Mushrooms (P. cubensis)
I knew full well that I had three pieces of coursework to be doing, but somewhere in the back of my mind I thought: 'You know what? That can all wait... there's something more important you need to do first!'. The important thing I had to do turned out to be ingesting around 3g of dried P. Cubensis.  This would turn out to be my fifth mushroom experience, and I felt very optimistic about it. That day just had a good vibe about it.
Only four days previously (third mushroom trip) I had had an incredibly confusing and dangerous experience where (as far as I can gather) I relieved a 'God Complex' repressed memory. Hopefully I will be able to share that experience with you also, but for now let's just focus on this trip.
Soon after ingestion, I began to wonder if I had actually taken enough (didn't have accurate enough scales to correctly measure). The only way to gauge it was to wait and see how strong the effects turned out to be. The first thing I did was to rapidly compose a Chillout playlist and lie back (for England!). Effects came on very gently and soon I found that I could switch between reality and mushroom-reality by thinking/focusing.
Picked up a mirror and found that if I stared at my reflection that half of my face looked as if it was melting away. One half would remain youthful, the rest would slowly dissolve into mush. After fooling around for another half hour with the mirror I started feeling really sick. Went upstairs to the bathroom, but by this stage I could feel it coming on really strongly and my thoughts were becoming very very irrational indeed.
I knew that I needed to barf, and I could just about figure out that I also needed to wee and poo also, but because I was so damn confused I ended up just standing there wondering what I should do.  I kept thinking to myself: 'You need to piss, shit, puke, you can't see a damn thing, and you can't stop yawning - this is pointless so you may as well go back downstairs and lay down!!!' So I managed to get back downstairs, although it took me about 2 minutes. Luckily, none of my housemates saw me, although I had a couple close calls.
So I lay back down again and by this time the music sounded fantastic. It couldn't have been more perfect. I still had the impression that I would be sick though. Back upstairs. Still confused. Still couldn't work out what I was supposed to do. For some reason I picked up a bottle of toothpaste, opened the window and tried to throw it outside. It didn't work though, and the bottle balanced itself neatly on the ledge, and I thought that was pointless so I picked it back up and put it back. For some reason I kept thinking over and over again: 'I am transcendental cheese!'. I also identified that there was actually eight days in a week, the eighth day being a day between Monday and Tuesday, namely - Cheeseday!!!
Back downstairs, and now things begin to get very silly indeed. I slipped into a trance state where I seemed to be reliving most of my childhood. Although I'm not totally clear on this, I remember being able to switch between just about every memory I had of my childhood, and I saw that they were all connected. At the time this was all very profound, and I felt that I was releasing more repressed memories. Probably around an hour later I snapped out of it and by this time I was feeling *perfect*! I was heavenly. I felt as if a big weight had been lifted off my shoulders and I was pure again. I was using an untainted mind, relieved of every hurt and anguish I had ever received.
I lay there for another hour. For some reason I felt the need to keep slapping the wall, and that continued for a good while. My uncontrollable yawning would not relent, so I just decided to flow with it and ended up creating some highly original and quite musical exotic sounding yawns. I got up and surveyed my room. The door was open and had probably been so for a long time so I closed it.
It didn't, and I just ended up wondering if I had totally lost my mind. I got up again and tried to put them back but couldn't remember where they were all supposed to go. More strange thoughts. I decided that the world would be a better place if instead of waging wars, world leaders would have an international 'BMX On The Moon' championship (BMX On The Moon - old computer game) to see who was best.
  I like BMX on the moon! I am transcendental cheese! Today is cheeseday!'. By now I was starting to drift back into reality and I could fully appreciate the hilarity of the situation. Visuals were still kind of strong though. With open eyes everything was so bright it was difficult to look at, with closed eyes I saw stunningly beautiful ribbons and rainbows.
I knew I was still tripping and that it would take another hour or two for the effects to wear off so I lay back once more, this time content in the knowledge that today I solved most of the problems in my life. What's more, it only took four hours!
The reason why this turned out to be a good trip is because I believed it would be a good trip beforehand.  They did, and it was the most confusing thing I've ever experienced. Being able to integrate the concept of transcendental cheese was a hell of a lot easier, that's for sure. :)
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Geoweaver
2018 ESIP Lab Incubator Project
Geoweaver is an in-browser software allowing users to easily compose and execute full-stack data processing workflows by taking advantage of online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides an all-in-one capacity covering server management, code repository, workflow orchestration software, and history recorder.
It can be run from both local and remote (distributed) machines.
GeoWeaver is a community effort. Any contribution is welcome and greatly appreciated!
Software Goals
Only two fundamental objectives:
-
Make it time affordable for scientists with less-coding experience (who are unaware of WfMS) to manage their data processing workflows.
-
Preserve every model run history and share them along with the code files.
Tutorial
Installation
Click here for the steps
Demo
A live demo site is available.
Citation
If you found Geoweaver helpful in your research.
please cite: Sun, Z. et al., "Geoweaver: Advanced cyberinfrastructure for managing hybrid geoscientific AI workflows." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 119.
Dependencies
This project is impossible without the support of several remarkable open-source libraries.
-
d3.js - BSD 3-Clause
-
graph-creator - MIT License
-
bootstrap - MIT License
-
CodeMirror - MIT License
-
JQuery Terminal - MIT License
Community
Geoweaver owes its success to the hard work of its many community members who generously devoted their time and effort to the project.
Please find the full contributor list below: developer list
License
MIT


skopt.plots module
Plotting functions.
"""Plotting functions.""" import sys import numpy as np from itertools import count from functools import partial from scipy.optimize import OptimizeResult from .space import Categorical # For plot tests, matplotlib must be set to headless mode early if 'pytest' in sys.modules: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.pyplot import cm from matplotlib.ticker import LogLocator from matplotlib.ticker import MaxNLocator, FuncFormatter # noqa: E402 def plot_convergence(*args, **kwargs): """Plot one or several convergence traces. Parameters ---------- * `args[i]` [`OptimizeResult`, list of `OptimizeResult`, or tuple]: The result(s) for which to plot the convergence trace. - if `OptimizeResult`, then draw the corresponding single trace; - if list of `OptimizeResult`, then draw the corresponding convergence traces in transparency, along with the average convergence trace; - if tuple, then `args[i][0]` should be a string label and `args[i][1]` an `OptimizeResult` or a list of `OptimizeResult`. * `ax` [`Axes`, optional]: The matplotlib axes on which to draw the plot, or `None` to create a new one. * `true_minimum` [float, optional]: The true minimum value of the function, if known. * `yscale` [None or string, optional]: The scale for the y-axis. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ # <3 legacy python ax = kwargs.get("ax", None) true_minimum = kwargs.get("true_minimum", None) yscale = kwargs.get("yscale", None) if ax is None: ax = plt.gca() ax.set_title("Convergence plot") ax.set_xlabel("Number of calls $n$") ax.set_ylabel(r"$\min f(x)$ after $n$ calls") ax.grid() if yscale is not None: ax.set_yscale(yscale) colors = cm.viridis(np.linspace(0.25, 1.0, len(args))) for results, color in zip(args, colors): if isinstance(results, tuple): name, results = results else: name = None if isinstance(results, OptimizeResult): n_calls = len(results.x_iters) mins = [np.min(results.func_vals[:i]) for i in range(1, n_calls + 1)] ax.plot(range(1, n_calls + 1), mins, c=color, marker=".", markersize=12, lw=2, label=name) elif isinstance(results, list): n_calls = len(results[0].x_iters) iterations = range(1, n_calls + 1) mins = [[np.min(r.func_vals[:i]) for i in iterations] for r in results] for m in mins: ax.plot(iterations, m, c=color, alpha=0.2) ax.plot(iterations, np.mean(mins, axis=0), c=color, marker=".", markersize=12, lw=2, label=name) if true_minimum: ax.axhline(true_minimum, linestyle="--", color="r", lw=1, label="True minimum") if true_minimum or name: ax.legend(loc="best") return ax def plot_regret(*args, **kwargs): """Plot one or several cumulative regret traces. Parameters ---------- * `args[i]` [`OptimizeResult`, list of `OptimizeResult`, or tuple]: The result(s) for which to plot the cumulative regret trace. - if `OptimizeResult`, then draw the corresponding single trace; - if list of `OptimizeResult`, then draw the corresponding cumulative regret traces in transparency, along with the average cumulative regret trace; - if tuple, then `args[i][0]` should be a string label and `args[i][1]` an `OptimizeResult` or a list of `OptimizeResult`. * `ax` [`Axes`, optional]: The matplotlib axes on which to draw the plot, or `None` to create a new one. * `true_minimum` [float, optional]: The true minimum value of the function, if known. * `yscale` [None or string, optional]: The scale for the y-axis. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ # <3 legacy python ax = kwargs.get("ax", None) true_minimum = kwargs.get("true_minimum", None) yscale = kwargs.get("yscale", None) if ax is None: ax = plt.gca() ax.set_title("Cumulative regret plot") ax.set_xlabel("Number of calls $n$") ax.set_ylabel(r"$\sum_{i=0}^n(f(x_i) - optimum)$ after $n$ calls") ax.grid() if yscale is not None: ax.set_yscale(yscale) colors = cm.viridis(np.linspace(0.25, 1.0, len(args))) if true_minimum is None: results = [] for res in args: if isinstance(res, tuple): res = res[1] if isinstance(res, OptimizeResult): results.append(res) elif isinstance(res, list): results.extend(res) true_minimum = np.min([np.min(r.func_vals) for r in results]) for results, color in zip(args, colors): if isinstance(results, tuple): name, results = results else: name = None if isinstance(results, OptimizeResult): n_calls = len(results.x_iters) regrets = [np.sum(results.func_vals[:i] - true_minimum) for i in range(1, n_calls + 1)] ax.plot(range(1, n_calls + 1), regrets, c=color, marker=".", markersize=12, lw=2, label=name) elif isinstance(results, list): n_calls = len(results[0].x_iters) iterations = range(1, n_calls + 1) regrets = [[np.sum(r.func_vals[:i] - true_minimum) for i in iterations] for r in results] for cr in regrets: ax.plot(iterations, cr, c=color, alpha=0.2) ax.plot(iterations, np.mean(regrets, axis=0), c=color, marker=".", markersize=12, lw=2, label=name) if name: ax.legend(loc="best") return ax def _format_scatter_plot_axes(ax, space, ylabel, dim_labels=None): # Work out min, max of y axis for the diagonal so we can adjust # them all to the same value diagonal_ylim = (np.min([ax[i, i].get_ylim()[0] for i in range(space.n_dims)]), np.max([ax[i, i].get_ylim()[1] for i in range(space.n_dims)])) if dim_labels is None: dim_labels = ["$X_{%i}$" % i if d.name is None else d.name for i, d in enumerate(space.dimensions)] # Axes for categorical dimensions are really integers; we have to # label them with the category names iscat = [isinstance(dim, Categorical) for dim in space.dimensions] # Deal with formatting of the axes for i in range(space.n_dims): # rows for j in range(space.n_dims): # columns ax_ = ax[i, j] if j > i: ax_.axis("off") elif i > j: # off-diagonal plots # plots on the diagonal are special, like Texas. They have # their own range so do not mess with them. if not iscat[i]: # bounds not meaningful for categoricals ax_.set_ylim(*space.dimensions[i].bounds) if iscat[j]: # partial() avoids creating closures in a loop ax_.xaxis.set_major_formatter(FuncFormatter( partial(_cat_format, space.dimensions[j]))) else: ax_.set_xlim(*space.dimensions[j].bounds) if j == 0: # only leftmost column (0) gets y labels ax_.set_ylabel(dim_labels[i]) if iscat[i]: # Set category labels for left column ax_.yaxis.set_major_formatter(FuncFormatter( partial(_cat_format, space.dimensions[i]))) else: ax_.set_yticklabels([]) # for all rows except ... if i < space.n_dims - 1: ax_.set_xticklabels([]) # ... the bottom row else: [l.set_rotation(45) for l in ax_.get_xticklabels()] ax_.set_xlabel(dim_labels[j]) # configure plot for linear vs log-scale if space.dimensions[j].prior == 'log-uniform': ax_.set_xscale('log') else: ax_.xaxis.set_major_locator(MaxNLocator(6, prune='both', integer=iscat[j])) if space.dimensions[i].prior == 'log-uniform': ax_.set_yscale('log') else: ax_.yaxis.set_major_locator(MaxNLocator(6, prune='both', integer=iscat[i])) else: # diagonal plots ax_.set_ylim(*diagonal_ylim) ax_.yaxis.tick_right() ax_.yaxis.set_label_position('right') ax_.yaxis.set_ticks_position('both') ax_.set_ylabel(ylabel) ax_.xaxis.tick_top() ax_.xaxis.set_label_position('top') ax_.set_xlabel(dim_labels[j]) if space.dimensions[i].prior == 'log-uniform': ax_.set_xscale('log') else: ax_.xaxis.set_major_locator(MaxNLocator(6, prune='both', integer=iscat[i])) if iscat[i]: ax_.xaxis.set_major_formatter(FuncFormatter( partial(_cat_format, space.dimensions[i]))) return ax def partial_dependence(space, model, i, j=None, sample_points=None, n_samples=250, n_points=40): """Calculate the partial dependence for dimensions `i` and `j` with respect to the objective value, as approximated by `model`. The partial dependence plot shows how the value of the dimensions `i` and `j` influence the `model` predictions after "averaging out" the influence of all other dimensions. Parameters ---------- * `space` [`Space`] The parameter space over which the minimization was performed. * `model` Surrogate model for the objective function. * `i` [int] The first dimension for which to calculate the partial dependence. * `j` [int, default=None] The second dimension for which to calculate the partial dependence. To calculate the 1D partial dependence on `i` alone set `j=None`. * `sample_points` [np.array, shape=(n_points, n_dims), default=None] Randomly sampled and transformed points to use when averaging the model function at each of the `n_points`. * `n_samples` [int, default=100] Number of random samples to use for averaging the model function at each of the `n_points`. Only used when `sample_points=None`. * `n_points` [int, default=40] Number of points at which to evaluate the partial dependence along each dimension `i` and `j`. Returns ------- For 1D partial dependence: * `xi`: [np.array]: The points at which the partial dependence was evaluated. * `yi`: [np.array]: The value of the model at each point `xi`. For 2D partial dependence: * `xi`: [np.array, shape=n_points]: The points at which the partial dependence was evaluated. * `yi`: [np.array, shape=n_points]: The points at which the partial dependence was evaluated. * `zi`: [np.array, shape=(n_points, n_points)]: The value of the model at each point `(xi, yi)`. For Categorical variables, the `xi` (and `yi` for 2D) returned are the indices of the variable in `Dimension.categories`. """ # The idea is to step through one dimension, evaluating the model with # that dimension fixed and averaging over random values in all other # dimensions. (Or step through 2 dimensions when i and j are given.) # Categorical dimensions make this interesting, because they are one- # hot-encoded, so there is a one-to-many mapping of input dimensions # to transformed (model) dimensions. if sample_points is None: sample_points = space.transform(space.rvs(n_samples=n_samples)) # dim_locs[i] is the (column index of the) start of dim i in sample_points dim_locs = np.cumsum([0] + [d.transformed_size for d in space.dimensions]) if j is None: xi, xi_transformed = _evenly_sample(space.dimensions[i], n_points) yi = [] for x_ in xi_transformed: rvs_ = np.array(sample_points) # copy rvs_[:, dim_locs[i]:dim_locs[i + 1]] = x_ yi.append(np.mean(model.predict(rvs_))) return xi, yi else: xi, xi_transformed = _evenly_sample(space.dimensions[j], n_points) yi, yi_transformed = _evenly_sample(space.dimensions[i], n_points) zi = [] for x_ in xi_transformed: row = [] for y_ in yi_transformed: rvs_ = np.array(sample_points) # copy rvs_[:, dim_locs[j]:dim_locs[j + 1]] = x_ rvs_[:, dim_locs[i]:dim_locs[i + 1]] = y_ row.append(np.mean(model.predict(rvs_))) zi.append(row) return xi, yi, np.array(zi).T def plot_objective(result, levels=10, n_points=40, n_samples=250, size=2, zscale='linear', dimensions=None): """Pairwise partial dependence plot of the objective function. The diagonal shows the partial dependence for dimension `i` with respect to the objective function. The off-diagonal shows the partial dependence for dimensions `i` and `j` with respect to the objective function. The objective function is approximated by `result.model.` Pairwise scatter plots of the points at which the objective function was directly evaluated are shown on the off-diagonal. A red point indicates the found minimum. Parameters ---------- * `result` [`OptimizeResult`] The result for which to create the scatter plot matrix. * `levels` [int, default=10] Number of levels to draw on the contour plot, passed directly to `plt.contour()`. * `n_points` [int, default=40] Number of points at which to evaluate the partial dependence along each dimension. * `n_samples` [int, default=250] Number of random samples to use for averaging the model function at each of the `n_points`. * `size` [float, default=2] Height (in inches) of each facet. * `zscale` [str, default='linear'] Scale to use for the z axis of the contour plots. Either 'linear' or 'log'. * `dimensions` [list of str, default=None] Labels of the dimension variables. `None` defaults to `space.dimensions[i].name`, or if also `None` to `['X_0', 'X_1', ..]`. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ space = result.space rvs_transformed = space.transform(space.rvs(n_samples=n_samples)) samples, minimum, _ = _map_categories(space, result.x_iters, result.x) if zscale == 'log': locator = LogLocator() elif zscale == 'linear': locator = None else: raise ValueError("Valid values for zscale are 'linear' and 'log'," " not '%s'." % zscale) fig, ax = plt.subplots(space.n_dims, space.n_dims, figsize=(size * space.n_dims, size * space.n_dims)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.1) for i in range(space.n_dims): for j in range(space.n_dims): if i == j: xi, yi = partial_dependence(space, result.models[-1], i, j=None, sample_points=rvs_transformed, n_points=n_points) ax[i, i].plot(xi, yi) ax[i, i].axvline(minimum[i], linestyle="--", color="r", lw=1) # lower triangle elif i > j: xi, yi, zi = partial_dependence(space, result.models[-1], i, j, rvs_transformed, n_points) ax[i, j].contourf(xi, yi, zi, levels, locator=locator, cmap='viridis_r') ax[i, j].scatter(samples[:, j], samples[:, i], c='k', s=10, lw=0.) ax[i, j].scatter(minimum[j], minimum[i], c=['r'], s=20, lw=0.) return _format_scatter_plot_axes(ax, space, ylabel="Partial dependence", dim_labels=dimensions) def plot_evaluations(result, bins=20, dimensions=None): """Visualize the order in which points where sampled. The scatter plot matrix shows at which points in the search space and in which order samples were evaluated. Pairwise scatter plots are shown on the off-diagonal for each dimension of the search space. The order in which samples were evaluated is encoded in each point's color. The diagonal shows a histogram of sampled values for each dimension. A red point indicates the found minimum. Parameters ---------- * `result` [`OptimizeResult`] The result for which to create the scatter plot matrix. * `bins` [int, bins=20]: Number of bins to use for histograms on the diagonal. * `dimensions` [list of str, default=None] Labels of the dimension variables. `None` defaults to `space.dimensions[i].name`, or if also `None` to `['X_0', 'X_1', ..]`. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ space = result.space # Convert categoricals to integers, so we can ensure consistent ordering. # Assign indices to categories in the order they appear in the Dimension. # Matplotlib's categorical plotting functions are only present in v 2.1+, # and may order categoricals differently in different plots anyway. samples, minimum, iscat = _map_categories(space, result.x_iters, result.x) order = range(samples.shape[0]) fig, ax = plt.subplots(space.n_dims, space.n_dims, figsize=(2 * space.n_dims, 2 * space.n_dims)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.1) for i in range(space.n_dims): for j in range(space.n_dims): if i == j: if iscat[j]: bins_ = len(space.dimensions[j].categories) elif space.dimensions[j].prior == 'log-uniform': low, high = space.bounds[j] bins_ = np.logspace(np.log10(low), np.log10(high), bins) else: bins_ = bins ax[i, i].hist(samples[:, j], bins=bins_, range=None if iscat[j] else space.dimensions[j].bounds) # lower triangle elif i > j: ax[i, j].scatter(samples[:, j], samples[:, i], c=order, s=40, lw=0., cmap='viridis') ax[i, j].scatter(minimum[j], minimum[i], c=['r'], s=20, lw=0.) return _format_scatter_plot_axes(ax, space, ylabel="Number of samples", dim_labels=dimensions) def _map_categories(space, points, minimum): """ Map categorical values to integers in a set of points. Returns ------- * `mapped_points` [np.array, shape=points.shape]: A copy of `points` with categoricals replaced with their indices in the corresponding `Dimension`. * `mapped_minimum` [np.array, shape=(space.n_dims,)]: A copy of `minimum` with categoricals replaced with their indices in the corresponding `Dimension`. * `iscat` [np.array, shape=(space.n_dims,)]: Boolean array indicating whether dimension `i` in the `space` is categorical. """ points = np.asarray(points, dtype=object) # Allow slicing, preserve cats iscat = np.repeat(False, space.n_dims) min_ = np.zeros(space.n_dims) pts_ = np.zeros(points.shape) for i, dim in enumerate(space.dimensions): if isinstance(dim, Categorical): iscat[i] = True catmap = dict(zip(dim.categories, count())) pts_[:, i] = [catmap[cat] for cat in points[:, i]] min_[i] = catmap[minimum[i]] else: pts_[:, i] = points[:, i] min_[i] = minimum[i] return pts_, min_, iscat def _evenly_sample(dim, n_points): """Return `n_points` evenly spaced points from a Dimension. Parameters ---------- * `dim` [`Dimension`] The Dimension to sample from. Can be categorical; evenly-spaced category indices are chosen in order without replacement (result may be smaller than `n_points`). * `n_points` [int] The number of points to sample from `dim`. Returns ------- * `xi`: [np.array]: The sampled points in the Dimension. For Categorical dimensions, returns the index of the value in `dim.categories`. * `xi_transformed`: [np.array]: The transformed values of `xi`, for feeding to a model. """ cats = np.array(getattr(dim, 'categories', []), dtype=object) if len(cats): # Sample categoricals while maintaining order xi = np.linspace(0, len(cats) - 1, min(len(cats), n_points), dtype=int) xi_transformed = dim.transform(cats[xi]) else: bounds = dim.bounds # XXX use linspace(*bounds, n_points) after python2 support ends xi = np.linspace(bounds[0], bounds[1], n_points) xi_transformed = dim.transform(xi) return xi, xi_transformed def _cat_format(dimension, x, _): """Categorical axis tick formatter function. Returns the name of category `x` in `dimension`. Used with `matplotlib.ticker.FuncFormatter`.""" return str(dimension.categories[int(x)])
Functions
def partial_dependence(
space, model, i, j=None, sample_points=None, n_samples=250, n_points=40)
Calculate the partial dependence for dimensions i
and j
with
respect to the objective value, as approximated by model
.
The partial dependence plot shows how the value of the dimensions
i
and j
influence the model
predictions after "averaging out"
the influence of all other dimensions.
Parameters
-
space
[Space
] The parameter space over which the minimization was performed. -
model
Surrogate model for the objective function. -
i
[int] The first dimension for which to calculate the partial dependence. -
j
[int, default=None] The second dimension for which to calculate the partial dependence. To calculate the 1D partial dependence oni
alone setj=None
. -
sample_points
[np.array, shape=(n_points, n_dims), default=None] Randomly sampled and transformed points to use when averaging the model function at each of then_points
. -
n_samples
[int, default=100] Number of random samples to use for averaging the model function at each of then_points
. Only used whensample_points=None
. -
n_points
[int, default=40] Number of points at which to evaluate the partial dependence along each dimensioni
andj
.
Returns
For 1D partial dependence:
-
xi
: [np.array]: The points at which the partial dependence was evaluated. -
yi
: [np.array]: The value of the model at each pointxi
.
For 2D partial dependence:
xi
: [np.array, shape=n_points]: The points at which the partial dependence was evaluated.yi
: [np.array, shape=n_points]: The points at which the partial dependence was evaluated.zi
: [np.array, shape=(n_points, n_points)]: The value of the model at each point(xi, yi)
.
For Categorical variables, the xi
(and yi
for 2D) returned are
the indices of the variable in Dimension.categories
.
def partial_dependence(space, model, i, j=None, sample_points=None, n_samples=250, n_points=40): """Calculate the partial dependence for dimensions `i` and `j` with respect to the objective value, as approximated by `model`. The partial dependence plot shows how the value of the dimensions `i` and `j` influence the `model` predictions after "averaging out" the influence of all other dimensions. Parameters ---------- * `space` [`Space`] The parameter space over which the minimization was performed. * `model` Surrogate model for the objective function. * `i` [int] The first dimension for which to calculate the partial dependence. * `j` [int, default=None] The second dimension for which to calculate the partial dependence. To calculate the 1D partial dependence on `i` alone set `j=None`. * `sample_points` [np.array, shape=(n_points, n_dims), default=None] Randomly sampled and transformed points to use when averaging the model function at each of the `n_points`. * `n_samples` [int, default=100] Number of random samples to use for averaging the model function at each of the `n_points`. Only used when `sample_points=None`. * `n_points` [int, default=40] Number of points at which to evaluate the partial dependence along each dimension `i` and `j`. Returns ------- For 1D partial dependence: * `xi`: [np.array]: The points at which the partial dependence was evaluated. * `yi`: [np.array]: The value of the model at each point `xi`. For 2D partial dependence: * `xi`: [np.array, shape=n_points]: The points at which the partial dependence was evaluated. * `yi`: [np.array, shape=n_points]: The points at which the partial dependence was evaluated. * `zi`: [np.array, shape=(n_points, n_points)]: The value of the model at each point `(xi, yi)`. For Categorical variables, the `xi` (and `yi` for 2D) returned are the indices of the variable in `Dimension.categories`. """ # The idea is to step through one dimension, evaluating the model with # that dimension fixed and averaging over random values in all other # dimensions. (Or step through 2 dimensions when i and j are given.) # Categorical dimensions make this interesting, because they are one- # hot-encoded, so there is a one-to-many mapping of input dimensions # to transformed (model) dimensions. if sample_points is None: sample_points = space.transform(space.rvs(n_samples=n_samples)) # dim_locs[i] is the (column index of the) start of dim i in sample_points dim_locs = np.cumsum([0] + [d.transformed_size for d in space.dimensions]) if j is None: xi, xi_transformed = _evenly_sample(space.dimensions[i], n_points) yi = [] for x_ in xi_transformed: rvs_ = np.array(sample_points) # copy rvs_[:, dim_locs[i]:dim_locs[i + 1]] = x_ yi.append(np.mean(model.predict(rvs_))) return xi, yi else: xi, xi_transformed = _evenly_sample(space.dimensions[j], n_points) yi, yi_transformed = _evenly_sample(space.dimensions[i], n_points) zi = [] for x_ in xi_transformed: row = [] for y_ in yi_transformed: rvs_ = np.array(sample_points) # copy rvs_[:, dim_locs[j]:dim_locs[j + 1]] = x_ rvs_[:, dim_locs[i]:dim_locs[i + 1]] = y_ row.append(np.mean(model.predict(rvs_))) zi.append(row) return xi, yi, np.array(zi).T
def plot_convergence(
*args, **kwargs)
Plot one or several convergence traces.
Parameters
-
args[i]
[OptimizeResult
, list ofOptimizeResult
, or tuple]: The result(s) for which to plot the convergence trace.- if
OptimizeResult
, then draw the corresponding single trace; - if list of
OptimizeResult
, then draw the corresponding convergence traces in transparency, along with the average convergence trace; - if tuple, then
args[i][0]
should be a string label andargs[i][1]
anOptimizeResult
or a list ofOptimizeResult
.
- if
-
ax
[Axes
, optional]: The matplotlib axes on which to draw the plot, orNone
to create a new one. -
true_minimum
[float, optional]: The true minimum value of the function, if known. -
yscale
[None or string, optional]: The scale for the y-axis.
Returns
ax
: [Axes
]: The matplotlib axes.
def plot_convergence(*args, **kwargs): """Plot one or several convergence traces. Parameters ---------- * `args[i]` [`OptimizeResult`, list of `OptimizeResult`, or tuple]: The result(s) for which to plot the convergence trace. - if `OptimizeResult`, then draw the corresponding single trace; - if list of `OptimizeResult`, then draw the corresponding convergence traces in transparency, along with the average convergence trace; - if tuple, then `args[i][0]` should be a string label and `args[i][1]` an `OptimizeResult` or a list of `OptimizeResult`. * `ax` [`Axes`, optional]: The matplotlib axes on which to draw the plot, or `None` to create a new one. * `true_minimum` [float, optional]: The true minimum value of the function, if known. * `yscale` [None or string, optional]: The scale for the y-axis. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ # <3 legacy python ax = kwargs.get("ax", None) true_minimum = kwargs.get("true_minimum", None) yscale = kwargs.get("yscale", None) if ax is None: ax = plt.gca() ax.set_title("Convergence plot") ax.set_xlabel("Number of calls $n$") ax.set_ylabel(r"$\min f(x)$ after $n$ calls") ax.grid() if yscale is not None: ax.set_yscale(yscale) colors = cm.viridis(np.linspace(0.25, 1.0, len(args))) for results, color in zip(args, colors): if isinstance(results, tuple): name, results = results else: name = None if isinstance(results, OptimizeResult): n_calls = len(results.x_iters) mins = [np.min(results.func_vals[:i]) for i in range(1, n_calls + 1)] ax.plot(range(1, n_calls + 1), mins, c=color, marker=".", markersize=12, lw=2, label=name) elif isinstance(results, list): n_calls = len(results[0].x_iters) iterations = range(1, n_calls + 1) mins = [[np.min(r.func_vals[:i]) for i in iterations] for r in results] for m in mins: ax.plot(iterations, m, c=color, alpha=0.2) ax.plot(iterations, np.mean(mins, axis=0), c=color, marker=".", markersize=12, lw=2, label=name) if true_minimum: ax.axhline(true_minimum, linestyle="--", color="r", lw=1, label="True minimum") if true_minimum or name: ax.legend(loc="best") return ax
def plot_evaluations(
result, bins=20, dimensions=None)
Visualize the order in which points where sampled.
The scatter plot matrix shows at which points in the search space and in which order samples were evaluated. Pairwise scatter plots are shown on the off-diagonal for each dimension of the search space. The order in which samples were evaluated is encoded in each point's color. The diagonal shows a histogram of sampled values for each dimension. A red point indicates the found minimum.
Parameters
-
result
[OptimizeResult
] The result for which to create the scatter plot matrix. -
bins
[int, bins=20]: Number of bins to use for histograms on the diagonal. -
dimensions
[list of str, default=None] Labels of the dimension variables.None
defaults tospace.dimensions[i].name
, or if alsoNone
to['X_0', 'X_1', ..]
.
Returns
ax
: [Axes
]: The matplotlib axes.
def plot_evaluations(result, bins=20, dimensions=None): """Visualize the order in which points where sampled. The scatter plot matrix shows at which points in the search space and in which order samples were evaluated. Pairwise scatter plots are shown on the off-diagonal for each dimension of the search space. The order in which samples were evaluated is encoded in each point's color. The diagonal shows a histogram of sampled values for each dimension. A red point indicates the found minimum. Parameters ---------- * `result` [`OptimizeResult`] The result for which to create the scatter plot matrix. * `bins` [int, bins=20]: Number of bins to use for histograms on the diagonal. * `dimensions` [list of str, default=None] Labels of the dimension variables. `None` defaults to `space.dimensions[i].name`, or if also `None` to `['X_0', 'X_1', ..]`. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ space = result.space # Convert categoricals to integers, so we can ensure consistent ordering. # Assign indices to categories in the order they appear in the Dimension. # Matplotlib's categorical plotting functions are only present in v 2.1+, # and may order categoricals differently in different plots anyway. samples, minimum, iscat = _map_categories(space, result.x_iters, result.x) order = range(samples.shape[0]) fig, ax = plt.subplots(space.n_dims, space.n_dims, figsize=(2 * space.n_dims, 2 * space.n_dims)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.1) for i in range(space.n_dims): for j in range(space.n_dims): if i == j: if iscat[j]: bins_ = len(space.dimensions[j].categories) elif space.dimensions[j].prior == 'log-uniform': low, high = space.bounds[j] bins_ = np.logspace(np.log10(low), np.log10(high), bins) else: bins_ = bins ax[i, i].hist(samples[:, j], bins=bins_, range=None if iscat[j] else space.dimensions[j].bounds) # lower triangle elif i > j: ax[i, j].scatter(samples[:, j], samples[:, i], c=order, s=40, lw=0., cmap='viridis') ax[i, j].scatter(minimum[j], minimum[i], c=['r'], s=20, lw=0.) return _format_scatter_plot_axes(ax, space, ylabel="Number of samples", dim_labels=dimensions)
def plot_objective(
result, levels=10, n_points=40, n_samples=250, size=2, zscale='linear', dimensions=None)
Pairwise partial dependence plot of the objective function.
The diagonal shows the partial dependence for dimension i
with
respect to the objective function. The off-diagonal shows the
partial dependence for dimensions i
and j
with
respect to the objective function. The objective function is
approximated by result.model.
Pairwise scatter plots of the points at which the objective function was directly evaluated are shown on the off-diagonal. A red point indicates the found minimum.
Parameters
-
result
[OptimizeResult
] The result for which to create the scatter plot matrix. -
levels
[int, default=10] Number of levels to draw on the contour plot, passed directly toplt.contour()
. -
n_points
[int, default=40] Number of points at which to evaluate the partial dependence along each dimension. -
n_samples
[int, default=250] Number of random samples to use for averaging the model function at each of then_points
. -
size
[float, default=2] Height (in inches) of each facet. -
zscale
[str, default='linear'] Scale to use for the z axis of the contour plots. Either 'linear' or 'log'. -
dimensions
[list of str, default=None] Labels of the dimension variables.None
defaults tospace.dimensions[i].name
, or if alsoNone
to['X_0', 'X_1', ..]
.
Returns
ax
: [Axes
]: The matplotlib axes.
def plot_objective(result, levels=10, n_points=40, n_samples=250, size=2, zscale='linear', dimensions=None): """Pairwise partial dependence plot of the objective function. The diagonal shows the partial dependence for dimension `i` with respect to the objective function. The off-diagonal shows the partial dependence for dimensions `i` and `j` with respect to the objective function. The objective function is approximated by `result.model.` Pairwise scatter plots of the points at which the objective function was directly evaluated are shown on the off-diagonal. A red point indicates the found minimum. Parameters ---------- * `result` [`OptimizeResult`] The result for which to create the scatter plot matrix. * `levels` [int, default=10] Number of levels to draw on the contour plot, passed directly to `plt.contour()`. * `n_points` [int, default=40] Number of points at which to evaluate the partial dependence along each dimension. * `n_samples` [int, default=250] Number of random samples to use for averaging the model function at each of the `n_points`. * `size` [float, default=2] Height (in inches) of each facet. * `zscale` [str, default='linear'] Scale to use for the z axis of the contour plots. Either 'linear' or 'log'. * `dimensions` [list of str, default=None] Labels of the dimension variables. `None` defaults to `space.dimensions[i].name`, or if also `None` to `['X_0', 'X_1', ..]`. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ space = result.space rvs_transformed = space.transform(space.rvs(n_samples=n_samples)) samples, minimum, _ = _map_categories(space, result.x_iters, result.x) if zscale == 'log': locator = LogLocator() elif zscale == 'linear': locator = None else: raise ValueError("Valid values for zscale are 'linear' and 'log'," " not '%s'." % zscale) fig, ax = plt.subplots(space.n_dims, space.n_dims, figsize=(size * space.n_dims, size * space.n_dims)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.1, wspace=0.1) for i in range(space.n_dims): for j in range(space.n_dims): if i == j: xi, yi = partial_dependence(space, result.models[-1], i, j=None, sample_points=rvs_transformed, n_points=n_points) ax[i, i].plot(xi, yi) ax[i, i].axvline(minimum[i], linestyle="--", color="r", lw=1) # lower triangle elif i > j: xi, yi, zi = partial_dependence(space, result.models[-1], i, j, rvs_transformed, n_points) ax[i, j].contourf(xi, yi, zi, levels, locator=locator, cmap='viridis_r') ax[i, j].scatter(samples[:, j], samples[:, i], c='k', s=10, lw=0.) ax[i, j].scatter(minimum[j], minimum[i], c=['r'], s=20, lw=0.) return _format_scatter_plot_axes(ax, space, ylabel="Partial dependence", dim_labels=dimensions)
def plot_regret(
*args, **kwargs)
Plot one or several cumulative regret traces.
Parameters
-
args[i]
[OptimizeResult
, list ofOptimizeResult
, or tuple]: The result(s) for which to plot the cumulative regret trace.- if
OptimizeResult
, then draw the corresponding single trace; - if list of
OptimizeResult
, then draw the corresponding cumulative regret traces in transparency, along with the average cumulative regret trace; - if tuple, then
args[i][0]
should be a string label andargs[i][1]
anOptimizeResult
or a list ofOptimizeResult
.
- if
-
ax
[Axes
, optional]: The matplotlib axes on which to draw the plot, orNone
to create a new one. -
true_minimum
[float, optional]: The true minimum value of the function, if known. -
yscale
[None or string, optional]: The scale for the y-axis.
Returns
ax
: [Axes
]: The matplotlib axes.
def plot_regret(*args, **kwargs): """Plot one or several cumulative regret traces. Parameters ---------- * `args[i]` [`OptimizeResult`, list of `OptimizeResult`, or tuple]: The result(s) for which to plot the cumulative regret trace. - if `OptimizeResult`, then draw the corresponding single trace; - if list of `OptimizeResult`, then draw the corresponding cumulative regret traces in transparency, along with the average cumulative regret trace; - if tuple, then `args[i][0]` should be a string label and `args[i][1]` an `OptimizeResult` or a list of `OptimizeResult`. * `ax` [`Axes`, optional]: The matplotlib axes on which to draw the plot, or `None` to create a new one. * `true_minimum` [float, optional]: The true minimum value of the function, if known. * `yscale` [None or string, optional]: The scale for the y-axis. Returns ------- * `ax`: [`Axes`]: The matplotlib axes. """ # <3 legacy python ax = kwargs.get("ax", None) true_minimum = kwargs.get("true_minimum", None) yscale = kwargs.get("yscale", None) if ax is None: ax = plt.gca() ax.set_title("Cumulative regret plot") ax.set_xlabel("Number of calls $n$") ax.set_ylabel(r"$\sum_{i=0}^n(f(x_i) - optimum)$ after $n$ calls") ax.grid() if yscale is not None: ax.set_yscale(yscale) colors = cm.viridis(np.linspace(0.25, 1.0, len(args))) if true_minimum is None: results = [] for res in args: if isinstance(res, tuple): res = res[1] if isinstance(res, OptimizeResult): results.append(res) elif isinstance(res, list): results.extend(res) true_minimum = np.min([np.min(r.func_vals) for r in results]) for results, color in zip(args, colors): if isinstance(results, tuple): name, results = results else: name = None if isinstance(results, OptimizeResult): n_calls = len(results.x_iters) regrets = [np.sum(results.func_vals[:i] - true_minimum) for i in range(1, n_calls + 1)] ax.plot(range(1, n_calls + 1), regrets, c=color, marker=".", markersize=12, lw=2, label=name) elif isinstance(results, list): n_calls = len(results[0].x_iters) iterations = range(1, n_calls + 1) regrets = [[np.sum(r.func_vals[:i] - true_minimum) for i in iterations] for r in results] for cr in regrets: ax.plot(iterations, cr, c=color, alpha=0.2) ax.plot(iterations, np.mean(regrets, axis=0), c=color, marker=".", markersize=12, lw=2, label=name) if name: ax.legend(loc="best") return ax


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THE GIRL NEXT DOOR A film review by Steve Rhodes Copyright 2000 Steve Rhodes RATING (0 TO ****): ***
"I never had anything I was good at," says Stacy Valentine about her growing up. Everyone wants to be good at something. It's human nature. Eventually, Stacy, the fascinating subject of Christine Fugate's THE GIRL NEXT DOOR, did find something at which she excelled. It's an activity practiced with pleasure by most of the world's adult population -- sex.
But, unlike most of us, Stacy found a way to make a living at it. She is what is euphemistically called "in the business," specifically, "in the adult entertainment industry." She makes a living starring in videos that people, mainly men, watch in the comfort of their homes.
As soon as most people hear the subject of the story, which Fugate carefully filmed herself over a two-year period, their minds are already closed. Most viewers are ready for a documentary that will ridicule Stacy, or at the very least, patronize and marginalize her. Americans stand foursquare behind their movies' right to splatter people's brains all over the ceiling, awarding stars, like Bruce Willis, millions of dollars to do exactly that. But, sex is another matter entirely. Porn stars are routinely detested by the media and the public.
The most shocking part of the documentary is how Stacy really does seem like the innocent girl next door. As Producer Adam Berns, who was at our screening, so aptly put it, "she is basically a very normal person. She isn't a whack job."
Good documentaries make you think, and this got me to thinking about the parallels with another movie, WITHOUT LIMITS, based on the life of the famous runner Steve Prefontaine. One of the most gruesome parts of that movie was when we see him pushing himself so hard in the service of his sport that he turns his feet into bloody pieces of raw meat. He knew a runner's career is short so he always pushed himself to a limit way past what most people would be willing to endure.
So it is with Stacy. As we see in some hard-to-watch footage, Stacy has surgery after surgery to mold her body into her ever-changing version of perfection. Her breasts, lips, thighs, hips, you name it, are put under the surgical scalpel and suction device. Berns says that Stacy, who loves watching herself on screen, cannot stomach these scenes and has vowed never to have such plastic surgery again. Like a runner, the life of a porn star doesn't last long so Stacy pushes herself in ways most people would never consider. Then again, traditional Hollywood stars have been known to invest quite a bit in such reconstructive surgery.
The movie, however, isn't some dry compendium of talking heads followed by carefully covered porn scenes. This sexually explicit, but not graphic, movie is extremely funny, thanks to the Fugate's approach and choice of subjects. Stacy's mother and stepfather, for example, who support her career but have never seen her videos, are a comedy duo, as they talk about their daughter and about themselves.
We find that the carefully controlled filming of pornographic movies is not without hilarious incidents. One producer has a man with a back-up organ in case the organ of the male lead fails. When the male lead disappears, she is left with nothing to film. Her backup doesn't have a suitable body, just an organ. In another scene we hear how the director has the male lead count off, "one-Mississippi, two-Mississippi, etc." so that his orgasm is precisely timed. And the director barks out orders to his camera operators about keeping everything in focus, as the stars are faking great passion.
Stacy's arc in the film has a certain predictability. At first she genuinely believes that she is in it because she loves sex so much, and she is ready to criticize many of her fellow stars for just being in it for the money. Eventually, her enthusiasm wanes as her income increases and as she becomes burnt-out. By the end, she says that she's ready to abandon it all for another career entirely.
Like a roman candle, Stacy shoots for her star and reaches it. It is a star that most of us would never want to aim for, and on her descent, it becomes clear that Stacy is beginning to harbor doubts as to whether she really wanted it in the first place.
THE GIRL NEXT DOOR runs 1:22. It is not rated but would be an R for strong sexual content and language and would not be appropriate for those under 19.
Email<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Meal planning, your way.
NEED TO CODE: Write JavaScript code that displays the recipe(s) customer selects in the Home page. Hypothetical example below.
| Recipe | |
|---|---|
| Recipe 1 | |
| Recipe 2 | |
| Recipe 5 |
Table will be built using JavaScript. Table will be populated with data extracted from JSON string received from backend server. Send SELECT command to Recipes_in_Orders table JOIN Recipe_Ratings table ON recipe_id WHERE customer_id = this.customer_id AND order_id = this.order_id ORDER BY order_date DESC.


10 medical reasons for feeling tired
Any serious illness, such as cancer or stroke, or recovering from medical treatments can make you tired. But other illnesses can also leave you feeling washed out.
What's the difference between tiredness and fatigue?
We all experience tiredness at times, which can be relieved by sleep and rest. Fatigue is when the tiredness is often overwhelming and isn't relieved by sleep and rest.
Here are 10 health conditions known to cause tiredness or fatigue.
Anaemia
One of the most common medical reasons for feeling constantly run down is iron deficiency anaemia.
Women with heavy periods and pregnant women are especially prone to anaemia.
But it can also affect men and postmenopausal women, when the cause is more likely to be problems with the stomach and intestines, such as an ulcer or taking non-steroidal anti-inflammatory drugs (NSAIDs).
Typically, you feel you can't be bothered to do anything, your muscles feel heavy, and you get tired very quickly.
Read more about iron deficiency anaemia.
Vitamin B12 or folate deficiency anaemia can also cause tiredness.
It's possible to have too much iron, which can also cause tiredness, when it's known as iron-overload disorder (haemochromatosis).
This is a relatively rare inherited condition that affects men and women between the ages of 30 and 60.
Read more about haemochromatosis.
Sleep apnoea
Sleep apnoea is a condition where your throat narrows or closes during sleep and repeatedly interrupts your breathing.
This results in loud snoring and a drop in your blood's oxygen levels. The difficulty in breathing means you wake up often in the night and feel exhausted the next day.
It's most common in overweight middle-aged men. Drinking alcohol and smoking makes it worse.
Read more about sleep apnoea.
 This makes you feel tired.
You're also likely to put on weight and have aching muscles and dry skin. It's most common in women and happens more often as you get older.
Your GP can diagnose an underactive thyroid by taking a blood test.
Read more about having an underactive thyroid (hypothyroidism).
Coeliac disease
This is a lifelong disease caused by the immune system reacting to gluten.
Gluten is a protein in wheat, barley and rye, and can be found in foods like pasta, bread, cakes and cereals.
One in 100 people in the UK are affected, but research suggests many of them don't know they have the condition, according to charity Coeliac UK.
Other symptoms of coeliac disease, apart from tiredness, are diarrhoea, bloating, anaemia and weight loss. Your GP can do a blood test to check if you may have coeliac disease.
Read more about coeliac disease.
Chronic fatigue syndrome
Chronic fatigue syndrome (also known as myalgic encephalomyelitis, or ME) is a severe and disabling fatigue that goes on for at least 4 months. There may be other symptoms, such as muscle or joint pain.
Read more about chronic fatigue syndrome.
Diabetes
One of the main symptoms of type 1 and type 2 diabetes is feeling very tired.
Other key symptoms are feeling very thirsty, peeing a lot (particularly at night) and weight loss. Speak to a GP if you think you might have diabetes symptoms.
Glandular fever
Glandular fever is a common viral infection that causes fatigue, along with fever, sore throat and swollen glands.
Most cases happen in teenagers and young adults. Symptoms usually clear up within 4 to 6 weeks, but the fatigue can linger for several more months.
Read more about glandular fever.
Depression
As well as making you feel very sad, depression can also make you feel drained of energy.
It can stop you falling asleep or cause you to wake up early in the morning, which makes you feel more tired during the day.
Read more about clinical depression.
Restless legs syndrome
This is when you get an overwhelming urge to move your legs, which can keep you awake at night.
You might also have an unpleasant crawling sensation or a deep ache in your legs.
Whatever your symptoms, your sleep will be disrupted and poor quality, so you'll feel very tired throughout the day.
Read more about restless legs syndrome.
Anxiety
Feeling anxious is sometimes perfectly normal. But some people have constant uncontrollable feelings of anxiety that are so strong they affect their daily life.
Doctors call this generalised anxiety disorder (GAD). It's a common condition, affecting slightly more women than men. As well as feeling worried and irritable, people with GAD often feel tired.
Read more about generalised anxiety disorder in adults.
Page last reviewed: 15 March 2021
Next review due: 15 March 2024


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Millions of Nigerians not bothered about Sowore’s rearrest —Presidency
by Eniola AkinkuotuEniola Akinkuotu, Abuja
The Presidency has said that millions of Nigerians are not bothered about the rearrest of SaharaReporters Publisher, Omoyele Sowore, at the Federal High Court in Abuja last Friday.
The Special Adviser to the President on Media and Publicity, Femi Adesina, said this during Channels Television’s Sunrise Daily programme on Monday.
Adesina was asked to react to the outrage that greeted the violent rearrest of Sowore.
He said the Presidency stands by the claim of the State Security Service that the video circulating did not reflect what actually transpired in court.
The President’s spokesman added, “I don’t particularly agree with you when you say a lot of Nigerians (are not happy), you know that all this noise has been coming from less than 100,000 Nigerians. And the noise will be so loud that you think it is the whole country.
“It is a country of 198 million people. When just 100,000 are making noise in the social and traditional media, you would think the whole country is in an uproar. There are millions and millions of people who are not bothered.
“There are millions of people who know what the issues are. So, you cannot just seek the opinion of a local minority and then conclude that the country is in an uproar.”


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/* Copyright (c) 2013 Eric Brisson
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE. */
/**
* The FiguredBassRend object takes care of drawing the figuration
* notation on the canvas.
*
* @class FiguredBassRend
* @namespace GUIState
*
* @constructor
* @param {Canvas} can An html5 canvas element.
* @param {Render.CoordManager} coordMgr The library's coordinate
* manager.
* @param {Render.GlyphProvider} glyphProvider The library's glyph
* provider.
* @param {GUIState.FiguredBass} figBass The figured bass to be
* rendered.
* @param {Number} staffID The zero-based ID of the staff.
* @param {Number} colID The zero-based ID of the note column.
* @param {Number} totalAccCols The number of accidental columns
* in the key signature portion of the staff.
* @return {undefined}
*/
MusThGUI.GUIState.FiguredBassRend = function(can, coordMgr, glyphProvider,
figBass, staffID, colID, totalAccCols) {
this.can = can;
this.coordMgr = coordMgr;
this.figBass = figBass;
this.staffID = staffID;
this.colID = colID;
this.totalAccCols = totalAccCols;
this.glyphProvider = glyphProvider;
};
/**
* Draws the figured bass on the canvas.
*
* @method draw
* @return {undefined}
*/
MusThGUI.GUIState.FiguredBassRend.prototype.draw = function() {
// Set lower left corner at the first ledger line below the staff
var lLCorner = this.coordMgr.getNotePos('A', 3, this.staffID, 'treble',
this.colID, this.totalAccCols);
var ctx = this.can.getContext('2d');
ctx.font = 'bold 13px Times New Roman';
ctx.textAlign = 'left';
ctx.fillStyle = 'black';
var i, j, x, y, col, img;
var forwardStroke = false;
for (i = 0; i < this.figBass.rows.length; i++) {
col = 0;
for (j = 0; j < this.figBass.rows[i].cols.length; j++) {
var char = this.figBass.rows[i].cols[j];
if (char === '/') {
forwardStroke = true;
}
else if (char === '#') {
img = this.glyphProvider.getAccidental(char, false, true);
ctx.drawImage(img,
lLCorner.x + 1 + col * 8,
lLCorner.y + i * 17 - 12);
col += 1;
}
else if (char === 'b') {
img = this.glyphProvider.getAccidental(char, false, true);
ctx.drawImage(img,
lLCorner.x + 1 + col * 8,
lLCorner.y + i * 17 - 13);
col += 1;
}
else if (char === 'n') {
img = this.glyphProvider.getAccidental(char, false, true);
ctx.drawImage(img,
lLCorner.x + 1 + col * 8,
lLCorner.y + i * 17 - 12);
col += 1;
}
else {
x = lLCorner.x + 1 + col * 8;
y = lLCorner.y + i * 17;
ctx.fillText(char, x, y);
col += 1;
if (forwardStroke) {
ctx.beginPath();
ctx.moveTo(x - 2, y);
ctx.lineTo(x + 10, y - 9);
ctx.lineWidth = 0.75;
ctx.stroke();
forwardStroke = false;
}
}
}
}
};


Julia Stone
Age:35
Technology
- Job:Librarian
- Devices:Samsung Galaxy Chromebook using Chrome for browser, Iphone XR IOS 14.2
- Proficiency:knows apple phones, but prefers windows for desktop
- Passion for technology:Moderate
User Story
- As a Moderate user i would like a simple layout
- As a full time employee i would like a fast loading website
- As a registered user i would like updates on the website/app
Frustrations
- slow loading time
- Sites that are not visually appealing
- hard to understand site
Goal
Julia's goal is to be able to get sucked into her reading but also remember to water her plants.


Recipe 13.14. Tunneling SSL Through a ProxyProblemYou need to tunnel SSL (Secure Socket Layer) communications through a proxy, but the Python Standard Library doesn't support that functionality out of the box. SolutionWe can code a generic proxy, defaulting to SSL but, in fact, good for all kinds of network protocols. Save the following code as module file pytunnel.py somewhere along your Python sys.path: import threading, socket, traceback, sys, base64, time def recv_all(the_socket, timeout=1): ''' receive all data available from the_socket, waiting no more than ``timeout'' seconds for new data to arrive; return data as string.''' # use non-blocking sockets the_socket.setblocking(0) total_data = [ ] begin = time.time( ) while True: ''' loop until timeout ''' if total_data and time.time( )-begin > timeout: break # if you got some data, then break after timeout seconds elif time.time( )-begin > timeout*2: break # if you got no data at all yet, wait a little longer try: data = the_socket.recv(4096) if data: total_data.append(data) begin = time.time( ) # reset start-of-wait time else: time.sleep(0.1) # give data some time to arrive except: pass return ''.join(total_data) class thread_it(threading.Thread): ''' thread instance to run a tunnel, or a tunnel-client ''' done = False def _ _init_ _(self, tid='', proxy='', server='', tunnel_client='', port=0, ip='', timeout=1): threading.Thread._ _init_ _(self) self.tid = tid self.proxy = proxy self.port = port self.server = server self.tunnel_client = tunnel_client self.ip = ip; self._port = port self.data = { } # store data here to get later self.timeout = timeout def run(self): try: if self.proxy and self.server: ''' running tunnel operation, so bridge server <-> proxy ''' new_socket = False while not thread_it.done: # loop until termination if not new_socket: new_socket, address = self.server.accept( ) else: self.proxy.sendall( recv_all(new_socket, timeout=self.timeout)) new_socket.sendall( recv_all(self.proxy, timeout=self.timeout)) elif self.tunnel_client: ''' running tunnel client, just mark down when it's done ''' self.tunnel_client(self.ip, self.port) thread_it.done = True # normal termination except Exception, error: print traceback.print_exc(sys.exc_info( )), error thread_it.done = True # orderly termination upon exception class build(object): ''' build a tunnel object, ready to run two threads as needed ''' def _ _init_ _(self, host='', port=443, proxy_host='', proxy_port=80, proxy_user='', proxy_pass='', proxy_type='', timeout=1): self._port=port; self.host=host; self._phost=proxy_host self._puser=proxy_user; self._pport=proxy_port; self._ppass=proxy_pass self._ptype=proxy_type; self.ip='<IP_ADDRESS>'; self.timeout=timeout self._server, self.server_port = self.get_server( ) def get_proxy(self): if not self._ptype: proxy = socket.socket(socket.AF_INET, socket.SOCK_STREAM) proxy.connect((self._phost, self._pport)) proxy_authorization = '' if self._puser: proxy_authorization = 'Proxy-authorization: Basic '+\ base64.encodestring(self._puser+':'+self._ppass ).strip( )+'\r\n' proxy_connect = 'CONNECT %s:%sHTTP/1.0\r\n' % ( self.host, self._port) user_agent = 'User-Agent: pytunnel\r\n' proxy_pieces = proxy_connect+proxy_authorization+user_agent+'\r\n' proxy.sendall(proxy_pieces+'\r\n') response = recv_all(proxy, timeout=0.5) status = response.split(None, 1)[1] if int(status)/100 != 2: print 'error', response raise RuntimeError(status) return proxy def get_server(self): port = 2222 server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server.bind(('localhost', port)) server.listen(5) return server, port def run(self, func): Threads = [ ] Threads.append(thread_it(tid=0, proxy=self.get_proxy( ), server=self._server, timeout=self.timeout)) Threads.append(thread_it(tid=1, tunnel_client=func, ip=self.ip, port=self.server_port, timeout=0.5)) for Thread in Threads: Thread.start( ) for Thread in Threads: Thread.join( ) DiscussionHere is how you would typically use this pytunnel module in a small example script that tunnels an SSL connection through a proxy: import pytunnel, httplib def tunnel_this(ip, port): conn = httplib.HTTPSConnection(ip, port=port) conn.putrequest('GET', '/') conn.endheaders( ) response = conn.getresponse( ) print response.read( ) tunnel = pytunnel.build(host='login.yahoo.com', proxy_host='h1', proxy_user='u', proxy_pass='p') tunnel.run(tunnel_this) This example assumes you have a proxy server running on host h1, which is ready to accept basic authentication for a proxy user named u with a proxy password of p. Since it's unlikely that this is, in fact, your specific setup, you'll have to tweak these parameters if you want to see an example of this recipe's code running. But you understand the general idea: you instantiate class pytunnel.build, with all appropriate parameters passed with named-argument syntax, to build a tunnel object; then, you call the tunnel object's method run, passing as its argument your function that you want to be "tunneled" through the proxy. That function, in turn, receives as its arguments an IP address and a port number, and can connect to that address and port via SSL or any protocol implying SSL/TLS (Transport Layer Security), such as HTTPS. Internally, the tunnel object instantiates two threads that are instances of thread_it, one to run the tunnel client function, the other to perform the tunneling operation itself. The tunneling operation, in turn, is nothing more than an endless loop where all data available are received from one party and resent to the other, and vice versa; function recv_all deals with the task of receiving all available data, while the socket method send_all does the sending. The thread_it instance which runs the tunneling operation, therefore, does no more than an endless loop of just such calls. The code shown in this recipe is still being actively developed at the time of writing. For the latest version, see http://ftp.gnu.org/pub/savannah/files/pytunnel/pytunnel.py. Another alternative worth considering for tunneling and forwarding is Twisted's simple proxy (http://www.twistedmatrix.com/), but I have not personally tried that one yet. See AlsoFor SSL/TLS standards, http://www.ietf.org/html.charters/tls-charter.html; documentation for the standard library modules socket, threading and time in the Library Reference and Python in a Nutshell. |


kotcity4
/
kotcity.data.buildings
/
Building
/
pollution
pollution
open
var
pollution
:
Double
How much pollution this building generates...


SQL database project - Extended multi-feature query processing engine
The Problem with Standard SQL:
Building and maintaining multi-dimensional queries is a challenge for multiple reasons. The language does not allow succinct representation of simple concepts such as Data pivots, aggregate functions over multiple groups of the same table, and hierarchical data comparisons. In order to represent most multi-dimensional queries, we must resort to a complicated and costly series of join operations across many different relational algebra operators and multiple sub-queries. Besides the extra confusion of how to actually build the query, This makes it difficult for a query engine to optimize without costly analysis, which is not feasible for ad-hoc queries.
The Solution: Extended SQL
Extended SQL and Multi Feature Queries were originally developed by Chatziantoniou et al across two publications: Querying Multiple Features of Groups in Relational Databases and Evaluation of ad hoc OLAP: in-place computation. The papers describe a syntax that allows succinct expression of the types of queries queries mentioned above. This benefits us in a number of ways:
- Easier for humans to understand and formulate: Less likely for bugs to occur and less difficult to maintain
- Fewer costly join operations needed from the beginning: Leading to faster processing even before further optimization
- More feasible to optimize: With the query represented by grouping variables and the Phi relational algebra operator, it does not require as much deep analysis to optimize the query in order to improve the processing time.
My Implementation
I created both an MF and EMF based query processing engine that takes queries constructed with the Phi operator (see details in the above publications) and generates python code that executes this query. During execution, the MF table is a series of nested default dictionaries.
- 1st level: Scan # (table scan pass)
- 2nd level: grouping attributes
- 3rd level: attribute we are aggregating
- 4th level: aggregate function (this is a Custom defaultdict made especially for storing aggregates where default values for min, max, and avg are Null and all others are 0.)
This allows us to not waste space or time on attributes or aggregate functions that are not used in this query, while still allowing fast access of any possible attributes or aggregates needed.
At a high level, the basic steps are as follows:
- Parse the query data
- Inject code for each grouping variable matching given conditions - One table scan per grouping variable, maintain aggregates of each.
- Inject code to Build the output, scanning through all found grouping attributes for valid rows
Overall Engine logic:
This was a fun and challenging project. It was the first time I created code that then created other code so it was interesting coming up with good ways to manage that. Here are some specific challenges I dealt with along the way:
| Problem | Analysis | Solution |
|---|---|---|
| I originally tried to generate everything about the output code structure in the processing engine first | This led to a lot of syntax and logic issues while debugging that were tedious to identify and correct | Instead, I created my output code by hand, keeping in mind which parts needed to be modular, then worked backwards to make the engine output that code |
| Expected outputs were missing on sparse data | Null results were not being detected correctly | Switched to using try/except for all condition checks to avoid special logic for detecting Null cases. |


This dialog appears, if you started processes which are expected to have a longer duration, e.g. when performing the Attack on the Hash Value of the Digital Signature.
This dialog indicates the progress that is being made on the function in operation as a percentage and also as a moving bar display.
The action in progress can be terminated in advance by clicking on the Cancel button.


Members
(constant) About
Renders the about page
(constant) Account
Renders a account page
- Source:
(constant) EditPost
Renders the editPost page
- Source:
(constant) Home
Renders the home page
(constant) Login
Renders a Login form
(constant) Logout
Renders the logout page
(constant) Post
Renders a account page
(constant) PostManagement
Renders PostManagement page
- Source:
(constant) Posts
Renders Posts page
(constant) Register
Renders a registration form
- Source:
(constant) Title
Renders postView page
- Source:


About Us
A creative tool needs a creative person using it, and that's you! Our tool uses deep learning to generate lip-synchronised content allowing you to focus on creativity and not worry about synchronisation.
We're still in beta mode. But if you'd like to try an early version of DeepSyncAI, we'd love for you to give it a try and tell us what you think.
Team
Pavan
Chief Executive OfficerRudrabha Mukhopadhyay
Co-FounderPrajwal Renukanand
Co-FounderFaizan Farooq Khan
Co-FounderAnchit Gupta
Co-founderContact
Location:
IIIT Hyderabad, Professor CR Rao Rd, Gachibowli, Hyderabad, Telangana 500032
Email:
to_do
Call:
to_do


Forever young!
Go for it!
You can do it!
good
love and peace
background-size:cover
background-size:contain
定义背景图在何处
background-attachment:scroll
background-attachment:fixed


Every Mjolnir project and module comes with the documentation built-in. We do not provide auto-generated source code documentation (though the source code is documented) but actual hand written documentation; and we have our documentation as an integral part of our process.
On any mjolnir based project you may generate a html and pdf version of the documentation by running ./order librarian --manual
on the project's root. You may also view the documentation directly, which is located typically in +Docs
folders under each module in the form of readable markdown files.
We recomend viewing documentation generated inside the project because it may include information provided by 3rd party modules.


--- layout: babyuhomepage ---
Problem: Children from low-income neighborhoods enter Kindergarten 60 percent behind their middle-class counterparts in learning development.
Solution: Baby U gives parents the tools to close this education gap, so that children in the South Toledo Kids Zone are ready to learn when they start school.
Why? We believe in order to improve a neighborhood, we must nurture it's greatest asset: The Family. Baby U educates and strengthens our urban families.


Treant Bremec:
... ... Goblin.forest.destroyer.ruthless.invader.you.punish.teach.forest.order ... ...
Say you will quit
Say you will continue


{% extends 'base.html' %} {% load staticfiles %} {% block main_content_block %}
The uploaded file '{{ file }}' could not be detected as an Image file.


conio.h
joystick.h
mouse.h
This file contains a short overview of the libraries available for the cc65 C compiler. Please have a look at the function reference for a list function by function. Since the function reference is not complete (I'm working on that) it may happen that you don't find a specific function. In this case, have a look into the header files. All functions, that are not defined by the ISO C standard have a short comment in the headers, explaining their use.
The C library contains a large subset of the ISO C library. Functions are usually missing in areas, where there is no support on typical 6502 systems. Wide character sets are an example for this.
I will not go into detail about the ISO functions. If a function is not mentioned here explicitly, expect it to be available and to behave as defined in the C standard.
Functions that are not available:
tmpfile/tmpnam
system
(cc65 alternative exec
)
ldiv
function (cc65 is currently not able to return structs
with a size not equal to 1, 2 or 4 bytes by value).
SIGSEGV
would be
cool:-)
setbuf/setvbuf
Functions not available on all supported systems:
fopen/fread/fwrite/fclose/fputs/fgets/fscanf
: The functions
are built on open/read/write/close. These latter functions are not available
on all systems.
ftell/fseek/fgetpos/fsetpos
: Support depends on the
capabilities of the target machine.
rename/remove/rewind
: Support depends on the capabilities of
the target machine.
time
: Since many of the supported systems do not have a real
time clock, which means that the time
function is not available. Please
note that the other functions from time.h
are available.Functions that are limited in any way:
strcspn/strpbrk/strspn
: These functions have a length
limitation of 256 for the second string argument. Since this string gives a
character set, and there are only 256 distinct characters, this shouldn't be
a problem.
getenv
: Since there is no such thing as an environment on all
supported systems, the getenv
function will always return a NULL
pointer.
locale
: There is no other locale than the "C" locale. The
native locale is identical to the "C" locale.In addition to these limitations, some more functions are limited if inlined versions are requested by using -Os:
strlen
function only works for strings with a maximum length
of 255 characters.
isxxx
character classification functions from
<ctype.h>
will give unpredictable results if the argument is not
in character range (0..255). This limitation may be removed by #undef'ing
the function name (when using -Os
, the functions are actually macros
that expand to inline assembler code, but the real functions are still
available if the macro definition is removed).The header file 6502.h contains some functions that make only sense with the 6502 CPU. Examples are macros to insert more or less useful instructions into your C code, or a function to call arbitrary machine language subroutines, passing registers in and out.
For each supported system there's a header file that contains calls or defines specific for this system. So, when programming for the C64, include c64.h, for the C128, include c128.h and so on. To make the task for the Commodore systems easier, there is also a header file named cbm.h that will define stuff common for all CBM systems, and include the header file for the specific target system.
The header files contain
SID
. Writing to the fields of this struct will write to the SID device
instead. Using these variables will make your program more readable and more
portable. Don't fear ineffective code when using these variables, the
compiler will translate reads and writes to these structs into direct memory
accesses.
conio.h
The conio.h
header file contains a large set of functions that do screen
and keyboard I/O. The functions will write directly to the screen or poll the
keyboard directly with no more help from the operating system than needed.
This has some disadvantages, but on the other side it's fast and reasonably
portable. conio implementations exist for the following targets:
The conio.h header file does also include the system specific header files which define constants for special characters and keys.
joystick.h
For systems that have a joystick, joystick.h
will define a subroutine to
read the current value, including constants to evaluate the result of this
function.
mouse.h
Some target machines support a mouse. Mouse support is currently available for the following targets:
The available functions are declared in mouse.h
.
This C runtime library implementation for the cc65 compiler is (C) Copyright 1998-2002 Ullrich von Bassewitz. For usage of the binaries and/or sources the following conditions do apply:
This software is provided 'as-is', without any expressed or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software.
Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions:


|
Sympathy for the Devil:
The victimization of a serial killer in Monster
By Kristen Morley
Prior to seeing the film Monster,
I knew very little about the film. After watching Monster,
I found it difficult to forget. Monster is based upon
the true story of Aileen Wournos, a highway hooker and America's
first female serial-killer, who was put to death in 1992 for
murdering seven men. Aileen's killing spree began when she shot
and killed a John after being brutally raped. The sense of empowerment
she derived from killing the John who raped her, and a desperate
desire to provide for her girlfriend Shelby, drove Aileen to
kill 6 more victims before she was apprehended by police.
Initially,
I attributed the title of the movie Monster as referring
to Aileen Wournos. She was a serial killer who took the lives
of seven men, how could she be anything but a monster? However,
by the end of the movie, I could not identify Aileen with that
label. Aileen Wournos is portrayed as a very sorrowful character.
Rather, it was the Johns who took advantage of Aileen and the
men who had victimized her all her life that became the monster
to me. When Aileen killed them, I felt "schadenfreude":
a degree of satisfaction in their deaths. Recognizing my own
reaction to this movie caused me to question my values: had I
simply been manipulated by the movie's sympathetic portrayal
of Aileen Wournos, or do I really believe that sexual victimization
justifies murder?
Monster fills in some of the gaps left by
the movie Dead Man Walking. Critics of Dead Man Walking
thought the film lacked social consciousness by failing to show
the disadvantaged background of most inmates on death row. Those
who have worked with death row inmates attest to the impact that
childhood physical and emotional abuse has in creating a criminal.
Through the characterization of Aileen Wournos, Monster
subtly and effectively reveals how Aileen's childhood abuse contributed
to her criminalization. Aileen's life is a tale of abuse and
abandonment. At age 8 she was repeatedly sexually abused by
her father's friend, and when her father found out, he beat her.
She gave up a baby for adoption at age 13. By the time she
was a teenager, she was homeless and forced to become a hooker
to survive. Where the movie picks up her life, Aileen is 28-years-old
and contemplating suicide. As a viewer, it is difficult not
to assess her later actions in the context of her personal history.
The magnitude of abuse she had suffered inspired in me a righteous
anger at those who contributed to the victimization, and the
social system that allowed it to happen.
Monster can be characterized as a revenge
film. As defined by William Miller in his article Clint Eastwood
and Equity: Popular Culture's Theory of Revenge, a revenge
narrative, "takes us from indignation and outrage at a wrong,
via fear and loathing of the wrongdoer, to a sense of satisfaction
of having righted on the body of the wrongdoer." The initial
wrong perceived in Monster that inspires our fear and
outrage is the rape of Aileen. When she kills the John, we feel
satisfaction that he got what he deserved. However, the later
murders committed by Aileen were not in response to the same
circumstances that occurred in the first instance. Whereas the
first murder could be construed as self-defence, the later murders
were much more predatory. With the exception of her final victim,
Aileen killed Johns simply because they were Johns. Yet, her
murders still find some legitimacy under the revenge model.
Even though she is not directly under attack, she is still the
victim of men degrading her for sexual gratification. Rather
than fighting against one specific instance of rape, she is fighting
against a society that has allowed her to suffer a lifetime of
sexual victimization. The wrong that inspires the revenge is
still there, but the dimension that changes in the subsequent
murders is the audience's lack of fear and loathing of the Johns.
Miller posits that the audience
acts as the judge in determining when an avenger crosses the
line and becomes a villain. The legitimacy of an avenger is
dependant upon whether their response is proportional to the
wrong committed. A case can be made that after committing the
first murder, Aileen became a villain rather than an avenger,
particularly as her victims became less culpable. It may be
legitimate to fear and loath a rapist and feel some satisfaction
in his death, yet it is difficult to argue that a man who visits
a prostitute because his wife is in a wheelchair should be killed
for his indiscretions. Aileen's final victim was a kindly older
man who picked her up not realizing she was a prostitute. Upon
seeing that she was in a desperate state, he offered her money
and a place to stay with him and his wife. Aileen did not want
to kill him, and was visibly distraught as she agonized over
what to do. However, in the end, she did kill him, afraid that
if she didn't he would blow her cover. With this murder, that
had no legitimacy or revenge motive, Aileen seemed to have firmly
crossed the line from avenger to villain.
Although the audience may not
find objective legitimacy for Aileen's actions, she strived psychologically
to legitimate her own actions. During her killing spree, Aileen
tried to justify her actions by casting all her potential victims
as violent abusers. In light of her history of being exploited
by men, it is plausible to believe that in her mind all men were
in fact violent abusers. In one instance, Aileen attempted to
vilify a John she was planning to murder, only to find he was
painfully shy with a stuttering problem and had never picked
up a hooker before. This discovery gave her pause and she did
not kill him. This shows that Aileen does not fit the mould
of a typical serial killer: she is not killing indiscriminately
but rather acting out against the very class of people who had
victimized her. Perhaps it is because Aileen herself is a victim
that it is difficult to characterize her wholly as a villain.
Aileen Wournos is a character
for whom I felt an immense amount of sympathy. This sympathy
was based not only on Aileen's sexual victimization, but also
on the corresponding betrayal she suffered from everyone in the
movie who could have been in a position to "save" her.
Authority figures not only failed to protect her, but contributed
to her victimization. As a child, Aileen's father punished her
for being the object of molestation.  Aileen's efforts to rehabilitate herself were frustrated
by lack of support. After being raped by the John, Aileen wanted
to stop hooking and tried to secure a legitimate job. She was
humiliated and rejected at every place she applied, and ultimately
returned to hooking, partially motivated by a desire to provide
for Shelby. Even Shelby betrays Aileen out of self-interest.
In the final scenes of the movie, Shelby testifies against
Aileen at her criminal trial, as part of a plea to exculpate
her own knowledge of and complicity in the murders. At the end
of the movie, I almost felt more animosity towards Shelby than
I did toward Aileen: a completely inappropriate response given
the relative weight of their crimes.
Monster ends with Aileen being lead away from
the courtroom and a post-script informs the audience that she
was executed after spending 12 years on death row. Though Aileen's
execution was off-screen, I was left with the feeling that it
was unjust for her to be put to death. My reaction to Monster
stands in direct contrast to my reaction to Dead Man Walking.
At the end of Dead Man Walking I felt that justice was
served by putting Mathew Ponselet to death. My reaction was
certainly not premised upon the relative severity of their crimes:
Ponselet being guilty of an isolated incident of rape and murder,
and Aileen being guilty of seven murders over a period of time.
Objectively, Aileen is more deserving of the death penalty.
However there are two striking differences that effect my assessment
of the culpability of these two characters. First, Aileen was
not just the perpetrator of crimes, she was very much a victim
herself and had been victimized her entire life. Her crimes
were not random; rather, they were the direct result of acting
out against the emotional abuse she had suffered through her
own victimization. Second, Aileen was a victim of sexual exploitation
and degradation, whereas Ponselet was the perpetrator of these
crimes. Though I am arguable displaying a gender bias in this
assessment, I have a disproportionate amount of sympathy for
victims of sexual exploitation and a disproportionate level of
condemnation for sexual aggressors.
The story of Aileen Wornous
is a tragedy. She was a victim, turned victimizer. At the end
of Monster, I felt an injustice had been committed. There is
more than one monster in this film, yet Aileen Wornous was the
only one held accountable for her crimes. A society that allows
women to be sexually victimized is also monstrous and this is
the monster that should be put to death.
Posted November 11, 2005
|


class: center, middle, inverse, title-slide # Ruth Wilson Gilmore ## Professional Cameo ### David Ranzolin ### 2021-11-02 ---  # Why I Chose RWG .center2[  ] --- # Ruth Wilson Gilmore .panelset[ .panel[.panel-name[Early Life] * Born April 2, 1950 in New Haven, Connecticut * Raised in a politically active family * First (and only) African-American student at her private school .center[  ] ] .panel[.panel-name[Education] * Earned undergraduate degree in Drama * A lifelong community organizer and educator * Studied geography and economics under Neil Smith at Rutgers University, earning her Ph.D in 1998 > *"I became a geographer to engage with questions of how we make the world and ourselves, and to study how everyday people do so with the dream of justice, equality, and beauty for all."*
 > *""I became a geographer when I was in my 40s because it seemed to me, at least in the context of US graduate education, it was the best way to pursue serious materialist analysis."* ] .panel[.panel-name[Current] .pull-left[ * Professor of Geography at the City University of New York * Director of the Center for Place, Culture, and Politics * Executive Committee member of the Institute for Research on the African Diaspora in the Americas and the Caribbean * Award-winning author, global speaker, and lifelong activist ] .pull-right[  ] ] .panel[.panel-name[Methods] > *"My methods are as diverse as my subjects: while ethnography, interviews, and community studies constitute the heart of my labors, I use archives and statistics for armature."* .center[  ] ] .panel[.panel-name[Influence] Co-founder of: * California Prison Moratorium Project * Critical Resistance * Central California Environmental Justice Network Winner of: * American Studies Association Angela Y. Davis Award for Public Scholarship * Association of American Geographers Harold Rose Award for Anti-Racist Research and Practice * SUNY-Purchase College Eugene V. Grant Distinguished Scholar Prize for Social and Environmental Justice * American Studies Association Richard A Yarborough Mentorship Award * Association of American Geographers Lifetime Achievement Award * Lannan Foundation Lifetime Cultural Freedom Prize ] ] --- # Abolition .center2[  ] --- # Abolition .center2[  ] --- # Abolition and Geography .center2[  ] --- # Abolition and Geography .center2[  ] --- # Abolition and Humanism .center2[  ] --- # Green, Red, Transnational .center2[  ] --- # Abolition and Racial Capitalism .center2[  ] --- class: middle, center .center2[  ]


Facts about Saturn:
- Sixth planet furthest from the sun
- Second larget planet in our solar system
- Surrounded by nine consecutive rings
Facts about the moons of Saturn:
- There are 62 moons
- Only 53 moons are named
- About a quarter of the moons have a diameter larger than 31 miles