMVPHrJ+/afPPA6ufM05UMqH606IZbn9AJvCfufXAK55rT18D0Pi/vib0vkfzu35psDwPyxS8QaNIwL+/SX/btmVdZiwue9/H3b+2eWWC6h8wdPgmC99KVTMqYrN4yW48Pg10O504Wr23C0bw7L7NmgsLcI7v3mX5QSiMMjPv+48pWc+DNiSRfzk3i8BLO6zg9/e9GOAoxSGpPoAAxlLLCakctPNVgjcB37zAcuy3IUuvOqkV8FnnvcZqx2kgtO3jFp6HtQXPjG1LMP1ApEULAkGBhccv8ZiWSqNNi8cYgX24I+1b43f+Y+vwC8fPmSJPf/5tefA259/knI2BB4WgW0h1DL82wtt7crakwHe9COANf0ZDZhjgZNkRXQsv9r9K3j9ta+32hF4y//Ki78CZ6/3H2sRhObu3fDEa15riSoxJv2YL34Bhi5UbyeYwO+ftRlGCllLO3LjY2rDMOHYZwIURu2DYnd4WwUxV23C666+Cf7ztt1Wxsbfv+J0eNeLTxVaC+QO+uoDX4XZGz4B8K0323qJsy4B+KP/p2StJoalfv/90JrppeRR4Hvpjy61otIxC+YLL/yCdgz89Je+BHv+6q8tgTXqVbZ+9rNCCcWq+NNn2Wv/32/eYX3vYwcyG+jG6zThI//yJSsMLs3cf+9/6ZMs95JpPDe3FtLdLtyby8CuzWcAvOH7dhaUQZSZjmX2hl/C5T+5HL750DetbCDUL73nae9RsrAT0MJ9xlFjA6FjSQqWBAMDPOjf+twTrI+/8OvHl2UDq261D5lTl2611PD/cfmF8OIzxPUeQaCCBYW3/3975wEV1dW14RelFwFFsVAs0dh7/dRoFFssRGMswcRYYm8xGk2xJCZq9MNEjdHYNRZssX6/sSLGDpbYYi+ggoAiTRSU+dfelxlBhzLDAFfZz1rj1DWMZ+6cu88+7373s+SUhm9UTklaBuoYS4ZQfXYChdI3Y9KVN2cQsJCV9rB9wxCfFM+pZ0r7uhcyrBEolUSSVoEElqRVoOoPm2ppMz55gZ2VOTrXViqnVh83UnxL2y9azUgm20KklSEdw7EbD3mVvfTTevBp4JnlP0WGW9RzJzYxFguCflEebDAY8P4NKGjcqp0yG9R7h3h84sVxcCb8DPrv6o/op9Go5lKNRZTkA2QslOoP952F+1MVh3JnHx+U8vVFAcucbcxKTrGkGSE/llXGfseGYGaGJHelLYzD/RP8PS/5tB56/ydzQb1R3DqEIn4fo+4TpfJxb30fwNb47ykzgXbowd04HnqM+3/NbTGXK8RMoTvTCW/zWMciAYugKlpXLs623bFPn2Hp4ZzNssQ8ScKw48o2VI0C1+H3SSVULmmatgBk0EUp2EdPHylbBFd2Aau6vNAy9N4O2GWsMbCtX18XsOjbO151cRW+P/o9d1P2LufNFSGGpn3JlOxWr4/x7P59WJYrh9Jr18CqbFmohY9StoV2X7iv6+NjMKnLm9PZg6fg+OMlx7lCpFhKhQj1hTGEgjDDGI1yPK0rZI+bTYYBbacpmbxsoHW9pb5CRGBYIAbsGYDYpFjULlYbC1stNDrdT1DPqtBvvsWDRYv4ftFRo+D67Tc5VraeGjqZDmymHG/LDt9ir4+cJCHxOZaGKEFwU/NL/D2/+7Zpsx06ru0FVnUFEuPQykZZmOy5m1bsaypiK5TCs4KAU/QzVH3igpXtVrLztqmo7eGEMtH3cPOcfguI3EICFkFVUOqd9CMEBSzRCTmTZYl7+gyfLj2B/aFWuIUSMEcyysalaA9MADUIa+amTBj7T/+ulDCSlqFCuyxrGWhf2szamoWPVFqcGhLU/RT4E98mh8opjadkyVo/NXGHDuN2n75Ijo6GTY0a8Fz1R46XLBsjzKzr6cw+EOuDjBTfvuUFmFsDUTeB+4rp2ctGYZ8sO4EL92LgYm+JNZ81NLyfFeljdoxCo9Mb0exxAp6ZmWFWgZh0DQmNWT1T2erhu4cxeO9gFlI2LNGQtQkk+DUWqgi7M3IUd/2lwKrED1PgMmhgrlWDEWT5XtLRmit0qCorp6DmpQNXnYRfhBIE1yxwHZVcjN8qyRDyWFnbEyDRffk28Oq8irdozkae5W7IpiQsPgx9AwbhUinlO5tq09Ow5qtZoFrsXfx0aD4+2+KLR7dTWonkARKwCKqDKjLKF7Pn7s3UGdXUPE58hr7LAnEqWPFYcajcOl0/FlNsC/nfPQwNbQtV6wZ0z7qWwczSEjYpOpb4I0d0j5P5EwnqCDKDG1V7lOFN6/buxZ3Bg6FJSIBdkybwWLaUTajUiLbEee2JEBZoGoyVPVCupd5tofinz9BnWSB7cDjbWmBV/wbcy8ggKGvz1zjg1AouVR1d+3MuZaVqreOh6Vf3ZBUucTczQ+KNG5i4ZRiePn/KZcu/tvw1y746+khOSMCdIUMRt28fH2tuc+fAqavp+i5lFQsyaGtShm8vPHjDeIF1BlAF4Ii1p3HwSgTCzEsh0aYoCpDb653MdU0Gc24jsO5jxV+psjfQfRVcCrmjtmvt9E3kjCQ0LpQN4UJiQxBcXgmyLU6n6uxsAqikPm7YIDgkJcC1jBtsHF8t688tJGARVJllGZ6SZSHxLa2ATQWlnPuvCOJGiyTo/KNffRSp3kavH0t2+U9EMKyTk3HXwhxXanwAdP49jddKVrB/5x2+jpg9h7Umi88t5kZ2BHWEHlZzmMHBSvT27byq1pCwsk0buP82DwVsc8Ea3UjaVS0BJ1sLLn0NuBJu3Jvocb2l7YF+KwLZTbeQNR0LDXSltgYFK3smAieo+7wZ61XKNhjKDSUJCizJyC87FHR0xNNyyjZGrUuJXHXyS/NfuITZWMi9NmTgIMQfOsSNC8ljxZhuv6aiR30P/g6oweCefxVLfFNBAdDYjWex64LiXrvok3qwLKf8rnBmjUn/Fk6vBjb1V8TW1XsAHyzVmcHpqoVMFLDcjbvLomvqD+Zm74ZuPafw44+PHsXzGNN4WdFCKbj/Z3y8kLC30sqlsHIyfvsxu0jAIqiS9tVKoFxRO94SMlWWhVPCf5zEkesPuD/I8r712e8DpZso1thUZhxlIuHfyRWw2f45GiUouot9pesYpWUo3MuHV9g0YVzo1wtLDylizqE1h2JIzSEGBytR69bj3pfj2IyMOiyX8v0vr67V7oLctbYb3159LNi4N6nQRvmOwy8AD65z4DrgjyCdwHZlvwaZtl3Qy4FpwJE5yu0OPwM1e+qCSQcLB3YZ3XZ9W7bEsEvOLcFGN8Vf41N/YLJVV4O3/1LzPDYWwf36s5U7tYDwWLyIOyznJfQd9GroqcuymAoavwlbz2PzaaXqa55PbcUEsnZvJcD8Zw0QuMQ0fyxwMbB1CP1VoE4f4P35acTWXh5efH0m4gzux2cvKLsTe4czKxS0eDh4YFnbZXBr0JxF82SFQJkz6vKdHWL9/bm8XfPkCezeaQr3BfP5eMlLJGARVAlNLsNbKFmWxYdusuYkO5C3xtDVpxBwJYJLl5f1qa9TvnPbdndFJ4DNA4FEpY280QQtBbaP4ImrZXHlffeHGJe9oWCi1OzZSChWCDbhMfjiz+cYVW1Y5t1f9fBg+XKETZrEWQHnj3qixNQfuQ3A60DPFPHt/svhuBNlhBcEVWaUUSrCnl3cjiGrT3EZuy0Frn3qoaYxrqZ/zwICFB0R2v4E1O2TxjBsYI2BfHvu6bnsQGwolJmh9gq/nPoF2xqYIbSuJwo+S0bosBHciNAYqDQ6+NM+LLYu4OgIj+XLYFunDtTAp41LcwaESmeDbr3kFmxksDJt5yWsPh7MUqJZ3WqwGSRTthnQcoJye+eXwO0XW65GcXQe8L8vXlSGUfD60gLF1c6VxfgEdU83lpCYEM6shMaHso3B0jZLuR8QWSG4z/8NBezteRvn7hdjWFBtDDF/7cKd4SOgSUyEQysvuP36KwpYZ8/gzhRIwCKoFvLhKOtix7boK48an2XRemvs/TccVuYFsKR3XdQv81JpYbvpAFVaBB8F1vYAkox03jyxCNjxuXK74RA0azePGw1ejrqMg3cOGjXpzr+9Cl97x+OxFVA5BGj3Z4hBjpP02sj58xE+XTm5Fu7XF64TJuRKFYipKFfUnk3G6L+9LjAkW9tCwYfWYf+lcFhb0LFQD3VLG1FmevQ3YN93ym1yK234agBJBm6Uqo9IiMDyC8sNensS1VKLhXWX17FYc2yDcWi+ZCtXjvF2zoCBSLxtWDaQuvkGf9I7jdeOGsrXtRRzsEaXlDL2302QZZmz75ouWzOtczV411TeW0eT0UCVLgDpy0hzQu7TxnBwJrDr65T3/FypDEsn86ndFtp9a7dRf+pG9A0OVrSeSxSsUCCkxbpiRQ5aaKFD2qTQSZMMdqeN3rYNd0ePBp49Q6H27VFq1qwcL2/PKq/PjCXkyyzL0HcVD4rFf99kgaShBD94jM7zD+tS/3SC+s9belw7qUNqr00AVVzcPJjiUGpg1+hjC4D/G6Pc/s9woM1UONk4o4W7Ir7V9np5QtVCWeBe3D2MPjAaC88uxF0XM4SM+ZBXbdEbN+HhihVZeg+arCJ8fVkDQ7iMGI5iY8bkahWIqdB6ovgFhiDpedZca1MTWaoVkmGGsk8vwt38EbuaklmhURm0XV8pt5uNV05SeqCGg6PrjtYJpbO6DUB9gfrt6qdrseDb3Jf9NApYWXHzSbLKf/7gAW/rJIVnTdOTFBaG270+Zh2UedGi8PxjJZ/c1MZn75Tlcz11S74WnnnDx/QEtpO2nsfPexWn4gkdKrNG5hXoD3n/qrhMP44E1vkAiQZkwigQ2DcF2P+Dcv/db4CWkzKsDNMGLKfCT2HykclZ6+hO319yEuvXum3vxq7GZR3L8jZQUdtXS+9pC7nULF9lrtj0JyJm/Zzl/1LU+vW4N248V71RD7GSM37izI1akIBFUDXeNUvCs4gtm3qtMrBz78nbD/H+b4dxIyIeJRyt2XMhwyaG7vUAnw0AVV5c3wes7w08S8x6SpgqRQg6gbWaopu4pjWdxqttgrrp9tjRI22/mZegKpAF/yyA9xZvTh1Txcn4+uPx/sffw3Xcl/ya8BkzERcQkKnVftjk7/BgsbJHX2zcOBQdYrjuRS1QOt/F3goRsU+x96JhGoAj1yPRdulVnE5WAuCVNS+haXnDfFZ0Ik1tBq3xSKD5+AxfTroF8kqhNg1zTqdoXTLgdsxt9Pq/XjgXeY69VRa3Waw7yRHkOuuxcCE3RUy6cwchnw3IUGBJZcsksr7t00sxBixZgsvXrd5SxkGNmbRWlZSMwSIjsiwk0O+/MggrjipzxdfvVUS/lAokvVjaAT3WKC06Qv8Btg1P16snDfSa3d8CfyvVevx7b/ZlpmXstHXTr2o/vr3p6iZ03NIRm69u5q7q6UEmgRSozD41m+cGKmen1hsuNunPZQ5eXijxvZIBJH8d2g7OiMQ7d3B/2jSETUy1ZfzDFJgVzLgdRW5jpsnrbkYmICYmBo6OjoiOjkahQqYx/hLUA/lvUM+PInaWODSuRaY9XYjt/9zjfjCkXaFeGJRZcS2UxT3YGwHAmm6Kbwr1oum6TL9TKf10ru0Djs0DrqdY8L8zVllp6Zm4yEJ/wuEJiEyIZJ+W4bWGo3fl3ihITfpSsiG0qp4ROIOV/0Rd17r4qsFX7KCqfU3YxIl4tGEjC+BK+62FVfnyaU5Qcf4HEL11K+IOHuS0Ln2W4pMnw7m7UrnyOjNz1yXM87+OpuVduKonKxUi8wOuw3f3ZVC17FjnAAxNSGk0WaUz8J5vpgZ+TPwDpRrozCrlfoNBQNvpWfJZOR95Hj3/pwSsfh38UKVIlVc/pyaZT0wj/Uey2WAp+1LssUL9gfSRGBKCWx99hOcRkbCpWwceixen0RhQE7wov3V4tHEj+/gQFu7u8Fy+jLtAqxlaaHww/yjrWQ6NexfFsvi7JW1Tv+VBuHw/lrf7fu5WE+2y6lpNDVBXeivbQ17fAU1GZdxlnbIqVMZOvPdfoP5nMIRT909hyrEpuPZI6UVLMxoAAA6QSURBVBNWs2hNfNvw2zT+KeSYTEHK+svruSeQs5UzxtYbiw5lO2R50RG5cBEiZilVhZQtceyU0luL5pLkZK4Si1q9RpkrUkKBwn36oNiXY3NtYWPI+VsCFkH1UPq/he8BhDxMwLftK6F/0/SdWJXuq9cxc5fS0Myrkivm9KwJW0sDxaVX9wJ+PRUvhapdgS4Lle6//IESgLPrgGPzgYgUzwNqFd/8a6DZ2AzfNupJFKeC94fs1wUkPzb5kVO+tF1EQQ1RzLYYxtYdizal27wycZAQjqs8AgNh4eaG0uvXsZ6BgpSYnX+xEVzqJoougwfxiutNIOThY7wz05/n1sHNy6F1ZVfUcHPS2+snKj4Ro9efgf/lCL7/YR03TOlUCdZHfBXdAZWeUqfsjrNfdHXWZwhHQQoFKwkptuSNhgGtfzDIFO6rv7/Cjhs7uNdPdZfqHJTQsUDX2ot2lU0BDXmsZLSCJp5cusTbPMlxcbBv2RJuv/zMze+i1q5FnL+/rtmjuasrnLp3g3PPnqr12nmZrvOPcLk5fcfj2ma+dXUqOAoDVgYhMi6RO2qTTo0rAA2t8mHhrBnw0XqgQoo/E0HfPZXEn9+kbBnzd2UGdJoD1P7EiP+hss2z5t81mHdmHmuWKJPqU8mHq/+O3TuGqcenIjxB2fLrVK4TxtQdw2Jug1suTJ+OhytWAubmcJ/3K5tEPtr0J6L8/JAU8kK3Y9e4MZx7+cC+efNczcJKwCK8cawLDMa4Ted4S8C3Ww02fCPfhkJ8bQFL8wIc2Hyz+RzWBynZib6Ny+Cb9pVYC2MUl3cC63opq66aPkCLCUDQEkXDQP2ACEsHoPbHQIOBgHPW+pHQT27LtS2YdmIaT1R2FnZIfJ7IExhlXijrQoZwGZmCUbXHrW7decIhN1wqPdRCJyhaSTl6d1Jt6j87DF97mjNoWshK36uyK28ZkTCXmrWdCXnEVWHk3UJC6yneVdGtXqoeS/dOA5sHAxEpW3M1PlLEkjapTnLkirtjNBCiWOLDtapS/eGutEwwBBJJdtjcgVP6GdHaszW7FmfVEI6CVgpeKYilqp/UwSr5ZlBq36FFi9emGkzL7gthGPDHSThYm+PoVy0z7KCcOptKDsUUrFAnaIOhU+GOUcDJ5YoAv/dWIPKqEqRQJjU5lR9UqTpA0y+Aiu2RXejYoKyq1p+F5gPqDUZQyfKERhN4G8hYKJNCupSY7dthZqV492ieKsdhAQcHOHXpDKcePWBVJoOtsxxEAhbhjYMmo3f/e4BPQPqgUmUKWsi3heKTSR2rmKah2cWtwIY+ymqcVlTksUA4eihBCgUrVBZtZHni+EPjcTbiLN9vXLIxa1VKO2btcz+9dg23evTkFbaZrS0KtWoFx/e9uZJEbXvPpj4Wdp4Pxe6L9xFwOSJNyTv561AF2KFrkUh6rkHpIrb4zaeO/h5RSU+AA1OBw6Qt0ShdfDvNBTwaKuXKpEuiYNXCDnj3a2UbyMgmhsTfd/7GkXtHWJtC6X0na6e011ZORvmrxO7bxyWolFGhbULy16FAxarcq13AXxdoK8/r5wDWn3kUtmXna48itnzbM+XazdmWq4Bm7VHEtS0rFsOcnrW4aabRkGZtRccXQWpqKGCt2kWpLCps+pM7HR+UVaHtYHMzc/Sp2ocXLtbUViKbaJKSEDJ0KOIP/s33rSpV4mPEsX37PDeNlIBFeCM5dDUS8/yvIepxItv2xyQkcZPE1JCvxq8f1UKLii9K/bLN2Q3An7RHrVH8WhoOASp2yNbJSwt1ciZjsaI2RdGkVBODU7EUtDy9fgP2TRrnualTXkBmgFQBtudiGFeW3I95kcFoW6U4ZnxYnTNwGRJ8HNgyCHiYIvIkAaY2g0al0KRVcVSM69QK9RlKCg1Fodat3pjjYOuZuxjpdyZLryVh7dfvZSOb+rJGZVELIDoEKFwOqNZVCVKK5XxVFVUQ7rq1iztwl3UybRPS5IQE3ja2qvA2bGrVVI34XgIWId9AJYy0wo5JeMbZFVp5OdrmQBkeVRBoy58F1a7Kz92NZo+Vkk7W6FbXPeuTMpkF7v0OOPH7iwzaezOBt9vm6GcWMoYyqjci4nD7wWPWL9F18EPlQr97i4JmmNypiq7k3WQ8fgjERwAuFUzSwFJIHwlYBEEQjCH4GBB2TtEsWaq3v1J+h05bUY+TOGBxyCyDJrwx5+/XS4klCIKQk5B+hS6CqqHMWWE7dbivCrmHGMcJgiAIgqB6JGARBEEQBEH1SMAiCIIgCILqkYBFEARBEATVIwGLIAiCIAiqRwIWQRAEQRBUjwQsgiAIgiCoHglYBEEQBEFQPRKwCIIgCIKgeiRgEQRBEARB9UjAIgiCIAiC6pGARRAEQRAE1SMBiyAIgiAIqsf8TWk1rm1TLQiCIAjC64H2vK09j7/xAUtsbCxfu7u75/VHEQRBEATBiPO4o6Njhq8x02QlrFE5ycnJuHfvHhwcHGBmZmby6I8CoZCQEBQqVMik7/0mIuNlODJmhiHjZTgyZoYh45V7Y0YhCAUrJUuWRIECBd78DAv9J93c3HL0b9AXIAdu1pHxMhwZM8OQ8TIcGTPDkPHKnTHLLLOiRUS3giAIgiCoHglYBEEQBEFQPRKwZIKVlRUmTZrE10LmyHgZjoyZYch4GY6MmWHIeKlzzN4I0a0gCIIgCG82kmERBEEQBEH1SMAiCIIgCILqkYBFEARBEATVIwGLIAiCIAiqRwKWTJg3bx5Kly4Na2trNGjQACdOnMjrj6QKDh48iI4dO7I7IbkLb9myJc3zpOWeOHEiSpQoARsbG3h5eeHq1avIr0ybNg316tVjN+ZixYrh/fffx+XLl9O85smTJxg6dCiKFCkCe3t7fPDBB7h//z7yK/Pnz0f16tV1RlSNGjXCzp07dc/LeGXM9OnT+bc5atQo3WMyZi+YPHkyj0/qS8WKFXXPy1jp5+7du+jVqxePC83t1apVQ1BQUK7M/RKwZMC6deswevRoLtU6deoUatSogTZt2iA8PBz5nfj4eB4PCuj0MWPGDMyZMwcLFizA8ePHYWdnx2NHk0B+JCAggCe/Y8eOYc+ePUhKSkLr1q15HLV8/vnn2L59OzZs2MCvp3YTXbp0QX6F3KvppHvy5EmeEFu0aAFvb29cuHCBn5fxSp/AwED8/vvvHPClRsYsLVWqVEFoaKjucujQId1zMlavEhUVhcaNG8PCwoIXDxcvXoSvry+cnZ1zZ+6nsmZBP/Xr19cMHTpUd//58+eakiVLaqZNm5ann0tt0GG0efNm3f3k5GRN8eLFNTNnztQ99ujRI42VlZVm7dq1efQp1UV4eDiPW0BAgG58LCwsNBs2bNC95t9//+XXHD16NA8/qbpwdnbWLF68WMYrA2JjYzXly5fX7NmzR9OsWTPNyJEj+XEZs7RMmjRJU6NGDb3PyVjpZ9y4cZomTZqk82zOz/2SYUmHxMREXtlROit1zyK6f/To0Tz9bGrn5s2bCAsLSzN21CuCttRk7BSio6P5unDhwnxNxxplXVKPGaWnPTw8ZMwAPH/+HH5+fpyRoq0hGa/0oUxe+/bt04wNIWP2KrRVQdvaZcuWhY+PD4KDg/lxGSv9bNu2DXXr1sWHH37IW9u1atXCokWLcm3ul4AlHSIjI3mSdHV1TfM43acvREgf7fjI2KXfXZx0BZRarVq1Kj9G42JpaQknJ6c0r83vY3bu3DnWD5B75qBBg7B582ZUrlxZxisdKKij7WvSTL2MjFla6CS6fPly/PXXX6yXopNt06ZNuXOwjJV+bty4wWNVvnx57Nq1C4MHD8aIESOwYsWKXJn734huzYLwuq2Az58/n2a/XNDP22+/jTNnznBGauPGjejduzfrCYRXCQkJwciRI1kjRUUCQsa0a9dOd5u0PhTAeHp6Yv369SwWFfQvtijDMnXqVL5PGRaay0ivQr/NnEYyLOng4uKCggULvqIKp/vFixfPs8/1OqAdHxm7Vxk2bBh27NgBf39/FpVqoXGhbchHjx6leX1+HzNa5b711luoU6cOZw1I6D179mwZLz3QNgYVBNSuXRvm5uZ8oeCOBJB0m1a5MmbpQ9mUChUq4Nq1a3J8pQNV/lCGMzWVKlXSbaXl9NwvAUsGEyVNkvv27UsTXdJ92kMX0qdMmTJ8cKYeu5iYGFaM59exI20yBSu0pbF//34eo9TQsUbK+9RjRmXPNBHk1zHTB/0Gnz59KuOlh5YtW/IWGmWktBdaDZM2Q3tbxix94uLicP36dT4py/GlH9rGftmO4cqVK5yZypW5P9uy3TcYPz8/VjcvX75cc/HiRc2AAQM0Tk5OmrCwME1+hyoRTp8+zRc6jGbNmsW3b9++zc9Pnz6dx2rr1q2as2fPary9vTVlypTRJCQkaPIjgwcP1jg6OmoOHDigCQ0N1V0eP36se82gQYM0Hh4emv3792uCgoI0jRo14kt+Zfz48VxFdfPmTT6G6L6ZmZlm9+7d/LyMV+akrhIiZMxe8MUXX/DvkY6vw4cPa7y8vDQuLi5cwUfIWL3KiRMnNObm5poff/xRc/XqVc3q1as1tra2mlWrVulek5NzvwQsmTB37lw+aC0tLbnM+dixY3n9kVSBv78/ByovX3r37q0rb5swYYLG1dWVg76WLVtqLl++rMmv6Bsruixbtkz3GvpBDxkyhEt3aRLo3LkzBzX5lb59+2o8PT35t1e0aFE+hrTBCiHjZXjAImP2gu7du2tKlCjBx1epUqX4/rVr13TPy1jpZ/v27ZqqVavyvF6xYkXNwoUL0zyfk3O/Gf2T/TyNIAiCIAhCziEaFkEQBEEQVI8ELIIgCIIgqB4JWARBEARBUD0SsAiCIAiCoHokYBEEQRAEQfVIwCIIgiAIguqRgEUQBEEQBNUjAYsgCIIgCKpHAhZBEARBEFSPBCyCIAiCIKgeCVgEQRAEQVA9ErAIgiAIggC18//a1Zk1xC3PgQAAAABJRU5ErkJggg==", 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", "text/plain": [ "
" ] @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "f8e16201", "metadata": {}, "outputs": [ @@ -279,10 +279,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "8b24c215", "metadata": {}, "outputs": [ @@ -311,7 +311,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fd (detection efficiency): 1.0301282272423373\n", + "Fd (detection efficiency): 1.0847722604952812\n", "Ff (estimation efficiency): 1.0\n", "Fc (confounding): 0.6781214203894617\n", "Fe (stimulus frequency balance): 0\n" @@ -329,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "c9399dd9", "metadata": {}, "outputs": [ @@ -340,81 +340,81 @@ "[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 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", 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", "text/plain": [ "
" ] @@ -442,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "38124127", "metadata": {}, "outputs": [], @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "dca4f78c", "metadata": {}, "outputs": [ @@ -494,10 +494,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "9a66ce14", "metadata": {}, "outputs": [ @@ -526,7 +526,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fd (detection efficiency): 1.0\n", + "Fd (detection efficiency): 1.0084361025301332\n", "Ff (estimation efficiency): 0.7333333333333334\n", "Fc (confounding): 0.33676975945017185\n", "Fe (stimulus frequency balance): 0\n" @@ -544,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": 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", 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", 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" ] diff --git a/tutorials/new_functions/tutorial_new_varied_ITI.ipynb b/tutorials/new_functions/tutorial_new_varied_ITI.ipynb index 925c699..5a270ed 100644 --- a/tutorials/new_functions/tutorial_new_varied_ITI.ipynb +++ b/tutorials/new_functions/tutorial_new_varied_ITI.ipynb @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "b6a9ae2f", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "id": "38c21e45", "metadata": {}, "outputs": [], @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "92d81053", "metadata": {}, "outputs": [], @@ -102,21 +102,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "16027493", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:639: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n", - " warnings.warn(\n", - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:821: RuntimeWarning: divide by zero encountered in log\n", - " res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h))\n" - ] - } - ], + "outputs": [], "source": [ "exp = neurodesign.Experiment(\n", " TR=2, # Repetition time (TR) of fMRI acquisition in seconds\n", @@ -138,27 +127,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "f8e16201", "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "
/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/rich/live.py:231: \n",
-       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
-       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
-       "
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balance): 0\n" ] } @@ -232,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "id": "c9399dd9", "metadata": {}, "outputs": [ @@ -241,198 +213,73 @@ "output_type": "stream", "text": [ "[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 9.09093612e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 8.47105211e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", - " [ 2.04073059e-01 2.78856076e-05 0.00000000e+00 0.00000000e+00]\n", - " [ 1.71554158e-01 4.82662401e-02 0.00000000e+00 0.00000000e+00]\n", - " [ 8.59319272e-02 1.87871688e-01 0.00000000e+00 0.00000000e+00]\n", - " [ 2.55981307e-02 1.90041761e-01 7.05554114e-05 0.00000000e+00]\n", - " [-5.13426811e-03 1.06224353e-01 5.49730130e-02 0.00000000e+00]\n", - " [-1.71045078e-02 3.74957617e-02 1.92051320e-01 8.93409566e-06]\n", - " [-1.81472623e-02 4.05126466e-04 1.86681821e-01 4.19453804e-02]\n", - " [-1.40373385e-02 -1.53927436e-02 1.02028309e-01 1.83234327e-01]\n", - 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-1.06299378e-03 0.00000000e+00]\n", + " [-1.01043918e-02 0.00000000e+00 -3.63902914e-04 0.00000000e+00]\n", + " [-5.18017345e-03 0.00000000e+00 -1.34806382e-04 0.00000000e+00]\n", + " [-2.39150710e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00]]\n" ] }, { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -460,26 +307,26 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "id": "b973d515", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -489,7 +336,7 @@ } ], "source": [ - "plt.xlim(0,60)\n", + "plt.xlim(0,45)\n", "plt.plot(best_design.Xconv)" ] }, diff --git a/tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb b/tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb index 9b39ed8..1f072be 100644 --- a/tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb +++ b/tutorials/new_functions/tutorial_new_varied_stimuli_durations.ipynb @@ -85,10 +85,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "16027493", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:643: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n", + " warnings.warn(\n", + "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:811: RuntimeWarning: divide by zero encountered in log\n", + " res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h))\n" + ] + } + ], "source": [ "exp = neurodesign.Experiment(\n", " TR=2, # Repetition time (TR) of fMRI acquisition in seconds\n", @@ -111,10 +122,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "f8e16201", "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + "
/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/rich/live.py:231: \n",
+       "UserWarning: install \"ipywidgets\" for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "
\n" + ], + "text/plain": [ + "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/rich/live.py:231: \n", + "UserWarning: install \"ipywidgets\" for Jupyter support\n", + " warnings.warn('install \"ipywidgets\" for Jupyter support')\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -138,10 +166,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "8b24c215", "metadata": {}, "outputs": [ @@ -170,9 +198,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fd (detection efficiency): 1.0035935666916187\n", - "Ff (estimation efficiency): 1.0\n", - "Fc (confounding): 0.6735395189003437\n", + "Fd (detection efficiency): 1.041688770971554\n", + "Ff (estimation efficiency): 0.9333333333333333\n", + "Fc 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[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", @@ -316,19 +344,19 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/tutorials/new_functions/tutorial_varied_ITI.ipynb b/tutorials/new_functions/tutorial_varied_ITI.ipynb deleted file mode 100644 index 925c699..0000000 --- a/tutorials/new_functions/tutorial_varied_ITI.ipynb +++ /dev/null @@ -1,529 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c4358087", - "metadata": {}, - "source": [ - "## **Varied Intertrial Intervals Tutorial**\n", - "\n", - "Another key consideration in fMRI experiments is allowing for different intertrial intervals (ITI) between certain stimuli. The base Neurodesign package allows the user to input one ITI, which is then used between all stimuli.\n", - "\n", - "This modificaiton allows you to add custom ITI for each pair of stimuli, representing the ITI distribution the occurs when a specific pairing of stimuli occurs in the order.\n", - "\n", - "* **`conditional_iti`** `(dict)`\n", - " * Adds a new input of ITI that can be calculated from the chosen probability distribution of the users. This variable holds the information about the probability distribution for an ITI between two specific stimuli.\n", - " * There is also a `'default'` key used if a relationship between two specific ITI in order is not provided.\n", - " * **Example Structure:**\n", - " ```python\n", - " self.conditional_ITI = {\n", - " (0, 1): {\"model\": \"exponential\", \"mean\": 2, \"min\": 1},\n", - " (1, 2): {\"model\": \"fixed\", \"mean\": 4},\n", - " \"default\": {\"model\": \"exponential\", \"mean\": 3, \"min\": 1}\n", - " }\n", - " ```\n", - "\n", - "**With conditional_ITI, we need a function the can read the dictionary and generate the current ITI based on the dictionary**\n", - "\n", - "* **`generate_iti(self, order, conditional_iti)`**\n", - " * Generates ITI with the first value using the `'default'` entry, then appended relative to distributions of stimuli in order.\n", - " * Ensures `n_trials` and ITI length are the same." - ] - }, - { - "cell_type": "markdown", - "id": "ef4ba6ca", - "metadata": {}, - "source": [ - "### **Example**\n", - "\n", - "For the following example, we will use the following conditional_iti:\n", - "\n", - "```python\n", - "observe_conditional_iti = {\n", - " (0, 1): {\"model\": \"uniform\", \"mean\": 2.5, \"min\": 1, \"max\": 4}, \n", - " (2, 3): {\"model\": \"exponential\", \"mean\": 2.5, \"min\": 1, \"max\": 4}, \n", - " \"default\": {\"model\": \"exponential\", \"mean\": 2, \"min\": 1, \"max\": 4} \n", - "}\n", - "```\n", - "\n", - "**What does this tell us about the relationships?**\n", - "\n", - "In the order, when we have the following two stimuli adjacent to each other: \n", - "\n", - "1. [0,1] then it will use the uniform distibution\n", - "\n", - "2. [2,3] then it will use the exponential distribution with mean 2.5\n", - "\n", - "3. else for all other pairings ([1,0], [3,2], [0,2], [0,3], etc.), it will us the default mapping\n", - "\n", - "**Note: When using conditional_ITI, you MUST define the ITI_max to be the highest possible ITI in all the distributions**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b6a9ae2f", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import neurodesign\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "38c21e45", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "# Since Neurodesign uses multi-threading internally, limit the number of threads\n", - "os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n", - "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\"\n", - "os.environ[\"MKL_NUM_THREADS\"] = \"1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "92d81053", - "metadata": {}, - "outputs": [], - "source": [ - "conditional_iti = {\n", - " (0, 1): {\"model\": \"uniform\", \"mean\": 7, \"min\": 1, \"max\": 8}, #1, 4\n", - " (2, 3): {\"model\": \"exponential\", \"mean\": 10, \"min\": 1, \"max\": 15}, #1, 4\n", - " \"default\": {\"model\": \"exponential\", \"mean\": 2, \"min\": 1, \"max\": 4} #1, 4\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "16027493", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:639: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n", - " warnings.warn(\n", - "/Users/atharvumap/Documents/Projects/neurodesign_plus/.venv/lib/python3.13/site-packages/neurodesign/classes.py:821: RuntimeWarning: divide by zero encountered in log\n", - " res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h))\n" - ] - } - ], - "source": [ - "exp = neurodesign.Experiment(\n", - " TR=2, # Repetition time (TR) of fMRI acquisition in seconds\n", - " n_trials=20, # Number of trials (short toy example)\n", - " P=[0.25, 0.25, 0.25, 0.25], # Condition probabilities: Condition 2 is slightly overrepresented\n", - " C=[[1,0, -1, 0],[1, 0, 0, -1], [0, 1, -1, 0], [0, 1, 0,-1]], # Contrasts: C1 vs C3, C1 vs C4\n", - " n_stimuli=4, # Total number of stimulus conditions\n", - " rho=0.3, # Temporal autocorrelation in fMRI noise\n", - " stim_duration=1, # Duration of each stimulus (in seconds)\n", - " t_pre=1, # Pre-stimulus baseline (e.g., fixation)\n", - " t_post=0.5, # Post-stimulus period (e.g., feedback)\n", - " ITImodel=\"exponential\", # Inter-trial interval (ITI) sampling model\n", - " ITImin=1, # Minimum ITI\n", - " ITImax=15, # Maximum ITI\n", - " ITImean=2.5, # Mean ITI\n", - " conditional_ITI = conditional_iti,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f8e16201", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
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-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "population = neurodesign.Optimisation(\n",
-    "    experiment=exp,\n",
-    "    R=[1, 0, 0], # 100% blocked, 0% random, 0% msequence\n",
-    "    weights=[0, 0.5, 0.25, 0.25],  # Weights for Fe, Fd, confounding, and frequency\n",
-    "    preruncycles=10,               # Warm-up iterations\n",
-    "    cycles=100,                    # Main genetic algorithm iterations\n",
-    "    folder=\"./\",                   # Output folder for results\n",
-    "    seed=100                       # Seed for reproducibility\n",
-    ")\n",
-    "\n",
-    "population.optimise()             # Run the optimization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8b24c215",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fd (detection efficiency): 0.8464233847515515\n",
-      "Ff (estimation efficiency): 1.0\n",
-      "Fc (confounding): 0.7479954180985109\n",
-      "Fe (stimulus frequency balance): 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "best_design = population.bestdesign  # The best subject from the final generation\n",
-    "\n",
-    "print(f\"Fd (detection efficiency): {best_design.Fd}\")\n",
-    "print(f\"Ff (estimation efficiency): {best_design.Ff}\")\n",
-    "print(f\"Fc (confounding): {best_design.Fc}\")\n",
-    "print(f\"Fe (stimulus frequency balance): {best_design.Fe}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c9399dd9",
-   "metadata": {},
-   "outputs": [
-    {
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-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
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-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n",
-      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[,\n",
-       " ,\n",
-       " ,\n",
-       " ]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Compute the convolved bestdesign matrix and evaluate design quality\n",
-    "best_design.designmatrix()\n",
-    "print(best_design.Xconv)\n",
-    "plt.plot(best_design.Xconv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd00e214",
-   "metadata": {},
-   "source": [
-    "**There is a lot of empty space offset, why is this?** Since we give a range of ITI along with distributions, the model uses the maximum possible ITI to calculate the timepoints.\n",
-    "\n",
-    "Then, it maps the real values onto the graph, ensuring any randomized mapping of timepoints will fit on the graph. An easy way to clean this is simply bounding the x-axis on the plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b973d515",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[,\n",
-       " ,\n",
-       " ,\n",
-       " ]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.xlim(0,60)\n",
-    "plt.plot(best_design.Xconv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13a7cae5",
-   "metadata": {},
-   "source": [
-    "#### **Observations**\n",
-    "1. Notice that the peaks are now all of different size, this is because of the varied ITI. The higher the ITI, the higher the peak displayed on the diagram as the set of timepoints considers both the stimuli_duration and the ITI. \n",
-    "\n",
-    "2. Each stimuli's peak begins when the previous one reaches its peak, but notice how with higher ITI and higher peaks, the stimuli takes more of the x-axis, reflecting different ITI as desired."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv (3.13.9)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/tutorial_neurodesign_base_overview.ipynb b/tutorials/tutorial_1_neurodesign_base_overview.ipynb
similarity index 100%
rename from tutorials/tutorial_neurodesign_base_overview.ipynb
rename to tutorials/tutorial_1_neurodesign_base_overview.ipynb
diff --git a/tutorials/tutorial_2_comparing_designs_across_experiments.ipynb b/tutorials/tutorial_2_comparing_designs_across_experiments.ipynb
new file mode 100644
index 0000000..9338d23
--- /dev/null
+++ b/tutorials/tutorial_2_comparing_designs_across_experiments.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing Designs Across Experimental Setups\n",
+    "\n",
+    "This tutorial addresses a critical question when optimizing your fMRI designs:  \n",
+    "**When can you compare efficiency metrics between different experimental configurations, and when can't you?**\n",
+    "\n",
+    "We'll work through concrete examples showing how to properly compare ITI models, stimulus timing strategies, and other parameters on equal footing.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from neurodesign import classes, generate\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (10, 4)\n",
+    "plt.rcParams['figure.dpi'] = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. What Makes Metrics Comparable?\n",
+    "\n",
+    "Fe (estimation efficiency) is computed as:\n",
+    "\n",
+    "$$\n",
+    "F_e = \\frac{n_{\\text{contrasts}}}{\\text{trace}(\\mathbf{C} \\cdot (\\mathbf{X}^T \\mathbf{W} \\mathbf{X})^{-1} \\cdot \\mathbf{C}^T)}\n",
+    "$$\n",
+    "\n",
+    "The design matrix **X** varies between designs — that's what we optimize. But the whitening matrix **W** is a property of the *experimental container*. It depends on:\n",
+    "\n",
+    "- **`n_scans`** = ceil(duration / TR)  \n",
+    "- **`rho`** (temporal autocorrelation)\n",
+    "- **Drift polynomials** (derived from `n_scans`)\n",
+    "\n",
+    "Two Experiments with the same `duration`, `TR`, and `rho` produce **identical W**. Their Fe values are directly, linearly comparable. Two Experiments with *different* W values are like rulers measured in different units — you cannot compare the numbers.\n",
+    "\n",
+    "Let's see this concretely."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Case 1: Comparing Designs Within One Experiment\n",
+    "\n",
+    "This is the simplest and most common case. All designs share a single `Experiment` object, so they share the same `W`. Fe values are always directly comparable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\vguigon\\Desktop\\Research_directory\\Lab_SLD\\neurodesign-plus\\neurodesign\\classes.py:638: UserWarning: the resolution is adjusted to be a multiple of the TR. New resolution: 0.1\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\vguigon\\Desktop\\Research_directory\\Lab_SLD\\neurodesign-plus\\neurodesign\\classes.py:806: RuntimeWarning: divide by zero encountered in log\n",
+      "  res = (h - 1) * np.log(s) + h * np.log(l) - l * s - np.log(gamma(h))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All designs share the same Experiment:\n",
+      "  Same object? True\n",
+      "  n_scans = 56, W shape = (56, 56)\n",
+      "\n",
+      "Design           Fe (raw)     Fd (raw)   Fe ratio vs D1\n",
+      "------------------------------------------------------\n",
+      "Cyclic              19.98         0.16             1.00x\n",
+      "Blocked            112.66         0.19             5.64x\n",
+      "Block+jitter       171.28         0.21             8.57x\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define one experiment\n",
+    "exp = classes.Experiment(\n",
+    "    TR=2,\n",
+    "    n_trials=20,\n",
+    "    P=[0.3, 0.3, 0.4],\n",
+    "    C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3,\n",
+    "    rho=0.3,\n",
+    "    stim_duration=1,\n",
+    "    t_pre=0.5,\n",
+    "    t_post=2,\n",
+    "    ITImodel=\"exponential\",\n",
+    "    ITImin=2,\n",
+    "    ITImax=4,\n",
+    "    ITImean=2.1,\n",
+    ")\n",
+    "\n",
+    "# Design 1: cyclic order, fixed ITI\n",
+    "design_1 = classes.Design(\n",
+    "    order=[0, 1, 2] * 6 + [0, 1],\n",
+    "    ITI=[2] * 20,\n",
+    "    experiment=exp,\n",
+    ")\n",
+    "design_1.designmatrix()\n",
+    "design_1.FeCalc()\n",
+    "design_1.FdCalc()\n",
+    "\n",
+    "# Design 2: blocked order, same fixed ITI\n",
+    "design_2 = classes.Design(\n",
+    "    order=[0, 0, 1, 1, 2, 2] * 3 + [0, 1],\n",
+    "    ITI=[2] * 20,\n",
+    "    experiment=exp,\n",
+    ")\n",
+    "design_2.designmatrix()\n",
+    "design_2.FeCalc()\n",
+    "design_2.FdCalc()\n",
+    "\n",
+    "# Design 3: blocked order, jittered ITI\n",
+    "iti_jittered = generate.iti(ntrials=20, model=\"exponential\", min=1, mean=2, max=4, seed=42)[0]\n",
+    "design_3 = classes.Design(\n",
+    "    order=[0, 0, 1, 1, 2, 2] * 3 + [0, 1],\n",
+    "    ITI=iti_jittered,\n",
+    "    experiment=exp,\n",
+    ")\n",
+    "design_3.designmatrix()\n",
+    "design_3.FeCalc()\n",
+    "design_3.FdCalc()\n",
+    "\n",
+    "print(\"All designs share the same Experiment:\")\n",
+    "print(f\"  Same object? {id(design_1.experiment) == id(design_2.experiment) == id(design_3.experiment)}\")\n",
+    "print(f\"  n_scans = {exp.n_scans}, W shape = {exp.white.shape}\")\n",
+    "print()\n",
+    "print(f\"{'Design':<12} {'Fe (raw)':>12} {'Fd (raw)':>12} {'Fe ratio vs D1':>16}\")\n",
+    "print(\"-\" * 54)\n",
+    "for name, d in [(\"Cyclic\", design_1), (\"Blocked\", design_2), (\"Block+jitter\", design_3)]:\n",
+    "    print(f\"{name:<12} {d.Fe:>12.2f} {d.Fd:>12.2f} {d.Fe / design_1.Fe:>16.2f}x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Key takeaway:** Within one Experiment, Fe ratios are directly meaningful. If Design A has 2× the Fe of Design B, it provides contrast estimates with half the variance.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Case 2: Comparing Across `n_trials` with Different ITI Models\n",
+    "\n",
+    "A natural question: *\"Is an exponential ITI model better than a uniform one?\"*\n",
+    "\n",
+    "The naive approach — create two Experiments with `n_trials=20` but different ITI models — **gives misleading results**. Here's why:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Comparing Experiments with n_trials=20:\n",
+      "  Exponential: duration = 112.0s, n_scans = 56, W shape = (56, 56)\n",
+      "  Uniform:     duration = 130.0s, n_scans = 65, W shape = (65, 65)\n",
+      "  Same shape? False\n",
+      "  Same W? False\n",
+      "\n",
+      "These Fe values are NOT comparable — different W means different ruler!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# WRONG: Different ITImean → different duration → different W\n",
+    "exp_exp = classes.Experiment(\n",
+    "    TR=2, n_trials=20,\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1, t_pre=0.5, t_post=2,\n",
+    "    ITImodel=\"exponential\", ITImin=2, ITImax=4, ITImean=2.1,\n",
+    ")\n",
+    "\n",
+    "exp_uni = classes.Experiment(\n",
+    "    TR=2, n_trials=20,\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1, t_pre=0.5, t_post=2,\n",
+    "    ITImodel=\"uniform\", ITImin=1, ITImax=5, ITImean=3.0,\n",
+    ")\n",
+    "\n",
+    "print(\"Comparing Experiments with n_trials=20:\")\n",
+    "print(f\"  Exponential: duration = {exp_exp.duration:.1f}s, n_scans = {exp_exp.n_scans}, W shape = {np.array(exp_exp.white).shape}\")\n",
+    "print(f\"  Uniform:     duration = {exp_uni.duration:.1f}s, n_scans = {exp_uni.n_scans}, W shape = {np.array(exp_uni.white).shape}\")\n",
+    "print(f\"  Same shape? {np.array(exp_exp.white).shape == np.array(exp_uni.white).shape}\")\n",
+    "print(f\"  Same W? {np.allclose(np.array(exp_exp.white), np.array(exp_uni.white)[:min(exp_exp.n_scans, exp_uni.n_scans), :min(exp_exp.n_scans, exp_uni.n_scans)])}\")\n",
+    "print()\n",
+    "print(\"These Fe values are NOT comparable — different W means different ruler!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exponential experiment (ITImean=2.1) has a shorter total duration than the uniform experiment (ITImean=3.0). Fewer scans means a different whitening matrix, so Fe is computed on a different scale. Comparing raw Fe between them is like comparing meters to feet.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Case 3: The Right Way — Fix Duration, Let Trials Vary\n",
+    "\n",
+    "To compare ITI models properly, **fix the total duration**. Both experiments get the same number of scans, the same whitening matrix, and Fe is directly comparable.\n",
+    "\n",
+    "Duration imposes a tradeoff: a shorter mean ITI fits more trials into the same window (more data points), while a longer mean ITI gives each trial more temporal separation (less overlap, potentially better deconvolution).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Comparing Experiments with duration=200:\n",
+      "  Exponential: n_trials = 35, n_scans = 100\n",
+      "  Uniform:     n_trials = 30, n_scans = 100\n",
+      "  Same W? True\n"
+     ]
+    }
+   ],
+   "source": [
+    "DURATION = 200  # Fix total duration for both experiments\n",
+    "\n",
+    "exp_exp_fixed = classes.Experiment(\n",
+    "    TR=2, duration=DURATION,    # ← duration, not n_trials\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1, t_pre=0.5, t_post=2,\n",
+    "    ITImodel=\"exponential\", ITImin=2, ITImax=4, ITImean=2.1,\n",
+    ")\n",
+    "\n",
+    "exp_uni_fixed = classes.Experiment(\n",
+    "    TR=2, duration=DURATION,    # ← same duration\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1, t_pre=0.5, t_post=2,\n",
+    "    ITImodel=\"uniform\", ITImin=1, ITImax=5, ITImean=3.0,\n",
+    ")\n",
+    "\n",
+    "print(\"Comparing Experiments with duration=200:\")\n",
+    "print(f\"  Exponential: n_trials = {exp_exp_fixed.n_trials}, n_scans = {exp_exp_fixed.n_scans}\")\n",
+    "print(f\"  Uniform:     n_trials = {exp_uni_fixed.n_trials}, n_scans = {exp_uni_fixed.n_scans}\")\n",
+    "print(f\"  Same W? {np.allclose(np.array(exp_exp_fixed.white), np.array(exp_uni_fixed.white))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metric          Exponential              Uniform  Ratio (exp/uni)\n",
+      "----------------------------------------------------------------\n",
+      "Fe            67.94 ±  5.56        73.69 ±  5.23           0.92x\n",
+      "Fd             0.28 ±  0.02         0.25 ±  0.02           1.15x\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample many designs under each ITI model and compare Fe distributions\n",
+    "def sample_fe_distribution(exp, n_samples=10, seed=42):\n",
+    "    \"\"\"Sample random designs and collect raw Fe values.\"\"\"\n",
+    "    rng = np.random.RandomState(seed)\n",
+    "    fes = []\n",
+    "    fds = []\n",
+    "    for _ in range(n_samples):\n",
+    "        order = list(rng.choice(exp.n_stimuli, size=exp.n_trials, p=exp.P))\n",
+    "        iti, _ = generate.iti(\n",
+    "            ntrials=exp.n_trials, model=exp.ITImodel,\n",
+    "            min=exp.ITImin, max=exp.ITImax, mean=exp.ITImean,\n",
+    "            seed=rng.randint(100000), resolution=exp.resolution,\n",
+    "        )\n",
+    "        try:\n",
+    "            des = classes.Design(order=order, ITI=np.array(iti), experiment=exp)\n",
+    "            result = des.designmatrix()\n",
+    "            if result is False:\n",
+    "                continue\n",
+    "            des.FeCalc()\n",
+    "            des.FdCalc()\n",
+    "            fes.append(des.Fe)\n",
+    "            fds.append(des.Fd)\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "    return np.array(fes), np.array(fds)\n",
+    "\n",
+    "fes_exp, fds_exp = sample_fe_distribution(exp_exp_fixed)\n",
+    "fes_uni, fds_uni = sample_fe_distribution(exp_uni_fixed)\n",
+    "\n",
+    "print(f\"{'Metric':<6} {'Exponential':>20} {'Uniform':>20} {'Ratio (exp/uni)':>16}\")\n",
+    "print(\"-\" * 64)\n",
+    "print(f\"{'Fe':<6} {fes_exp.mean():>12.2f} ± {fes_exp.std():>5.2f} \"\n",
+    "      f\"{fes_uni.mean():>12.2f} ± {fes_uni.std():>5.2f} \"\n",
+    "      f\"{fes_exp.mean()/fes_uni.mean():>14.2f}x\")\n",
+    "print(f\"{'Fd':<6} {fds_exp.mean():>12.2f} ± {fds_exp.std():>5.2f} \"\n",
+    "      f\"{fds_uni.mean():>12.2f} ± {fds_uni.std():>5.2f} \"\n",
+    "      f\"{fds_exp.mean()/fds_uni.mean():>14.2f}x\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qlDNe+9m1a1fbuHFj0LlWyZRqUGVV4yzZVLVXzZ3U8aaoqrlq4agKbyiVOqmUWCWZ6mfDo9K8kiVL+o+1SutUW0+dIgb2R+Q1NwukUkP136GSmcDvi9eRf6zfF5WsimrSBPI+d+rUqa50OhKdo9ASe9Xg0fUR2MzPu5YCS2V1HYi2/dhjj80w37vGfv/9d1diphJRfY+9fda2q/RSTUMya+Kpa00laYHfT91fVGp/pHR9es3wAtftnUfV9tL2q8RXzQ2XLFmS47633rlPx1GNgLwuUmwVeN9QLYxAeibFi9fsO1KMFe2zJCt6PkZLzxnVUPDo+aLntLYhtOlTPEX7zA88doH9Mem5oRgp2vgqmv1UfKXmVYHNm1R7Rs+kePS1GU96FqkGSmB8pWZaqo0dyoubVDM7sJ8h1dBRfOQd602bNrkayqoZHRhPqRaxnpOh8ZWW0d8C4ysdY52rWOMrPfuVXwmNrxQ36VyriWtmzQID4ysv9tFzXd8jdWcQSNsXWGtaz3v9tlG86MVU4eIrbZ/66lKtdf07cL8VXymOyyxuSeQ9RrFk6DkKPS46ftpGHZfsxleJ/N4SXyUeSSnETF9YdfoWOIl+UHo/TPXDKXBSlVs9dI6UqgJnNrKUbnz6cammQ2qTroe6muXE0kePfvSqbXi0jj/++AzzTjjhhCNurpgVdU6qm66q5gZSszE9wPT3QIHJgsCbbLTt66PZTwUAak/vVTHXD3kFJToPOXFEMFUlV/CiJlKqmh2parl3LBUcBNIDTH38eH/3/h96rFQFWssF0vdFTbBCvys6ppLd74vXHNGjvrOUAFEzNH0nFJzox09ooumrr75y32U1pdP1o21RUz4JXTb0WvICRDUvCDffu8Z0nLV9atYbut/qWyur/dbx1fUV2mdW6HnJjtBRrzzqx0DJSwXL6vdA26rAJvSY5ITvrXfuI/XNAiD9YqvA+0ZofzbxuPd5vKZWkZ7V0T5LsnuvjSXu0I95NaVOlGif+R7FjKH33XjEV4H7qQSNRqYObMKnf6u5VujzJCdQPKXEqpqxqT+mWOMrb5+ziq/CvVfxla5LdcMRGmvoOo9XfKVmZWrar+Ze6sNJfWmpyZ36mQqkWE/N+xUTKTmi7fCSIaHfn3DXkt6XVXyl60QVA9Snaeg+q2lhNPFVou4xkb7zqkCg61fxlbpM8LpDOJL4KlHfW+KrxMsbQ3khKbx+Y9TeVz+wQh3pyCqq+fTTTz9lOkSybhYqRVKfB2rPrlImdfintsKaF/pjNhw9ZAIz7PGg7Qp9mEk8SvqivUFGGqEi3HZllz5DgcfLL7/s2tYr0aGaUzmtFM+jWn3qH0ulKEo4qM15Mr8v6vRz6NChYf8eGoBkRdsv4R6muv5VW0udNCpBrJIw9ceg74Qeyur76bzzznMBoLZHn62HuErO1OdRYJ9QmV1LWV1j3nruuusu94MmnHgF17F+5wJL7AJHM9RxUy1B9U2hANfr80LH7Ei3L97fW+/c6wcjAMRC/aXoHqcfzZFk9SyJRrh7bSLupYmsSZWK+MqrLXXbbbe5vhRV2KrjHjj4T06iPjMVT6tmk7Y1maPWKdbQtRyuDy4JrRWdFSVMdJ2Fi69UK0e1k5R4028OFbrpO6Fa4eovSYkiFZjre6W+k5T0URJGtYHUf1G84yvF25H62VQBWyq+c+G+8xoASNeIEnn6vaACRxXgqiJBaCf5iUJ8lbOQlELceNl1PQgCS/jiRcmmffv2RfwxG0iZd02PPfaYu7mps3N1aq1O/OKd5fZqiAVS8ixwZApl3sNVBw3N2seybaoGrYeQPl/VegM7OtRDMFw16UTvpxc0KXBVUlClR3r4R3POUkG1UNQJpqpeq0lYpMSpdyxXrlwZVONJ1YQ1xLF3vXvL6Vh5zfC8hKqW84ba9r4v3377rUsGxeOa1L7owa/PCUcJME0PPPCAqxWm/R41apTrLFznSkGjRsALrJkTjya3gbxjp8AjO/cIHd8ZM2a4ks7ABLPOS6hov3NZ3XO0zepwNfAcebW6ctr3VudeAVOsATeAnM27bygZHlgLINy9Lzs0cpvWHU0BUmbPEolnjBUp7lAze+8+p3t9uBFYw93ro922aJ/5ydxPUbMu1VZTDXTFw3qWZmdglGRQPKICHRXutG/fPuKP+cBjHRg3efO8vwfGV6FCvweKr9SUS9dmPJKgig21zkjxlf6mJviatH0NGzZ0cbD2XfGluihQHKEEjCfSurLLGzFPiaHsxlfR3mNi+c5FoqaGSs4pkafkpUdJqZz4vdX7VWHBa82A+KP5HuJGiQeVBGgEhXD9OR1JVWv9eFdphG6EGlUhEmWyQzPcejiI14TP6zMo3A01O1Q6EtgPzoIFC+zrr792D+HAB5bajQceA+2TahIFimXbNAKHhI6o4dW8ideoPLHsp1cSo0lV/PXQURB1pLXkEkmBtJIMmfVLpAeZag5p9MHA60sj7amasXes1deQAgMF6HoIBo68EnpOVWqo46laZaEUbGpklVgoONXnh45Co5FC1OdEIP2g0MPV+054pUWB+6b9ChccHAklrDWKkEZCUf8Qsd4jdM1rX7zR7kQBmEbNCxXtdy4z4Y6LrvnQobdzyvd28eLFOWoEJgDx4T1n9QwKFM2IWlnRD0nVftIzTjVCI4nmWSJqAh6v+Er32sD+ZTSKrWppqVazd3/WvV7Pq2XLlvmX0/Ml3Giu0W5btM/8eIlmP0WJHV0LSnaoFpD6bcrJNTdUK1rxlWoPRaK4RbGB4qbA60iFmhql1zvWqkmjeF5N6gObd6mJoEb5Do2vFBsMGjQow+fpGs7O9alna2h8peaV+/fvD5qn61HJocziK8WHqh0UT/ocdV+iuDvc6JRZxVex3GNi+c5ltr1KNgXWrlJ3IPqtkRO/t4qv1BdtaP+wiJ+c+0sRaUcJKf1YvOaaa9ywqUpG6Ae62pOrDxaVWERTzVhVOnWT141KpQv6EakaHLoR6IYXrmmgRw8r3ejVdls3TXUoqB/92jbvx6BKTdThnjqIVMZb1XLVJDCzZoGZUXMjDVurmjZ6COkGrqZUd999t38ZNSHUj04l7jQ0q9p16wGsG1zg8KKxbJtq3aiKrtqPe9WDlSjSMVDpVOvWrbO1P0eyn4G1pRSMSE5tuufRcdOUGV3HauanvsoUBKrKsUpidK2pjwdvH5UYUpJLw9WqxE8lmCpdUXIntE8pfU8mTpzoOn1VjSR9P3TNK5Gi+So9UrAWC/Xddf/997trymuCoSrkGhpYw+zqmlJApia2XgAjCnz1IFcVdG27aiLpe6NAMVzy6EioDxJdR/oxo2aTOi6qJaSgXE0SlDiKRNun46RhlBW86Lui0sdw/Q9E+53LjIZl1vp1P1Ewo3Opdehzvf5Xcsr3VvunADGzpD2A9KQf42purmeO7nfqNFq1RtVPXyyU+FBCQzUidP9ZuHCh+xGrH4d6LmTWvCeaZ4moM2nVUtH9V8Oyqz+ZwA6aY6F7qO7haiao2hTeD3k9iz2KNdUMSvdpLadEgWJRbWNoh8nRblu0z/x4iWY/A+OrSy+91P07XNIlJ9HzLrCGeDiKm9Qvk/o90vNQ17lighEjRria+HfccYd/WTWL07NYMYSe8eqAXIVSeq4HPpO1HsUyWl6doyvG0eeoFpP6EdW6vWMYS3yl61012LzaMvq3arsrCaYYQAWw+p2i7fc6K9d3VQXqeu7r/HrftXg37ZQnnnjCxZO6phVfaZt0jPQ90HWvf8fjHhPLdy4SnUd9D/X9UrcfimEUH+q3RmCyKyd8b1XR4vPPP3ed8SOBEjSqH3Ihb+jNhQsXZjn0rIaS1/CjGtL2uOOO83Xv3j1ouNtI7wscCrlQoUK+ChUq+M455xzfY489lmG49nDDgS5ZssQNN3zssce6oXM1zOyFF16Y4bM1nH3jxo3d0KGBQ51qyFENPRpOpKHkNRTskCFDfNWrV3efqeHpv/322wzvf/PNN321a9d2n6lhbTWca7hhVSNtW7ihVjVM7cMPP+yrVauWO17aBg0bHDjErOgzOnTokGGbohkKN9b9lE2bNvkKFCjgO+GEE3zxur60/eGGoI1muONw+5KZSNeBhpU98cQT3bGuVKmS78Ybb3RDAod6/vnn3TnRcWrSpIlvzpw5YY+1hr998sknfSeffLJbtkyZMu7c65x6w3KL9jvccLihtmzZ4oaH9oZhll9++cV37bXXuu+hvo9ly5b1tW7d2vfZZ58FvffDDz/01a9f3y1Ts2ZNt10aPjfw+5XZteQNBR3N8V69erWva9eubuhdHctq1aq57+m7776b5T7u2LHDd8011/hKlizp7jH6tzdUeeDQyNF+5zK7Jg4fPux7/PHH3fI6P40aNXLDVOfE7+0LL7zghpfevXt3lscQQPrFVvv27fPdeuutvnLlyrnnU8eOHX3r16/PMFx7ON59zpv0nNCzoFmzZu7es3bt2ojPVf0/lmeJhrBX3FasWDH3fu/Z5d0Lt23bFvVQ8nqm6D5+/PHH++/B3vYEmjZtmhtKXvffunXruveEW2ekbQuNJWN55uterGd4qHDPiXBi2U9v2HrFCnr+6ZqIRlYxkj4/0k/CaOOmwH3JTKTrYMKECW6/tf+6tq666irfhg0bMrz/vffe85100kluuXr16vkmTZoU8Vi/9NJL7rms812iRAnfqaee6rv77rt9GzdujCkO9o57+fLlfYMGDfLP2759u9tfXSP6Tuqc6Ds1ceLEoPd+9dVXvjPOOMNtR9WqVd02KB4J/H5ldi3FEncpDtQ8xRW6bhVnnXfeee5YZCWWe0y037nMrolXX33Vf83rGOp7mBO/t59++qn7nJ9//jnLY4jsy6f/JDLpBSDv0ZCpqmo9YMCATKttI/5UK0ild6pxiLxDHaqqaaQ6pgcA5E6qoabaIqo5rGZJSB7VTFPNd9W4itRJNnIf1WJXDbdYmicidvQpBSDu1IeSmqKpiRqSS/03qElGLH0nIb1NmTLFBcmqtg4AyL3U5476B1IzPiSXmhKqmaAGTkLeoH7NPvrooxzfVDY3oKYUgLhRnxPqcFK1o9Q3jvrjAQAAQPZpkA31taMfx+rcPNq+ewAgHZCUAhA3aj7kDRGtzlSrVauW6k0CAABIaxohUXGVOqRWbfTsDs4DADkRSSkAAAAAAAAkHX1KAQAAAAAAIOlISgEAAAAAACDpCloec/jwYdu4caOVKFHCDe8IAACg3gz+/PNPN9x6/vyU2cWK+AoAAGQntspzSSkFTNWrV0/1ZgAAgBxo/fr1dswxx6R6M9IO8RUAAMhObJXnklIqwfMOTMmSJVO9OQAAIAfYvXu3S6p4cQJiQ3wFAACyE1vluaSUV6VcARNBEwAACETTs+whvgIAANmJreg0AQAAAAAAAElHUgoAAAAAAABJR1IKAAAAAAAASZfn+pQCgJzg0KFD9vfff6d6M4A8o1ChQlagQIFUbwYAIEEOHz5sBw8eTPVmAHlGoTjFViSlACCJfD6fbd682Xbu3JnqTQHynNKlS1vlypXpzBwAchklo9asWeMSUwDSK7YiKQUASeQlpCpWrGjFixfnxzGQpGTwX3/9ZVu3bnWvq1SpkupNAgDE8R6/adMmV2NDw8/nz08PNUA6xVYkpQAgSdRkz0tIlStXLtWbA+QpxYoVc/9X8KTvIE35ACB3+Oeff9yP46pVq7oCPwDpFVulNI38wgsvWP369a1kyZJuat68uX366aeZvuedd96xE0880YoWLWqnnnqqffLJJ0nbXgA4El4fUgRMQGp437107c9t8ODBdvrpp1uJEiVc8HfxxRfbypUrs3xfVrGTSjsHDBjgSjkVYLZp08Z+/vnnBO4JAMS30E8KFy6c6k0B8pzicYitUpqUOuaYY+yJJ56wxYsX26JFi+zcc8+1Tp062YoVK8IuP3fuXOvSpYv17NnTvvnmGxeMaVq+fHnStx0Asosme0BqpPt37/PPP7ebb77Z5s+fb9OnT3cBYNu2bW3v3r0R3xNN7PTUU0/Zf//7Xxs1apR9/fXXdtRRR1m7du1s//79SdozADhy6X6PB/Lq9y6fT8VjOUjZsmXt6aefdsFTqM6dO7vA66OPPvLPO+OMM6xhw4YukIrG7t27rVSpUrZr1y5XOwsAkkU/8NQJZ61atVyNBQA55zuYjvHBtm3bXI0pJavOOeecsMtkFTspDFSTlzvvvNPuuusu93cdg0qVKtmYMWPsiiuuiGpb0vH4AcgdiK+A9I6t8uekapfjx493gZOa8YUzb948V6U8kEryNB8AgESrWbOmDR8+POrl9aNeo5Jk5dVXX3U1XnKzX3/91ZWmLV26NOr3dO/e3dXqica9995rt9xyi+UlCvK8Ar1IsoqdFEhqAIbAZRRANmvWjPgKAJBwxFbZl1tiq5R3dP7dd9+5JJQybEcffbS9//77Vq9evbDLKmhSyV0gvdb8SA4cOOCmwGwdAOQ0PccsTNpnvdr99JjfowfY66+/nmG+ftxOmTLFchsFPLfffrvrmD7QwoULXdOmeNLz78EHH3T9/iTKjh077KqrrrJly5a5f6t2jZrLP/744/6Sq9mzZ1vr1q0zvFcjGmmo31iOUzgaEUnrKl++vCWCavnUrl3b7rjjDvf/3E7DnuvYn3nmmXbKKadEXC6r2Mn7P/EVgNwmmbFVduIrYqv/h9gqWF6MrVKelKpbt67L7Km0791337Vu3bq5auiRElPZ6RT04Ycfjsu6AOQg4zrHtvyVExK1JXnGBRdcYK+99lrQvCJFilheUqFChbivU88+BS9KLiSKhsdWoPToo4+6fVi1apXrm+j333+3cePGBS2rjrMDq1gryDpSBw8edB3QRgrA4kEBmQJ5DaKibgByO50/9Qv15ZdfpuTzia+Q60UbZxBf5E5/R+6rL0ihI0umEFsRW2VXboqtUt58TweyTp061rhxYxfgNGjQwEaMGBF2WR3wLVu2BM3T68xORP/+/V3Cy5vWr18f930AgLxAQZLut4FTmTJl/CVBup9/8cUXQZ0n66Hr3bdbtWplffr0cZOaB+lBp1KswK4N//jjD+vatatbr0bzaN++fdAoYF6V7alTp9pJJ53katgqoFMpUaBXXnnF/V1t2zXq2PPPP5+hqvOkSZNc6ZU+R88er6mS9qVHjx7umaHlND300ENhq5gPHTrUjWamEj6VVt100022Z8+emI6rmq537NgxbNXqZ555xo2IVq5cORfoZHdkEx3PG2+80Zo0aWI1atSw8847z21r4Pny6JwFnmMFXeFkdZwGDRrkzqWCsOuuuy5DFXM121f/keqDQCO+qZAq0vM/MMjU8dbyOiZqchbYybeOo45nbqfvkPqImjVrlhs0JjNZxU7e/4mvACD5iK2IrQLl1dgq5UmpcNXRA6uDB1IzvxkzZgTN0+gzkfqg8r7oOmmBEwAgvhQUqarxNddc4x6kGuVLQZECmMBmQaqmXrBgQVuwYIF7SCrw0DKBAYNGY/3www9dIKOg6l//+ldQwPDXX3+5gOKNN96wOXPm2Lp16/wdNMtbb73lhrd/7LHH7IcffnDVqLUtoVXk77//fvc+PchPOOEEN0LZP//8Yy1atHDBkZ4XCsg0Ba4/kIIKjVqmUWO1/pkzZ9rdd98d07FTTRcFNKGUcFi9erX7v9atoFGT54YbbnCBY2ZTJBs3bnSBY8uWLTP8TR1gK1g7//zz7auvvoq4jqyOk86RAlLvWgj3vFdCRVXrv//+e3fO7rvvPps4cWLYz9P6dY6uvfZad14VuF1yySVBgXfTpk1tw4YNLkjLjbSv+uGhrg50rSnozEpWsZPWoQA5cBk1xdMofMRXAJA6xFbEVpvySGyV0uZ7KmVTpvbYY4+1P//801Vz04FQllaUBaxWrZqrQSW33XabO8lDhgyxDh06uIydvmAvvfRSKncDAPIE1cwIfRjrQadJVH1ZP3ZVcqNmRWqOfdFFFwUtrxKvYcOGuVIdld6oX0G97t27tyu1U8Ckh7Ueyl4QpPdMnjzZLrvsMjdPQZRGDTvuuOPca/1If+SRR/yfMXDgQPec0EPV+9GtB/OLL77otsmjh7yeJaJmSCeffLKreq3SP5U2ahuzqhKtYNGjEiwdAwU0gaWHmVF/AQo0NfpZuBK45557zgoUKOC2SduqxIGOlWifIwV0kSjw+OCDD2zfvn2u5CswaFWwpOOqIE6FQ/qbAmIlJ0477bQM61LpbWbH6dxzz3UjunlCg5lChQoFNf/SeVKwrMDp8ssvDxs4KbDVeVWJpKhkL5B3HNeuXevOR26jEl3FSjqHJUqU8Pf5pPOgEs7sxE46f7qOde0ef/zx7jwo0NWxjLYjVABA9hBbZURsVSrPxVYpTUpt3brVBU86GDr49evXdwkpZRBFGdrAqm36IikYe+CBB9wXVcGTvkyZdfAJAIgPVcdWm/JAgaN+6UGqQEf3cj3YFBCF0lD0etB6VBNDQY6qG6uERiV9GvXLo2rECrD0N4+qhHtBk/fA1/NEVN1YJWCquuwFGKIHrp4zgbSdgesQrUdBSrQ+++wz9+P/xx9/dLVL9DnqXFMljtrOrCiAkXBDWCuQU9AUuI0KNAOrgsfaJ4HOiQLLn376yRUM9e3b1x/k6ThrCnzm6ljqPSo5jVW4EspQI0eOtNGjR7vnvY6F+kdQaWI4KhlU1XgFS+rfQCPqXHrppf5mDuIlZnT8cyPv+6eANpD6I1FJeHZjJ5VA67ujHz0K5s866yzXyS5DqwNAYhFbZURslfdiq5QmpTRMY2ZUayqUsrleRhcAkDxq268+ADMzd+5c93918qgp3qOpeKVAgRSIedWMvT4HXn755aAATAKDkND1eMGcqj1HS6VTF154oetPQNXZFUSquriCNgUA0QROCgz12ervIZr9DNw+lRq++eabma4/tA8Gry8DBYfa3rPPPtvVivECx1Cqsp3djrSzOveqsaPSSAXOCqBV80edaKr0MBydP5UW6xqbNm2aPfvss66ZgJb3mrHpmktUp6k5QWB1+njGTrq2VDocWCoOAEg8YqtgxFZ5M7ZK+eh7AIDcQSU/GjJWQcuECRNcdW6VdgXW2gh9KM6fP9/V3NBDUZ1nqjRMy3hVzDXErkYsiXZEVvWxoGrGv/zyixumN7tUMqkSxswsXrzYBTJ68Hv7GKnNfmafo31TFXiVTsUiO1XMA3lBWKR+HEV9QkQKqqI9TpF4TQnUKWjgNZQZBY8aSUeT+klQqbH6V1KppKhpgwJOlYQCAJDuiK2IrfJCbEVSCgAQFT1gvT5sPKoSrpFe9PC8+uqrXdVfjRqiUVtUFVhBRb9+/fzLqyqxHnLXX3+9LVmyxJXIaBlRAKWhdVU1XH0UqHTn3nvvdf3jaH601Jb+1ltvdVXKtR3abvWhoxIz7wGbFbWZV0mY+hlQ1WaVzIWWzqlkU30waB/Uh4ACAfUbECsdM5WYBfahEI1Yqph/8sknbqSe008/3fVdoc5DdV4UgHj9A6hjTZWKKehQNXn1e6DORVVydiTHKRKd77Fjx7pm+/pcVWNfuHBhxM67FVDrcxRgar/1etu2bS7g9mjEG5VQelXNAQDIyYitiK1C5cXYKseNvgcAyJnUx4xKdgIn9T0jqmKtDhAV8Ij+po6U1Y/Nt99+61+H+hFU+3ZVXVanzeqEWf3YBPaN07hxY1d1W9WOVXVcD/3Q6taZ6dWrl3voa10K3tTJs0ZWiWakMo9KmVSFu3Pnzq66soZgDqVAQSPcPPnkk65/HvX54HUuHQtVSdc+qlPORFEgoVJWnS8FGip1VUep6mDVo2rx6jzTO2Y6byqNVV8DR3KcIlHwrI419V41B1DJbWDJXiiNRKMRgTRikEb00bWloFsDpgRWWw/s7wIAgJyM2CoYsZXlydgqny+aDgpyEXWWpgyvLlCGLwbS2LjOsS1/5QRLNZWQrFmzxj3A82IHwuqcWR0tqtQIwdTfj0ZhUQe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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the distributions\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].hist(fes_exp, bins=30, alpha=0.7, label=f\"Exponential (n={exp_exp_fixed.n_trials} trials)\")\n",
+    "axes[0].hist(fes_uni, bins=30, alpha=0.7, label=f\"Uniform (n={exp_uni_fixed.n_trials} trials)\")\n",
+    "axes[0].set_xlabel(\"Raw Fe (estimation efficiency)\")\n",
+    "axes[0].set_ylabel(\"Count\")\n",
+    "axes[0].set_title(\"Fe Distribution by ITI Model (same duration)\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].hist(fds_exp, bins=30, alpha=0.7, label=f\"Exponential (n={exp_exp_fixed.n_trials} trials)\")\n",
+    "axes[1].hist(fds_uni, bins=30, alpha=0.7, label=f\"Uniform (n={exp_uni_fixed.n_trials} trials)\")\n",
+    "axes[1].set_xlabel(\"Raw Fd (detection efficiency)\")\n",
+    "axes[1].set_ylabel(\"Count\")\n",
+    "axes[1].set_title(\"Fd Distribution by ITI Model (same duration)\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because both experiments have the same duration (and therefore the same W), these Fe and Fd values are directly comparable. The difference you see reflects the genuine impact of the ITI model on design efficiency.\n",
+    "\n",
+    "Note how the exponential model gets more trials into the same window (shorter mean ITI).  \n",
+    "\n",
+    "More trials generally means higher Fe, but it also means more temporal overlap between HRFs, which can hurt estimation. The distributions show this tradeoff, with in our present case exponential ITI increasing the Fd but uniform ITI dominating the Fe increase.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Systematic Comparison: Sweeping a Parameter\n",
+    "\n",
+    "Let's sweep across ITI mean values to see how the amount of temporal jitter affects efficiency. We fix duration=200 throughout, so all comparisons are fair.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\vguigon\\AppData\\Local\\Temp\\ipykernel_25048\\853093107.py:19: DeprecationWarning: The numpy.linalg.linalg has been made private and renamed to numpy.linalg._linalg. All public functions exported by it are available from numpy.linalg. Please use numpy.linalg.LinAlgError instead.\n",
+      "  des.FeCalc()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ITImean  n_trials    Fe mean   Fe std    Fd mean   Fd std\n",
+      "--------------------------------------------------------\n",
+      "     1.5        40      92.97     7.99       0.30     0.03\n",
+      "     2.0        36      84.01     6.47       0.29     0.03\n",
+      "     2.5        33      81.75     6.90       0.26     0.02\n",
+      "     3.0        30        nan      nan       0.25     0.02\n",
+      "     3.5        28      70.37     9.39       0.22     0.02\n",
+      "     4.0        26      63.11     5.78       0.21     0.02\n",
+      "     5.0        23      59.27     3.83       0.18     0.02\n"
+     ]
+    }
+   ],
+   "source": [
+    "iti_means = [1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0]\n",
+    "results = []\n",
+    "\n",
+    "for mean_iti in iti_means:\n",
+    "    try:\n",
+    "        exp_sweep = classes.Experiment(\n",
+    "            TR=2, duration=200,\n",
+    "            P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "            n_stimuli=3, rho=0.3, stim_duration=1, t_pre=0.5, t_post=2,\n",
+    "            ITImodel=\"exponential\",\n",
+    "            ITImin=max(0.5, mean_iti - 1),\n",
+    "            ITImax=mean_iti + 2,\n",
+    "            ITImean=mean_iti,\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print(f\"  ITImean={mean_iti}: skipped ({e})\")\n",
+    "        continue\n",
+    "    \n",
+    "    fes, fds = sample_fe_distribution(exp_sweep, n_samples=10, seed=42)\n",
+    "    \n",
+    "    if len(fes) > 0:\n",
+    "        results.append({\n",
+    "            \"iti_mean\": mean_iti,\n",
+    "            \"n_trials\": exp_sweep.n_trials,\n",
+    "            \"fe_mean\": fes.mean(),\n",
+    "            \"fe_std\": fes.std(),\n",
+    "            \"fd_mean\": fds.mean(),\n",
+    "            \"fd_std\": fds.std(),\n",
+    "        })\n",
+    "\n",
+    "print(f\"{'ITImean':>8} {'n_trials':>9} {'Fe mean':>10} {'Fe std':>8} {'Fd mean':>10} {'Fd std':>8}\")\n",
+    "print(\"-\" * 56)\n",
+    "for r in results:\n",
+    "    print(f\"{r['iti_mean']:>8.1f} {r['n_trials']:>9d} \"\n",
+    "          f\"{r['fe_mean']:>10.2f} {r['fe_std']:>8.2f} \"\n",
+    "          f\"{r['fd_mean']:>10.2f} {r['fd_std']:>8.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the tradeoff\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 5))\n",
+    "\n",
+    "iti_vals = [r[\"iti_mean\"] for r in results]\n",
+    "fe_vals = [r[\"fe_mean\"] for r in results]\n",
+    "fd_vals = [r[\"fd_mean\"] for r in results]\n",
+    "fe_errs = [r[\"fe_std\"] for r in results]\n",
+    "fd_errs = [r[\"fd_std\"] for r in results]\n",
+    "trial_counts = [r[\"n_trials\"] for r in results]\n",
+    "\n",
+    "color_fe = \"tab:blue\"\n",
+    "color_fd = \"tab:red\"\n",
+    "\n",
+    "ax1.errorbar(iti_vals, fe_vals, yerr=fe_errs, color=color_fe, marker=\"o\", \n",
+    "             capsize=4, label=\"Fe (estimation)\")\n",
+    "ax1.set_xlabel(\"Mean ITI (seconds)\")\n",
+    "ax1.set_ylabel(\"Fe (raw)\", color=color_fe)\n",
+    "ax1.tick_params(axis=\"y\", labelcolor=color_fe)\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.errorbar(iti_vals, fd_vals, yerr=fd_errs, color=color_fd, marker=\"s\", \n",
+    "             capsize=4, label=\"Fd (detection)\")\n",
+    "ax2.set_ylabel(\"Fd (raw)\", color=color_fd)\n",
+    "ax2.tick_params(axis=\"y\", labelcolor=color_fd)\n",
+    "\n",
+    "# Annotate trial counts\n",
+    "for x, n in zip(iti_vals, trial_counts):\n",
+    "    ax1.annotate(f\"n={n}\", (x, 0), textcoords=\"offset points\", \n",
+    "                xytext=(0, -20), ha=\"center\", fontsize=8, color=\"gray\")\n",
+    "\n",
+    "ax1.set_title(\"Efficiency vs Mean ITI (duration fixed at 200s)\")\n",
+    "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.88, 0.88))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows the tradeoff:\n",
+    "\n",
+    "- **Short ITIs → more trials → generally higher Fe** (more data points for deconvolution), but diminishing returns as overlap increases\n",
+    "- **Long ITIs → fewer trials → generally higher Fd per trial** (cleaner HRF separation), but fewer data points\n",
+    "\n",
+    "The exact crossover point depends on your specific contrast structure, number of conditions, and HRF characteristics. This sweep helps you identify the sweet spot for your experiment.\n",
+    "\n",
+    "All these comparisons are valid because every experiment has `duration=200` → same `n_scans` → same `W`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Fc and Ff: Always Comparable\n",
+    "\n",
+    "Unlike Fe and Fd, the confounding efficiency (Fc) and frequency accuracy (Ff) do not depend on the whitening matrix at all. They depend only on:\n",
+    "\n",
+    "- The stimulus order (sequence of condition labels)\n",
+    "- The target probabilities `P`\n",
+    "- The number of stimuli\n",
+    "\n",
+    "This means **Fc and Ff are always comparable** across any two designs with the same number of conditions and target probabilities, regardless of timing parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Short (100s): n_trials= 33, Fc=0.3490, Ff=0.8615, Fe=56.62\n",
+      "    Long (300s): n_trials=100, Fc=0.3603, Ff=0.8571, Fe=97.10\n",
+      "\n",
+      "Fc and Ff are properties of the ORDER, not the timing.\n",
+      "Fe differs because the whitening matrix differs (different duration).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Demonstrate: same order in two different experiments → same Fc, Ff\n",
+    "order = [0, 1, 2] * 6 + [0, 1]\n",
+    "\n",
+    "exp_short = classes.Experiment(\n",
+    "    TR=2, duration=100,\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1,\n",
+    "    ITImodel=\"fixed\", ITImean=2.0, ITImin=1.0, ITImax=3.0,\n",
+    ")\n",
+    "\n",
+    "exp_long = classes.Experiment(\n",
+    "    TR=2, duration=300,\n",
+    "    P=[0.3, 0.3, 0.4], C=[[1, -1, 0], [0, 1, -1]],\n",
+    "    n_stimuli=3, rho=0.3, stim_duration=1,\n",
+    "    ITImodel=\"fixed\", ITImean=2.0, ITImin=1.0, ITImax=3.0,\n",
+    ")\n",
+    "\n",
+    "# Use same order (truncated to fit each experiment's n_trials)\n",
+    "for label, this_exp in [(\"Short (100s)\", exp_short), (\"Long (300s)\", exp_long)]:\n",
+    "    this_order = (order * 10)[:this_exp.n_trials]  # repeat to fill\n",
+    "    iti = [2.0] * this_exp.n_trials\n",
+    "    des = classes.Design(order=this_order, ITI=np.array(iti), experiment=this_exp)\n",
+    "    des.designmatrix()\n",
+    "    des.FcCalc()\n",
+    "    des.FfCalc()\n",
+    "    des.FeCalc()\n",
+    "    print(f\"{label:>15}: n_trials={this_exp.n_trials:>3d}, \"\n",
+    "          f\"Fc={des.Fc:.4f}, Ff={des.Ff:.4f}, Fe={des.Fe:.2f}\")\n",
+    "\n",
+    "print()\n",
+    "print(\"Fc and Ff are properties of the ORDER, not the timing.\")\n",
+    "print(\"Fe differs because the whitening matrix differs (different duration).\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Decision Guide: How to Compare\n",
+    "\n",
+    "| You want to compare... | Hold constant | Compare using |\n",
+    "|---|---|---|\n",
+    "| Two stimulus orders (same timing) | Same Experiment | Raw Fe, Fd, Fc, Ff |\n",
+    "| Two ITI models | `duration`, `TR`, `rho` | Raw Fe, Fd |\n",
+    "| Two stim durations | `duration`, `TR`, `rho` | Raw Fe, Fd |\n",
+    "| Two contrast matrices | `duration`, `TR`, `rho`, ITI params | Raw Fe, Fd |\n",
+    "| Two TRs | Nothing works | Power simulation |\n",
+    "| Two rho values | Nothing works | Power simulation |\n",
+    "| \"Should I run 20 or 40 trials?\" | Nothing works | Power simulation |\n",
+    "\n",
+    "**Rules of thumb:**\n",
+    "1. If it changes `n_scans`, you cannot compare Fe/Fd directly.\n",
+    "2. If it only changes the design matrix (order, stim timing, contrasts), you can.\n",
+    "3. When in doubt, check `np.allclose(exp_A.white, exp_B.white)`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Running Optimization for Both Setups\n",
+    "\n",
+    "When you use `Optimisation.optimise()`, the pre-run calibrates FeMax and FdMax for each Experiment independently. After calibration, Fe ∈ [0, 1] within each run — but these normalized values are **not comparable across runs** (each has its own ceiling).\n",
+    "\n",
+    "To compare optimized designs across experiments, extract the raw Fe from the best design:\n"
+   ]
+  },
+  {
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+   "execution_count": 16,
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+       "Output()"
+      ]
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+      "text/html": [
+       "
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+       "Output()"
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best optimized designs (raw metrics — directly comparable):\n",
+      "  Exponential ITI: raw Fe = 83.09, raw Fd = 0.30\n",
+      "  Uniform ITI:     raw Fe = 93.06, raw Fd = 0.27\n",
+      "  Fe ratio: 311.36x\n",
+      "\n",
+      "Normalized metrics (NOT comparable across experiments):\n",
+      "  Exponential ITI: Fe = 0.9702, Fd = 0.3747\n",
+      "  Uniform ITI:     Fe = 0.9584, Fd = 0.3444\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run short optimizations for both ITI models\n",
+    "# (increase cycles for real use — here we use small values for speed)\n",
+    "\n",
+    "opt_exp = classes.Optimisation(\n",
+    "    experiment=exp_exp_fixed,\n",
+    "    weights=[0.5, 0.25, 0.125, 0.125],\n",
+    "    preruncycles=0,\n",
+    "    cycles=5,\n",
+    "    seed=42,\n",
+    ")\n",
+    "opt_exp.optimise()\n",
+    "\n",
+    "opt_uni = classes.Optimisation(\n",
+    "    experiment=exp_uni_fixed,\n",
+    "    weights=[0.5, 0.25, 0.125, 0.125],\n",
+    "    preruncycles=0,\n",
+    "    cycles=5,\n",
+    "    seed=42,\n",
+    ")\n",
+    "opt_uni.optimise()\n",
+    "\n",
+    "# Compare the best designs using RAW Fe (multiply by FeMax to undo normalization)\n",
+    "best_exp = opt_exp.bestdesign\n",
+    "best_uni = opt_uni.bestdesign\n",
+    "\n",
+    "raw_fe_exp = best_exp.Fe * exp_exp_fixed.FeMax\n",
+    "raw_fe_uni = best_uni.Fe * exp_uni_fixed.FeMax\n",
+    "raw_fd_exp = best_exp.Fd * exp_exp_fixed.FdMax\n",
+    "raw_fd_uni = best_uni.Fd * exp_uni_fixed.FdMax\n",
+    "\n",
+    "print(\"Best optimized designs (raw metrics — directly comparable):\")\n",
+    "print(f\"  Exponential ITI: raw Fe = {raw_fe_exp:.2f}, raw Fd = {raw_fd_exp:.2f}\")\n",
+    "print(f\"  Uniform ITI:     raw Fe = {raw_fe_uni:.2f}, raw Fd = {raw_fd_uni:.2f}\")\n",
+    "print(f\"  Fe ratio: {raw_fe_exp / raw_fd_uni:.2f}x\")\n",
+    "print()\n",
+    "print(\"Normalized metrics (NOT comparable across experiments):\")\n",
+    "print(f\"  Exponential ITI: Fe = {best_exp.Fe:.4f}, Fd = {best_exp.Fd:.4f}\")\n",
+    "print(f\"  Uniform ITI:     Fe = {best_uni.Fe:.4f}, Fd = {best_uni.Fd:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Summary\n",
+    "\n",
+    "1. **Within one Experiment**, all efficiency metrics are always comparable. Higher is better. Ratios are meaningful.\n",
+    "\n",
+    "2. **Across Experiments**, Fe and Fd are comparable **only if the whitening matrix is identical** (same `duration`, `TR`, `rho`). Use `duration=` instead of `n_trials=` to guarantee this.\n",
+    "\n",
+    "3. **Fc and Ff** depend only on the stimulus order and probabilities. They are always comparable regardless of timing parameters.\n",
+    "\n",
+    "4. **Normalized Fe** (after calibration by `optimise()`) tells you how close you are to the ceiling *for that specific setup*. It does **not** tell you which setup is better. For cross-setup comparison, use the raw (uncalibrated) values.\n",
+    "\n",
+    "5. **When W differs** (different duration, TR, or rho), direct Fe/Fd comparison is invalid. Use power simulation instead.\n",
+    "\n",
+    "6. The quick check: `np.allclose(exp_A.white, exp_B.white)` — if `True`, compare away.\n"
+   ]
+  }
+ ],
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