BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def reorder(self, updates_ids, offset=None, utc=None): ''' Edit the order at which statuses for the specified social media profile will be sent out of the buffer. ''' url = PATHS['REORDER'] % self.profile_id order_format = "order[]=%s&" post_data = '' if offset: post_data +=...
def new(self, text, shorten=None, now=None, top=None, media=None, when=None): ''' Create one or more new status updates. ''' url = PATHS['CREATE'] post_data = "text=%s&" % text post_data += "profile_ids[]=%s&" % self.profile_id if shorten: post_data += "shorten=%s&" % shorten ...
def set_level(self, level='info', handlers=None): """ Set the logging level (which types of logs are actually printed / recorded) to one of ['debug', 'info', 'warn', 'error', 'fatal'] in that order of severity """ for h in self.get_handlers(handlers): h.setLev...
def set_formatter(self, formatter='standard', handlers=None): """ Set the text format of messages to one of the pre-determined forms, one of ['quiet', 'minimal', 'standard', 'verbose'] """ for h in self.get_handlers(handlers): h.setFormatter(logging.Formatter(formatte...
def add_handler(self, name='console-color', level='info', formatter='standard', **kwargs): """ Add another handler to the logging system if not present already. Available handlers are currently: ['console-bw', 'console-color', 'rotating-log'] """ # make sure the the log file has ...
def remove_handler(self, name): """ Remove handler from the logging system if present already. Available handlers are currently: ['console-bw', 'console-color', 'rotating-log'] """ if name in self.handlers: self.log.removeHandler(self.handlers[name])
def noformat(self): """ Temporarily do not use any formatter so that text printed is raw """ try: formats = {} for h in self.get_handlers(): formats[h] = h.formatter self.set_formatter(formatter='quiet') yield except Exception as e:...
def set_verbosity(self, verbosity='vvv', handlers=None): """ Set the verbosity level of a certain log handler or of all handlers. Parameters ---------- verbosity : 'v' to 'vvvvv' the level of verbosity, more v's is more verbose handlers : string, or list of ...
def normalize(im, invert=False, scale=None, dtype=np.float64): """ Normalize a field to a (min, max) exposure range, default is (0, 255). (min, max) exposure values. Invert the image if requested. """ if dtype not in {np.float16, np.float32, np.float64}: raise ValueError('dtype must be numpy...
def generate_sphere(radius): """Generates a centered boolean mask of a 3D sphere""" rint = np.ceil(radius).astype('int') t = np.arange(-rint, rint+1, 1) x,y,z = np.meshgrid(t, t, t, indexing='ij') r = np.sqrt(xx + yy + z*z) sphere = r < radius return sphere
def local_max_featuring(im, radius=2.5, noise_size=1., bkg_size=None, minmass=1., trim_edge=False): """Local max featuring to identify bright spherical particles on a dark background. Parameters ---------- im : numpy.ndarray The image to identify particles in. radius...
def otsu_threshold(data, bins=255): """ Otsu threshold on data. Otsu thresholding [1]_is a method for selecting an intensity value for thresholding an image into foreground and background. The sel- ected intensity threshold maximizes the inter-class variance. Parameters ---------- ...
def harris_feature(im, region_size=5, to_return='harris', scale=0.05): """ Harris-motivated feature detection on a d-dimensional image. Parameters --------- im region_size to_return : {'harris','matrix','trace-determinant'} """ ndim = im.ndim #1. Gradient of image ...
def identify_slab(im, sigma=5., region_size=10, masscut=1e4, asdict=False): """ Identifies slabs in an image. Functions by running a Harris-inspired edge detection on the image, thresholding the edge, then clustering. Parameters ---------- im : numpy.ndarray 3D array of the...
def plot_errors_single(rad, crb, errors, labels=['trackpy', 'peri']): fig = pl.figure() comps = ['z', 'y', 'x'] markers = ['o', '^', '*'] colors = COLORS for i in reversed(range(3)): pl.plot(rad, crb[:,0,i], lw=2.5, label='CRB-'+comps[i], color=colors[i]) for c, (error, label) in enume...
def sphere_triangle_cdf(dr, a, alpha): """ Cumulative distribution function for the traingle distribution """ p0 = (dr+alpha)*2/(2alpha*2)(0 > dr)(dr>-alpha) p1 = 1(dr>0)-(alpha-dr)*2/(2alpha*2)(0<dr)*(dr<alpha) return (1-np.clip(p0+p1, 0, 1))
def sphere_analytical_gaussian(dr, a, alpha=0.2765): """ Analytically calculate the sphere's functional form by convolving the Heavyside function with first order approximation to the sinc, a Gaussian. The alpha parameters controls the width of the approximation -- should be 1, but is fit to be roug...
def sphere_analytical_gaussian_trim(dr, a, alpha=0.2765, cut=1.6): """ See sphere_analytical_gaussian_exact. I trimmed to terms from the functional form that are essentially zero (1e-8) for r0 > cut (~1.5), a fine approximation for these platonic anyway. """ m = np.abs(dr) <= cut # only co...
def sphere_analytical_gaussian_fast(dr, a, alpha=0.2765, cut=1.20): """ See sphere_analytical_gaussian_trim, but implemented in C with fast erf and exp approximations found at Abramowitz and Stegun: Handbook of Mathematical Functions A Fast, Compact Approximation of the Exponential Function ...
def sphere_constrained_cubic(dr, a, alpha): """ Sphere generated by a cubic interpolant constrained to be (1,0) on (r0-sqrt(3)/2, r0+sqrt(3)/2), the size of the cube in the (111) direction. """ sqrt3 = np.sqrt(3) b_coeff = a0.5/sqrt3(1 - 0.6sqrt3alpha)/(0.15 + a*a) rscl = np.clip(dr, -0...
def exact_volume_sphere(rvec, pos, radius, zscale=1.0, volume_error=1e-5, function=sphere_analytical_gaussian, max_radius_change=1e-2, args=()): """ Perform an iterative method to calculate the effective sphere that perfectly (up to the volume_error) conserves volume. Return the resulting image ...
def _tile(self, n): """Get the update tile surrounding particle `n` """ pos = self._trans(self.pos[n]) return Tile(pos, pos).pad(self.support_pad)
def _p2i(self, param): """ Parameter to indices, returns (coord, index), e.g. for a pos pos : ('x', 100) """ g = param.split('-') if len(g) == 3: return g[2], int(g[1]) else: raise ValueError('`param` passed as incorrect format')
def initialize(self): """Start from scratch and initialize all objects / draw self.particles""" self.particles = np.zeros(self.shape.shape, dtype=self.float_precision) for p0, arg0 in zip(self.pos, self._drawargs()): self._draw_particle(p0, *listify(arg0))
def _vps(self, inds): """Clips a list of inds to be on [0, self.N]""" return [j for j in inds if j >= 0 and j < self.N]
def _i2p(self, ind, coord): """ Translate index info to parameter name """ return '-'.join([self.param_prefix, str(ind), coord])
def get_update_tile(self, params, values): """ Get the amount of support size required for a particular update.""" doglobal, particles = self._update_type(params) if doglobal: return self.shape.copy() # 1) store the current parameters of interest values0 = self.get_v...
def update(self, params, values): """ Update the particles field given new parameter values """ #1. Figure out if we're going to do a global update, in which # case we just draw from scratch. global_update, particles = self._update_type(params) # if we are doin...
def param_particle(self, ind): """ Get position and radius of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, j) for i in ind for j in ['z', 'y', 'x', 'a']]
def param_particle_pos(self, ind): """ Get position of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, j) for i in ind for j in ['z', 'y', 'x']]
def param_particle_rad(self, ind): """ Get radius of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, 'a') for i in ind]
def add_particle(self, pos, rad): """ Add a particle or list of particles given by a list of positions and radii, both need to be array-like. Parameters ---------- pos : array-like [N, 3] Positions of all new particles rad : array-like [N] ...
def remove_particle(self, inds): """ Remove the particle at index `inds`, may be a list. Returns [3,N], [N] element numpy.ndarray of pos, rad. """ if self.rad.shape[0] == 0: return inds = listify(inds) # Here's the game plan: # 1. get all p...
def _update_type(self, params): """ Returns dozscale and particle list of update """ dozscale = False particles = [] for p in listify(params): typ, ind = self._p2i(p) particles.append(ind) dozscale = dozscale or typ == 'zscale' particles = set(...
def _tile(self, n): """ Get the tile surrounding particle `n` """ zsc = np.array([1.0/self.zscale, 1, 1]) pos, rad = self.pos[n], self.rad[n] pos = self._trans(pos) return Tile(pos - zscrad, pos + zscrad).pad(self.support_pad)
def update(self, params, values): """Calls an update, but clips radii to be > 0""" # radparams = self.param_radii() params = listify(params) values = listify(values) for i, p in enumerate(params): # if (p in radparams) & (values[i] < 0): if (p[-2:] == '-a'...
def rmatrix(self): """ Generate the composite rotation matrix that rotates the slab normal. The rotation is a rotation about the x-axis, followed by a rotation about the z-axis. """ t = self.param_dict[self.lbl_theta] r0 = np.array([ [np.cos(t), -np.sin(t), 0], ...
def j2(x): """ A fast j2 defined in terms of other special functions """ to_return = 2./(x+1e-15)*j1(x) - j0(x) to_return[x==0] = 0 return to_return
def calc_pts_hg(npts=20): """Returns Hermite-Gauss quadrature points for even functions""" pts_hg, wts_hg = np.polynomial.hermite.hermgauss(npts2) pts_hg = pts_hg[npts:] wts_hg = wts_hg[npts:] np.exp(pts_hg*pts_hg) return pts_hg, wts_hg
def calc_pts_lag(npts=20): """ Returns Gauss-Laguerre quadrature points rescaled for line scan integration Parameters ---------- npts : {15, 20, 25}, optional The number of points to Notes ----- The scale is set internally as the best rescaling for a line scan ...
def f_theta(cos_theta, zint, z, n2n1=0.95, sph6_ab=None, **kwargs): """ Returns the wavefront aberration for an aberrated, defocused lens. Calculates the portions of the wavefront distortion due to z, theta only, for a lens with defocus and spherical aberration induced by coverslip mismatch. (The r...
def get_Kprefactor(z, cos_theta, zint=100.0, n2n1=0.95, get_hdet=False, **kwargs): """ Returns a prefactor in the electric field integral. This is an internal function called by get_K. The returned prefactor in the integrand is independent of which integral is being called; it is a combinat...
def get_K(rho, z, alpha=1.0, zint=100.0, n2n1=0.95, get_hdet=False, K=1, Kprefactor=None, return_Kprefactor=False, npts=20, **kwargs): """ Calculates one of three electric field integrals. Internal function for calculating point spread functions. Returns one of three electric field integrals th...
def get_hsym_asym(rho, z, get_hdet=False, include_K3_det=True, **kwargs): """ Calculates the symmetric and asymmetric portions of a confocal PSF. Parameters ---------- rho : numpy.ndarray Rho in cylindrical coordinates, in units of 1/k. z : numpy.ndarray Z in cyl...
def calculate_pinhole_psf(x, y, z, kfki=0.89, zint=100.0, normalize=False, **kwargs): """ Calculates the perfect-pinhole PSF, for a set of points (x,y,z). Parameters ----------- x : numpy.ndarray The x-coordinate of the PSF in units of 1/ the wavevector of the in...
def get_polydisp_pts_wts(kfki, sigkf, dist_type='gaussian', nkpts=3): """ Calculates a set of Gauss quadrature points & weights for polydisperse light. Returns a list of points and weights of the final wavevector's distri- bution, in units of the initial wavevector. Parameters ---------- ...
def calculate_polychrome_pinhole_psf(x, y, z, normalize=False, kfki=0.889, sigkf=0.1, zint=100., nkpts=3, dist_type='gaussian', **kwargs): """ Calculates the perfect-pinhole PSF, for a set of points (x,y,z). Parameters ----------- x : numpy.ndarray The x-coordinate of the PS...
def get_psf_scalar(x, y, z, kfki=1., zint=100.0, normalize=False, **kwargs): """ Calculates a scalar (non-vectorial light) approximation to a confocal PSF The calculation is approximate, since it ignores the effects of polarization and apodization, but should be ~3x faster. Parameters --------...
def calculate_linescan_ilm_psf(y,z, polar_angle=0., nlpts=1, pinhole_width=1, use_laggauss=False, **kwargs): """ Calculates the illumination PSF for a line-scanning confocal with the confocal line oriented along the x direction. Parameters ---------- y : numpy.ndarray Th...
def calculate_linescan_psf(x, y, z, normalize=False, kfki=0.889, zint=100., polar_angle=0., wrap=True, **kwargs): """ Calculates the point spread function of a line-scanning confocal. Make x,y,z __1D__ numpy.arrays, with x the direction along the scan line. (to make the calculation faster sinc...
def calculate_polychrome_linescan_psf(x, y, z, normalize=False, kfki=0.889, sigkf=0.1, zint=100., nkpts=3, dist_type='gaussian', wrap=True, **kwargs): """ Calculates the point spread function of a line-scanning confocal with polydisperse dye emission. Make x,y,z __1D__ numpy.arrays, wi...
def wrap_and_calc_psf(xpts, ypts, zpts, func, **kwargs): """ Wraps a point-spread function in x and y. Speeds up psf calculations by a factor of 4 for free / some broadcasting by exploiting the x->-x, y->-y symmetry of a psf function. Pass x and y as the positive (say) values of the coordinates at ...
def vec_to_halfvec(vec): """Transforms a vector np.arange(-N, M, dx) to np.arange(min(\|vec\|), max(N,M),dx)]""" d = vec[1:] - vec[:-1] if ((d/d.mean()).std() > 1e-14) or (d.mean() < 0): raise ValueError('vec must be np.arange() in increasing order') dx = d.mean() lowest = np.abs(vec).min() ...
def listify(a): """ Convert a scalar ``a`` to a list and all iterables to list as well. Examples -------- >>> listify(0) [0] >>> listify([1,2,3]) [1, 2, 3] >>> listify('a') ['a'] >>> listify(np.array([1,2,3])) [1, 2, 3] >>> listify('string') ['string'] ""...
def delistify(a, b=None): """ If a single element list, extract the element as an object, otherwise leave as it is. Examples -------- >>> delistify('string') 'string' >>> delistify(['string']) 'string' >>> delistify(['string', 'other']) ['string', 'other'] >>> delisti...
def aN(a, dim=3, dtype='int'): """ Convert an integer or iterable list to numpy array of length dim. This func is used to allow other methods to take both scalars non-numpy arrays with flexibility. Parameters ---------- a : number, iterable, array-like The object to convert to numpy...
def cdd(d, k): """ Conditionally delete key (or list of keys) 'k' from dict 'd' """ if not isinstance(k, list): k = [k] for i in k: if i in d: d.pop(i)
def patch_docs(subclass, superclass): """ Apply the documentation from ``superclass`` to ``subclass`` by filling in all overridden member function docstrings with those from the parent class """ funcs0 = inspect.getmembers(subclass, predicate=inspect.ismethod) funcs1 = inspect.getmembers(sup...
def indir(path): """ Context manager for switching the current path of the process. Can be used: with indir('/tmp'): <do something in tmp> """ cwd = os.getcwd() try: os.chdir(path) yield except Exception as e: raise finally: os.chdir(cwd)
def slicer(self): """ Array slicer object for this tile >>> Tile((2,3)).slicer (slice(0, 2, None), slice(0, 3, None)) >>> np.arange(10)[Tile((4,)).slicer] array([0, 1, 2, 3]) """ return tuple(np.s_[l:r] for l,r in zip(*self.bounds))
def oslicer(self, tile): """ Opposite slicer, the outer part wrt to a field """ mask = None vecs = tile.coords(form='meshed') for v in vecs: v[self.slicer] = -1 mask = mask & (v > 0) if mask is not None else (v>0) return tuple(np.array(i).astype('int') for...
def kcenter(self): """ Return the frequency center of the tile (says fftshift) """ return np.array([ np.abs(np.fft.fftshift(np.fft.fftfreq(q))).argmin() for q in self.shape ]).astype('float')
def corners(self): """ Iterate the vector of all corners of the hyperrectangles >>> Tile(3, dim=2).corners array([[0, 0], [0, 3], [3, 0], [3, 3]]) """ corners = [] for ind in itertools.product(((0,1),)self.dim): ...
def _format_vector(self, vecs, form='broadcast'): """ Format a 3d vector field in certain ways, see `coords` for a description of each formatting method. """ if form == 'meshed': return np.meshgrid(*vecs, indexing='ij') elif form == 'vector': vecs ...
def coords(self, norm=False, form='broadcast'): """ Returns the coordinate vectors associated with the tile. Parameters ----------- norm : boolean can rescale the coordinates for you. False is no rescaling, True is rescaling so that all coordinates are fr...
def kvectors(self, norm=False, form='broadcast', real=False, shift=False): """ Return the kvectors associated with this tile, given the standard form of -0.5 to 0.5. `norm` and `form` arguments arethe same as that passed to `Tile.coords`. Parameters ----------- r...
def contains(self, items, pad=0): """ Test whether coordinates are contained within this tile. Parameters ---------- items : ndarray [3] or [N, 3] N coordinates to check are within the bounds of the tile pad : integer or ndarray [3] anisotropic p...
def intersection(tiles, *args): """ Intersection of tiles, returned as a tile >>> Tile.intersection(Tile([0, 1], [5, 4]), Tile([1, 0], [4, 5])) Tile [1, 1] -> [4, 4] ([3, 3]) """ tiles = listify(tiles) + listify(args) if len(tiles) < 2: return tiles[...
def translate(self, dr): """ Translate a tile by an amount dr >>> Tile(5).translate(1) Tile [1, 1, 1] -> [6, 6, 6] ([5, 5, 5]) """ tile = self.copy() tile.l += dr tile.r += dr return tile
def pad(self, pad): """ Pad this tile by an equal amount on each side as specified by pad >>> Tile(10).pad(2) Tile [-2, -2, -2] -> [12, 12, 12] ([14, 14, 14]) >>> Tile(10).pad([1,2,3]) Tile [-1, -2, -3] -> [11, 12, 13] ([12, 14, 16]) """ tile = self.copy...
def overhang(self, tile): """ Get the left and right absolute overflow -- the amount of box overhanging `tile`, can be viewed as self \\ tile (set theory relative complement, but in a bounding sense) """ ll = np.abs(amin(self.l - tile.l, aN(0, dim=self.dim))) rr =...
def reflect_overhang(self, clip): """ Compute the overhang and reflect it internally so respect periodic padding rules (see states._tile_from_particle_change). Returns both the inner tile and the inner tile with necessary pad. """ orig = self.copy() tile = self.co...
def filtered_image(self, im): """Returns a filtered image after applying the Fourier-space filters""" q = np.fft.fftn(im) for k,v in self.filters: q[k] -= v return np.real(np.fft.ifftn(q))
def set_filter(self, slices, values): """ Sets Fourier-space filters for the image. The image is filtered by subtracting values from the image at slices. Parameters ---------- slices : List of indices or slice objects. The q-values in Fourier space to filter....
def load_image(self): """ Read the file and perform any transforms to get a loaded image """ try: image = initializers.load_tiff(self.filename) image = initializers.normalize( image, invert=self.invert, scale=self.exposure, dtype=self.float_precisi...
def get_scale(self): """ If exposure was not set in the __init__, get the exposure associated with this RawImage so that it may be used in other :class:`~peri.util.RawImage`. This is useful for transferring exposure parameters to a series of images. Returns -----...
def get_scale_from_raw(raw, scaled): """ When given a raw image and the scaled version of the same image, it extracts the ``exposure`` parameters associated with those images. This is useful when Parameters ---------- raw : array_like The image loaded...
def _draw(self): """ Interal draw method, simply prints to screen """ if self.display: print(self._formatstr.format(**self.__dict__), end='') sys.stdout.flush()
def update(self, value=0): """ Update the value of the progress and update progress bar. Parameters ----------- value : integer The current iteration of the progress """ self._deltas.append(time.time()) self.value = value self._percen...
def init_app(self, app): """Flask application initialization.""" self.init_config(app) app.register_blueprint(blueprint) app.extensions['invenio-groups'] = self
def check_consistency(self): """ Make sure that the required comps are included in the list of components supplied by the user. Also check that the parameters are consistent across the many components. """ error = False regex = re.compile('([a-zA-Z_][a-zA-Z0-9_]*)...
def check_inputs(self, comps): """ Check that the list of components `comp` is compatible with both the varmap and modelstr for this Model """ error = False compcats = [c.category for c in comps] # Check that the components are all provided, given the categories ...
def get_difference_model(self, category): """ Get the equation corresponding to a variation wrt category. For example if:: modelstr = { 'full' :'H(I) + B', 'dH' : 'dH(I)', 'dI' : 'H(dI)', 'dB' : 'dB' } ...
def map_vars(self, comps, funcname='get', diffmap=None, **kwargs): """ Map component function ``funcname`` result into model variables dictionary for use in eval of the model. If ``diffmap`` is provided then that symbol is translated into 'd'+diffmap.key and is replaced by diffma...
def evaluate(self, comps, funcname='get', diffmap=None, **kwargs): """ Calculate the output of a model. It is recommended that at some point before using `evaluate`, that you make sure the inputs are valid using :class:`~peri.models.Model.check_inputs` Parameters -------...
def lbl(axis, label, size=22): """ Put a figure label in an axis """ at = AnchoredText(label, loc=2, prop=dict(size=size), frameon=True) at.patch.set_boxstyle("round,pad=0.,rounding_size=0.0") #bb = axis.get_yaxis_transform() #at = AnchoredText(label, # loc=3, prop=dict(size=18), frameon=...
def generative_model(s,x,y,z,r, factor=1.1): """ Samples x,y,z,r are created by: b = s.blocks_particle(#) h = runner.sample_state(s, b, stepout=0.05, N=2000, doprint=True) z,y,x,r = h.get_histogram().T """ pl.close('all') slicez = int(round(z.mean())) slicex = s.image.shape[2]//2 ...
def examine_unexplained_noise(state, bins=1000, xlim=(-10,10)): """ Compares a state's residuals in real and Fourier space with a Gaussian. Point out that Fourier space should always be Gaussian and white Parameters ---------- state : `peri.states.State` The state to examine. ...
def compare_data_model_residuals(s, tile, data_vmin='calc', data_vmax='calc', res_vmin=-0.1, res_vmax=0.1, edgepts='calc', do_imshow=True, data_cmap=plt.cm.bone, res_cmap=plt.cm.RdBu): """ Compare the data, model, and residuals of a state. Makes an image of any 2D slice of a state that co...
def trisect_image(imshape, edgepts='calc'): """ Returns 3 masks that trisect an image into 3 triangular portions. Parameters ---------- imshape : 2-element list-like of ints The shape of the image. Elements after the first 2 are ignored. edgepts : Nested list-like, float, o...
def center_data(data, vmin, vmax): """Clips data on [vmin, vmax]; then rescales to [0,1]""" ans = data - vmin ans /= (vmax - vmin) return np.clip(ans, 0, 1)
def sim_crb_diff(std0, std1, N=10000): """ each element of std0 should correspond with the element of std1 """ a = std0np.random.randn(N, len(std0)) b = std1np.random.randn(N, len(std1)) return a - b
def crb_compare(state0, samples0, state1, samples1, crb0=None, crb1=None, zlayer=None, xlayer=None): """ To run, do: s,h = pickle... s1,h1 = pickle... i.e. /media/scratch/bamf/vacancy/vacancy_zoom-1.tif_t002.tif-featured-v2.pkl i.e. /media/scratch/bamf/frozen-particles/0.tif-fea...
def twoslice(field, center=None, size=6.0, cmap='bone_r', vmin=0, vmax=1, orientation='vertical', figpad=1.09, off=0.01): """ Plot two parts of the ortho view, the two sections given by ``orientation``. """ center = center or [i//2 for i in field.shape] slices = [] for i,c in enumerate(c...
def circles(st, layer, axis, ax=None, talpha=1.0, cedge='white', cface='white'): """ Plots a set of circles corresponding to a slice through the platonic structure. Copied from twoslice_overlay with comments, standaloneness. Inputs ------ pos : array of particle positions; [N,3] rad...
def missing_particle(separation=0.0, radius=RADIUS, SNR=20): """ create a two particle state and compare it to featuring using a single particle guess """ # create a base image of one particle s = init.create_two_particle_state(imsize=6*radius+4, axis='x', sigma=1.0/SNR, delta=separation, radius...
def get_rand_Japprox(s, params, num_inds=1000, include_cost=False, **kwargs): """ Calculates a random approximation to J by returning J only at a set of random pixel/voxel locations. Parameters ---------- s : :class:`peri.states.State` The state to calculate J for. param...
def name_globals(s, remove_params=None): """ Returns a list of the global parameter names. Parameters ---------- s : :class:`peri.states.ImageState` The state to name the globals of. remove_params : Set or None A set of unique additional parameters to remove from...
def get_num_px_jtj(s, nparams, decimate=1, max_mem=1e9, min_redundant=20): """ Calculates the number of pixels to use for J at a given memory usage. Tries to pick a number of pixels as (size of image / `decimate`). However, clips this to a maximum size and minimum size to ensure that (1) too much m...
def vectorize_damping(params, damping=1.0, increase_list=[['psf-', 1e4]]): """ Returns a non-constant damping vector, allowing certain parameters to be more strongly damped than others. Parameters ---------- params : List The list of parameter names, in order. damping : ...

def reorder(self, updates_ids, offset=None, utc=None): ''' Edit the order at which statuses for the specified social media profile will be sent out of the buffer. ''' url = PATHS['REORDER'] % self.profile_id order_format = "order[]=%s&" post_data = '' if offset: post_data +=...

def new(self, text, shorten=None, now=None, top=None, media=None, when=None): ''' Create one or more new status updates. ''' url = PATHS['CREATE'] post_data = "text=%s&" % text post_data += "profile_ids[]=%s&" % self.profile_id if shorten: post_data += "shorten=%s&" % shorten ...

def set_level(self, level='info', handlers=None): """ Set the logging level (which types of logs are actually printed / recorded) to one of ['debug', 'info', 'warn', 'error', 'fatal'] in that order of severity """ for h in self.get_handlers(handlers): h.setLev...

def set_formatter(self, formatter='standard', handlers=None): """ Set the text format of messages to one of the pre-determined forms, one of ['quiet', 'minimal', 'standard', 'verbose'] """ for h in self.get_handlers(handlers): h.setFormatter(logging.Formatter(formatte...

def add_handler(self, name='console-color', level='info', formatter='standard', **kwargs): """ Add another handler to the logging system if not present already. Available handlers are currently: ['console-bw', 'console-color', 'rotating-log'] """ # make sure the the log file has ...

def remove_handler(self, name): """ Remove handler from the logging system if present already. Available handlers are currently: ['console-bw', 'console-color', 'rotating-log'] """ if name in self.handlers: self.log.removeHandler(self.handlers[name])

def noformat(self): """ Temporarily do not use any formatter so that text printed is raw """ try: formats = {} for h in self.get_handlers(): formats[h] = h.formatter self.set_formatter(formatter='quiet') yield except Exception as e:...

def set_verbosity(self, verbosity='vvv', handlers=None): """ Set the verbosity level of a certain log handler or of all handlers. Parameters ---------- verbosity : 'v' to 'vvvvv' the level of verbosity, more v's is more verbose handlers : string, or list of ...

def normalize(im, invert=False, scale=None, dtype=np.float64): """ Normalize a field to a (min, max) exposure range, default is (0, 255). (min, max) exposure values. Invert the image if requested. """ if dtype not in {np.float16, np.float32, np.float64}: raise ValueError('dtype must be numpy...

def generate_sphere(radius): """Generates a centered boolean mask of a 3D sphere""" rint = np.ceil(radius).astype('int') t = np.arange(-rint, rint+1, 1) x,y,z = np.meshgrid(t, t, t, indexing='ij') r = np.sqrt(x*x + y*y + z*z) sphere = r < radius return sphere

def local_max_featuring(im, radius=2.5, noise_size=1., bkg_size=None, minmass=1., trim_edge=False): """Local max featuring to identify bright spherical particles on a dark background. Parameters ---------- im : numpy.ndarray The image to identify particles in. radius...

def otsu_threshold(data, bins=255): """ Otsu threshold on data. Otsu thresholding [1]_is a method for selecting an intensity value for thresholding an image into foreground and background. The sel- ected intensity threshold maximizes the inter-class variance. Parameters ---------- ...

def harris_feature(im, region_size=5, to_return='harris', scale=0.05): """ Harris-motivated feature detection on a d-dimensional image. Parameters --------- im region_size to_return : {'harris','matrix','trace-determinant'} """ ndim = im.ndim #1. Gradient of image ...

def identify_slab(im, sigma=5., region_size=10, masscut=1e4, asdict=False): """ Identifies slabs in an image. Functions by running a Harris-inspired edge detection on the image, thresholding the edge, then clustering. Parameters ---------- im : numpy.ndarray 3D array of the...

def plot_errors_single(rad, crb, errors, labels=['trackpy', 'peri']): fig = pl.figure() comps = ['z', 'y', 'x'] markers = ['o', '^', '*'] colors = COLORS for i in reversed(range(3)): pl.plot(rad, crb[:,0,i], lw=2.5, label='CRB-'+comps[i], color=colors[i]) for c, (error, label) in enume...

def sphere_triangle_cdf(dr, a, alpha): """ Cumulative distribution function for the traingle distribution """ p0 = (dr+alpha)**2/(2*alpha**2)*(0 > dr)*(dr>-alpha) p1 = 1*(dr>0)-(alpha-dr)**2/(2*alpha**2)*(0<dr)*(dr<alpha) return (1-np.clip(p0+p1, 0, 1))

def sphere_analytical_gaussian(dr, a, alpha=0.2765): """ Analytically calculate the sphere's functional form by convolving the Heavyside function with first order approximation to the sinc, a Gaussian. The alpha parameters controls the width of the approximation -- should be 1, but is fit to be roug...

def sphere_analytical_gaussian_trim(dr, a, alpha=0.2765, cut=1.6): """ See sphere_analytical_gaussian_exact. I trimmed to terms from the functional form that are essentially zero (1e-8) for r0 > cut (~1.5), a fine approximation for these platonic anyway. """ m = np.abs(dr) <= cut # only co...

def sphere_analytical_gaussian_fast(dr, a, alpha=0.2765, cut=1.20): """ See sphere_analytical_gaussian_trim, but implemented in C with fast erf and exp approximations found at Abramowitz and Stegun: Handbook of Mathematical Functions A Fast, Compact Approximation of the Exponential Function ...

def sphere_constrained_cubic(dr, a, alpha): """ Sphere generated by a cubic interpolant constrained to be (1,0) on (r0-sqrt(3)/2, r0+sqrt(3)/2), the size of the cube in the (111) direction. """ sqrt3 = np.sqrt(3) b_coeff = a*0.5/sqrt3*(1 - 0.6*sqrt3*alpha)/(0.15 + a*a) rscl = np.clip(dr, -0...

def exact_volume_sphere(rvec, pos, radius, zscale=1.0, volume_error=1e-5, function=sphere_analytical_gaussian, max_radius_change=1e-2, args=()): """ Perform an iterative method to calculate the effective sphere that perfectly (up to the volume_error) conserves volume. Return the resulting image ...

def _tile(self, n): """Get the update tile surrounding particle `n` """ pos = self._trans(self.pos[n]) return Tile(pos, pos).pad(self.support_pad)

def _p2i(self, param): """ Parameter to indices, returns (coord, index), e.g. for a pos pos : ('x', 100) """ g = param.split('-') if len(g) == 3: return g[2], int(g[1]) else: raise ValueError('`param` passed as incorrect format')

def initialize(self): """Start from scratch and initialize all objects / draw self.particles""" self.particles = np.zeros(self.shape.shape, dtype=self.float_precision) for p0, arg0 in zip(self.pos, self._drawargs()): self._draw_particle(p0, *listify(arg0))

def _vps(self, inds): """Clips a list of inds to be on [0, self.N]""" return [j for j in inds if j >= 0 and j < self.N]

def _i2p(self, ind, coord): """ Translate index info to parameter name """ return '-'.join([self.param_prefix, str(ind), coord])

def get_update_tile(self, params, values): """ Get the amount of support size required for a particular update.""" doglobal, particles = self._update_type(params) if doglobal: return self.shape.copy() # 1) store the current parameters of interest values0 = self.get_v...

def update(self, params, values): """ Update the particles field given new parameter values """ #1. Figure out if we're going to do a global update, in which # case we just draw from scratch. global_update, particles = self._update_type(params) # if we are doin...

def param_particle(self, ind): """ Get position and radius of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, j) for i in ind for j in ['z', 'y', 'x', 'a']]

def param_particle_pos(self, ind): """ Get position of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, j) for i in ind for j in ['z', 'y', 'x']]

def param_particle_rad(self, ind): """ Get radius of one or more particles """ ind = self._vps(listify(ind)) return [self._i2p(i, 'a') for i in ind]

def add_particle(self, pos, rad): """ Add a particle or list of particles given by a list of positions and radii, both need to be array-like. Parameters ---------- pos : array-like [N, 3] Positions of all new particles rad : array-like [N] ...

def remove_particle(self, inds): """ Remove the particle at index `inds`, may be a list. Returns [3,N], [N] element numpy.ndarray of pos, rad. """ if self.rad.shape[0] == 0: return inds = listify(inds) # Here's the game plan: # 1. get all p...

def _update_type(self, params): """ Returns dozscale and particle list of update """ dozscale = False particles = [] for p in listify(params): typ, ind = self._p2i(p) particles.append(ind) dozscale = dozscale or typ == 'zscale' particles = set(...

def _tile(self, n): """ Get the tile surrounding particle `n` """ zsc = np.array([1.0/self.zscale, 1, 1]) pos, rad = self.pos[n], self.rad[n] pos = self._trans(pos) return Tile(pos - zsc*rad, pos + zsc*rad).pad(self.support_pad)

def update(self, params, values): """Calls an update, but clips radii to be > 0""" # radparams = self.param_radii() params = listify(params) values = listify(values) for i, p in enumerate(params): # if (p in radparams) & (values[i] < 0): if (p[-2:] == '-a'...

def rmatrix(self): """ Generate the composite rotation matrix that rotates the slab normal. The rotation is a rotation about the x-axis, followed by a rotation about the z-axis. """ t = self.param_dict[self.lbl_theta] r0 = np.array([ [np.cos(t), -np.sin(t), 0], ...

def j2(x): """ A fast j2 defined in terms of other special functions """ to_return = 2./(x+1e-15)*j1(x) - j0(x) to_return[x==0] = 0 return to_return

def calc_pts_hg(npts=20): """Returns Hermite-Gauss quadrature points for even functions""" pts_hg, wts_hg = np.polynomial.hermite.hermgauss(npts*2) pts_hg = pts_hg[npts:] wts_hg = wts_hg[npts:] * np.exp(pts_hg*pts_hg) return pts_hg, wts_hg

def calc_pts_lag(npts=20): """ Returns Gauss-Laguerre quadrature points rescaled for line scan integration Parameters ---------- npts : {15, 20, 25}, optional The number of points to Notes ----- The scale is set internally as the best rescaling for a line scan ...

def f_theta(cos_theta, zint, z, n2n1=0.95, sph6_ab=None, **kwargs): """ Returns the wavefront aberration for an aberrated, defocused lens. Calculates the portions of the wavefront distortion due to z, theta only, for a lens with defocus and spherical aberration induced by coverslip mismatch. (The r...

def get_Kprefactor(z, cos_theta, zint=100.0, n2n1=0.95, get_hdet=False, **kwargs): """ Returns a prefactor in the electric field integral. This is an internal function called by get_K. The returned prefactor in the integrand is independent of which integral is being called; it is a combinat...

def get_K(rho, z, alpha=1.0, zint=100.0, n2n1=0.95, get_hdet=False, K=1, Kprefactor=None, return_Kprefactor=False, npts=20, **kwargs): """ Calculates one of three electric field integrals. Internal function for calculating point spread functions. Returns one of three electric field integrals th...

def get_hsym_asym(rho, z, get_hdet=False, include_K3_det=True, **kwargs): """ Calculates the symmetric and asymmetric portions of a confocal PSF. Parameters ---------- rho : numpy.ndarray Rho in cylindrical coordinates, in units of 1/k. z : numpy.ndarray Z in cyl...

def calculate_pinhole_psf(x, y, z, kfki=0.89, zint=100.0, normalize=False, **kwargs): """ Calculates the perfect-pinhole PSF, for a set of points (x,y,z). Parameters ----------- x : numpy.ndarray The x-coordinate of the PSF in units of 1/ the wavevector of the in...

def get_polydisp_pts_wts(kfki, sigkf, dist_type='gaussian', nkpts=3): """ Calculates a set of Gauss quadrature points & weights for polydisperse light. Returns a list of points and weights of the final wavevector's distri- bution, in units of the initial wavevector. Parameters ---------- ...

def calculate_polychrome_pinhole_psf(x, y, z, normalize=False, kfki=0.889, sigkf=0.1, zint=100., nkpts=3, dist_type='gaussian', **kwargs): """ Calculates the perfect-pinhole PSF, for a set of points (x,y,z). Parameters ----------- x : numpy.ndarray The x-coordinate of the PS...

def get_psf_scalar(x, y, z, kfki=1., zint=100.0, normalize=False, **kwargs): """ Calculates a scalar (non-vectorial light) approximation to a confocal PSF The calculation is approximate, since it ignores the effects of polarization and apodization, but should be ~3x faster. Parameters --------...

def calculate_linescan_ilm_psf(y,z, polar_angle=0., nlpts=1, pinhole_width=1, use_laggauss=False, **kwargs): """ Calculates the illumination PSF for a line-scanning confocal with the confocal line oriented along the x direction. Parameters ---------- y : numpy.ndarray Th...

def calculate_linescan_psf(x, y, z, normalize=False, kfki=0.889, zint=100., polar_angle=0., wrap=True, **kwargs): """ Calculates the point spread function of a line-scanning confocal. Make x,y,z __1D__ numpy.arrays, with x the direction along the scan line. (to make the calculation faster sinc...

def calculate_polychrome_linescan_psf(x, y, z, normalize=False, kfki=0.889, sigkf=0.1, zint=100., nkpts=3, dist_type='gaussian', wrap=True, **kwargs): """ Calculates the point spread function of a line-scanning confocal with polydisperse dye emission. Make x,y,z __1D__ numpy.arrays, wi...

def wrap_and_calc_psf(xpts, ypts, zpts, func, **kwargs): """ Wraps a point-spread function in x and y. Speeds up psf calculations by a factor of 4 for free / some broadcasting by exploiting the x->-x, y->-y symmetry of a psf function. Pass x and y as the positive (say) values of the coordinates at ...

def vec_to_halfvec(vec): """Transforms a vector np.arange(-N, M, dx) to np.arange(min(|vec|), max(N,M),dx)]""" d = vec[1:] - vec[:-1] if ((d/d.mean()).std() > 1e-14) or (d.mean() < 0): raise ValueError('vec must be np.arange() in increasing order') dx = d.mean() lowest = np.abs(vec).min() ...

def listify(a): """ Convert a scalar ``a`` to a list and all iterables to list as well. Examples -------- >>> listify(0) [0] >>> listify([1,2,3]) [1, 2, 3] >>> listify('a') ['a'] >>> listify(np.array([1,2,3])) [1, 2, 3] >>> listify('string') ['string'] ""...

def delistify(a, b=None): """ If a single element list, extract the element as an object, otherwise leave as it is. Examples -------- >>> delistify('string') 'string' >>> delistify(['string']) 'string' >>> delistify(['string', 'other']) ['string', 'other'] >>> delisti...

def aN(a, dim=3, dtype='int'): """ Convert an integer or iterable list to numpy array of length dim. This func is used to allow other methods to take both scalars non-numpy arrays with flexibility. Parameters ---------- a : number, iterable, array-like The object to convert to numpy...

def cdd(d, k): """ Conditionally delete key (or list of keys) 'k' from dict 'd' """ if not isinstance(k, list): k = [k] for i in k: if i in d: d.pop(i)

def patch_docs(subclass, superclass): """ Apply the documentation from ``superclass`` to ``subclass`` by filling in all overridden member function docstrings with those from the parent class """ funcs0 = inspect.getmembers(subclass, predicate=inspect.ismethod) funcs1 = inspect.getmembers(sup...

def indir(path): """ Context manager for switching the current path of the process. Can be used: with indir('/tmp'): <do something in tmp> """ cwd = os.getcwd() try: os.chdir(path) yield except Exception as e: raise finally: os.chdir(cwd)

def slicer(self): """ Array slicer object for this tile >>> Tile((2,3)).slicer (slice(0, 2, None), slice(0, 3, None)) >>> np.arange(10)[Tile((4,)).slicer] array([0, 1, 2, 3]) """ return tuple(np.s_[l:r] for l,r in zip(*self.bounds))

def oslicer(self, tile): """ Opposite slicer, the outer part wrt to a field """ mask = None vecs = tile.coords(form='meshed') for v in vecs: v[self.slicer] = -1 mask = mask & (v > 0) if mask is not None else (v>0) return tuple(np.array(i).astype('int') for...

def kcenter(self): """ Return the frequency center of the tile (says fftshift) """ return np.array([ np.abs(np.fft.fftshift(np.fft.fftfreq(q))).argmin() for q in self.shape ]).astype('float')

def corners(self): """ Iterate the vector of all corners of the hyperrectangles >>> Tile(3, dim=2).corners array([[0, 0], [0, 3], [3, 0], [3, 3]]) """ corners = [] for ind in itertools.product(*((0,1),)*self.dim): ...

def _format_vector(self, vecs, form='broadcast'): """ Format a 3d vector field in certain ways, see `coords` for a description of each formatting method. """ if form == 'meshed': return np.meshgrid(*vecs, indexing='ij') elif form == 'vector': vecs ...

def coords(self, norm=False, form='broadcast'): """ Returns the coordinate vectors associated with the tile. Parameters ----------- norm : boolean can rescale the coordinates for you. False is no rescaling, True is rescaling so that all coordinates are fr...

def kvectors(self, norm=False, form='broadcast', real=False, shift=False): """ Return the kvectors associated with this tile, given the standard form of -0.5 to 0.5. `norm` and `form` arguments arethe same as that passed to `Tile.coords`. Parameters ----------- r...

def contains(self, items, pad=0): """ Test whether coordinates are contained within this tile. Parameters ---------- items : ndarray [3] or [N, 3] N coordinates to check are within the bounds of the tile pad : integer or ndarray [3] anisotropic p...

def intersection(tiles, *args): """ Intersection of tiles, returned as a tile >>> Tile.intersection(Tile([0, 1], [5, 4]), Tile([1, 0], [4, 5])) Tile [1, 1] -> [4, 4] ([3, 3]) """ tiles = listify(tiles) + listify(args) if len(tiles) < 2: return tiles[...

def translate(self, dr): """ Translate a tile by an amount dr >>> Tile(5).translate(1) Tile [1, 1, 1] -> [6, 6, 6] ([5, 5, 5]) """ tile = self.copy() tile.l += dr tile.r += dr return tile

def pad(self, pad): """ Pad this tile by an equal amount on each side as specified by pad >>> Tile(10).pad(2) Tile [-2, -2, -2] -> [12, 12, 12] ([14, 14, 14]) >>> Tile(10).pad([1,2,3]) Tile [-1, -2, -3] -> [11, 12, 13] ([12, 14, 16]) """ tile = self.copy...

def overhang(self, tile): """ Get the left and right absolute overflow -- the amount of box overhanging `tile`, can be viewed as self \\ tile (set theory relative complement, but in a bounding sense) """ ll = np.abs(amin(self.l - tile.l, aN(0, dim=self.dim))) rr =...

def reflect_overhang(self, clip): """ Compute the overhang and reflect it internally so respect periodic padding rules (see states._tile_from_particle_change). Returns both the inner tile and the inner tile with necessary pad. """ orig = self.copy() tile = self.co...

def filtered_image(self, im): """Returns a filtered image after applying the Fourier-space filters""" q = np.fft.fftn(im) for k,v in self.filters: q[k] -= v return np.real(np.fft.ifftn(q))

def set_filter(self, slices, values): """ Sets Fourier-space filters for the image. The image is filtered by subtracting values from the image at slices. Parameters ---------- slices : List of indices or slice objects. The q-values in Fourier space to filter....

def load_image(self): """ Read the file and perform any transforms to get a loaded image """ try: image = initializers.load_tiff(self.filename) image = initializers.normalize( image, invert=self.invert, scale=self.exposure, dtype=self.float_precisi...

def get_scale(self): """ If exposure was not set in the __init__, get the exposure associated with this RawImage so that it may be used in other :class:`~peri.util.RawImage`. This is useful for transferring exposure parameters to a series of images. Returns -----...

def get_scale_from_raw(raw, scaled): """ When given a raw image and the scaled version of the same image, it extracts the ``exposure`` parameters associated with those images. This is useful when Parameters ---------- raw : array_like The image loaded...

def _draw(self): """ Interal draw method, simply prints to screen """ if self.display: print(self._formatstr.format(**self.__dict__), end='') sys.stdout.flush()

def update(self, value=0): """ Update the value of the progress and update progress bar. Parameters ----------- value : integer The current iteration of the progress """ self._deltas.append(time.time()) self.value = value self._percen...

def init_app(self, app): """Flask application initialization.""" self.init_config(app) app.register_blueprint(blueprint) app.extensions['invenio-groups'] = self

def check_consistency(self): """ Make sure that the required comps are included in the list of components supplied by the user. Also check that the parameters are consistent across the many components. """ error = False regex = re.compile('([a-zA-Z_][a-zA-Z0-9_]*)...

def check_inputs(self, comps): """ Check that the list of components `comp` is compatible with both the varmap and modelstr for this Model """ error = False compcats = [c.category for c in comps] # Check that the components are all provided, given the categories ...

def get_difference_model(self, category): """ Get the equation corresponding to a variation wrt category. For example if:: modelstr = { 'full' :'H(I) + B', 'dH' : 'dH(I)', 'dI' : 'H(dI)', 'dB' : 'dB' } ...

def map_vars(self, comps, funcname='get', diffmap=None, **kwargs): """ Map component function ``funcname`` result into model variables dictionary for use in eval of the model. If ``diffmap`` is provided then that symbol is translated into 'd'+diffmap.key and is replaced by diffma...

def evaluate(self, comps, funcname='get', diffmap=None, **kwargs): """ Calculate the output of a model. It is recommended that at some point before using `evaluate`, that you make sure the inputs are valid using :class:`~peri.models.Model.check_inputs` Parameters -------...

def lbl(axis, label, size=22): """ Put a figure label in an axis """ at = AnchoredText(label, loc=2, prop=dict(size=size), frameon=True) at.patch.set_boxstyle("round,pad=0.,rounding_size=0.0") #bb = axis.get_yaxis_transform() #at = AnchoredText(label, # loc=3, prop=dict(size=18), frameon=...

def generative_model(s,x,y,z,r, factor=1.1): """ Samples x,y,z,r are created by: b = s.blocks_particle(#) h = runner.sample_state(s, b, stepout=0.05, N=2000, doprint=True) z,y,x,r = h.get_histogram().T """ pl.close('all') slicez = int(round(z.mean())) slicex = s.image.shape[2]//2 ...

def examine_unexplained_noise(state, bins=1000, xlim=(-10,10)): """ Compares a state's residuals in real and Fourier space with a Gaussian. Point out that Fourier space should always be Gaussian and white Parameters ---------- state : `peri.states.State` The state to examine. ...

def compare_data_model_residuals(s, tile, data_vmin='calc', data_vmax='calc', res_vmin=-0.1, res_vmax=0.1, edgepts='calc', do_imshow=True, data_cmap=plt.cm.bone, res_cmap=plt.cm.RdBu): """ Compare the data, model, and residuals of a state. Makes an image of any 2D slice of a state that co...

def trisect_image(imshape, edgepts='calc'): """ Returns 3 masks that trisect an image into 3 triangular portions. Parameters ---------- imshape : 2-element list-like of ints The shape of the image. Elements after the first 2 are ignored. edgepts : Nested list-like, float, o...

def center_data(data, vmin, vmax): """Clips data on [vmin, vmax]; then rescales to [0,1]""" ans = data - vmin ans /= (vmax - vmin) return np.clip(ans, 0, 1)

def sim_crb_diff(std0, std1, N=10000): """ each element of std0 should correspond with the element of std1 """ a = std0*np.random.randn(N, len(std0)) b = std1*np.random.randn(N, len(std1)) return a - b

def crb_compare(state0, samples0, state1, samples1, crb0=None, crb1=None, zlayer=None, xlayer=None): """ To run, do: s,h = pickle... s1,h1 = pickle... i.e. /media/scratch/bamf/vacancy/vacancy_zoom-1.tif_t002.tif-featured-v2.pkl i.e. /media/scratch/bamf/frozen-particles/0.tif-fea...

def twoslice(field, center=None, size=6.0, cmap='bone_r', vmin=0, vmax=1, orientation='vertical', figpad=1.09, off=0.01): """ Plot two parts of the ortho view, the two sections given by ``orientation``. """ center = center or [i//2 for i in field.shape] slices = [] for i,c in enumerate(c...

def circles(st, layer, axis, ax=None, talpha=1.0, cedge='white', cface='white'): """ Plots a set of circles corresponding to a slice through the platonic structure. Copied from twoslice_overlay with comments, standaloneness. Inputs ------ pos : array of particle positions; [N,3] rad...

def missing_particle(separation=0.0, radius=RADIUS, SNR=20): """ create a two particle state and compare it to featuring using a single particle guess """ # create a base image of one particle s = init.create_two_particle_state(imsize=6*radius+4, axis='x', sigma=1.0/SNR, delta=separation, radius...

def get_rand_Japprox(s, params, num_inds=1000, include_cost=False, **kwargs): """ Calculates a random approximation to J by returning J only at a set of random pixel/voxel locations. Parameters ---------- s : :class:`peri.states.State` The state to calculate J for. param...

def name_globals(s, remove_params=None): """ Returns a list of the global parameter names. Parameters ---------- s : :class:`peri.states.ImageState` The state to name the globals of. remove_params : Set or None A set of unique additional parameters to remove from...

def get_num_px_jtj(s, nparams, decimate=1, max_mem=1e9, min_redundant=20): """ Calculates the number of pixels to use for J at a given memory usage. Tries to pick a number of pixels as (size of image / `decimate`). However, clips this to a maximum size and minimum size to ensure that (1) too much m...

def vectorize_damping(params, damping=1.0, increase_list=[['psf-', 1e4]]): """ Returns a non-constant damping vector, allowing certain parameters to be more strongly damped than others. Parameters ---------- params : List The list of parameter names, in order. damping : ...