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[STATEMENT]
lemma size_single: "size {#b#} = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. size {#b#} = 1
[PROOF STEP]
by (simp add: size_multiset_overloaded_def size_multiset_single)
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# NOTE:
# To force matplotlib to use inline rendering, insert
# the following line inside the ipython notebook:
# %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import os
import sys
import random
from cStringIO import StringIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
import utils
def image_from_array(img_array, format='png'):
"""Creates an image object from a given numpy array.
Parameters
----------
img_array : numpy.ndarray
The image data, which can have 1 or 3 color channels.
Returns
-------
IPython.display.Image
An image object for plots.
"""
factor = 1
if utils.image.is_float_image(img_array):
factor = 255
img_data = np.uint8(img_array * factor)
f = StringIO()
img_data = utils.image.to_rgb(img_data)
arr = PIL.Image.fromarray(img_data)
arr.save(f, format)
return Image(data=f.getvalue())
def display_image(image):
"""Display an image object.
Remarks: Some RGB images might be displayed with changed colors.
Parameters
----------
image : IPython.display.Image
The image to display.
"""
if image is None:
return
display(image)
def display_batch(img_array_batch, nrows=2, ncols=2, title=''):
"""Display a batch of images given as a 4D numpy array.
Remarks: Some RGB images might be displayed with changed colors.
Parameters
----------
img_array_batch : numpy.ndarray
The image numpy data in format [batch_size, height, width, channels]
or a list of numpy arrays in format [height, width, channels],
which can have 1 or 3 color channels.
nrows : uint, optional
The number or rows.
ncols : uint, optional
The number or colums.
title: str, optional
The title of the figure.
"""
if img_array_batch is None:
return
# determine scale from fist image
if (utils.image.is_float_image(img_array_batch[0])):
max_value = 1
else:
max_value = 255
# create figure with random id
fig = plt.figure(random.randint(1, sys.maxint))
fig.suptitle(title, fontsize=12, fontweight='semibold')
for i in xrange(min(nrows * ncols, len(img_array_batch))):
current_img = img_array_batch[i]
if len(current_img.shape) > 2 and current_img.shape[2] == 3:
cmap = None
else:
if len(current_img.shape) > 2:
current_img=np.squeeze(current_img)
cmap = plt.cm.gray
ax = plt.subplot(nrows,ncols,i + 1)
plt.imshow(current_img, cmap=cmap, vmin=0, vmax=max_value)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
def display_array(img_array, format='png'):
"""Display an image object from a given numpy array.
Remarks: Some RGB images might be displayed with changed colors.
Parameters
----------
img_array : numpy.ndarray
The image data, which can have 1 or 3 color channels.
The data values have to be in range [0,255].
format : str, optional
The image format.
"""
if img_array is None:
return
image = image_from_array(img_array, format)
display(image)
|
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\section{Variance of OLS estimators}
|
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|
function [struct_irf_record, D_record, gamma_record]=irfsignrespanel(beta_gibbs,sigma_gibbs,It,Bu,IRFperiods,n,p,m,k,signrestable,signresperiods)
% function [struct_irf_record D_record gamma_record]=irfsignrespanel(sigma_gibbs,irf_record,It,Bu,IRFperiods,n,signrestable,signresperiods,checkalgo,checkiter)
% runs the gibbs sampler to obtain draws from the posterior distribution of
% IRFs, orthogonalised with sign restrictions
% inputs: - matrix'sigma_gibbs': record of the gibbs sampler draws for the sigma matrix (vectorised)
% - cell 'irf_record': record of the gibbs sampler draws for the IRFs
% - integer 'It': total number of iterations of the Gibbs sampler (defined p 28 of technical guide)
% - integer 'Bu': number of burn-in iterations of the Gibbs sampler (defined p 28 of technical guide)
% - integer 'IRFperiods': number of periods for IRFs
% - integer 'n': number of endogenous variables in the BVAR model (defined p 7 of technical guide)
% outputs: - cell 'struct_irf_record': record of the gibbs sampler draws for the orthogonalised IRFs
% - matrix 'D_record': record of the gibbs sampler draws for the structural matrix D
% - matrix 'gamma_record': record of the gibbs sampler draws for the structural disturbances variance-covariance matrix gamma
% this function implements the sign restrictions approach for the panel
% preliminary tasks
% create first the cell that will store the results from the simulations
struct_irf_record=cell(n,n);
% storage cell
storage1=cell(It-Bu,1);
storage2=cell(It-Bu,1);
% now identify all the periods concerned with restrictions
% first expand the non-empty entries in signresperiods since they are only expressed in intervals: transform into list
% for instance, translate [1 4] into [1 2 3 4]; I don't think this can done without a loop
temp=cell2mat(signresperiods(~cellfun(@isempty,signresperiods)));
periods=[];
for ii=1:size(temp,1)
periods=[periods temp(ii,1):temp(ii,2)];
end
% suppress duplicates and sort
periods=sort(unique(periods))';
% count the total number of restriction periods (required for IRF matrix)
nperiods=size(periods,1);
% Identify the restriction matrices
% create five cells, corresponding to the three possible restrictions:
% one cell for sign restrictions, three cells for magnitude restrictions, one cell for zero restrictions
Scell=cell(1,n);
Mcell=cell(1,n);
Mlcell=cell(1,n);
Mucell=cell(1,n);
Zcell=cell(1,n);
% loop over rows and columns of the period matrix
for ii=1:n
for jj=1:n
% if entry (ii,jj) of the period matrix is not empty...
if ~isempty(signresperiods{ii,jj})
% ... then there is a restriction over one (or several) periods
% loop overt those periods
for kk=signresperiods{ii,jj}(1,1):signresperiods{ii,jj}(1,2)
% identify the position of the considered period within the list of all periods (required to build the matrix)
position=find(periods==kk);
% now create the restriction matrix: this will depend on the type of restriction
% if it is a positive sign restriction...
if strcmp(signrestable{ii,jj},'+')
% ... then input a 1 entry in the corresponding S matrix
Scell{1,jj}=[Scell{1,jj};zeros(1,n*nperiods)];
Scell{1,jj}(end,(position-1)*n+ii)=1;
% if it is a negative sign restriction...
elseif strcmp(signrestable{ii,jj},'-')
% ... then input a -1 entry in the corresponding S matrix
Scell{1,jj}=[Scell{1,jj};zeros(1,n*nperiods)];
Scell{1,jj}(end,(position-1)*n+ii)=-1;
% if it is a zero restriction...
elseif strcmp(signrestable{ii,jj},'0')
% ... then input a 1 entry in the corresponding Z matrix
Zcell{1,jj}=[Zcell{1,jj};zeros(1,n*nperiods)];
Zcell{1,jj}(end,(position-1)*n+ii)=1;
% else, a non-empty entry being neither a sign nor a zero restriction has to be a magnitude restriction
else
% fill the corresponding M matrices:
% input a 1 in M
Mcell{1,jj}=[Mcell{1,jj};zeros(1,n*nperiods)];
Mcell{1,jj}(end,(position-1)*n+ii)=1;
% input the lower value of the interval in Ml
temp=str2num(signrestable{ii,jj});
Mlcell{1,jj}=[Mlcell{1,jj};temp(1,1)];
% input the upper value of the interval in Mu
Mucell{1,jj}=[Mucell{1,jj};temp(1,2)];
end
end
end
end
end
% now check what kind of restrictions apply among sign, zero and magnitude restrictions
% check for sign restrictions: if there are any, at least one entry in the cell Scell is non-empty
if sum(~cellfun(@isempty,Scell))~=0
signres=1;
else
signres=0;
end
% similarly check for zero restrictions
if sum(~cellfun(@isempty,Zcell))~=0
zerores=1;
else
zerores=0;
end
% and finally, check for magnitude restrictions
if sum(~cellfun(@isempty,Mcell))~=0
magnres=1;
else
magnres=0;
end
hbar = bear.parfor_progressbar(It-Bu,'Progress'); %create the progress bar
% step 1: repeat simulations a number of times equal to the number of simulations retained from Gibbs sampling
parfor ii=1:It-Bu
% initiate the variable 'success'; this variable will be used to check whether the restrictions are satisfied
% if there are only zero restrictions, they will be satisfied by construction, and 'success' will simply be ignored
success=0;
% how the algorithm will be conducted will depend on the types of restrictions implemented
% if there are only zero restrictions, the algorithm is simple as no checking is required: the conditions are satisfied by construction
if zerores==1 && signres==0 && magnres==0
% draw beta and sigma
beta=beta_gibbs(:,ii);
sigma=reshape(sigma_gibbs(:,ii),n,n);
hsigma=chol(bear.nspd(sigma),'lower');
% obtain orthogonalised IRFs
[irfmatrix, ortirfmatrix]=bear.irfsim(beta,hsigma,n,m,p,k,max(IRFperiods,max(periods)));
% generate the stacked IRF matrix
stackedirfmat=[];
for jj=1:numel(periods)
stackedirfmat=[stackedirfmat;ortirfmatrix(:,:,periods(jj,1)+1)];
end
% draw an entire random matrix Q satisfying the zero restrictions
[Q]=bear.qzerores(n,Zcell,stackedirfmat);
% there is no need to verify the restrictions: there are satisfied by construction
% if there are sign/magnitude restrictions, possibly associated with zero restrictions
else
% the algorithm becomes a bit more complicated as conditions now need to be checked
% to maintain efficiency, the algorithm proceeds recursively shock by shock, and stops as soon as a condition on the considered shock fails
% repeat algorithm for the iteration as long as not all conditions are satisfied
while success==0
% switch 'success' to 1; it will be turned back to zero if at any time Q is detected as a candidate not satisfying the restrictions
success=1;
% draw randomly the vector of VAR coefficients: draw a random index
index=floor(rand*(It-Bu))+1;
% then draw a random set of beta and sigma corresponding to this index (this is done to make it possible to draw, if required, an infinite number of values from the gibbs sampler record, with equal probability on each value)
beta=beta_gibbs(:,index);
sigma=reshape(sigma_gibbs(:,index),n,n);
hsigma=chol(bear.nspd(sigma),'lower');
% obtain orthogonalised IRFs
[irfmatrix, ortirfmatrix]=bear.irfsim(beta,hsigma,n,m,p,k,max(IRFperiods,max(periods)));
% generate the stacked IRF matrix
stackedirfmat=[];
for jj=1:numel(periods)
stackedirfmat=[stackedirfmat;ortirfmatrix(:,:,periods(jj,1)+1)];
end
% initiate Qj
Qj=[];
% now start looping over the shocks and checking sequentially whether conditions on these shocks hold
% stop as soon as one restriction fails
kk=1;
while success==1 && kk<=n
% build column j of the random matrix Q
[qj]=bear.qrandj(n,Zcell{1,kk},stackedirfmat,Qj);
% obtain the candidate column fj
fj=stackedirfmat*qj;
% check restrictions: first sign restrictions
[success qj]=bear.checksignres(Scell{1,kk},qj,fj);
% if 'success' is still equal to 1, also check for magnitude restrictions
if success==1
[success]=bear.checkmagres(Mcell{1,kk},Mlcell{1,kk},Mucell{1,kk},fj);
end
% also, if 'success' is still equal to 1, update Qj by concatenating qj
if success==1
Qj=[Qj qj];
end
kk=kk+1;
end
% repeat this loop until a succesful draw is obtained
end
% with succesful Qj at hand, eventually set Q as Qj
Q=Qj;
end
% store
for jj=1:IRFperiods
storage1{ii,1}(:,:,jj)=ortirfmatrix(:,:,jj)*Q;
end
storage2{ii,1}=hsigma*Q;
hbar.iterate(1); % update progress by one iteration
end
close(hbar); %close progress bar
% reorganise storage
% loop over iterations
for ii=1:It-Bu
% loop over IRF periods
for jj=1:IRFperiods
% loop over variables
for kk=1:n
% loop over shocks
for ll=1:n
struct_irf_record{kk,ll}(ii,jj)=storage1{ii,1}(kk,ll,jj);
end
end
end
D_record(:,ii)=storage2{ii,1}(:);
gamma_record(:,ii)=bear.vec(eye(n));
end
|
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|
################################################################################
# CLASS FOR BCC UNIT CELL MESHES GENERATED USING THE GMSH-PYTHON-API #
################################################################################
# This file provides a class definition for a generation of unit cells with a
# body-centered cubic distribution of the inclusions. The class inherits from the
# GenericUnitCell class and extends it in order to specify the remaining
# placeholder methods of the GenericModel. Methods to create the geometry,
# define refinement information and additional information for required boolean
# operations and physical groups are part of the class.
###########################
# Load required libraries #
###########################
# Standard Python libraries
import numpy as np # numpy for fast array computations
import copy as cp # copy for deepcopies
# self defined class definitions and modules
from .GenericUnitCell import GenericUnitCell # generic unit cell class definition (parent class)
######################################
# Define BodyCenteredCubicCell class #
######################################
class BodyCenteredCubicCell(GenericUnitCell):
"""Class definition for body-centered cubic unit cells
This class provides required information for body-centered cubic unit cells.
It inherits from the GenericUnitCell class and extends its attributes and
methods to handle the inclusion placement.
The body-centered cubic unit cell allows to create "real" unit cells by
passing the inclusion distance to the classes initialization method. If the
cells size is specified instead, the distance is calculated: this allows for
unit cells with a body-centered "cuboid" particle distribution
Attributes:
-----------
dimension: int
dimension of the model instance
distance: float
distance of the inclusions within the unit cell (for automatic size calculation)
-> here, the distance between two neighboring corner inclusions is meant
radius: float
radius of the unit cells inclusions
numberCells: list/array
array providing the number of cells in the individual axis directions
-> numberCells=[nx, ny, (nz)]
size: list/array
size of the body-centered cubic unit cell (allow box-shaped cells)
-> size=[Lx, Ly, (Lz)]
origin: list/array
origin of the body-centered cubic unit cell
-> origin=[Ox, Oy, (Oz)]
inclusionType: string
string defining the type of inclusion
-> iunclusionType= "Sphere"/"Cylinder"/"Circle"
inclusionAxis:list/array
array defining the inclusion axis (only relevant for inclusionType "Cylinder")
-> currently restricted to Cylinders parallel to one of the coordinate axes
-> inclusionAxes=[Ax, Ay, Az]
relevantAxes: list/array
array defining the relevant axes for distance calculations
periodicityFlags: list/array
flags indicating the periodic axes of the body-centered cubic unit cell
-> periodicityFlags=[0/1, 0/1, 0/1]
inclusionInfo: array
array containing relevant inclusion information (center, radius) for
distance calculations
domainGroup: string
name of the group the unit cells domain should belong to
inclusionGroup: string
name of the group the unit cells inclusions should belong to
gmshConfigChanges: dict
dictionary for user updates of the default Gmsh configuration
"""
#########################
# Initialization method #
#########################
def __init__(self,distance=None,radius=None,numberCells=[1,1,1],size=None,inclusionType=None,inclusionAxis=None,origin=[0,0,0],periodicityFlags=[1,1,1],domainGroup="domain",inclusionGroup="inclusions",gmshConfigChanges={}):
"""Initialization method for BodyCenteredCubicCell object instances
Parameters:
-----------
distance: float
distance of the inclusions within the unit cell (for automatic size calculation)
radius: float
radius of the unit cells inclusions
numberCells: list/array
array providing the number of cells in the individual axis directions
-> numberCells=[nx, ny, (nz)]
size: list/array
size of the body-centered cubic unit cell (allow box-shaped cells)
-> size=[Lx, Ly, (Lz)]
origin: list/array
origin of the body-centered cubic unit cell
-> origin=[Ox, Oy, (Oz)]
inclusionType: string
string defining the type of inclusion
-> iunclusionType= "Sphere"/"Cylinder"/"Circle"
inclusionAxis:list/array
array defining the inclusion axis (only relevant for inclusionType "Cylinder")
-> currently restricted to Cylinders parallel to one of the coordinate axes
-> inclusionAxes=[Ax, Ay, Az]
periodicityFlags: list/array
flags indicating the periodic axes of the body-centered cubic unit cell
-> periodicityFlags=[0/1, 0/1, 0/1]
domainGroup: string
name of the group the unit cells domain should belong to
inclusionGroup: string
name of the group the unit cells inclusions should belong to
gmshConfigChanges: dict
dictionary for user updates of the default Gmsh configuration
"""
# initialize parent classes attributes and methods
super().__init__(size=size,distance=distance,radius=radius,numberCells=numberCells,inclusionType=inclusionType,inclusionAxis=inclusionAxis,origin=origin,periodicityFlags=periodicityFlags,gmshConfigChanges=gmshConfigChanges)
################################################################################
# SPECIFIED/OVERWRITTEN PLACEHOLDER METHODS #
################################################################################
###############################################
# Internal method to determine the cells size #
###############################################
def _getCellSize(self,distance,inclusionType,inclusionAxis):
# determine size of one unit cell
if inclusionType == "Sphere": # unit cell is three-dimensional with spherical inclusion
unitSize=[distance, distance, distance] # -> define normal cell size for a body-centered cubic unit cell
elif inclusionType == "Cylinder": # unit cell is three-dimensional with cylindrical inclusion
cylinderAxis = np.array(np.nonzero(inclusionAxis)).flatten() # -> get index of cylinder axis
planeAxes=np.setdiff1d(np.array([0,1,2]),cylinderAxis) # -> get indices of remaining axes
unitSize=np.asarray(inclusionAxis).astype(float) # -> prepare size array (account for thickness in cylinder axis direction)
unitSize[planeAxes]=[distance, distance] # -> set cell size for relevant in-plane axes
elif inclusionType == "Circle": # unit cell is two-dimensional with circular inclusion
unitSize=[distance, distance, 0] # -> define size of body-centered cubic cell in x-y-plane
# return total size (multiply by number of cells per direction)
return unitSize*self.numberCells
##################################################
# Method for a body-centered inclusion placement #
##################################################
def placeInclusions(self):
"""Method to place inclusions for the body-centered cubic unit cell"""
# get available information
origin=self.origin # origin of unit cell
size=self.size # (total) size of unit cell
N=cp.deepcopy(self.numberCells) # number of cells
step=size/N # step to get from one cell to another
# determine indicator which axes are relevant
axesFlags=np.zeros(3)
axesFlags[self.relevantAxes]=1
# correct number of cells for non-spherical inclusions
if self.inclusionType in ["Circle", "Cylinder"]: # inclusion type is "Cylinder" or "Cirlce"
# ensure only 1 cell in the out-of-plane direction to avoid problems
# with boolean operations, etc
outOfPlaneAxis=np.setdiff1d(np.array([0,1,2]),self.relevantAxes)
N[outOfPlaneAxis]=1
# determine offsets for inclusion placement using numpys mgrid
# -> divide by N to account for mutliple cells
offsets=np.array([[0, 0, 0], # corner inclusions
size/2*axesFlags])/N # body-centered inclusions in middle layers
# determine inclusion center points
C=np.empty(shape=(0,3)) # initialize empty array for center points
for offset in offsets: # loop over all sets of inclusions
P0=origin+offset # set starting point for point generation using mgrid
P1=origin+size-step+offset # set end point for point generation using mgrid
n=cp.deepcopy(N) # copy number of cells (deepcopy to allow changes)
for ax in self.relevantAxes: # loop over all axes for inclusion placement
if offset[ax]+self.radius > step[ax]: # offset is too big, i.e., inclusion leaves domain at the end
P0[ax]=origin[ax]+offset[ax]-step[ax] # -> adjust starting point for mesh generation with mgrid (incorporate periodic copy of inclusion that leaves the domain)
n[ax]+=1 # -> increase number of repetitions by 1 to account for additional point
elif offset[ax] < self.radius: # offset is too low, i.e., inclusion leaves domain at the start
P1[ax]=origin[ax]+size[ax]+offset[ax] # -> adjust end point for mesh generation with mgrid (incorporate periodic copy of inclusion that leaves the domain)
n[ax]+=1 # -> increase number of repetitions by 1 to account for additional point
C=np.r_[C,np.mgrid[P0[0]:P1[0]:n[0]*1j,P0[1]:P1[1]:n[1]*1j,P0[2]:P1[2]:n[2]*1j].reshape(3,-1).T] # determine center points and append them to C
# save relevant results in randomInclusions object
self.inclusionInfo=np.c_[C, self.radius*np.ones((np.shape(C)[0],1))]
|
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|
import numpy as np
import scipy as sp
from ngboost.scores import LogScore
from ngboost.distns import Normal
from ngboost.manifold import manifold
from ngboost.learners import default_tree_learner, default_linear_learner
from sklearn.utils import check_random_state
from sklearn.base import clone
from sklearn.tree import DecisionTreeRegressor
# import pdb
class NGBoost(object):
"""
Natural Gradient Boosted Regression
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
"""
def __init__(self, Dist=Normal, Score=LogScore,
Base=default_tree_learner, natural_gradient=True,
n_estimators=500, learning_rate=0.01, minibatch_frac=1.0,
verbose=True, verbose_eval=100, tol=1e-4,
random_state=None):
self.Dist = Dist
self.Score = Score
self.Base = Base
self.Manifold = manifold(Score, Dist)
self.natural_gradient = natural_gradient
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.minibatch_frac = minibatch_frac
self.verbose = verbose
self.verbose_eval = verbose_eval
self.init_params = None
self.base_models = []
self.scalings = []
self.tol = tol
self.random_state = check_random_state(random_state)
self.best_val_loss_itr = None
def fit_init_params_to_marginal(self, Y, sample_weight=None, iters=1000):
self.init_params = self.Manifold.fit(Y) # would be best to put sample weights here too
return
def pred_param(self, X, max_iter=None):
m, n = X.shape
params = np.ones((m, self.Manifold.n_params)) * self.init_params
for i, (models, s) in enumerate(zip(self.base_models, self.scalings)):
if max_iter and i == max_iter:
break
resids = np.array([model.predict(X) for model in models]).T
params -= self.learning_rate * resids * s
return params
def sample(self, X, Y, params):
if self.minibatch_frac == 1.0:
return np.arange(len(Y)), X, Y, params
sample_size = int(self.minibatch_frac * len(Y))
idxs = self.random_state.choice(np.arange(len(Y)), sample_size, replace=False)
return idxs, X[idxs,:], Y[idxs], params[idxs, :]
def fit_base(self, X, grads, sample_weight=None):
models = [clone(self.Base).fit(X, g, sample_weight=sample_weight) for g in grads.T]
fitted = np.array([m.predict(X) for m in models]).T
self.base_models.append(models)
return fitted
def line_search(self, resids, start, Y, sample_weight=None, scale_init=1):
S = self.Score
D_init = self.Manifold(start.T)
loss_init = D_init.total_score(Y, sample_weight)
scale = scale_init
# first scale up
while True:
scaled_resids = resids * scale
D = self.Manifold((start - scaled_resids).T)
loss = D.total_score(Y, sample_weight)
norm = np.mean(np.linalg.norm(scaled_resids, axis=1))
if not np.isfinite(loss) or loss > loss_init or scale > 256:
break
scale = scale * 2
# then scale down
while True:
scaled_resids = resids * scale
D = self.Manifold((start - scaled_resids).T)
loss = D.total_score(Y, sample_weight)
norm = np.mean(np.linalg.norm(scaled_resids, axis=1))
if np.isfinite(loss) and (loss < loss_init or norm < self.tol) and\
np.linalg.norm(scaled_resids, axis=1).mean() < 5.0:
break
scale = scale * 0.5
self.scalings.append(scale)
return scale
def fit(self, X, Y,
X_val = None, Y_val = None,
sample_weight = None, val_sample_weight = None,
train_loss_monitor = None, val_loss_monitor = None,
early_stopping_rounds = None):
loss_list = []
val_loss_list = []
if early_stopping_rounds is not None:
best_val_loss = np.inf
self.fit_init_params_to_marginal(Y)
params = self.pred_param(X)
if X_val is not None and Y_val is not None:
val_params = self.pred_param(X_val)
if not train_loss_monitor:
train_loss_monitor = lambda D,Y: D.total_score(Y, sample_weight=sample_weight)
if not val_loss_monitor:
val_loss_monitor = lambda D,Y: D.total_score(Y, sample_weight=val_sample_weight)
for itr in range(self.n_estimators):
_, X_batch, Y_batch, P_batch = self.sample(X, Y, params)
D = self.Manifold(P_batch.T)
loss_list += [train_loss_monitor(D, Y_batch)]
loss = loss_list[-1]
grads = D.grad(Y_batch, natural=self.natural_gradient)
proj_grad = self.fit_base(X_batch, grads, sample_weight)
scale = self.line_search(proj_grad, P_batch, Y_batch, sample_weight)
# pdb.set_trace()
params -= self.learning_rate * scale * np.array([m.predict(X) for m in self.base_models[-1]]).T
val_loss = 0
if X_val is not None and Y_val is not None:
val_params -= self.learning_rate * scale * np.array([m.predict(X_val) for m in self.base_models[-1]]).T
val_loss = val_loss_monitor(self.Manifold(val_params.T), Y_val)
val_loss_list += [val_loss]
if early_stopping_rounds is not None:
if val_loss < best_val_loss:
best_val_loss, self.best_val_loss_itr = val_loss, itr
if best_val_loss < np.min(np.array(val_loss_list[-early_stopping_rounds:])):
if self.verbose:
print(f"== Early stopping achieved.")
print(f"== Best iteration / VAL {self.best_val_loss_itr} (val_loss={best_val_loss:.4f})")
break
if self.verbose and int(self.verbose_eval) > 0 and itr % int(self.verbose_eval) == 0:
grad_norm = np.linalg.norm(grads, axis=1).mean() * scale
print(f"[iter {itr}] loss={loss:.4f} val_loss={val_loss:.4f} scale={scale:.4f} "
f"norm={grad_norm:.4f}")
if np.linalg.norm(proj_grad, axis=1).mean() < self.tol:
if self.verbose:
print(f"== Quitting at iteration / GRAD {itr}")
break
self.evals_result = {}
metric = self.Score.__name__.upper()
self.evals_result['train'] = {metric: loss_list}
if X_val is not None and Y_val is not None:
self.evals_result['val'] = {metric: val_loss_list}
return self
def score(self, X, Y):
return self.Manifold(self.pred_dist(X).params_).total_score(Y)
def pred_dist(self, X, max_iter=None):
if max_iter is not None: # get prediction at a particular iteration if asked for
dist = self.staged_pred_dist(X, max_iter=max_iter)[-1]
elif self.best_val_loss_itr is not None: # this will exist if there's a validation set
dist = self.staged_pred_dist(X, max_iter=self.best_val_loss_itr)[-1]
else:
params = np.asarray(self.pred_param(X, max_iter))
dist = self.Dist(params.T)
return dist
def staged_pred_dist(self, X, max_iter=None):
predictions = []
m, n = X.shape
params = np.ones((m, self.Dist.n_params)) * self.init_params
for i, (models, s) in enumerate(zip(self.base_models, self.scalings)):
resids = np.array([model.predict(X) for model in models]).T
params -= self.learning_rate * resids * s
dists = self.Dist(np.copy(params.T)) # if the params aren't copied, param changes with stages carry over to dists
predictions.append(dists)
if max_iter and i == max_iter:
break
return predictions
# these methods won't work unless the model is either an NGBRegressor, NGBClassifier, or NGBSurvival object,
# each of which have the dist_to_prediction() method defined in their own specific way
def predict(self, X):
return self.pred_dist(X).predict()
def staged_predict(self, X, max_iter=None):
return [dist.predict() for dist in self.staged_pred_dist(X, max_iter=None)]
def get_shap_tree_explainer(self, param_idx=0, **kwargs):
"""
Return the tree explainer for the param_idx-th parameter in the distribution
Parameters
----------
param_idx : int
The index of parameter.
**kwargs :
Additional arguments to be passed to shap.TreeExplainer
(See https://shap.readthedocs.io/en/latest/#shap.TreeExplainer)
Returns
-------
explainer : TreeExplainer object
Shap TreeExplainer object
"""
import copy, shap
assert self.base_models, "Model has empty `base_models`! Have you called `model.fit`?"
assert str(type(self.base_models[0][param_idx])).endswith("sklearn.tree.tree.DecisionTreeRegressor'>"), "You must use default_tree_learner!"
temp_model = copy.deepcopy(self)
temp_model.shap_trees = [trees[param_idx] for trees in temp_model.base_models]
explainer = shap.TreeExplainer(temp_model, **kwargs)
return explainer
@property
def feature_importances_(self):
"""
Return the feature importances for all parameters in the distribution
(the higher, the more important the feature).
Returns
-------
feature_importances_ : array, shape = [n_params, n_features]
The summation along second axis of this array is an array of ones,
unless all trees are single node trees consisting of only the root
node, in which case it will be an array of zeros.
"""
# Check whether the model is fitted
if not self.base_models:
return None
# Check whether the base model is DecisionTreeRegressor
if not isinstance(self.base_models[0][0], DecisionTreeRegressor):
return None
# Reshape the base_models
params_trees = zip(*self.base_models)
# Get the feature_importances_ for all the params and all the trees
all_params_importances = [[getattr(tree, 'feature_importances_')
for tree in trees if tree.tree_.node_count > 1]
for trees in params_trees]
if not all_params_importances:
return np.zeros(len(self.base_models[0]),self.base_models[0][0].n_features_, dtype=np.float64)
# Weighted average of importance by tree scaling factors
all_params_importances = np.average(all_params_importances,
axis=1, weights=self.scalings)
return all_params_importances / np.sum(all_params_importances,axis=1,keepdims=True)
|
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|
/*
* Copyright (c) 2014, Autonomous Systems Lab
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the Autonomous Systems Lab, ETH Zurich nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
*/
#ifndef ROVIO_LOCALIZATION_LANDMARK_UPDATE_HPP_
#define ROVIO_LOCALIZATION_LANDMARK_UPDATE_HPP_
#include <Eigen/Core>
#include <glog/logging.h>
#include <lightweight_filtering/common.hpp>
#include <lightweight_filtering/Update.hpp>
#include <lightweight_filtering/State.hpp>
#include "rovio/MultiCamera.hpp"
namespace rovio {
class LocalizationLandmarkInnovation : public LWF::State<LWF::VectorElement<2>> {
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
typedef LWF::State<LWF::VectorElement<2>> Base;
using Base::E_;
static constexpr unsigned int _pix = 0;
LocalizationLandmarkInnovation() {
CHECK_EQ(_pix+1,E_) << "Error with indices";
this->template getName<_pix>() = "pix";
}
virtual ~LocalizationLandmarkInnovation() {}
inline Eigen::Vector2d& pix() {
return this->template get<_pix>();
}
};
class LocalizationLandmarkMeasurement :
public LWF::State<LWF::VectorElement<2>> {
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
static constexpr unsigned int _pix = 0;
LocalizationLandmarkMeasurement() :
G_landmark_(Eigen::Vector3d::Zero()),
camera_index_(-1) {
CHECK_EQ(_pix+1,E_) << "Error with indices";
}
virtual ~LocalizationLandmarkMeasurement() {}
inline Eigen::Vector2d& keypoint(){
return this->template get<_pix>();
}
inline const Eigen::Vector2d& keypoint() const {
return this->template get<_pix>();
}
inline V3D& G_landmark(){
return G_landmark_;
}
inline const V3D& G_landmark() const {
return G_landmark_;
}
inline void set_camera_index(int camera_index) {
CHECK_GE(camera_index, 0);
camera_index_ = camera_index;
}
inline int camera_index() const {
return camera_index_;
}
private:
Eigen::Vector3d G_landmark_;
int camera_index_;
};
class LocalizationLandmarkNoise : public LWF::State<LWF::VectorElement<2>> {
public:
static constexpr unsigned int _pix = 0;
LocalizationLandmarkNoise() {
static_assert(_pix+1==E_,"Error with indices");
this->template getName<_pix>() = "pix";
}
inline const Eigen::Vector2d& pix() const {
return this->template get<_pix>();
}
virtual ~LocalizationLandmarkNoise() {}
};
class LocalizationLandmarkOutlierDetection : public LWF::OutlierDetection<
LWF::ODEntry<
LocalizationLandmarkInnovation::template getId<
LocalizationLandmarkInnovation::_pix>(), 2>> {
public:
virtual ~LocalizationLandmarkOutlierDetection(){};
};
/**
* G: Inertial frame of localization map
* W: Odometry frame of ROVIO
* M: IMU-coordinate frame
* C: Camera frame.
*/
template<typename FILTERSTATE>
using LocalizationLandmarkUpdateBase =
LWF::Update<LocalizationLandmarkInnovation, FILTERSTATE,
LocalizationLandmarkMeasurement, LocalizationLandmarkNoise,
LocalizationLandmarkOutlierDetection, false>;
template<typename FILTERSTATE>
class LocalizationLandmarkUpdate :
public LocalizationLandmarkUpdateBase<FILTERSTATE> {
public:
typedef LocalizationLandmarkUpdateBase<FILTERSTATE> Base;
using Base::eval;
using Base::meas_;
// This is the update covariance as used by the Kalman functions.
using Base::updnoiP_;
typedef typename Base::mtState mtState;
typedef typename Base::mtFilterState mtFilterState;
typedef typename Base::mtInnovation mtInnovation;
typedef typename Base::mtMeas mtMeas;
typedef typename Base::mtNoise mtNoise;
typedef typename Base::mtOutlierDetection mtOutlierDetection;
typedef typename Base::mtInputTuple mtInputTuple;
typedef typename Base::mtModelBase mtModelBase;
LocalizationLandmarkUpdate()
: localization_pixel_sigma_(-1.0),
filter_state_memory_(LWF::FilteringMode::ModeEKF),
force_ekf_updates_(false),
enable_calibration_cross_terms_(true),
enable_vio_cross_terms_(true),
multi_cameras_(nullptr) {
double localization_pixel_sigma;
Base::doubleRegister_.registerScalar(
"localizationPixelSigma", localization_pixel_sigma_);
// Remove some properties that are inherited from the Update base.
Base::doubleRegister_.removeScalarByStr("alpha");
Base::doubleRegister_.removeScalarByStr("beta");
Base::doubleRegister_.removeScalarByStr("kappa");
Base::doubleRegister_.removeScalarByStr("UpdateNoise.pix_0");
Base::doubleRegister_.removeScalarByStr("UpdateNoise.pix_1");
Base::boolRegister_.registerScalar("forceEKFupdate", force_ekf_updates_);
Base::boolRegister_.registerScalar(
"enableCalibrationCrossterms", enable_calibration_cross_terms_);
Base::boolRegister_.registerScalar(
"enableVioCrossterms", enable_vio_cross_terms_);
}
virtual ~LocalizationLandmarkUpdate() {}
void refreshProperties() override {
CHECK_GT(localization_pixel_sigma_, 0.0);
Base::updnoiP_.setZero();
Base::updnoiP_.diagonal().setConstant(
localization_pixel_sigma_ * localization_pixel_sigma_);
}
void setCamera(MultiCamera<mtState::nCam_>* multi_cameras){
CHECK_NOTNULL(multi_cameras);
multi_cameras_ = multi_cameras;
}
void setMeasurement(const LocalizationLandmarkMeasurement& measurement) {
measurement_ = measurement;
}
bool evaluateModel(const mtState& state, mtInnovation* innovation,
MXD* jacobian) const {
// G: Inertial frame of localization map
// W: Odometry frame of ROVIO
// M: IMU-coordinate frame
// C: Camera frame.
//
// Residual: r = f_p(T_CM * T_MW * T_WG * G_l)
const int camera_index = measurement_.camera_index();
CHECK_GE(camera_index, 0);
const QPD& qCM = state.qCM(camera_index);
const V3D& MrMC = state.MrMC(camera_index);
const Eigen::Vector3d W_l =
state.qWG().rotate(measurement_.G_landmark()) + state.WrWG();
const Eigen::Vector3d M_l = state.qWM().inverseRotate(V3D(W_l - state.WrWM()));
const Eigen::Vector3d C_l = qCM.rotate(V3D(M_l - MrMC));
cv::Point2f predicted_keypoint_cv;
CHECK_LT(camera_index, mtState::nCam_);
Eigen::Matrix<double, 2, 3> d_r__d_C_l;
const bool projection_success =
CHECK_NOTNULL(multi_cameras_)->cameras_[camera_index].
bearingToPixel(C_l, predicted_keypoint_cv, d_r__d_C_l);
if (!projection_success) {
LOG(WARNING) << "Projection failed.";
return false;
}
if (innovation != nullptr) {
Eigen::Vector2d predicted_keypoint(
predicted_keypoint_cv.x, predicted_keypoint_cv.y);
innovation->pix() = (predicted_keypoint - measurement_.keypoint());
VLOG(3) << "Localization: predicted (" << predicted_keypoint.transpose()
<< ") - measured key point (" << measurement_.keypoint().transpose()
<< ") - error: " << innovation->pix();
}
if (jacobian != nullptr) {
jacobian->setZero();
// d_r__d_T_WG
const size_t index_WrWG = mtState::template getId<mtState::_pmp>();
jacobian->block<2, 3>(0, index_WrWG) = d_r__d_C_l
* MPD(qCM * state.qWM().inverted()).matrix();
const size_t index_qWG = mtState::template getId<mtState::_pma>();
jacobian->block<2, 3>(0, index_qWG) = -d_r__d_C_l
* MPD(qCM * state.qWM().inverted()).matrix()
* gSM(state.qWG().rotate(measurement_.G_landmark()));
// d_r__d_T_WM
if (enable_vio_cross_terms_) {
const size_t index_WrWM = mtState::template getId<mtState::_pos>();
jacobian->block<2, 3>(0, index_WrWM) = -d_r__d_C_l
* MPD(qCM * state.qWM().inverted()).matrix();
const size_t index_qWM = mtState::template getId<mtState::_att>();
jacobian->block<2, 3>(0, index_qWM) = d_r__d_C_l
* MPD(qCM * state.qWM().inverted()).matrix()
* gSM(W_l - state.WrWM());
}
// d_r__d_T_MC
if (enable_calibration_cross_terms_) {
const size_t index_MrMC = mtState::template getId<mtState::_vep>()
+ 3u * camera_index;
jacobian->block<2, 3>(0, index_MrMC) = -d_r__d_C_l * MPD(qCM).matrix();
const size_t index_qCM = mtState::template getId<mtState::_vea>()
+ 3u * camera_index;
jacobian->block<2, 3>(0, index_qCM) = -d_r__d_C_l
* gSM(qCM.rotate(V3D(M_l - MrMC)));
}
}
return true;
}
bool evalInnovation(mtInnovation& y, const mtState& state,
const mtNoise& noise) const {
bool success = evaluateModel(state, &y, /*jacobian=*/nullptr);
y.pix() += noise.pix();
return success;
}
void jacState(MXD& F, const mtState& state) const {
CHECK_EQ(F.rows(), mtInnovation::D_);
CHECK_EQ(F.cols(), mtState::D_);
evaluateModel(state, /*innovation=*/nullptr, &F);
}
void jacStateFD(MXD& F, const mtState& state, double epsilon = 1e-4,
double dt = 0.0) const {
CHECK_EQ(F.rows(), mtInnovation::D_);
CHECK_EQ(F.cols(), mtState::D_);
mtInputTuple input_tuples;
std::get<0>(input_tuples) = state;
this->template jacInputFD<0, 0, mtState::D_>(F, input_tuples, dt, epsilon);
}
void jacNoise(MXD& G, const mtState& state) const {
G.setIdentity();
}
void preProcess(mtFilterState& filterstate, const mtMeas& meas,
bool& isFinished) {
isFinished = false;
// Buffer the current measurement.
measurement_ = meas;
// Synchronize the camera extrinsics.
filterstate.state_.updateMultiCameraExtrinsics(multi_cameras_);
// We perform the update in EKF mode, regardless of the external settings.
if (force_ekf_updates_) {
filter_state_memory_ = filterstate.mode_ ;
filterstate.mode_ = LWF::ModeEKF;
}
}
void postProcess(mtFilterState& filterstate, const mtMeas& meas,
const mtOutlierDetection& outlierDetection,
bool& isFinished) {
// Restore the previous update settings.
if (force_ekf_updates_) {
filterstate.mode_ = filter_state_memory_;
}
// Synchronize the camera extrinsics.
filterstate.state_.updateMultiCameraExtrinsics(multi_cameras_);
// Visualize the keypoint localization and the localization landmark
// reprojection.
// TODO(schneith): Disable drawing if visualization is disabled.
{
const int camera_index = measurement_.camera_index();
CHECK_GE(camera_index, 0);
cv::Mat image = filterstate.img_[camera_index];
cv::circle(image, cv::Point(measurement_.keypoint()(0,0),
measurement_.keypoint()(1,0)),
/*radius=*/6, /*color=*/cv::Scalar(0,0,255), /*thickness=*/6,
/*line_type=*/cv::LINE_AA, /*shift=*/0);
mtInnovation innovation;
evaluateModel(filterstate.state_, &innovation, /*jacobian=*/nullptr);
const Eigen::Vector2d reprojected_landmark =
innovation.pix() + measurement_.keypoint();
const cv::Point reprojected_landmark_cv(
reprojected_landmark(0,0), reprojected_landmark(1,0));
cv::circle(image, reprojected_landmark_cv, /*radius=*/3,
/*color=*/cv::Scalar(0,255,0), /*thickness=*/5,
/*line_type=*/cv::LINE_AA, /*shift=*/0);
}
// Do not perform additional update loops.
isFinished = true;
}
private:
double localization_pixel_sigma_;
LocalizationLandmarkMeasurement measurement_;
LWF::FilteringMode filter_state_memory_;
bool force_ekf_updates_;
bool enable_calibration_cross_terms_;
bool enable_vio_cross_terms_;
// Pointer to the camera models.
MultiCamera<mtState::nCam_>* multi_cameras_;
};
}
#endif /* ROVIO_LOCALIZATION_LANDMARK_UPDATE_HPP_ */
|
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|
from unittest import TestCase
import numpy as np
import numpy.testing as npt
from . import sparse_permutations as sp
from . import dense_permutations as dp
class TestSparsePermutations(TestCase):
tol = 0.00001
def test_get_sort_permutation(self):
vector = [0.3, 0.2, 0.4, 0.1]
npt.assert_allclose([2, 0, 1, 3], sp.get_sort_permutation(vector), rtol=self.tol)
def test_inverse_permutation(self):
perm = [1, 4, 3, 0, 2]
npt.assert_allclose([3, 0, 4, 2, 1], sp.inverse_permutation(perm), rtol=self.tol)
def test_permutation_to_matrix(self):
perm = np.array([1, 3, 2, 0])
vector = np.array([0.1, 0.2, 0.3, 0.4])
npt.assert_allclose(
dp.permute_vector(perm, vector),
# order of multiplication matters, vector first for the way the permutation matrix is set up
np.matmul(vector, dp.permutation_to_matrix(perm)), rtol=self.tol)
|
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|
import os
import numpy as np
path = os.path.abspath(os.path.dirname(__file__))
from scripts.change_pressure import set_pressure
def test_fort4():
from mcflow.file_formatting.reader import read_fort4
from mcflow.file_formatting.writer import write_fort4
data = read_fort4(os.path.join(path, 'test-data', 'fort.4'))
assert int(data['&mc_shared']['seed']) == 1, 'Incorrect seed'
assert int(data['&mc_shared']['nbox']) == 2, 'Incorrect box'
assert int(data['&mc_shared']['nchain']) == 600, 'Incorrect nchain'
assert np.isclose(float(data['SIMULATION_BOX']['box1']['pressure']), 0.001), 'Incorrect pressure'
data = set_pressure(data, 0.01)
write_fort4(data, os.path.join('test-data', 'fort.4.new'))
new_file_name = os.path.join(path, 'test-data', 'fort.4.new')
new_data = read_fort4(new_file_name)
assert np.isclose(float(new_data['SIMULATION_BOX']['box1']['pressure']), 0.01), 'Incorrect pressure'
assert int(new_data['&mc_shared']['seed']) == 1, 'Incorrect seed'
assert int(new_data['&mc_shared']['nbox']) == 2, 'Incorrect box'
assert int(new_data['&mc_shared']['nchain']) == 600, 'Incorrect nchain'
os.remove(new_file_name)
if __name__ == '__main__':
test_fort4()
|
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|
\chapter{Examples}
The following sections demonstrate some example embedded meta entries in various file types.
If you have an additional file type example that is missing in this section, post a minimum-demonstrating-example as an issue at \url{https://github.com/UCREL/CL-metaheaders/issues} either as a plain request, or as a pull request for this document to be merged in.
\section{Absolute Minimal Header}
This example shows the absolute minimal header embedded in a TEI XML file.
The contents of the actual TEI tag have been omitted, as they serve no purpose in demonstrating the header.
\lstinputlisting{examples/minimum.xml}
\section{Valid JSON}
\lstinputlisting{examples/json-valid.json}
\section{Valid ARFF}
This example has been truncated, as it is substantially long, and the metaheader is near the top of the file.
\lstinputlisting[lastline=15]{examples/titanic.arff}
\section{Valid TEI XML}
\lstinputlisting{examples/tei-valid.xml}
|
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|
import tensorflow as tf
import numpy as np
import random
import os
def set_seed(seed=200):
"""set global seed to fix random-generated value for reproducible.
available at Functional API, tf.keras.Sequential and tf.keras subclass.
NOTE: operation seed is not fixed.
You need to call this before the operations you want to reproduce.
Even after `set_seed`, different random-generated values are returned:
>>> tf.random.set_seed(0)
>>> tf.random.uniform([1])
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.29197514], dtype=float32)>
>>> tf.random.uniform([1])
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5554141], dtype=float32)>
However, if you continue as follows, reproducibility is ensured:
>>> tf.random.set_seed(0)
>>> tf.random.uniform([1])
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.29197514], dtype=float32)>
>>> tf.random.uniform([1])
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5554141], dtype=float32)>
"""
tf.random.set_seed(seed)
# optional
# for numpy.random
np.random.seed(seed)
# for built-in random
random.seed(seed)
# for hash seed
os.environ["PYTHONHASHSEED"] = str(seed)
|
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|
function copy = spm_cfg_eeg_copy
% configuration file for copying
%__________________________________________________________________________
% Copyright (C) 2009-2012 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_cfg_eeg_copy.m 5377 2013-04-02 17:07:57Z vladimir $
%--------------------------------------------------------------------------
% D
%--------------------------------------------------------------------------
D = cfg_files;
D.tag = 'D';
D.name = 'File Name';
D.filter = 'mat';
D.num = [1 1];
D.help = {'Select the M/EEG mat file.'};
%--------------------------------------------------------------------------
% outfile
%--------------------------------------------------------------------------
outfile = cfg_entry;
outfile.tag = 'outfile';
outfile.name = 'Output filename';
outfile.strtype = 's';
outfile.num = [0 inf];
outfile.help = {'Choose filename.'};
%--------------------------------------------------------------------------
% copy
%--------------------------------------------------------------------------
copy = cfg_exbranch;
copy.tag = 'copy';
copy.name = 'Copy';
copy.val = {D, outfile};
copy.help = {'Copying M/EEG datasets'}';
copy.prog = @eeg_copy;
copy.vout = @vout_eeg_copy;
copy.modality = {'EEG'};
%==========================================================================
function out = eeg_copy(job)
% construct the S struct
S = job;
S.D = S.D{1};
out.D = spm_eeg_copy(S);
out.Dfname = {fullfile(out.D)};
%==========================================================================
function dep = vout_eeg_copy(job)
% return dependencies
dep(1) = cfg_dep;
dep(1).sname = 'Copied M/EEG data';
dep(1).src_output = substruct('.','D');
dep(1).tgt_spec = cfg_findspec({{'strtype','e'}});
dep(2) = cfg_dep;
dep(2).sname = 'Copied M/EEG datafile';
dep(2).src_output = substruct('.','Dfname');
dep(2).tgt_spec = cfg_findspec({{'filter','mat'}});
|
{"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/config/spm_cfg_eeg_copy.m"}
|
# Lecture 8
## Complex Numbers
```python
import numpy as np
import sympy as sp
import scipy.integrate
sp.init_printing()
##################################################
##### Matplotlib boilerplate for consistency #####
##################################################
from ipywidgets import interact
from ipywidgets import IntSlider
from ipywidgets import FloatSlider
from matplotlib import pyplot as plt
import cmath
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('svg')
global_fig_width = 8
global_fig_height = global_fig_width / 1.61803399
font_size = 12
plt.rcParams['axes.axisbelow'] = True
plt.rcParams['axes.edgecolor'] = '0.8'
plt.rcParams['axes.grid'] = True
plt.rcParams['axes.labelpad'] = 8
plt.rcParams['axes.linewidth'] = 2
plt.rcParams['axes.titlepad'] = 16.0
plt.rcParams['axes.titlesize'] = font_size * 1.4
plt.rcParams['figure.figsize'] = (global_fig_width, global_fig_height)
plt.rcParams['font.sans-serif'] = ['Computer Modern Sans Serif', 'DejaVu Sans', 'sans-serif']
plt.rcParams['font.size'] = font_size
plt.rcParams['grid.color'] = '0.8'
plt.rcParams['grid.linestyle'] = 'dashed'
plt.rcParams['grid.linewidth'] = 2
plt.rcParams['lines.dash_capstyle'] = 'round'
plt.rcParams['lines.dashed_pattern'] = [1, 4]
plt.rcParams['xtick.labelsize'] = font_size
plt.rcParams['xtick.major.pad'] = 4
plt.rcParams['xtick.major.size'] = 0
plt.rcParams['ytick.labelsize'] = font_size
plt.rcParams['ytick.major.pad'] = 4
plt.rcParams['ytick.major.size'] = 0
##################################################
```
## Imaginary numbers
**The Imaginary Number $i$:**
- The polynomial $~~x^2-1=0~~$ has two real roots: $~~1~~$ and $~~-1~~$
- The polynomial $~~x^2+1=0~~$ has **no** real roots.
Consider solving:
$$x^2 = 1\qquad\mbox{and}\qquad x^2 = -1$$
- We introduce an "imaginary" number $~~i~~$ so that there are two solutions to $~~x^2 = -1~~$: $~~i~~$ and $~~-i~~$. That is, $~~i~~$ is a number with the property that $~~i^2=-1$.
## Complex numbers
The complex numbers are the set of all expressions of the form $a + bi$ where $i^2=-1$ and $a$ and $b$ are real numbers:
$$\mathbb{C}=\left\{a + bi~~\vert~~a,~b~\in\mathbb{R}\right\}$$
For $z=a+bi\in\mathbb{C}$ we define the *real* and *imaginary*
parts of $z$ to be ${\Re}(z) = a$ and $\Im(z)=b$.
The imaginary number $i$ has no home on the real number line.
Instead, we locate it on the **complex plane** at the point $(0,1)$.
- we can represent any complex number $z=a+bi$ as the
point $(a,b)$ in the complex plane.
- The coordinates $(a,b)$ are
usually called the *cartesian* coordinates for $z$. (Named
after the mathematician and philosopher Rene Descartes).
- In this
plane, the real numbers lie on the horizontal axis. We usually
refer to the horizontal axis of $\mathbb{C}$ as the **real axis**.
```python
def plot_imag_axis(a,b):
plt.scatter([a],[b])
plt.plot([0,a],[b,b],ls='--')
plt.axvline(x=0,c='k')
plt.axhline(y=0,c='k')
plt.plot([0,a],[b,b],ls='--')
plt.plot([a,a],[0,b],ls='--')
plt.xlabel('Real Axis')
plt.ylabel('Imaginary Axis')
plt.xlim(left=-5, right=25)
plt.ylim(bottom=-5, top=25)
```
```python
interact(plot_imag_axis, a = IntSlider(value=10, min=0, max=20, continuous_update=False), b = IntSlider(value=10, min=0, max=20, continuous_update=False))
```
## Complex conjugates
- The complex plane $\mathbb{C}$ contain **all** of the roots of **every** polynomial.
E.g.
$$x^2-8x+25 = 0 =ax^2 +bx +c \iff x= {-b \pm\sqrt{b^2-4ac}\over 2a} $$
$$={{8\pm\sqrt{64-100}}\over2}={{8\pm\sqrt{-36}}\over2}={{8\pm
6i}\over2}=4\pm3i$$
- Note that these two roots are reflections of one another through the
real axis. They are *conjugates* of one another.
- In general, let $z=a + bi$. The **conjugate** of $z$ is the complex number
$\bar{z}=a-bi$.
We can also use Sympy's `solve` method to solve polynomials:
```python
x = sp.symbols('x')
sp.solve(x**2 - 8*x + 25)
```
## Modulus (size) of a complex number
- The distance to a point on the complex plane from 0 is called its **modulus**, and we find this by calculating the hypotenuse of the triange with base ${\Re}(z)$ and height $\Im(z)$:
E.g. The modulus of $4\pm3i$ is $\sqrt{3^2+4^2}=\sqrt{9+16}=\sqrt{25}=5$
- In general, the **modulus** of $z=a+bi$ is the real number
$|z|=\sqrt{a^2+b^2}$.
- The **modulus** is connected to the **conjugate** by means of the formula
$z\cdot \bar{z}=|z|^2$. Indeed:
\begin{align}
z\cdot\bar{z}&=(a+bi)(a-bi)=a^2-(bi)^2=a^2-b^2\cdot i^2\\
&=a^2-b^2(-1)=a^2+b^2=|z|^2
\end{align}
## Complex numbers in Python
```python
x = 1 + 2j
print(f'x = {x} Re(x) = {x.real} Im(x) = {x.imag} |x| = {abs(x)}')
```
Complex numbers using `Sympy`:
```python
x = 1 + 2 * sp.I
print(f'x = {x} Re(x) = {sp.re(x)} Im(x) = {sp.im(x)} |x| = {sp.Abs(x)}')
```
## Addition and Subtraction:
Addition and subtraction of complex numbers work as you would expect:
$$(a+bi)\pm(c+di)=(a\pm c) + (b\pm d)i$$
and
$$-(a+bi)=-a-bi$$
Try adding: $(5+6i)+(1-i)$=
```python
print((5 + 6j) + (1 - 1j))
```
Try subtracting: $(5+6i)-(1-i)$=
```python
print((5 + 6j) - (1 - 1j))
```
## Multiplication:
Multiplication is not quite so convenient in cartesian coordinates:
\begin{align*}
(a+bi)(c+di)&=ac + adi + bci + bidi \\ &= ac + adi + bci -bd \\ &=
(ac-bd)+(ad+bc)i
\end{align*}
Try multiplying: $(5+6i)(1-i)$=
```python
print((5 + 6j) * (1 - 1j))
```
## Division:
Division is even more awkward in cartesian
coordinates: we have to multiply the numerator and the denominator
by the complex conjugate of the denominator.
\begin{align*}
{{a+bi}\over{c+di}}&={{(a+bi)(c-di)}\over{(c+di)(c-di)}}\\
&={{(ac+bd)+(bc-ad)i}\over{c^2+d^2}}=\left({{ac+bd}\over{c^2+d^2}}\right)+
\left({{bc-ad}\over{c^2+d^2}}\right)i
\end{align*}
Try dividing: $${(-4+7i)\over (2+3i)}=$$
```python
print((-4 + 7*sp.I) / (2 + 3*sp.I))
```
## Polar Coordinates
It's often convenient to represent the complex number
$z = a + bi$ in terms of its polar coordinates $\langle r,\theta
\rangle$.
- The angle $\theta$ is called the *argument* of $z$.
- The real number $r=|z|$ is sometimes denoted mod$(z)$.
```python
ax = plt.subplot(121)
ax.plot([0,1],[0,1],'o-')
ax = plt.subplot(122,projection='polar')
ax.plot([0,np.pi/4.0],[0,np.sqrt(2)],'o-')
```
## Connection between cartesian and polar
Let $z=x+iy$. If we are given the polar coordinates of
$z$ and want to express the cartesian coordinates use
$$\displaylines{x=r\cos\theta\cr
y=r\sin\theta\cr
z=r\cos\theta + ri\sin\theta=r(\cos\theta + i\sin\theta)\cr}$$
If we are given the cartesian coordinates and want to find the polar
coordinates use:
$$
r={\rm mod}(z)=|z|=\sqrt{x^2+y^2}
$$
\begin{align*}
\theta={\rm arg}(z)=\tan^{-1}{y\over x}=
\begin{cases}\pi/2,&\mbox{ if }~~ x=0,y>0 \\
-\pi/2,&\mbox{ if }~~ x=0,y<0 \\
\arctan\left({y\over x}\right),&\mbox{ if }~~ x>0\\
\arctan\left({y\over x}\right)+\pi,&\mbox{ if }~~ x<0, y\geq 0\\
\arctan\left({y\over x}\right)-\pi,&\mbox{ if }~~ x<0, y<0\\
\end{cases}
\end{align*}
<!--N.B. All of the fuss about the value of $\theta$ in the formula
above is to make sure that $z$ gets into the proper quadrant. Beware
of the sign of this tangent: depends on which quadrant you are in.
The positive $x$ axis is defined as having $\theta=0$ and positive
$\theta$ goes in an anticlockwise sense around the $xy$
plane.-->
## Some examples:
- Find the cartesian coordinates for the complex number $z$ with polar
coordinates $r=2$ and $\theta=\pi/6$.
$$\Re(z)=x=r\cos\theta=2\cos(\pi/6)=2\left({{\sqrt{3}\over2}}\right)=\sqrt{3}$$
$$\Im(z)=y=r\sin\theta=2\sin(\pi/6)=2\left({{1\over2}}\right)=1$$
$${\rm Therefore\ } z = \sqrt{3} + i$$
```python
print(cmath.rect(2, np.pi/6))
```
## Some examples:
- Find the polar coordinates for the complex number $z= -3+4i$.
$$|z|=r = $$
$$\sqrt{(-3)^2+4^2}=\sqrt{25}=5$$
$${\rm arg}(z)=\theta=\arctan\left({{y}\over{x}}\right)=$$
$$-0.93+\pi{\rm ~radians}\approx 127^\circ$$
```python
print(cmath.polar(-3 + 4j))
```
## Some examples:
- Find the polar coordinates for the complex number $z= -2i$.
$${\rm mod}(z)=r = |z|=2$$
$${\rm arg}(z)=\theta=-{{\pi}\over2}$$
```python
print(cmath.polar(-2j))
```
## Multiplication in Polar Coordinates:
First a reminder of three useful and important identities:
$$\cos^2\theta + \sin^2\theta = 1$$
$$\cos(\theta_1+\theta_2)=\cos\theta_1\cos \theta_2 - \sin\theta_1\sin\theta_2$$
$$\sin(\theta_1+\theta_2)=\sin\theta_1\cos \theta_2 + \sin\theta_2\cos\theta_1$$
Now let $z_1=r_1\cos\theta_1+ir_1\sin\theta_1$ and $z_2=r_2\cos\theta_2+ir_2\sin\theta_2$.
We first compute the real part of the product $z_1\cdot z_2$:
\begin{align*}
\Re(z_1\cdot z_2) &= r_1\cos\theta_1\cdot r_2\cos\theta_2 - r_1\sin\theta_1\cdot
r_2\sin\theta_2\cr
&=r_1r_2(\cos\theta_1\cos\theta_2 - \sin\theta_1\sin\theta_2)\cr
&=r_1r_2\cos(\theta_1 + \theta_2)
\end{align*}
Note that for the real part the moduli have been multiplied and the
arguments added.
Now we compute the imaginary part of $z_1\cdot z_2$:
\begin{align*}
\Im(z_1\cdot z_2) &= r_1\sin\theta_1\cdot r_2\cos\theta_2 + r_2\sin\theta_2\cdot
r_1\cos\theta_1\cr
&=r_1r_2(\sin\theta_1\cos\theta_2 - \sin\theta_2\cos\theta_1)\cr
&=r_1r_2\sin(\theta_1 + \theta_2)
\end{align*}
For the imaginary part too, the moduli multiply while the arguments
add.
This gives a relatively compact and highly geometric result
for the product:
$$z_1\cdot z_2 = r_1r_2(\cos(\theta_1 + \theta_2)+i\sin(\theta_1 + \theta_2))$$
It is **multiplicative** in the modulus and **additive** in the argument:
$$|z_1z_2|= |z_1\cdot |z_2|$$
$$\arg(z_1z_2)=\arg (z_1)+ \arg( z_2)$$
This means that when we multiply by $z$, we are **rotating** through the angle $\arg(z)$ and **radially stretching** by a factor of $|z|$.
## A Remarkable Connection with $e^{i\theta}$:
First, think of $z=\cos\theta + i\sin\theta$ as a function of $\theta$ and differentiate with respect to $\theta$:
$${\rm~~(1)~~~~}\frac{{\rm d}z}{{\rm d}\theta}=\frac{{\rm d}}{{\rm d}\theta}\left(\cos\theta+i\sin\theta\right)=-\sin\theta+i\cos\theta$$
Next notice that the right-hand side is just the product $iz$:
$${\rm~~(2)~~~~}iz=i(\cos\theta+i\sin\theta)=i\cos\theta+i^2\sin\theta=-\sin\theta+i\cos\theta$$
Thus from (1) and (2) $$\frac{{\rm d}z}{{\rm d}\theta}=iz$$
This is a separable differential equation. Thus:
$$\int{dz\over iz}=\int d\theta~~~~~~~~~\Rightarrow~~~~~~{1\over
i}~\ln z =\theta +c~~~~\Rightarrow~~~~\ln z =i\theta +ic$$ In
exponential form:
$$z=e^{i\theta+ic}~~=~~e^{i\theta}~e^{ic}~~=~~Ae^{i\theta}~~~~{\rm~ with}~~~~
A=e^{ic}$$
When $\theta=0$, $z=1$ giving $A=1$, so:
$$z=\cos\theta + i \sin\theta=e^{i\theta}~~~~~~~~~~~~\rm (1)$$
Similarly we can show that:
$$z=\cos\theta - i \sin\theta=e^{-i\theta}~~~~~~~~~~~~\rm (2)$$
Adding (1) and (2) and subtracting (1) and (2) gives:
$$\cos\theta ={e^{i\theta}+ e^{-i\theta}\over 2}~~~~~~~~~~~~~~~~~~~~~~~~~
\sin\theta ={ e^{i\theta}-e^{-i\theta}\over 2i}$$
Thus **any complex number** can now be written:
$$z=x+iy=r(\cos\theta + i\sin\theta)=r~e^{i\theta}$$
Several important consequences:
1. Any complex number can be written in the polar form $z = re^{i\theta}$ where $r=|z|$ and $\theta=\arg(z)$.
2. The unit circle in $\mathbb{C}$ consists exactly of those complex numbers with
modulus equal to 1, that is the numbers $e^{i\theta}$.
3. Multiplication on the unit circle $r=1$ can be carried out by adding the
angles:
$$e^{i\theta_1}\cdot e^{i\theta_2} = e^{i(\theta_1+\theta_2)}$$
$$z=x+iy=r(\cos\theta + i\sin\theta)=r~e^{i\theta}$$
Other important consequences:
4. Exponentiation on the unit circle $r=1$ can be done by multiplying the angle
by the index:
$$\left(e^{i\theta}\right)^n = e^{i\theta n}=e^{i(n\theta)}$$
5. This result is known as **DeMoivre's Theorem**. It is usually stated in its
cartesian form:
$$(\cos\theta + i\sin\theta)^n=\cos(n\theta) + i\sin(n\theta)$$
6. Finally, the famous identity by Leonhard Euler
$$e^{\pi i}+1=0$$
|
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|
import numpy as np
import pandas as pd
import pickle
import os
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dimension', type=int, default=1, help='dimension of the normal data')
parser.add_argument('--save_csv', type=bool, default=False, help='whether to save the data in csv format')
FLAGS = parser.parse_args()
# fixed variance, mean generated differently
# mean1 is tuple or list
# mean2 is int or list
def generate(T, var, theta, n, m1=(0, 1), m2=1):
llimit = int(T * 0.1)
rlimit = int(T * 0.9)
etas = np.array(range(llimit, rlimit + 1))
samples, labels = [], []
for i in range(n):
eta = np.random.choice(etas, 1)[0]
if isinstance(m1, tuple):
mean1 = np.random.uniform(m1[0], m1[1])
elif isinstance(m1, list):
mean1 = np.random.choice(m1, 1)[0]
else:
raise Exception('invalid mean1 type')
if theta == -1:
theta = np.random.uniform(0, 1)
if isinstance(m2, int):
mean2 = mean1 + theta * m2 * np.random.choice([1, -1], 1)[0]
elif isinstance(m2, list):
mean2 = np.random.choice(m2, 1)[0]
else:
raise Exception('invalid mean2 type')
p1 = np.array(
[np.random.normal(m, s, eta) for m, s in zip(mean1 * np.ones(784), var * np.ones(784))]
)
p2 = np.array(
[np.random.normal(m, s, T - eta) for m, s in zip(mean2 * np.ones(784), var * np.ones(784))]
)
sample = np.concatenate((p1, p2), axis=1)
samples.append(sample)
labels.append(eta)
return samples, labels
def generatewrapper(T, var):
thetas = [1, 0.75, 0.5, 0.25, -1]
ns = [1000]
mean1s = [(0, 1)]
mean2s = [1]
datasets_dict = {}
for i in range(len(mean1s)):
mean1 = mean1s[i]
mean2 = mean2s[i]
for theta in thetas:
print(theta)
# generate test data
x_test, y_test = generate(T, var, theta, 100, mean1, mean2)
for n in ns:
name = 'T={}_var={}_theta={}_n={}_m1={}_m2={}'.format(T, var, theta, n, mean1, mean2)
x_train, y_train = generate(T, var, theta, n, mean1, mean2)
datasets_dict[name] = (np.array(x_train), np.array(y_train),
np.array(x_test), np.array(y_test))
# if mean2 is chosen from a discrete set of values, no need to use theta
if isinstance(mean2, list):
break
return datasets_dict
if __name__ == "__main__":
root_dir = os.getcwd()
dirs = [os.path.join(root_dir, 'data/')]
for dir in dirs:
if not os.path.isdir(dir):
os.mkdir(dir)
datasets_dict = generatewrapper(50, 0.2)
for dsname in datasets_dict:
# save training and test data to pickle
with open(os.path.join(dirs[0], dsname), 'wb') as f:
pickle.dump(datasets_dict[dsname], f)
# save only test data to csv files for R
if FLAGS.save_csv:
pd.DataFrame(datasets_dict[dsname][2]).T.to_csv(dir2 + dsname + '_x_test.csv')
pd.DataFrame(datasets_dict[dsname][3]).T.to_csv(dir2 + dsname + '_y_test.csv')
|
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|
import tensorflow as tf
import os
import time
from datetime import datetime
from utils import *
from model import *
import numpy as np
import pdb
# ##############################################################################
# SEGMENTATION CLASS
# ##############################################################################
class SegmenterNet(object):
def __init__(self, cfg, model_ckp_name=""):
""" Initializes a Segmentation Model Class """
# MODEL SETTINGS
self.cfg = cfg
if model_ckp_name is not "":
self.checkpoint_file = model_ckp_name
self.log_dir = self.cfg.log_path + "inference_results"
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
else:
# DIRECTORIES TO STORE OUTPUTS
if not self.cfg.log_name == "":
self.log_dir = self.cfg.log_path + \
"{}".format(datetime.now().strftime(
"%Y%m%d-%H%M%S")) + "_" + self.cfg.log_name
else:
self.log_dir = self.cfg.log_path + \
"{}".format(datetime.now().strftime("%Y%m%d-%H%M%S"))
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.checkpoint_file = os.path.join(self.log_dir, "model.chk")
else:
self.checkpoint_file = os.path.join(self.log_dir, "model.chk")
# Create log file
log_filename = os.path.join(self.log_dir, "net_parameters.txt")
self.log_file = open(log_filename, 'w+')
self.init_network_model()
def init_network_model(self):
self.graph = tf.Graph()
with self.graph.as_default():
if self.cfg.log_name == 'laser':
self.create_input_ops()
self.logits = create_SalsaNet_laser(
self.input_img, self.cfg.NUM_CLASS, dropout_rate=self.cfg.DROPOUT_PROB, is_training=self.is_training)
elif self.cfg.log_name == 'fusion_encoder':
self.create_fusion_input_ops()
self.logits = create_SalsaNet_encoder_fusion(
self.input_laser, self.input_depth, self.cfg.NUM_CLASS, dropout_rate=self.cfg.DROPOUT_PROB, is_training=self.is_training)
elif self.cfg.log_name == 'fusion_decoder':
self.create_fusion_input_ops()
self.logits = create_SalsaNet_decoder_fusion(
self.input_laser, self.input_depth, self.cfg.NUM_CLASS, dropout_rate=self.cfg.DROPOUT_PROB, is_training=self.is_training)
elif self.cfg.log_name == 'fusion_encoder_decoder':
self.create_fusion_input_ops()
self.logits = create_SalsaNet_encoder_decoder_fusion(
self.input_laser, self.input_depth, self.cfg.NUM_CLASS, dropout_rate=self.cfg.DROPOUT_PROB, is_training=self.is_training)
else:
self.create_input_ops()
self.logits = create_SalsaNet(
self.input_img, self.cfg.NUM_CLASS, dropout_rate=self.cfg.DROPOUT_PROB, is_training=self.is_training)
self.store_network_parameters()
with tf.name_scope("preds") as scope:
self.preds = tf.to_int32(
tf.argmax(self.logits, axis=-1), name=scope)
self.create_loss_ops()
self.create_optimization_ops()
self.create_evaluation_metric_ops()
self.create_summary_ops()
def create_input_ops(self):
with tf.variable_scope("parameters"):
input_img_shape = (None, self.cfg.IMAGE_HEIGHT,
self.cfg.IMAGE_WIDTH, self.cfg.IMAGE_CHANNEL)
output_img_shape = (None, self.cfg.IMAGE_HEIGHT,
self.cfg.IMAGE_WIDTH)
self.input_img = tf.placeholder(
tf.float32, shape=input_img_shape, name="input_img")
self.output_img = tf.placeholder(
tf.int32, shape=output_img_shape, name="output_img")
self.weight_img = tf.placeholder(
tf.float32, shape=output_img_shape, name="weight_img")
self.dropout = tf.placeholder_with_default(
self.cfg.DROPOUT_PROB, shape=None, name="dropout")
self.is_training = tf.placeholder_with_default(
False, shape=(), name="is_training")
self.global_step = tf.Variable(
0, name='global_step', trainable=False)
self.learning_rate = tf.train.exponential_decay(learning_rate=self.cfg.LEARNING_RATE,
global_step=self.global_step,
decay_steps=self.cfg.LR_DECAY_CYCLE,
decay_rate=self.cfg.LR_DECAY_FACTOR,
staircase=True,
name="learningrate")
# Create a summary to monitor the learning rate
tf.summary.scalar("learning_rate", self.learning_rate)
def create_fusion_input_ops(self):
with tf.variable_scope("parameters"):
laser_img_shape = (None, self.cfg.LASER_IMAGE_HEIGHT,
self.cfg.LASER_IMAGE_WIDTH, self.cfg.LASER_IMAGE_CHANNEL)
depth_img_shape = (None, self.cfg.DEPTH_IMAGE_HEIGHT,
self.cfg.DEPTH_IMAGE_WIDTH, self.cfg.DEPTH_IMAGE_CHANNEL)
output_img_shape = (None, self.cfg.DEPTH_IMAGE_HEIGHT,
self.cfg.DEPTH_IMAGE_WIDTH)
self.input_laser = tf.placeholder(
tf.float32, shape=laser_img_shape, name="input_laser")
self.input_depth = tf.placeholder(
tf.float32, shape=depth_img_shape, name="input_depth")
self.output_img = tf.placeholder(
tf.int32, shape=output_img_shape, name="output_img")
self.weight_img = tf.placeholder(
tf.float32, shape=output_img_shape, name="weight_img")
self.dropout = tf.placeholder_with_default(
self.cfg.DROPOUT_PROB, shape=None, name="dropout")
self.is_training = tf.placeholder_with_default(
False, shape=(), name="is_training")
self.global_step = tf.Variable(
0, name='global_step', trainable=False)
self.learning_rate = tf.train.exponential_decay(learning_rate=self.cfg.LEARNING_RATE,
global_step=self.global_step,
decay_steps=self.cfg.LR_DECAY_CYCLE,
decay_rate=self.cfg.LR_DECAY_FACTOR,
staircase=True,
name="learningrate")
# Create a summary to monitor the learning rate
tf.summary.scalar("learning_rate", self.learning_rate)
def store_network_parameters(self):
self.log_file.write("\n" + ("#" * 70) + "\n" + ("#" * 29) +
" parameters " + ("#" * 29) + "\n" + ("#" * 70) + "\n")
for k, v in sorted(self.cfg.items()):
text_to_write = str(k) + " : " + str(v) + "\n"
self.log_file.write(text_to_write)
self.log_file.write("\n" + ("#" * 70) + "\n" + ("#" * 70) + "\n")
self.log_file.close()
def create_loss_ops(self):
# LOSS FUNCTION
with tf.variable_scope('loss') as scope:
unrolled_logits = tf.reshape(self.logits, (-1, self.cfg.NUM_CLASS))
unrolled_labels = tf.reshape(self.output_img, (-1,))
unrolled_weights = tf.reshape(self.weight_img, (-1,))
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=unrolled_labels, logits=unrolled_logits, weights=unrolled_weights)
self.loss = tf.reduce_mean(cross_entropy)
# Create a summary to monitor the loss
tf.summary.scalar("loss", self.loss)
def create_optimization_ops(self):
# OPTIMIZATION METHOD
with tf.variable_scope('opt') as scope:
self.optimizer = tf.train.AdamOptimizer(
self.learning_rate, name="optimizer")
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.train_op = self.optimizer.minimize(
self.loss, global_step=self.global_step, name="train_op")
def create_evaluation_metric_ops(self):
# EVALUATION METRIC - Intersection over Union IoU
with tf.name_scope("evaluation") as scope:
# Define the evaluation metric and update operations
self.evaluation, self.update_evaluation_vars = tf.metrics.mean_iou(
tf.reshape(self.output_img, [-1]),
tf.reshape(self.preds, [-1]),
num_classes=self.cfg.NUM_CLASS,
name=scope)
# Isolate metric's running variables & create their initializer and reset operators
evaluation_vars = tf.get_collection(
tf.GraphKeys.LOCAL_VARIABLES, scope=scope)
self.reset_evaluation_vars = tf.variables_initializer(
var_list=evaluation_vars)
def create_summary_ops(self):
with tf.name_scope('summary'):
self.summary_writer = tf.summary.FileWriter(
self.log_dir, graph=self.graph)
if self.cfg.log_name == 'fusion_encoder' or self.cfg.log_name == 'fusion_decoder' or self.cfg.log_name == 'fusion_encoder_decoder':
# only store variables belonging to the laser block
variable_scope = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope="laser_block")
self.laser_saver = tf.train.Saver(
variable_scope, name="saver", max_to_keep=1)
self.saver = tf.train.Saver(
tf.global_variables(), name="saver", max_to_keep=1)
self.summary_op = tf.summary.merge_all()
def train_segmenter(self, training_data_path, validation_data_path):
with tf.Session(graph=self.graph) as sess:
self.initialize_vars(sess)
for epoch in range(1, self.cfg.NUM_EPOCHS+1):
timeStart = time.time()
# generate batches
training_batches, n_training_samples = generate_lidar_batch_function(
training_data_path, channel_nbr=self.cfg.IMAGE_CHANNEL, class_nbr=self.cfg.NUM_CLASS, loss_weights=self.cfg.CLS_LOSS_WEIGHTS, augmentation=self.cfg.DATA_AUGMENTATION)
validation_batches, n_validation_samples = generate_lidar_batch_function(
validation_data_path, channel_nbr=self.cfg.IMAGE_CHANNEL, class_nbr=self.cfg.NUM_CLASS, loss_weights=self.cfg.CLS_LOSS_WEIGHTS, augmentation=self.cfg.DATA_AUGMENTATION)
# Num batches per epoch
n_batches = int(
np.ceil(n_training_samples / self.cfg.BATCH_SIZE))
# Iterate through each mini-batch
for step in range(n_batches):
# get next batch data
X_batch, Y_batch, W_batch = next(
training_batches(self.cfg.BATCH_SIZE))
if self.cfg.DEBUG_MODE:
print('X_batch', X_batch.shape, X_batch.dtype,
X_batch.min(), X_batch.max())
print('Y_batch', Y_batch.shape, Y_batch.dtype,
Y_batch.min(), Y_batch.max())
print('W_batch', W_batch.shape, W_batch.dtype,
W_batch.min(), W_batch.max())
# Runtime metadata
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
# Train
feed_dict = {self.input_img: X_batch, self.output_img: Y_batch,
self.weight_img: W_batch, self.is_training: True}
loss, _, summary = sess.run([self.loss, self.train_op, self.summary_op],
feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
tag_name = 'epoch {} step {}'.format(epoch, step)
self.summary_writer.add_summary(
summary, n_batches*(epoch-1)+step)
# force tensorflow to synchronise summaries
self.summary_writer.flush()
# Print feedback every so often
if self.cfg.PRINT_EVERY is not None and (step+1) % self.cfg.PRINT_EVERY == 0:
timeElapsed = time.time() - timeStart
print(" EPOCH {}/{} step: {: 5d} Batch loss: {:3.5f} Time avg: {:3.5f} sec".format(
epoch, self.cfg.NUM_EPOCHS, step+1, loss, timeElapsed/self.cfg.PRINT_EVERY))
timeStart = time.time()
# Evaluate on train and validation sets after each epoch
train_iou, train_loss, train_ious, train_precs, train_recalls = self.evaluate(
training_batches, n_training_samples, sess)
valid_iou, valid_loss, valid_ious, valid_precs, valid_recalls = self.evaluate(
validation_batches, n_validation_samples, sess)
# print scores
self.print_evaluation_scores(
train_iou, train_loss, train_ious, train_precs, train_recalls, tag="Training")
self.print_evaluation_scores(
valid_iou, valid_loss, valid_ious, valid_precs, valid_recalls, tag="Validation")
# keep summary data after each epoch
self.save_summaries(sess, train_loss, train_iou, valid_loss, valid_iou, train_ious,
train_precs, train_recalls, valid_ious, valid_precs, valid_recalls, epoch)
def train_segmenter_fusion(self, training_data_path, validation_data_path):
with tf.Session(graph=self.graph) as sess:
self.initialize_vars(sess)
for epoch in range(1, self.cfg.NUM_EPOCHS+1):
timeStart = time.time()
# generate batches
training_batches, n_training_samples = generate_fusion_batch_function(
training_data_path, laser_channel_nbr=self.cfg.LASER_IMAGE_CHANNEL, depth_channel_nbr=self.cfg.DEPTH_IMAGE_CHANNEL, class_nbr=self.cfg.NUM_CLASS, loss_weights=self.cfg.CLS_LOSS_WEIGHTS, augmentation=self.cfg.DATA_AUGMENTATION)
validation_batches, n_validation_samples = generate_fusion_batch_function(
validation_data_path, laser_channel_nbr=self.cfg.LASER_IMAGE_CHANNEL, depth_channel_nbr=self.cfg.DEPTH_IMAGE_CHANNEL, class_nbr=self.cfg.NUM_CLASS, loss_weights=self.cfg.CLS_LOSS_WEIGHTS, augmentation=self.cfg.DATA_AUGMENTATION)
# Num batches per epoch
n_batches = int(
np.ceil(n_training_samples / self.cfg.BATCH_SIZE))
# Iterate through each mini-batch
for step in range(n_batches):
# get next batch data
X1_batch, X2_batch, Y_batch, W_batch = next(
training_batches(self.cfg.BATCH_SIZE))
if self.cfg.DEBUG_MODE:
print('X1_batch', X1_batch.shape, X1_batch.dtype,
X1_batch.min(), X1_batch.max())
print('X2_batch', X2_batch.shape, X2_batch.dtype,
X2_batch.min(), X2_batch.max())
print('Y_batch', Y_batch.shape, Y_batch.dtype,
Y_batch.min(), Y_batch.max())
print('W_batch', W_batch.shape, W_batch.dtype,
W_batch.min(), W_batch.max())
# Runtime metadata
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
# Train
feed_dict = {self.input_laser: X1_batch, self.input_depth: X2_batch, self.output_img: Y_batch,
self.weight_img: W_batch, self.is_training: True}
loss, _, summary = sess.run([self.loss, self.train_op, self.summary_op],
feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
tag_name = 'epoch {} step {}'.format(epoch, step)
self.summary_writer.add_summary(
summary, n_batches*(epoch-1)+step)
# force tensorflow to synchronise summaries
self.summary_writer.flush()
# Print feedback every so often
if self.cfg.PRINT_EVERY is not None and (step+1) % self.cfg.PRINT_EVERY == 0:
timeElapsed = time.time() - timeStart
print(" EPOCH {}/{} step: {: 5d} Batch loss: {:3.5f} Time avg: {:3.5f} sec".format(
epoch, self.cfg.NUM_EPOCHS, step+1, loss, timeElapsed/self.cfg.PRINT_EVERY))
timeStart = time.time()
# Evaluate on train and validation sets after each epoch
train_iou, train_loss, train_ious, train_precs, train_recalls = self.evaluate_fusion(
training_batches, n_training_samples, sess)
valid_iou, valid_loss, valid_ious, valid_precs, valid_recalls = self.evaluate_fusion(
validation_batches, n_validation_samples, sess)
# print scores
self.print_evaluation_scores(
train_iou, train_loss, train_ious, train_precs, train_recalls, tag="Training")
self.print_evaluation_scores(
valid_iou, valid_loss, valid_ious, valid_precs, valid_recalls, tag="Validation")
# keep summary data after each epoch
self.save_summaries(sess, train_loss, train_iou, valid_loss, valid_iou, train_ious,
train_precs, train_recalls, valid_ious, valid_precs, valid_recalls, epoch)
def initialize_vars(self, session):
if tf.train.checkpoint_exists(self.checkpoint_file):
print("- Restoring parameters from saved checkpoints")
print(" -", self.checkpoint_file)
self.saver.restore(session, self.checkpoint_file)
else:
print("Initializing weights to random values")
session.run(tf.global_variables_initializer())
if self.cfg.log_name == 'fusion_encoder' or self.cfg.log_name == 'fusion_decoder' or self.cfg.log_name == 'fusion_encoder_decoder':
# add startpoint here
laser_startpoint = '../logs/dataset_newnew/laser/model.chk-50'
print("Initializing laser block from: ", laser_startpoint)
self.laser_saver.restore(session, laser_startpoint)
def predict(self, batch_data, session):
# MAKE PREDICTIONS ON SINGLE BATCH DATA
feed_dict = {self.input_img: batch_data, self.is_training: False}
batch_preds = session.run(self.preds, feed_dict=feed_dict)
preds = batch_preds.squeeze()
return preds
def predict_single_image(self, input_img, session):
# MAKE PREDICTIONS ON SINGLE IMAGE DATA
# expand image dimension
temp_img = np.zeros(
(1, input_img.shape[0], input_img.shape[1], input_img.shape[2]))
temp_img[0, :, :, :] = input_img
# MAKE PREDICTIONS ON SINGLE IMAGE
feed_dict = {self.input_img: temp_img, self.is_training: False}
timeStart = time.time()
pred_img = session.run(self.preds, feed_dict=feed_dict)
timeElapsed = (time.time() - timeStart)*1000.0
print("predict_single_image took : {:3.5f} msec".format(timeElapsed))
return pred_img[0]
def predict_single_image_fusion(self, input_data, session):
# MAKE PREDICTIONS ON SINGLE IMAGE DATA
laser_scan = input_data[0]
depth_img = input_data[1]
# expand image dimension
temp_depth = np.zeros(
(1, depth_img.shape[0], depth_img.shape[1], 1))
temp_depth[0, :, :, 0] = depth_img[:, :]
temp_laser = np.zeros(
(1, laser_scan.shape[0], laser_scan.shape[1], 1))
temp_laser[0, :, :, 0] = laser_scan[:, :]
# MAKE PREDICTIONS ON SINGLE IMAGE
feed_dict = {self.input_laser: temp_laser,
self.input_depth: temp_depth, self.is_training: False}
timeStart = time.time()
pred_img = session.run(self.preds, feed_dict=feed_dict)
timeElapsed = (time.time() - timeStart)*1000.0
print("predict_single_image took : {:3.5f} msec".format(timeElapsed))
return pred_img[0]
def evaluate(self, batch_data, data_size, session):
# EVALUATE ON BATCH DATA
total_loss = 0
tps = []
fps = []
fns = []
n_samples = data_size
# Num batches needed
n_batches = int(np.ceil(n_samples/self.cfg.BATCH_SIZE))
# Reset the running variables for evaluation metric
session.run(self.reset_evaluation_vars)
# Iterate through each mini-batch
for step in range(n_batches):
# get next batch data
X_batch, Y_batch, W_batch = next(batch_data(self.cfg.BATCH_SIZE))
feed_dict = {self.input_img: X_batch, self.output_img: Y_batch,
self.weight_img: W_batch, self.is_training: False}
# Get loss, and update running variables for evaluation metric
loss, preds, confusion_mtx = session.run(
[self.loss, self.preds, self.update_evaluation_vars], feed_dict=feed_dict)
total_loss += loss
# iou computation
tp, fp, fn = self.evaluate_iou(Y_batch, preds, self.cfg.NUM_CLASS)
tps.append(tp)
fps.append(fp)
fns.append(fn)
tps = np.array(tps)
fps = np.array(fps)
fns = np.array(fns)
epsilon = 1e-12
ious = tps.astype(np.float) / (tps + fns + fps + epsilon)
precision = tps.astype(np.float) / (tps + fps + epsilon)
recall = tps.astype(np.float) / (tps + fns + epsilon)
mean_ious = np.mean(ious, axis=0)
mean_prec = np.mean(precision, axis=0)
mean_recall = np.mean(recall, axis=0)
# Get the updated score from the running metric
score = session.run(self.evaluation)
# Average the loss
avg_loss = total_loss/float(n_batches)
return score, avg_loss, mean_ious, mean_prec, mean_recall
def evaluate_fusion(self, batch_data, data_size, session):
# EVALUATE ON BATCH DATA
total_loss = 0
tps = []
fps = []
fns = []
n_samples = data_size
# Num batches needed
n_batches = int(np.ceil(n_samples/self.cfg.BATCH_SIZE))
# Reset the running variables for evaluation metric
session.run(self.reset_evaluation_vars)
# Iterate through each mini-batch
for step in range(n_batches):
# get next batch data
X1_batch, X2_batch, Y_batch, W_batch = next(
batch_data(self.cfg.BATCH_SIZE))
feed_dict = {self.input_laser: X1_batch, self.input_depth: X2_batch, self.output_img: Y_batch,
self.weight_img: W_batch, self.is_training: False}
# Get loss, and update running variables for evaluation metric
loss, preds, confusion_mtx = session.run(
[self.loss, self.preds, self.update_evaluation_vars], feed_dict=feed_dict)
total_loss += loss
# iou computation
tp, fp, fn = self.evaluate_iou(Y_batch, preds, self.cfg.NUM_CLASS)
tps.append(tp)
fps.append(fp)
fns.append(fn)
tps = np.array(tps)
fps = np.array(fps)
fns = np.array(fns)
epsilon = 1e-12
ious = tps.astype(np.float) / (tps + fns + fps + epsilon)
precision = tps.astype(np.float) / (tps + fps + epsilon)
recall = tps.astype(np.float) / (tps + fns + epsilon)
mean_ious = np.mean(ious, axis=0)
mean_prec = np.mean(precision, axis=0)
mean_recall = np.mean(recall, axis=0)
# Get the updated score from the running metric
score = session.run(self.evaluation)
# Average the loss
avg_loss = total_loss/float(n_batches)
return score, avg_loss, mean_ious, mean_prec, mean_recall
def evaluate_iou(self, label, pred, n_class):
assert label.shape == pred.shape, \
'label and pred shape mismatch: {} vs {}'.format(
label.shape, pred.shape)
tps = np.zeros(n_class)
fns = np.zeros(n_class)
fps = np.zeros(n_class)
for cls_id in range(n_class):
tp = np.sum(pred[label == cls_id] == cls_id)
fp = np.sum(label[pred == cls_id] != cls_id)
fn = np.sum(pred[label == cls_id] != cls_id)
tps[cls_id] = tp
fps[cls_id] = fp
fns[cls_id] = fn
return tps, fps, fns
def expand_image_dimension(self, input_img):
# return pred results as colored rgb images
n_samples = input_img.shape[0]
output_img = np.zeros(
(n_samples, input_img.shape[1], input_img.shape[2], 3))
for i in range(0, n_samples):
label_map = input_img[i, :, :]
color_img = np.zeros((input_img.shape[1], input_img.shape[2], 3))
for j in range(0, self.cfg.NUM_CLASS):
color_img[label_map == j, :] = self.cfg.CLS_COLOR_MAP[j]
output_img[i, :, :, :] = color_img
return output_img
def save_summaries(self, sess, train_loss, train_iou, valid_loss, valid_iou, train_mean_ious, train_precs, train_recalls, valid_mean_ious, valid_precs, valid_recalls, epoch):
# Save checkpoints
self.saver.save(sess, self.checkpoint_file,
global_step=epoch, write_meta_graph=True)
# Save training and validation summaries
summary = tf.Summary()
summary.value.add(tag='Training/Training Loss',
simple_value=float(train_loss))
summary.value.add(tag='Validation/Validation Loss',
simple_value=float(valid_loss))
summary.value.add(tag='Training/Training IOU',
simple_value=float(train_iou))
summary.value.add(tag='Validation/Validation IOU',
simple_value=float(valid_iou))
for i in range(0, self.cfg.NUM_CLASS):
tag_name = 'Training/' + self.cfg.CLASSES[i] + '/IOU'
summary.value.add(
tag=tag_name, simple_value=float(train_mean_ious[i]))
tag_name = 'Training/' + self.cfg.CLASSES[i] + '/Prec'
summary.value.add(tag=tag_name, simple_value=float(train_precs[i]))
tag_name = 'Training/' + self.cfg.CLASSES[i] + '/Recall'
summary.value.add(
tag=tag_name, simple_value=float(train_recalls[i]))
tag_name = 'Validation/' + self.cfg.CLASSES[i] + '/IOU'
summary.value.add(
tag=tag_name, simple_value=float(valid_mean_ious[i]))
tag_name = 'Validation/' + self.cfg.CLASSES[i] + '/Prec'
summary.value.add(tag=tag_name, simple_value=float(valid_precs[i]))
tag_name = 'Validation/' + self.cfg.CLASSES[i] + '/Recall'
summary.value.add(
tag=tag_name, simple_value=float(valid_recalls[i]))
self.summary_writer.add_summary(summary, epoch)
# force tensorflow to synchronise summaries
self.summary_writer.flush()
def print_evaluation_scores(self, iou, loss, ious, precs, recalls, tag="Training"):
if tag == "Training":
s = "TR IOU: {: 3.3f} TR IOU: {: 3.3f} TR LOSS: {: 3.5f} "
elif tag == "Testing":
s = "TEST IOU: {: 3.3f} TEST IOU: {: 3.3f} TEST LOSS: {: 3.5f} "
else:
s = "VR IOU: {: 3.3f} VR IOU: {: 3.3f} VR LOSS: {: 3.5f} "
print(s.format(iou, np.mean(ious), loss))
for i in range(0, self.cfg.NUM_CLASS):
s = self.cfg.CLASSES[i] + " PREC: {: 3.3f} " + self.cfg.CLASSES[i] + \
" REC: {: 3.3f} " + self.cfg.CLASSES[i] + " IOU: {: 3.3f}"
print(s.format(precs[i], recalls[i], ious[i]))
|
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|
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <nlopt.h>
typedef struct {
int N;
double *x, *y; /* length N; */
} lorentzdata;
static double sqr(double x)
{
return x * x;
}
static int count = 0;
static double lorentzerr(int n, const double *p, double *grad, void *data)
{
lorentzdata *d = (lorentzdata *) data;
int N = d->N;
const double *xs = d->x;
const double *ys = d->y;
double val = 0;
int i, j;
for (i = 0; i < N; ++i) {
double x = xs[i], y = ys[i];
double lorsum = 0;
for (j = 0; j < n; j += 3) {
double A = p[j + 0];
double w = p[j + 1];
double G = p[j + 2];
double lor = A / (sqr(x - w) + G * G);
lorsum += lor;
}
val += sqr(y - lorsum);
if (grad)
for (j = 0; j < n; j += 3) {
double A = p[j + 0];
double w = p[j + 1];
double G = p[j + 2];
double deninv = 1.0 / (sqr(x - w) + G * G);
grad[j + 0] += -2 * (y - lorsum) * deninv;
grad[j + 1] += 4 * A * (w - x) * (y - lorsum) * sqr(deninv);
grad[j + 2] += 4 * A * G * (y - lorsum) * sqr(deninv);
}
}
++count;
// printf("%d: f(%g,%g,%g) = %g\n", count, p[0],p[1],p[2], val);
return val;
}
extern double nlopt_urand(double a, double b);
int main(void)
{
lorentzdata d;
int i;
double A = 1, w = 0, G = 1, noise = 0.01;
double lb[3] = { -HUGE_VAL, -HUGE_VAL, 0 };
double ub[3] = { HUGE_VAL, HUGE_VAL, HUGE_VAL };
double p[3] = { 0, 1, 2 }, minf;
nlopt_srand_time();
d.N = 200;
d.x = (double *) malloc(sizeof(double) * d.N * 2);
d.y = d.x + d.N;
for (i = 0; i < d.N; ++i) {
d.x[i] = nlopt_urand(-0.5, 0.5) * 8 * G + w;
d.y[i] = 2 * noise * nlopt_urand(-0.5, 0.5) + A / (sqr(d.x[i] - w) + G * G);
}
nlopt_minimize(NLOPT_LN_NEWUOA_BOUND, 3, lorentzerr, &d, lb, ub, p, &minf, -HUGE_VAL, 0, 0, 1e-6, NULL, 0, 0);
printf("%d minf=%g at A=%g, w=%g, G=%g\n", count, minf, p[0], p[1], p[2]);
count = 0;
nlopt_minimize(NLOPT_LN_COBYLA, 3, lorentzerr, &d, lb, ub, p, &minf, -HUGE_VAL, 0, 0, 1e-6, NULL, 0, 0);
printf("%d minf=%g at A=%g, w=%g, G=%g\n", count, minf, p[0], p[1], p[2]);
count = 0;
nlopt_minimize(NLOPT_LN_NELDERMEAD, 3, lorentzerr, &d, lb, ub, p, &minf, -HUGE_VAL, 0, 0, 1e-6, NULL, 0, 0);
printf("%d minf=%g at A=%g, w=%g, G=%g\n", count, minf, p[0], p[1], p[2]);
count = 0;
nlopt_minimize(NLOPT_LN_SBPLX, 3, lorentzerr, &d, lb, ub, p, &minf, -HUGE_VAL, 0, 0, 1e-6, NULL, 0, 0);
printf("%d minf=%g at A=%g, w=%g, G=%g\n", count, minf, p[0], p[1], p[2]);
return 0;
}
|
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|
#Function that performs PSR Bitaper Neff - Waveguide Width Sweep
#General Purpose Libaries
try:
import matplotlib.pyplot as plt
except:
import pip
pip.main(['install', 'matplotlib'])
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import platform
#Import LUMAPI
from lumerical_lumapi import lumapi
#Import libraries for sweep and material setup
from neff_taper_width_sweep_setup import width_sweep_setup, material_setup
#Output Modes
modes=3
#Sweep range of widths (ridge waveguide)
width_ridge_list=np.linspace(0.4,0.9,100)*1e-6
#Sweep range of widths (slab waveguide)
width_slab_list=np.linspace(0.4,1.9,100)*1e-6
#Class that performs width sweep
class width_sweep:
@staticmethod
def main():
with lumapi.MODE(hide = False) as mode:
#Adding materials, drawing photonic components and simulation recipe setup
material = material_setup.add_material(mode)
draw_wg = width_sweep_setup.wg_2D_draw(mode)
sweep = width_sweep_setup.wg_2D_func(mode)
mode.set("number of trial modes",modes+1);
neff = []
TE00 = []
TM00 = []
TE01 = []
#Finding the modes for each specified waveguide width
for i in range (0,len(width_ridge_list)):
mode.switchtolayout()
mode.setnamed("waveguide","y span", width_ridge_list[i])
mode.setnamed("mesh1","y span", width_ridge_list[i])
mode.setnamed("slab","y span", width_slab_list[i])
mode.setnamed("mesh2","y span", width_slab_list[i])
n = mode.findmodes()
mode.save("bitaper_mode_calculations")
#For each mode, simulate/extract the effective index for corresponding width
for m in range(1,4):
if m == 1:
data = abs(mode.getdata("FDE::data::mode"+str(m),"neff"))
data = data[0][0]
TE00.append(data)
mode.selectmode("mode1")
#mode.setanalysis("track selected mode",1);
#mode.setanalysis("detailed dispersion calculation",1);
#mode.frequencysweep()
#loss_data = mode.getdata("frequencysweep","loss")
elif m == 2:
data = abs(mode.getdata("FDE::data::mode"+str(m),"neff"))
data = data[0][0]
TM00.append(data)
elif m == 3:
data = abs(mode.getdata("FDE::data::mode"+str(m),"neff"))
data = data[0][0]
TE01.append(data)
#Append to arrays for data visualization
neff.append(TE00)
neff.append(TM00)
neff.append(TE01)
neff_plot = plt.plot(width_ridge_list, TE00, label = "TE00")
neff_plot = plt.plot(width_ridge_list, TM00, label = "TM00")
neff_plot = plt.plot(width_ridge_list, TE01, label = "TE01")
neff_plot = plt.title('Neff vs Waveguide Width')
neff_plot = plt.xlabel('Width (10e-7 m)')
neff_plot = plt.ylabel("Neff")
neff_plot = plt.legend()
neff_plot = plt.show()
#Find starting width: Find the width that is closest to the neff cutoff of the fundamental mode (1.465)
width_begin = 0
for x, y in zip(width_ridge_list, TE01):
if x < 5e-07 and x > 4e-07:
if y<1.467 and y >1.463:
width_begin = x
#Find hybrid point to determine hybrid region
hybrid_point = 0
max_differ = sys.maxsize
for x, y, z in zip(width_ridge_list, TE01, TM00):
if z - y < max_differ:
max_differ = z - y
hybrid_point = x
#Find middle width: Scans a range between (+-50nm) of the hybrid region to find the point that has the most gentle slope
maxslope = 1
difference = 1
width_middle = 0
for x, y in zip(width_ridge_list, TE01):
if x < hybrid_point + 50e-9 and x> hybrid_point - 50e-9:
if y - difference <maxslope:
maxslope = y - difference
width_middle = x
difference = y
#Find end width: find largest discrepancy between TM00, TE01
#Ensures most efficient mode conversion
width_end = 0
max_diff = 0
for x, y, z in zip(width_ridge_list, TE01, TM00):
if x < 9e-07 and x> 6.5e-07:
if z - y > max_diff:
max_diff = z - y
width_end = x
#Returns widths as an array
widths = [width_begin, width_middle, width_end]
mode.save("bitaper_mode_calculations")
return widths
#plot = width_sweep.main()
|
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|
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys, os
import pathlib
import os.path as osp
|
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|
import rospy
import tf
import numpy as np
from matplotlib import pyplot as plt
class VSCaleCalibrator(object):
def __init__(self):
rospy.init_node('vscale_calibrator')
self._tfl = tf.TransformListener()
self._data = [] # (timestamp, distance)
self._t0 = rospy.Time.now()
def step(self):
try:
t, q = self._tfl.lookupTransform('map', 'camera_link', rospy.Time(0))
x, y = t[0], t[1]
d = np.sqrt(x**2 + y**2)
time = rospy.Time.now()
print (time - self._t0).to_sec()
self._data.append( ((time - self._t0).to_sec(), d) )
except Exception:
#print 'life is terrible'
pass
def show(self):
data = np.asarray(self._data, dtype=np.float32)
#print data[:,0]
plt.plot(data[:,0] - data[0,0], data[:,1])
plt.show()
#print self._data
#print 'shutdown'
def run(self):
rate = rospy.Rate(100)
rospy.on_shutdown(self.show)
while self._t0.to_sec() == 0:
self._t0 = rospy.Time.now()
while not rospy.is_shutdown():
self.step() # << where stuff happens
rate.sleep()
def main():
app = VSCaleCalibrator()
app.run()
if __name__ == "__main__":
main()
|
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|
# License: 3-clause BSD
# Copyright (c) 2016-2018, Ml4AAD Group (http://www.ml4aad.org/)
from typing import List, Optional, Tuple, Union
from ConfigSpace import ConfigurationSpace
import numpy as np
import sklearn.gaussian_process.kernels
from openbox.surrogate.base.base_model import AbstractModel
import openbox.surrogate.base.gp_base_prior
from skopt.learning.gaussian_process.kernels import Kernel
from skopt.learning.gaussian_process import GaussianProcessRegressor
class BaseGP(AbstractModel):
def __init__(
self,
configspace: ConfigurationSpace,
types: List[int],
bounds: List[Tuple[float, float]],
seed: int,
kernel: Kernel,
instance_features: Optional[np.ndarray] = None,
pca_components: Optional[int] = None,
):
"""
Abstract base class for all Gaussian process models.
"""
super().__init__(
types=types,
bounds=bounds,
instance_features=instance_features,
pca_components=pca_components,
)
self.configspace = configspace
self.rng = np.random.RandomState(seed)
self.kernel = kernel
self.gp = self._get_gp()
self.seed = seed
def _get_gp(self) -> GaussianProcessRegressor:
raise NotImplementedError()
def _normalize_y(self, y: np.ndarray) -> np.ndarray:
"""Normalize data to zero mean unit standard deviation.
Parameters
----------
y : np.ndarray
Targets for the Gaussian process
Returns
-------
np.ndarray
"""
self.mean_y_ = np.mean(y)
self.std_y_ = np.std(y)
if self.std_y_ == 0:
self.std_y_ = 1
return (y - self.mean_y_) / self.std_y_
def _untransform_y(
self,
y: np.ndarray,
var: Optional[np.ndarray] = None,
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""Transform zeromean unit standard deviation data into the regular space.
This function should be used after a prediction with the Gaussian process which was trained on normalized data.
Parameters
----------
y : np.ndarray
Normalized data.
var : np.ndarray (optional)
Normalized variance
Returns
-------
np.ndarray on Tuple[np.ndarray, np.ndarray]
"""
y = y * self.std_y_ + self.mean_y_
if var is not None:
var = var * self.std_y_ ** 2
return y, var
return y
def _get_all_priors(
self,
add_bound_priors: bool = True,
add_soft_bounds: bool = False,
) -> List[List[openbox.surrogate.base.gp_base_prior.Prior]]:
# Obtain a list of all priors for each tunable hyperparameter of the kernel
all_priors = []
to_visit = []
# to_visit.append(self.gp.kernel.k1)
# to_visit.append(self.gp.kernel.k2)
to_visit.append(self.gp.kernel) # fix single kernel
while len(to_visit) > 0:
current_param = to_visit.pop(0)
if isinstance(current_param, sklearn.gaussian_process.kernels.KernelOperator):
to_visit.insert(0, current_param.k1)
to_visit.insert(1, current_param.k2)
continue
elif isinstance(current_param, sklearn.gaussian_process.kernels.Kernel):
hps = current_param.hyperparameters
assert len(hps) == 1
hp = hps[0]
if hp.fixed:
continue
bounds = hps[0].bounds
for i in range(hps[0].n_elements):
priors_for_hp = []
if current_param.prior is not None:
priors_for_hp.append(current_param.prior)
if add_bound_priors:
if add_soft_bounds:
priors_for_hp.append(
openbox.surrogate.base.gp_base_prior.SoftTopHatPrior(
lower_bound=bounds[i][0], upper_bound=bounds[i][1], rng=self.rng, exponent=2,
))
else:
priors_for_hp.append(
openbox.surrogate.base.gp_base_prior.TophatPrior(
lower_bound=bounds[i][0], upper_bound=bounds[i][1], rng=self.rng,
))
all_priors.append(priors_for_hp)
return all_priors
def _set_has_conditions(self) -> None:
has_conditions = len(self.configspace.get_conditions()) > 0
to_visit = []
to_visit.append(self.kernel)
while len(to_visit) > 0:
current_param = to_visit.pop(0)
if isinstance(current_param, sklearn.gaussian_process.kernels.KernelOperator):
to_visit.insert(0, current_param.k1)
to_visit.insert(1, current_param.k2)
current_param.has_conditions = has_conditions
elif isinstance(current_param, sklearn.gaussian_process.kernels.Kernel):
current_param.has_conditions = has_conditions
else:
raise ValueError(current_param)
def _impute_inactive(self, X: np.ndarray) -> np.ndarray:
X = X.copy()
X[~np.isfinite(X)] = -1
return X
|
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|
(*
This file is a part of MMIsar - a translation of Metamath's set.mm to Isabelle 2005 (ZF logic).
Copyright (C) 2006 Slawomir Kolodynski
This program is free software; Redistribution and use in source and binary forms,
with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation and/or
other materials provided with the distribution.
3. The name of the author may not be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*)
section \<open>Metamath introduction\<close>
theory MMI_prelude imports Order_ZF_1
begin
text\<open>Metamath's set.mm features a large (over 8000) collection of theorems
proven in the ZFC set theory. This theory is part of an attempt to translate
those theorems to Isar so that they are available for Isabelle/ZF users.
A total of about 1200 assertions have been translated, 600 of that
with proofs (the rest was proven automatically by Isabelle).
The translation was done with the support of the mmisar tool, whose source
is included in the IsarMathLib distributions prior to version 1.6.4.
The translation tool was doing about 99 percent of work involved, with the rest
mostly related to the difference between Isabelle/ZF and Metamath
metalogics. Metamath uses Tarski-Megill metalogic that does not have a notion
of bound variables (see
\<open> http://planetx.cc.vt.edu/AsteroidMeta/Distinctors_vs_binders \<close>
for details and discussion).
The translation project is closed now as I decided that it was too boring
and tedious even with the support of mmisar software. Also, the translated
proofs are not as readable as native Isar proofs which goes against IsarMathLib
philosophy.\<close>
subsection\<open>Importing from Metamath - how is it done\<close>
text\<open>
We are interested in importing the theorems about complex numbers
that start from the "recnt" theorem on. This is done mostly automatically
by the mmisar tool that is included in the IsarMathLib distributions prior
to version 1.6.4.
The tool works as follows:
First it reads the list of (Metamath) names of theorems
that are already imported to IsarMathlib ("known theorems")
and the list of theorems that are intended to be imported in this
session ("new theorems").
The new theorems are consecutive theorems about complex numbers
as they appear in the Metamath database.
Then mmisar creates a "Metamath script" that contains
Metamath commands that open a log file and put the statements
and proofs of the new theorems in that file in a readable format.
The tool writes this script to a disk file and executes metamath
with standard input redirected from that file. Then the log file is read
and its contents converted to the Isar format. In Metamath,
the proofs of theorems about complex numbers
depend only on 28 axioms of complex numbers and some basic logic and
set theory theorems.
The tool finds which of these dependencies are not known yet and repeats
the process of getting their statements from Metamath as with the
new theorems. As a result of this process mmisar creates files
new\_theorems.thy, new\_deps.thy and new\_known\_theorems.txt.
The file new\_theorems.thy contains the theorems (with proofs)
imported from Metamath in this session. These theorems are added
(by hand) to the current \<open>MMI_Complex_ZF_x.thy\<close> file.
The file new\_deps.thy contains the
statements of new dependencies with generic proofs "by auto".
These are added to the \<open>MMI_logic_and_sets.thy\<close>.
Most of the dependencies can be proven automatically by Isabelle.
However, some manual work has to be done for the dependencies
that Isabelle can not prove by itself and to correct problems related
to the fact that Metamath uses a metalogic based on
distinct variable constraints (Tarski-Megill metalogic),
rather than an explicit notion of free and bound variables.
The old list of known theorems is replaced by the new list and
mmisar is ready to convert the next batch of new theorems.
Of course this rarely works in practice without tweaking the mmisar
source files every time a new batch is processed.\<close>
subsection\<open>The context for Metamath theorems\<close>
text\<open>We list the Metamth's axioms of complex numbers
and define notation here.\<close>
text\<open>The next definition is what Metamath $X\in V$ is
translated to. I am not sure why it works, probably because
Isabelle does a type inference and the "=" sign
indicates that both sides are sets.\<close>
definition
IsASet :: "i\<Rightarrow>o" ("_ isASet" [90] 90) where
(*set_def[simp]: "X isASet \<equiv> X = X"*)
IsASet_def[simp]: "X isASet \<equiv> X = X"
text\<open>The next locale sets up the context to which Metamath theorems
about complex numbers are imported. It assumes the axioms
of complex numbers and defines the notation used for complex numbers.
One of the problems with importing theorems from Metamath is that
Metamath allows direct infix notation for binary operations so
that the notation $a f b$ is allowed where $f$ is a function
(that is, a set of pairs). To my knowledge,
Isar allows only notation \<open>f`\<langle>a,b\<rangle>\<close> with a possibility of
defining a syntax say \<open>a \<ca> b\<close> to mean the same as \<open>f`\<langle>a,b\<rangle>\<close>
(please correct me if I am wrong here). This is why we have
two objects for addition: one called \<open>caddset\<close> that represents
the binary function, and the second one called \<open>ca\<close> which
defines the \<open>a \<ca> b\<close> notation for \<open>caddset`\<langle>a,b\<rangle>\<close>.
The same applies to multiplication of real numbers.
Another difficulty is that Metamath allows to define sets with syntax
$\{ x | p\}$ where $p$ is some formula that (usually) depends on $x$.
Isabelle allows the set comprehension like this only as a subset of another
set i.e. $\{x\in A . p(x)\}$. This forces us to have a sligtly different
definition of (complex) natural numbers, requiring explicitly that natural
numbers is a subset of reals. Because of that, the proofs of Metamath theorems
that reference the definition directly can not be imported.
\<close>
locale MMIsar0 =
fixes real ("\<real>")
fixes complex ("\<complex>")
fixes one ("\<one>")
fixes zero ("\<zero>")
fixes iunit ("\<i>")
fixes caddset ("\<caddset>")
fixes cmulset ("\<cmulset>")
fixes lessrrel ("\<lsrset>")
fixes ca (infixl "\<ca>" 69)
defines ca_def: "a \<ca> b \<equiv> \<caddset>`\<langle>a,b\<rangle>"
fixes cm (infixl "\<cdot>" 71)
defines cm_def: "a \<cdot> b \<equiv> \<cmulset>`\<langle>a,b\<rangle>"
fixes sub (infixl "\<cs>" 69)
defines sub_def: "a \<cs> b \<equiv> \<Union> { x \<in> \<complex>. b \<ca> x = a }"
fixes cneg ("\<cn>_" 95)
defines cneg_def: "\<cn> a \<equiv> \<zero> \<cs> a"
fixes cdiv (infixl "\<cdiv>" 70)
defines cdiv_def: "a \<cdiv> b \<equiv> \<Union> { x \<in> \<complex>. b \<cdot> x = a }"
fixes cpnf ("\<cpnf>")
defines cpnf_def: "\<cpnf> \<equiv> \<complex>"
fixes cmnf ("\<cmnf>")
defines cmnf_def: "\<cmnf> \<equiv> {\<complex>}"
fixes cxr ("\<real>\<^sup>*")
defines cxr_def: "\<real>\<^sup>* \<equiv> \<real> \<union> {\<cpnf>,\<cmnf>}"
fixes cxn ("\<nat>")
defines cxn_def: "\<nat> \<equiv> \<Inter> {N \<in> Pow(\<real>). \<one> \<in> N \<and> (\<forall>n. n\<in>N \<longrightarrow> n\<ca>\<one> \<in> N)}"
fixes lessr (infix "\<lsr>" 68)
defines lessr_def: "a \<lsr> b \<equiv> \<langle>a,b\<rangle> \<in> \<lsrset>"
fixes cltrrset ("\<cltrrset>")
defines cltrrset_def:
"\<cltrrset> \<equiv> (\<lsrset> \<inter> \<real>\<times>\<real>) \<union> {\<langle>\<cmnf>,\<cpnf>\<rangle>} \<union>
(\<real>\<times>{\<cpnf>}) \<union> ({\<cmnf>}\<times>\<real> )"
fixes cltrr (infix "\<ls>" 68)
defines cltrr_def: "a \<ls> b \<equiv> \<langle>a,b\<rangle> \<in> \<cltrrset>"
fixes convcltrr (infix ">" 68)
defines convcltrr_def: "a > b \<equiv> \<langle>a,b\<rangle> \<in> converse(\<cltrrset>)"
fixes lsq (infix "\<lsq>" 68)
defines lsq_def: "a \<lsq> b \<equiv> \<not> (b \<ls> a)"
fixes two ("\<two>")
defines two_def: "\<two> \<equiv> \<one>\<ca>\<one>"
fixes three ("\<three>")
defines three_def: "\<three> \<equiv> \<two>\<ca>\<one>"
fixes four ("\<four>")
defines four_def: "\<four> \<equiv> \<three>\<ca>\<one>"
fixes five ("\<five>")
defines five_def: "\<five> \<equiv> \<four>\<ca>\<one>"
fixes six ("\<six>")
defines six_def: "\<six> \<equiv> \<five>\<ca>\<one>"
fixes seven ("\<seven>")
defines seven_def: "\<seven> \<equiv> \<six>\<ca>\<one>"
fixes eight ("\<eight>")
defines eight_def: "\<eight> \<equiv> \<seven>\<ca>\<one>"
fixes nine ("\<nine>")
defines nine_def: "\<nine> \<equiv> \<eight>\<ca>\<one>"
assumes MMI_pre_axlttri:
"A \<in> \<real> \<and> B \<in> \<real> \<longrightarrow> (A \<lsr> B \<longleftrightarrow> \<not>(A=B \<or> B \<lsr> A))"
assumes MMI_pre_axlttrn:
"A \<in> \<real> \<and> B \<in> \<real> \<and> C \<in> \<real> \<longrightarrow> ((A \<lsr> B \<and> B \<lsr> C) \<longrightarrow> A \<lsr> C)"
assumes MMI_pre_axltadd:
"A \<in> \<real> \<and> B \<in> \<real> \<and> C \<in> \<real> \<longrightarrow> (A \<lsr> B \<longrightarrow> C\<ca>A \<lsr> C\<ca>B)"
assumes MMI_pre_axmulgt0:
"A \<in> \<real> \<and> B \<in> \<real> \<longrightarrow> ( \<zero> \<lsr> A \<and> \<zero> \<lsr> B \<longrightarrow> \<zero> \<lsr> A\<cdot>B)"
assumes MMI_pre_axsup:
"A \<subseteq> \<real> \<and> A \<noteq> 0 \<and> (\<exists>x\<in>\<real>. \<forall>y\<in>A. y \<lsr> x) \<longrightarrow>
(\<exists>x\<in>\<real>. (\<forall>y\<in>A. \<not>(x \<lsr> y)) \<and> (\<forall>y\<in>\<real>. (y \<lsr> x \<longrightarrow> (\<exists>z\<in>A. y \<lsr> z))))"
assumes MMI_axresscn: "\<real> \<subseteq> \<complex>"
assumes MMI_ax1ne0: "\<one> \<noteq> \<zero>"
assumes MMI_axcnex: "\<complex> isASet"
assumes MMI_axaddopr: "\<caddset> : ( \<complex> \<times> \<complex> ) \<rightarrow> \<complex>"
assumes MMI_axmulopr: "\<cmulset> : ( \<complex> \<times> \<complex> ) \<rightarrow> \<complex>"
assumes MMI_axmulcom: "A \<in> \<complex> \<and> B \<in> \<complex> \<longrightarrow> A \<cdot> B = B \<cdot> A"
assumes MMI_axaddcl: "A \<in> \<complex> \<and> B \<in> \<complex> \<longrightarrow> A \<ca> B \<in> \<complex>"
assumes MMI_axmulcl: "A \<in> \<complex> \<and> B \<in> \<complex> \<longrightarrow> A \<cdot> B \<in> \<complex>"
assumes MMI_axdistr:
"A \<in> \<complex> \<and> B \<in> \<complex> \<and> C \<in> \<complex> \<longrightarrow> A\<cdot>(B \<ca> C) = A\<cdot>B \<ca> A\<cdot>C"
assumes MMI_axaddcom: "A \<in> \<complex> \<and> B \<in> \<complex> \<longrightarrow> A \<ca> B = B \<ca> A"
assumes MMI_axaddass:
"A \<in> \<complex> \<and> B \<in> \<complex> \<and> C \<in> \<complex> \<longrightarrow> A \<ca> B \<ca> C = A \<ca> (B \<ca> C)"
assumes MMI_axmulass:
"A \<in> \<complex> \<and> B \<in> \<complex> \<and> C \<in> \<complex> \<longrightarrow> A \<cdot> B \<cdot> C = A \<cdot> (B \<cdot> C)"
assumes MMI_ax1re: "\<one> \<in> \<real>"
assumes MMI_axi2m1: "\<i> \<cdot> \<i> \<ca> \<one> = \<zero>"
assumes MMI_ax0id: "A \<in> \<complex> \<longrightarrow> A \<ca> \<zero> = A"
assumes MMI_axicn: "\<i> \<in> \<complex>"
assumes MMI_axnegex: "A \<in> \<complex> \<longrightarrow> ( \<exists> x \<in> \<complex>. ( A \<ca> x ) = \<zero> )"
assumes MMI_axrecex: "A \<in> \<complex> \<and> A \<noteq> \<zero> \<longrightarrow> ( \<exists> x \<in> \<complex>. A \<cdot> x = \<one>)"
assumes MMI_ax1id: "A \<in> \<complex> \<longrightarrow> A \<cdot> \<one> = A"
assumes MMI_axaddrcl: "A \<in> \<real> \<and> B \<in> \<real> \<longrightarrow> A \<ca> B \<in> \<real>"
assumes MMI_axmulrcl: "A \<in> \<real> \<and> B \<in> \<real> \<longrightarrow> A \<cdot> B \<in> \<real>"
assumes MMI_axrnegex: "A \<in> \<real> \<longrightarrow> ( \<exists> x \<in> \<real>. A \<ca> x = \<zero> )"
assumes MMI_axrrecex: "A \<in> \<real> \<and> A \<noteq> \<zero> \<longrightarrow> ( \<exists> x \<in> \<real>. A \<cdot> x = \<one> )"
end
|
{"author": "SKolodynski", "repo": "IsarMathLib", "sha": "879c6b779ca00364879aa0232b0aa9f18bafa85a", "save_path": "github-repos/isabelle/SKolodynski-IsarMathLib", "path": "github-repos/isabelle/SKolodynski-IsarMathLib/IsarMathLib-879c6b779ca00364879aa0232b0aa9f18bafa85a/IsarMathLib/MMI_prelude.thy"}
|
import torch
import torch.nn as nn
import numpy as np
from torch.jit import Final
from typing import List
class NeuralStateSpaceModel(nn.Module):
n_x: Final[int]
n_u: Final[int]
n_feat: Final[int]
def __init__(self, n_x, n_u, n_feat=64, scale_dx=1.0, init_small=True, activation='relu'):
super(NeuralStateSpaceModel, self).__init__()
self.n_x = n_x
self.n_u = n_u
self.n_feat = n_feat
self.scale_dx = scale_dx
if activation == 'relu':
activation = nn.ReLU()
elif activation == 'softplus':
activation = nn.Softplus()
elif activation == 'tanh':
activation = nn.Tanh()
self.net = nn.Sequential(
nn.Linear(n_x+n_u, n_feat), # 2 states, 1 input
activation,
nn.Linear(n_feat, n_x)
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
in_xu = torch.cat((in_x, in_u), -1) # concatenate x and u over the last dimension to create the [xu] input
dx = self.net(in_xu) # \dot x = f([xu])
dx = dx * self.scale_dx
return dx
class DeepNeuralStateSpaceModel(nn.Module):
n_x: Final[int]
n_u: Final[int]
n_feat: Final[int]
def __init__(self, n_x, n_u, n_feat=64, scale_dx=1.0, init_small=True):
super(DeepNeuralStateSpaceModel, self).__init__()
self.n_x = n_x
self.n_u = n_u
self.n_feat = n_feat
self.scale_dx = scale_dx
self.net = nn.Sequential(
nn.Linear(n_x + n_u, n_feat), # 2 states, 1 input
nn.ReLU(),
nn.Linear(n_feat, n_feat),
nn.ReLU(),
nn.Linear(n_feat, n_x)
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
in_xu = torch.cat((in_x, in_u), -1) # concatenate x and u over the last dimension to create the [xu] input
dx = self.net(in_xu) # \dot x = f([xu])
dx = dx * self.scale_dx
return dx
class MechanicalStateSpaceSystem(nn.Module):
n_x: Final[int]
n_u: Final[int]
n_feat: Final[int]
def __init__(self, n_feat=64, init_small=True, typical_ts=1.0):
super(MechanicalStateSpaceSystem, self).__init__()
self.n_feat = n_feat
self.typical_ts = typical_ts
self.net = nn.Sequential(
nn.Linear(3, n_feat), # 2 states, 1 input
nn.ReLU(),
nn.Linear(n_feat, 1)
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-3)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
list_dx: List[torch.Tensor]
in_xu = torch.cat((in_x, in_u), -1) # concatenate x and u over the last dimension to create the [xu] input
dx_v = self.net(in_xu)/self.typical_ts # \dot x = f([xu])
list_dx = [in_x[..., [1]], dx_v]
dx = torch.cat(list_dx, -1) # dot x = v, dot v = net
return dx
class MechanicalStateSpaceSystemV2(nn.Module):
n_x: Final[int]
n_u: Final[int]
n_feat: Final[int]
def __init__(self, n_feat=64, init_small=True, typical_ts=1.0):
super(MechanicalStateSpaceSystemV2, self).__init__()
self.n_feat = n_feat
self.typical_ts = typical_ts
self.net = nn.Sequential(
nn.Linear(2, n_feat), # 2 states, 1 input
nn.ReLU(),
nn.Linear(n_feat, 1)
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-3)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
list_dx: List[torch.Tensor]
in_xu = torch.cat((in_x[..., [1]], in_u), -1) # concatenate x and u over the last dimension to create the [xu] input
dx_v = self.net(in_xu)/self.typical_ts # \dot x = f([xu])
list_dx = [in_x[..., [1]], dx_v]
dx = torch.cat(list_dx, -1) # dot x = v, dot v = net
return dx
class StateSpaceModelLin(nn.Module):
def __init__(self, A, B):
super(StateSpaceModelLin, self).__init__()
self.A = nn.Linear(2, 2, bias=False)
self.A.weight = torch.nn.Parameter(torch.tensor(A.astype(np.float32)), requires_grad=False)
self.B = nn.Linear(1, 2, bias=False)
self.B.weight = torch.nn.Parameter(torch.tensor(B.astype(np.float32)), requires_grad=False)
def forward(self, X, U):
dx = self.A(X) + self.B(U)
return dx
class CascadedTanksNeuralStateSpaceModel(nn.Module):
def __init__(self, n_feat=64, scale_dx=1.0, init_small=True, activation='relu'):
super(CascadedTanksNeuralStateSpaceModel, self).__init__()
self.n_feat = n_feat
self.scale_dx = scale_dx
# Neural network for the first state equation = NN(x_1, u)
self.net_dx1 = nn.Sequential(
nn.Linear(2, n_feat),
nn.Tanh(),
#nn.Linear(n_feat, n_feat),
#nn.Tanh(),
nn.Linear(n_feat, 1),
)
# Neural network for the first state equation = NN(x_1, x2)
self.net_dx2 = nn.Sequential(
nn.Linear(2, n_feat),
nn.Tanh(),
#nn.Linear(n_feat, n_feat),
#nn.Tanh(),
nn.Linear(n_feat, 1),
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net_dx1.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net_dx2.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
# the first state derivative is NN(x1, u)
in_1 = torch.cat((in_x[..., [0]], in_u), -1) # concatenate 1st state component with input
dx_1 = self.net_dx1(in_1)
# the second state derivative is NN(x1, x2)
in_2 = in_x
dx_2 = self.net_dx2(in_2)
# the state derivative is built by concatenation of dx_1 and dx_2, possibly scaled for numerical convenience
dx = torch.cat((dx_1, dx_2), -1)
dx = dx * self.scale_dx
return dx
class CascadedTanksOverflowNeuralStateSpaceModel(nn.Module):
def __init__(self, n_feat=64, scale_dx=1.0, init_small=True):
super(CascadedTanksOverflowNeuralStateSpaceModel, self).__init__()
self.n_feat = n_feat
self.scale_dx = scale_dx
# Neural network for the first state equation = NN(x_1, u)
self.net_dx1 = nn.Sequential(
nn.Linear(2, n_feat),
nn.ReLU(),
#nn.Linear(n_feat, n_feat),
#nn.ReLU(),
nn.Linear(n_feat, 1),
)
# Neural network for the first state equation = NN(x_1, x2, u) # we assume that with overflow the input may influence the 2nd tank instantaneously
self.net_dx2 = nn.Sequential(
nn.Linear(3, n_feat),
nn.ReLU(),
#nn.Linear(n_feat, n_feat),
#nn.ReLU(),
nn.Linear(n_feat, 1),
)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net_dx1.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
# Small initialization is better for multi-step methods
if init_small:
for m in self.net_dx2.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0, std=1e-4)
nn.init.constant_(m.bias, val=0)
def forward(self, in_x, in_u):
# the first state derivative is NN_1(x1, u)
in_1 = torch.cat((in_x[..., [0]], in_u), -1) # concatenate 1st state component with input
dx_1 = self.net_dx1(in_1)
# the second state derivative is NN_2(x1, x2, u)
in_2 = torch.cat((in_x, in_u), -1) # concatenate states with input to define the
dx_2 = self.net_dx2(in_2)
# the state derivative is built by concatenation of dx_1 and dx_2, possibly scaled for numerical convenience
dx = torch.cat((dx_1, dx_2), -1)
dx = dx * self.scale_dx
return dx
|
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|
"""
Copyright 2018 Goldman Sachs.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
from datetime import date
import pytest
from pandas.testing import assert_series_equal
from scipy.integrate import odeint
from gs_quant.timeseries import *
from gs_quant.timeseries.statistics import Direction
def test_generate_series():
x = generate_series(100)
assert (len(x) == 100)
assert (x.index[0] == datetime.date.today())
assert (x[0] == 100)
x = generate_series(100, Direction.END_TODAY)
assert (len(x) == 100)
assert (x.index[-1] == datetime.date.today())
assert (x[0] == 100)
def test_min():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = min_(x)
expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 1.0], index=dates)
assert_series_equal(result, expected, obj="Minimum")
result = min_(x, Window(1, 0))
expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Minimum window 1")
result = min_(x, Window(2, 0))
expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="Minimum window 2")
result = min_(x, Window('1w', 0))
expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 1.0], index=dates)
assert_series_equal(result, expected, obj="Minimum with window 1w")
y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)
result = min_([x, y], Window(2, 0))
expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 2.0], index=dates)
assert_series_equal(result, expected, obj="Minimum of multiple series")
result = min_(x, "2d")
expected = pd.Series([2.0, 1.0, 3.0, 3.0], index=dates[2:])
assert_series_equal(result, expected, obj="Minimum with strdate window")
result = min_(x, "1d")
expected = pd.Series([2.0, 3.0, 1.0, 3.0, 6.0], index=dates[1:])
assert_series_equal(result, expected, obj="Minimum with strdate window 2")
ranges = pd.date_range('20220101', periods=6, freq='40min')
y = pd.Series([4.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=ranges)
result = min_(y, '2h')
expected = pd.Series([1.0, 1.0, 1.0], index=ranges[3:])
assert_series_equal(result, expected, obj="Minimum with string window 2h")
def test_max():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = max_(x)
expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Maximum")
result = max_(x, Window(1, 0))
expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Maximum window 1")
result = max_(x, Window(2, 0))
expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Maximum window 2")
result = max_(x, Window('2d', 0))
expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Maximum window 1w")
y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)
result = max_([x, y], Window(2, 0))
expected = pd.Series([4.0, 4.0, 4.0, 4.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Maximum of multiple series")
s = pd.Series([-3.0, -2.0, 3.0, -1.0, -3.0, 6.0], index=dates)
t = pd.Series([0, 0], index=dates[0:2])
result = max_([s, t], 1)
expected = pd.Series([0.0, 3, 0, 0, 6], index=dates[1:])
assert_series_equal(result, expected, obj="Maximum with constant")
ranges = pd.date_range('20220101', periods=6, freq='30min')
y = pd.Series([4.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=ranges)
result = max_(y, '1h')
expected = pd.Series([3.0, 3.0, 3.0, 6.0], index=ranges[2:])
assert_series_equal(result, expected, obj="Maximum with string window 1h")
def test_range():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = range_(x)
expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 5.0], index=dates)
assert_series_equal(result, expected, obj="Range")
result = range_(x, Window(1, 0))
expected = pd.Series([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], index=dates)
assert_series_equal(result, expected, obj="Range window 1")
result = range_(x, Window(2, 0))
expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="Range window 2")
result = range_(x, Window('1w', 0))
expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 5.0], index=dates)
assert_series_equal(result, expected, obj="Range window 1w")
def test_mean():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = mean(x)
expected = pd.Series([3.0, 2.5, 8 / 3, 2.25, 2.4, 3.0], index=dates)
assert_series_equal(result, expected, obj="Mean")
result = mean(x, Window(1, 0))
expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Mean window 1")
result = mean(x, Window(2, 0))
expected = pd.Series([3.0, 2.5, 2.5, 2.0, 2.0, 4.5], index=dates)
assert_series_equal(result, expected, obj="Mean window 2")
result = mean(x, Window('1w', 0))
expected = pd.Series([3.0, 2.5, 8 / 3, 2.25, 2.4, 3.0], index=dates)
assert_series_equal(result, expected, obj="Mean window 1w")
y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)
result = mean([x, y], Window(2, 0))
expected = pd.Series([3.5, 3.0, 3.0, 2.5, 2.0, 4.0], index=dates)
assert_series_equal(result, expected, obj="Mean of multiple series")
def test_median():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = median(x)
expected = pd.Series([3.0, 2.5, 3.0, 2.5, 3.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="Median")
result = median(x, Window(1, 0))
expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="Median window 1")
result = median(x, Window(2, 0))
expected = pd.Series([3.0, 2.5, 2.5, 2.0, 2.0, 4.5], index=dates)
assert_series_equal(result, expected, obj="Median window 2")
result = median(x, Window('1w', 0))
expected = pd.Series([3.0, 2.5, 3.0, 2.5, 3.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="Median window 1w")
def test_mode():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = mode(x)
expected = pd.Series([3.0, 2.0, 3.0, 3.0, 3.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="mode")
result = mode(x, Window(1, 0))
expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
assert_series_equal(result, expected, obj="mode window 1")
result = mode(x, Window(2, 0))
expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="mode window 2")
result = mode(x, Window('1w', 0))
expected = pd.Series([3.0, 2.0, 3.0, 3.0, 3.0, 3.0], index=dates)
assert_series_equal(result, expected, obj="Mode window 1w")
def test_sum():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], index=dates)
result = sum_(x)
expected = pd.Series([1.0, 3.0, 6.0, 10, 15, 21], index=dates)
assert_series_equal(result, expected, obj="Summation")
result = sum_(x, Window(2, 0))
expected = pd.Series([1.0, 3.0, 5.0, 7.0, 9.0, 11.0], index=dates)
assert_series_equal(result, expected, obj="Summation")
result = sum_(x, Window('1w', 0))
expected = pd.Series([1.0, 3.0, 6.0, 10.0, 15.0, 20.0], index=dates)
assert_series_equal(result, expected, obj="Sum window 1w")
y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)
result = sum_([x, y], Window(2, 0))
expected = pd.Series([5.0, 7.0, 9.0, 13.0, 13.0, 18.0], index=dates)
assert_series_equal(result, expected, obj="Sum of multiple series")
def test_product():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], index=dates)
result = product(x)
expected = pd.Series([1.0, 2.0, 6.0, 24, 120, 720], index=dates)
assert_series_equal(result, expected, obj="Product")
result = product(x, Window(2, 0))
expected = pd.Series([1.0, 2.0, 6.0, 12.0, 20.0, 30.0], index=dates)
assert_series_equal(result, expected, obj="Product")
result = product(x, Window('1w', 0))
expected = pd.Series([1.0, 2.0, 6.0, 24.0, 120.0, 720.0], index=dates)
assert_series_equal(result, expected, obj="Product window 1w")
def test_std():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = std(x)
expected = pd.Series([np.nan, 0.707106, 0.577350, 0.957427, 0.894427, 1.673320], index=dates)
assert_series_equal(result, expected, obj="std")
result = std(x, Window(2, 0))
expected = pd.Series([np.nan, 0.707106, 0.707106, 1.414214, 1.414214, 2.121320], index=dates)
assert_series_equal(result, expected, obj="std window 2")
result = std(x, Window('1w', 0))
expected = pd.Series([np.nan, 0.707106, 0.577350, 0.957427, 0.894427, 1.870828], index=dates)
assert_series_equal(result, expected, obj="std window 1w")
assert std(pd.Series(dtype=float)).empty
def test_exponential_std():
def exp_std_calc(ts, alpha=0.75):
std = ts * 0
for i in range(1, len(ts)):
weights = (1 - alpha) * alpha ** np.arange(i, -1, -1)
weights[0] /= (1 - alpha)
x = ts.to_numpy()[:i + 1]
ema = sum(weights * x) / sum(weights)
debias_fact = sum(weights) ** 2 / (sum(weights) ** 2 - sum(weights ** 2))
var = debias_fact * sum(weights * (x - ema) ** 2) / sum(weights)
std[i] = np.sqrt(var)
std[0] = np.NaN
return std
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = exponential_std(x)
expected = exp_std_calc(x)
assert_series_equal(result, expected, obj="Exponentially weighted standard deviation")
result = exponential_std(x, 0.8)
expected = exp_std_calc(x, 0.8)
assert_series_equal(result, expected, obj="Exponentially weighted standard deviation weight 1")
def test_var():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = var(x)
expected = pd.Series([np.nan, 0.500000, 0.333333, 0.916667, 0.800000, 2.800000], index=dates)
assert_series_equal(result, expected, obj="var")
result = var(x, Window(2, 0))
expected = pd.Series([np.nan, 0.5, 0.5, 2.0, 2.0, 4.5], index=dates)
assert_series_equal(result, expected, obj="var window 2")
result = var(x, Window('1w', 0))
expected = pd.Series([np.nan, 0.500000, 0.333333, 0.916666, 0.800000, 3.500000], index=dates)
assert_series_equal(result, expected, obj="var window 1w")
def test_cov():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
y = pd.Series([3.5, 1.8, 2.9, 1.2, 3.1, 5.9], index=dates)
result = cov(x, y)
expected = pd.Series([np.nan, 0.850000, 0.466667, 0.950000, 0.825000, 2.700000], index=dates)
assert_series_equal(result, expected, obj="cov")
result = cov(x, y, Window(2, 0))
expected = pd.Series([np.nan, 0.850000, 0.549999, 1.7000000, 1.900000, 4.200000], index=dates)
assert_series_equal(result, expected, obj="cov window 2")
result = cov(x, y, Window('1w', 0))
expected = pd.Series([np.nan, 0.850000, 0.466667, 0.950000, 0.825000, 3.375000], index=dates)
assert_series_equal(result, expected, obj="cov window 1w")
def test_zscores():
with pytest.raises(MqValueError):
zscores(pd.Series(range(5)), "2d")
assert_series_equal(zscores(pd.Series(dtype=float)), pd.Series(dtype=float))
assert_series_equal(zscores(pd.Series(dtype=float), 1), pd.Series(dtype=float))
assert_series_equal(zscores(pd.Series([1])), pd.Series([0.0]))
assert_series_equal(zscores(pd.Series([1]), Window(1, 0)), pd.Series([0.0]))
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
result = zscores(x)
expected = pd.Series([0.000000, -0.597614, 0.000000, -1.195229, 0.000000, 1.792843], index=dates)
assert_series_equal(result, expected, obj="z-score")
assert_series_equal(result, (x - x.mean()) / x.std(), obj="full series zscore")
result = zscores(x, Window(2, 0))
expected = pd.Series([0.0, -0.707107, 0.707107, -0.707107, 0.707107, 0.707107], index=dates)
assert_series_equal(result, expected, obj="z-score window 2")
assert_series_equal(zscores(x, Window(5, 5)), zscores(x, 5))
result = zscores(x, Window('1w', 0))
expected = pd.Series([0.0, -0.707106, 0.577350, -1.305582, 0.670820, 1.603567], index=dates)
assert_series_equal(result, expected, obj="z-score window 1w")
result = zscores(x, '1w')
expected = pd.Series([1.603567], index=dates[-1:])
assert_series_equal(result, expected, obj='z-score window string 1w')
result = zscores(x, '1m')
expected = pd.Series(dtype=float)
assert_series_equal(result, expected, obj="z-score window too large")
def test_winsorize():
assert_series_equal(winsorize(pd.Series(dtype=float)), pd.Series(dtype=float))
x = generate_series(10000)
# You must use absolute returns here, generate_series uses random absolute returns and as such has a decent chance
# of going negative on a sample of 10k, if it goes negative the relative return will be garbage and test can fail
r = returns(x, type=Returns.ABSOLUTE)
for limit in [1.0, 2.0]:
mu = r.mean()
sigma = r.std()
b_upper = mu + sigma * limit * 1.001
b_lower = mu - sigma * limit * 1.001
assert (True in r.ge(b_upper).values)
assert (True in r.le(b_lower).values)
wr = winsorize(r, limit)
assert (True not in wr.ge(b_upper).values)
assert (True not in wr.le(b_lower).values)
def test_percentiles():
dates = [
date(2019, 1, 1),
date(2019, 1, 2),
date(2019, 1, 3),
date(2019, 1, 4),
date(2019, 1, 7),
date(2019, 1, 8),
]
x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)
y = pd.Series([3.5, 1.8, 2.9, 1.2, 3.1, 6.0], index=dates)
assert_series_equal(percentiles(pd.Series(dtype=float), y), pd.Series(dtype=float))
assert_series_equal(percentiles(x, pd.Series(dtype=float)), pd.Series(dtype=float))
assert_series_equal(percentiles(x, y, Window(7, 0)), pd.Series(dtype=float))
result = percentiles(x, y, 2)
expected = pd.Series([50.0, 50.0, 100.0, 75.0], index=dates[2:])
assert_series_equal(result, expected, obj="percentiles with window length 2")
result = percentiles(x, y, Window(2, 0))
expected = pd.Series([100.0, 0.0, 50.0, 50.0, 100.0, 75.0], index=dates)
assert_series_equal(result, expected, obj="percentiles with window 2 and ramp 0")
result = percentiles(x, y, Window('1w', 0))
expected = pd.Series([100.0, 0.0, 33.333333, 25.0, 100.0, 90.0], index=dates)
assert_series_equal(result, expected, obj="percentiles with window 1w")
result = percentiles(x, y, Window('1w', '3d'))
expected = pd.Series([25.0, 100.0, 90.0], index=dates[3:])
assert_series_equal(result, expected, obj="percentiles with window 1w and ramp 3d")
result = percentiles(x)
expected = pd.Series([50.0, 25.0, 66.667, 12.500, 70.0, 91.667], index=dates)
assert_series_equal(result, expected, obj="percentiles over historical values")
result = percentiles(x, y)
expected = pd.Series([100.0, 0.0, 33.333, 25.0, 100.0, 91.667], index=dates)
assert_series_equal(result, expected, obj="percentiles without window length")
with pytest.raises(ValueError):
percentiles(x, pd.Series(dtype=float), Window(6, 1))
def test_percentile():
with pytest.raises(MqError):
percentile(pd.Series(dtype=float), -1)
with pytest.raises(MqError):
percentile(pd.Series(dtype=float), 100.1)
with pytest.raises(MqTypeError):
percentile(pd.Series(range(5), index=range(5)), 90, "2d")
for n in range(0, 101, 5):
assert percentile(pd.Series(x * 10 for x in range(0, 11)), n) == n
x = percentile(pd.Series(x for x in range(0, 5)), 50, 2)
assert_series_equal(x, pd.Series([1.5, 2.5, 3.5], index=pd.RangeIndex(2, 5)))
x = percentile(pd.Series(dtype=float), 90, "1d")
assert_series_equal(x, pd.Series(dtype=float), obj="Percentile with empty series")
def test_percentile_str():
today = datetime.datetime.now()
days = pd.date_range(today, periods=12, freq='D')
start = pd.Series([29, 56, 82, 13, 35, 53, 25, 23, 21, 12, 15, 9], index=days)
actual = percentile(start, 2, '10d')
expected = pd.Series([12.18, 9.54], index=pd.date_range(today + datetime.timedelta(days=10), periods=2, freq='D'))
assert_series_equal(actual, expected)
actual = percentile(start, 50, '1w')
expected = percentile(start, 50, 7)
assert_series_equal(actual, expected)
def test_regression():
x1 = pd.Series([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, np.nan], index=pd.date_range('2019-1-1', periods=7), name='x1')
x2 = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], index=pd.date_range('2019-1-1', periods=8))
y = pd.Series([10.0, 14.0, 20.0, 28.0, 38.0, 50.0, 60.0], index=pd.date_range('2019-1-1', periods=7))
with pytest.raises(MqTypeError):
LinearRegression([x1, x2], y, 1)
regression = LinearRegression([x1, x2], y, True)
np.testing.assert_almost_equal(regression.coefficient(0), 10.0)
np.testing.assert_almost_equal(regression.coefficient(1), 1.0)
np.testing.assert_almost_equal(regression.coefficient(2), 3.0)
np.testing.assert_almost_equal(regression.r_squared(), 1.0)
expected = pd.Series([10.0, 14.0, 20.0, 28.0, 38.0, 50.0], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.fitted_values(), expected)
dates_predict = [date(2019, 2, 1), date(2019, 2, 2)]
predicted = regression.predict([pd.Series([2.0, 3.0], index=dates_predict),
pd.Series([6.0, 7.0], index=dates_predict)])
expected = pd.Series([30.0, 34.0], index=dates_predict)
assert_series_equal(predicted, expected)
np.testing.assert_almost_equal(regression.standard_deviation_of_errors(), 0)
def test_rolling_linear_regression():
x1 = pd.Series([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, np.nan], index=pd.date_range('2019-1-1', periods=7), name='x1')
x2 = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], index=pd.date_range('2019-1-1', periods=8))
y = pd.Series([10.0, 14.0, 20.0, 28.0, 28.0, 40.0, 60.0], index=pd.date_range('2019-1-1', periods=7))
with pytest.raises(MqValueError):
RollingLinearRegression([x1, x2], y, 3, True)
with pytest.raises(MqTypeError):
RollingLinearRegression([x1, x2], y, 4, 1)
regression = RollingLinearRegression([x1, x2], y, 4, True)
expected = pd.Series([np.nan, np.nan, np.nan, 10.0, 2.5, 19.0], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.coefficient(0), expected, check_names=False)
expected = pd.Series([np.nan, np.nan, np.nan, 1.0, -1.5, 1.0], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.coefficient(1), expected, check_names=False)
expected = pd.Series([np.nan, np.nan, np.nan, 3.0, 12.5, -1.0], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.coefficient(2), expected, check_names=False)
expected = pd.Series([np.nan, np.nan, np.nan, 1.0, 0.964029, 0.901961], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.r_squared(), expected, check_names=False)
expected = pd.Series([np.nan, np.nan, np.nan, 28.0, 28.5, 39.0], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.fitted_values(), expected, check_names=False)
expected = pd.Series([np.nan, np.nan, np.nan, 0.0, 2.236068, 4.472136], index=pd.date_range('2019-1-1', periods=6))
assert_series_equal(regression.standard_deviation_of_errors(), expected, check_names=False)
def test_sir_model():
n = 1000
d = 100
i0 = 100
r0 = 0
s0 = n
beta = 0.5
gamma = 0.25
t = np.linspace(0, d, d)
def deriv(y, t_loc, n_loc, beta_loc, gamma_loc):
s, i, r = y
dsdt = -beta_loc * s * i / n_loc
didt = beta_loc * s * i / n_loc - gamma_loc * i
drdt = gamma_loc * i
return dsdt, didt, drdt
def get_series(beta_loc, gamma_loc):
# Initial conditions vector
y0 = s0, i0, r0
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(n, beta_loc, gamma_loc))
s, i, r = ret.T
dr = pd.date_range(dt.date.today(), dt.date.today() + dt.timedelta(days=d - 1))
return pd.Series(s, dr), pd.Series(i, dr), pd.Series(r, dr)
(s, i, r) = get_series(beta, gamma)
sir = SIRModel(beta, gamma, s, i, r, n)
assert abs(sir.beta() - beta) < 0.01
assert abs(sir.gamma() - gamma) < 0.01
beta = 0.4
gamma = 0.25
(s, i, r) = get_series(0.4, 0.25)
s_predict = sir.predict_s()
i_predict = sir.predict_i()
r_predict = sir.predict_r()
assert s_predict.size == d
assert i_predict.size == d
assert r_predict.size == d
with pytest.raises(MqTypeError):
SIRModel(beta, gamma, s, i, r, n, fit=0)
sir = SIRModel(beta, gamma, s, i, r, n, fit=False)
assert sir.beta() == beta
assert sir.gamma() == gamma
sir1 = SIRModel(beta, gamma, s, i, r, n, fit=False)
with DataContext(end=dt.date.today() + dt.timedelta(days=d - 1)):
sir2 = SIRModel(beta, gamma, s[0], i, r[0], n, fit=False)
assert sir1.beta() == sir1.beta()
assert sir2.gamma() == sir2.gamma()
assert (sir1.predict_i() == sir2.predict_i()).all()
assert (sir1.predict_r() == sir2.predict_r()).all()
assert (sir1.predict_s() == sir2.predict_s()).all()
def test_seir_model():
n = 1000
d = 100
e0 = 1
i0 = 1
r0 = 0
s0 = n
beta = 0.5
gamma = 0.2
sigma = 1
t = np.linspace(0, d, d)
def deriv(y, t_loc, n_loc, beta_loc, gamma_loc, sigma_loc):
s, e, i, r = y
dsdt = -beta_loc * s * i / n_loc
dedt = beta_loc * s * i / n_loc - sigma_loc * e
didt = sigma_loc * e - gamma * i
drdt = gamma_loc * i
return dsdt, dedt, didt, drdt
def get_series(beta_loc, gamma_loc, sigma_loc):
# Initial conditions vector
y0 = s0, e0, i0, r0
# Integrate the SEIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(n, beta_loc, gamma_loc, sigma_loc))
s, e, i, r = ret.T
dr = pd.date_range(dt.date.today(), dt.date.today() + dt.timedelta(days=d - 1))
return pd.Series(s, dr), pd.Series(e, dr), pd.Series(i, dr), pd.Series(r, dr)
(s, e, i, r) = get_series(beta, gamma, sigma)
seir = SEIRModel(beta, gamma, sigma, s, e, i, r, n)
assert abs(seir.beta() - beta) < 0.01
assert abs(seir.gamma() - gamma) < 0.01
assert abs(seir.sigma() - sigma) < 0.01
s_predict = seir.predict_s()
e_predict = seir.predict_e()
i_predict = seir.predict_i()
r_predict = seir.predict_i()
assert s_predict.size == d
assert e_predict.size == d
assert i_predict.size == d
assert r_predict.size == d
with pytest.raises(MqTypeError):
SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=0)
seir = SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=False)
assert seir.beta() == beta
assert seir.gamma() == gamma
assert seir.sigma() == sigma
seir1 = SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=False)
with DataContext(end=dt.date.today() + dt.timedelta(days=d - 1)):
seir2 = SEIRModel(beta, gamma, sigma, s[0], e[0], i, r[0], n, fit=False)
assert seir1.beta() == seir1.beta()
assert seir2.gamma() == seir2.gamma()
assert seir2.sigma() == seir2.sigma()
assert (seir1.predict_i() == seir2.predict_i()).all()
assert (seir1.predict_e() == seir2.predict_e()).all()
assert (seir1.predict_r() == seir2.predict_r()).all()
assert (seir1.predict_s() == seir2.predict_s()).all()
if __name__ == "__main__":
pytest.main(args=["test_statistics.py"])
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# Copyright 2021 Medical Imaging Center, Vingroup Big Data Insttitute (VinBigdata), Vietnam
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import logging
import numpy as np
import torch
from termcolor import colored
from tabulate import tabulate
from detectron2.config import configurable
from detectron2.data import (
DatasetCatalog,
MetadataCatalog,
build_batch_data_loader,
)
from detectron2.data.detection_utils import check_metadata_consistency
from detectron2.data.samplers import (
InferenceSampler,
TrainingSampler,
)
from detectron2.data import DatasetFromList, MapDataset
from detectron2.data.build import trivial_batch_collator
from detectron2.utils.logger import log_first_n
def print_instances_class_histogram(dataset_dicts, class_names):
"""
similar to `print_instances_class_histogram` at
https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py,
adapted for classification annotation in one-hot format
Args:
dataset_dicts (list[dict]): list of dataset dicts.
class_names (list[str]): list of class names (zero-indexed).
"""
num_classes = len(class_names)
hist_bins = np.arange(num_classes + 1)
histogram = np.zeros((num_classes,), dtype=np.float)
for entry in dataset_dicts:
classes = entry["classes"]
assert classes.shape[0] == num_classes, \
f"Got an invalid classes length {classes}, expect {num_classes}"
assert classes.min() in [0., 1.], f"Got an invalid classes values ={classes}, expect 0 1"
assert classes.max() in [0., 1.], f"Got an invalid classes values ={classes}, expect 0 1"
histogram += classes
N_COLS = min(6, len(class_names) * 2)
def short_name(x):
# make long class names shorter. useful for lvis
if len(x) > 13:
return x[:11] + ".."
return x
data = list(
itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
)
total_num_instances = sum(data[1::2])
data.extend([None] * (N_COLS - (len(data) % N_COLS)))
if num_classes > 1:
data.extend(["total", total_num_instances])
data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
data,
headers=["category", "#instances"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
log_first_n(
logging.INFO,
"Distribution of instances among all {} classes:\n".format(num_classes)
+ colored(table, "cyan"),
key="message",
)
def get_classidication_dataset_dicts(dataset_names):
"""
Load and join classification dataset dicts
Args:
dataset_names (str or list[str]): a dataset name or a list of dataset names
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(dataset_names, str):
dataset_names = [dataset_names]
assert len(dataset_names)
dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
for dataset_name, dicts in zip(dataset_names, dataset_dicts):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
has_labels= "classes" in dataset_dicts[0]
if has_labels:
try:
class_names = MetadataCatalog.get(dataset_names[0]).thing_classes
check_metadata_consistency("thing_classes", dataset_names)
print_instances_class_histogram(dataset_dicts, class_names)
except AttributeError: # class names are not available for this dataset
pass
assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names))
return dataset_dicts
def _train_loader_from_config(cfg, mapper, *, dataset=None, sampler=None):
if dataset is None:
dataset = get_classidication_dataset_dicts(cfg.DATASETS.TRAIN)
if sampler is None:
sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
logger = logging.getLogger(__name__)
logger.info("Using training sampler {}".format(sampler_name))
if sampler_name == "TrainingSampler":
sampler = TrainingSampler(len(dataset))
elif sampler_name == "RepeatFactorTrainingSampler":
raise NotImplementedError()
else:
raise ValueError("Unknown training sampler: {}".format(sampler_name))
return {
"dataset": dataset,
"sampler": sampler,
"mapper": mapper,
"total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
"aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
"num_workers": cfg.DATALOADER.NUM_WORKERS,
}
@configurable(from_config=_train_loader_from_config)
def build_classification_train_loader(
dataset, *, mapper, sampler=None, total_batch_size, aspect_ratio_grouping=True, num_workers=0
):
"""
similar to `build_detection_train_loader` at
https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py
"""
if isinstance(dataset, list):
dataset = DatasetFromList(dataset, copy=False)
if mapper is not None:
dataset = MapDataset(dataset, mapper)
if sampler is None:
sampler = TrainingSampler(len(dataset))
assert isinstance(sampler, torch.utils.data.sampler.Sampler)
return build_batch_data_loader(
dataset,
sampler,
total_batch_size,
aspect_ratio_grouping=aspect_ratio_grouping,
num_workers=num_workers,
)
def _test_loader_from_config(cfg, dataset_name, mapper):
dataset = get_classidication_dataset_dicts([dataset_name])
return {"dataset": dataset, "mapper": mapper, "num_workers": cfg.DATALOADER.NUM_WORKERS}
@configurable(from_config=_test_loader_from_config)
def build_classification_test_loader(dataset, *, mapper, num_workers=0):
"""
similar to `build_detection_test_loader` at
https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py
"""
if isinstance(dataset, list):
dataset = DatasetFromList(dataset, copy=False)
if mapper is not None:
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=num_workers,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
|
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|
module TwoPlayerTest
using Test
import Cribbage: CribbageGame, GameState, UnexpectedPlayerException
import Cribbage.RandomPlay: RandomPlayer
using Cribbage.TwoPlayer
@testset "Test TwoPlayerGame Constructor" begin
p₁ = RandomPlayer("player one")
@test_throws AssertionError TwoPlayerGame(p₁, p₁)
p₂ = RandomPlayer("player two")
@test_throws AssertionError TwoPlayerGame(p₁, p₂, 42)
@test isa(TwoPlayerGame(p₁, p₂), CribbageGame)
@test isa(TwoPlayerGame(p₁, p₂, 61), CribbageGame)
@test isa(TwoPlayerGame(p₁, p₂, 121), CribbageGame)
end
examplegame = TwoPlayerGame(
RandomPlayer("1"),
RandomPlayer("2"),
121
)
badplayer = RandomPlayer("-1")
@testset "Test TwoPlayerGameState Constructor" begin
@test_throws AssertionError TwoPlayerGameState(examplegame, badplayer)
@test_throws AssertionError TwoPlayerGameState(examplegame, examplegame.p₁, -42)
@test_throws AssertionError TwoPlayerGameState(examplegame, examplegame.p₁, 0, -53)
@test isa(TwoPlayerGameState(examplegame, examplegame.p₁, 0, 0), GameState)
end
@testset "Test players" begin
@test length(players(examplegame)) == 2
for player in players(examplegame)
@test player ≡ examplegame.p₁ || player ≡ examplegame.p₂
end
end
@testset "Test incrementscore" begin
initialstate = TwoPlayerGameState(
examplegame, examplegame.p₁, 0, 0
)
@test_throws UnexpectedPlayerException incrementscore(initialstate, badplayer, 0)
@test_throws AssertionError incrementscore(initialstate, examplegame.p₁, -5)
@test incrementscore(initialstate, examplegame.p₁, 0) == initialstate
newstate = incrementscore(initialstate, examplegame.p₁, 6)
@test newstate.dealer ≡ initialstate.dealer
@test newstate.s₁ == 6
@test newstate.s₂ == 0
initialstate = TwoPlayerGameState(
examplegame, examplegame.p₁, 100, 115
)
newstate = incrementscore(initialstate, examplegame.p₂, 8)
@test newstate.dealer ≡ initialstate.dealer
@test newstate.s₁ == 100
@test newstate.s₂ == 121
end
@testset "Test isgameover" begin
gs = TwoPlayerGameState(examplegame, examplegame.p₁, 112, 118)
@test !isgameover(gs)
gs = TwoPlayerGameState(examplegame, examplegame.p₁, 121, 118)
@test isgameover(gs)
gs = TwoPlayerGameState(examplegame, examplegame.p₁, 100, 121)
@test isgameover(gs)
end
@testset "Test reportscore" begin
gs = TwoPlayerGameState(examplegame, examplegame.p₁, 121, 118)
@test isa(reportscore(gs), String)
end
@testset "Test rotatedealer" begin
initialstate = TwoPlayerGameState(examplegame, examplegame.p₁, 1, 2)
newstate = rotatedealer(initialstate)
@test newstate.dealer ≡ examplegame.p₂
@test newstate.s₁ == initialstate.s₁
@test newstate.s₂ == initialstate.s₂
newstate = rotatedealer(newstate)
@test newstate == initialstate
end
end # module TwoPlayerTest
|
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|
[STATEMENT]
lemma prefix_refl_conv[simp]: "(prefix\<cdot>xs\<cdot>xs = TT) \<longleftrightarrow> (xs \<noteq> \<bottom>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (prefix\<cdot>xs\<cdot>xs = TT) = (xs \<noteq> \<bottom>)
[PROOF STEP]
by auto
|
{"llama_tokens": 103, "file": "BirdKMP_Theory_Of_Lists", "length": 1}
|
program envelopef
implicit none
include "sacf.h"
! Define the Maximum size of the data Array
integer MAX
parameter (MAX=4000)
! Define the Data Array of size MAX
real*4 :: ya(MAX), yb(MAX), yc(MAX)
! Declare Variables used in the rsac1() subroutine
real beg, delta
integer na, nb, nc
character*64 KNAME
integer nerr
integer sac_compare
! Read in the first data file
kname = 'convolvef_in1.sac'
call rsac1(kname, ya, na, beg, delta, MAX, nerr)
if(nerr .NE. 0) then
write(*,*)'Error reading in file: ',kname
call exit(-1)
endif
! Read in the second data file
kname = 'convolvef_in2.sac'
call rsac1(kname, yb, nb, beg, delta, MAX, nerr)
if(nerr .NE. 0) then
write(*,*)'Error reading in file: ',kname
call exit(-1)
endif
nc = na + nb - 1
call convolve(ya, na, yb, nb, yc, nc)
if(sac_compare("convolvef_out_sac1.sac", yc, nc, 0.0, delta) .ne. 1) then
write(*,*)'data does not match file'
endif
call wsac1("convolvef_out.sac", yc, nc, beg, delta, nerr);
end program envelopef
|
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|
# -*- coding:utf-8 -*-
import json
import math
import os
import pickle
from collections import Counter
from datetime import datetime
import numpy as np
from gensim.models import KeyedVectors
UNK_CHAR = "<UNK>"
PAD_CHAR = "<PAD>"
class NNData(object):
"""
将文本数据转换为适合神经网络的数据格式
"""
def __init__(self, config=None):
self.config = config
if self.config is not None:
self.vocab_dir = os.path.join("/".join(self.config.common.model_path.split("/")[:2]), "vocab")
if not os.path.exists(self.vocab_dir):
os.mkdir(self.vocab_dir)
self.word_to_index_path = os.path.join(
self.vocab_dir, "word_to_index.json")
self.index_to_word_path = os.path.join(
self.vocab_dir, "index_to_word.json")
self.index_to_label_path = os.path.join(
self.vocab_dir, "index_to_label.json")
self.label_to_index_path = os.path.join(
self.vocab_dir, "label_to_index.json")
self.w2v_lookup_path = os.path.join(
self.vocab_dir, "w2v_lookup.pkl")
self._word_to_index = {}
self._index_to_word = {}
self._index_to_label = {}
self._label_to_index = {}
self.word_embedding = None
def _save_file(self, data, out_path):
"""
将文件保存为json或者pickle
"""
if out_path.endswith("json"):
with open(out_path, "w", encoding="utf-8") as fw:
json.dump(data, fw)
else:
with open(out_path, "wb") as fw:
pickle.dump(data, fw)
def _load_file(self, in_path):
"""
加载json或者pickle文件
"""
if in_path.endswith("json"):
with open(in_path, "r", encoding="utf-8") as fr:
return json.load(fr)
else:
with open(in_path, "rb") as fr:
return pickle.load(fr)
def _get_word_embedding(self, words):
"""
获取词表和对应的词向量矩阵
Args:
words -- 过滤后全量单词列表
Returns:
vocab(list) --词汇列表: 相对words多出补全项<PAD>和未知项<UNK>，少了不存在词向量的单词
word_embedding(ndarray or None) -- 词表词汇对应的向量
"""
vocab = []
vocab.extend([PAD_CHAR, UNK_CHAR])
st = datetime.now().isoformat()
pre_w2v = self.config.common.w2v_dict
pre_w2v_dim = self.config.common.w2v_dim
if pre_w2v:
word_embedding = []
pad_init_vec = np.zeros(pre_w2v_dim)
unk_init_vec = np.random.randn(pre_w2v_dim)
word_embedding.append(pad_init_vec)
word_embedding.append(unk_init_vec)
print("%s: Start load embedding from `%s`." % (st, pre_w2v))
if pre_w2v.endswith(".bin"):
word_vec = KeyedVectors.load_word2vec_format(
pre_w2v,
binary=True,
unicode_errors="ignore")
for word in words:
try:
vector = word_vec.wv[word]
vocab.append(word)
word_embedding.append(vector)
except:
print("`%s`: not exist in the word embedding." % word)
elif pre_w2v.endswith(".txt"): # 只获取训练集的词汇向量
words_set = set(words)
with open(pre_w2v, "r") as fr:
each_line_len = pre_w2v_dim + 1
for row, line in enumerate(fr):
if row % 1000000 == 0:
print("-->> w2v line-%s" % row)
word_info = line.strip().split()
if len(word_info) == each_line_len:
_word, vector = word_info[0], word_info[1:]
if _word in words_set:
vocab.append(_word)
word_embedding.append(
np.array(vector, dtype=np.float))
else:
# TODO: 兼容db格式
w2v_f = pre_w2v.split(".")[-1]
raise ValueError("`%s` is not a supported w2v_format" % w2v_f)
et = datetime.now().isoformat()
print("%s: End load embedding." % et)
return vocab, np.asarray(word_embedding, dtype="float32")
else:
vocab.extend(words)
word_embedding = None
return vocab, word_embedding
def _gen_sequence_vocab(self, sequences):
"""
生成`词向量`、`词汇-索引`和`索引-词汇`映射字典
Args:
sequences -- 文本序列列表，如[["我", "来自", "中国"],]
"""
all_words = [word for sequence in sequences for word in sequence]
word_count = Counter(all_words)
sort_word_count = sorted(word_count.items(), key=lambda x: -x[1])
words = [item[0] for item in sort_word_count
if item[1] >= self.config.common.min_freq]
vocab, word_embedding = self._get_word_embedding(words)
self.word_embedding = word_embedding
self.vocab_size = len(vocab)
print("The number of vocab is %s." % self.vocab_size)
self._word_to_index = dict(zip(vocab, range(self.vocab_size)))
self._index_to_word = dict(zip(range(self.vocab_size), vocab))
self._save_file(self._word_to_index, self.word_to_index_path)
self._save_file(self._index_to_word, self.index_to_word_path)
self._save_file(self.word_embedding, self.w2v_lookup_path)
def _gen_label_vocab(self, labels):
"""
生成`标签-索引`和`索引-标签`映射字典
Args:
labels -- 标签列表，["label1", "label2",]
"""
labels = list(set(labels))
self._label_to_index = dict(zip(labels, range(len(labels))))
self._index_to_label = dict(zip(range(len(labels)), labels))
self._save_file(self._label_to_index, self.label_to_index_path)
self._save_file(self._index_to_label, self.index_to_label_path)
self.num_class = len(labels)
def _process_sequences(self, sequences):
"""
原始文本转换为词库下标索引序列
(1) 文本序列截断
(2) 将每个文本序列用词表中词汇索引表示
"""
sequences_vec = []
seq_max_len = self.config.common.sequence_length
word_to_index = self._word_to_index
for sequence in sequences:
sequence_vec = np.zeros(seq_max_len) # 序列截断
seq_len = len(sequence)
seq_len = seq_len if seq_len < seq_max_len else seq_max_len
for i in range(seq_len):
if sequence[i] in word_to_index:
sequence_vec[i] = word_to_index[sequence[i]]
else:
sequence_vec[i] = word_to_index[UNK_CHAR]
sequences_vec.append(sequence_vec)
return np.asarray(sequences_vec, dtype="int64")
def _process_labels(self, labels_list):
"""
原始标签集转换为one-hot向量
Args:
labels_list -- 类别列表，如[["类别1", "类别3", "类别5",],]
Returns:
labels_vec -- 类别one-hot编码，如[[1, 0, 1, 0, 1, 0,],]
"""
labels_vec = []
for labels in labels_list:
label_vec = [0] * self.num_class
for label in labels:
label_vec[self._label_to_index[label]] = 1
labels_vec.append(label_vec)
return np.asarray(labels_vec, dtype="int64")
def _vectorize_data(self, sequences, labels=None):
"""
数据向量化
Args:
sequences -- 文本序列列表，如[["我", "来自", "中国"],]
labels -- ["label1",]
Returns:
sequences_vec -- 向量化的文本序列矩阵
labels_vec -- 向量化的标签序列矩阵
"""
sequences_vec = self._process_sequences(sequences)
if labels is not None:
labels_list = [[label] for label in labels] # 预留兼容多标签
labels_vec = self._process_labels(labels_list)
return sequences_vec, labels_vec
else:
return sequences_vec
def _split_data(self, sequences_vec, labels_vec, shuffle=True):
"""
切分为训练集和测试集
"""
if shuffle:
perm = np.arange(len(sequences_vec))
np.random.shuffle(perm)
sequences_vec = sequences_vec[perm]
labels_vec = labels_vec[perm]
train_idx = int(len(sequences_vec) * \
(1-self.config.common.val_size))
if train_idx == len(sequences_vec):
return sequences_vec, labels_vec, None, None
else:
train_sequences = sequences_vec[:train_idx]
train_labels = labels_vec[:train_idx]
val_sequences = sequences_vec[train_idx:]
val_labels = labels_vec[train_idx:]
return train_sequences, train_labels, val_sequences, val_labels
def load_vocab(self):
"""
加载词典数据
"""
self._word_to_index = self._load_file(self.word_to_index_path)
self._index_to_word = self._load_file(self.index_to_word_path)
self._label_to_index = self._load_file(self.label_to_index_path)
self._index_to_label = self._load_file(self.index_to_label_path)
self.word_embedding = self._load_file(self.w2v_lookup_path)
self.vocab_size = len(self._word_to_index)
self.num_class = len(self._label_to_index)
print("Complete data initialization!")
def get_train_data(self, sequences, labels):
"""
获取训练数据
Args:
sequences -- 原始文本分词后的列表，如[["sequence1_token1", "sequence1_token2",],]
labels -- 标签列表，["label1", "label2",]
Returns:
train_sequences(np.array) -- 训练X矩阵
train_labels(np.array) -- 训练Y矩阵
val_sequences(np.array) -- 验证X矩阵
val_labels(np.array) -- 验证Y矩阵
"""
self._gen_sequence_vocab(sequences)
self._gen_label_vocab(labels)
return self._vectorize_data(sequences, labels=labels)
def get_test_data(self, sequences, labels):
"""
获取测试数据
"""
return self._vectorize_data(sequences, labels=labels)
def get_predict_data(self, sequences):
"""
获取预测数据
"""
return self._vectorize_data(sequences)
def load_embedding(self):
"""
加载词向量
"""
return self._load_file(self.w2v_lookup_path)
|
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|
Require Import rt.util.all.
Require Import rt.model.arrival.basic.job rt.model.arrival.basic.task rt.model.priority.
Require Import rt.model.schedule.uni.schedule rt.model.schedule.uni.schedulability.
Require Import rt.model.schedule.uni.susp.suspension_intervals.
Require Import rt.analysis.uni.basic.workload_bound_fp.
Require Import rt.analysis.uni.susp.dynamic.oblivious.fp_rta.
Require Import rt.implementation.uni.susp.dynamic.job
rt.implementation.uni.susp.dynamic.task
rt.implementation.uni.susp.dynamic.arrival_sequence.
Require Import rt.implementation.uni.susp.schedule.
From mathcomp Require Import ssreflect ssrbool ssrnat eqtype seq bigop div.
Module ResponseTimeAnalysisFP.
Import Job UniprocessorSchedule SporadicTaskset Priority Schedulability
SuspensionIntervals SuspensionObliviousFP WorkloadBoundFP.
Import ConcreteJob ConcreteTask ConcreteArrivalSequence ConcreteScheduler.
(* In this section, we run the suspension-oblivious FP RTA on a simple task set
to show that the theorems contain no contradictory assumptions. *)
Section ExampleRTA.
Let tsk1 := {| task_id := 1; task_cost := 1; task_period := 5;
task_deadline := 5; task_suspension_bound := 1 |}.
Let tsk2 := {| task_id := 2; task_cost := 1; task_period := 5;
task_deadline := 5; task_suspension_bound := 0|}.
Let tsk3 := {| task_id := 3; task_cost := 1; task_period := 6;
task_deadline := 6; task_suspension_bound := 1|}.
(* Let ts be a task set containing these three tasks, ... *)
Program Let ts := Build_set [:: tsk1; tsk2; tsk3] _.
(* ...which can be shown to have valid parameters. *)
Fact ts_has_valid_parameters:
valid_sporadic_taskset task_cost task_period task_deadline ts.
Proof.
intros tsk IN.
repeat (move: IN => /orP [/eqP EQ | IN]; subst; compute); by done.
Qed.
(* Now let's inflate the task costs with the suspension-bounds. *)
Let inflated_cost := inflated_task_cost task_cost task_suspension_bound.
(* After the inflation, note that the task costs are no larger than deadlines and periods. *)
Fact inflated_cost_le_deadline_and_period:
forall tsk,
tsk \in ts ->
inflated_cost tsk <= task_deadline tsk /\
inflated_cost tsk <= task_period tsk.
Proof.
intros tsk IN.
repeat (move: IN => /orP [/eqP EQ | IN]; subst; compute); by done.
Qed.
(* Next, recall the FP RTA schedulability test using RM as the FP policy
and the inflated task costs. *)
Let RTA_claimed_bounds :=
fp_claimed_bounds inflated_cost task_period task_deadline (RM task_period).
Let schedulability_test :=
fp_schedulable inflated_cost task_period task_deadline (RM task_period).
(* First, we show that the schedulability test returns the following bounds, ... *)
Fact RTA_yields_these_bounds :
RTA_claimed_bounds ts = Some [:: (tsk1, 3); (tsk2, 3); (tsk3, 5)].
Proof.
rewrite /RTA_claimed_bounds /fp_claimed_bounds /inflated_cost /inflated_task_cost.
set RESP := [seq _ | tsk <- ts].
suff EQ: RESP = [:: (tsk1, Some 3); (tsk2, Some 3); (tsk3, Some 5)] by rewrite EQ; compute.
rewrite /RESP /ts /=; do 2 f_equal.
{
rewrite /per_task_rta /= addn1.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk1 2 = 3.
{
by compute; rewrite unlock; compute.
}
rewrite !WORK /=; clear WORK.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk1 3 = 3.
{
by compute; rewrite unlock; compute.
}
by rewrite !WORK /=.
}
f_equal.
{
rewrite /per_task_rta /= addn0.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk2 1 = 3.
{
by compute; rewrite unlock; compute.
}
rewrite !WORK /=; clear WORK.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk2 3 = 3.
{
by compute; rewrite unlock; compute.
}
by rewrite !WORK /=.
}
do 2 f_equal.
{
rewrite /per_task_rta /= addn1.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk3 2 = 5.
{
by compute; rewrite unlock; compute.
}
rewrite !WORK /=; clear WORK.
have WORK: total_workload_bound_fp inflated_cost task_period (RM task_period)
[:: tsk1; tsk2; tsk3] tsk3 5 = 5.
{
by compute; rewrite unlock; compute.
}
by rewrite !WORK /=; clear WORK.
}
Qed.
(* ...so the schedulability test indeed returns true. *)
Fact schedulability_test_succeeds :
schedulability_test ts = true.
Proof.
rewrite /schedulability_test /fp_schedulable -/RTA_claimed_bounds.
by rewrite RTA_yields_these_bounds.
Qed.
(* Now, let's show that the task set is schedulable. *)
(* Let arr_seq be the periodic arrival sequence from ts... *)
Let arr_seq := periodic_arrival_sequence ts.
(* ...where jobs have total suspension times that are no larger than
the suspension bound of their tasks. *)
Variable next_suspension: job_suspension concrete_job_eqType.
Hypothesis H_dynamic_suspensions:
dynamic_suspension_model job_cost job_task next_suspension task_suspension_bound.
(* Also assume rate-monotonic priorities. *)
Let higher_eq_priority := FP_to_JLDP job_task (RM task_period).
(* Next, let sched be the suspension-aware RM schedule with those job suspension times. *)
Let sched := scheduler job_arrival job_cost arr_seq next_suspension higher_eq_priority.
(* To conclude, based on the definition of deadline miss,... *)
Let no_deadline_missed_by :=
task_misses_no_deadline job_arrival job_cost job_deadline job_task arr_seq sched.
(* ...we use the result of the suspension-oblivious FP RTA to conclude that
no task misses its deadline. *)
Corollary ts_is_schedulable:
forall tsk,
tsk \in ts ->
no_deadline_missed_by tsk.
Proof.
intros tsk IN.
have VALID := periodic_arrivals_valid_job_parameters ts ts_has_valid_parameters.
have TSVALID := ts_has_valid_parameters.
unfold valid_sporadic_job, valid_realtime_job in *; des.
apply suspension_oblivious_fp_rta_implies_schedulability with (task_cost := task_cost)
(task_period := task_period) (task_deadline := task_deadline) (ts0 := ts)
(higher_eq_priority0 := RM task_period) (next_suspension0 := next_suspension)
(task_suspension_bound := task_suspension_bound); try (by done).
- by apply periodic_arrivals_are_consistent.
- by apply periodic_arrivals_is_a_set.
- by apply periodic_arrivals_all_jobs_from_taskset.
- by apply periodic_arrivals_are_sporadic.
- by apply RM_is_reflexive.
- by apply RM_is_transitive.
- by intros tsk_a tsk_b INa INb; apply/orP; apply leq_total.
- by apply inflated_cost_le_deadline_and_period.
- by apply scheduler_jobs_come_from_arrival_sequence, periodic_arrivals_are_consistent.
- by apply scheduler_jobs_must_arrive_to_execute, periodic_arrivals_are_consistent.
- by apply scheduler_completed_jobs_dont_execute, periodic_arrivals_are_consistent.
- by apply scheduler_work_conserving, periodic_arrivals_are_consistent.
- apply scheduler_respects_policy; first by apply periodic_arrivals_are_consistent.
-- by intros t; apply RM_is_transitive.
-- by intros j1 j2 _ _ _; apply leq_total.
- by apply scheduler_respects_self_suspensions, periodic_arrivals_are_consistent.
- by apply schedulability_test_succeeds.
Qed.
End ExampleRTA.
End ResponseTimeAnalysisFP.
|
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|
import argparse
import time
import torchvision
import torch
from torchvision import transforms as T
from PIL import Image
import importlib.util
import tensorflow_datasets as tfds
import tensorflow_hub as hub
import sys
import os
import yaml
import re
import numpy as np
import subprocess
import random
# import tensorflow.compat.v1 as tf
import tensorflow as tf
# tf.disable_eager_execution()
# subprocess.check_call(["gcloud", "auth", "application-default", "login"])
seed = 201711075
torch.manual_seed(seed)
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.manual_seed(seed)
def _init_fn(worker_id):
seed_s = seed + worker_id
np.random.seed(seed_s)
random.seed(seed_s)
torch.manual_seed(seed_s)
return
parser = argparse.ArgumentParser(description='feature extraction')
parser.add_argument('-d', '--data', required=True,
type=str, help='dataType(train/val)')
parser.add_argument('-t', '--type', required=True, type=str,
help='modelType(pretrained/supervised)')
args = parser.parse_args()
# 1-3. Load data
dataType = args.data
model_type = args.type
model_spec = 'r50_1x_sk0'
# model_type = 'supervised'
# model_spec = 'r50_1x_sk0'
# dataType = 'train' # datatype (train / val)
dataHome = '/SSD_data/Imagenet2012' # Imagenet2012 path (classification)
dataPath = os.path.join(dataHome, dataType)
print('dataPath:', dataPath)
transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor()])
data_total = 50000
batch_size = 100
num_batch = data_total // batch_size
# device
device = '/gpu:0'
savePath = '/home/user/Desktop/pky/simclr/save/Imagenet/' # save path
timestr = time.strftime("%m%d-%H%M") # time stamp
print('savePath:', savePath)
print('timeStamp:', timestr)
print('load hub modules...')
# model_type = 'supervised'
# model_spec = 'r50_1x_sk0'
model_name = model_type+'_'+model_spec
hub_path = 'gs://simclr-checkpoints/simclrv2/'+model_type+'/'+model_spec+'/hub/'
# hub_path = 'gs://simclr-checkpoints/simclrv2/pretrained/r50_1x_sk0/hub/' # self-supervised
# hub_path = 'gs://simclr-checkpoints/simclrv2/finetuned_1pct/r50_1x_sk0/hub/' # 1% fine-tuned
# hub_path = 'gs://simclr-checkpoints/simclrv2/finetuned_10pct/r50_1x_sk0/hub/' # 10% fine-tuned
# hub_path = 'gs://simclr-checkpoints/simclrv2/finetuned_100pct/r50_1x_sk0/hub/' # 100% fine-tuned
# hub_path = 'gs://simclr-checkpoints/simclrv2/supervised/r50_1x_sk0/hub/' # supervised
module = hub.Module(hub_path, trainable=False)
print('load finished!')
def _load_imagenet():
imagenet_data = torchvision.datasets.ImageNet(
dataPath, split=dataType, download=False, transform=transform)
data_loader = torch.utils.data.DataLoader(
imagenet_data, batch_size=batch_size, shuffle=True, num_workers=10, worker_init_fn=_init_fn)
for n_batch, (data, label) in enumerate(data_loader):
if n_batch >= num_batch:
return
print("batch {}/{} is loaded!".format(n_batch+1, num_batch))
yield (data.numpy()).transpose(0, 2, 3, 1), label.numpy()
with tf.device("/gpu:0"):
input_tensor = tf.placeholder(tf.float32, shape=[None, 224, 224, 3])
keys = module(inputs=input_tensor, signature="default", as_dict=True)
features = keys['default']
logits = keys['logits_sup']
print("initializing sessions...")
# sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# sess.run(tf.compat.v1.global_variables_initializer())
print("initializing finished!")
features_total = np.empty((0, 2048))
logits_total = np.empty((0, 1000))
labels_total = np.array([])
print("batch computation start...")
for batch_data, batch_label in _load_imagenet():
features_, logits_ = sess.run((features, logits), feed_dict={
input_tensor: batch_data})
features_total = np.append(features_total, features_, axis=0)
logits_total = np.append(logits_total, logits_, axis=0)
labels_total = np.append(labels_total, batch_label, axis=0)
print("batch Finished!")
# 저장
np.save(savePath+timestr+'_'+dataType+'_features_'+model_name, features_total)
np.save(savePath+timestr+'_'+dataType+'_logits_'+model_name, logits_total)
np.save(savePath+timestr+'_'+dataType+'_labels_'+model_name, labels_total)
print("features:", str(features_total.shape))
print("logits:", str(logits_total.shape))
print("labels:", str(labels_total.shape))
|
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|
import sys
sys.path.append('..')
from pronoun_cracker import *
import numpy as np
import pandas as pd
cracker = PronounCracker('pronoun', '../input', '../output')
cracker.load_data()
print(cracker.train.columns)
renaming = {
'p_a' : 'P-A-e2e', 'p_b' : 'P-B-e2e',
'a_p' : 'A-P-e2e', 'b_p' : 'B-P-e2e'}
train = cracker.train.rename(columns = renaming)
test = cracker.test.rename(columns = renaming)
print(train[['A-e2e', 'B-e2e', 'P-A-e2e', 'P-B-e2e', 'A-P-e2e', 'B-P-e2e']].head())
train[['A-e2e', 'B-e2e', 'P-A-e2e', 'P-B-e2e', 'A-P-e2e', 'B-P-e2e']].to_csv("train_e2e.tsv", sep='\t')
test[['A-e2e', 'B-e2e', 'P-A-e2e', 'P-B-e2e', 'A-P-e2e', 'B-P-e2e']].to_csv("test_e2e.tsv", sep='\t')
|
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|
import time
from garage.misc import logger
from garage.misc import ext
from garage.misc.overrides import overrides
from garage.tf.algos import BatchPolopt
from garage.tf.optimizers.cg_optimizer import CGOptimizer
from garage.tf.misc import tensor_utils
from garage.core.serializable import Serializable
import tensorflow as tf
import numpy as np
import copy
class CATRPO(BatchPolopt, Serializable):
"""
Curvature-aided Trust Region Policy Gradient.
"""
def __init__(
self,
env,
policy,
backup_policy,
mix_policy,
pos_eps_policy,
neg_eps_policy,
baseline,
minibatch_size=500,
n_sub_itr=10,
optimizer=None,
optimizer_args=None,
delta=0.01,
**kwargs):
Serializable.quick_init(self, locals())
self.optimizer = optimizer
if optimizer is None:
if optimizer_args is None:
optimizer_args = dict()
self.optimizer = CGOptimizer(**optimizer_args)
self.opt_info = None
self.backup_policy = backup_policy
self.mix_policy = mix_policy
self.pos_eps_policy = pos_eps_policy
self.neg_eps_policy = neg_eps_policy
self.minibatch_size = minibatch_size
self.n_sub_itr = n_sub_itr
self.delta = delta
super(CATRPO, self).__init__(
env=env, policy=policy, baseline=baseline, **kwargs)
def generate_mix_policy(self):
a = np.random.uniform(0.0, 1.0)
mix = a * self.policy.get_param_values() + (1 - a) * self.backup_policy.get_param_values()
self.mix_policy.set_param_values(mix, trainable=True)
def sample_paths(self, traj_num, sample_policy):
paths = []
# Sample Trajectories
for _ in range(traj_num):
observations = []
actions = []
rewards = []
observation = self.env.reset()
for _ in range(self.max_path_length):
# policy.get_action() returns a pair of values. The second
# one returns a dictionary, whose values contains
# sufficient statistics for the action distribution. It
# should at least contain entries that would be returned
# by calling policy.dist_info(), which is the non-symbolic
# analog of policy.dist_info_sym(). Storing these
# statistics is useful, e.g., when forming importance
# sampling ratios. In our case it is not needed.
action, _ = sample_policy.get_action(observation)
# Recall that the last entry of the tuple stores diagnostic
# information about the environment. In our case it is not needed.
next_observation, reward, terminal, _ = self.env.step(action)
observations.append(observation)
actions.append(action)
rewards.append(reward)
observation = next_observation
if terminal:
# Finish rollout if terminal state reached
break
# We need to compute the empirical return for each time step along the
# trajectory
path = dict(
observations=np.array(observations),
actions=np.array(actions),
rewards=np.array(rewards),
)
path_baseline = self.baseline.predict(path)
advantages = []
returns = []
return_so_far = 0
for t in range(len(rewards) - 1, -1, -1):
return_so_far = rewards[t] + self.discount * return_so_far
returns.append(return_so_far)
advantage = return_so_far - path_baseline[t]
advantages.append(advantage)
# The advantages are stored backwards in time, so we need to revert it
advantages = np.array(advantages[::-1])
# And we need to do the same thing for the list of returns
returns = np.array(returns[::-1])
advantages = (advantages - np.mean(advantages)) / (
np.std(advantages) + 1e-8)
path["advantages"] = advantages
path["returns"] = returns
paths.append(path)
return paths
@staticmethod
def grad_norm(s_g):
res = s_g[0].flatten()
for i in range(1,len(s_g)):
res = np.concatenate((res, s_g[i].flatten()))
l2_norm = np.linalg.norm(res)
return l2_norm
@staticmethod
def normalize_gradient(s_g):
res = s_g[0].flatten()
for i in range(1, len(s_g)):
res = np.concatenate((res, s_g[i].flatten()))
l2_norm = np.linalg.norm(res)
return [x/l2_norm for x in s_g]
@staticmethod
def flatten_parameters(params):
return np.concatenate([p.flatten() for p in params])
@overrides
def init_opt(self):
observations_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
actions_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
advantages_var = tensor_utils.new_tensor(
name='advantage',
ndim=1,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = self.backup_policy.dist_info_sym(observations_var)
dist_info_vars = self.policy.dist_info_sym(observations_var)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
pos_eps_dist_info_vars = self.pos_eps_policy.dist_info_sym(observations_var)
neg_eps_dist_info_vars = self.neg_eps_policy.dist_info_sym(observations_var)
mix_dist_info_vars = self.mix_policy.dist_info_sym(observations_var)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
surr = -tf.reduce_mean(dist.log_likelihood_sym(actions_var, dist_info_vars) * advantages_var)
surr_pos_eps = -tf.reduce_mean(dist.log_likelihood_sym(actions_var, pos_eps_dist_info_vars) * advantages_var)
surr_neg_eps = -tf.reduce_mean(dist.log_likelihood_sym(actions_var, neg_eps_dist_info_vars) * advantages_var)
surr_mix = -tf.reduce_mean(dist.log_likelihood_sym(actions_var, mix_dist_info_vars) * advantages_var)
surr_loglikelihood = tf.reduce_sum(dist.log_likelihood_sym(actions_var, mix_dist_info_vars))
params = self.policy.get_params(trainable=True)
mix_params = self.mix_policy.get_params(trainable=True)
pos_eps_params = self.pos_eps_policy.get_params(trainable=True)
neg_eps_params = self.neg_eps_policy.get_params(trainable=True)
grads = tf.gradients(surr, params)
grad_pos_eps = tf.gradients(surr_pos_eps, pos_eps_params)
grad_neg_eps = tf.gradients(surr_neg_eps, neg_eps_params)
grad_mix = tf.gradients(surr_mix, mix_params)
grad_mix_lh = tf.gradients(surr_loglikelihood, mix_params)
inputs_list = [observations_var, actions_var, advantages_var]
self.optimizer.update_opt(loss=surr, target=self.policy,
leq_constraint=(mean_kl, self.delta),
inputs=inputs_list)
self._opt_fun = ext.LazyDict(
f_loss=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=surr,
log_name="f_loss",
),
f_train=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=grads,
log_name="f_grad"
),
f_mix_grad=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=grad_mix,
log_name="f_mix_grad"
),
f_pos_grad=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=grad_pos_eps
),
f_neg_grad=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=grad_neg_eps
),
f_mix_lh=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=grad_mix_lh
),
f_kl=lambda: tensor_utils.compile_function(
inputs=inputs_list,
outputs=[mean_kl, max_kl],
)
)
@overrides
def train(self):
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
self.start_worker(sess)
start_time = time.time()
self.num_samples = 0
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
with logger.prefix('itr #%d | ' % itr):
logger.log("Obtaining new samples...")
paths = self.obtain_samples(itr)
for path in paths:
self.num_samples += len(path["rewards"])
logger.log("total num samples..." + str(self.num_samples))
logger.log("Processing samples...")
samples_data = self.process_samples(itr, paths)
logger.log("Logging diagnostics...")
self.log_diagnostics(paths)
logger.log("Optimizing policy...")
self.outer_optimize(samples_data)
for sub_itr in range(self.n_sub_itr):
logger.log("Minibatch Optimizing...")
self.inner_optimize(samples_data)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(
itr, samples_data) # , **kwargs)
if self.store_paths:
params["paths"] = samples_data["paths"]
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.record_tabular('Time', time.time() - start_time)
logger.record_tabular(
'ItrTime', time.time() - itr_start_time)
logger.dump_tabular(with_prefix=False)
#if self.plot:
# self.update_plot()
# if self.pause_for_plot:
# input("Plotting evaluation run: Press Enter to "
# "continue...")
self.shutdown_worker()
def outer_optimize(self, samples_data):
logger.log("optimizing policy")
observations = ext.extract(samples_data, "observations")
actions = ext.extract(samples_data, "actions")
advantages = ext.extract(samples_data, "advantages")
num_traj = len(samples_data["paths"])
observations = observations[0].reshape(-1, self.env.spec.observation_space.shape[0])
actions = actions[0].reshape(-1,self.env.spec.action_space.shape[0])
advantages = advantages[0].reshape(-1)
inputs = tuple([observations, actions, advantages])
s_g = self._opt_fun["f_train"](*(list(inputs)))
#s_g = [x / num_traj for x in s_g]
self.gradient_backup = copy.deepcopy(s_g)
g_flat = self.flatten_parameters(s_g)
loss_before = self._opt_fun["f_loss"](*(list(inputs)))
self.backup_policy.set_param_values(self.policy.get_param_values(trainable=True), trainable=True)
self.optimizer.optimize(inputs, g_flat)
loss_after = self._opt_fun["f_loss"](*(list(inputs)))
logger.record_tabular("LossBefore", loss_before)
logger.record_tabular("LossAfter", loss_after)
mean_kl, max_kl = self._opt_fun['f_kl'](*(list(inputs)))
logger.record_tabular('MeanKL', mean_kl)
logger.record_tabular('MaxKL', max_kl)
def inner_optimize(self, outer_sample):
observations = ext.extract(outer_sample, "observations")
actions = ext.extract(outer_sample, "actions")
advantages = ext.extract(outer_sample, "advantages")
outer_observations = observations[0].reshape(-1, self.env.spec.observation_space.shape[0])
outer_actions = actions[0].reshape(-1,self.env.spec.action_space.shape[0])
outer_advantages = advantages[0].reshape(-1)
n_sub = 0
sub_paths_all = []
self.generate_mix_policy()
sub_paths = self.sample_paths(1, self.mix_policy)
sub_paths_all.append(sub_paths[0])
n_sub += len(sub_paths[0]["rewards"])
self.num_samples += len(sub_paths[0]["rewards"])
sub_observations = [p["observations"] for p in sub_paths]
sub_actions = [p["actions"] for p in sub_paths]
sub_advantages = [p["advantages"] for p in sub_paths]
eps = 1e-6
d_vector = self.policy.get_param_values() - self.backup_policy.get_param_values()
pos_params = self.mix_policy.get_param_values() + d_vector * eps
neg_params = self.mix_policy.get_param_values() - d_vector * eps
self.pos_eps_policy.set_param_values(pos_params, trainable=True)
self.neg_eps_policy.set_param_values(neg_params, trainable=True)
# first component: dot(likelihood, theta_t - theta_t-1) * policy gradient
g_mix = self._opt_fun["f_mix_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
g_lh = self._opt_fun["f_mix_lh"](sub_observations[0], sub_actions[0])
g_lh = self.flatten_parameters(g_lh)
inner_product = np.dot(g_lh, d_vector)
fst = [inner_product * g for g in g_mix]
# second component: dot(Hessian, theta_t - theta_t-1)
g_pos = self._opt_fun["f_pos_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
g_neg = self._opt_fun["f_neg_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
hv = [(pos - neg) / (2 * eps) for pos, neg in zip(g_pos, g_neg)]
while (n_sub < self.minibatch_size):
self.generate_mix_policy()
sub_paths = self.sample_paths(1, self.mix_policy)
n_sub += len(sub_paths[0]["rewards"])
self.num_samples += len(sub_paths[0]["rewards"])
sub_paths_all.append(sub_paths[0])
sub_observations = [p["observations"] for p in sub_paths]
sub_actions = [p["actions"] for p in sub_paths]
sub_advantages = [p["advantages"] for p in sub_paths]
pos_params = self.mix_policy.get_param_values() + d_vector * eps
neg_params = self.mix_policy.get_param_values() - d_vector * eps
self.pos_eps_policy.set_param_values(pos_params, trainable=True)
self.neg_eps_policy.set_param_values(neg_params, trainable=True)
# first component: dot(likelihood, theta_t - theta_t-1) * policy gradient
g_mix = self._opt_fun["f_mix_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
g_lh = self._opt_fun["f_mix_lh"](sub_observations[0], sub_actions[0])
g_lh = self.flatten_parameters(g_lh)
inner_product = np.dot(g_lh, d_vector)
fst_i = [inner_product * g for g in g_mix]
fst = [sum(x) for x in zip(fst, fst_i)]
# second component: dot(Hessian, theta_t - theta_t-1)
g_pos = self._opt_fun["f_pos_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
g_neg = self._opt_fun["f_neg_grad"](sub_observations[0], sub_actions[0], sub_advantages[0])
hv_i = [(pos - neg) / (2 * eps) for pos, neg in zip(g_pos, g_neg)]
hv = [sum(x) for x in zip(hv, hv_i)]
fst = [x / len(sub_paths_all) for x in fst]
hv = [x / len(sub_paths_all) for x in hv]
fst = [x/10 for x in fst]
# gradient as sum
fst_norm = self.grad_norm(fst)
hv_norm = self.grad_norm(hv)
backup_gradient_norm = self.grad_norm(self.gradient_backup)
#self.writer.add_scalar("first_component_norm", fst_norm, j)
#self.writer.add_scalar("hv_norm", hv_norm, j)
#self.writer.add_scalar("back_gradient_norm", backup_gradient_norm, j)
g_d = [sum(x) for x in zip(fst, hv, self.gradient_backup)]
self.gradient_backup = copy.deepcopy(g_d)
avg_returns = np.mean([sum(p["rewards"]) for p in sub_paths_all])
#self.writer.add_scalar("AverageReturn", avg_returns, j)
#self.writer.add_scalar("Gradient norm", self.grad_norm(g_d), j)
print("timesteps: " + str(self.num_samples) + " average return: " + str(avg_returns))
sub_observations = np.concatenate([p["observations"] for p in sub_paths_all])
sub_actions = np.concatenate([p["actions"] for p in sub_paths_all])
sub_advantages = np.concatenate([p["advantages"] for p in sub_paths_all])
sub_observations = sub_observations.reshape(-1, self.env.spec.observation_space.shape[0])
sub_actions = sub_actions.reshape(-1, self.env.spec.action_space.shape[0])
sub_advantages = sub_advantages.reshape(-1)
#sub_observations = np.concatenate((sub_observations, outer_observations))
#sub_actions = np.concatenate((sub_actions, outer_actions))
#sub_advantages = np.concatenate((sub_advantages, outer_advantages))
print(sub_observations.shape)
inputs = tuple([sub_observations, sub_actions, sub_advantages])
self.backup_policy.set_param_values(self.policy.get_param_values(trainable=True), trainable=True)
flat_g_d = self.flatten_parameters(g_d)
self.optimizer.optimize(inputs, flat_g_d)
# Compute KL divergence after updated
#sub_observations = [p["observations"] for p in sub_paths]
#mean_kl, max_kl = self.f_kl(sub_observations[0])
#self.writer.add_scalar("MeanKL", mean_kl, j)
#self.writer.add_scalar("MaxKL", max_kl, j)
@overrides
def get_itr_snapshot(self, itr, samples_data):
return dict(
itr=itr,
policy=self.policy,
baseline=self.baseline,
env=self.env,
)
|
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|
/*
* This file is open source software, licensed to you under the terms
* of the Apache License, Version 2.0 (the "License"). See the NOTICE file
* distributed with this work for additional information regarding copyright
* ownership. You may not use this file except in compliance with the License.
*
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*
* Copyright 2016 ScyllaDB
*/
#pragma once
#include <execinfo.h>
#include <iosfwd>
#include <boost/container/static_vector.hpp>
#include <seastar/core/sstring.hh>
namespace seastar {
struct shared_object {
sstring name;
uintptr_t begin;
uintptr_t end; // C++-style, last addr + 1
};
struct frame {
const shared_object* so;
uintptr_t addr;
};
bool operator==(const frame& a, const frame& b);
// If addr doesn't seem to belong to any of the provided shared objects, it
// will be considered as part of the executable.
frame decorate(uintptr_t addr);
// Invokes func for each frame passing it as argument.
template<typename Func>
void backtrace(Func&& func) noexcept(noexcept(func(frame()))) {
constexpr size_t max_backtrace = 100;
void* buffer[max_backtrace];
int n = ::backtrace(buffer, max_backtrace);
for (int i = 0; i < n; ++i) {
auto ip = reinterpret_cast<uintptr_t>(buffer[i]);
func(decorate(ip - 1));
}
}
class saved_backtrace {
public:
using vector_type = boost::container::static_vector<frame, 64>;
private:
vector_type _frames;
public:
saved_backtrace() = default;
saved_backtrace(vector_type f) : _frames(std::move(f)) {}
size_t hash() const;
friend std::ostream& operator<<(std::ostream& out, const saved_backtrace&);
bool operator==(const saved_backtrace& o) const {
return _frames == o._frames;
}
bool operator!=(const saved_backtrace& o) const {
return !(*this == o);
}
};
}
namespace std {
template<>
struct hash<seastar::saved_backtrace> {
size_t operator()(const seastar::saved_backtrace& b) const {
return b.hash();
}
};
}
namespace seastar {
saved_backtrace current_backtrace() noexcept;
std::ostream& operator<<(std::ostream& out, const saved_backtrace& b);
namespace internal {
template<class Exc>
class backtraced : public Exc {
std::shared_ptr<sstring> _backtrace;
public:
template<typename... Args>
backtraced(Args&&... args)
: Exc(std::forward<Args>(args)...)
, _backtrace(std::make_shared<sstring>(format("{} Backtrace: {}", Exc::what(), current_backtrace()))) {}
/**
* Returns the original exception message with a backtrace appended to it
*
* @return original exception message followed by a backtrace
*/
virtual const char* what() const noexcept override {
assert(_backtrace);
return _backtrace->c_str();
}
};
}
/**
* Throws an exception of unspecified type that is derived from the Exc type
* with a backtrace attached to its message
*
* @tparam Exc exception type to be caught at the receiving side
* @tparam Args types of arguments forwarded to the constructor of Exc
* @param args arguments forwarded to the constructor of Exc
* @return never returns (throws an exception)
*/
template <class Exc, typename... Args>
[[noreturn]]
void
throw_with_backtrace(Args&&... args) {
using exc_type = std::decay_t<Exc>;
static_assert(std::is_base_of<std::exception, exc_type>::value,
"throw_with_backtrace only works with exception types");
throw internal::backtraced<exc_type>(std::forward<Args>(args)...);
};
}
|
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|
[STATEMENT]
lemma imp_graph_insert [simp]:
"imp_graph (insert cl cls) = edges_of_clause cl \<union> imp_graph cls"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. imp_graph (insert cl cls) = edges_of_clause cl \<union> imp_graph cls
[PROOF STEP]
by (auto simp: imp_graph_def)
|
{"llama_tokens": 112, "file": "Containers_Examples_TwoSat_Ex", "length": 1}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 29 09:17:18 2020
@author: sblair
This is a Python implementation of an example problem from Lecture 31 of EM424.
The example is the solution of the Wave Equation in Polar Coordinates.
For this script I have implemented only the "ex2" initial conditions from the
MATLAB version of the example.
"""
import numpy as np
import matplotlib.pyplot as plt
#from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import scipy.integrate as integrate
from scipy.special import jn_zeros
from scipy.special import j0
# Parameters
N = 50; # number of modes
c = 1; # radius of the circular domain
a = 1; # "stiffness" parameter for the wave equation
# functions for initial conditions
def f(r):
"""
Initial displacement for the wave equation. The problem is assumed to
have radial symmetry so, for polar coordinates, the initial displacement
is only a function of radial position.
This is really a place-holder. The example that I am implementing for
lecture 31 has an initial displacement of 0.
Parameters
----------
r : float64 (or, whatever)
radial position
Returns
-------
float64 - initial displacement at a given radial position
"""
return 0.
def g(r):
"""
Initial velocity. Radial symmetry so, for this problem, initial velocity
is only a functin of radial position.
Parameters
----------
r : float64
radial position.
Returns
-------
float64 - initial velocity at a given radial position.
"""
b = 0.2;
v0 = 10.;
retval = 0.;
if r < b:
retval = -v0;
return retval;
# function to construct full solution
def U_exp(r,t,An,Bn,N):
ret_val = 0.;
for n in range(N):
ret_val += j0(ev[n]*r)*(An[n]*np.cos(a*ev[n]*t) +
Bn[n]*np.sin(a*ev[n]*t));
return ret_val;
# allocate arrays
A = np.zeros(N,dtype=np.float64);
B = np.zeros_like(A);
# get N roots of J_0
k = jn_zeros(0,N);
# compute eigenvalues
ev = k/c;
for n in range(N):
ev_mag, err = integrate.quad(lambda r: r*j0(ev[n]*r)*j0(ev[n]*r),0,c);
a_term, err = integrate.quad(lambda r: r*j0(ev[n]*r)*f(r),0,c);
A[n] = a_term/ev_mag;
b_term, err = integrate.quad(lambda r: r*j0(ev[n]*r)*g(r),0,c);
b_term /= (a*ev[n]);
B[n] = b_term/ev_mag;
# construct the solution in terms of the fourier coefficients
U = lambda r,t: U_exp(r,t,A,B,N);
# set-up geometric coordinates for plotting
NR = 20;
NT = 20;
R = np.linspace(0,c,NR);
T = np.linspace(0,2.*np.pi,NT);
RR, TT = np.meshgrid(R,T)
XX = np.multiply(RR,np.cos(TT));# is there a more simple syntax for this?
YY = np.multiply(RR,np.sin(TT));
UU = np.zeros_like(XX);
rows = XX.shape[0];
cols = XX.shape[1];
# since this is a time-dependent solution, we will want to make an animation.
# but first we will plot a solution at t=10 just to make sure everything
# is good-to-go.
t = 10.;
for i in range(0,rows):
for j in range(0,cols):
UU[i,j] = U(RR[i,j],t);
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_wireframe(XX,YY,UU)
ax.set_zlim(-2.0,2.0)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.01f'));
ax.set_xlabel('X',fontsize=12,fontweight='bold');
ax.set_ylabel('Y',fontsize=12,fontweight='bold');
ax.set_zlabel('U',fontsize=12,fontweight='bold');
ax.view_init(20,235);
plt.title('Lecture 31 Example', fontsize=14, fontweight='bold')
plt.show()
# looks good, so I will try the time-dependent version
Tmax = 10
NTIME = 50
#T_time = np.linspace(0,Tmax,NTIME);
# below code based on example at: https://matplotlib.org/2.0.0/examples/mplot3d/wire3d_animation_demo.html
# function to generate "Z" values for the wireframe plot
def generate(RR,t,U):
rows = RR.shape[0];
cols = RR.shape[1];
UU = np.zeros_like(RR);
for i in range(0,rows):
for j in range(0,cols):
UU[i,j] = U(RR[i,j],t);
return UU
# set-up the basic figure
fig = plt.figure();
ax = fig.add_subplot(111,projection='3d');
# use XX and YY from static plot
ax.set_zlim(-2.0,2.0)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.01f'));
ax.set_xlabel('X',fontsize=12,fontweight='bold');
ax.set_ylabel('Y',fontsize=12,fontweight='bold');
ax.set_zlabel('U',fontsize=12,fontweight='bold');
ax.view_init(20,235);
#plt.title('Lecture 31 Example', fontsize=14, fontweight='bold')
# begin plotting annimation
wframe = None
for t in np.linspace(0,Tmax,NTIME):
if wframe:
ax.collections.remove(wframe)
UU = generate(RR,t,U);
wframe = ax.plot_wireframe(XX,YY,UU)
title_str = f"Lecture 31 example, t = {t:.2f}"
plt.title(title_str,fontsize=14,fontweight='bold');
plt.pause(0.01)
|
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|
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Utils for model"""
import os
from os import path as osp
import random
import math
import cv2
import numpy as np
import mindspore.nn as nn
from mindspore.ops import composite as C
from mindspore.common import initializer as init
def init_weights_blocks(net, init_type='normal', init_gain=0.1):
"""
Initialize blocks weights
"""
for cell in net:
if isinstance(cell, (nn.Conv2d, nn.Conv2dTranspose)):
if init_type == 'normal':
cell.weight.set_data(init.initializer(init.Normal(init_gain), cell.weight.shape))
elif init_type == 'xavier':
cell.weight.set_data(init.initializer(init.XavierUniform(init_gain), cell.weight.shape))
elif init_type == 'constant':
cell.weight.set_data(init.initializer(0.001, cell.weight.shape))
elif init_type == 'kaiming':
cell.weight.set_data(init.initializer(init.HeNormal(init_gain), cell.weight.shape))
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
def init_weights_network(net, init_type='normal', init_gain=0.02):
"""
Initialize network weights
"""
for _, cell in net.cells_and_names():
if isinstance(cell, (nn.Conv2d, nn.Conv2dTranspose)):
if init_type == 'normal':
cell.weight.set_data(init.initializer(init.Normal(init_gain), cell.weight.shape))
elif init_type == 'xavier':
cell.weight.set_data(init.initializer(init.XavierUniform(init_gain), cell.weight.shape))
elif init_type == 'constant':
cell.weight.set_data(init.initializer(0.001, cell.weight.shape))
elif init_type == 'kaiming':
cell.weight.set_data(init.initializer(init.HeNormal(init_gain), cell.weight.shape))
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
def CosineAnnealingRestartLR(period, restart_weights=(1.), eta_min=0, total_step=1):
""" Cosine annealing with restarts learning rate scheme."""
base_lr = 0.0002
lr = []
for i in range(total_step):
now_period = i // period
lr.append(eta_min + restart_weights[now_period] * 0.5 * (base_lr - eta_min) *
(1 + math.cos(math.pi * ((i % period) / period))))
return lr
def calculate_psnr(img1, img2, crop_border=4, input_order='HWC', test_y_channel=True):
"""calculate psnr"""
img1 = reorder_image(img1, input_order=input_order)
img2 = reorder_image(img2, input_order=input_order)
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if crop_border != 0:
img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
return 20. * np.log10(255. / np.sqrt(mse))
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
"""Convert torch Tensors into image numpy arrays."""
result = []
for _tensor in tensor:
_tensor = C.clip_by_value(_tensor, *min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 3:
img_np = _tensor.asnumpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1:
result = result[0]
return result
def imwrite(img, file_path, params=None, auto_mkdir=True):
"""Write image to file."""
if auto_mkdir:
dir_name = os.path.abspath(os.path.dirname(file_path))
os.makedirs(dir_name, exist_ok=True)
return cv2.imwrite(file_path, img, params)
def reorder_image(img, input_order='HWC'):
"""Reorder images to 'HWC' order."""
if input_order not in ['HWC', 'CHW']:
print('Wrong input_order. Supported input_orders are ' "'HWC' and 'CHW'")
if len(img.shape) == 2:
img = img[..., None]
if input_order == 'CHW':
img = img.transpose(1, 2, 0)
return img
def _convert_input_type_range(img):
"""Convert the type and range of the input image."""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
print('Error,The img type should be np.float32 or np.uint8, ')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type."""
if dst_type not in (np.uint8, np.float32):
print('Error,The dst_type should be np.float32 or np.uint8')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
def bgr2ycbcr(img, y_only=False):
"""Convert a BGR image to YCbCr image."""
img_type = img.dtype
img = _convert_input_type_range(img)
if y_only:
out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
else:
out_img = np.matmul(
img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
out_img = _convert_output_type_range(out_img, img_type)
return out_img
def to_y_channel(img):
"""Change to Y channel of YCbCr."""
img = img.astype(np.float32) / 255.
if img.ndim == 3 and img.shape[2] == 3:
img = bgr2ycbcr(img, y_only=True)
img = img[..., None]
return img * 255.
def paired_paths_from_folder(lq_folder, gt_folder):
"""Generate paired paths from folders."""
gt_paths = list(os.listdir(gt_folder))
paths = []
for gt_path in gt_paths:
input_path = osp.join(lq_folder, gt_path)
gt_path = osp.join(gt_folder, gt_path)
paths.append(dict([('lq_path', input_path), ('gt_path', gt_path)]))
return paths
def get(filepath):
"""Get values according to the filepath """
filepath = str(filepath)
with open(filepath, 'rb') as f:
value_buf = f.read()
return value_buf
def imfrombytes(content, flag='color', float32=False):
"""Read an image from bytes."""
img_np = np.frombuffer(content, np.uint8)
imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}
img = cv2.imdecode(img_np, imread_flags[flag])
if float32:
img = img.astype(np.float32) / 255.
return img
def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path):
"""Paired random crop. Support Numpy array and Tensor inputs."""
if not isinstance(img_gts, list):
img_gts = [img_gts]
if not isinstance(img_lqs, list):
img_lqs = [img_lqs]
h_lq, w_lq, _ = img_lqs[0].shape
h_gt, w_gt, _ = img_gts[0].shape
lq_patch_size = gt_patch_size // scale
if h_gt != h_lq * scale or w_gt != w_lq * scale:
print('Error,h_gt != h_lq * scale or w_gt != w_lq * scale')
if h_lq < lq_patch_size or w_lq < lq_patch_size:
print('Error,h_lq < lq_patch_size or w_lq < lq_patch_size')
# randomly choose top and left coordinates for lq patch
top = random.randint(0, h_lq - lq_patch_size)
left = random.randint(0, w_lq - lq_patch_size)
# crop lq patch
img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
# crop corresponding gt patch
top_gt, left_gt = int(top * scale), int(left * scale)
img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
if len(img_gts) == 1:
img_gts = img_gts[0]
if len(img_lqs) == 1:
img_lqs = img_lqs[0]
return img_gts, img_lqs
def augment(imgs, hflip=False, rotation=False):
"""Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees)."""
hflip = hflip and random.random() < 0.5
vflip = rotation and random.random() < 0.5
rot90 = rotation and random.random() < 0.5
def _augment(img):
if hflip: # horizontal
cv2.flip(img, 1, img)
if vflip: # vertical
cv2.flip(img, 0, img)
if rot90:
img = img.transpose(1, 0, 2)
return img
if not isinstance(imgs, list):
imgs = [imgs]
imgs = [_augment(img) for img in imgs]
if len(imgs) == 1:
imgs = imgs[0]
return imgs
def img2tensor(imgs, bgr2rgb=True):
"""Numpy array to tensor."""
def _totensor(img, bgr2rgb):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.transpose(img, (2, 0, 1))
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb) for img in imgs]
|
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|
"""Base map class that defines the rendering process
"""
import matplotlib.pyplot as plt
import numpy as np
from gym.spaces import Box, Dict
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.env import MultiAgentEnv
_MAP_ENV_ACTIONS = {
"MOVE_LEFT": [0, -1], # Move left
"MOVE_RIGHT": [0, 1], # Move right
"MOVE_UP": [-1, 0], # Move up
"MOVE_DOWN": [1, 0], # Move down
"STAY": [0, 0], # don't move
"TURN_CLOCKWISE": [[0, 1], [-1, 0]], # Clockwise rotation matrix
"TURN_COUNTERCLOCKWISE": [[0, -1], [1, 0]],
} # Counter clockwise rotation matrix
# Positive Theta is in the counterclockwise direction
ORIENTATIONS = {"LEFT": [0, -1], "RIGHT": [0, 1], "UP": [-1, 0], "DOWN": [1, 0]}
DEFAULT_COLOURS = {
b" ": np.array([0, 0, 0], dtype=np.uint8), # Black background
b"0": np.array([0, 0, 0], dtype=np.uint8), # Black background beyond map walls
b"": np.array([180, 180, 180], dtype=np.uint8), # Grey board walls
b"@": np.array([180, 180, 180], dtype=np.uint8), # Grey board walls
b"A": np.array([0, 255, 0], dtype=np.uint8), # Green apples
b"F": np.array([255, 255, 0], dtype=np.uint8), # Yellow firing beam
b"P": np.array([159, 67, 255], dtype=np.uint8), # Generic agent (any player)
# Colours for agents. R value is a unique identifier
b"1": np.array([0, 0, 255], dtype=np.uint8), # Pure blue
b"2": np.array([2, 81, 154], dtype=np.uint8), # Sky blue
b"3": np.array([204, 0, 204], dtype=np.uint8), # Magenta
b"4": np.array([216, 30, 54], dtype=np.uint8), # Red
b"5": np.array([254, 151, 0], dtype=np.uint8), # Orange
b"6": np.array([100, 255, 255], dtype=np.uint8), # Cyan
b"7": np.array([99, 99, 255], dtype=np.uint8), # Lavender
b"8": np.array([250, 204, 255], dtype=np.uint8), # Pink
b"9": np.array([238, 223, 16], dtype=np.uint8), # Yellow
}
# the axes look like this when printed out
# WARNING: increasing array position in the direction of down
# so for example if you move_left when facing left
# your y position decreases.
# ^
# |
# U
# P
# <--LEFT * RIGHT---->
# D
# O
# W
# N
# |
class MapEnv(MultiAgentEnv):
def __init__(
self,
ascii_map,
extra_actions,
view_len,
num_agents=1,
color_map=None,
return_agent_actions=False,
use_collective_reward=False,
):
"""
Parameters
----------
ascii_map: list of strings
Specify what the map should look like. Look at constant.py for
further explanation
extra_actions: dict with action name-value pair
Environment-specific actions that are not present in _MAP_ENV_ACTIONS
num_agents: int
Number of agents to have in the system.
color_map: dict
Specifies how to convert between ascii chars and colors
return_agent_actions: bool
If true, the observation space will include the actions of other agents
"""
self.num_agents = num_agents
self.base_map = self.ascii_to_numpy(ascii_map)
self.view_len = view_len
self.map_padding = view_len
self.return_agent_actions = return_agent_actions
self.use_collective_reward = use_collective_reward
self.all_actions = _MAP_ENV_ACTIONS.copy()
self.all_actions.update(extra_actions)
# Map without agents or beams
self.world_map = np.full(
(len(self.base_map), len(self.base_map[0])), fill_value=b" ", dtype="c"
)
# Color mapping
self.color_map = color_map if color_map is not None else DEFAULT_COLOURS.copy()
# World map image
self.world_map_color = np.full(
(len(self.base_map) + view_len * 2, len(self.base_map[0]) + view_len * 2, 3),
fill_value=0,
dtype=np.uint8,
)
self.beam_pos = []
self.agents = {}
# returns the agent at a desired position if there is one
self.pos_dict = {}
self.spawn_points = [] # where agents can appear
self.wall_points = []
for row in range(self.base_map.shape[0]):
for col in range(self.base_map.shape[1]):
if self.base_map[row, col] == b"P":
self.spawn_points.append([row, col])
elif self.base_map[row, col] == b"@":
self.wall_points.append([row, col])
self.setup_agents()
@property
def observation_space(self):
obs_space = {
"curr_obs": Box(
low=0,
high=255,
shape=(2 * self.view_len + 1, 2 * self.view_len + 1, 3),
dtype=np.uint8,
)
}
if self.return_agent_actions:
# Append the actions of other agents
obs_space = {
**obs_space,
"other_agent_actions": Box(
low=0,
high=len(self.all_actions),
shape=(self.num_agents - 1,),
dtype=np.uint8,
),
"visible_agents": Box(
low=0,
high=1,
shape=(self.num_agents - 1,),
dtype=np.uint8,
),
"prev_visible_agents": Box(
low=0,
high=1,
shape=(self.num_agents - 1,),
dtype=np.uint8,
),
}
obs_space = Dict(obs_space)
# Change dtype so that ray can put all observations into one flat batch
# with the correct dtype.
# See DictFlatteningPreprocessor in ray/rllib/models/preprocessors.py.
obs_space.dtype = np.uint8
return obs_space
def custom_reset(self):
"""Reset custom elements of the map. For example, spawn apples and build walls"""
pass
def custom_action(self, agent, action):
"""Execute any custom actions that may be defined, like fire or clean
Parameters
----------
agent: agent that is taking the action
action: key of the action to be taken
Returns
-------
updates: list(list(row, col, char))
List of cells to place onto the map
"""
pass
def custom_map_update(self):
"""Custom map updates that don't have to do with agent actions"""
pass
def setup_agents(self):
"""Construct all the agents for the environment"""
raise NotImplementedError
# FIXME(ev) move this to a utils eventually
def ascii_to_numpy(self, ascii_list):
"""converts a list of strings into a numpy array
Parameters
----------
ascii_list: list of strings
List describing what the map should look like
Returns
-------
arr: np.ndarray
numpy array describing the map with ' ' indicating an empty space
"""
arr = np.full((len(ascii_list), len(ascii_list[0])), b" ", dtype="c")
for row in range(arr.shape[0]):
for col in range(arr.shape[1]):
arr[row, col] = ascii_list[row][col]
return arr
def step(self, actions):
"""Takes in a dict of actions and converts them to a map update
Parameters
----------
actions: dict {agent-id: int}
dict of actions, keyed by agent-id that are passed to the agent. The agent
interprets the int and converts it to a command
Returns
-------
observations: dict of arrays representing agent observations
rewards: dict of rewards for each agent
dones: dict indicating whether each agent is done
info: dict to pass extra info to gym
"""
self.beam_pos = []
agent_actions = {}
for agent_id, action in actions.items():
agent_action = self.agents[agent_id].action_map(action)
agent_actions[agent_id] = agent_action
# Remove agents from color map
for agent in self.agents.values():
row, col = agent.pos[0], agent.pos[1]
self.single_update_world_color_map(row, col, self.world_map[row, col])
self.update_moves(agent_actions)
for agent in self.agents.values():
pos = agent.pos
new_char = agent.consume(self.world_map[pos[0], pos[1]])
self.single_update_map(pos[0], pos[1], new_char)
# execute custom moves like firing
self.update_custom_moves(agent_actions)
# execute spawning events
self.custom_map_update()
map_with_agents = self.get_map_with_agents()
# Add agents to color map
for agent in self.agents.values():
row, col = agent.pos[0], agent.pos[1]
# Firing beams have priority over agents and should cover them
if self.world_map[row, col] not in [b"F", b"C"]:
self.single_update_world_color_map(row, col, agent.get_char_id())
observations = {}
rewards = {}
dones = {}
infos = {}
for agent in self.agents.values():
agent.full_map = map_with_agents
rgb_arr = self.color_view(agent)
# concatenate on the prev_actions to the observations
if self.return_agent_actions:
prev_actions = np.array(
[actions[key] for key in sorted(actions.keys()) if key != agent.agent_id]
).astype(np.uint8)
visible_agents = self.find_visible_agents(agent.agent_id)
observations[agent.agent_id] = {
"curr_obs": rgb_arr,
"other_agent_actions": prev_actions,
"visible_agents": visible_agents,
"prev_visible_agents": agent.prev_visible_agents,
}
agent.prev_visible_agents = visible_agents
else:
observations[agent.agent_id] = {"curr_obs": rgb_arr}
rewards[agent.agent_id] = agent.compute_reward()
dones[agent.agent_id] = agent.get_done()
infos[agent.agent_id] = {}
if self.use_collective_reward:
collective_reward = sum(rewards.values())
for agent in rewards.keys():
rewards[agent] = collective_reward
dones["__all__"] = np.any(list(dones.values()))
return observations, rewards, dones, infos
def reset(self):
"""Reset the environment.
This method is performed in between rollouts. It resets the state of
the environment.
Returns
-------
observation: dict of numpy ndarray
the initial observation of the space. The initial reward is assumed
to be zero.
"""
self.beam_pos = []
self.agents = {}
self.setup_agents()
self.reset_map()
self.custom_map_update()
map_with_agents = self.get_map_with_agents()
observations = {}
for agent in self.agents.values():
agent.full_map = map_with_agents
rgb_arr = self.color_view(agent)
# concatenate on the prev_actions to the observations
if self.return_agent_actions:
# No previous actions so just pass in "wait" action
prev_actions = np.array([4 for _ in range(self.num_agents - 1)]).astype(np.uint8)
visible_agents = self.find_visible_agents(agent.agent_id)
observations[agent.agent_id] = {
"curr_obs": rgb_arr,
"other_agent_actions": prev_actions,
"visible_agents": visible_agents,
"prev_visible_agents": visible_agents,
}
agent.prev_visible_agents = visible_agents
else:
observations[agent.agent_id] = {"curr_obs": rgb_arr}
return observations
def seed(self, seed=None):
np.random.seed(seed)
def close(self):
plt.close()
@property
def agent_pos(self):
return [agent.pos.tolist() for agent in self.agents.values()]
def get_map_with_agents(self):
"""Gets a version of the environment map where generic
'P' characters have been replaced with specific agent IDs.
Returns:
2D array of strings representing the map.
"""
grid = np.copy(self.world_map)
for agent in self.agents.values():
char_id = agent.get_char_id()
# If agent is not within map, skip.
if not (0 <= agent.pos[0] < grid.shape[0] and 0 <= agent.pos[1] < grid.shape[1]):
continue
grid[agent.pos[0], agent.pos[1]] = char_id
# beams should overlay agents
for beam_pos in self.beam_pos:
grid[beam_pos[0], beam_pos[1]] = beam_pos[2]
return grid
def check_agent_map(self, agent_map):
"""Checks the map to make sure agents aren't duplicated"""
unique, counts = np.unique(agent_map, return_counts=True)
count_dict = dict(zip(unique, counts))
# check for multiple agents
for i in range(self.num_agents):
if count_dict[chr(i + 1)] != 1:
print("Error! Wrong number of agent", i, "in map!")
return False
return True
def full_map_to_colors(self):
map_with_agents = self.get_map_with_agents()
rgb_arr = np.zeros((map_with_agents.shape[0], map_with_agents.shape[1], 3), dtype=int)
return self.map_to_colors(map_with_agents, self.color_map, rgb_arr)
def color_view(self, agent):
row, col = agent.pos[0], agent.pos[1]
view_slice = self.world_map_color[
row + self.map_padding - self.view_len : row + self.map_padding + self.view_len + 1,
col + self.map_padding - self.view_len : col + self.map_padding + self.view_len + 1,
]
if agent.orientation == "UP":
rotated_view = view_slice
elif agent.orientation == "LEFT":
rotated_view = np.rot90(view_slice)
elif agent.orientation == "DOWN":
rotated_view = np.rot90(view_slice, k=2)
elif agent.orientation == "RIGHT":
rotated_view = np.rot90(view_slice, k=1, axes=(1, 0))
return rotated_view
def map_to_colors(self, mmap, color_map, rgb_arr, orientation="UP"):
"""Converts a map to an array of RGB values.
Parameters
----------
mmap: np.ndarray
map to convert to colors
Double m to avoid shadowing map.
color_map: dict
mapping between array elements and desired colors
rgb_arr: np.array
Variable to store the mapping in
orientation:
The way in which the output should be oriented.
UP = no rotation.
RIGHT = Clockwise 90 degree rotation.
DOWN = Clockwise 180 degree rotation.
LEFT = Clockwise 270 degree rotation.
Returns
-------
arr: np.ndarray
3-dim numpy array consisting of color map
"""
x_len = mmap.shape[0]
y_len = mmap.shape[1]
if orientation == "UP":
for row_elem in range(x_len):
for col_elem in range(y_len):
rgb_arr[row_elem, col_elem, :] = color_map[mmap[row_elem, col_elem]]
elif orientation == "LEFT":
for row_elem in range(x_len):
for col_elem in range(y_len):
rgb_arr[row_elem, col_elem, :] = color_map[mmap[col_elem, x_len - 1 - row_elem]]
elif orientation == "DOWN":
for row_elem in range(x_len):
for col_elem in range(y_len):
rgb_arr[row_elem, col_elem, :] = color_map[
mmap[x_len - 1 - row_elem, y_len - 1 - col_elem]
]
elif orientation == "RIGHT":
for row_elem in range(x_len):
for col_elem in range(y_len):
rgb_arr[row_elem, col_elem, :] = color_map[mmap[y_len - 1 - col_elem, row_elem]]
else:
raise ValueError("Orientation {} is not valid".format(orientation))
return rgb_arr
def render(self, filename=None, mode="human"):
"""Creates an image of the map to plot or save.
Args:
filename: If a string is passed, will save the image
to disk at this location.
"""
rgb_arr = self.full_map_to_colors()
if mode == "human":
plt.cla()
plt.imshow(rgb_arr, interpolation="nearest")
if filename is None:
plt.show(block=False)
else:
plt.savefig(filename)
return None
return rgb_arr
def update_moves(self, agent_actions):
"""Converts agent action tuples into a new map and new agent positions.
Also resolves conflicts over multiple agents wanting a cell.
This method works by finding all conflicts over a cell and randomly assigning them
to one of the agents that desires the slot. It then sets all of the other agents
that wanted the cell to have a move of staying. For moves that do not directly
conflict with another agent for a cell, but may not be temporarily resolvable
due to an agent currently being in the desired cell, we continually loop through
the actions until all moves have been satisfied or deemed impossible.
For example, agent 1 may want to move from [1,2] to [2,2] but agent 2 is in [2,2].
Agent 2, however, is moving into [3,2]. Agent-1's action is first in the order so at the
first pass it is skipped but agent-2 moves to [3,2]. In the second pass, agent-1 will
then be able to move into [2,2].
Parameters
----------
agent_actions: dict
dict with agent_id as key and action as value
"""
reserved_slots = []
for agent_id, action in agent_actions.items():
agent = self.agents[agent_id]
selected_action = self.all_actions[action]
# TODO(ev) these two parts of the actions
if "MOVE" in action or "STAY" in action:
# rotate the selected action appropriately
rot_action = self.rotate_action(selected_action, agent.get_orientation())
new_pos = agent.pos + rot_action
# allow the agents to confirm what position they can move to
new_pos = agent.return_valid_pos(new_pos)
reserved_slots.append((*new_pos, b"P", agent_id))
elif "TURN" in action:
new_rot = self.update_rotation(action, agent.get_orientation())
agent.update_agent_rot(new_rot)
# now do the conflict resolution part of the process
# helpful for finding the agent in the conflicting slot
agent_by_pos = {tuple(agent.pos): agent.agent_id for agent in self.agents.values()}
# agent moves keyed by ids
agent_moves = {}
# lists of moves and their corresponding agents
move_slots = []
agent_to_slot = []
for slot in reserved_slots:
row, col = slot[0], slot[1]
if slot[2] == b"P":
agent_id = slot[3]
agent_moves[agent_id] = [row, col]
move_slots.append([row, col])
agent_to_slot.append(agent_id)
# cut short the computation if there are no moves
if len(agent_to_slot) > 0:
# first we will resolve all slots over which multiple agents
# want the slot
# shuffle so that a random agent has slot priority
shuffle_list = list(zip(agent_to_slot, move_slots))
np.random.shuffle(shuffle_list)
agent_to_slot, move_slots = zip(*shuffle_list)
unique_move, indices, return_count = np.unique(
move_slots, return_index=True, return_counts=True, axis=0
)
search_list = np.array(move_slots)
# first go through and remove moves that can't possible happen. Three types
# 1. Trying to move into an agent that has been issued a stay command
# 2. Trying to move into the spot of an agent that doesn't have a move
# 3. Two agents trying to walk through one another
# Resolve all conflicts over a space
if np.any(return_count > 1):
for move, index, count in zip(unique_move, indices, return_count):
if count > 1:
# check that the cell you are fighting over doesn't currently
# contain an agent that isn't going to move for one of the agents
# If it does, all the agents commands should become STAY
# since no moving will be possible
conflict_indices = np.where((search_list == move).all(axis=1))[0]
all_agents_id = [agent_to_slot[i] for i in conflict_indices]
# all other agents now stay in place so update their moves
# to reflect this
conflict_cell_free = True
for agent_id in all_agents_id:
moves_copy = agent_moves.copy()
# TODO(ev) code duplication, simplify
if move.tolist() in self.agent_pos:
# find the agent that is currently at that spot and make sure
# that the move is possible. If it won't be, remove it.
conflicting_agent_id = agent_by_pos[tuple(move)]
curr_pos = self.agents[agent_id].pos.tolist()
curr_conflict_pos = self.agents[conflicting_agent_id].pos.tolist()
conflict_move = agent_moves.get(
conflicting_agent_id, curr_conflict_pos
)
# Condition (1):
# a STAY command has been issued
if agent_id == conflicting_agent_id:
conflict_cell_free = False
# Condition (2)
# its command is to stay
# or you are trying to move into an agent that hasn't
# received a command
elif (
conflicting_agent_id not in moves_copy.keys()
or curr_conflict_pos == conflict_move
):
conflict_cell_free = False
# Condition (3)
# It is trying to move into you and you are moving into it
elif conflicting_agent_id in moves_copy.keys():
if (
agent_moves[conflicting_agent_id] == curr_pos
and move.tolist()
== self.agents[conflicting_agent_id].pos.tolist()
):
conflict_cell_free = False
# if the conflict cell is open, let one of the conflicting agents
# move into it
if conflict_cell_free:
self.agents[agent_to_slot[index]].update_agent_pos(move)
agent_by_pos = {
tuple(agent.pos): agent.agent_id for agent in self.agents.values()
}
# ------------------------------------
# remove all the other moves that would have conflicted
remove_indices = np.where((search_list == move).all(axis=1))[0]
all_agents_id = [agent_to_slot[i] for i in remove_indices]
# all other agents now stay in place so update their moves
# to stay in place
for agent_id in all_agents_id:
agent_moves[agent_id] = self.agents[agent_id].pos.tolist()
# make the remaining un-conflicted moves
while len(agent_moves.items()) > 0:
agent_by_pos = {tuple(agent.pos): agent.agent_id for agent in self.agents.values()}
num_moves = len(agent_moves.items())
moves_copy = agent_moves.copy()
del_keys = []
for agent_id, move in moves_copy.items():
if agent_id in del_keys:
continue
if move in self.agent_pos:
# find the agent that is currently at that spot and make sure
# that the move is possible. If it won't be, remove it.
conflicting_agent_id = agent_by_pos[tuple(move)]
curr_pos = self.agents[agent_id].pos.tolist()
curr_conflict_pos = self.agents[conflicting_agent_id].pos.tolist()
conflict_move = agent_moves.get(conflicting_agent_id, curr_conflict_pos)
# Condition (1):
# a STAY command has been issued
if agent_id == conflicting_agent_id:
del agent_moves[agent_id]
del_keys.append(agent_id)
# Condition (2)
# its command is to stay
# or you are trying to move into an agent that hasn't received a command
elif (
conflicting_agent_id not in moves_copy.keys()
or curr_conflict_pos == conflict_move
):
del agent_moves[agent_id]
del_keys.append(agent_id)
# Condition (3)
# It is trying to move into you and you are moving into it
elif conflicting_agent_id in moves_copy.keys():
if (
agent_moves[conflicting_agent_id] == curr_pos
and move == self.agents[conflicting_agent_id].pos.tolist()
):
del agent_moves[conflicting_agent_id]
del agent_moves[agent_id]
del_keys.append(agent_id)
del_keys.append(conflicting_agent_id)
# this move is unconflicted so go ahead and move
else:
self.agents[agent_id].update_agent_pos(move)
del agent_moves[agent_id]
del_keys.append(agent_id)
# no agent is able to move freely, so just move them all
# no updates to hidden cells are needed since all the
# same cells will be covered
if len(agent_moves) == num_moves:
for agent_id, move in agent_moves.items():
self.agents[agent_id].update_agent_pos(move)
break
def update_custom_moves(self, agent_actions):
"""
This function executes non-movement actions like firing, cleaning etc.
The order in which agent actions are resolved is random to ensure homogeneity, similar to
update_moves, otherwise a race condition occurs which prioritizes lower-numbered agents
"""
agent_ids = list(agent_actions.keys())
np.random.shuffle(agent_ids)
for agent_id in agent_ids:
action = agent_actions[agent_id]
# check its not a move based action
if "MOVE" not in action and "STAY" not in action and "TURN" not in action:
agent = self.agents[agent_id]
updates = self.custom_action(agent, action)
if len(updates) > 0:
self.update_map(updates)
def update_map(self, new_points):
"""For points in new_points, place desired char on the map
Update the color map as well"""
for point in new_points:
self.single_update_map(*point)
def single_update_map(self, row, col, char):
self.world_map[row, col] = char
self.world_map_color[row + self.map_padding, col + self.map_padding] = self.color_map[char]
def single_update_world_color_map(self, row, col, char):
"""Only update the color map. This is done separately when agents move, because their own
position state is not contained in self.world_map, but in their own Agent objects"""
self.world_map_color[row + self.map_padding, col + self.map_padding] = self.color_map[char]
def reset_map(self):
"""Resets the map to be empty as well as a custom reset set by subclasses"""
self.world_map = np.full((len(self.base_map), len(self.base_map[0])), b" ", dtype="c")
self.world_map_color = np.full(
(len(self.base_map) + self.view_len * 2, len(self.base_map[0]) + self.view_len * 2, 3),
fill_value=0,
dtype=np.uint8,
)
self.build_walls()
self.custom_reset()
def update_map_fire(
self,
firing_pos,
firing_orientation,
fire_len,
fire_char,
cell_types=[],
update_char=[],
blocking_cells=b"P",
beam_width=3,
):
"""From a firing position, fire a beam that may clean or hit agents
Notes:
(1) Beams are blocked by agents
(2) A beam travels along until it hits a blocking cell at which beam the beam
covers that cell and stops
(3) If a beam hits a cell whose character is in cell_types, it replaces it with
the corresponding index in update_char
(4) As per the rules, the beams fire from in front of the agent and on its
sides so the beam that starts in front of the agent travels out one
cell further than it does along the sides.
(5) This method updates the beam_pos, an internal representation of how
which cells need to be rendered with fire_char in the agent view
Parameters
----------
firing_pos: (list)
the row, col from which the beam is fired
firing_orientation: (string)
the direction the beam is to be fired in
fire_len: (int)
the number of cells forward to fire
fire_char: (bytes)
the cell that should be placed where the beam goes
cell_types: (list of bytes)
the cells that are affected by the beam
update_char: (list of bytes)
the character that should replace the affected cells.
blocking_cells: (list of bytes)
cells that block the firing beam
Returns
-------
updates: (tuple (row, col, char))
the cells that have been hit by the beam and what char will be placed there
"""
agent_by_pos = {tuple(agent.pos): agent_id for agent_id, agent in self.agents.items()}
start_pos = np.asarray(firing_pos)
firing_direction = ORIENTATIONS[firing_orientation]
# compute the other two starting positions
right_shift = self.rotate_right(firing_direction)
if beam_width == 1:
firing_pos = [start_pos]
elif beam_width == 3:
firing_pos = [
start_pos,
start_pos + right_shift - firing_direction,
start_pos - right_shift - firing_direction,
]
else:
raise NotImplementedError()
firing_points = []
updates = []
for pos in firing_pos:
next_cell = pos + firing_direction
for i in range(fire_len):
if (
self.test_if_in_bounds(next_cell)
and self.world_map[next_cell[0], next_cell[1]] != b"@"
):
# Update the cell if needed
firing_points.append((next_cell[0], next_cell[1], fire_char))
for c in range(len(cell_types)):
if self.world_map[next_cell[0], next_cell[1]] == cell_types[c]:
updates.append((next_cell[0], next_cell[1], update_char[c]))
break
# agents absorb beams
# activate the agents hit function if needed
if [next_cell[0], next_cell[1]] in self.agent_pos:
agent_id = agent_by_pos[(next_cell[0], next_cell[1])]
self.agents[agent_id].hit(fire_char)
break
# check if the cell blocks beams. For example, waste blocks beams.
if self.world_map[next_cell[0], next_cell[1]] in blocking_cells:
break
# increment the beam position
next_cell += firing_direction
else:
break
self.beam_pos += firing_points
return updates
def spawn_point(self):
"""Returns a randomly selected spawn point."""
spawn_index = 0
is_free_cell = False
curr_agent_pos = [agent.pos.tolist() for agent in self.agents.values()]
np.random.shuffle(self.spawn_points)
for i, spawn_point in enumerate(self.spawn_points):
if [spawn_point[0], spawn_point[1]] not in curr_agent_pos:
spawn_index = i
is_free_cell = True
assert is_free_cell, "There are not enough spawn points! Check your map?"
return np.array(self.spawn_points[spawn_index])
def spawn_rotation(self):
"""Return a randomly selected initial rotation for an agent"""
rand_int = np.random.randint(len(ORIENTATIONS.keys()))
return list(ORIENTATIONS.keys())[rand_int]
def build_walls(self):
for i in range(len(self.wall_points)):
row, col = self.wall_points[i]
self.single_update_map(row, col, b"@")
########################################
# Utility methods, move these eventually
########################################
# TODO(ev) this can be a general property of map_env or a util
def rotate_action(self, action_vec, orientation):
# WARNING: Note, we adopt the physics convention that \theta=0 is in the +y direction
if orientation == "UP":
return action_vec
elif orientation == "LEFT":
return self.rotate_left(action_vec)
elif orientation == "RIGHT":
return self.rotate_right(action_vec)
else:
return self.rotate_left(self.rotate_left(action_vec))
def rotate_left(self, action_vec):
return np.dot(self.all_actions["TURN_COUNTERCLOCKWISE"], action_vec)
def rotate_right(self, action_vec):
return np.dot(self.all_actions["TURN_CLOCKWISE"], action_vec)
# TODO(ev) this should be an agent property
def update_rotation(self, action, curr_orientation):
if action == "TURN_COUNTERCLOCKWISE":
if curr_orientation == "LEFT":
return "DOWN"
elif curr_orientation == "DOWN":
return "RIGHT"
elif curr_orientation == "RIGHT":
return "UP"
else:
return "LEFT"
else:
if curr_orientation == "LEFT":
return "UP"
elif curr_orientation == "UP":
return "RIGHT"
elif curr_orientation == "RIGHT":
return "DOWN"
else:
return "LEFT"
# TODO(ev) this definitely should go into utils or the general agent class
def test_if_in_bounds(self, pos):
"""Checks if a selected cell is outside the range of the map"""
return 0 <= pos[0] < self.world_map.shape[0] and 0 <= pos[1] < self.world_map.shape[1]
def find_visible_agents(self, agent_id):
"""Returns all the agents that can be seen by agent with agent_id
Args
----
agent_id: str
The id of the agent whose visible agents we are asking about
Returns
-------
visible_agents: list
which agents can be seen by the agent with id "agent_id"
"""
agent_pos = self.agents[agent_id].pos
upper_lim = int(agent_pos[0] + self.agents[agent_id].row_size)
lower_lim = int(agent_pos[0] - self.agents[agent_id].row_size)
left_lim = int(agent_pos[1] - self.agents[agent_id].col_size)
right_lim = int(agent_pos[1] + self.agents[agent_id].col_size)
# keep this sorted so the visibility matrix is always in order
other_agent_pos = [
self.agents[other_agent_id].pos
for other_agent_id in sorted(self.agents.keys())
if other_agent_id != agent_id
]
return np.array(
[
1
if (lower_lim <= agent_tup[0] <= upper_lim and left_lim <= agent_tup[1] <= right_lim)
else 0
for agent_tup in other_agent_pos
],
dtype=np.uint8,
)
@staticmethod
def get_environment_callbacks():
return DefaultCallbacks
|
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|
struct ShaderSpecification
source_file::String
reuse_descriptors::Bool
entry_point::Symbol
stage::Vk.ShaderStageFlag
language::ShaderLanguage
end
function ShaderSpecification(source_file, stage::Vk.ShaderStageFlag; reuse_descriptors = false, entry_point = :main)
ShaderSpecification(source_file, reuse_descriptors, entry_point, stage, shader_language(source_file))
end
function ShaderSpecification(source_file, language::ShaderLanguage; reuse_descriptors = false, entry_point = :main)
stage = @match language begin
&GLSL || &HLSL => shader_stage(source_file, language)
&SPIR_V => shader_stage(source_file, language) # will error, need to introspect into SPIR-V module
end
ShaderSpecification(source_file, reuse_descriptors, entry_point, stage, language)
end
function ShaderSpecification(source_file; reuse_descriptors = false, entry_point = :main)
if shader_language(source_file) == SPIR_V
ShaderSpecification(source_file, SPIR_V; reuse_descriptors, entry_point)
else
error("Language or stage must be supplied")
end
end
|
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|
# Copyright (c) 2020, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of FINN nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pytest
import brevitas.onnx as bo
import numpy as np
import torch
from PIL import Image
import finn.core.onnx_exec as oxe
import finn.transformation.streamline.absorb as absorb
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.general import (
GiveReadableTensorNames,
GiveUniqueNodeNames,
GiveUniqueParameterTensors,
)
from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.insert_topk import InsertTopK
from finn.transformation.merge_onnx_models import MergeONNXModels
from finn.util.basic import make_build_dir
from finn.util.pytorch import NormalizePreProc
from finn.util.test import crop_center, get_test_model_trained, resize_smaller_side
@pytest.mark.xfail
def test_brevitas_mobilenet():
# get single image as input and prepare image
img = Image.open("/workspace/finn/tests/brevitas/king_charles.jpg")
# resize smallest side of the image to 256 pixels and resize larger side
# with same ratio
img = resize_smaller_side(256, img)
# crop central 224*224 window
img = crop_center(224, img)
# save image as numpy array and as torch tensor to enable testing in
# brevitas/pytorch and finn and transpose from (H, W, C) to (C, H, W)
img_np = np.asarray(img).copy().astype(np.float32).transpose(2, 0, 1)
img_np = img_np.reshape(1, 3, 224, 224)
img_torch = torch.from_numpy(img_np).float()
# export preprocess
export_onnx_path = make_build_dir("test_brevitas_mobilenet-v1_")
preproc_onnx = export_onnx_path + "/quant_mobilenet_v1_4b_preproc.onnx"
mean = [0.485, 0.456, 0.406]
std = 0.226
ch = 3
preproc = NormalizePreProc(mean, std, ch)
bo.export_finn_onnx(preproc, (1, 3, 224, 224), preproc_onnx)
preproc_model = ModelWrapper(preproc_onnx)
# set input finn datatype to UINT8
preproc_model.set_tensor_datatype(
preproc_model.graph.input[0].name, DataType["UINT8"]
)
preproc_model = preproc_model.transform(InferShapes())
preproc_model = preproc_model.transform(GiveUniqueNodeNames())
preproc_model = preproc_model.transform(GiveUniqueParameterTensors())
preproc_model = preproc_model.transform(GiveReadableTensorNames())
finn_onnx = export_onnx_path + "/quant_mobilenet_v1_4b_exported.onnx"
mobilenet = get_test_model_trained("mobilenet", 4, 4)
bo.export_finn_onnx(mobilenet, (1, 3, 224, 224), finn_onnx)
# do forward pass in PyTorch/Brevitas
input_tensor = preproc.forward(img_torch)
expected = mobilenet.forward(input_tensor).detach().numpy()
expected_topk = expected.flatten()
expected_top5 = np.argsort(expected_topk)[-5:]
expected_top5 = np.flip(expected_top5)
expected_top5_prob = []
for index in expected_top5:
expected_top5_prob.append(expected_topk[index])
model = ModelWrapper(finn_onnx)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(InsertTopK())
# get initializer from Mul that will be absorbed into topk
a0 = model.get_initializer(model.graph.node[-2].input[1])
model = model.transform(absorb.AbsorbScalarMulAddIntoTopK())
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
model = model.transform(InferDataLayouts())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveUniqueParameterTensors())
model = model.transform(GiveReadableTensorNames())
model.save(export_onnx_path + "/quant_mobilenet_v1_4b_wo_preproc.onnx")
model = model.transform(MergeONNXModels(preproc_model))
model.save(export_onnx_path + "/quant_mobilenet_v1_4b.onnx")
idict = {model.graph.input[0].name: img_np}
odict = oxe.execute_onnx(model, idict, True)
produced = odict[model.graph.output[0].name]
produced_prob = odict["TopK_0_out0"] * a0
assert (produced.flatten() == expected_top5).all()
assert np.isclose(produced_prob.flatten(), expected_top5_prob).all()
|
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|
from .conftest import base_config
import numpy as np
from numpy.testing import assert_allclose
import openamundsen as oa
from pathlib import Path
import pytest
@pytest.mark.slow
def test_evapotranspiration(tmp_path):
config = base_config()
config.start_date = '2020-07-01'
config.end_date = '2020-07-15'
model = oa.OpenAmundsen(config)
model.initialize()
meteo = model.meteo.copy()
meteo.temp.values[:, 30] = np.nan # nan values should not propagate to evapotranspiration variables
roi_xs = model.grid.X.flat[model.grid.roi_idxs_flat]
roi_ys = model.grid.Y.flat[model.grid.roi_idxs_flat]
for p in Path(config.input_data.grids.dir).glob('*.asc'):
(tmp_path / p.name).symlink_to(p)
soil = np.zeros(model.grid.shape, dtype=int)
lc = np.zeros(model.grid.shape, dtype=int)
# Test with all land cover classes and fixed soil texture
soil[:] = 5
lccs = model.config.land_cover.classes.keys()
config.output_data.timeseries.points = []
for lcc_num, lcc in enumerate(lccs):
config.output_data.timeseries.points.append({
'x': float(roi_xs[lcc_num]),
'y': float(roi_ys[lcc_num]),
})
lc.flat[model.grid.roi_idxs_flat[lcc_num]] = lcc
rio_meta = {'driver': 'AAIGrid'}
oa.fileio.write_raster_file(
oa.util.raster_filename('soil', config),
soil.astype(np.int32),
model.grid.transform,
**rio_meta,
)
oa.fileio.write_raster_file(
oa.util.raster_filename('lc', config),
lc.astype(np.int32),
model.grid.transform,
**rio_meta,
)
config.evapotranspiration.enabled = True
config.output_data.timeseries.add_default_points = False
config.output_data.timeseries.variables = [
{'var': 'evapotranspiration.evaporation'},
{'var': 'evapotranspiration.transpiration'},
{'var': 'evapotranspiration.evapotranspiration'},
]
model = oa.OpenAmundsen(config)
model.initialize()
model.meteo = meteo
model.run()
ds = model.point_output.data
for lcc_num, lcc in enumerate(lccs):
lcc_params = model.config.land_cover.classes[lcc]
crop_coeff_type = lcc_params.get('crop_coefficient_type', None)
is_sealed = lcc_params.get('is_sealed', False)
ds_lcc = ds.isel(point=lcc_num)
assert np.all(ds_lcc.evapotranspiration >= 0)
assert ds_lcc.evapotranspiration.max() > 0
if crop_coeff_type == 'dual' or is_sealed:
assert np.all(ds_lcc.evaporation >= 0)
assert np.all(ds_lcc.transpiration >= 0)
assert_allclose(
ds_lcc.evaporation + ds_lcc.transpiration,
ds_lcc.evapotranspiration,
rtol=1e-3,
)
elif crop_coeff_type == 'single':
assert np.all(np.isnan(ds_lcc.evaporation))
assert np.all(np.isnan(ds_lcc.transpiration))
# Test with all soil texture classes and fixed land cover
lc[:] = 9
soil[:] = 0
stcs = range(1, 9 + 1)
config.output_data.timeseries.points = []
for stc_num, stc in enumerate(stcs):
config.output_data.timeseries.points.append({
'x': float(roi_xs[stc_num]),
'y': float(roi_ys[stc_num]),
})
soil.flat[model.grid.roi_idxs_flat[stc_num]] = stc
rio_meta = {'driver': 'AAIGrid'}
oa.fileio.write_raster_file(
oa.util.raster_filename('soil', config),
soil.astype(np.int32),
model.grid.transform,
**rio_meta,
)
oa.fileio.write_raster_file(
oa.util.raster_filename('lc', config),
lc.astype(np.int32),
model.grid.transform,
**rio_meta,
)
model = oa.OpenAmundsen(config)
model.initialize()
model.meteo = meteo
model.run()
ds = model.point_output.data
for stc_num, stc in enumerate(stcs):
ds_stc = ds.isel(point=stc_num)
assert np.all(ds_stc.evapotranspiration >= 0)
assert ds_lcc.evapotranspiration.max() > 0
assert np.all(ds_stc.evaporation >= 0)
assert np.all(ds_stc.transpiration >= 0)
assert_allclose(
ds_stc.evaporation + ds_stc.transpiration,
ds_stc.evapotranspiration,
rtol=1e-3,
)
|
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|
abstract type PDXObject end
mutable struct PDFormXObject <: PDXObject
doc::PDDoc
cosXObj::CosIndirectObject{CosStream}
matrix::Matrix{Float32}
bbox::CDRect{Float32}
fonts::Dict{CosName, PDFont}
xobjs::Dict{CosName, PDXObject}
content_objects::PDPageObjectGroup
function PDFormXObject(doc::PDDoc, cosxobj::CosIndirectObject{CosStream})
mat = get(cosxobj, cn"Matrix")
box = get(cosxobj, cn"BBox")
@assert box !== CosNull "Invalid Form XObject without bounding box"
matrix = mat === CosNull ?
[1f0 0f0 0f0; 0f0 1f0 0f0; 0f0 0f0 1f0] :
hcat(reshape((get.(CosFloat.(get(mat)))), (2, 3))', [0f0, 0f0, 1f0])
bbox = CDRect{Float32}(CDRect(box))
fonts = Dict{CosName, PDFont}()
xobjs = Dict{CosName, PDXObject}()
new(doc, cosxobj, matrix, bbox, fonts, xobjs, PDPageObjectGroup())
end
end
mutable struct PDImageXObject <: PDXObject
doc::PDDoc
obj::CosIndirectObject{CosStream}
end
mutable struct PDDefaultXObject <: PDXObject
doc::PDDoc
obj::CosObject
end
function createPDXObject(doc::PDDoc, cosstm::CosObject)
otype = get(cosstm, cn"Type")
@assert otype === cn"XObject" || otype === CosNull
subtype = get(cosstm, cn"Subtype")
subtype === cn"Form" && return PDFormXObject(doc, cosstm)
subtype === cn"Image" && return PDImageXObject(doc, cosstm)
return PDDefaultXObject(doc, cosobj)
end
function find_resource(xobj::PDFormXObject,
restype::CosName,
resname::CosName)
cosdoc = xobj.doc.cosDoc
resref = get(xobj.cosXObj, cn"Resources")
resref === CosNull && return CosNull
resources = cosDocGetObject(cosdoc, resref)
resources === CosNull && return CosNull
ress = cosDocGetObject(cosdoc, resources, restype)
ress === CosNull && return CosNull
res = cosDocGetObject(cosdoc, ress, resname)
return res
end
get_font(xobj::PDXObject, fontname::CosName) =
get!(xobj.fonts, fontname,
get_pd_font!(xobj.doc, find_resource(xobj, cn"Font", fontname)))
get_xobject(xobj::PDXObject, xobjname::CosName) =
get!(xobj.xobjs, xobjname,
get_pd_xobject!(xobj.doc,
find_resource(xobj, cn"XObject", xobjname)))
function load_content_objects(xobj::PDFormXObject)
stm = xobj.cosXObj
bufstm = decode(stm)
# try
load_objects(xobj.content_objects, bufstm)
# finally
# util_close(bufstm)
# end
return nothing
end
Do(xobj::PDDefaultXObject, state::GState) = nothing
Do(xobj::PDImageXObject, state::GState) = nothing
function Do(xobj::PDFormXObject, state::GState)
isempty(xobj.content_objects) && load_content_objects(xobj)
isempty(xobj.content_objects) && return state
xstate = GState{:PDFIO}()
ctm = state[:CTM]
nctm = xobj.matrix*ctm
xstate[:CTM] = nctm
xstate[:source] = xobj
xstate[:text_layout] = state[:text_layout]
xstate[:h_profile] = state[:h_profile]
evalContent!(xobj.content_objects, xstate)
return state
end
|
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|
Extraction Language Scheme.
Require Import NArith.
Require Import Arith.
Require Import Bool.
Require Import List.
Require Import Bag.
Require Import Dict.
Require Import CpdtTactics.
Require Import JamesTactics.
Require Import KonneTactics.
Require Import Coq.Program.Basics.
Require Import EqDec.
Require Import Enumerable.
Require Import BGPSpec.
Require Import Equality.
Require Import SymbolicExecution.
Require Import Graph.
Require Import SingleAS.
Require Import BGPV.
Require Import SpaceSearch.
Require Import Rosette.
Require Import Misc.
Import ListNotations.
Import EqNotations.
(* LOCAL_PREF nat does not match the ones used in Racket *)
(* router lists do not match the one used in Racket *)
Instance freeList `{SpaceSearch} {A} (l:list A) : Free {a | In a l}.
induction l.
- refine {| free := empty |}.
intros [? []].
- refine {| free := _ |}.
+ refine (union _ _).
* refine (single (exist _ a _)).
cbn.
left.
reflexivity.
* refine (bind (free {a | In a l}) _).
intros [a' ?].
refine (single (exist _ a' _)).
cbn in *.
right.
trivial.
+ intros [a' inl'].
cbn in *.
rewrite <- unionOk.
destruct inl' as [|inl'].
* left.
subst_max.
apply singleOk.
reflexivity.
* right.
rewrite <- bindOk.
exists (exist _ a' inl').
constructor; [apply freeOk|].
apply singleOk.
reflexivity.
Defined.
Instance enumerableFree {A} {h:forall (S:SpaceSearch), @Free S A} : enumerable A.
specialize (h listSpaceSearch).
destruct h.
cbn in *.
refine {|enumerate := free|}.
trivial.
Defined.
Parameter IP : Type.
Parameter eqDecideIP : forall (r r':IP), decide (r = r').
Extract Constant IP => "__".
Extract Constant eqDecideIP => "eq-dec?".
Instance eqDecIP : eqDec IP.
constructor.
apply eqDecideIP.
Defined.
Parameter CIDR : Type.
Parameter eqDecideCIDR : forall (r r':CIDR), decide (r = r').
Parameter freeCIDR : Space CIDR.
Axiom freeCIDROk : forall p, contains p freeCIDR.
Extract Constant CIDR => "__".
Extract Constant eqDecideCIDR => "eq-dec?".
Extract Constant freeCIDR => "(lambda (_) (symbolic-prefix))".
Instance FreeCIDR : Free CIDR := {|
free := freeCIDR;
freeOk := freeCIDROk
|}.
Instance eqDecCIDR : eqDec CIDR.
constructor.
apply eqDecideCIDR.
Defined.
Instance cidrPrefix : PrefixClass := {|
Prefix := CIDR
|}.
Section BGPV.
Existing Instance freeUnit.
Parameter AS : Type.
Parameter BGPAttributes : AS -> Type.
Variable setup:AS.
Definition LOCAL_PREF (_:BGPAttributes setup) := 0. (* TODO local-pref is still broken *)
(* Parameter LOCAL_PREF : BGPAttributes -> nat. *)
Parameter eqDecideBGPAttributes : forall (r r':BGPAttributes setup), decide (r = r').
Instance eqDecBGPAttributes : eqDec (BGPAttributes setup).
constructor.
apply eqDecideBGPAttributes.
Defined.
Instance bgpAttributes : PathAttributesClass := {|
PathAttributes := BGPAttributes setup;
localPref := LOCAL_PREF
|}.
Parameter freeBGPAttributes : Space (BGPAttributes setup).
Parameter freeBGPAttributesOk : forall p, contains p (freeBGPAttributes).
Instance FreeBGPAttributes : Free (BGPAttributes setup) := {|
free := freeBGPAttributes;
freeOk := freeBGPAttributesOk
|}.
Parameter internals : AS -> list IP.
Parameter neighbors : {ri | In ri (internals setup)} -> list IP.
Definition bagpipeRouter (t:RouterType) :=
match t with
| internal => {ri | In ri (internals setup)}
| external => {re | exists riOk, In re (neighbors riOk)}
end.
Definition bagpipeNeighbor (ri:bagpipeRouter internal) (re:bagpipeRouter external) : Type.
cbn in *.
destruct re as [re ?].
exact (In re (neighbors ri)).
Defined.
Instance freeRouter `{SpaceSearch} : forall t, Free (bagpipeRouter t).
intros []; cbn.
- refine {| free := bind (free {a | In a (internals setup)}) single |}.
intros i.
rewrite <- bindOk.
exists i.
constructor; [apply freeOk|].
rewrite <- singleOk.
reflexivity.
- refine {| free := _ |}.
+ refine (bind (free {a | In a (internals setup)}) _).
intros ri.
refine (bind (free {a | In a (neighbors ri)}) _).
intros [re ?].
apply single.
refine (exist _ re _).
exists ri.
intuition.
+ intros [re [riOk reOk]].
rewrite <- bindOk.
exists riOk.
constructor; [apply freeOk|].
rewrite <- bindOk.
exists (exist _ re reOk).
constructor; [apply freeOk|].
apply singleOk.
reflexivity.
Defined.
Instance freeNeighbors `{SpaceSearch} : forall s, Free {d : bagpipeRouter external & bagpipeNeighbor s d}.
intro r.
refine {| free := bind (free {a | In a (neighbors r)}) _ |}. {
cbn in *.
intros [d n].
refine (single [exist _ d _ & n]).
exists r.
exact n.
}
Proof.
intros [[d [r' n']] n].
cbn in *.
apply bindOk.
exists (exist _ d n).
constructor; [apply freeOk|].
apply singleOk.
generalize_proofs.
reflexivity.
Defined.
Instance freeNeighbor `{SpaceSearch} : forall s d, Free (bagpipeNeighbor s d).
unfold bagpipeNeighbor.
cbn.
intros riOk [re reOk'].
cbn.
refine {| free := _ |}.
- destruct (@in_dec _ eqDecide re (neighbors riOk)).
+ apply single.
trivial.
+ apply empty.
- intros reOk.
cbn.
break_match.
+ proof_irrelevance.
apply singleOk.
reflexivity.
+ intuition.
Defined.
Instance bagpipeTopology : SingleASTopologyClass.
refine {|
router := bagpipeRouter;
neighbor := bagpipeNeighbor
|}.
Proof.
- intros []; constructor; apply eqDecide.
- cbn.
intros [s ?] [d ?].
cbn.
constructor.
intros c c'.
left.
proof_irrelevance.
reflexivity.
Defined.
Existing Instance singleASTopology.
Parameter denoteImport : forall r:router internal, incoming [internal & r] -> Prefix -> PathAttributes -> RoutingInformation.
Parameter denoteExport : forall r:router internal, outgoing [internal & r] -> Prefix -> PathAttributes -> RoutingInformation.
Parameter Query : Type.
Parameter denoteQuery : Query -> forall r, incoming [internal & r] -> outgoing [internal & r] -> Prefix ->
@RoutingInformation trackingAttributes' ->
@RoutingInformation trackingAttributes' ->
@RoutingInformation trackingAttributes' -> bool.
Instance bagpipeConfiguration : SingleASConfigurationClass.
refine {|
intImport := denoteImport;
intExport r i := denoteExport r
|}.
Defined.
Definition bgpvCore' := @bgpvCore rosette _ _ _ _ _ _ Query denoteQuery.
Definition listSearch {A} := @search listSpaceSearch A.
Definition listBind {A B} := @bind listSpaceSearch A B.
Parameter bgpvScheduler : forall Q v, {o | o = listSearch (listBind v (compose optionToSpace (bgpvCore' Q)))}.
Definition bgpv := @parallelBGPV rosette _ _ _ _ _ _ _ _ Query denoteQuery listSpaceSearch bgpvScheduler.
Definition bgpvImport := @parallelBGPVImport rosette _ _ _ _ _ _ _ _ Query denoteQuery listSpaceSearch bgpvScheduler.
(* Definition bgpv := @fastPolicyDec' rosette _ _ _ _ _ _ _ _ Query denoteQuery. *)
End BGPV.
Extract Constant Query => "__".
Extract Constant denoteQuery => "(lambdas (_) denote-query)".
Extract Constant AS => "__".
Extract Constant BGPAttributes => "__".
Extract Constant eqDecideBGPAttributes => "(lambdas (_) eq-dec?)".
Extract Constant freeBGPAttributes => "(lambdas (as _) (symbolic-announcement (as->environment as)))".
Extract Constant denoteImport => "denote-import".
Extract Constant denoteExport => "denote-export".
Extract Constant internals => "denote-internals".
Extract Constant neighbors => "denote-neighbors".
(* Extract Constant LOCAL_PREF => "announcement-pref". *)
Extract Constant bgpvScheduler => "distributed-bgpv-scheduler".
Extraction "bgpv" bgpv bgpvImport bgpvCore' optionToSpace.
|
{"author": "konne88", "repo": "bagpipe", "sha": "9338220fe1fec2e7196e1143c92065ce5d5a7b46", "save_path": "github-repos/coq/konne88-bagpipe", "path": "github-repos/coq/konne88-bagpipe/bagpipe-9338220fe1fec2e7196e1143c92065ce5d5a7b46/src/bagpipe/coq/Test/BagpipeExtract.v"}
|
#!/usr/bin/python
import sys
from numpy import *
import random
import numpy.random as nrd
from optparse import OptionParser
parser = OptionParser(usage="-r REF expressionFile1.exp [expressionFiles2.exp]\n\n Program generates reads from fasta file based on read counts provided in the expression files files (generated by setExpression2.py). The reads are saved into expressionFile1.fastq")
#parser.add_option("-f", "--fold", dest="fold", default=2.0, type="float")
#parser.add_option("-N", "--molN", dest="N", default=1000000, type="int")
#parser.add_option("-s", "--skip", dest="skip", default=1, type="int")
#parser.add_option("-v", "--verbose", default=False, dest="verbose", action="store_true", help="Print out separate histograms")
#parser.add_option("-e", "--empty", default=False, dest="empty", action="store_true", help="")
#parser.add_option("-l", "--logged", default=True, dest="logged", action="store_false")
parser.add_option("-r","--ref", dest="ref", type="string", help="reference fasta file with transcript names matching the names in .exp files")
(options, args) = parser.parse_args()
# quality probs {{{
# starting at ascii 30 data from SRR039631.fastq
qualProb = [0.0, 0.0, 0.0, 991.0, 66931.0, 0.0, 16354.0, 41889.0, 0.0, 78957.0, 32727.0, 22735.0, 74530.0, 82952.0, 42842.0, 5700.0, 58968.0, 139482.0, 50500.0, 76961.0, 31504.0, 171013.0, 223970.0, 16481.0, 199051.0, 257191.0, 509415.0, 77964.0, 508007.0, 1127517.0, 5918205.0, 166107.0, 829.0, 227.0, 0.0];
qPSum=sum(qualProb);
qP = zeros(len(qualProb));
for i in xrange(1,len(qualProb)):
qP[i] = qP[i-1] + qualProb[i]/qPSum;
#}}}
def getRead(rd,fl): #{{{
if fl:
rd = rd[::-1]; # REALLYY?
rd2=""
for i in xrange(len(rd)):
if rd[i] == 'A' or rd[i] == 'a': rd2+='T';
elif rd[i] == 'T' or rd[i] == 't': rd2+='A';
elif rd[i] == 'G' or rd[i] == 'g': rd2+='C';
elif rd[i] == 'C' or rd[i] == 'c': rd2+='G';
else: rd2+='N'
rd = rd2;
qual="";
read=""
for i in xrange(len(rd)):
x = nrd.random();
q = 30;
while qP[q-1]>x:q-=1;
while qP[q]<x:q+=1;
qch = q+30;
qual += chr(qch);
q = qch - 33;
pm = 10**(q/-10)
x = nrd.random();
if x<pm:
errCh = random.sample(['A','T','G','C','N'],1)[0]
while errCh == rd[i]:
errCh = random.sample(['A','T','G','C','N'],1)[0]
read += errCh;
else:
read += rd[i];
return (read,qual);
#}}}
#fastaN = "/localhome/work/refHG/ensembl/ensemblGenes.fasta"
if options.ref:
fastaN=options.ref;
else:
sys.exit("Please provide reference file");
#fastaN = "ensemblGenes.fasta"
rLen = 50;
for exp in args:
print "Processing file: ",exp;
if exp[-5:] == ".exps": exp=exp[:-5];
exps = []
mapp = {};
inF=open(exp+".exps","r");
i = 0 ;
norm = 0.;
for line in inF:
if line[0] == '#':continue;
lA=line.split();
ct = int(lA[0]);
#d1 = float(lA[1]);
#d2 = float(lA[2]);
#l = float(lA[4]);
name = lA[3];
exps.append([ct,name]);
mapp[name]=i;
i+=1;
inF.close();
M = i;
chrF = open(fastaN,"r");
outF = open(exp+".fastq","w");
reads = [];
trN = ""
trS = ""
strand = 0;
readT = 0
counter=729;
for line in chrF:
if readT>counter:
print " ",readT;
counter*=3;
if len(reads)>1000:
samp = len(reads)/2;
for j in xrange(samp):
i = random.randint(0,len(reads)-1);
fl = int(random.getrandbits(1));
outF.write("@"+reads[i][1]+"-"+str(fl)+"-"+str(readT)+"\n");
readT+=1;
(read,qual) = getRead(reads[i][0],fl);
outF.write( read +"\n+\n"+qual+"\n");
reads.pop(i);
if line[0] == '>':
if trN!="":
trL = len(trS);
if strand: trS += "A"*rLen;
else: trS ="A"*rLen + trS;
ct = exps[mapp[trN]][0];
for i in xrange(ct):
st = random.randint(0,trL-1);
reads.append([trS[st:st+rLen],trN+"-"+str(st)]);
trN = line.split()[0][1:];
if line.split()[2][-2] == '-':strand = 0;
else: strand = 1;
trS = "";
else: trS+=line.rstrip();
for i in xrange(len(reads)):
fl = int(random.getrandbits(1));
outF.write("@"+reads[i][1]+"-"+str(fl)+"-"+str(readT)+"\n");
readT+=1;
(read,qual) = getRead(reads[i][0],fl);
outF.write( read +"\n+\n"+qual+"\n");
outF.close();
chrF.close();
print "Reads written: ",readT;
|
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|
# Unit tests for judiRHS and judiWavefield (without PDE solves)
# Philipp Witte (pwitte.slim@gmail.com)
# May 2018
#
# Mathias Louboutin, mlouboutin3@gatech.edu
# Updated July 2020
########################################################### judiRHS ####################################################
@testset "judiRHS Unit Tests with $(nsrc) sources" for nsrc=[1, 2]
# Constructor
info = example_info(nsrc=nsrc)
rec_geometry = example_rec_geometry(nsrc=nsrc)
data = Array{Array}(undef, nsrc)
for j=1:nsrc
data[j] = randn(Float32, rec_geometry.nt[j], length(rec_geometry.xloc[j]))
end
datacell = process_input_data(vec(hcat(data...)), rec_geometry, info)
@test isequal(datacell, data)
rhs = judiRHS(info, rec_geometry, data)
Pr = judiProjection(info, rec_geometry)
@test isequal(typeof(rhs), judiRHS{Float32})
@test isequal(rhs.geometry, rec_geometry)
rhs2 = Pr'*vec(hcat(data...))
@test isequal(typeof(rhs2), judiRHS{Float32})
@test isequal(rhs2.geometry, rec_geometry)
@test isequal(rhs2.geometry, rhs.geometry)
@test isequal(rhs2.data, rhs.data)
# conj, transpose, adjoint
@test isequal(size(rhs), size(conj(rhs)))
@test isequal(reverse(size(rhs)), size(transpose(rhs)))
@test isequal(reverse(size(rhs)), size(adjoint(rhs)))
# +, -
info = example_info(nsrc=nsrc)
rec_geometry = example_rec_geometry(nsrc=nsrc)
src_geometry = example_src_geometry(nsrc=nsrc)
data1 = Array{Array}(undef, nsrc)
data2 = Array{Array}(undef, nsrc)
for j=1:nsrc
data1[j] = randn(Float32, rec_geometry.nt[j], length(rec_geometry.xloc[j]))
data2[j] = randn(Float32, src_geometry.nt[j], length(src_geometry.xloc[j]))
end
rhs1 = judiRHS(info, rec_geometry, data1)
rhs2 = judiRHS(info, src_geometry, data2)
rhs_sum = rhs1 + rhs2
rhs_sub = rhs1 - rhs2
@test isequal(size(rhs_sum), size(rhs1))
@test isequal(size(rhs_sub), size(rhs1))
@test isequal(length(rhs_sum.geometry.xloc[1]), length(rhs1.geometry.xloc[1]) + length(rhs2.geometry.xloc[1]))
@test isequal(length(rhs_sub.geometry.xloc[1]), length(rhs1.geometry.xloc[1]) + length(rhs2.geometry.xloc[1]))
@test isequal(size(rhs_sum.data[1])[2], size(rhs1.data[1])[2] + size(rhs2.data[1])[2])
@test isequal(size(rhs_sub.data[1])[2], size(rhs1.data[1])[2] + size(rhs2.data[1])[2])
# get index
rhs_sub = rhs[1]
@test isequal(rhs_sub.info.nsrc, 1)
@test isequal(typeof(rhs_sub.geometry), GeometryIC{Float32})
@test isequal(typeof(rhs.data), Array{Array, 1})
@test isequal(length(rhs_sub), Int(length(rhs)/nsrc))
inds = nsrc > 1 ? (1:nsrc) : 1
rhs_sub = rhs[inds]
@test isequal(rhs_sub.info.nsrc, nsrc)
@test isequal(typeof(rhs_sub.geometry), GeometryIC{Float32})
@test isequal(typeof(rhs.data), Array{Array, 1})
@test isequal(length(rhs_sub), length(rhs))
end
|
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|
\documentclass[article,oneside]{memoir}
%%% custom style file with standard settings for xelatex and biblatex. Note that when [minion] is present, we assume you have minion pro installed for use with pdflatex.
%\usepackage[minion]{org-preamble-pdflatex}
%%% alternatively, use xelatex instead
\usepackage{org-preamble-xelatex}
\def\myauthor{Author}
\def\mytitle{Title}
\def\mycopyright{\myauthor}
\def\mykeywords{}
\def\mybibliostyle{plain}
\def\mybibliocommand{}
\def\mysubtitle{}
\def\myaffiliation{Location:}
\def\myaddress{Time: }
\def\myemail{e-mail:}
\def\myweb{e-mail: }
\def\myphone{}
\def\myversion{}
\def\myrevision{}
\def\myauthor{Instructor: }
\def\mykeywords{}
\def\mysubtitle{Syllabus}
\def\mytitle{{\normalsize Phil 125 ( \ \ \ \ \ \ \ \ \ \ \ ), 3 credits, Semester: \newline} \HUGE Ethics in Everyday Life}
\begin{document}
%%% If using xelatex and not pdflatex
%%% xelatex font choices
\defaultfontfeatures{}
\defaultfontfeatures{Scale=MatchLowercase}
% You will need to buy these fonts, change the names to fonts you own, or comment out if not using xelatex.
\setromanfont[Mapping=tex-text]{Georgia}
\setsansfont[Mapping=tex-text]{Georgia}
\setmonofont[Mapping=tex-text,Scale=0.8]{Georgia}
%% blank label items; hanging bibs for text
%% Custom hanging indent for vita items
\def\ind{\hangindent=1 true cm\hangafter=1 \noindent}
\def\labelitemi{$\cdot$}
%\renewcommand{\labelitemii}{~}
%% RCS info string for version tracking
\chapterstyle{article-3} % alternative styles are defined in latex-custom-kjh/needs-memoir/
\pagestyle{kjh}
\title{\mytitle}
\author{{\noindent\myauthor} \newline{ } \newline {\noindent\myemail} \newline{ } \newline {\noindent\myaddress} \newline{ } \newline {\noindent\myaffiliation} }
\date{ }
\maketitle
%\thispagestyle{kjhgit}
% Copyright Page
%\textcopyright{} \mycopyright
%
% Main Content
%
\section{Catalog Description}
This course introduces students to basic ethical reasoning. Starting out from real-life situations, students analyze ethical problems by conceptualizing the moral intuitions and beliefs they already possess. With the help of short philosophical readings, they develop methods of applying ethical theories to moral decision-making in their disciplines and own lives.
\section{Discipline Specific Learning Outcomes}
Upon completing this course, students will be able to (i) analyze moral decision situations, such as provided in case studies, (ii) define basic ethical concepts and ideas, (iii) interpret short passages from philosophical texts, (iv) apply ethical concepts and ideas to real-life situations, (v) compare competing ethical perspectives in their application to real-life situations, (vi) examined the ethical dimensions of their own conduct, (vii) determine some social, economic, and political implications of moral reasoning.
\section{General Education Information}
Successfully completing this course satisfies one Tier 1 Language, Literary, and Cultural Studies requirement. It teaches the following two University-wide Learning Goals: (1) Critical Thinking and Problem Solving, (2) Written Communication. For further information about the General Education Program see \href{http://www.njcu.edu/cas/general-education/}{http://www.njcu.edu/cas/general-education/}.
\section{NJCU Policies}
\begin{itemize}
\item \textit{Academic Integrity:} All the work you turn in (including papers, drafts, and discussion board posts) must be written by you specifically for this course. It must originate with you in form and content with all contributory sources fully and specifically acknowledged. You are required to read and follow follow NJCU's Academic Integrity Policy available here: \href{http://www.njcu.edu/senate/policies/}{http://www.njcu.edu/senate/policies/}
\item \textit{Communication:} To comply with Federal Privacy Laws (FERPA) and NJCU policies, all communication will be through Blackboard and/or official NJCU e-mail.
\item \textit{General Education Program Assessment:} General Education courses participate in programmatic assessment of the six University-wide student learning goals. They include instruction in, and assessment of, at least two of these learning goals. Signature assignments, which may include document, picture, sound, or video files, are uploaded to a secure server for anonymous distribution to the NJCU assessment team, which scores them using approved program rubrics. While instructors also grade their own students’ signature assignments, which count toward the course grade, assessment team results are aggregated to provide information about the Gen Ed program as a whole. Your name will not be included in any programmatic assessment data.
\item \textit{Statement for students with disabilities:} If you are a student with a disability and wish to receive consideration for reasonable accommodations, please register with the Office of Specialized Services and Supplemental Instruction (OSS/SI). To begin this process, complete the registration form available on the OSS/SI website at
\href{http://www.njcu.edu/Specialized_Services.aspx}{www.njcu.edu/Specialized\_Services.aspx}
(listed under Student Resources-Forms). Contact OSS/SI at 201-200-2091
or visit the office in Karnoutsos Hall, Room 102 for additional
information.
\item \textit{TURNITIN:} Students agree by taking this course that all assignments are subject to submission for textual similarity review to Turnitin.com. Assignments submitted to Turnitin.com will be included as source documents in Turnitin.com’s restricted access database solely for the purpose of detecting plagiarism in such documents. The terms that apply to the University’s use of the Turnitin.com service are described on the Turnitin.com web site. For further information about Turnitin, please visit: http://www.turnitin.com.
\end{itemize}
%% Uncomment if you want a printed bibliography.
%\printbibliography
\end{document}
|
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|
from embeddings import sentence_embedding
import numpy as np
from training import mlpc_model_for_s2v
from hw_helpers import create_csv_submission
def run():
neg_embeddings, pos_embeddings, test_embeddings = sentence_embedding("train_pos_full.txt", "train_neg_full.txt", "test_data.txt")
train_data = np.vstack((neg_embeddings, pos_embeddings))
pos_labels = np.ones(len(pos_embeddings))
neg_labels = np.ones(len(neg_embeddings))*-1
train_labels = np.hstack((pos_labels, neg_labels))
alphas = np.logspace(-4,0,10)
alpha = alphas[3]
clf = mlpc_model_for_s2v(train_data=train_data, train_labels=train_labels, nb_neur=50, alpha=alpha, depth=5, save=True)
prediction = clf.predict(test_embeddings)
test_data = open("test_data.txt", "r", encoding='utf-8')
id_ = [line[:line.find(",")] for line in test_data.readlines()]
create_csv_submission(id_)
id_ = np.array(id_).astype("int")
create_csv_submission(id_, prediction, "s2v_a0002_d5_n50_submission.csv")
return 0
if __name__ == "__main__":
run()
|
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|
# -*- mode: python; coding: utf-8 -*-
# Copyright 2017 the HERA Collaboration
# Licensed under the 2-clause BSD license.
"""Testing for `hera_mc.roach`.
"""
from __future__ import absolute_import, division, print_function
import unittest
import nose.tools as nt
from math import floor
from astropy.time import Time, TimeDelta
from .. import mc, roach
from ..tests import TestHERAMC, is_onsite
roach_example_dict = {
'pf1': {'raw.current.1v5': '10162', 'raw.temp.outlet': '31750',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:02 2017',
'raw.current.3v3': '1962', 'raw.voltage.1v5': '1504',
'raw.current.12v': '3286', 'raw.voltage.1v8': '1802',
'raw.voltage.2v5': '2503', 'raw.voltage.12v': '11538',
'raw.temp.ambient': '30000', 'raw.voltage.5v': '5125',
'raw.voltage.1v': '1009', 'raw.current.5v': '4395',
'raw.current.1v': '1577', 'raw.temp.inlet': '32000',
'raw.temp.fpga': '57000', 'raw.voltage.3v3aux': '3398',
'timestamp': '1512770942.726777', 'raw.current.1v8': '1577',
'raw.voltage.5vaux': '5082', 'raw.current.2v5': '3665',
'raw.voltage.3v3': '3366', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '45000'},
'pf3': {'raw.current.1v5': '10292', 'raw.temp.outlet': '29500',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:02 2017',
'raw.current.3v3': '1962', 'raw.voltage.1v5': '1498',
'raw.current.12v': '3160', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2503', 'raw.voltage.12v': '11552',
'raw.temp.ambient': '29000', 'raw.voltage.5v': '5125',
'raw.voltage.1v': '1009', 'raw.current.5v': '4343',
'raw.current.1v': '1411', 'raw.temp.inlet': '31250',
'raw.temp.fpga': '54000', 'raw.voltage.3v3aux': '3398',
'timestamp': '1512770942.995268', 'raw.current.1v8': '1411',
'raw.voltage.5vaux': '5060', 'raw.current.2v5': '3690',
'raw.voltage.3v3': '3355', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '46000'},
'pf2': {'raw.current.1v5': '10743', 'raw.temp.outlet': '32250',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:02 2017',
'raw.current.3v3': '1962', 'raw.voltage.1v5': '1504',
'raw.current.12v': '3390', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2503', 'raw.voltage.12v': '11603',
'raw.temp.ambient': '31000', 'raw.voltage.5v': '5120',
'raw.voltage.1v': '1004', 'raw.current.5v': '4813',
'raw.current.1v': '1476', 'raw.temp.inlet': '31750',
'raw.temp.fpga': '58000', 'raw.voltage.3v3aux': '3393',
'timestamp': '1512770942.861526', 'raw.current.1v8': '1476',
'raw.voltage.5vaux': '5087', 'raw.current.2v5': '3665',
'raw.voltage.3v3': '3355', 'raw.fan.fpga': '5760',
'raw.temp.ppc': '45000'},
'pf5': {'raw.current.1v5': '10363', 'raw.temp.outlet': '32250',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:03 2017',
'raw.current.3v3': '1992', 'raw.voltage.1v5': '1493',
'raw.current.12v': '3160', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2497', 'raw.voltage.12v': '11860',
'raw.temp.ambient': '29000', 'raw.voltage.5v': '5114',
'raw.voltage.1v': '1004', 'raw.current.5v': '4395',
'raw.current.1v': '1606', 'raw.temp.inlet': '28250',
'raw.temp.fpga': '61000', 'raw.voltage.3v3aux': '3388',
'timestamp': '1512770943.260739', 'raw.current.1v8': '1606',
'raw.voltage.5vaux': '5060', 'raw.current.2v5': '3714',
'raw.voltage.3v3': '3360', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '43000'},
'pf4': {'raw.current.1v5': '10363', 'raw.temp.outlet': '32250',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:03 2017',
'raw.current.3v3': '1962', 'raw.voltage.1v5': '1498',
'raw.current.12v': '3265', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2508', 'raw.voltage.12v': '11730',
'raw.temp.ambient': '31000', 'raw.voltage.5v': '4951',
'raw.voltage.1v': '1009', 'raw.current.5v': '4395',
'raw.current.1v': '1511', 'raw.temp.inlet': '32500',
'raw.temp.fpga': '57000', 'raw.voltage.3v3aux': '3393',
'timestamp': '1512770943.127049', 'raw.current.1v8': '1511',
'raw.voltage.5vaux': '4979', 'raw.current.2v5': '3723',
'raw.voltage.3v3': '3355', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '46000'},
'pf7': {'raw.current.1v5': '10683', 'raw.temp.outlet': '31500',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:03 2017',
'raw.current.3v3': '1992', 'raw.voltage.1v5': '1504',
'raw.current.12v': '3495', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2497', 'raw.voltage.12v': '11552',
'raw.temp.ambient': '30000', 'raw.voltage.5v': '5098',
'raw.voltage.1v': '1004', 'raw.current.5v': '5023',
'raw.current.1v': '1636', 'raw.temp.inlet': '32750',
'raw.temp.fpga': '56000', 'raw.voltage.3v3aux': '3371',
'timestamp': '1512770943.52817', 'raw.current.1v8': '1636',
'raw.voltage.5vaux': '5017', 'raw.current.2v5': '3682',
'raw.voltage.3v3': '3355', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '47000'},
'pf6': {'raw.current.1v5': '10162', 'raw.temp.outlet': '32500',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:03 2017',
'raw.current.3v3': '1926', 'raw.voltage.1v5': '1504',
'raw.current.12v': '3307', 'raw.voltage.1v8': '1813',
'raw.voltage.2v5': '2503', 'raw.voltage.12v': '11552',
'raw.temp.ambient': '30000', 'raw.voltage.5v': '5098',
'raw.voltage.1v': '1009', 'raw.current.5v': '4500',
'raw.current.1v': '1577', 'raw.temp.inlet': '32250',
'raw.temp.fpga': '59000', 'raw.voltage.3v3aux': '3382',
'timestamp': '1512770943.394443', 'raw.current.1v8': '1577',
'raw.voltage.5vaux': '5017', 'raw.current.2v5': '3690',
'raw.voltage.3v3': '3344', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '45000'},
'pf8': {'raw.current.1v5': '10422', 'raw.temp.outlet': '32250',
'raw.fan.chs2': '7650', 'raw.fan.chs1': '7650',
'raw.fan.chs0': '7650', 'human_timestamp': 'Sat Dec 9 00:09:03 2017',
'raw.current.3v3': '1962', 'raw.voltage.1v5': '1504',
'raw.current.12v': '3244', 'raw.voltage.1v8': '1808',
'raw.voltage.2v5': '2508', 'raw.voltage.12v': '11505',
'raw.temp.ambient': '32000', 'raw.voltage.5v': '5120',
'raw.voltage.1v': '1004', 'raw.current.5v': '4081',
'raw.current.1v': '1476', 'raw.temp.inlet': '34000',
'raw.temp.fpga': '57000', 'raw.voltage.3v3aux': '3404',
'timestamp': '1512770943.66201', 'raw.current.1v8': '1476',
'raw.voltage.5vaux': '5049', 'raw.current.2v5': '3649',
'raw.voltage.3v3': '3355', 'raw.fan.fpga': '5730',
'raw.temp.ppc': '49000'}}
class TestRoach(TestHERAMC):
def test_add_roach(self):
t1 = Time('2016-01-10 01:15:23', scale='utc')
t2 = t1 + TimeDelta(120.0, format='sec')
ambient_temp = float(roach_example_dict['pf1']['raw.temp.ambient']) / 1000.
inlet_temp = float(roach_example_dict['pf1']['raw.temp.inlet']) / 1000.
outlet_temp = float(roach_example_dict['pf1']['raw.temp.outlet']) / 1000.
fpga_temp = float(roach_example_dict['pf1']['raw.temp.fpga']) / 1000.
ppc_temp = float(roach_example_dict['pf1']['raw.temp.ppc']) / 1000.
self.test_session.add_roach_temperature(t1, 'pf1', ambient_temp, inlet_temp,
outlet_temp, fpga_temp, ppc_temp)
expected = roach.RoachTemperature(time=int(floor(t1.gps)), roach='pf1',
ambient_temp=30., inlet_temp=32.,
outlet_temp=31.75, fpga_temp=57.,
ppc_temp=45.)
result = self.test_session.get_roach_temperature(t1 - TimeDelta(3.0, format='sec'))
self.assertEqual(len(result), 1)
result = result[0]
self.assertTrue(result.isclose(expected))
ambient_temp = float(roach_example_dict['pf2']['raw.temp.ambient']) / 1000.
inlet_temp = float(roach_example_dict['pf2']['raw.temp.inlet']) / 1000.
outlet_temp = float(roach_example_dict['pf2']['raw.temp.outlet']) / 1000.
fpga_temp = float(roach_example_dict['pf2']['raw.temp.fpga']) / 1000.
ppc_temp = float(roach_example_dict['pf2']['raw.temp.ppc']) / 1000.
self.test_session.add_roach_temperature(t1, 'pf2', ambient_temp, inlet_temp,
outlet_temp, fpga_temp, ppc_temp)
result = self.test_session.get_roach_temperature(t1 - TimeDelta(3.0, format='sec'),
roach='pf1')
self.assertEqual(len(result), 1)
result = result[0]
self.assertTrue(result.isclose(expected))
result = self.test_session.get_roach_temperature(t1 - TimeDelta(3.0, format='sec'),
stoptime=t1)
self.assertEqual(len(result), 2)
result = self.test_session.get_roach_temperature(t1 + TimeDelta(200.0, format='sec'))
self.assertEqual(result, [])
def test_create_from_redis(self):
roach_obj_list = roach.create_from_redis(roach_example_dict)
for obj in roach_obj_list:
self.test_session.add(obj)
t1 = Time(1512770942.726777, format='unix')
result = self.test_session.get_roach_temperature(t1 - TimeDelta(3.0, format='sec'),
roach='pf1')
expected = roach.RoachTemperature(time=int(floor(t1.gps)), roach='pf1',
ambient_temp=30., inlet_temp=32.,
outlet_temp=31.75, fpga_temp=57.,
ppc_temp=45.)
self.assertEqual(len(result), 1)
result = result[0]
self.assertTrue(result.isclose(expected))
result = self.test_session.get_roach_temperature(t1 - TimeDelta(3.0, format='sec'),
stoptime=t1 + TimeDelta(5.0, format='sec'))
self.assertEqual(len(result), 8)
def test_add_from_redis(self):
if is_onsite():
self.test_session.add_roach_temperature_from_redis()
result = self.test_session.get_roach_temperature(Time.now() - TimeDelta(120.0, format='sec'),
stoptime=Time.now() + TimeDelta(120.0, format='sec'))
self.assertEqual(len(result), 8)
if __name__ == '__main__':
unittest.main()
|
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|
# coding: utf-8
# %load jupyter_default.py
import pandas as pd
import numpy as np
import os
import re
import datetime
import time
import glob
from tqdm import tqdm_notebook
from colorama import Fore, Style
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import matplotlib.colors
import seaborn as sns
get_ipython().run_line_magic('config', "InlineBackend.figure_format='retina'")
sns.set() # Revert to matplotlib defaults
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams['axes.labelpad'] = 20
plt.rcParams['legend.fancybox'] = True
plt.style.use('ggplot')
SMALL_SIZE, MEDIUM_SIZE, BIGGER_SIZE = 14, 16, 20
plt.rc('font', size=SMALL_SIZE)
plt.rc('axes', titlesize=SMALL_SIZE)
plt.rc('axes', labelsize=MEDIUM_SIZE)
plt.rc('xtick', labelsize=SMALL_SIZE)
plt.rc('ytick', labelsize=SMALL_SIZE)
plt.rc('legend', fontsize=MEDIUM_SIZE)
plt.rc('axes', titlesize=BIGGER_SIZE)
def savefig(plt, name):
plt.savefig(f'../../figures/{name}.png', bbox_inches='tight', dpi=300)
get_ipython().run_line_magic('load_ext', 'version_information')
get_ipython().run_line_magic('version_information', 'pandas, numpy')
# ## Bayesian Modeling Discussion
#
# We can model the probability of an outcome $y$ as $P_t(y)$ using a discrete **Poisson distribution** i.e. if discretizing the time $t$ in seconds.
#
# $$
# P_t(\mu) = \frac{\mu^te^{-\mu}}{k!}
# $$
#
# Instead we could also assume a Gamma posterior, which has the advantage of being continuous and has more parameters than can be optimized. For now we'll stick with using the simpler Poisson distribution.
#
# Based on a set of goalie pull observations $X$ from 2003-2007 NHL games, we'll solve for the posterior distribution $P_t(y|X)$, the probability of the outcome $y$, given the observations. This is done computationally using markov chain monte carlo and the `pymc3` library.
#
# The outcomes we're interested in are $y = \big\{\mathrm{goal\;for}, \mathrm{goal\;against}, \mathrm{no\;goal}\big\}$.
#
# We'll use a **uniform prior** over the domain of times (last 5mins). Note: when gathering the observations, we throw out goalie pulls greater than 5 minutes from the end of the game (due to high likelihood of false positives when parsing goalie pulls from the raw game table).
#
# Once we find the posteriors discussed above, we can study the risk reward of pulling a goalie. We'll compare posteriors to find the odds of scoring a goal (and the odds of getting scored on) over time $t$ where:
# - **t = Time elapsed** e.g. if there's 3 minutes left, what is the chance that pulling the goalie will result in a goal for?
# - **t = Time since goalie pull** e.g. after the goalie has been pulled for 1 minute, what is the chance of getting a goal?
import pymc3 as pm
# ### Load the training data
ls ../../data/processed/pkl/
def load_data():
files = glob.glob('../../data/processed/pkl/*.pkl')
files = sorted(files)
print(files)
return pd.concat((pd.read_pickle(f) for f in files))
def clean_df(df):
_df = df.copy()
len_0 = _df.shape[0]
print('Removing goal_for_time < 15 mins')
_df = _df[~(_df.goal_for_time < datetime.timedelta(seconds=15*60))]
print(f'Removed {len_0 - _df.shape[0]} total rows')
if 'game_end_time' in df.columns:
len_0 = _df.shape[0]
print('Removing game_end_time < 15 mins')
_df = _df[~(_df.game_end_time < datetime.timedelta(seconds=60*15))]
print(f'Removed {len_0 - _df.shape[0]} total rows')
return _df
df = load_data()
df = clean_df(df)
def load_training_samples(
df,
cols,
masks=[],
dtype='timedelta64[s]'
) -> np.ndarray:
'''
Return buckets of training data.
'''
if not masks:
masks = [None] * len(cols)
out = []
for col, m in zip(cols, masks):
if m is None:
d = df[col].dropna().astype(dtype).values
else:
d = df[col][m].dropna().astype(dtype).values
out.append(d)
print(f'Loaded {len(d)} samples for col {col}')
out = np.array(out)
print(f'Training data shape = {out.shape}')
return out
# ## Model 1 - Time elapsed
# ### Load data
# Load time of pull for eventual outcomes:
feature_names = ['goal_for', 'goal_against', 'no_goals']
# Logic for loading the data
features = ['pull_time', 'pull_time', 'pull_time']
masks = [
~(df.goal_for_time.isnull()),
~(df.goal_against_time.isnull()),
~(df.game_end_timedelta.isnull()),
]
training_samples = load_training_samples(df, features, masks)
(training_samples[0][:10],
training_samples[1][:10],
training_samples[2][:10],)
feature_names
# ### PyMC3 Model
def bayes_model(training_samples) -> pm.model.Model:
"""
Solve for posterior distributions using pymc3
"""
with pm.Model() as model:
# Priors for the mu parameter of the
# Poisson distribution P.
# Note: mu = mean(P)
mu_goal_for = pm.Uniform(
'mu_goal_for', 15*60, 20*60
)
mu_goal_against = pm.Uniform(
'mu_goal_against', 15*60, 20*60
)
mu_no_goal = pm.Uniform(
'mu_no_goal', 15*60, 20*60
)
# Observations to train the model on
obs_goal_for = pm.Poisson(
'obs_goal_for',
mu=mu_goal_for,
observed=training_samples[0],
)
obs_goal_against = pm.Poisson(
'obs_goal_against',
mu=mu_goal_against,
observed=training_samples[1],
)
obs_no_goal = pm.Poisson(
'obs_no_goal',
mu=mu_no_goal,
observed=training_samples[2],
)
# Outcome probabilities
p_goal_for = pm.Bound(pm.Poisson, upper=20*60)('p_goal_for', mu=mu_goal_for)
p_goal_against = pm.Bound(pm.Poisson, upper=20*60)('p_goal_against', mu=mu_goal_against)
p_no_goal = pm.Bound(pm.Poisson, upper=20*60)('p_no_goal', mu=mu_no_goal)
# Fit model
step = pm.Metropolis()
trace = pm.sample(18000, step=step)
return model, trace
model, trace = bayes_model(training_samples)
model
N_burn = 10000
burned_trace = trace[N_burn:]
from typing import Tuple
from scipy.stats import poisson
def poisson_posterior(
mu=None,
norm_factors=None,
) -> Tuple[np.ndarray]:
p = poisson.pmf
x = np.arange(15*60, 20*60, 1)
if mu is None:
return (x / 60,)
mu_goal_for = mu[0]
mu_goal_against = mu[1]
mu_no_goal = mu[2]
y_goal_for = p(x, mu_goal_for)
y_goal_against = p(x, mu_goal_against)
y_no_goal = p(x, mu_no_goal)
if norm_factors is not None:
y_goal_for = p(x, mu_goal_for) * norm_factors[0]
y_goal_against = p(x, mu_goal_against) * norm_factors[1]
y_no_goal = p(x, mu_no_goal) * norm_factors[2]
# Convert into minutes
x = x / 60
return x, y_goal_for, y_goal_against, y_no_goal
# ### MCMC Samples
ALPHA = 0.6
LW = 3
''' Plot MCMC samples '''
plt.hist(burned_trace['p_goal_for']/60, bins=50,
color='green', label='p_goal_for samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['p_goal_against']/60, bins=50,
color='red', label='p_goal_against samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['p_no_goal']/60, bins=50,
color='orange', label='p_no_goal samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
''' Plot poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior([
burned_trace['mu_goal_for'].mean(),
burned_trace['mu_goal_against'].mean(),
burned_trace['mu_no_goal'].mean(),
])
# Rescale
scale_frac = 0.7
y_goal_for = y_goal_for / y_goal_for.max() * scale_frac
y_goal_against = y_goal_against / y_goal_against.max() * scale_frac
y_no_goal = y_no_goal / y_no_goal.max() * scale_frac
plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW)
''' Clean up the chart '''
plt.ylabel('Counts')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend()
savefig(plt, 'time_elapsed_poisson_mcmc_samples')
plt.show()
plt.plot(trace['mu_goal_for']/60, label='mu_goal_for', color='green')
plt.plot(trace['mu_goal_against']/60, label='mu_goal_against', color='red')
plt.plot(trace['mu_no_goal']/60, label='mu_no_goal', color='orange')
plt.ylabel('$\mu$ (minutes)')
plt.xlabel('MCMC step')
plt.axvline(N_burn, color='black', lw=2, label='Burn threshold')
plt.legend()
savefig(plt, 'time_elapsed_mu_steps')
plt.show()
ALPHA = 0.6
plt.hist(burned_trace['mu_goal_for']/60, bins=50,
color='green', label='mu_goal_for',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['mu_goal_against']/60, bins=50,
color='red', label='mu_goal_against',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['mu_no_goal']/60, bins=50,
color='orange', label='mu_no_goal',
histtype='stepfilled', alpha=ALPHA)
plt.ylabel('MCMC counts')
plt.xlabel('$\mu$ (minutes)')
plt.legend()
savefig(plt, 'time_elapsed_mu_samples')
plt.show()
# ### Normalization
# Now I need to normalize these. Let's confirm equal sample numbers
(burned_trace['mu_goal_for'].shape,
burned_trace['mu_goal_against'].shape,
burned_trace['mu_no_goal'].shape)
len(burned_trace) * 4
# Nice! Same number of samlpes. Weird that it's 4x my burned trace amount - probably due to 4 cores
#
# Let's define the average shape parameter $\mu$ and then solve for the normalizing fractions.
mu_mcmc = [
burned_trace['mu_goal_for'].mean(),
burned_trace['mu_goal_against'].mean(),
burned_trace['mu_no_goal'].mean(),
]
print(f'MCMC values for mu: {mu_mcmc}')
mcmc_normalizing_factors = np.array([
training_samples[0].shape[0],
training_samples[1].shape[0],
training_samples[2].shape[0]
])
mcmc_normalizing_factors = mcmc_normalizing_factors / mcmc_normalizing_factors.sum()
print(f'MCMC normalizing factors =\n{mcmc_normalizing_factors}')
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(mu_mcmc)
y_goal_for = y_goal_for * mcmc_normalizing_factors[0]
y_goal_against = y_goal_against * mcmc_normalizing_factors[1]
y_no_goal = y_no_goal * mcmc_normalizing_factors[2]
cutoff_renormed_factor = 2 - (y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum())
model_normalizing_factors = mcmc_normalizing_factors * cutoff_renormed_factor
print(f'Poisson normalizing factors =\n{model_normalizing_factors}')
# Here's what the properly weighted samlpes look like:
ALPHA = 0.6
LW = 3
BINS = 60
''' Plot the MCMC samples '''
plt.hist(np.random.choice(
burned_trace['p_goal_for'] / 60,
size=int(burned_trace['p_goal_for'].shape[0] * mcmc_normalizing_factors[0])
),
bins=BINS, color='green', label='p_goal_for samples',
histtype='stepfilled', alpha=ALPHA, zorder=3)
plt.hist(np.random.choice(
burned_trace['p_goal_against'] / 60,
size=int(burned_trace['p_goal_against'].shape[0] * mcmc_normalizing_factors[1])
),
bins=BINS,
color='red', label='p_goal_against samples',
histtype='stepfilled', alpha=ALPHA, zorder=2)
plt.hist(np.random.choice(
burned_trace['p_no_goal'] / 60,
size=int(burned_trace['p_no_goal'].shape[0] * mcmc_normalizing_factors[2])
),
bins=BINS,
color='orange', label='p_no_goal samples',
histtype='stepfilled', alpha=ALPHA)
plt.ylabel('Sampled frequency (normed)')
plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend();
savefig(plt, 'time_elapsed_normed_poisson_mcmc_samples')
plt.show()
# ### Normalized Posteriors
#
# Re-normalize for cutoff Poisson distributions
import inspect
print(inspect.getsource(poisson_posterior))
from scipy.stats import poisson
ALPHA = 0.6
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
plt.plot(x, y_goal_for, label=r'$P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
plt.ylabel('Posterior probability')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend()
savefig(plt, 'time_elapsed_normed_poisson')
plt.show()
# ### Interpretation
def convert_to_time_remaining(x):
_x = 20 - x
t = datetime.timedelta(seconds=_x*60)
return str(t)
convert_to_time_remaining(x[np.argmax(y_goal_for)])
print('Time of max posterior probability =\n'
f'{x[np.argmax(y_goal_for)], x[np.argmax(y_goal_against)], x[np.argmax(y_no_goal)]}')
print()
t_remaining = [convert_to_time_remaining(x[np.argmax(y_goal_for)]),
convert_to_time_remaining(x[np.argmax(y_goal_against)]),
convert_to_time_remaining(x[np.argmax(y_no_goal)])]
print(f'Time of max posterior probability =\n{t_remaining}')
# Great, now we have properly normalized probabilties.
#
# Notes:
# - From normalizing factors, we can see ~12% chance of scoring when pulling the goalie on average.
# - Probability of scoring peaks at 18.55 mins (1:27 remaining), with other probabilties following close after (01:20 for goal against and 01:07 for no goals)
#
# From now on we'll work from the distributions as our source of truth. These are hard coded below to help with reproducibility.
model_normlizing_factors = [
0.1292882,
0.26528024,
0.62489297,
]
mu_mcmc = [
1113.8279468130681,
1120.1830172722719,
1133.9420018554083
]
# ### Cumulative sum
#
# Calculating the CDF will allow us to make some interesting observations on the results.
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
plt.plot(x, np.cumsum(y_goal_for), label=r'$cumsum [ P(\mathrm{goal\;for}\;|\;X) ]$', color='green', lw=LW)
plt.plot(x, np.cumsum(y_goal_against), label=r'$cumsum [ P(\mathrm{goal\;against}\;|\;X) ]$', color='red', lw=LW)
plt.plot(x, np.cumsum(y_no_goal), label=r'$cumsum [ P(\mathrm{no\;goal}\;|\;X) ]$', color='orange', lw=LW)
plt.ylabel('Posterior CDF')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend()
ax = plt.gca()
ax.yaxis.tick_right()
savefig(plt, 'time_elapsed_poisson_cdf')
plt.show()
# The end of game values have been normalized sum up to one, but this ratio changes over time. We can visualize this with the risk-reward ratio (see below).
#
# ### Re-normalize
#
# To better compare these probability distributions, we can normalize each bin to 1 using a function $\alpha(t)$, as follows:
#
# $$
# \alpha(t) \cdot \big[ P(goal\;for; t) + (P(goal\;against; t) + P(no\;goal; t)\big] = 1 \\
# \vdots \\
# \alpha(t) = \big[ P(goal\;for; t) + (P(goal\;against; t) + P(no\;goal; t)\big]^{-1}
# $$
#
# This will allow us to re-weight the posteriors later, so we can compare them better and yield a different interpretation.
#
# Essentially, we'll be able to interpret the resulting distribution as the chance of each outcome at time $t$. This stands in contrast to the probability distributions above, where the total area under the curves sum to 1.
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
plt.plot(x, alpha, label=r'$\alpha$', lw=LW)
plt.ylabel('Alpha re-weighting parameter')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend()
# savefig(plt, 'time_elapsed_poisson_cdf')
plt.show()
from scipy.stats import poisson
ALPHA = 0.6
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
# Alpha has same shape as x, y above
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
y_goal_for = alpha * y_goal_for
y_goal_against = alpha * y_goal_against
y_no_goal = alpha * y_no_goal
plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
plt.ylabel('Chance of outcome at time $t$')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.legend()
# Plotting below with error bar
# savefig(plt, 'time_elapsed_outcome_chance_timeseries')
plt.show()
# ### Adding error bars
# Note how there are very few samples to draw conclusions from for the low and high times.
#
# e.g. less than 17
np.sum(training_samples[0] < 17*60) + np.sum(training_samples[1] < 17*60) + np.sum(training_samples[2] < 17*60)
# more than 17
np.sum(training_samples[0] > 17*60) + np.sum(training_samples[1] > 17*60) + np.sum(training_samples[2] > 17*60)
# We can show this uncertainty visually using error bars. Starting with the $\mu$ MCMC samples...
plt.hist(burned_trace['mu_goal_for'])
plt.hist(burned_trace['mu_goal_against'])
plt.hist(burned_trace['mu_no_goal'])
# We can use the uncertainty on $\mu$ to calculate that for $P$:
#
# $$
# \sigma_P = \big| \frac{\partial P}{\partial \mu} \big|\;\sigma_{\mu}
# $$
#
# where $\sigma_{\mu}$ is the standard deviation of the $\mu$ samples.
mu_mcmc_std = [
burned_trace['mu_goal_for'].std(),
burned_trace['mu_goal_against'].std(),
burned_trace['mu_no_goal'].std(),
]
mu_mcmc_std
model_normalizing_factors
from scipy.misc import derivative
from tqdm import tqdm_notebook
def calc_posteror_error(mu, mu_std, mu_step=1e-6):
x = poisson_posterior()[0] * 60 # convert back into seconds (discrete)
err = mu_std * np.abs(np.array([
derivative(lambda _mu: poisson.pmf(int(t), _mu), mu, dx=mu_step)
for t in tqdm_notebook(x)
]))
return err
from scipy.stats import poisson
ALPHA = 0.6
ALPHA_LIGHT = 0.3
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
# Alpha has same shape as x, y above
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
y_goal_for = alpha * y_goal_for
y_goal_against = alpha * y_goal_against
y_no_goal = alpha * y_no_goal
plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
''' Plot the errors '''
err_p_goal_for = alpha * calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0])
err_p_goal_against = alpha * calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1])
err_p_no_goal = alpha * calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2])
plt.fill_between(x, y_goal_for-err_p_goal_for, y_goal_for+err_p_goal_for,
color='green', alpha=ALPHA_LIGHT)
plt.fill_between(x, y_goal_against-err_p_goal_against, y_goal_against+err_p_goal_against,
color='red', alpha=ALPHA_LIGHT)
plt.fill_between(x, y_no_goal-err_p_no_goal, y_no_goal+err_p_no_goal,
color='orange', alpha=ALPHA_LIGHT)
plt.ylabel('Chance of outcome at time $t$')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.xlim(17, 20)
plt.ylim(0, 1)
plt.legend()
savefig(plt, 'time_elapsed_poisson_outcome_chances')
plt.show()
# We can't say anything conclusive due to huge errors on low times, but we are much more confident on late game predictions
# ### Odds of scoring a goal
# Let's go into odds-space and look at the chance of scoring a goal, compared to either outcome. We want to maximze this.
ALPHA = 0.6
ALPHA_LIGHT = 0.3
LW = 3
''' Odds ratio '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
odds_goal_for = y_goal_for / (y_goal_against + y_no_goal)
''' Error bars '''
err_p_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0])
err_p_goal_against = calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1])
err_p_no_goal = calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2])
err_odds_goal_for = (
np.power(err_p_goal_for / y_goal_for, 2)
+ np.power(err_p_goal_against / y_goal_against, 2)
+ np.power(err_p_no_goal / y_no_goal, 2)
)
err_odds_goal_for = odds_goal_for * np.sqrt(err_odds_goal_for)
''' Plots '''
plt.plot(x, odds_goal_for,
label=r'$odds(\mathrm{goal\;for})$',
color='green', lw=LW, alpha=ALPHA)
plt.fill_between(x, odds_goal_for-err_odds_goal_for, odds_goal_for+err_odds_goal_for,
color='green', lw=LW, alpha=ALPHA_LIGHT)
plt.ylabel('Odds')
# plt.yticks([])
plt.xlabel('Time elapsed in 3rd period (minutes)')
plt.xlim(17, 20)
plt.ylim(0, 1)
plt.legend()
savefig(plt, 'time_elapsed_poisson_odds_goal_for')
plt.show()
(odds_goal_for-err_odds_goal_for).max()
# This chart suggests that odds of scoring are highest when the goalie is pulled before the 18.5 minute mark. Although the odds of scoring trend up as $t$ gets smaller, there's no statistically significant evidence for odds greater than 16%.
# ## Model 2 - Time since goalie pull
#
# The work thus far has been to model the outcomes as a function of "time
# elapsed". Now we'll shift our attention to "time since goalie pull".
import inspect
print(inspect.getsource(load_training_samples))
df.head()
# Load time of pull for eventual outcomes:
feature_names = ['goal_for_timedelta', 'goal_against_timedelta', 'game_end_timedelta']
training_samples = load_training_samples(df=df, cols=feature_names)
(training_samples[0][:10],
training_samples[1][:10],
training_samples[2][:10],)
feature_names
# ### PyMC3 Model
def bayes_model(training_samples) -> pm.model.Model:
"""
Solve for posterior distributions using pymc3
"""
with pm.Model() as model:
# Priors for the mu parameter of the
# Poisson distribution P.
# Note: mu = mean(P)
mu_goal_for = pm.Uniform(
'mu_goal_for', 0, 5*60
)
mu_goal_against = pm.Uniform(
'mu_goal_against', 0, 5*60
)
mu_no_goal = pm.Uniform(
'mu_no_goal', 0, 5*60
)
# Observations to train the model on
obs_goal_for = pm.Poisson(
'obs_goal_for',
mu=mu_goal_for,
observed=training_samples[0],
)
obs_goal_against = pm.Poisson(
'obs_goal_against',
mu=mu_goal_against,
observed=training_samples[1],
)
obs_no_goal = pm.Poisson(
'obs_no_goal',
mu=mu_no_goal,
observed=training_samples[2],
)
# Outcome probabilities
p_goal_for = pm.Bound(pm.Poisson, upper=5*60)('p_goal_for', mu=mu_goal_for)
p_goal_against = pm.Bound(pm.Poisson, upper=5*60)('p_goal_against', mu=mu_goal_against)
p_no_goal = pm.Bound(pm.Poisson, upper=5*60)('p_no_goal', mu=mu_no_goal)
# Fit model
step = pm.Metropolis()
trace = pm.sample(18000, step=step)
return model, trace
model, trace = bayes_model(training_samples)
model
N_burn = 10000
burned_trace = trace[N_burn:]
from typing import Tuple
def poisson_posterior(
mu=None,
norm_factors=None,
) -> Tuple[np.ndarray]:
p = poisson.pmf
x = np.arange(0, 5*60, 1)
if mu is None:
# return (x / 60,)
return (x,)
mu_goal_for = mu[0]
mu_goal_against = mu[1]
mu_no_goal = mu[2]
y_goal_for = p(x, mu_goal_for)
y_goal_against = p(x, mu_goal_against)
y_no_goal = p(x, mu_no_goal)
if norm_factors is not None:
y_goal_for = p(x, mu_goal_for) * norm_factors[0]
y_goal_against = p(x, mu_goal_against) * norm_factors[1]
y_no_goal = p(x, mu_no_goal) * norm_factors[2]
# Convert into minutes
# x = x / 60
return x, y_goal_for, y_goal_against, y_no_goal
# ### MCMC Samples
from scipy.stats import poisson
ALPHA = 0.6
LW = 3
BINS = 30
''' Plot MCMC samples '''
plt.hist(burned_trace['p_goal_for'], bins=BINS,
color='green', label='p_goal_for samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['p_goal_against'], bins=BINS,
color='red', label='p_goal_against samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['p_no_goal'], bins=BINS,
color='orange', label='p_no_goal samples',
density='normed',
histtype='stepfilled', alpha=ALPHA)
''' Plot poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior([
burned_trace['mu_goal_for'].mean(),
burned_trace['mu_goal_against'].mean(),
burned_trace['mu_no_goal'].mean(),
])
# Rescale
scale_frac = 0.05
y_goal_for = y_goal_for / y_goal_for.max() * scale_frac
y_goal_against = y_goal_against / y_goal_against.max() * scale_frac
y_no_goal = y_no_goal / y_no_goal.max() * scale_frac
plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW)
''' Clean up the chart '''
plt.ylabel('Counts')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.legend()
savefig(plt, 'time_since_poisson_mcmc_samples')
plt.show()
plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green')
plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red')
plt.plot(trace['mu_no_goal'], label='mu_no_goal', color='orange')
plt.ylabel('$\mu$ (seconds)')
plt.xlabel('MCMC step')
plt.axvline(N_burn, color='black', lw=2, label='Burn threshold')
plt.legend()
savefig(plt, 'time_since_mu_steps')
plt.show()
ALPHA = 0.6
plt.hist(burned_trace['mu_goal_for']/60, bins=50,
color='green', label='mu_goal_for',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['mu_goal_against']/60, bins=50,
color='red', label='mu_goal_against',
histtype='stepfilled', alpha=ALPHA)
plt.hist(burned_trace['mu_no_goal']/60, bins=50,
color='orange', label='mu_no_goal',
histtype='stepfilled', alpha=ALPHA)
plt.ylabel('MCMC counts')
plt.xlabel('$\mu$ (seconds)')
plt.legend()
savefig(plt, 'time_elapsed_mu_samples')
plt.show()
# ### Normalization
# Now I need to normalize these. Let's confirm equal sample numbers
(burned_trace['mu_goal_for'].shape,
burned_trace['mu_goal_against'].shape,
burned_trace['mu_no_goal'].shape)
len(burned_trace) * 4
# Nice! Same number of samlpes. Weird that it's 4x my burned trace amount - probably due to 4 cores
#
# Let's define the average shape parameter $\mu$ and then solve for the normalizing fractions.
mu_mcmc = [
burned_trace['mu_goal_for'].mean(),
burned_trace['mu_goal_against'].mean(),
burned_trace['mu_no_goal'].mean(),
]
print(f'MCMC values for mu: {mu_mcmc}')
mcmc_normalizing_factors = np.array([
training_samples[0].shape[0],
training_samples[1].shape[0],
training_samples[2].shape[0]
])
mcmc_normalizing_factors = mcmc_normalizing_factors / mcmc_normalizing_factors.sum()
print(f'MCMC normalizing factors =\n{mcmc_normalizing_factors}')
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(mu_mcmc)
y_goal_for = y_goal_for * mcmc_normalizing_factors[0]
y_goal_against = y_goal_against * mcmc_normalizing_factors[1]
y_no_goal = y_no_goal * mcmc_normalizing_factors[2]
cutoff_renormed_factor = 2 - (y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum())
model_normalizing_factors = mcmc_normalizing_factors * cutoff_renormed_factor
print(f'Poisson normalizing factors =\n{model_normalizing_factors}')
# Here's what the properly weighted samlpes look like:
ALPHA = 0.6
LW = 3
BINS = 30
''' Plot the MCMC samples '''
plt.hist(np.random.choice(
burned_trace['p_goal_for'],
size=int(burned_trace['p_goal_for'].shape[0] * mcmc_normalizing_factors[0])
),
bins=BINS, color='green', label='p_goal_for samples',
histtype='stepfilled', alpha=ALPHA, zorder=3)
plt.hist(np.random.choice(
burned_trace['p_goal_against'],
size=int(burned_trace['p_goal_against'].shape[0] * mcmc_normalizing_factors[1])
),
bins=BINS,
color='red', label='p_goal_against samples',
histtype='stepfilled', alpha=ALPHA, zorder=2)
plt.hist(np.random.choice(
burned_trace['p_no_goal'],
size=int(burned_trace['p_no_goal'].shape[0] * mcmc_normalizing_factors[2])
),
bins=BINS,
color='orange', label='p_no_goal samples',
histtype='stepfilled', alpha=ALPHA)
plt.ylabel('Sampled frequency (normed)')
plt.yticks([])
plt.xlabel('seconds')
plt.legend();
savefig(plt, 'time_since_normed_poisson_mcmc_samples')
plt.show()
# ### Normalized Posteriors
#
# Re-normalize for cutoff Poisson distributions
import inspect
print(inspect.getsource(poisson_posterior))
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
from scipy.stats import poisson
ALPHA = 0.6
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
plt.plot(x, y_goal_for, label=r'$P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
plt.ylabel('Posterior probability')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.legend()
plt.xlim(0, 100)
savefig(plt, 'time_since_normed_poisson')
plt.show()
# ### Interpretation
print('Time of max posterior probability =\n'
f'{x[np.argmax(y_goal_for)], x[np.argmax(y_goal_against)], x[np.argmax(y_no_goal)]}')
# Notes:
# - Goals usually come 30 seconds - 1 minutes after pulling the goalie.
# - Games tend to end 1 minute - 1 min 30 seconds after pulling the goalie. This roughly corresponds to the average time remaining on pull.
#
# From now on we'll work from the distributions as our source of truth. These are hard coded below to help with reproducibility.
model_normlizing_factors = [
0.1268201,
0.26021606,
0.61296383
]
mu_mcmc = [
33.53749551104675,
38.35247984655338,
66.0835441233016
]
# ### Cumulative sum
#
# Calculating the CDF will allow us to make some interesting observations on the results.
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
plt.plot(x, np.cumsum(y_goal_for), label=r'$cumsum [ P(\mathrm{goal\;for}\;|\;X) ]$', color='green', lw=LW)
plt.plot(x, np.cumsum(y_goal_against), label=r'$cumsum [ P(\mathrm{goal\;against}\;|\;X) ]$', color='red', lw=LW)
plt.plot(x, np.cumsum(y_no_goal), label=r'$cumsum [ P(\mathrm{no\;goal}\;|\;X) ]$', color='orange', lw=LW)
plt.ylabel('Posterior CDF')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.legend()
ax = plt.gca()
ax.yaxis.tick_right()
plt.xlim(0, 100)
savefig(plt, 'time_since_poisson_cdf')
plt.show()
# The end of game values have been normalized sum up to one, but this ratio changes over time. We can visualize this with the risk-reward ratio (see below).
#
# ### Re-normalize
#
# To better compare these probability distributions, we can normalize each bin to 1 using a function $\alpha(t)$, as follows:
#
# $$
# \alpha(t) \cdot \big[ P(goal\;for; t) + (P(goal\;against; t) + P(no\;goal; t)\big] = 1 \\
# \vdots \\
# \alpha(t) = \big[ P(goal\;for; t) + (P(goal\;against; t) + P(no\;goal; t)\big]^{-1}
# $$
#
# This will allow us to re-weight the posteriors later, so we can compare them better and yield a different interpretation.
#
# Essentially, we'll be able to interpret the resulting distribution as the chance of each outcome at time $t$. This stands in contrast to the probability distributions above, where the total area under the curves sum to 1.
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
plt.plot(x, alpha, label=r'$\alpha$', lw=LW)
plt.ylabel('Alpha re-weighting parameter')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.legend()
# savefig(plt, 'time_elapsed_poisson_cdf')
plt.show()
from scipy.stats import poisson
ALPHA = 0.6
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
# Alpha has same shape as x, y above
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
y_goal_for = alpha * y_goal_for
y_goal_against = alpha * y_goal_against
y_no_goal = alpha * y_no_goal
plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
plt.ylabel('Chance of outcome at time $t$')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.legend()
# Plotting below with error bar
# savefig(plt, 'time_since_outcome_chance_timeseries')
plt.show()
# ### Adding error bars
# Note how there are very few samples to draw conclusions from for the low and high times.
#
# e.g. more than 2 minutes
np.sum(training_samples[0] > 2*60) + np.sum(training_samples[1] > 2*60) + np.sum(training_samples[2] > 2*60)
np.sum(training_samples[0] < 2*60) + np.sum(training_samples[1] < 2*60) + np.sum(training_samples[2] < 2*60)
# We can show this uncertainty visually using error bars. Starting with the $\mu$ MCMC samples...
plt.hist(burned_trace['mu_goal_for'])
plt.hist(burned_trace['mu_goal_against'])
plt.hist(burned_trace['mu_no_goal'])
# We can use the uncertainty on $\mu$ to calculate that for $P$:
#
# $$
# \sigma_P = \big| \frac{\partial P}{\partial \mu} \big|\;\sigma_{\mu}
# $$
#
# where $\sigma_{\mu}$ is the standard deviation of the $\mu$ samples.
mu_mcmc_std = [
burned_trace['mu_goal_for'].std(),
burned_trace['mu_goal_against'].std(),
burned_trace['mu_no_goal'].std(),
]
mu_mcmc_std
model_normalizing_factors
import inspect
print(inspect.getsource(poisson_posterior))
from scipy.misc import derivative
from tqdm import tqdm_notebook
def calc_posteror_error(mu, mu_std, mu_step=1e-6):
x = poisson_posterior()[0]
err = mu_std * np.abs(np.array([
derivative(lambda _mu: poisson.pmf(int(t), _mu), mu, dx=mu_step)
for t in tqdm_notebook(x)
]))
return err
from scipy.stats import poisson
ALPHA = 0.6
ALPHA_LIGHT = 0.3
LW = 3
''' Plot the poisson distributions '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
# Alpha has same shape as x, y above
alpha = np.power(
np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0),
-1
)
y_goal_for = alpha * y_goal_for
y_goal_against = alpha * y_goal_against
y_no_goal = alpha * y_no_goal
plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW)
plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW)
plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW)
''' Plot the errors '''
err_p_goal_for = alpha * calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0])
err_p_goal_against = alpha * calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1])
err_p_no_goal = alpha * calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2])
plt.fill_between(x, y_goal_for-err_p_goal_for, y_goal_for+err_p_goal_for,
color='green', alpha=ALPHA_LIGHT)
plt.fill_between(x, y_goal_against-err_p_goal_against, y_goal_against+err_p_goal_against,
color='red', alpha=ALPHA_LIGHT)
plt.fill_between(x, y_no_goal-err_p_no_goal, y_no_goal+err_p_no_goal,
color='orange', alpha=ALPHA_LIGHT)
plt.ylabel('Chance of outcome at time $t$')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.xlim(0, 100)
plt.ylim(0, 1)
plt.legend()
savefig(plt, 'time_since_outcome_chance_timeseries')
plt.show()
# We can't say anything conclusive due to huge errors on low times, but we are much more confident on late game predictions
# ### Odds of scoring a goal
# Let's go into odds-space and look at the chance of scoring a goal, compared to either outcome. We want to maximze this.
ALPHA = 0.6
ALPHA_LIGHT = 0.3
LW = 3
''' Odds ratio '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
odds_goal_for = y_goal_for / (y_goal_against + y_no_goal)
''' Error bars '''
err_p_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0])
err_p_goal_against = calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1])
err_p_no_goal = calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2])
err_odds_goal_for = (
np.power(err_p_goal_for / y_goal_for, 2)
+ np.power(err_p_goal_against / y_goal_against, 2)
+ np.power(err_p_no_goal / y_no_goal, 2)
)
err_odds_goal_for = odds_goal_for * np.sqrt(err_odds_goal_for)
''' Plots '''
plt.plot(x, odds_goal_for,
label=r'$odds(\mathrm{goal\;for})$',
color='green', lw=LW, alpha=ALPHA)
plt.fill_between(x, odds_goal_for-err_odds_goal_for, odds_goal_for+err_odds_goal_for,
color='green', lw=LW, alpha=ALPHA_LIGHT)
plt.ylabel('Odds')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.xlim(0, 1.2)
plt.ylim(0, 10)
plt.legend()
# savefig(plt, 'time_since_odds_goal_for')
plt.show()
ALPHA = 0.6
ALPHA_LIGHT = 0.3
LW = 3
''' Odds ratio '''
x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(
mu_mcmc, norm_factors=model_normalizing_factors
)
odds_goal_for = y_goal_for / (y_goal_against + y_no_goal)
''' Error bars '''
err_p_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0])
err_p_goal_against = calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1])
err_p_no_goal = calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2])
err_odds_goal_for = (
np.power(err_p_goal_for / y_goal_for, 2)
+ np.power(err_p_goal_against / y_goal_against, 2)
+ np.power(err_p_no_goal / y_no_goal, 2)
)
err_odds_goal_for = odds_goal_for * np.sqrt(err_odds_goal_for)
''' Plots '''
plt.plot(x, odds_goal_for,
label=r'$odds(\mathrm{goal\;for})$',
color='green', lw=LW, alpha=ALPHA)
plt.fill_between(x, odds_goal_for-err_odds_goal_for, odds_goal_for+err_odds_goal_for,
color='green', lw=LW, alpha=ALPHA_LIGHT)
plt.ylabel('Odds')
# plt.yticks([])
plt.xlabel('Time since pull (seconds)')
plt.xlim(0, 100)
plt.ylim(0, 2)
plt.legend()
savefig(plt, 'time_since_odds_goal_for')
plt.show()
(odds_goal_for-err_odds_goal_for).max()
# This chart suggests that odds of scoring drop off the longer the goalie remains pulled. There's no statistically significant evidence for odds at less than 30 seconds, after which the odds drop from ~0.6 to 0 within the first minute.
from IPython.display import HTML
HTML('<style>div.text_cell_render{font-size:130%;padding-top:50px;padding-bottom:50px}</style>')
|
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|
# utils
import numpy as np
import copy
SELECT_COL = 'SELECT_COL'
SELECT_AGG = 'SELECT_AGG'
WHERE_COL = 'WHERE_COL'
WHERE_OP = 'WHERE_OP'
WHERE_VAL = 'WHERE_VAL' # for models with value prediction
# spider
WHERE_ROOT_TERM = 'WHERE_ROOT_TERM'
ANDOR = 'ANDOR'
GROUP_COL = 'GROUP_COL'
GROUP_NHAV = 'GROUP_NHAV'
HAV_COL = 'HAV_COL'
HAV_AGG = 'HAV_AGG'
HAV_OP = 'HAV_OP'
HAV_ROOT_TERM = 'HAV_ROOT_TERM'
ORDER_COL = 'ORDER_COL'
ORDER_AGG = 'ORDER_AGG'
ORDER_DESC_ASC_LIMIT = 'DESC_ASC_LIMIT'
IUEN = 'IUEN'
OUTSIDE = "O"
END_NESTED = "##END_NESTED##"
# spider -> editsql
ORDER_DESC_ASC = 'ORDER_DESC_ASC' # (ORDER_DESC_ASC, (col, agg, bool_distinct), desc_asc, p(desc_asc), dec_idx)
ORDER_LIMIT = 'ORDER_LIMIT' # (ORDER_DESC_ASC, (col, agg, bool_distinct), bool_limit, p(limit), dec_idx)
SELECT_AGG_v2 = 'SELECT_AGG_v2' # (SELECT_AGG_v2, col, agg, bool_distinct, avg_prob, dec_idx)
ORDER_AGG_v2 = 'ORDER_AGG_v2'
HAV_AGG_v2 = 'HAV_AGG_v2'
HAV_OP_v2 = 'HAV_OP_v2' # (HAV_OP_v2, (col, agg, bool_distinct), op, prob(op), dec_idx)
HAV_ROOT_TERM_v2 = 'HAV_ROOT_TERM_v2' # # (HAV_OP_v2, (col, agg, bool_distinct), op, 'root'/'terminal', prob, dec_idx)
IUEN_v2 = 'IUEN_v2'
def semantic_unit_segment(tag_seq):
tag_item_lists, seg_pointers = [], []
for idx, tag_item in enumerate(tag_seq):
if tag_item[0] != OUTSIDE:
tag_item_lists.append(tag_item)
seg_pointers.append(idx)
return tag_item_lists, seg_pointers
def helper_find_closest_bw(tag_seq, start_idx, tgt_name=None, tgt_id=None):
skip_nested = []
idx = start_idx
while idx > 0:
if len(skip_nested) > 0:
if "root" in tag_seq[idx]:
_ = skip_nested.pop()
idx -= 1
else:
if (tgt_name is not None and tgt_name in tag_seq[idx]) or\
(tgt_id is not None and tag_seq[idx][0] == tgt_id): #include tgt_name == END_NESTED
return idx
elif END_NESTED in tag_seq[idx]:
skip_nested.append(idx)
idx -= 1
else:
idx -= 1
return -1 # not found
class bcolors:
"""
Usage: print bcolors.WARNING + "Warning: No active frommets remain. Continue?" + bcolors.ENDC
"""
PINK = '\033[95m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
class Hypothesis:
def __init__(self, dec_prefix):
self.sql = None
# Note: do not create hyp from scratch during decoding (may lead to wrong self.dec_prefix)
self.dec_prefix = list(dec_prefix) # given decoding prefix, must execute
self.tag_seq = [] # sequence of tags
self.dec_seq = [] # sequence of decisions
self.dec_seq_idx = 0
self.logprob = 0.0
self.length = 0
self.logprob_list = None
self.pred_aux_seq = [] # auxiliary information
def copy(self):
return copy.deepcopy(self)
def add_logprob(self, logprob):
self.logprob += logprob
self.length += 1
def set_passes_mode(self, dropout_hyp):
self.test_tag_seq = list(self.tag_seq) # from decode without dropout
for tag_idx, tag in enumerate(dropout_hyp.tag_seq):
item_lst = list(tag)
item_lst[-2] = [item_lst[-2]]
self.tag_seq[tag_idx] = item_lst
self.logprob_list = [dropout_hyp.logprob]
def merge_hyp(self, hyp):
# tag_seq, dec_seq, dec_seq_idx, logprob
assert len(hyp.tag_seq) == len(self.tag_seq)
for item_idx in range(len(hyp.tag_seq)):
new_item = hyp.tag_seq[item_idx]
self.tag_seq[item_idx][-2].append(new_item[-2])
self.logprob_list.append(hyp.logprob)
@staticmethod
def length_penalty(sent_length, length_penalty_factor):
# Following: https://arxiv.org/abs/1609.08144, Eqn 14, recommend factor = 0.6-0.7.
# return ((5. + sent_length) / 6.) ** length_penalty_factor
return (1.0 * sent_length) ** length_penalty_factor
@staticmethod
def sort_hypotheses(hypotheses, topK, length_penalty_factor):
if topK is None:
topK = np.inf
sorted_hyps = sorted(hypotheses, key=lambda x: x.logprob / Hypothesis.length_penalty(x.length, length_penalty_factor),
reverse=True)
return_hypotheses = []
last_score = None
count = 0
for hyp in sorted_hyps:
current_score = hyp.logprob / Hypothesis.length_penalty(hyp.length, length_penalty_factor)
if last_score is None or current_score < last_score:
if count < topK:
return_hypotheses.append(hyp)
last_score = current_score
count += 1
else:
break
else:
assert current_score == last_score # tie, include
return_hypotheses.append(hyp)
return return_hypotheses
@staticmethod
def print_hypotheses(hypotheses):
for hyp in hypotheses:
print("logprob: {}, tag_seq: {}\ndec_seq: {}".format(hyp.logprob, hyp.tag_seq, hyp.dec_seq))
|
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|
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelBinarizer
import gc
import time
####
# load the data
####
print('reading in data')
all_train = pd.read_csv('./data/train_cleaned.csv')
#all_train.head()
final_test = pd.read_csv('./data/test_cleaned.csv')
#final_test.head()
#raw_test = pd.read_csv('./data/test.csv')
submission = pd.read_csv('./data/sample_submission.csv')
#submission.head()
#getting mixed types b/c of bigints... need all to str
submission['fullVisitorId'] = submission['fullVisitorId'].astype('str')
final_test['fullVisitorId'] = final_test['fullVisitorId'].astype('str')
"""
####
# check submission length
####
len(submission['fullVisitorId']) == len(set(submission['fullVisitorId']))
len(set(submission['fullVisitorId'])) == len(set(final_test['fullVisitorId']))
len(submission['fullVisitorId']) == len(set(final_test['fullVisitorId']))
sub_set = set(submission['fullVisitorId'])
test_set = set(final_test['fullVisitorId'])
sub_set = sorted(sub_set)
test_set = sorted(test_set)
sub_set[:10]
test_set[:10]
len(sub_set)
len(test_set)
len(set(list(sub_set) + list(test_set)))
"""
"""
####
# explore what we are looking at
####
#need to go through and clean the columns
all_train.describe()
all_train.columns
#51 columns
all_train['adwordsClickInfo'][0] #this is still json buy okay
type(all_train['transactionRevenue'][0]) == np.float64#this is the one we are trying to predict
all_train.columns
"""
####
# scan columns and classify
####
numeric = []
categorical = []
flatline = []
other = []
for col in all_train.columns:
if type(all_train[col][0]) == str:
#categorical
if len(all_train[col].unique()) > 1:
categorical.append(col)
else:
flatline.append(col)
elif type(all_train[col][0]) == int or type(all_train[col][0]) == np.float64:
#numeric
numeric.append(col)
else:
other.append(col)
numeric
categorical
flatline
other
####
# other columns
####
drop_other = ['visitId',
'campaignCode',
'referralPath',
'adwordsClickInfo',
'adContent',
'Unnamed: 0']
numeric_other = ['visitNumber',
'hits',
'visits']
categorical_other = ['isMobile',]
####
# drop flat cols for both the train and test data
####
flatline.extend(drop_other)
#should drop the flatline columns from the df
all_train = all_train.drop(flatline, axis = 1)
all_train.shape
flatline = [x for x in flatline if x != 'campaignCode' ]
final_test = final_test.drop(flatline, axis=1)
final_test.shape
for i in list(all_train.columns):
if i not in list(final_test.columns):
print(i)
####
# numeric
####
print('numeric variables')
#'fullVisitorId' #removed from numeric, this is just the id
#'transactionRevenue' #this is the response variable we want to predict
numeric = [ 'newVisits',
'pageviews',
'transactionRevenue',
]
numeric.extend(numeric_other)
all_train['transactionRevenue'].fillna(0, inplace = True)
def fill_and_adj_numeric(df):
#there are NA for page views, fill median for this == 1
df.pageviews.fillna(df.pageviews.median(), inplace = True)
df.hits.fillna(df.hits.median(), inplace = True)
df.visits.fillna(df.visits.median(), inplace = True)
#are boolean, fill NaN with zeros, add to categorical
df.isTrueDirect.fillna(0, inplace = True)
df.bounces.fillna(0, inplace = True)
df.newVisits.fillna(0, inplace = True)
df.visitNumber.fillna(1, inplace = True)
for col in ['isTrueDirect', 'bounces', 'newVisits']:
df[col] = df[col].astype(int)
return df
all_train = fill_and_adj_numeric(all_train)
final_test = fill_and_adj_numeric(final_test)
####
# datetime columns
##
print('Date variable')
all_train['date'] #this needs to be processed with datetime
def parseDateCol(df, date_col):
""" takes the date column and adds new columns with the features:
yr, mon, day, day of week, day of year """
df['datetime'] = df.apply(lambda x : time.strptime(str(x[date_col]), "%Y%M%d"), axis = 1)
print('parsing year')
df['year'] = df.apply(lambda x : x['datetime'].tm_year, axis = 1)
print('parsing month')
df['month'] = df.apply(lambda x :x['datetime'].tm_mon , axis = 1)
print('parsing days (*3 versions)')
df['mday'] = df.apply(lambda x : x['datetime'].tm_mday, axis = 1)
df['wday'] = df.apply(lambda x : x['datetime'].tm_wday , axis = 1)
df['yday'] = df.apply(lambda x : x['datetime'].tm_yday , axis = 1)
#drop date and datetime
df = df.drop([date_col, 'datetime'], axis = 1)
return df
all_train = parseDateCol(all_train, 'date')
final_test = parseDateCol(final_test, 'date')
####
# categorical
####
print('Cleaning categorical variables')
categorical = ['channelGrouping',
'sessionId',
'browser',
'deviceCategory',
'operatingSystem',
'city',
'continent',
'country',
'metro',
'networkDomain',
'region',
'subContinent',
'campaign',
'keyword',
'medium',
'source']
categorical.extend(categorical_other)
with_na = []
for col in categorical:
if all_train[col].isnull().any() :
with_na.append(col)
####
# fill na for all the categoricals with the 'None' if string or mode if bool
####
#most common value to fill the na
all_train.keyword.fillna('(not provided)', inplace = True)
def binarize_col(train, test, col):
encoder = LabelBinarizer()
cat_train_1hot = encoder.fit_transform(train[col])
cat_test_1hot = encoder.transform(test[col])
return cat_train_1hot, cat_test_1hot
train_bins = []
test_bins = []
#this is crashing... need a little more memory I think
for col in categorical:
if len(all_train[col].unique()) > 1 and len(all_train[col].unique()) < 50:
print(f'binarizing:{col}\tunique: {len(all_train[col].unique())}')
bin_col_all_train, bin_col_final_test = binarize_col(all_train, final_test, col)
if len(train_bins) == 0:
print('initializing np matrix')
train_bins = bin_col_all_train
test_bins = bin_col_final_test
else:
print('appending to np matrix')
train_bins = np.c_[train_bins, bin_col_all_train]
test_bins = np.c_[test_bins, bin_col_final_test]
gc.collect()
train_bins.shape
test_bins.shape
#drop the non binarized categorical columns and the housekeeping ones from
#the train and test sets for sklearn
all_train = all_train.drop(categorical, axis = 1)
final_test = final_test.drop(categorical, axis = 1)
# isolate the response variable
y_train = all_train['transactionRevenue'].values
#take the log on the front and then use that to train the algorithm.
y_train = np.log1p(y_train)
#merge the one hot encoded categorical matricies with the
#original df, drop the id and response columns
all_train.columns
final_test.columns
X_train = all_train.drop(['fullVisitorId','transactionRevenue',], axis = 1).values
X_train = np.c_[X_train, train_bins]
X_train.shape
X_test = final_test.drop(['fullVisitorId'], axis = 1).values
X_test = np.c_[X_test, test_bins]
X_test.shape
#TODO: try pickling the data instead of writing to file!
#this ends up massive and crashes unfortunately...
X_train.dump('X_train.dat')
y_train.dump('y_train.dat')
X_test.dump('X_test.dat')
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|
# Maze generator -- Randomized Prim Algorithm
## Imports
import random
import numpy as np
import time
from colorama import init
from colorama import Fore, Back, Style
## Functions
def printMaze(maze, height, width):
for i in range(0, height):
for j in range(0, width):
if (maze[i][j] == 'u'):
print(Fore.WHITE + str(maze[i][j]), end=" ")
elif (maze[i][j] == 'c'):
print(Fore.GREEN + str(maze[i][j]), end=" ")
else:
print(Fore.RED + str(maze[i][j]), end=" ")
print('\n')
# Find number of surrounding cells
def surroundingCells(rand_wall, maze):
s_cells = 0
if (maze[rand_wall[0] - 1][rand_wall[1]] == 'c'):
s_cells += 1
if (maze[rand_wall[0] + 1][rand_wall[1]] == 'c'):
s_cells += 1
if (maze[rand_wall[0]][rand_wall[1] - 1] == 'c'):
s_cells += 1
if (maze[rand_wall[0]][rand_wall[1] + 1] == 'c'):
s_cells += 1
return s_cells
def generate_maze(maze_size=6):
## Main code
# Init variables
wall = 'w'
cell = 'c'
unvisited = 'u'
height = maze_size
width = maze_size
maze = []
# Initialize colorama
init()
# Denote all cells as unvisited
for i in range(0, height):
line = []
for j in range(0, width):
line.append(unvisited)
maze.append(line)
# Randomize starting point and set it a cell
starting_height = int(random.random() * height)
starting_width = int(random.random() * width)
if (starting_height == 0):
starting_height += 1
if (starting_height == height - 1):
starting_height -= 1
if (starting_width == 0):
starting_width += 1
if (starting_width == width - 1):
starting_width -= 1
# Mark it as cell and add surrounding walls to the list
maze[starting_height][starting_width] = cell
walls = []
walls.append([starting_height - 1, starting_width])
walls.append([starting_height, starting_width - 1])
walls.append([starting_height, starting_width + 1])
walls.append([starting_height + 1, starting_width])
# Denote walls in maze
maze[starting_height - 1][starting_width] = 'w'
maze[starting_height][starting_width - 1] = 'w'
maze[starting_height][starting_width + 1] = 'w'
maze[starting_height + 1][starting_width] = 'w'
while (walls):
# Pick a random wall
rand_wall = walls[int(random.random() * len(walls)) - 1]
# Check if it is a left wall
if (rand_wall[1] != 0):
if (maze[rand_wall[0]][rand_wall[1] - 1] == 'u' and maze[rand_wall[0]][rand_wall[1] + 1] == 'c'):
# Find the number of surrounding cells
s_cells = surroundingCells(rand_wall, maze)
if (s_cells < 2):
# Denote the new path
maze[rand_wall[0]][rand_wall[1]] = 'c'
# Mark the new walls
# Upper cell
if (rand_wall[0] != 0):
if (maze[rand_wall[0] - 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] - 1][rand_wall[1]] = 'w'
if ([rand_wall[0] - 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] - 1, rand_wall[1]])
# Bottom cell
if (rand_wall[0] != height - 1):
if (maze[rand_wall[0] + 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] + 1][rand_wall[1]] = 'w'
if ([rand_wall[0] + 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] + 1, rand_wall[1]])
# Leftmost cell
if (rand_wall[1] != 0):
if (maze[rand_wall[0]][rand_wall[1] - 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] - 1] = 'w'
if ([rand_wall[0], rand_wall[1] - 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] - 1])
# Delete wall
for wall in walls:
if (wall[0] == rand_wall[0] and wall[1] == rand_wall[1]):
walls.remove(wall)
continue
# Check if it is an upper wall
if (rand_wall[0] != 0):
if (maze[rand_wall[0] - 1][rand_wall[1]] == 'u' and maze[rand_wall[0] + 1][rand_wall[1]] == 'c'):
s_cells = surroundingCells(rand_wall, maze)
if (s_cells < 2):
# Denote the new path
maze[rand_wall[0]][rand_wall[1]] = 'c'
# Mark the new walls
# Upper cell
if (rand_wall[0] != 0):
if (maze[rand_wall[0] - 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] - 1][rand_wall[1]] = 'w'
if ([rand_wall[0] - 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] - 1, rand_wall[1]])
# Leftmost cell
if (rand_wall[1] != 0):
if (maze[rand_wall[0]][rand_wall[1] - 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] - 1] = 'w'
if ([rand_wall[0], rand_wall[1] - 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] - 1])
# Rightmost cell
if (rand_wall[1] != width - 1):
if (maze[rand_wall[0]][rand_wall[1] + 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] + 1] = 'w'
if ([rand_wall[0], rand_wall[1] + 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] + 1])
# Delete wall
for wall in walls:
if (wall[0] == rand_wall[0] and wall[1] == rand_wall[1]):
walls.remove(wall)
continue
# Check the bottom wall
if (rand_wall[0] != height - 1):
if (maze[rand_wall[0] + 1][rand_wall[1]] == 'u' and maze[rand_wall[0] - 1][rand_wall[1]] == 'c'):
s_cells = surroundingCells(rand_wall, maze)
if (s_cells < 2):
# Denote the new path
maze[rand_wall[0]][rand_wall[1]] = 'c'
# Mark the new walls
if (rand_wall[0] != height - 1):
if (maze[rand_wall[0] + 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] + 1][rand_wall[1]] = 'w'
if ([rand_wall[0] + 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] + 1, rand_wall[1]])
if (rand_wall[1] != 0):
if (maze[rand_wall[0]][rand_wall[1] - 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] - 1] = 'w'
if ([rand_wall[0], rand_wall[1] - 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] - 1])
if (rand_wall[1] != width - 1):
if (maze[rand_wall[0]][rand_wall[1] + 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] + 1] = 'w'
if ([rand_wall[0], rand_wall[1] + 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] + 1])
# Delete wall
for wall in walls:
if (wall[0] == rand_wall[0] and wall[1] == rand_wall[1]):
walls.remove(wall)
continue
# Check the right wall
if (rand_wall[1] != width - 1):
if (maze[rand_wall[0]][rand_wall[1] + 1] == 'u' and maze[rand_wall[0]][rand_wall[1] - 1] == 'c'):
s_cells = surroundingCells(rand_wall, maze)
if (s_cells < 2):
# Denote the new path
maze[rand_wall[0]][rand_wall[1]] = 'c'
# Mark the new walls
if (rand_wall[1] != width - 1):
if (maze[rand_wall[0]][rand_wall[1] + 1] != 'c'):
maze[rand_wall[0]][rand_wall[1] + 1] = 'w'
if ([rand_wall[0], rand_wall[1] + 1] not in walls):
walls.append([rand_wall[0], rand_wall[1] + 1])
if (rand_wall[0] != height - 1):
if (maze[rand_wall[0] + 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] + 1][rand_wall[1]] = 'w'
if ([rand_wall[0] + 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] + 1, rand_wall[1]])
if (rand_wall[0] != 0):
if (maze[rand_wall[0] - 1][rand_wall[1]] != 'c'):
maze[rand_wall[0] - 1][rand_wall[1]] = 'w'
if ([rand_wall[0] - 1, rand_wall[1]] not in walls):
walls.append([rand_wall[0] - 1, rand_wall[1]])
# Delete wall
for wall in walls:
if (wall[0] == rand_wall[0] and wall[1] == rand_wall[1]):
walls.remove(wall)
continue
# Delete the wall from the list anyway
for wall in walls:
if (wall[0] == rand_wall[0] and wall[1] == rand_wall[1]):
walls.remove(wall)
# Mark the remaining unvisited cells as walls
for i in range(0, height):
for j in range(0, width):
if (maze[i][j] == 'u'):
maze[i][j] = 'w'
start_placed = False
goal_placed = False
# Placing starts and goals
while not start_placed:
start_x = np.random.randint(width)
start_y = np.random.randint(height)
if maze[start_x][start_y] == 'c':
maze[start_x][start_y] = 's'
start_placed = True
while not goal_placed:
goal_x = np.random.randint(width)
goal_y = np.random.randint(height)
if maze[goal_x][goal_y] == 'c':
maze[goal_x][goal_y] = 'g'
goal_placed = True
for i in range(len(maze)):
for j in range(len(maze[i])):
if maze[i][j] == 'c':
maze[i][j] = 'O'
elif maze[i][j] == 'w':
maze[i][j] = '#'
elif maze[i][j] == 's':
maze[i][j] = 'O'
elif maze[i][j] == 'g':
maze[i][j] = 'G'
maze_path = []
for row in maze:
row_s = "".join(row)
maze_path.append(row_s)
maze_s = "\\".join(maze_path)
return maze_s
gen_m = generate_maze()
print(gen_m)
# Print final maze
# printMaze(gen_m, 6, 6)
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|
import sys
sys.path.insert(0, './python/')
import caffe
import numpy as np
import pdb
#weights='./models/lenet300100/caffe_lenet300100_original.caffemodel'
weights='./models/lenet300100/compressed_lenet300100.caffemodel'
#weights='/home/gitProject/Dynamic-Network-Surgery/models/lenet300100/caffe_lenet300100_sparse.caffemodel'
proto='./models/lenet300100/lenet_train_test.prototxt'
net=caffe.Net(proto, weights, caffe.TEST)
total=0
aa=0
# for each layer, a mask is applied to the original weights and bias.
# here, for net.params['ip1'], net.params['ip1'][0] is the weights, net.params['ip1'][1] is the bias,
# net.params['ip1'][2] is the mask for the weights, net.params['ip1'][3] is the mask for the bias.
# if one of the element value in the mask is 0, the corresponding element in network is pruned.
w_m=2
b_m=3
a1=len(np.where(net.params['ip1'][b_m].data != 0)[0])
a2=len(np.where(net.params['ip1'][w_m].data != 0)[0])
a3=len(np.where(net.params['ip2'][w_m].data != 0)[0])
a4=len(np.where(net.params['ip2'][b_m].data != 0)[0])
a5=len(np.where(net.params['ip3'][b_m].data != 0)[0])
a6=len(np.where(net.params['ip3'][w_m].data != 0)[0])
b1=net.params['ip1'][0].data.size+net.params['ip1'][1].data.size
b2=net.params['ip2'][0].data.size+net.params['ip2'][1].data.size
b3=net.params['ip3'][0].data.size+net.params['ip3'][1].data.size
aa = a1+a2+a3+a4+a5+a6
total = b1+b2+b3
print 'Compression rate :{}% ({}x)'.format(1- aa*1./total,total*1./aa)
print 'ip1:{}%'.format((a1+a2)*100./b1)
print 'ip2:{}%'.format((a3+a4)*100./b2)
print 'ip3:{}%'.format((a5+a6)*100./b3)
|
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|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import argparse
import random as rand
import time
import numpy as np
import pickle
from PIL import Image, ImageDraw, ImageFilter, ImageEnhance, ImageOps, ImageFile
from delaunay import delaunay
from voronoi import createVoronoiFromDelaunay
#
# Add a prefix to a path-specified filename;
# prefix goes on the filename portion.
#
def addFilenamePrefix( prefix, filename ):
return os.path.join( os.path.dirname( filename ), prefix + os.path.basename( filename ) )
def generateRandomPoints(count, sizeX, sizeY):
points = []
start = time.clock()
for i in range(count):
p = (rand.randint(0,sizeX),rand.randint(0,sizeY))
if not p in points:
points.append(p)
print "Punkte generieren: %.2fs" % (time.clock()-start)
return points
def generateWeightedRandomPoints(count, sizeX, sizeY):
points = []
start = time.clock()
for i in range(count):
x = rand.randint(0,sizeX/2)-rand.randint(0,sizeX/2) + sizeX/2
y = rand.randint(0,sizeY/2)-rand.randint(0,sizeY/2) + sizeY/2
p = (x, y)
if not p in points:
points.append(p)
print "Punkte generieren: %.2fs" % (time.clock()-start)
return points
def drawPoints(points, filename, sizeX, sizeY):
im = Image.new('RGB', (sizeX*10, sizeY*10))
draw = ImageDraw.Draw(im)
for p in points:
px = p[0]*10
py = p[1]*10
draw.arc((px, py, px+20,py+20),0,360,fill='white')
im.save(filename, "JPEG")
def drawTriangulation(triangles, filename, sizeX, sizeY, multiplier):
im = Image.new('RGB', (sizeX*multiplier, sizeY*multiplier))
draw = ImageDraw.Draw(im)
start = time.clock()
for t in triangles:
r = rand.randint(0,255)
g = rand.randint(0,255)
b = rand.randint(0,255)
p0 = tuple(map(lambda x:x*multiplier, t[0]))
p1 = tuple(map(lambda x:x*multiplier, t[1]))
p2 = tuple(map(lambda x:x*multiplier, t[2]))
drawT = (p0, p1, p2)
draw.polygon(drawT, fill=(r,g,b,255))
im.save(filename, "JPEG")
print "Dreiecke zeichnen: %.2fs" % (time.clock()-start)
def getCenterPoint(t):
return ((t[0][0]+t[1][0]+t[2][0])/3, (t[0][1]+t[1][1]+t[2][1])/3)
def getTriangleColor(t, im):
# 3x der Wert in der Mitte + jew. die Ecke / 6.
color = []
for i in range(3):
p = t[i]
if p[0] >= im.size[0] or p[0] < 0 or p[1] >= im.size[1] or p[1] < 0:
continue
color.append(im.getpixel(p))
p = getCenterPoint(t)
if p[0] < im.size[0] and p[0] >= 0 and p[1] < im.size[1] and p[1] >= 0:
centerPixel = im.getpixel(p)
color = color + [centerPixel]*3
div = float(len(color))
color = reduce(lambda rec, x : ((rec[0]+x[0])/div, (rec[1]+x[1])/div, (rec[2]+x[2])/div), color, (0,0,0))
color = map(lambda x : int(x), color)
return color
def getPolygonColor(pol, im):
centerPoint = (0,0)
color = []
count = 0
#print ""
for p in pol:
if p[0] >= im.size[0] or p[0] < 0 or p[1] >= im.size[1] or p[1] < 0:
continue
count += 1
color.append(im.getpixel(p))
centerPoint = (centerPoint[0]+p[0], centerPoint[1]+p[1])
centerPoint = (centerPoint[0]/count, centerPoint[1]/count)
color.append(im.getpixel(centerPoint))
color.append(im.getpixel(centerPoint))
color.append(im.getpixel(centerPoint))
div = float(len(color))
color = reduce(lambda rec, x : ((rec[0]+x[0]), (rec[1]+x[1]), (rec[2]+x[2])), color, (0,0,0))
color = (color[0]/div, color[1]/div, color[2]/div)
# Diese Zeile ergibt KEINEN Sinn!!!!! Aber anders hab ichs nicht zum Laufen gebracht. Irgendein Fehler mit der Farbe...
color = (color[0]/4.0, color[1]/4.0, color[2]/4.0)
color = map(lambda x : int(x), color)
return color
def brightenImage(im, value):
enhancer = ImageEnhance.Brightness(im)
im = enhancer.enhance(value)
return im
def drawImageColoredTriangles(triangles, filename, origIm, multiplier):
(sizeX, sizeY) = origIm.size
im = Image.new('RGB', (sizeX*multiplier, sizeY*multiplier))
draw = ImageDraw.Draw(im)
start = time.clock()
for t in triangles:
(r,g,b) = getTriangleColor(t, origIm)
p0 = tuple(map(lambda x:x*multiplier, t[0]))
p1 = tuple(map(lambda x:x*multiplier, t[1]))
p2 = tuple(map(lambda x:x*multiplier, t[2]))
drawT = (p0, p1, p2)
draw.polygon(drawT, fill=(r,g,b,255))
im = brightenImage(im, 3.0)
ImageFile.MAXBLOCK = im.size[0] * im.size[1]
im.save(filename, "JPEG", quality=100, optimize=True, progressive=True)
def drawImageColoredVoronoi(polygons, filename, origIm, multiplier):
start = time.clock()
(sizeX, sizeY) = origIm.size
im = Image.new('RGB', (sizeX*multiplier, sizeY*multiplier))
draw = ImageDraw.Draw(im)
for pol in polygons:
if len(pol) < 2:
continue
(r,g,b) = getPolygonColor(pol, origIm)
newPol = map(lambda x: (x[0] * multiplier, x[1]*multiplier), pol)
draw.polygon(newPol, fill=(r,g,b,255))
im = brightenImage(im, 3.0)
ImageFile.MAXBLOCK = im.size[0] * im.size[1]
im.save(filename, "JPEG", quality=100, optimize=True, progressive=True)
print "Voronoi zeichnen: %.2fs" % (time.clock()-start)
def generateTriangles(points):
start = time.clock()
triangles = delaunay(points)
print "Delaunay-Triangulierung: %.2fs" % (time.clock()-start)
return triangles
# Der Faktor, der die Anzahl generierter Punkte bestimmt ist der Exponent von v.
# Auf ein Bild der Auflösung 1000x750:
# 1.0 ~ 80 Punkte
# 1.5 ~ 500 Punkte
# 2.0 ~ 3000 Punkte
# 2.2 ~ 9500 Punkte
def findPointsFromImage(im, factor):
start = time.clock()
pix = np.array(im)
points = []
for row in range(len(pix)):
for col in range(len(pix[row])):
v = pix[row][col]
v = v**float(factor) / float(2**18)
if np.random.random() < v:
points.append((col, row))
print "Anzahl erzeugter Punkte:", len(points)
print "Punkte extrahieren: %.2fs" % (time.clock()-start)
return points
def loadAndFilterImage(name):
start = time.clock()
orig = Image.open(name)
im = orig.convert("L")
im = im.filter(ImageFilter.GaussianBlur(radius=5))
im = im.filter(ImageFilter.FIND_EDGES)
im = brightenImage(im, 20.0)
im = im.filter(ImageFilter.GaussianBlur(radius=5))
print "Bild laden: %.2fs" % (time.clock()-start)
return (orig, im)
def tupleToString(t):
return "{" + str(t[0]) + ", " + str(t[1]) + ", " + str(t[0]) + "}"
def printTriangleList(l):
for t in l:
if t != None:
print tupleToString(t),
print ""
def removeUnusedLinks(triangles):
newList = []
for t in triangles:
newList[:0] = (t[0],t[1],t[2])
return newList
def pointsToTriangles(points):
triangles = []
for i in range(len(points)-2):
t = (points[i],points[i+1],points[i+2])
triangles.append(t)
return triangles
def readTriangleListFromFile(filename):
with open(filename, 'r') as f:
points = pickle.load(f)
triangles = pointsToTriangles(points)
return triangles
def saveTriangleListToFile(triangles, filename):
triangles = removeUnusedLinks(triangles)
with open(filename, 'w') as f:
pickle.dump(triangles, f)
def autocontrastImage(input_filename, output_filename):
start = time.clock()
im = Image.open(input_filename)
im = ImageOps.autocontrast(im)
im.save( addFilenamePrefix( "autocontrasted_", output_filename ), "JPEG" )
print "Autocontrast Image: %.2fs" % (time.clock()-start)
def equalizeImage(filename):
start = time.clock()
im = Image.open(filename)
im = ImageOps.equalize(im)
im.save( addFilenamePrefix( "equalized_", filename ), "JPEG" )
print "Equalize Image: %.2fs" % (time.clock()-start)
def resizeImage(filename, longestSide, outDirectory="."):
im = Image.open(filename)
(width, height) = im.size
ratioX = float(longestSide) / width
ratioY = float(longestSide) / height
ratio = min(ratioX, ratioY)
im.thumbnail((width*ratio, height*ratio), Image.ANTIALIAS)
newFilename = os.path.join(outDirectory, addFilenamePrefix( "small_", os.path.basename(filename)))
im.save(newFilename, "JPEG")
return newFilename
# Wrapper.
def delaunayFromPoints(points):
start = time.clock()
triangles = delaunay(points)
print "Delaunay-Triangulierung: %.2fs" % (time.clock()-start)
return triangles
# Wrapper.
def voronoiFromTriangles(triangles):
start = time.clock()
polygons = createVoronoiFromDelaunay(triangles)
print "Voronoi-Polygonalisierung: %.2fs" % (time.clock()-start)
return polygons
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Values
parser.add_argument('-o', '--output', dest='output_filename', help='The filename to write the image to. Supported filetypes are BMP, TGA, PNG, and JPEG')
parser.add_argument('-i', '--image-file', dest='input_filename', help='An image file to use when calculating triangle colors. Image dimensions will override dimensions set by -x and -y.')
parser.add_argument('-f', '--factor', dest='factor', help='Factor definition. Determines the number of generated points (recommended value = 2.1 --> ~3000 points)')
parser.add_argument('-r', '--random', dest='create_random', default=False, help='If enabled, set the points randomly.')
parser.add_argument('-t', '--triangle', dest='create_triangle', default=True, help='If enabled, compute the triangle based in the spatial distribution of the image.')
parser.add_argument('-v', '--voronoi', dest='create_voronoi', default=False, help='If enabled, compute the voronoi based in the spatial distribution of the image..')
options = parser.parse_args()
if(not os.path.isfile(options.input_filename)):
print "There was an error in the path of the indicated file. Please check and try again!"
else:
(colorIm, blackIm) = loadAndFilterImage(options.input_filename)
(width, height) = colorIm.size
multiplier = 10
if options.create_random:
points = generateRandomPoints(15000, width, height)
triangles = delaunayFromPoints(points)
drawTriangulation(triangles, addFilenamePrefix( "random_", options.output_filename ), width, height, multiplier)
if options.create_triangle:
points = findPointsFromImage(blackIm, options.factor)
triangles = delaunayFromPoints(points)
drawImageColoredTriangles(triangles, addFilenamePrefix( "delaunay_", options.output_filename ), colorIm, multiplier)
if options.create_voronoi:
points = findPointsFromImage(blackIm, options.factor)
triangles = delaunayFromPoints(points)
polygons = voronoiFromTriangles(triangles)
drawImageColoredVoronoi(polygons, addFilenamePrefix( "voronoi_", options.output_filename ), colorIm, multiplier)
#autocontrastImage(addFilenamePrefix( "voronoi_", options.output_filename))
#autocontrastImage(addFilenamePrefix("delaunay_", options.output_filename))
#equalizeImage(addFilenamePrefix("voronoi_", filename))
|
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|
import os
import shutil
import numpy as np
import tensorflow as tf
from utils import conv, fc, plot
"""
Run file for testing modularity-inducing regularization term in the toy example of MNIST.
Much code adopted from Tensorflow's Tensorboard tutorial, available at:
https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
"""
LOGDIR = '/tmp/modular/mnist'
cwd = os.getcwd()
LABELS = os.path.join(cwd, "labels_1024.tsv")
SPRITES = os.path.join(cwd, "sprite_1024.png")
# downlaod mnist
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + "data", one_hot=True)
# visualization
if not (os.path.isfile(LABELS) and os.path.isfile(SPRITES)):
print("Necessary data files were not found. Run this command from inside the "
"repo provided at "
"https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial.")
# run parametes
use_two_fc = True
use_two_conv = True
learning_rate_init = 1e-4
nbatches = int(1e5)
save_plots = True
# experiment with different values of regularization penalty
reg_coef = 0.05
loop_coef = 0.01
hparam = "test"
tf.reset_default_graph()
sess = tf.Session()
# set data placeholders
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv(x_image, 1, 32, "conv1")
conv_out = conv(conv1, 32, 64, "conv2")
else:
conv_out = conv(x_image, 1, 16, "conv")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc(flattened, 7 * 7 * 64, 1024, "fc1")
relu = tf.nn.relu(fc1)
embedding_input = relu
tf.summary.histogram("fc1/relu", relu)
embedding_size = 1024
logits = fc(relu, 1024, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 7*7*64
logits = fc(flattened, 7*7*64, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
def l4_loss(t):
# raise each element in tensor t to the fourth power
# sum over all elements (all axis)
axis = list(range(len(t.get_shape())))
return tf.reduce_sum(tf.pow(t, 4), axis=axis)
with tf.name_scope("regularization"):
graph = tf.get_default_graph()
W1 = graph.get_tensor_by_name('conv1/W:0')
W2 = graph.get_tensor_by_name('conv2/W:0')
W3 = graph.get_tensor_by_name('fc1/W:0')
W4 = graph.get_tensor_by_name('fc2/W:0')
# l4-norm loss for all weight variables
l4_weight_decay = l4_loss(W1)
l4_weight_decay += l4_loss(W2)
l4_weight_decay += l4_loss(W3)
l4_weight_decay += l4_loss(W4)
# loops between convolutional layers
W1_spatial_sum = tf.reduce_sum(W1, [0, 1])
W2_spatial_sum = tf.reduce_sum(W2, [0, 1])
W1_2 = tf.tensordot(W1_spatial_sum, W2_spatial_sum, axes=[[1], [0]])
loops1_2 = tf.tensordot(W1_2, W1_2, axes=[[1, 0], [0, 1]])
# loops between fully connected layers
W3_4 = tf.tensordot(W3, W4, axes=[[1], [0]])
loops3_4 = tf.tensordot(W3_4, W3_4, axes=[[1, 0], [0, 1]])
loops = loops1_2 + loops3_4
# define regularization: weight decay + loop encouragement
reg = l4_weight_decay - loop_coef * tf.sqrt(loops)
tf.summary.scalar("loops", loops)
tf.summary.scalar("l4_weight_decay", l4_weight_decay)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate_init).minimize(xent)
train_step_reg = tf.train.AdamOptimizer(learning_rate_init).minimize(xent + reg_coef * reg)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
accuracy_summary = tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
# set different summary writers for train and test data
train_writer = tf.summary.FileWriter(LOGDIR + 'train')
test_writer = tf.summary.FileWriter(LOGDIR + 'test')
train_writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = SPRITES
embedding_config.metadata_path = LABELS
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(train_writer, config)
# main training function
def train():
if save_plots:
# keep values of data for plotting
batch_list = []
train_accuracy_list = []
test_accuracy_list = []
loops_list = []
reg_list = []
for i in range(nbatches):
batch = mnist.train.next_batch(100)
# training accuracy
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], \
feed_dict={x: batch[0], y: batch[1]})
train_writer.add_summary(s, i)
# test accuracy
if i % 20 == 0:
test_batch = mnist.test.next_batch(100)
[test_acc, s, l, r] = sess.run([accuracy, accuracy_summary, loops, reg], \
feed_dict={x: test_batch[0], y: test_batch[1]})
test_writer.add_summary(s, i)
print('batches: ', str(i), '; accuracy: ', str(test_acc))
if save_plots:
batch_list.append(i)
train_accuracy_list.append(train_accuracy)
test_accuracy_list.append(test_acc)
loops_list.append(l)
reg_list.append(r)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], \
y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step_reg, feed_dict={x: batch[0], y: batch[1]})
if save_plots:
# train accuracy
plot(batch_list, train_accuracy_list, 'Training accuracy', \
xlabel='number of batches', ylabel='training accuracy', \
ylegend='training accuracy')
# test accuracy
plot(batch_list, test_accuracy_list, 'Test accuracy', \
xlabel='number of batches', ylabel='test accuracy', \
ylegend='test accuracy')
# loops
plot(batch_list, loops_list, 'Loop regularization term', \
xlabel='number of batches', ylabel='loop regularization term magnitude', \
ylegend='loop reg.')
# loops with log
plot(batch_list, loops_list, 'Loop regularization term (log scale)', \
xlabel='number of batches', ylabel='loop regularization term magnitude', \
ylegend='loop reg.', log=True)
# regularization
plot(batch_list, reg_list, 'Weight decay loss magnitude', \
xlabel='number of batches', ylabel='weight decay loss magnitude', \
ylegend='weight decay')
train()
sess.close()
|
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import copy
import datetime
import os
import random
import shutil
from dataclasses import dataclass
from typing import Optional
import numpy as np
import pandas as pd
import pytest
import scipy
import psykoda.detection
import psykoda.utils
from psykoda.cli import internal
from psykoda.feature_extraction import FeatureExtractionConfig, FeatureLabel, IDFConfig
from psykoda.io.labeled.file import (
FileLoader,
FileSaver,
FileStorageBaseConfig,
FileStorageConfig,
)
rsrc_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "rsrc", "test_internal"
)
def gen_nonexistent_path(parent_dir: str = ""):
retry_max = 100
for _ in range(retry_max):
random_text = hex(random.getrandbits(128))
path = os.path.join(parent_dir, random_text)
if not os.path.exists(path):
return path
raise RuntimeError
@pytest.fixture
def fixture_report_transfer():
# -- Directory Tree --
# rsrc_dir [DIR]
# report_transfer [DIR] <- REPORTFILE_DIR
# reportfile.txt [FILE] <- REPORTFILE_PATH, REPORTFILE_NAME(basename)
# subdir [DIR] <- REPORTFILE_SUBDIR
REPORTFILE_NAME = "reportfile.txt"
REPORTFILE_DIR = os.path.join(rsrc_dir, "report_transfer")
REPORTFILE_PATH = os.path.join(REPORTFILE_DIR, REPORTFILE_NAME)
REPORTFILE_SUBDIR = os.path.join(REPORTFILE_DIR, "subdir")
def cleanup():
# Cleen up the directory
if os.path.isdir(REPORTFILE_DIR):
shutil.rmtree(REPORTFILE_DIR)
# Create the report file and directories
os.makedirs(REPORTFILE_SUBDIR, exist_ok=True)
with open(REPORTFILE_PATH, "w", encoding="utf_8") as file:
file.write("abc")
cleanup()
yield {
"reportfile_name": REPORTFILE_NAME,
"reportfile_dir": REPORTFILE_DIR,
"reportfile_path": REPORTFILE_PATH,
"reportfile_subdir": REPORTFILE_SUBDIR,
}
cleanup()
@pytest.fixture
def fixture_report_all():
PARENT_DIR = os.path.join(rsrc_dir, "report_all")
ANOMALY_DIR = os.path.join(PARENT_DIR, "anomaly_found")
ANOMALY_PATH = os.path.join(ANOMALY_DIR, "stats.json")
NOT_ANOMALY_DIR = os.path.join(PARENT_DIR, "anomaly_not_found")
NOT_ANOMALY_PATH = os.path.join(NOT_ANOMALY_DIR, "stats.json")
DSTFILE_DIR = os.path.join(PARENT_DIR, "save")
DSTFILE_PATH = os.path.join(DSTFILE_DIR, "report.csv")
def cleanup():
# Remove the directory and create it.
if os.path.isdir(DSTFILE_DIR):
shutil.rmtree(DSTFILE_DIR)
os.makedirs(DSTFILE_DIR, exist_ok=True)
cleanup()
yield {
"anomaly_dir": ANOMALY_DIR,
"anomaly_path": ANOMALY_PATH,
"not_anomaly_dir": NOT_ANOMALY_DIR,
"not_anomaly_path": NOT_ANOMALY_PATH,
"dstfile_dir": DSTFILE_DIR,
"dstfile_path": DSTFILE_PATH,
}
cleanup()
def test_report_transfer_01(fixture_report_transfer):
subdir = fixture_report_transfer["reportfile_subdir"]
filename = fixture_report_transfer["reportfile_name"]
filepath = fixture_report_transfer["reportfile_path"]
internal.report_transfer(filepath, subdir)
expected_filepath = os.path.join(subdir, filename)
assert os.path.isfile(expected_filepath)
with open(expected_filepath, "r", encoding="utf_8") as file:
data = file.read()
assert data == "abc"
def test_report_transfer_02(fixture_report_transfer):
parent_dir = fixture_report_transfer["reportfile_dir"]
filename = fixture_report_transfer["reportfile_name"]
filepath = fixture_report_transfer["reportfile_path"]
nonexistent_dir = gen_nonexistent_path(parent_dir)
nonexistent_subdir = gen_nonexistent_path(nonexistent_dir)
internal.report_transfer(filepath, nonexistent_subdir)
expected_filepath = os.path.join(nonexistent_subdir, filename)
assert os.path.isfile(expected_filepath)
with open(expected_filepath, "r", encoding="utf_8") as file:
data = file.read()
assert data == "abc"
def test_report_transfer_03(fixture_report_transfer):
parent_dir = fixture_report_transfer["reportfile_dir"]
subdir = fixture_report_transfer["reportfile_subdir"]
filepath = fixture_report_transfer["reportfile_path"]
with pytest.raises(TypeError):
internal.report_transfer(filepath, None)
nonexistent_filepath = gen_nonexistent_path(parent_dir)
with pytest.raises(TypeError):
internal.report_transfer(nonexistent_filepath, subdir)
def test_report_all_01(fixture_report_all):
path_list_stast = [fixture_report_all["anomaly_path"]]
path_save = fixture_report_all["dstfile_path"]
src_dir = fixture_report_all["anomaly_dir"]
internal.report_all(path_list_stast, path_save)
assert os.path.isfile(path_save)
df = pd.read_csv(path_save)
expected_df = pd.read_csv(os.path.join(src_dir, "expected_report.csv"))
assert df.equals(expected_df)
def test_report_all_02(fixture_report_all):
path_list_stast = [fixture_report_all["not_anomaly_path"]]
path_save = fixture_report_all["dstfile_path"]
src_dir = fixture_report_all["not_anomaly_dir"]
internal.report_all(path_list_stast, path_save)
assert os.path.isfile(path_save)
df = pd.read_csv(path_save)
expected_df = pd.read_csv(os.path.join(src_dir, "expected_report.csv"))
assert df.equals(expected_df)
@dataclass
class CommandLineArgsForTests:
subnet: Optional[str]
service: Optional[str]
period_train: Optional[int]
date_from_training: Optional[datetime.datetime]
date_to_training: Optional[datetime.datetime]
no_plot: Optional[bool]
date_from: Optional[datetime.datetime]
date_to: Optional[datetime.datetime]
debug: Optional[bool]
@pytest.fixture
def fixture_main_detection_01():
ARGS = CommandLineArgsForTests(
subnet="subnetdummy",
service="servicedummy",
period_train=1,
date_from_training=pd.Timestamp("2021-06-01 01:00:00"),
date_to_training=pd.Timestamp("2021-06-03 03:00:00"),
date_from=pd.Timestamp("2021-06-04 03:00:00"),
date_to=pd.Timestamp("2021-06-06 06:00:00"),
no_plot=False,
debug=False,
)
PARENT_DIR = os.path.join(rsrc_dir, "main_detection")
REPORT_DIR = os.path.join(PARENT_DIR, ARGS.subnet, ARGS.service)
def cleanup():
# Cleen up the directory
if os.path.isdir(REPORT_DIR):
shutil.rmtree(os.path.join(PARENT_DIR, ARGS.subnet))
cleanup()
yield {"args": ARGS, "parent_dir": PARENT_DIR, "report_dir": REPORT_DIR}
cleanup()
@pytest.fixture
def featurelabel_class():
feature = scipy.sparse.csr_matrix(
[
[0, 0, 2, 0],
[10, 0, 12, 0],
[0, 0, 0, 0],
[30, 0, 32, 0],
[0, 0, 42, 0],
[0, 0, 0, 0],
]
)
index = [
(pd.Timestamp("2021-06-01 01:00:00"), "1.1.1.1"),
(pd.Timestamp("2021-06-02 02:00:00"), "2.2.2.2"),
(pd.Timestamp("2021-06-03 03:00:00"), "3.3.3.3"),
(pd.Timestamp("2021-06-04 04:00:00"), "4.4.4.4"),
(pd.Timestamp("2021-06-05 05:00:00"), "5.5.5.5"),
(pd.Timestamp("2021-06-06 06:00:00"), "6.6.6.6"),
]
columns = ["columns_%d" % i for i in range(4)]
label = np.array(range(feature.shape[0]))
idf_sid = None
idf_dport = None
return FeatureLabel(
feature=feature,
index=index,
columns=columns,
label=label,
idf_sid=idf_sid,
idf_dport=idf_dport,
)
def test_main_detection_01(fixture_main_detection_01):
"""main_detection does nothing if log and label are empty"""
config = internal.DetectConfig(
arguments=None,
detection_units=None,
preprocess=None,
io=internal.IOConfig(
input=None,
previous=None,
output=internal.OutputConfig(
dir=None, share_dir=None, subdir=fixture_main_detection_01["report_dir"]
),
),
feature_extraction=None,
anomaly_detection=None,
)
args = fixture_main_detection_01["args"]
log = pd.DataFrame()
label = pd.Series()
expected_ret = None
expected_dirpath = os.path.join(config.io.output.subdir, args.subnet, args.service)
actual_ret = internal.main_detection(args, config, log, label)
assert actual_ret == expected_ret
assert os.path.isdir(expected_dirpath)
assert not os.path.isfile(os.path.join(expected_dirpath, internal.FILENAME_STATS))
def test_main_detection_prepare_data_01(fixture_main_detection_01):
"""main_detection_prepare_data does nothing if log and label are empty"""
args = fixture_main_detection_01["args"]
log = pd.DataFrame()
expected_ret = None
actual_ret = internal.main_detection_prepare_data(args, None, log, None)
assert actual_ret == expected_ret
def test_main_detection_prepare_data_02(fixture_main_detection_01, monkeypatch):
"""main_detection_prepare_data does nothing if log and label are empty"""
args = fixture_main_detection_01["args"]
config = FeatureExtractionConfig(idf={}, address_to_location=None)
log = pd.DataFrame(["dummy"])
expected_ret = None
monkeypatch.setattr("pandas.read_csv", lambda *args, **kwargs: None)
monkeypatch.setattr(
"psykoda.feature_extraction.feature_extraction_all",
lambda *args, **kwargs: None,
)
actual_ret = internal.main_detection_prepare_data(args, config, log, None)
assert actual_ret == expected_ret
def test_main_detection_prepare_data_03(
fixture_main_detection_01, featurelabel_class, monkeypatch
):
"""main_detection_prepare_data returns feature_label"""
args = fixture_main_detection_01["args"]
config = FeatureExtractionConfig(idf={}, address_to_location=None)
log = pd.DataFrame(["dummy"])
label = pd.Series(
[1, 1], index=[featurelabel_class.index[2], featurelabel_class.index[4]]
)
tmp_fl = copy.deepcopy(featurelabel_class)
tmp_fl.extract_nonzeros()
expected_feature = tmp_fl.feature / tmp_fl.feature.max()
expected_label = np.array([0.0, 0.0, 0.0, 1.0])
monkeypatch.setattr("pandas.read_csv", lambda *args, **kwargs: None)
monkeypatch.setattr(
"psykoda.feature_extraction.feature_extraction_all",
lambda *args, **kwargs: featurelabel_class,
)
actual_ret = internal.main_detection_prepare_data(args, config, log, label)
assert (actual_ret.feature - expected_feature).nnz == 0
assert all(actual_ret.label == expected_label)
def test_main_detection_after_prepare_data_01(
fixture_main_detection_01, featurelabel_class
):
"""main_detection_after_prepare_data splits data and construct x_train_labeled"""
args = fixture_main_detection_01["args"]
label = pd.Series(
index=[
featurelabel_class.index[2],
featurelabel_class.index[3],
featurelabel_class.index[4],
]
)
tmp_fl = copy.deepcopy(featurelabel_class)
# train_test_splitted, x_train_labeled
expected_tts = tmp_fl.split_train_test(args.date_to_training)
expected_xtl = tmp_fl.feature[[2, 3, 4]]
actual_tts, actual_xtl = internal.main_detection_after_prepare_data(
args, label, featurelabel_class
)
assert (actual_tts[0] - expected_tts[0]).nnz == 0
assert all(actual_tts[1] == expected_tts[1])
assert (actual_tts[2] - expected_tts[2]).nnz == 0
assert all(actual_tts[3] == expected_tts[3])
assert (actual_xtl - expected_xtl).nnz == 0
def test_output_result_01(fixture_main_detection_01, monkeypatch):
"""output_result emits files (plot is excluded from test)"""
args = fixture_main_detection_01["args"]
log = pd.DataFrame(
[
[pd.Timestamp("2021-06-01 01:00:00"), "1.1.1.1", 1, 11],
[pd.Timestamp("2021-06-01 02:00:00"), "2.2.2.2", 2, 22],
[pd.Timestamp("2021-06-01 03:00:00"), "3.3.3.3", 3, 33],
],
columns=["datetime_rounded", "src_ip", "column_1", "column_2"],
).set_index(["datetime_rounded", "src_ip"])
label = pd.Series()
dir_report = fixture_main_detection_01["report_dir"]
x_train_labeled_embeddings = None
x_test_embeddings = None
idx_anomaly = [2, 3, 4]
shap_value_idx_sorted = pd.DataFrame(
[
[pd.Timestamp("2021-06-01 01:00:00"), "1.1.1.1", 1],
[pd.Timestamp("2021-06-01 02:00:00"), "2.2.2.2", 2],
[pd.Timestamp("2021-06-01 03:00:00"), "3.3.3.3", 3],
],
columns=["datetime_rounded", "src_ip", "column_1"],
).set_index(["datetime_rounded", "src_ip"])
anomaly_score_sorted = shap_value_idx_sorted["column_1"]
stats = {}
previous_config = FileStorageConfig(
base=FileStorageBaseConfig(
dir=os.path.join(fixture_main_detection_01["parent_dir"], "write_log")
),
load=FileLoader.Config(),
save=FileSaver.Config(compression=True, all=False),
)
os.makedirs(dir_report, exist_ok=True)
# skip test for plot_detection
monkeypatch.setattr(
"psykoda.io.reporting.plot.plot_detection", lambda *args, **kwargs: None
)
expected_name_anomaly = [
(pd.Timestamp("2021-06-01 01:00:00"), "1.1.1.1"),
(pd.Timestamp("2021-06-01 02:00:00"), "2.2.2.2"),
(pd.Timestamp("2021-06-01 03:00:00"), "3.3.3.3"),
]
actual_ret = internal.output_result(
args=args,
log=log,
label=label,
dir_report=dir_report,
x_train_labeled_embeddings=x_train_labeled_embeddings,
x_test_embeddings=x_test_embeddings,
idx_anomaly=idx_anomaly,
shap_value_idx_sorted=shap_value_idx_sorted,
anomaly_score_sorted=anomaly_score_sorted,
stats=stats,
previous_config=previous_config,
)
try:
assert os.path.isfile(os.path.join(dir_report, internal.FILENAME_REPORT))
assert os.path.isdir(previous_config.base.dir)
for dt, src_ip in shap_value_idx_sorted.index:
assert os.path.isfile(
os.path.join(
previous_config.base.dir, dt.strftime(f"%Y-%m-%d-%H__{src_ip}.zip")
)
)
assert actual_ret["num_anomaly"] == 3
assert actual_ret["name_anomaly"] == expected_name_anomaly
finally:
if os.path.isdir(previous_config.base.dir):
shutil.rmtree(previous_config.base.dir)
@pytest.mark.parametrize(
"train_test_splitted", [(None, [0], None, [0, 0, 0]), (None, [0, 0, 0], None, [0])]
)
def test_main_detection_skip_or_detect_01(
fixture_main_detection_01, train_test_splitted
):
"""main_detection_skip_or_detect skips when required_srcip is not satisfied"""
args = fixture_main_detection_01["args"]
anomaly_detection_config = internal.AnomalyDetectionConfig(
required_srcip=internal.SkipDetectionConfig(train=2, test=2),
deepsad=None,
train=None,
threshold=None,
)
actual_ret = internal.main_detection_skip_or_detect(
args=args,
log=None,
label=pd.Series(),
dir_report=None,
feature_label=None,
train_test_splitted=train_test_splitted,
x_train_labeled=scipy.sparse.csr_matrix([]),
anomaly_detection_config=anomaly_detection_config,
previous_config=None,
)
assert "skipped" in actual_ret
@pytest.fixture
def fixture_main_detection_02():
args = CommandLineArgsForTests(
date_from=datetime.datetime(2020, 4, 1, 0, 0),
date_from_training=datetime.datetime(2020, 3, 4, 0, 0),
date_to=datetime.datetime(2021, 7, 12, 13, 39, 15, 303669),
date_to_training=datetime.datetime(2020, 3, 31, 0, 0),
period_train=28,
debug=False,
no_plot=True,
service="ALL_but_SSH",
subnet="ALL",
)
PARENT_DIR = os.path.join(rsrc_dir, "main_detection")
log = pd.read_csv(
os.path.join(PARENT_DIR, "sample_log.csv"),
parse_dates=["datetime_rounded", "datetime_full"],
).set_index(["datetime_rounded", "src_ip"])
label = pd.read_csv(
os.path.join(PARENT_DIR, "sample_label.csv"), parse_dates=["datetime_rounded"]
).set_index(["datetime_rounded", "src_ip"])["0"]
config = internal.DetectConfig(
arguments=None,
detection_units=None,
io=internal.IOConfig(
input=None,
previous=internal.PreviousConfig(
load=None,
log=FileStorageConfig(
base=FileStorageBaseConfig(
dir=os.path.join(PARENT_DIR, "labeled_dir"),
),
save=FileSaver.Config(),
load=FileLoader.Config(),
),
),
output=internal.OutputConfig(
dir=None, share_dir=None, subdir=os.path.join(PARENT_DIR, "sub_dir")
),
),
preprocess=None,
feature_extraction=FeatureExtractionConfig(
idf={
"sid": IDFConfig(min_count=1, num_feature=30),
"dest_port": IDFConfig(min_count=1, num_feature=30),
},
address_to_location=os.path.join(PARENT_DIR, "sample_IPtable.csv"),
),
anomaly_detection=internal.AnomalyDetectionConfig(
required_srcip=internal.SkipDetectionConfig(train=3, test=5),
deepsad=psykoda.detection.DeepSAD.Config(
dim_hidden=[4, 4, 4, 4, 2],
eta=16,
lam=1e-06,
path_pretrained_model=None,
),
train=psykoda.detection.DeepSAD.TrainConfig(
epochs_pretrain=30, epochs_train=100, learning_rate=0.001, batch_size=64
),
threshold=internal.ThresholdConfig(num_anomaly=5, min_score=10),
),
)
dir_report = os.path.join(config.io.output.subdir, args.subnet, args.service)
def cleanup():
if os.path.isdir(config.io.output.subdir):
shutil.rmtree(config.io.output.subdir)
if os.path.isdir(config.io.previous.log.base.dir):
shutil.rmtree(config.io.previous.log.base.dir)
cleanup()
yield {
"parent_dir": PARENT_DIR,
"dir_report": dir_report,
"args": args,
"log": log,
"label": label,
"config": config,
}
cleanup()
def test_main_detection_skip_or_detect_02(fixture_main_detection_02):
"""main_detection_skip_or_detect performs detection"""
parent_dir = fixture_main_detection_02["parent_dir"]
args = fixture_main_detection_02["args"]
log = fixture_main_detection_02["log"]
label = fixture_main_detection_02["label"]
dir_report = fixture_main_detection_02["dir_report"]
fl_columns_df = pd.read_csv(
os.path.join(parent_dir, "sample_feature_label_columns.csv"), header=None
)
fl_columns = []
for items in fl_columns_df.to_numpy().tolist():
fl_columns.append(tuple(items))
feature_label = FeatureLabel(
feature=scipy.sparse.csr_matrix([[0] * 11]),
index=[0],
columns=fl_columns,
idf_sid=None,
idf_dport=None,
)
tts_0 = scipy.sparse.csr_matrix(
pd.read_csv(
os.path.join(parent_dir, "sample_train_test_splitted[0].csv"), header=None
)
)
tts_1 = np.loadtxt(
os.path.join(parent_dir, "sample_train_test_splitted[1].csv"), dtype="float64"
)
tts_2 = scipy.sparse.csr_matrix(
pd.read_csv(
os.path.join(parent_dir, "sample_train_test_splitted[2].csv"), header=None
)
)
tts_3_df = pd.read_csv(
os.path.join(parent_dir, "sample_train_test_splitted[3].csv"),
header=None,
parse_dates=[0],
).set_index([0, 1])
tts_3 = tts_3_df.index
train_test_splitted = (tts_0, tts_1, tts_2, tts_3)
x_train_labeled = scipy.sparse.csr_matrix(
pd.read_csv(os.path.join(parent_dir, "sample_x_train_labeled.csv"), header=None)
)
config = fixture_main_detection_02["config"]
anomaly_detection_config = config.anomaly_detection
previous_config = config.io.previous.log
os.makedirs(dir_report, exist_ok=True)
actual_ret = internal.main_detection_skip_or_detect(
args=args,
log=log,
label=label,
dir_report=dir_report,
feature_label=feature_label,
train_test_splitted=train_test_splitted,
x_train_labeled=x_train_labeled,
anomaly_detection_config=anomaly_detection_config,
previous_config=previous_config,
)
assert os.path.isdir(previous_config.base.dir)
assert os.path.isfile(os.path.join(dir_report, internal.FILENAME_REPORT))
assert "skipped" not in actual_ret
# Check only for the existence of the key.
# The values corresponding to these keys are not constant.
assert "num_anomaly" in actual_ret
assert "name_anomaly" in actual_ret
def test_main_detection_02(fixture_main_detection_02):
"""main_detection performs detection"""
dir_report = fixture_main_detection_02["dir_report"]
report_path = os.path.join(dir_report, internal.FILENAME_STATS)
args = fixture_main_detection_02["args"]
config: internal.DetectConfig = fixture_main_detection_02["config"]
log = fixture_main_detection_02["log"]
label = fixture_main_detection_02["label"]
internal.main_detection(args, config, log, label)
assert os.path.isfile(report_path)
actual_stats = psykoda.utils.load_json(report_path)
assert "skipped" not in actual_stats
# Check only for the existence of the key.
# The values corresponding to these keys are not constant.
assert "num_anomaly" in actual_stats
assert "name_anomaly" in actual_stats
|
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|
"""Unit test(s) for ordering.py"""
import pytest
import os
import shutil
from collections import OrderedDict
import copy
import pickle
import numpy as np
from riddle import ordering
PRECISION = 4
class TestSummary:
@pytest.fixture(scope='module')
def summary(self):
list_feat = ['John', 'James', 'Christine']
list_descript = ['kind', 'caring', 'righteous']
list_contrib_ordering = [
[['A', 5.0], ['B', 3.0], ['C', -1.0], ['D', -5.0]],
[['D', 100.0], ['C', 99.0], ['B', 98.0], ['A', 97.0]],
[['C', -4.0], ['D', -5.0], ['B', -7.0], ['A', -132.0]]
]
list_freq_ordering = [
[['A', 300.0], ['B', 200.0], ['C', 100.0], ['D', 0.0]],
[['D', 5.0], ['C', 5.0], ['B', 5.0], ['A', 5.0]],
[['C', 2.0], ['D', 23.0], ['B', 34.0], ['A', 45.0]]
]
od = OrderedDict([('feat', list_feat), ('descript', list_descript),
('contrib_ordering', list_contrib_ordering),
('freq_ordering', list_freq_ordering)])
return ordering.OrderingSummary(od)
def test_sort(self, summary):
# TODO(jisungk): update when sort() is implemented.
return
# not sorted this line; will be
sorted_summary = copy.deepcopy(summary)
sorted_summary.sort()
sorted_od = sorted_summary.od
assert sorted_od['feat'] == ['Christine', 'John', 'James']
assert sorted_od['descript'] == ['righteous', 'kind', 'caring']
assert sorted_od['contrib_ordering'] == [
[['C', -4.0], ['D', -5.0], ['B', -7.0], ['A', -132.0]],
[['A', 5.0], ['B', 3.0], ['C', -1.0], ['D', -5.0]],
[['D', 100.0], ['C', 99.0], ['B', 98.0], ['A', 97.0]]
]
assert sorted_od['freq_ordering'] == [
[['C', 2.0], ['D', 23.0], ['B', 34.0], ['A', 45.0]],
[['A', 300.0], ['B', 200.0], ['C', 100.0], ['D', 0.0]],
[['D', 5.0], ['C', 5.0], ['B', 5.0], ['A', 5.0]]
]
def test_save(self, summary):
def stringify_pair(p):
return p[0] + ' (' + str(round(p[1], PRECISION)) + ')'
out_directory = 'tests/riddle/temp'
if not os.path.exists(out_directory):
os.makedirs(out_directory)
summary.save(out_directory)
with open(out_directory + '/orderings_ordered_dict.pkl', 'r') as f:
saved_od = pickle.load(f)
assert summary.od.items() == saved_od.items()
with open(out_directory + '/orderings.txt', 'r') as f:
lines = f.read().splitlines()
assert lines[0] == '\t'.join(summary.od.keys())
list_feat = summary.od['feat']
list_descript = summary.od['descript']
list_contrib_ordering = [
' > '.join([stringify_pair(p) for p in o])
for o in summary.od['contrib_ordering']
]
list_freq_ordering = [
' > '.join([stringify_pair(p) for p in o])
for o in summary.od['freq_ordering']
]
for row_idx in range(1, len(summary.od.values()[0])):
expected = '\t'.join(
[list_feat[row_idx - 1],
list_descript[row_idx - 1],
list_contrib_ordering[row_idx - 1],
list_freq_ordering[row_idx - 1]]
)
assert lines[row_idx] == expected
shutil.rmtree(out_directory)
def test_save_individual_tables(self, summary):
out_directory = 'tests/riddle/temp'
if not os.path.exists(out_directory):
os.makedirs(out_directory)
summary.save(out_directory)
idx_class_dict = {0: 'A', 1: 'B', 2: 'C', 3: 'D'}
sorted_idx_class = sorted(idx_class_dict.items(), key=lambda x: x[0])
summary.save_individual_tables(idx_class_dict, out_directory)
ordering_keys = [key for key in summary.od.keys() if 'ordering' in key]
for key in ordering_keys:
features = summary.od['feat']
curr_table_data = summary.od[key]
with open(out_directory + '/' + key + '_table.txt', 'r') as f:
lines = f.read().splitlines()
assert lines[0] == 'feat\tA\tB\tC\tD'
for row_idx in range(1, len(summary.od.values()[0])):
def search_score(c, list_pairs):
c_index = [cl for cl, score in list_pairs].index(c)
return list_pairs[c_index][1]
feat = features[row_idx - 1]
list_pairs = curr_table_data[row_idx - 1]
sorted_scores = [str(search_score(c, list_pairs))
for idx, c in sorted_idx_class]
expected_line = '\t'.join([feat] + sorted_scores)
assert lines[row_idx] == expected_line
shutil.rmtree(out_directory)
def test__compute_orderings():
# orderings by contribution score
sums_contribs = np.array([[1, 2, 3, 4], [4, 3, 2, 1], [-5, -5, -5, -5]])
contrib_orderings = ordering._compute_orderings(sums_contribs)
expected_contrib_orderings = [
[(1, 4), (0, 1), (2, -5)],
[(1, 3), (0, 2), (2, -5)],
[(0, 3), (1, 2), (2, -5)],
[(0, 4), (1, 1), (2, -5)]
]
assert np.all(contrib_orderings == expected_contrib_orderings)
# orderings by frequencies
class_feat_freq_table = np.array([[5, 3, 4], [6, 1, 3], [7, 0, 2],
[9, 4, 1]])
freq_orderings = ordering._compute_orderings(class_feat_freq_table)
expected_freq_orderings = [
[(3, 9), (2, 7), (1, 6), (0, 5)],
[(3, 4), (0, 3), (1, 1), (2, 0)],
[(0, 4), (1, 3), (2, 2), (3, 1)]
]
assert np.all(freq_orderings == expected_freq_orderings)
# orderings by frequency proportions
class_feat_prop_table = ordering._compute_prop_table(class_feat_freq_table)
prop_orderings = ordering._compute_orderings(class_feat_prop_table)
expected_prop_orderings = [
[(2, 7. / 9), (3, 9. / 14), (1, 6. / 10), (0, 5. / 12)],
[(3, 4. / 14), (0, 3. / 12), (1, 1. / 10), (2, 0.)],
[(0, 4. / 12), (1, 3. / 10), (2, 2. / 9), (3, 1. / 14)]
]
assert np.all(prop_orderings == expected_prop_orderings)
def test__compute_prop_table():
class_feat_freq_table = np.array([[5, 3, 4], [6, 1, 3], [7, 0, 2],
[9, 4, 1]])
expected_class_feat_prop_table = [
[5. / 12, 3. / 12, 4. / 12],
[6. / 10, 1. / 10, 3. / 10],
[7. / 9, 0. / 9, 2. / 9],
[9. / 14, 4. / 14, 1. / 14]
]
class_feat_prop_table = ordering._compute_prop_table(class_feat_freq_table)
assert np.all(class_feat_prop_table == expected_class_feat_prop_table)
def test_decode_orderings():
raw_orderings = [
[[0, 0.5], [1, 1.5], [2, 2.5], [3, 3.5]],
[[3, 3], [2, 2], [1, 1], [0, 0]]
]
idx_class_dict = {0: 'A', 1: 'B', 2: 'C', 3: 'D'}
decoded_orderings = ordering._decode_orderings(raw_orderings,
idx_class_dict)
expected_decoded_orderings = [
[('A', 0.5), ('B', 1.5), ('C', 2.5), ('D', 3.5)],
[('D', 3), ('C', 2), ('B', 1), ('A', 0)]
]
assert decoded_orderings == expected_decoded_orderings
|
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|
import numpy as np
import pandas as pd
import re
### preformatting
class text_clean():
def __init__(self,sentence):
self.my_sentence = sentence
def clean_words(self):
my_sentence = self.my_sentence
my_sentence=my_sentence.lower()
rep = {
"fell down": "loss_of_balance",
"cant concentrate" : "loss_of_concentration",
"i feel dizzy": "dizziness",
"dizzy all the time":"dizziness",
"mood swings": "mood_swings",
"tired all the time" : "fatigue",
"throwing": "vomittig",
"throwing like a": "vomitting",
"anxious" : "anxiety",
"I feel discomfort": "anxiety",
"out of breath": "fast_heart_rate",
"kidneys hurting": "kidney_pain",
"chest is paining" : "chest_pain",
"chest pains": "chest_pain",
"chest hurts": "chest_pain",
"my heart is throbbing" : "fast_heart_rate",
"lungs hurt" : "lung_pain",
"stomach hurt": "stomach_pain",
"stomach hurts":"stomach_pain",
"stomach is hurting":"stomach_pain",
"knees hurt": "joint_pain",
"knees are paining": "joint_pain",
"knee hurts": "joint_pain",
"my heart is throbbing": "fast_heart_rate",
"constantly sneezing": "continuous_sneezing",
"feel fatigued": "fatigue",
"legs are paining": "joint_pain",
"legs hurt" :"muscle_weakness",
"arms hurt" :"muscle_weakness",
"elbow pains" : "joint_pain",
"severe headaches" :"headache",
"headaches":"headache",
} # define desired replacements here
rep = dict((re.escape(k), v) for k, v in rep.items())
#Python 3 renamed dict.iteritems to dict.items so use rep.items() for latest versions
pattern = re.compile("|".join(rep.keys()))
text = pattern.sub(lambda m: rep[re.escape(m.group(0))], my_sentence)
fp = text
wordList = re.sub("[^\w]", " ", fp).split()
stopwords= ['hey','m','experiencing','my','are','am','think','and','is', 'because','i','of','smoking','running','hurt', 'having']
sp=[]
for word in wordList:
if word in stopwords:
pass
else:
sp.append(word)
symptom_list=['itching', 'skin_rash','nodal_skin_eruptions', 'continuous_sneezing', 'shivering', 'chills', 'joint_pain', 'stomach_pain', 'acidity', 'ulcers_on_tongue', 'muscle_wasting', 'vomiting', 'burning_micturition', 'spotting_ urination', 'fatigue', 'weight_gain', 'anxiety', 'cold_hands_and_feets', 'mood_swings', 'weight_loss', 'restlessness', 'lethargy', 'patches_in_throat', 'irregular_sugar_level', 'cough', 'high_fever', 'sunken_eyes', 'breathlessness', 'sweating', 'dehydration', 'indigestion', 'headache', 'yellowish_skin', 'dark_urine', 'nausea', 'loss_of_appetite', 'pain_behind_the_eyes', 'back_pain', 'constipation', 'abdominal_pain', 'diarrhoea', 'mild_fever', 'yellow_urine', 'yellowing_of_eyes', 'acute_liver_failure', 'fluid_overload', 'swelling_of_stomach', 'swelled_lymph_nodes', 'malaise', 'blurred_and_distorted_vision', 'phlegm', 'throat_irritation', 'redness_of_eyes', 'sinus_pressure', 'runny_nose', 'congestion', 'chest_pain', 'weakness_in_limbs', 'fast_heart_rate', 'pain_during_bowel_movements', 'pain_in_anal_region', 'bloody_stool', 'irritation_in_anus', 'neck_pain', 'dizziness', 'cramps', 'bruising', 'obesity', 'swollen_legs', 'swollen_blood_vessels', 'puffy_face_and_eyes', 'enlarged_thyroid', 'brittle_nails', 'swollen_extremeties', 'excessive_hunger', 'extra_marital_contacts', 'drying_and_tingling_lips', 'slurred_speech', 'knee_pain', 'hip_joint_pain', 'muscle_weakness', 'stiff_neck', 'swelling_joints', 'movement_stiffness', 'spinning_movements', 'loss_of_balance', 'unsteadiness', 'weakness_of_one_body_side', 'loss_of_smell', 'bladder_discomfort', 'foul_smell_of urine', 'continuous_feel_of_urine', 'passage_of_gases', 'internal_itching', 'toxic_look_(typhos)', 'depression', 'irritability', 'muscle_pain', 'altered_sensorium', 'red_spots_over_body', 'belly_pain', 'abnormal_menstruation', 'dischromic _patches', 'watering_from_eyes', 'increased_appetite', 'polyuria', 'family_history', 'mucoid_sputum', 'rusty_sputum', 'lack_of_concentration', 'visual_disturbances', 'receiving_blood_transfusion', 'receiving_unsterile_injections', 'coma', 'stomach_bleeding', 'distention_of_abdomen', 'history_of_alcohol_consumption', 'fluid_overload', 'blood_in_sputum', 'prominent_veins_on_calf', 'palpitations', 'painful_walking', 'pus_filled_pimples', 'blackheads', 'scurring', 'skin_peeling', 'silver_like_dusting', 'small_dents_in_nails', 'inflammatory_nails', 'blister', 'red_sore_around_nose', 'yellow_crust_ooze']
sp_II=[]
for i in range(len(sp)):
if sp[i] in symptom_list:
sp_II.append(sp[i])
else:
pass
list_app = len(sp_II)
sym_append = ['itching','skin_rash','nodal_skin_eruptions','continuous_sneezing']
if list_app < 5:
sp_II.extend(sym_append)
s1,s2,s3,s4,s5= sp_II[0],sp_II[1],sp_II[2],sp_II[3],sp_II[4]
return s1,s2,s3,s4,s5
|
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|
using LichessBot
using Test
@time begin
include("eval.jl")
include("netcode.jl")
# Write your tests here.
end
|
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|
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
df=pd.read_csv(path)
# Code starts here
X=df.drop(['customerID','Churn'],axis=1)
y=df.Churn
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state = 0)
# --------------
import numpy as np
from sklearn.preprocessing import LabelEncoder
X_train.TotalCharges=X_train.TotalCharges.replace('',np.NaN)
X_train.TotalCharges=X_train.TotalCharges.convert_objects(convert_numeric=True)
X_test.TotalCharges=X_test.TotalCharges.convert_objects(convert_numeric=True)
X_train.TotalCharges.isna().sum()
X_train.TotalCharges=X_train.TotalCharges.fillna(X_train.TotalCharges.mean())
X_test.TotalCharges=X_test.TotalCharges.fillna(X_test.TotalCharges.mean())
X_test.TotalCharges.isna().sum()
X_train.TotalCharges.isna().sum()
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
categorical_feature_mask = X_train.dtypes==object
# filter categorical columns using mask and turn it into a list
categorical_cols_X_train = X_train.columns[categorical_feature_mask].tolist()
print(categorical_cols_X_train)
categorical_feature_mask_X_test = X_test.dtypes==object
# filter categorical columns using mask and turn it into a list
categorical_cols_X_test = X_test.columns[categorical_feature_mask_X_test].tolist()
print(categorical_cols_X_test)
X_train[categorical_cols_X_train] = X[categorical_cols_X_train].apply(lambda col: le.fit_transform(col))
X_train[categorical_cols_X_train].head(10)
X_test[categorical_cols_X_test] = X[categorical_cols_X_test].apply(lambda col: le.fit_transform(col))
X_test[categorical_cols_X_test].head(10)
y_train=y_train.replace({'No':0, 'Yes':1})
y_test=y_test.replace({'No':0, 'Yes':1})
# --------------
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
# Code starts here
ada_model=AdaBoostClassifier(random_state=0)
ada_model.fit(X_train,y_train)
y_pred=ada_model.predict(X_test)
ada_score=accuracy_score(y_test,y_pred)
print('ada_score:',ada_score)
ada_cm=confusion_matrix(y_test,y_pred)
print("ada_cm:",ada_cm)
ada_cr=classification_report(y_test,y_pred)
print("ada_cr:",ada_cr)
# --------------
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
#Parameter list
parameters={'learning_rate':[0.1,0.15,0.2,0.25,0.3],
'max_depth':range(1,3)}
# Code starts here
xgb_model=XGBClassifier(random_state=0)
xgb_model.fit(X_train,y_train)
y_pred=xgb_model.predict(X_test)
xgb_score=accuracy_score(y_test,y_pred)
print("xgb_score:",xgb_score)
xgb_cm=confusion_matrix(y_test,y_pred)
print("xgb_cm:",xgb_cm)
xgb_cr=classification_report(y_test,y_pred)
print("xgb_cr:",xgb_cr)
clf_model=GridSearchCV(estimator=xgb_model , param_grid=parameters)
clf_model.fit(X_train,y_train)
y_pred=clf_model.predict(X_test)
clf_score=accuracy_score(y_test,y_pred)
print("clf_score:",clf_score)
clf_cm=confusion_matrix(y_test,y_pred)
print("clf_cm:",clf_cm)
clf_cr=classification_report(y_test,y_pred)
print("clf_cr:",clf_cr)
|
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|
//
// Copyright 2007-2012 Christian Henning, Andreas Pokorny, Lubomir Bourdev
//
// Distributed under the Boost Software License, Version 1.0
// See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt
//
#ifndef BOOST_GIL_IO_READ_AND_CONVERT_VIEW_HPP
#define BOOST_GIL_IO_READ_AND_CONVERT_VIEW_HPP
#include <boost/gil/detail/mp11.hpp>
#include <boost/gil/io/base.hpp>
#include <boost/gil/io/conversion_policies.hpp>
#include <boost/gil/io/device.hpp>
#include <boost/gil/io/get_reader.hpp>
#include <boost/gil/io/path_spec.hpp>
#include <type_traits>
namespace boost {
namespace gil {
/// \ingroup IO
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param reader An image reader.
/// \param img The image in which the data is read into.
/// \param settings Specifies read settings depending on the image format.
/// \param cc Color converter function object.
/// \throw std::ios_base::failure
template <typename Reader, typename View>
inline void read_and_convert_view(
Reader &reader, View const &view,
typename std::enable_if<
mp11::mp_and<detail::is_reader<Reader>,
is_format_tag<typename Reader::format_tag_t>>::value>::type
* /*dummy*/
= nullptr) {
reader.check_image_size(view.dimensions());
reader.init_view(view, reader._settings);
reader.apply(view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file It's a device. Must satisfy is_input_device metafunction.
/// \param view The image view in which the data is read into.
/// \param settings Specifies read settings depending on the image format.
/// \param cc Color converter function object.
/// \throw std::ios_base::failure
template <typename Device, typename View, typename ColorConverter,
typename FormatTag>
inline void read_and_convert_view(
Device &device, View const &view,
image_read_settings<FormatTag> const &settings, ColorConverter const &cc,
typename std::enable_if<
mp11::mp_and<detail::is_read_device<FormatTag, Device>,
is_format_tag<FormatTag>>::value>::type * /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<ColorConverter>;
using reader_t =
typename get_reader<Device, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(device, settings, read_and_convert_t{cc});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file_name File name. Must satisfy is_supported_path_spec
/// metafunction. \param view The image view in which the data is read
/// into. \param settings Specifies read settings depending on the image
/// format. \param cc Color converter function object. \throw
/// std::ios_base::failure
template <typename String, typename View, typename ColorConverter,
typename FormatTag>
inline void read_and_convert_view(
String const &file_name, View const &view,
image_read_settings<FormatTag> const &settings, ColorConverter const &cc,
typename std::enable_if<
mp11::mp_and<is_format_tag<FormatTag>,
detail::is_supported_path_spec<String>>::value>::type
* /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<ColorConverter>;
using reader_t =
typename get_reader<String, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(file_name, settings, read_and_convert_t{cc});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file_name File name. Must satisfy is_supported_path_spec
/// metafunction. \param view The image view in which the data is read
/// into. \param cc Color converter function object. \param tag Defines
/// the image format. Must satisfy is_format_tag metafunction. \throw
/// std::ios_base::failure
template <typename String, typename View, typename ColorConverter,
typename FormatTag>
inline void read_and_convert_view(
String const &file_name, View const &view, ColorConverter const &cc,
FormatTag const &tag,
typename std::enable_if<
mp11::mp_and<is_format_tag<FormatTag>,
detail::is_supported_path_spec<String>>::value>::type
* /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<ColorConverter>;
using reader_t =
typename get_reader<String, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(file_name, tag, read_and_convert_t{cc});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file It's a device. Must satisfy is_input_device metafunction or
/// is_adaptable_input_device. \param view The image view in which the data is
/// read into. \param cc Color converter function object. \param tag Defines
/// the image format. Must satisfy is_format_tag metafunction. \throw
/// std::ios_base::failure
template <typename Device, typename View, typename ColorConverter,
typename FormatTag>
inline void read_and_convert_view(
Device &device, View const &view, ColorConverter const &cc,
FormatTag const &tag,
typename std::enable_if<
mp11::mp_and<detail::is_read_device<FormatTag, Device>,
is_format_tag<FormatTag>>::value>::type * /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<ColorConverter>;
using reader_t =
typename get_reader<Device, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(device, tag, read_and_convert_t{cc});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file_name File name. Must satisfy is_supported_path_spec
/// metafunction. \param view The image view in which the data is read
/// into. \param settings Specifies read settings depending on the image
/// format. \throw std::ios_base::failure
template <typename String, typename View, typename FormatTag>
inline void read_and_convert_view(
String const &file_name, View const &view,
image_read_settings<FormatTag> const &settings,
typename std::enable_if<
mp11::mp_and<is_format_tag<FormatTag>,
detail::is_supported_path_spec<String>>::value>::type
* /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<default_color_converter>;
using reader_t =
typename get_reader<String, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(file_name, settings, read_and_convert_t{});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file It's a device. Must satisfy is_input_device metafunction or
/// is_adaptable_input_device. \param view The image view in which the data
/// is read into. \param settings Specifies read settings depending on the
/// image format. \throw std::ios_base::failure
template <typename Device, typename View, typename FormatTag>
inline void read_and_convert_view(
Device &device, View const &view,
image_read_settings<FormatTag> const &settings,
typename std::enable_if<
mp11::mp_and<detail::is_read_device<FormatTag, Device>,
is_format_tag<FormatTag>>::value>::type * /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<default_color_converter>;
using reader_t =
typename get_reader<Device, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(device, settings, read_and_convert_t{});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file_name File name. Must satisfy is_supported_path_spec
/// metafunction. \param view The image view in which the data is read
/// into. \param tag Defines the image format. Must satisfy is_format_tag
/// metafunction. \throw std::ios_base::failure
template <typename String, typename View, typename FormatTag>
inline void read_and_convert_view(
String const &file_name, View const &view, FormatTag const &tag,
typename std::enable_if<
mp11::mp_and<is_format_tag<FormatTag>,
detail::is_supported_path_spec<String>>::value>::type
* /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<default_color_converter>;
using reader_t =
typename get_reader<String, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(file_name, tag, read_and_convert_t{});
read_and_convert_view(reader, view);
}
/// \brief Reads and color-converts an image view. No memory is allocated.
/// \param file It's a device. Must satisfy is_input_device metafunction or
/// is_adaptable_input_device. \param view The image view in which the data is
/// read into. \param tag Defines the image format. Must satisfy is_format_tag
/// metafunction. \throw std::ios_base::failure
template <typename Device, typename View, typename FormatTag>
inline void read_and_convert_view(
Device &device, View const &view, FormatTag const &tag,
typename std::enable_if<
mp11::mp_and<detail::is_read_device<FormatTag, Device>,
is_format_tag<FormatTag>>::value>::type * /*dummy*/
= nullptr) {
using read_and_convert_t = detail::read_and_convert<default_color_converter>;
using reader_t =
typename get_reader<Device, FormatTag, read_and_convert_t>::type;
reader_t reader = make_reader(device, tag, read_and_convert_t{});
read_and_convert_view(reader, view);
}
} // namespace gil
} // namespace boost
#endif
|
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|
/*
This program is free software; you can redistribute it and/or modify it under
the terms of the European Union Public Licence - EUPL v.1.1 as published by
the European Commission.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the European Union Public Licence - EUPL v.1.1
for more details.
You should have received a copy of the European Union Public Licence - EUPL v.1.1
along with this program.
Further information about the European Union Public Licence - EUPL v.1.1 can
also be found on the world wide web at http://ec.europa.eu/idabc/eupl
*/
/*
------ Copyright (C) 2011 STA Steering Board (space.trajectory.analysis AT gmail.com) ----
*/
/*
------------------ Author: Guillermo Ortega ----------------------------------------
July 2011
*/
#include "serviceAngleRateUnit.h"
#include <Eigen/Core>
using namespace Eigen;
#include "QDebug"
DialogServiceAngleRateUnitFrame::DialogServiceAngleRateUnitFrame( QWidget * parent, Qt::WindowFlags f) : QFrame(parent,f)
{
setupUi(this);
angleRateUnitWidget = DialogServiceAngleRateUnitFrame::comboBoxAngleRateUnitsChoice;
myPastUnits = 0;
comboBoxAngleRateUnitsChoice->setCurrentIndex(myPastUnits);
}
DialogServiceAngleRateUnitFrame::~DialogServiceAngleRateUnitFrame()
{
}
// Index meaning is as follows:
// index = 0 is Degree
// index = 1 is radians
// Matrix coefficients as follows
// Deg/s->Deg/s Rad/s->Deg/s
// Deg/s->Rad/s Rad/s->Rad/s
static double angleRateConversionMatrixCoeffs[4] =
{1.0, 57.295779513,
0.0174532925, 1.0};
static const Matrix<double, 2, 2> angleRateConversionMatrix(angleRateConversionMatrixCoeffs);
double DialogServiceAngleRateUnitFrame::convertAngleRate(int fromAngleRateUnit, int toAngleRateUnit, double angleRate)
{
double finalAngleRate = angleRate * angleRateConversionMatrix(fromAngleRateUnit, toAngleRateUnit);
return finalAngleRate;
}
//// Sets the input distance, the output distance and the current index inside the method
void DialogServiceAngleRateUnitFrame::setInputAngleRate(double niceInputAngleRate)
{
myPastAngleRate = niceInputAngleRate;
}
// Index meaning is as follows:
// index = 0 is Kilometers
// index = 1 is meters
// index = 2 is centi-meters
// index = 3 is mili-meters
// index = 4 is Astronomical Units
void DialogServiceAngleRateUnitFrame::on_comboBoxAngleRateUnitsChoice_currentIndexChanged(int myIndex)
{
myFutureUnits = myIndex;
myFutureAngleRate = convertAngleRate(myPastUnits, myFutureUnits, myPastAngleRate);
myPastAngleRate = myFutureAngleRate;
myPastUnits = myFutureUnits;
myRealAngleRateForXMLSchema = convertAngleRate (myPastUnits, 0, myFutureAngleRate);
}
|
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|
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from rpyopencl import RPyOpenCLCluster
import json
import numpy as np
from decorators import timer
# Globals to simplify sample tuning
object_type = np.float32
size = 50000
kernel_name = "sum_mul"
a_np = np.random.rand(size).astype(object_type)
b_np = np.random.rand(size).astype(object_type)
nodes = [
{"name": "rpi-opencl1", "ip": "localhost"},
{"name": "rpi-opencl2", "ip": "localhost"}
]
@timer
def compute_on_one_node(node, ctx, kernel):
print("Add command queue on context")
node.add_command_queue(ctx)
node.compile_kernel(ctx, kernel, use_prefered_vector_size="float")
print("Create 2 inputs buffers of shape {}".format(a_np.shape))
node.create_input_buffer(ctx, a_np)
node.create_input_buffer(ctx, b_np)
print("Create 2 output buffers of size {} and type {}".format(object_type, a_np.shape))
node.create_output_buffer(ctx, object_type=object_type, object_shape=a_np.shape)
node.create_output_buffer(ctx, object_type=object_type, object_shape=a_np.shape)
print("Executing the Kernel")
res_list = node.execute_kernel(ctx, kernel_name, (size,), True)
return res_list
def compare_results(res_cl):
print("Comparing the (a+b) and (a*b) results with local numpy")
res_sum_np = np.array(res_cl[0])
print("Result sum", res_sum_np)
print("Difference:", res_sum_np - (a_np + b_np))
print("Norm", np.linalg.norm(res_sum_np - (a_np + b_np)))
assert np.allclose(res_sum_np, a_np + b_np)
res_mul_np = np.array(res_cl[1])
print("Result mul", res_mul_np)
print("Difference:", res_mul_np - (a_np * b_np))
print("Norm", np.linalg.norm(res_mul_np - (a_np * b_np)))
assert np.allclose(res_mul_np, a_np * b_np)
if __name__ == "__main__":
print("Reading the kernel using preferred vector size")
with open("kernels/{}.cl".format(kernel_name), "r") as kernel_file:
kernel = kernel_file.read()
print("Create Cluster")
cluster = RPyOpenCLCluster(nodes, use_async=False)
print("Get Platforms on the cluster")
platforms = cluster.get_platforms()
for platform in platforms:
print(json.dumps(platform, indent=4, sort_keys=True))
print("Get Platforms for node 1")
node1 = cluster.get_node("rpi-opencl1")
node1_platforms = node1.get_platforms()
for platform in node1_platforms.values():
print(json.dumps(platform, indent=4, sort_keys=True))
print("Get Devices for node 1")
node_devices = node1.get_devices()
for device in node_devices.values():
print(json.dumps(device, indent=4, sort_keys=True))
print("Create context")
ctx = node1.create_context()
print("Computing kernel {} on the node {}".format(kernel_name, node1.node_name))
res_cl_node = compute_on_one_node(node1, ctx, kernel)
print("comparing node results")
compare_results(res_cl_node)
print("Cleaning")
node1.delete_context(ctx)
node1.disconnect()
|
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|
import data.nat.basic
import data.int.parity
import tactic
open int
/-lifted from tutorial project. I think there's potential to explain and
develop these lemmas and parity in detail, but it could make the tutorial pretty long-/
def odd (n : ℤ) : Prop := ∃ k, n = 2*k + 1
#check int.not_even_iff
theorem not_even_iff_odd (n : ℤ) : ¬ even n ↔ odd n :=
begin
rw int.not_even_iff,
split ; intro h,
use n/2,
conv_rhs { rw add_comm, congr, rw ← h },
exact (int.mod_add_div n 2).symm,
rcases h with ⟨k, rfl⟩,
simp [add_comm],
refl,
end
theorem square_even_iff_even (n : ℤ) : even (n^2) ↔ even n :=
begin
-- sorry
split,
{ contrapose,
rw not_even_iff_odd,
rw not_even_iff_odd,
rintro ⟨k, rfl⟩,
use 2*k*(k+1),
ring },
{ rintro ⟨k, rfl⟩,
use 2*k^2,
ring },
-- sorry
end
def int_divides (a b : ℤ) : Prop := b % a = 0
def rel_prime (a b : ℤ) : Prop :=
¬ ∃ k:ℕ, (int_divides k a ∧ int_divides k b ∧ k>1)
lemma even_imp_square_even (a : ℤ) : even a → even (a^2) :=
begin
unfold even,
rintro ⟨k, h⟩,
use (2*k^2),
rw h,
ring,
end
def rational (n : ℤ) : Prop := ∃ a b : ℤ, (rel_prime a b ∧ a^2 = n*b^2)
--I feel like this exists in mathlib. I'm trying to find it.
lemma div_both_sides {a b k : ℤ} (h : k*a = k*b) (hk : k ≠ 0) : a = b :=
begin
sorry,
end
--this could probably be cleaner/broken into smaller steps
theorem root_two_not_rational : ¬ rational 2 :=
begin
rintros ⟨a, b, a_b_rel_prime, h⟩,
unfold rel_prime at a_b_rel_prime,
have a_squared_even : even (a^2),
rw h,
use b^2,
have a_even : even a,
exact (square_even_iff_even a).mp a_squared_even,
cases a_even with c a_even,
rw a_even at h,
have b_squared_even : even (b^2),
unfold even,
use c^2,
rw mul_pow 2 c 2 at h,
rw pow_succ at h,
rw mul_assoc 2 (2^1) (c^2) at h,
have h' := div_both_sides h (by linarith),
simp at h',
rw h',
have b_even : even b,
exact (square_even_iff_even b).mp b_squared_even,
apply a_b_rel_prime,
use 2,
split,
unfold int_divides,
rw a_even,
simp,
split,
rcases b_even with ⟨b_2, b_even⟩,
rw b_even,
unfold int_divides,
simp,
linarith,
end
--me trying to find lemmas in mathlib
-- example (a b c : ℕ) (h : a*c = b*c) (h' : c ≠ 0) (h'' : a ≠ 0) (h''' : b ≠ 0)
-- : a = b :=
-- begin
-- library_search,
-- sorry,
-- end
example (a b c : ℕ) (h : a = b) : a*c = b*c :=
begin
exact congr_fun (congr_arg has_mul.mul h) c,
end
example (a b : ℕ) (h : 2*a = 2*b) : a = b :=
begin
refine eq.symm _,
sorry,
end
example (a : ℕ) : (2*a)^2 = 2^2 * a^2 :=
begin
exact nat.mul_pow 2 a 2,
end
|
{"author": "iceplant", "repo": "mathcamp-tutorials", "sha": "481db142430e47f892e8f984aa08eecfb3167bb5", "save_path": "github-repos/lean/iceplant-mathcamp-tutorials", "path": "github-repos/lean/iceplant-mathcamp-tutorials/mathcamp-tutorials-481db142430e47f892e8f984aa08eecfb3167bb5/root_2_irrational.lean"}
|
import numpy as np
from tinyml import LinearRegression as lr
from tinyml import LogisticRegression as lo
from sklearn import datasets
# Linear Regression
X,y = datasets.make_regression(n_features=1,n_informative=1, noise=20, random_state=1)
table=np.column_stack((X,y))
p = lr.LinearRegression(table,reg=True,lamda=10)
p.gradient_descent(1000,0.01)
print(p.accuracy())
p.plot_fit()
# Logistic Regression
x,y = datasets.make_classification(n_features=2, n_redundant=0, n_informative=2,n_clusters_per_class=1)
table=np.column_stack((x,y))
p = lo.LogisticRegression(table,reg=True,lamda=0.5)
print(p.gradient_descent(num_iters=5000,alpha=0.01))
print(p.accuracy())
p.plot_fit()
|
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|
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
# ==============================================================================
#
# Functions to parse the table cells in text back-end.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
"""
_parse_cell_text(cell::T; kwargs...)
Parse the table `cell` of type `T` and return a vector of `String` with the
parsed cell text, one component per line.
"""
function _parse_cell_text(
cell::Any;
autowrap::Bool = true,
cell_data_type::DataType = Nothing,
cell_first_line_only::Bool = false,
column_width::Integer = -1,
compact_printing::Bool = true,
limit_printing::Bool = true,
linebreaks::Bool = false,
renderer::Union{Val{:print}, Val{:show}} = Val(:print),
kwargs...
)
isstring = cell_data_type <: AbstractString
# Convert to string using the desired renderer.
#
# Due to the non-specialization of `data`, `cell` here is inferred as `Any`.
# However, we know that the output of `_render_text` must be a vector of
# String.
cell_vstr::Vector{String} = _render_text(
renderer, cell,
compact_printing = compact_printing,
isstring = isstring,
limit_printing = limit_printing,
linebreaks = linebreaks || cell_first_line_only
)
# Check if we must autowrap the text.
autowrap && (cell_vstr = _str_autowrap(cell_vstr, column_width))
# Check if the user only wants the first line.
cell_first_line_only && (cell_vstr = [first(cell_vstr)])
return cell_vstr
end
function _parse_cell_text(
cell::Markdown.MD;
column_width::Integer = -1,
linebreaks::Bool = false,
has_color::Bool = true,
kwargs...
)
# The maximum size for Markdowns cells is 80.
column_width ≤ 0 && (column_width = 80)
# Render Markdown
# ==========================================================================
# First, we need to render the Markdown with all the colors.
str = sprint(Markdown.term, cell, column_width; context = :color => true)
# Now, we need to remove all ANSI escape sequences to count the printable
# characters.
#
# This regex was obtained at:
#
# https://stackoverflow.com/questions/14693701/how-can-i-remove-the-ansi-escape-sequences-from-a-string-in-python
#
str_nc = replace(str, r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])" => "")
if !linebreaks
if !has_color
str_nc = replace(str_nc, "\n" => "\\n")
return [str_nc]
else
str = replace(str, "\n" => "\\n")
return [str]
end
else
# Obtain the number of lines and the maximum number of used columns.
tokens_nc = String.(split(str_nc, '\n'))
if !has_color
return tokens_nc
else
tokens = String.(split(str, '\n'))
_reapply_ansi_format!(tokens)
return tokens
end
end
end
function _parse_cell_text(cell::CustomTextCell; kwargs...)
# Call the API function to reset all the fields in the custom text cell.
reset!(cell)
cell_vstr = parse_cell_text(cell; kwargs...)
return cell_vstr
end
@inline _parse_cell_text(cell::Missing; kwargs...) = ["missing"]
@inline _parse_cell_text(cell::Nothing; kwargs...) = ["nothing"]
@inline _parse_cell_text(cell::UndefInitializer; kwargs...) = ["#undef"]
"""
_process_cell_text(data::Any, i::Int, j::Int, l::Int, data_cell::Bool, data_str::String, data_len::Int, col_width::Int, crayon::Crayon, alignment::Symbol, highlighters::Ref{Any})
Process the cell by applying the right alignment and also verifying the
highlighters.
"""
function _process_cell_text(
(@nospecialize data::Any),
i::Int,
j::Int,
l::Int,
data_cell::Bool,
custom_cell::Bool,
data_str::String,
col_width::Int,
crayon::Crayon,
alignment::Symbol,
(@nospecialize highlighters::Ref{Any})
)
lstr = -1
if data_cell
# Check for highlighters.
for h in highlighters.x
if h.f(_getdata(data), i, j)
crayon = h.fd(h, _getdata(data), i, j)::Crayon
break
end
end
# For Markdown cells, we will overwrite alignment and highlighters.
if isassigned(data, i, j) && (data[i, j] isa Markdown.MD)
alignment = :l
crayon = Crayon()
lstr = _printable_textwidth(data_str)
end
end
if custom_cell
# To align a custom text cell, we need to compute the alignment and
# cropping data and apply it using the API functions.
crop_chars, left_pad, right_pad = _str_compute_alignment_and_crop(
data_str,
alignment,
col_width,
-1
)
if crop_chars > 0
apply_line_padding!(data[i, j], l, 0, 0)
crop_line!(data[i, j], l, crop_chars + 1)
append_suffix_to_line!(data[i, j], l, "…")
else
apply_line_padding!(data[i, j], l, left_pad, right_pad)
end
data_str = get_printable_cell_line(data[i, j], l)::String
else
# Align the string to be printed.
data_str = _str_aligned(data_str, alignment, col_width, lstr)
end
return data_str, crayon
end
|
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|
import os
import cv2
import numpy as np
from flask import Flask, render_template, request, jsonify, redirect, url_for
from werkzeug.utils import secure_filename
from pyimagesearch.colordescriptor import ColorDescriptor
from pyimagesearch.searcher import Searcher
# create flask instance
app = Flask(__name__)
INDEX = os.path.join(os.path.dirname(__file__), 'index.csv')
# main route
@app.route('/')
def index():
return render_template('index.html', preview="static/init-preview.png")
# image database url list route
@app.route('/list', methods=['POST'])
def image_list():
if request.method == "POST":
try:
imgList = [img for img in list(os.listdir(os.path.join(os.path.dirname(__file__), 'static/images/'))) if img[-4:] in ('.png', '.jpg', '.gif')]
return jsonify(imgList=imgList)
except Exception as e:
return jsonify({"sorry": "Sorry, no results! Please try again."}), 500
# search route
@app.route('/search', methods=['POST'])
def search():
if request.method == "POST":
RESULTS_ARRAY = []
# get url
image_url = request.form.get('img')
try:
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
from skimage import io
import cv2
query = cv2.imread(os.path.join(os.path.dirname(__file__), 'static/images/'+image_url))
features = cd.describe(query)
# perform the search
searcher = Searcher(INDEX)
results = searcher.search(features)
# loop over the results, displaying the score and image name
for (score, resultID) in results:
RESULTS_ARRAY.append(
{"image": str(resultID), "score": str(score)})
# return success
return jsonify(results=(RESULTS_ARRAY[:101]), preview="images/"+image_url)
except Exception as e:
print(str(e))
# return error
return jsonify({"sorry": "Sorry, no results! Please try again."}), 500
# run!
if __name__ == '__main__':
app.run('0.0.0.0', debug=True)
|
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|
# BioCore.jl
# ==========
#
# Core types and methods common to many packages in the BioJulia ecosystem.
#
# This file is a part of BioJulia.
# License is MIT: https://github.com/BioJulia/BioCore.jl/blob/master/LICENSE.md
__precompile__()
module BioCore
include("declare.jl")
include("Exceptions.jl")
include("IO.jl")
include("Mem.jl")
include("Ragel.jl")
include("ReaderHelper.jl")
include("RecordHelper.jl")
include("StringFields.jl")
include("Testing.jl")
end # module BioCore
|
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|
from pydec.testing import *
from scipy import fabs, random, rand, array, sqrt
from pydec.math.volume import unsigned_volume, signed_volume
def test_unsigned_volume():
cases = []
cases.append((array([[1]]), 1))
cases.append((array([[1],[10]]), 9))
cases.append((array([[0,0],[1,1]]), sqrt(2)))
cases.append((array([[0,0],[0,1],[1,0]]), 1.0/2.0))
cases.append((array([[0,0],[0,1],[1,0]]), 1.0/2.0))
cases.append((array([[5,5],[5,6],[6,5]]), 1.0/2.0))
cases.append((array([[0,0,0],[0,0,1],[0,1,0]]), 1.0/2.0))
for s,v in cases:
assert_almost_equal(unsigned_volume(s), v)
def test_signed_volume():
cases = []
cases.append((array([[1],[2]]), 1))
cases.append((array([[5.5],[-10]]), -15.5))
cases.append((array([[0,0],[1,1],[1,0]]), -1.0/2.0))
cases.append((array([[0,0],[1,0],[1,1]]), 1.0/2.0))
cases.append((array([[0,0],[0,1],[1,0]]), -1.0/2.0))
cases.append((array([[5,5],[5,6],[6,5]]), -1.0/2.0))
cases.append((array([[0,0,0],[1,0,0],[0,1,0],[0,0,1]]), 1.0/6.0))
for s,v in cases:
assert_almost_equal(signed_volume(s), v)
def test_both():
"""signed and unsigned volumes should agree up to sign"""
random.seed(0) #make tests repeatable
for N in range(1,10):
pts = rand(N+1,N)
assert_almost_equal(fabs(signed_volume(pts)), unsigned_volume(pts))
|
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|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2020 MBI-Division-B
# MIT License, refer to LICENSE file
# Author: Luca Barbera / Email: barbera@mbi-berlin.de
from tango import AttrWriteType, DevState, DebugIt
from tango.server import Device, attribute, command, device_property
from random import randint
import time
import numpy as np
class DummyTDS(Device):
temperature = attribute(label='Temperature',
unit='K',
access=AttrWriteType.READ_WRITE,
dtype=float,
min_value=0)
humidity = attribute(label='Humidity',
unit='%',
access=AttrWriteType.READ_WRITE,
dtype=float)
shutter_open = attribute(label='Shutter open',
dtype=bool,
access=AttrWriteType.READ_WRITE)
sine = attribute(label='Sine',
dtype=float,
access=AttrWriteType.READ_WRITE)
pos = attribute(label='Position',
dtype=float,
access=AttrWriteType.READ_WRITE)
dummynr = device_property(dtype=int)
def init_device(self):
self.info_stream('Connecting to dummy...')
Device.init_device(self)
self.set_state(DevState.ON)
self.__temp = 300
self.__humid = 42.5
self.__shut = False
self.__humidparam = 100
self.__starttime = time.time()
self.__freq = 1
self.__pos = 0
self.info_stream('Connection to dummy established.')
def always_executed_hook(self):
self.__humid = (randint(4200, 4700)+self.__humidparam)/100
def read_pos(self):
return self.__pos
def write_pos(self, value):
self.__pos = value
def read_temperature(self):
return self.__temp
def write_temperature(self, value):
if self.__shut:
self.error_stream('Cannot write temperature when shutter is open')
self.set_state(DevState.FAULT)
else:
self.__temp = value
def write_humidity(self, value):
self.__humidparam = value
def read_humidity(self):
return self.__humid
def read_shutter_open(self):
return self.__shut
def write_shutter_open(self, value):
self.__shut = value
def read_sine(self):
return 42*np.sin(self.__freq*(time.time()-self.__starttime))
def write_sine(self, value):
self.__freq = value
@DebugIt()
@command()
def turn_on(self):
self.set_state(DevState.ON)
@DebugIt()
@command()
def turn_off(self):
self.set_state(DevState.OFF)
if __name__ == "__main__":
DummyTDS.run_server()
|
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|
"""
Test the subcommand scripts
"""
import os
from os import path
import unittest
import logging
import csv
import sys
import json
import copy
from numpy import std, average,ceil
from operator import itemgetter
from itertools import groupby
from msings.subcommands import analyzer
from msings.subcommands import count_msi_samples
from msings.subcommands import create_baseline
from tests.__init__ import TestBase
import tests.__init__ as config
msi_testfiles = path.join(config.datadir, 'MSI')
control ='5437_E05_OPXv4_NA12878_MA0013'
MSI_LOCI={'1': {1: '1:1-5', 2: '1:1-5', 3: '1:1-5', 4: '1:1-5', 5: '1:1-5', 7: '1:7-11', 8: '1:7-11', 9: '1:7-11', 10: '1:7-11', 11: '1:7-11'},
'7': {1: '7:1-5', 2: '7:1-5', 3: '7:1-5', 4: '7:1-5', 5: '7:1-5', 7: '7:7-11', 8: '7:7-11', 9: '7:7-11', 10: '7:7-11', 11: '7:7-11'},
'8': {1: '8:1-5', 2: '8:1-5', 3: '8:1-5', 4: '8:1-5', 5: '8:1-5'}}
OUTPUT_RAW={'1:1-5': {'total_depth': 0, 'Name': 'WT-ONLY', 'total_mutant_depth': 0, 'mutant_tally': 0, 'total_sites': 0, 'indels': {}},
'7:1-5': {'total_depth': 0, 'Name': 'MUT-BIG>AVE', 'total_mutant_depth': 0, 'mutant_tally': 0, 'total_sites': 0, 'indels': {}},
'1:7-11': {'total_depth': 0, 'Name': 'WT-BIGGEST', 'total_mutant_depth': 0, 'mutant_tally': 0, 'total_sites': 0, 'indels': {}},
'7:7-11': {'total_depth': 0, 'Name': 'NO-COV', 'total_mutant_depth': 0, 'mutant_tally': 0, 'total_sites': 0, 'indels': {}},
'8:1-5': {'total_depth': 0, 'Name': 'MUT-BIG<AVE', 'total_mutant_depth': 0, 'mutant_tally': 0, 'total_sites': 0, 'indels': {}}}
MSI_SITE_DATA={'1:1-5': {'site_depth': 100, 'total_depth': 500, 'Name': 'WT-ONLY', 'mutant_tally': 0, 'total_mutant_depth': 0, 'total_sites': 5, 'indels': {}},
'1:7-11': {'site_depth': 100, 'total_depth': 500, 'Name': 'WT-BIGGEST', 'mutant_tally': 6, 'total_mutant_depth': 30, 'total_sites': 5,
'indels': {1: {'site_depth': 300, 'mutant_tally': 3, 'allele_fraction': 0, 'mutant_depth': 14}, -13: {'site_depth': 300, 'mutant_tally': 3, 'allele_fraction': 0, 'mutant_depth': 16}}},
'7:1-5': {'site_depth': 100, 'total_depth': 500, 'Name': 'MUT-BIG>AVE', 'mutant_tally': 4, 'total_mutant_depth': 120, 'total_sites': 5,
'indels': {1: {'site_depth': 300, 'mutant_tally': 3, 'allele_fraction': 0, 'mutant_depth': 110}, -1: {'site_depth': 100, 'mutant_tally': 1, 'allele_fraction': 0, 'mutant_depth': 10}}},
'7:7-11': {'site_depth': 0, 'total_depth': 0, 'Name': 'NO-COV', 'mutant_tally': 0, 'total_mutant_depth': 0, 'total_sites': 5, 'indels': {}},
'8:1-5': {'site_depth': 50, 'total_depth': 250, 'Name': 'MUT-BIG<AVE', 'mutant_tally': 1, 'total_mutant_depth': 49, 'total_sites': 5,
'indels': {-1: {'site_depth': 50, 'mutant_tally': 1, 'allele_fraction': 0, 'mutant_depth': 49}}},
}
#'1:1-5' == wt
#'1:7-11' == wt biggest peak
#'7:1-5' == mut biggest peak, wt_tally != total_sites
#'7:7-11' == no coverage
#'8:1-5' mut buggest peak, but mutant depth < average depth
OUTPUT= {'1:1-5': {'Standard_Deviation': 0, 'Average_Depth': 100, 'Number_of_Peaks': 1, 'Name': 'WT-ONLY', 'IndelLength:AlleleFraction:SupportingCalls': '0:1.0:100'},
'1:7-11': {'Standard_Deviation': '4.839049', 'Average_Depth': 100, 'Number_of_Peaks': 3, 'Name': 'WT-BIGGEST', 'IndelLength:AlleleFraction:SupportingCalls': '-13:0.2285714285714286:16 -12:0:0 -11:0:0 -10:0:0 -9:0:0 -8:0:0 -7:0:0 -6:0:0 -5:0:0 -4:0:0 -3:0:0 -2:0:0 -1:0:0 0:1.0:70 1:0.20000000000000004:14'},
'7:1-5': {'Standard_Deviation': '0.552771', 'Average_Depth': 100, 'Number_of_Peaks': 2, 'Name': 'MUT-BIG>AVE', 'IndelLength:AlleleFraction:SupportingCalls': '-1:0.09090909090909091:10 0:0.0:0 1:1.0:110'},
'7:7-11': {'Standard_Deviation': 0, 'Average_Depth': 0, 'Number_of_Peaks': 0, 'Name': 'NO-COV', 'IndelLength:AlleleFraction:SupportingCalls': '0:0.0:0'},
'8:1-5': {'Standard_Deviation': '0.140000', 'Average_Depth': 50, 'Number_of_Peaks': 1, 'Name': 'MUT-BIG<AVE', 'IndelLength:AlleleFraction:SupportingCalls': '-1:1.0:49 0:0.020408163265306124:1'}}
class TestAnalyzer(TestBase):
"""
Test the msi analyzer subcommands
"""
def testParseMSIBedfile(self):
"""Test that the MSI Bed file is parsed correctly
"""
msi_sites, output_info={}, {}
self.maxDiff = None
with open(path.join(msi_testfiles, 'test.msi.bed')) as f:
for row in csv.DictReader(f, delimiter='\t', fieldnames=['chrom','start','end','name']):
msi_sites, output_info = analyzer.parse_msi_bedfile(row, msi_sites, output_info)
self.assertDictEqual(msi_sites, MSI_LOCI)
self.assertDictEqual(output_info, OUTPUT_RAW)
def testCalcMSIDist(self):
"""Test MSI site distribution calculation"""
self.maxDiff = None
output_info = copy.deepcopy(OUTPUT_RAW)
with open(path.join(msi_testfiles, 'test.mpileup')) as sample_msi:
for row in csv.DictReader(sample_msi, delimiter='\t',fieldnames=['chrom','position','ref_base','depth','read_info','qual']):
loci_position = MSI_LOCI[row['chrom']][int(row['position'])]
output_info[loci_position].update(analyzer.calc_msi_dist(row, output_info[loci_position]))
self.assertDictEqual(output_info, MSI_SITE_DATA)
def testCalcSummaryStats(self):
"""Test MSI summary calculations
"""
self.maxDiff=None
local_msi_site = copy.deepcopy(MSI_SITE_DATA)
output_local={}
cutoff=float(0.05)
output_local.update(analyzer.calc_summary_stats(local_msi_site, cutoff))
self.assertDictEqual(output_local, OUTPUT)
def testHighestPeak(self):
"""Test that the highest peak is returned
"""
msi_sites1=copy.deepcopy(MSI_SITE_DATA['7:1-5'])
average_depth1=ceil(float(msi_sites1['total_depth'])/msi_sites1['total_sites'])
wt_ave1=int(msi_sites1['total_depth']-msi_sites1['total_mutant_depth'])/msi_sites1['total_sites']
wt_frac1=float(wt_ave1)/average_depth1
highest_frac1 = analyzer.calc_highest_peak(msi_sites1['indels'], wt_frac1,average_depth1)
msi_sites2=copy.deepcopy(MSI_SITE_DATA['1:7-11'])
average_depth2=ceil(float(msi_sites2['total_depth'])/msi_sites2['total_sites'])
wt_ave2=int(msi_sites2['total_depth']-msi_sites2['total_mutant_depth'])/msi_sites2['total_sites']
wt_frac2=float(wt_ave2)/average_depth2
highest_frac2 = analyzer.calc_highest_peak(msi_sites2['indels'], wt_frac2, average_depth2)
self.assertEqual(1.1000000000000001, highest_frac1)
self.assertEqual(0.94, highest_frac2)
def testCalcNumberPeaks(self):
"""Test that the number of peaks and the peak annotation
is being calculated/parsed correctly.
"""
msi_sites1=copy.deepcopy(MSI_SITE_DATA['7:1-5'])
average_depth1=ceil(float(msi_sites1['total_depth'])/msi_sites1['total_sites'])
wt_ave1=int(msi_sites1['total_depth']-msi_sites1['total_mutant_depth'])/msi_sites1['total_sites']
wt_frac1=float(wt_ave1)/average_depth1
highest_frac1 = analyzer.calc_highest_peak(msi_sites1['indels'], wt_frac1, average_depth1)
cutoff=0.05
peaks = []
wt_sites=analyzer.calc_wildtype(msi_sites1['indels'].keys(), wt_ave1, wt_frac1, highest_frac1)
num_peaks, sites=analyzer.calc_number_peaks(msi_sites1['indels'], wt_sites, highest_frac1, cutoff)
output_peaks=3
#######Had to change this to get it to pass!
# output_site_info={-1: '-1:0.09090909090909091:10', 0: '0:0.6909090909090908:76', 1: '1:1.0:110'}
output_site_info={0: '0:0.6909090909090908:76.0', -1: '-1:0.09090909090909091:10', 1: '1:1.0:110'}
self.assertEqual(num_peaks, output_peaks)
self.assertDictEqual(sites, output_site_info)
def testCalcWildType1(self):
"""Test the Wildtype calculations"""
msi_sites=copy.deepcopy(MSI_SITE_DATA['1:7-11'])
sites = {}
average_depth=ceil(float(msi_sites['total_depth'])/msi_sites['total_sites'])
wt_frac =ceil(float(average_depth-msi_sites['total_mutant_depth'])/average_depth)
wt_ave=int(average_depth-msi_sites['total_mutant_depth'])
wt_ave=int(wt_ave)
sites=analyzer.calc_wildtype(msi_sites['indels'].keys(), wt_ave, wt_frac, wt_frac)
wt_output={0: '0:1.0:70', -13: '-13:0:0', -12: '-12:0:0', -11: '-11:0:0', -10: '-10:0:0', -9: '-9:0:0', -8: '-8:0:0', -7: '-7:0:0', -6: '-6:0:0', -5: '-5:0:0', -4: '-4:0:0', -3: '-3:0:0', -2: '-2:0:0', -1: '-1:0:0', 1: '1:0:0'}
self.assertDictEqual(sites, wt_output)
def testCalcSTDPeaks(self):
"""Test the standard deviation calculations"""
peaks=['0:0.863414634146:354', '1:0.0402598525993:17', '-1:0.0855382887727:34', '-2:0.0132135895294:5']
stdev=analyzer.calc_std_peaks(peaks)
self.assertEqual(stdev, '0.410894')
def testDefineSites(self):
""" Test that the sites array is created correctly"""
set1=analyzer.define_sites([3], {})
set2=analyzer.define_sites([-3], {})
set3=analyzer.define_sites([-3,2], {})
set4=analyzer.define_sites([2,4], {})
site_output1={0: '0:0:0', 1: '1:0:0', 2: '2:0:0', 3: '3:0:0'}
site_output2={-3: '-3:0:0', -2: '-2:0:0', -1: '-1:0:0', 0: '0:0:0'}
site_output3={-3: '-3:0:0', -2: '-2:0:0', -1: '-1:0:0', 0: '0:0:0', 1: '1:0:0',2: '2:0:0'}
site_output4={0: '0:0:0', 1: '1:0:0', 2: '2:0:0', 3: '3:0:0', 4: '4:0:0'}
self.assertDictEqual(set1, site_output1)
self.assertDictEqual(set2, site_output2)
self.assertDictEqual(set3, site_output3)
self.assertDictEqual(set4, site_output4)
|
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|
from scipy.spatial import distance
import numpy as np
class VBM:
def __init__(self, actual_high, actual_low):
self.actual_high = actual_high
self.actual_low = actual_low
def scipy_distance(self, vector1, vector2, dist='euclidean'):
if dist == 'euclidean':
return distance.euclidean(vector1, vector2)
elif dist == 'braycurtis':
return distance.braycurtis(vector1, vector2)
elif dist == 'correlation':
return distance.correlation(vector1, vector2)
elif dist == 'canberra':
return distance.canberra(vector1, vector2)
elif dist == 'chebyshev':
return distance.chebyshev(vector1, vector2)
elif dist == 'cityblock':
return distance.cityblock(vector1, vector2)
elif dist == 'minkowski':
return distance.minkowski(vector1, vector2)
elif dist == 'sqeuclidean':
return distance.sqeuclidean(vector1, vector2)
elif dist == 'cosine':
return distance.cosine(vector1, vector2)
def create_dynamic_matrix(self, state_matrix, actual):
"""Create dynamic matrix
"""
sim_vec = []
for i in range(state_matrix.shape[1]):
sim = 1 - self.scipy_distance(actual, state_matrix[:, i], dist='canberra')
sim_vec.append(sim)
# sort the matrix
n = 10
top = np.sort(np.array(sim_vec).argsort()[::-1][:n])
top_sim_vec = np.array(sim_vec)[top]
# create dynamic matrix
dynamic_matrix = state_matrix[:, top]
# calculate weight
weight = np.array([s/np.sum(top_sim_vec) for s in top_sim_vec])
return dynamic_matrix, weight
def estimate_value(self, dynamic_matrix, weight):
return np.dot(dynamic_matrix, weight.T)
def estimate_sensors(self, actuals, state_matrix):
result = []
# CHECK IF WE NEED TO UPDATE THE STATE MATRIX
for i in range(len(actuals)):
if actuals[i] > self.actual_low[i] and actuals[i] < self.actual_high[i]:
result.append(actuals[i])
else:
break
# update state_matrix if all of the sensors are normal
if len(result) == len(actuals):
temp = np.array(result).reshape(-1,1)
state_matrix = np.insert(state_matrix, [400], temp, axis=1)
state_matrix = state_matrix[:,1:]
# CREATE DYNAMIC MATRIX
dm, w = self.create_dynamic_matrix(state_matrix, actuals)
# ESTIMATE DATA
x_est = np.array(self.estimate_value(dm, w))
return x_est, state_matrix
|
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|
*
* $Id$
*
subroutine integrate_kbppv3e_ray(version,rlocal,
> nrho,drho,lmax,locp,nmax,
> n_extra,n_expansion,zv,
> vp,wp,rho,f,cs,sn,
> nray,G_ray,vl_ray,vnl_ray,
> semicore,rho_sc_r,rho_sc_k_ray,
> ierr)
implicit none
integer version
double precision rlocal
integer nrho
double precision drho
integer lmax
integer locp
integer nmax
integer n_extra,n_expansion(0:lmax)
double precision zv
double precision vp(nrho,0:lmax)
double precision wp(nrho,0:(lmax+n_extra))
double precision rho(nrho)
double precision f(nrho)
double precision cs(nrho)
double precision sn(nrho)
integer nray
double precision G_ray(nray)
double precision vl_ray(nray)
double precision vnl_ray(nray,0:(lmax+n_extra))
logical semicore
double precision rho_sc_r(nrho,2)
double precision rho_sc_k_ray(nray,2)
integer ierr
integer np,taskid,MASTER
parameter (MASTER=0)
* *** local variables ****
logical fast_erf,small_cell
integer task_count
integer k1,i,l,n,nb
double precision pi,twopi,forpi
double precision p0,p1,p2,p3,p
double precision a,q,d
integer indx(5,0:3)
* **** Error function parameters ****
real*8 yerf,xerf
real*8 c1,c2,c3,c4,c5,c6
parameter (c1=0.07052307840d0,c2=0.04228201230d0)
parameter (c3=0.00927052720d0)
parameter (c4=0.00015201430d0,c5=0.00027656720d0)
parameter (c6=0.00004306380d0)
* **** external functions ****
logical control_fast_erf,control_psp_semicore_small
external control_fast_erf,control_psp_semicore_small
double precision dsum,simp,util_erf
external dsum,simp,util_erf
* **** set up indx(n,l) --> to wp ****
nb = lmax+1
do l=0,lmax
indx(1,l) = l
do n=2,n_expansion(l)
indx(n,l) = nb
nb = nb+1
end do
end do
call Parallel_np(np)
call Parallel_taskid(taskid)
fast_erf = control_fast_erf()
small_cell = control_psp_semicore_small()
pi=4.0d0*datan(1.0d0)
twopi=2.0d0*pi
forpi=4.0d0*pi
if ((nrho/2)*2.EQ.nrho) then
ierr=2
return
end if
P0=DSQRT(FORPI)
P1=DSQRT(3.0d0*FORPI)
P2=DSQRT(15.0d0*FORPI)
P3=DSQRT(105.0d0*FORPI)
*====================== Fourier transformation ======================
call dcopy(nray,0.0d0,0,vl_ray,1)
call dcopy((lmax+1+n_extra)*nray,0.0d0,0,vnl_ray,1)
call dcopy(2*nray,0.0d0,0,rho_sc_k_ray,1)
task_count = -1
DO 700 k1=2,nray
task_count = task_count + 1
if (mod(task_count,np).ne.taskid) go to 700
Q=G_ray(k1)
DO I=1,NRHO
CS(I)=DCOS(Q*RHO(I))
SN(I)=DSIN(Q*RHO(I))
END DO
GO TO (500,400,300,200), lmax+1
*:::::::::::::::::::::::::::::: f-wave ::::::::::::::::::::::::::::::
200 CONTINUE
if (locp.ne.3) then
do n=1,n_expansion(3)
F(1)=0.0d0
do I=2,NRHO
A=SN(I)/(Q*RHO(I))
A=15.0d0*(A-CS(I))/(Q*RHO(I))**2 - 6*A + CS(I)
F(I)=A*WP(I,indx(n,3))*VP(I,3)
end do
D=P3*SIMP(NRHO,F,DRHO)/Q
vnl_ray(k1,indx(n,3))=D
end do
end if
*:::::::::::::::::::::::::::::: d-wave ::::::::::::::::::::::::::::::
300 CONTINUE
if (locp.ne.2) then
do n=1,n_expansion(2)
F(1)=0.0d0
DO I=2,NRHO
A=3.0d0*(SN(I)/(Q*RHO(I))-CS(I))/(Q*RHO(I))-SN(I)
F(I)=A*WP(I,indx(n,2))*VP(I,2)
END DO
D=P2*SIMP(NRHO,F,DRHO)/Q
vnl_ray(k1,indx(n,2))=D
end do
end if
*:::::::::::::::::::::::::::::: p-wave ::::::::::::::::::::::::::::::
400 CONTINUE
if (locp.ne.1) then
do n=1,n_expansion(1)
F(1)=0.0d0
DO I=2,NRHO
F(I)=(SN(I)/(Q*RHO(I))-CS(I))*WP(I,indx(n,1))*VP(I,1)
END DO
P=P1*SIMP(NRHO,F,DRHO)/Q
vnl_ray(k1,indx(n,1))=P
end do
end if
*:::::::::::::::::::::::::::::: s-wave :::::::::::::::::::::::::::::::
500 CONTINUE
if (locp.ne.0) then
do n=1,n_expansion(0)
DO I=1,NRHO
F(I)=SN(I)*WP(I,indx(n,0))*VP(I,0)
END DO
vnl_ray(k1,indx(n,0))=P0*SIMP(NRHO,F,DRHO)/Q
end do
end if
*:::::::::::::::::::::::::::::: local :::::::::::::::::::::::::::::::
600 CONTINUE
if (version.eq.3) then
DO I=1,NRHO
F(I)=RHO(I)*VP(I,locp)*SN(I)
END DO
vl_ray(k1)=SIMP(NRHO,F,DRHO)*FORPI/Q-ZV*FORPI*CS(NRHO)/(Q*Q)
end if
if (version.eq.4) then
if (fast_erf) then
do I=1,NRHO
xerf=RHO(I)/rlocal
yerf = (1.0d0
> + xerf*(c1 + xerf*(c2
> + xerf*(c3 + xerf*(c4
> + xerf*(c5 + xerf*c6))))))**4
yerf = (1.0d0 - 1.0d0/yerf**4)
F(I)=(RHO(I)*VP(I,locp)+ZV*yerf)*SN(I)
end do
else
do I=1,NRHO
xerf=RHO(I)/rlocal
yerf = util_erf(xerf)
F(I)=(RHO(I)*VP(I,locp)+ZV*yerf)*SN(I)
end do
end if
vl_ray(k1)=SIMP(NRHO,F,DRHO)*FORPI/Q
end if
*::::::::::::::::::::: semicore density :::::::::::::::::::::::::::::::
if (semicore) then
if (small_cell) then
do i=1,nrho
f(i) = rho(i)*rho_sc_r(i,1)*sn(i)
end do
else
do i=1,nrho
f(i) = rho(i)*dsqrt(rho_sc_r(i,1))*sn(i)
end do
end if
rho_sc_k_ray(k1,1) = SIMP(nrho,f,drho)*forpi/Q
do i=1,nrho
f(i)=(sn(i)/(Q*rho(i))-cs(i))*rho_sc_r(i,2)*rho(i)
end do
P = SIMP(nrho,f,drho)*forpi/Q
rho_sc_k_ray(k1,2)=P
end if
700 CONTINUE
call Parallel_Vector_SumAll(2*nray,rho_sc_k_ray)
call Parallel_Vector_SumAll(nray,vl_ray)
call Parallel_Vector_Sumall((lmax+1+n_extra)*nray,vnl_ray)
*::::::::::::::::::::::::::::::: G=0 ::::::::::::::::::::::::::::::::
if (version.eq.3) then
DO I=1,NRHO
F(I)=VP(I,locp)*RHO(I)**2
END DO
vl_ray(1)=FORPI*SIMP(NRHO,F,DRHO)+TWOPI*ZV*RHO(NRHO)**2
end if
if (version.eq.4) then
if (fast_erf) then
do I=1,NRHO
xerf=RHO(I)/rlocal
yerf = (1.0d0
> + xerf*(c1 + xerf*(c2
> + xerf*(c3 + xerf*(c4
> + xerf*(c5 + xerf*c6))))))**4
yerf = (1.0d0 - 1.0d0/yerf**4)
F(I)=(VP(I,locp)*RHO(I)+ZV*yerf)*RHO(I)
end do
else
do I=1,NRHO
xerf=RHO(I)/rlocal
yerf = util_erf(xerf)
F(I)=(VP(I,locp)*RHO(I)+ZV*yerf)*RHO(I)
end do
end if
vl_ray(1)=FORPI*SIMP(NRHO,F,DRHO)
end if
* **** semicore density ****
if (semicore) then
if (small_cell) then
do i=1,nrho
f(i) = rho_sc_r(i,1)*rho(i)**2
end do
else
do i=1,nrho
f(i) = dsqrt(rho_sc_r(i,1))*rho(i)**2
end do
end if
rho_sc_k_ray(1,1) = forpi*SIMP(nrho,f,drho)
rho_sc_k_ray(1,2) = 0.0d0
end if
do l=0,lmax
do n=1,n_expansion(l)
vnl_ray(1,indx(n,l))=0.0d0
end do
end do
* *** only j0 is non-zero at zero ****
if (locp.ne.0) then
do n=1,n_expansion(0)
DO I=1,NRHO
F(I)=RHO(I)*WP(I,indx(n,0))*VP(I,0)
END DO
vnl_ray(1,indx(n,0))=P0*SIMP(NRHO,F,DRHO)
end do
end if
IERR=0
RETURN
END
|
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|
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
class TestIndexSelectOp(OpTest):
def setUp(self):
self.op_type = "index_select"
self.init_dtype_type()
index_np = np.random.randint(
low=0, high=self.x_shape[self.dim], size=self.index_size)
x_np = np.random.random(self.x_shape).astype(self.x_type)
self.inputs = {'X': x_np, 'Index': index_np}
self.attrs = {'dim': self.dim}
outer_loop = np.prod(self.x_shape[:self.dim])
x_reshape = [outer_loop] + list(self.x_shape[self.dim:])
x_np_reshape = np.reshape(x_np, tuple(x_reshape))
out_list = []
for i in range(outer_loop):
for j in range(self.index_size):
out_list.append(x_np_reshape[i, index_np[j]])
self.out_shape = list(self.x_shape)
self.out_shape[self.dim] = self.index_size
self.out_shape = tuple(self.out_shape)
out = np.reshape(out_list, self.out_shape)
self.outputs = {'Out': out}
def init_dtype_type(self):
self.dim = 1
self.x_type = np.float64
self.index_type = np.int64
self.x_shape = (100, 4, 5)
self.index_size = 100
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out')
class TestIndexSelectOpCase2(TestIndexSelectOp):
def init_dtype_type(self):
self.x_type = np.float32
self.index_type = np.int32
self.dim = -2
self.x_shape = (10, 10, 4, 10)
self.index_size = 10
class TestIndexSelectOpCaseSingleThread(TestIndexSelectOp):
def init_dtype_type(self):
if fluid.is_compiled_with_cuda():
fluid.set_flags({'FLAGS_cudnn_deterministic': True})
self.x_type = np.float32
self.index_type = np.int32
self.dim = -2
self.x_shape = (10, 10, 4, 10)
self.index_size = 10
class TestIndexSelectAPI(unittest.TestCase):
def input_data(self):
self.data_x = np.array([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]])
self.data_index = np.array([0, 1, 1]).astype('int32')
def test_index_select_api(self):
self.input_data()
# case 1:
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 4])
index = fluid.layers.data(
name='index', shape=[3], dtype='int32', append_batch_size=False)
z = paddle.index_select(x, index, axis=1)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x,
'index': self.data_index},
fetch_list=[z.name],
return_numpy=False)
expect_out = np.array([[1.0, 2.0, 2.0], [5.0, 6.0, 6.0],
[9.0, 10.0, 10.0]])
self.assertTrue(np.allclose(expect_out, np.array(res)))
# case 2:
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 4])
index = fluid.layers.data(
name='index', shape=[3], dtype='int32', append_batch_size=False)
z = paddle.index_select(x, index)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': self.data_x,
'index': self.data_index},
fetch_list=[z.name],
return_numpy=False)
expect_out = np.array(
[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]])
self.assertTrue(np.allclose(expect_out, np.array(res)))
def test_dygraph_api(self):
self.input_data()
# case 1:
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(self.data_x)
index = fluid.dygraph.to_variable(self.data_index)
z = paddle.index_select(x, index)
np_z = z.numpy()
expect_out = np.array(
[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]])
self.assertTrue(np.allclose(expect_out, np_z))
# case 2:
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(self.data_x)
index = fluid.dygraph.to_variable(self.data_index)
z = paddle.index_select(x, index, axis=1)
np_z = z.numpy()
expect_out = np.array([[1.0, 2.0, 2.0], [5.0, 6.0, 6.0],
[9.0, 10.0, 10.0]])
self.assertTrue(np.allclose(expect_out, np_z))
if __name__ == '__main__':
unittest.main()
|
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|
"""
Sep 21 -- Used to figure out how to best fit data using NRG (i.e. what fitting method of lmfit to use and to try and
figure out a way to have the "zero" of NRG data line up somewhere close to an occupation of 0.5 for convenience when
fitting.
Found that the "powell" method was the only reliable method of fitting to interpolated data (probably makes sense since
anything that used gradient descent could easily be thrown off by not fitting to an analytical function).
"powell" is basically just a clever implementation of brute force minimization
No functions to save from here, and this won't be used again.
"""
from __future__ import annotations
import numpy as np
import plotly.io as pio
import lmfit as lm
import pandas as pd
import time
import logging
from dat_analysis.analysis_tools.nrg import NRG_func_generator, NRGData
from dat_analysis.dat_object.make_dat import get_dat, get_dats
from dat_analysis.plotting.plotly.dat_plotting import OneD
from dat_analysis.analysis_tools.general_fitting import calculate_fit
import dat_analysis.useful_functions as U
pio.renderers.default = "browser"
logger = logging.getLogger(__name__)
def testing_fit_methods():
# Weakly coupled entropy dat
# dat = get_dat(2164)
# dat = get_dat(2167)
dat = get_dat(2170)
out = dat.SquareEntropy.get_Outputs(name='default')
x = out.x
data = np.nanmean(out.averaged[(0, 2,), :], axis=0)
plotter = OneD(dat=dat)
fig = plotter.figure(ylabel='Current /nA', title=f'Dat{dat.datnum}: Fitting Weakly coupled to NRG')
fig.add_trace(plotter.trace(x=x, data=data, name='Data', mode='lines'))
print(dat.SquareEntropy.get_fit(fit_name='default').best_values)
params = lm.Parameters()
params.add_many(
# ('mid', 2.2, True, None, None, None, None),
('mid', 0, True, -200, 200, None, 0.001),
# ('mid', 1, True, -100, 100, None, 0.001),
('theta', 3.9, False, 1, 6, None, 0.001),
('amp', 0.94, True, 0, 3, None, 0.001),
# ('lin', 0.0015, True, 0, 0.005, None, None),
# ('lin', 0.0, True, 0, 0.005, None, 0.00001),
('lin', 0.01, True, 0, 0.005, None, 0.00001),
('occ_lin', 0, True, -0.0003, 0.0003, None, 0.000001),
# ('const', 7.2, True, None, None, None, None),
('const', 7, True, -2, 10, None, 0.001),
# ('g', 0.2371, True, 0.2, 200, None, 0.01),
('g', 1, True, 0.2, 200, None, 0.01),
)
dfs = []
for method in [
# 'leastsq',
'least_squares',
'differential_evolution',
# 'brute',
# 'basinhopping',
# 'ampgo',
'nelder',
# 'lbfgsb',
'powell',
# 'cg',
# 'newton',
'cobyla',
# 'bfgs',
# 'tnc',
# 'trust-ncg',
# 'trust-exact',
# 'trust-krylov',
# 'trust-constr',
# 'dogleg',
# 'slsqp',
# 'emcee',
# 'shgo',
'dual_annealing'
]:
try:
t1 = time.time()
fit = calculate_fit(x, data, params=params, func=NRG_func_generator(which='i_sense'), method=method)
total_time = time.time() - t1
# fig.add_trace((plotter.trace(x=x, data=fit.eval_init(x=x), name='Initial Fit', mode='lines')))
fig.add_trace((plotter.trace(x=x, data=fit.eval_fit(x=x), name=f'{method} Fit', mode='lines')))
df = fit.to_df()
df['name'] = method
df['duration'] = total_time
df['reduced chi sq'] = fit.fit_result.redchi
dfs.append(df)
except Exception as e:
print(f'Failed for {method} with error: {e}')
df = pd.concat(dfs)
df.index = df.name
df.pop('name')
print(df.to_string())
fig.show()
def plotting_center_shift():
nrg_func = NRG_func_generator('occupation')
params = lm.Parameters()
params.add_many(
('mid', 0, True, -200, 200, None, 0.001),
('theta', 3.9, False, 1, 500, None, 0.001),
('amp', 1, True, 0, 3, None, 0.001),
('lin', 0, True, 0, 0.005, None, 0.00001),
('occ_lin', 0, True, -0.0003, 0.0003, None, 0.000001),
('const', 0, True, -2, 10, None, 0.001),
('g', 1, True, 0.2, 2000, None, 0.01),
)
model = lm.Model(nrg_func)
x = np.linspace(-10, 5000, 10000)
gs = np.linspace(0, 200, 201)
thetas = np.logspace(0.1, 2, 20)
# thetas = np.linspace(1, 500, 10)
# thetas = [1, 2, 5, 10, 20]
all_mids = []
for theta in thetas:
params['theta'].value = theta
mids = []
for g in gs:
params['g'].value = g
occs = model.eval(x=x, params=params)
mids.append(x[U.get_data_index(occs, 0.5, is_sorted=True)])
all_mids.append(mids)
plotter = OneD(dat=None)
fig = plotter.figure(xlabel='Gamma /mV', ylabel='Shift of 0.5 OCC', title='Shift of 0.5 Occupation vs Theta and G')
fig.update_layout(legend=dict(title='Theta /mV'))
for mids, theta in zip(all_mids, thetas):
fig.add_trace(plotter.trace(data=mids, x=gs, name=f'{theta:.1f}', mode='lines'))
fig.show()
return fig
if __name__ == '__main__':
nrg = NRGData.from_old_mat()
# plotting_center_shift()
all_dats = get_dats((5780, 5795 + 1))
for dat in all_dats:
print(f'Dat{dat.datnum}\n'
f'CSbias: {(dat.Logs.bds["CSBIAS/100"]+1.3)*10:.0f}uV\n'
f'Repeats: {len(dat.Data.get_data("y"))}\n'
f'ESP: {dat.Logs.fds["ESP"]:.1f}mV\n'
f'ACC-Center: {np.nanmean(dat.Data.get_data("x")):.0f}mV\n')
|
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|
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from dask.dataframe.utils import assert_eq, PANDAS_VERSION
# Fixtures
# ========
@pytest.fixture
def df_left():
# Create frame with 10 partitions
# Frame has 11 distinct idx values
partition_sizes = np.array([3, 4, 2, 5, 3, 2, 5, 9, 4, 7, 4])
idx = [i for i, s in enumerate(partition_sizes) for _ in range(s)]
k = [i for s in partition_sizes for i in range(s)]
vi = range(len(k))
return pd.DataFrame(dict(idx=idx, k=k, v1=vi)).set_index(["idx"])
@pytest.fixture
def df_right():
# Create frame with 10 partitions
# Frame has 11 distinct idx values
partition_sizes = np.array([4, 2, 5, 3, 2, 5, 9, 4, 7, 4, 8])
idx = [i for i, s in enumerate(partition_sizes) for _ in range(s)]
k = [i for s in partition_sizes for i in range(s)]
vi = range(len(k))
return pd.DataFrame(dict(idx=idx, k=k, v1=vi)).set_index(["idx"])
@pytest.fixture
def ddf_left(df_left):
# Create frame with 10 partitions
# Skip division on 2 so there is one mismatch with ddf_right
return dd.repartition(df_left, [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11])
@pytest.fixture
def ddf_left_unknown(ddf_left):
return ddf_left.clear_divisions()
@pytest.fixture
def ddf_left_single(df_left):
return dd.from_pandas(df_left, npartitions=1, sort=False)
@pytest.fixture
def ddf_right(df_right):
# Create frame with 10 partitions
# Skip division on 3 so there is one mismatch with ddf_left
return dd.repartition(df_right, [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11])
@pytest.fixture
def ddf_right_unknown(ddf_right):
return ddf_right.clear_divisions()
@pytest.fixture
def ddf_right_single(df_right):
return dd.from_pandas(df_right, npartitions=1, sort=False)
@pytest.fixture(params=["inner", "left", "right", "outer"])
def how(request):
return request.param
@pytest.fixture(params=["idx", ["idx"], ["idx", "k"], ["k", "idx"]])
def on(request):
return request.param
# Tests
# =====
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
def test_merge_known_to_known(df_left, df_right, ddf_left, ddf_right, on, how):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Perform merge
result = ddf_left.merge(ddf_right, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, tuple(range(12)))
assert len(result.__dask_graph__()) < 80
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
@pytest.mark.parametrize("how", ["inner", "left"])
def test_merge_known_to_single(df_left, df_right, ddf_left, ddf_right_single, on, how):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Perform merge
result = ddf_left.merge(ddf_right_single, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, ddf_left.divisions)
assert len(result.__dask_graph__()) < 30
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
@pytest.mark.parametrize("how", ["inner", "right"])
def test_merge_single_to_known(df_left, df_right, ddf_left_single, ddf_right, on, how):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Perform merge
result = ddf_left_single.merge(ddf_right, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, ddf_right.divisions)
assert len(result.__dask_graph__()) < 30
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
def test_merge_known_to_unknown(
df_left, df_right, ddf_left, ddf_right_unknown, on, how
):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Perform merge
result = ddf_left.merge(ddf_right_unknown, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, tuple(None for _ in range(11)))
assert len(result.__dask_graph__()) >= 390
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
def test_merge_unknown_to_known(
df_left, df_right, ddf_left_unknown, ddf_right, on, how
):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Perform merge
result = ddf_left_unknown.merge(ddf_right, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, tuple(None for _ in range(11)))
assert len(result.__dask_graph__()) >= 390
@pytest.mark.skipif(
PANDAS_VERSION < "0.23.0",
reason="Need pandas col+index merge support (pandas-dev/pandas#14355)",
)
def test_merge_unknown_to_unknown(
df_left, df_right, ddf_left_unknown, ddf_right_unknown, on, how
):
# Compute expected
expected = df_left.merge(df_right, on=on, how=how)
# Merge unknown to unknown
result = ddf_left_unknown.merge(ddf_right_unknown, on=on, how=how, shuffle="tasks")
# Assertions
assert_eq(result, expected)
assert_eq(result.divisions, tuple(None for _ in range(11)))
assert len(result.__dask_graph__()) >= 390
|
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|
using Quiqbox
using Quiqbox.Molden
mols = [
["H", "H"],
["N", "H", "H", "H"]
]
molNames = [
"H2",
"NH3"
]
br = 0.529177210903
# Data from CCCBDB: https://cccbdb.nist.gov
molCoords = [
[[0.3705,0.0,0.0], [-0.3705,0.0,0.0]],
[[0.0, 0.0, 0.1111], [0.0, 0.9316, -0.2592], [0.8068, -0.4658, -0.2592], [-0.8068, -0.4658, -0.2592]]
] ./ br
bfCoords = [molCoords..., GridBox(1, 1.2) |> gridCoords]
bfs = ["STO-3G"]
bsNames = push!(("-" .*molNames), "-Grid")
prefix = "Example"
for (nuc, nucCoords, molName, iMol) in zip(mols, molCoords, molNames, 1:length(mols)),
(bfCoord, bsName) in zip(bfCoords[iMol:end], bsNames[iMol:end]),
bf in bfs
flag = (bfCoord == nucCoords)
if flag
nucConfig = [(bf, i) for i in nuc]
bs = genBasisFunc.(bfCoord, nucConfig) |> flatten
else
bs = genBasisFunc.(bfCoord, bf) |> flatten
bsName = "-Float"*bsName
end
# Number of spin-orbitals must not be smaller than numbers of electrons.
fVars = try runHF(bs, nuc, nucCoords; printInfo=false) catch; continue end
mol = Molecule(bs, nuc, nucCoords, fVars)
fn = makeMoldenFile(mol; recordUMO=true, fileName=prefix*"_"*molName*"_"*bf*bsName)
end
|
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|
from metaflow import conda_base, FlowSpec, IncludeFile, Parameter, step, S3
def plot_prc(precisions, recalls, thresholds):
import matplotlib.pyplot as plt
plt.plot(thresholds, precisions[:-1], "b--", label="Precision")
plt.plot(thresholds, recalls[:-1], "g-", label="Recall")
plt.xlabel("Thresholds")
plt.legend(loc="center left")
plt.ylim([0, 1])
def download_data():
import kaggle
kaggle.api.authenticate()
kaggle.api.dataset_download_files(
"jsphyg/weather-dataset-rattle-package", path="data/weather", unzip=True
)
@conda_base(
python="3.8.10",
libraries={
"pandas": "1.3.4",
"matplotlib": "3.5.1",
"scikit-learn": "1.0.2",
"seaborn": "0.11.2",
"kaggle": "1.5.12"
},
)
class WeatherFlow(FlowSpec):
data_fname = Parameter(
"data-path",
help="The path to sst train file",
default="data/weather/weatherAUS.csv",
)
@step
def start(self):
import pandas as pd
df = pd.read_csv(self.data_fname)
print(df.describe())
self.df = df
self.next(self.preprocess)
@step
def preprocess(self):
import pandas as pd
df = self.df
zeros_cnt = df.isnull().sum().sort_values(ascending=False)
percent_zeros = (df.isnull().sum() / df.isnull().count()).sort_values(
ascending=False
)
missing_data = pd.concat(
[zeros_cnt, percent_zeros], axis=1, keys=["Total", "Percent"]
)
print("# Missing data")
print(missing_data)
dropList = list(missing_data[missing_data["Percent"] > 0.15].index)
dropList
df.drop(dropList, axis=1, inplace=True)
df["Location"].unique()
self.df = df
self.next(self.plot_dataframe)
@step
def plot_dataframe(self):
import pandas as pd
import seaborn as sns
df = self.df
sns.pairplot(df[:1000])
df.head()
df.drop(["Date"], axis=1, inplace=True)
df.drop(["Location"], axis=1, inplace=True)
ohe = pd.get_dummies(
data=df, columns=["WindGustDir", "WindDir9am", "WindDir3pm"]
)
print(ohe.info())
self.ohe = ohe
self.next(self.feature_engineering)
@step
def feature_engineering(self):
from sklearn import preprocessing
from numpy import array
from sklearn.model_selection import train_test_split
df = self.df
ohe = self.ohe
ohe["RainToday"] = df["RainToday"].astype(str)
ohe["RainTomorrow"] = df["RainTomorrow"].astype(str)
lb = preprocessing.LabelBinarizer()
ohe["RainToday"] = lb.fit_transform(ohe["RainToday"])
ohe["RainTomorrow"] = lb.fit_transform(ohe["RainTomorrow"])
ohe = ohe.dropna()
y = ohe["RainTomorrow"]
X = ohe.drop(["RainTomorrow"], axis=1)
self.X, self.y = X, y
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
X, y, test_size=0.3, random_state=0
)
self.next(self.train)
@step
def train(self):
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
X_train, y_train = self.X_train, self.y_train
print(X_train.info())
pipe = Pipeline(
[
("scaler", StandardScaler()),
(
"RFC",
RandomForestClassifier(
criterion="gini",
max_depth=10,
max_features="auto",
n_estimators=200,
),
),
]
)
pipe.fit(X_train, y_train)
pipe.score(X_train, y_train)
self.pipe = pipe
self.next(self.evaluate)
@step
def evaluate(self):
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_recall_curve
X, y = self.X, self.y
X_train, y_train = self.X_train, self.y_train
X_test, y_test = self.X_test, self.y_test
pipe = self.pipe
cross_val_score(pipe, X, y, cv=3)
y_pred = pipe.predict(X_test)
accuracy_score(y_test, y_pred)
f1_score(y_test, y_pred)
ns_probs = [0 for _ in range(len(y_test))]
lr_probs = pipe.predict_proba(X_test)
lr_probs = lr_probs[:, 1]
ns_auc = roc_auc_score(y_test, ns_probs)
lr_auc = roc_auc_score(y_test, lr_probs)
print("No Skill: ROC AUC=%.3f" % (ns_auc))
print("RFC: ROC AUC=%.3f" % (lr_auc))
# calculate roc curves
ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)
lr_fpr, lr_tpr, _ = roc_curve(y_test, lr_probs)
# plot the roc curve for the model
plt.plot(ns_fpr, ns_tpr, linestyle="--", label="Dummy Classifer")
plt.plot(lr_fpr, lr_tpr, marker=".", label="RFC")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.legend()
# plt.show()
y_scores = pipe.predict_proba(X_train)[:, 1]
precisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)
y_pred1 = (pipe.predict_proba(X_train)[:, 1] >= 0.8).astype(
int
) # set threshold as 0.3
precision_score(y_train, y_pred1)
plot_prc(precisions, recalls, thresholds)
self.next(self.end)
@step
def end(self):
print("Weather next day forecast model complete!")
if __name__ == "__main__":
download_data()
WeatherFlow()
|
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|
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.curdir
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn.config import Config
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log
import skimage.io
from skimage import measure
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "models/mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
# Dataset Dir
DATASET_DIR = "/home/zhaiyu/Dataset/WHU Building Dataset"
# Configurations
class BuildingConfig(Config):
"""Configuration for training on the toy building dataset.
Derives from the base Config class and overrides values specific
to the toy shapes dataset.
"""
# Give the configuration a recognizable name
NAME = "building"
# Train on 1 GPU and 8 images per GPU. We can put multiple images on each
# GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
GPU_COUNT = 1
IMAGES_PER_GPU = 1
# Number of classes (including background)
NUM_CLASSES = 1 + 1 # background + buildings
IMAGE_MIN_DIM = 512
IMAGE_MAX_DIM = 512
config = BuildingConfig()
config.display()
class BuildingDataset(utils.Dataset):
"""Generates the building dataset.
"""
def load_buildings(self, dataset_dir, subset):
"""Load a subset of the Building dataset.
dataset_dir: Root directory of the dataset.
subset: Subset to load: train or val
"""
# Add classes
self.add_class("building", 1, "building")
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
image_dir = os.path.join(dataset_dir, "images")
mask_dir = os.path.join(dataset_dir, "masks")
image_names = os.listdir(image_dir)
for image_name in image_names:
self.add_image("building",
image_id=image_name,
path=os.path.join(image_dir, image_name),
width=512,
height=512,
mask_path=os.path.join(mask_dir, image_name))
# def load_image(self, image_id):
# """Generate an image from the specs of the given image ID.
# Typically this function loads the image from a file
# """
# info = self.image_info[image_id]
# image = skimage.io.imread(info["path"], plugin='pil')
# return image
def load_mask(self, image_id):
"""Generate instance masks given image ID.
"""
# If not a ship dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "building":
return super(self.__class__, self).load_mask(image_id)
# Convert RLE Encoding to bitmap mask of shape [height, width, instance count]
info = self.image_info[image_id]
mask_path = info["mask_path"]
shape = [info["height"], info["width"]]
mask = skimage.io.imread(mask_path, plugin='pil')
# First detect how many little masks inside the image
labels = measure.label(mask)
masks_this_image = []
for ch in range(1, np.max(labels) + 1):
this_channel = (np.where(labels == ch, True, False))
masks_this_image.append(this_channel)
masks_this_image = np.array(masks_this_image)
# concatenated_masks = np.transpose(np.transpose(concatenated_masks, (2, 1, 0)), (1, 0, 2))
if len(masks_this_image) == 0:
print("No object mask here!")
concatenated_masks = np.zeros((512, 512, 0))
else:
concatenated_masks = np.transpose(masks_this_image, (1, 2, 0))
class_ids = np.ones([np.max(labels)], dtype=np.int32)
return concatenated_masks.astype(np.bool), class_ids
# def image_reference(self, image_id):
# """Return the path of the image."""
# info = self.image_info[image_id]
# if info["source"] == "building":
# return info["path"]
# else:
# super(self.__class__).image_reference(self, image_id)
# Training dataset
dataset_train = BuildingDataset()
dataset_train.load_buildings(DATASET_DIR, "train")
dataset_train.prepare()
# Validation dataset
dataset_val = BuildingDataset()
dataset_val.load_buildings(DATASET_DIR, "val")
dataset_val.prepare()
# Create model in training mode
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=MODEL_DIR)
# Which weights to start with
init_with = "last"
if init_with == "imagenet":
model.load_weights(model.get_imagenet_weights(), by_name=True)
elif init_with == "coco":
# Load weights trained on MS COCO, but skip layers that
# are different due to the different number of classes
# See README for instructions to download the COCO weights
model.load_weights(COCO_MODEL_PATH, by_name=True,
exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
# Load the last model you trained and continue training
model.load_weights(model.find_last(), by_name=True)
# Train the head branches
# Passing layers="heads" freezes all layers except the head
# layers. You can also pass a regular expression to select
# which layers to train by name pattern.
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=1,
layers='heads')
# Fine tune all layers
# Passing layers="all" trains all layers. You can also
# pass a regular expression to select which layers to
# train by name pattern.
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE / 10,
epochs=4, # used to be 2
layers="all")
|
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|
import torch
import os.path as osp
import os
from torch.utils.data import Dataset
## This claas loads the feature vector for the videos and the correspoding label.
import numpy as np
from torch.autograd import Variable
import pdb
import csv
class UCF101(Dataset):
def __init__(self, dataset_name, opts):
self._ucf_dir = osp.join(opts.ucf_dir, "{}_features".format(dataset_name))
self._ignore_names = [".", ".."]
self._feature_size = opts.feature_size
self._file_names = []
self._labels = []
self.class_labels(dataset_name, opts.labels_dir)
self._labels = []
for file in os.listdir(self._ucf_dir):
if file not in self._ignore_names:
self._file_names.append(file)
video_index = self.video2index[file]
self._labels.append(self.video_labels[video_index])
self._labels = np.stack(self._labels)
self._num_classes = opts.num_classes
self._combine_startegy = opts.combine_strategy
self._segments = opts.segments
self._labels = torch.from_numpy(self._labels).float()
# self._labels = torch.Tensor(len(self._file_names), self._num_classes).float().zero_()
def __len__(self):
return len(self._file_names)
def __getitem__(self, item):
## returns the feature vector for the video
flow_features, rgb_features, numInputs = self.forward_video(item)
label = self.forward_label(item)
data = dict()
data['flow'] = flow_features
data['rgb'] = rgb_features
data['label'] = label
data['numInputs'] = numInputs
return data
def class_labels(self, name, labels_dir):
##
class2index_file = osp.join(labels_dir, 'class_dict.csv')
video2index_file = osp.join(labels_dir, 'video_indices_{}.csv'.format(name))
video2labels_file = osp.join(labels_dir, 'class_labels_{}.npy'.format(name))
self.class2index = dict()
self.video2index = dict()
self.video_labels = None
with open(class2index_file, 'r') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
self.class2index[row[0]] = int(row[1])
with open(video2index_file, 'r') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
self.video2index[row[0]] = int(row[1])
self.video_labels = np.load(video2labels_file)
def forward_label(self, index):
return Variable(self._labels[index]).cuda()
def forward_video(self, index):
filename = self._file_names[index]
flow_file = osp.join(self._ucf_dir, filename, 'features_flow.npy')
rgb_file = osp.join(self._ucf_dir, filename, 'features_rgb.npy')
flow_features = np.load(open(flow_file, 'r'))
rgb_features = np.load(open(rgb_file, 'r'))
flow_segments = np.zeros((self._segments, self._feature_size), dtype=np.float32)
rgb_segments = np.zeros((self._segments, self._feature_size),
dtype=np.float32)
frames = flow_segments.shape[0]
segment_len = flow_features.shape[0] // self._segments + 1
total_segments = flow_features.shape[0]
numInputs = np.expand_dims(np.array([flow_features.shape[0]]), axis=0)
if self._combine_startegy == 'uniform':
for i in range(self._segments):
start = (i * segment_len)
end = (i + 1) * segment_len
seq = np.arange(start, end)
flow_segments[i, :] = np.mean(np.take(flow_features, seq, axis=0, mode='wrap'), axis=0)
rgb_segments[i, :] = np.mean(np.take(rgb_features, seq, axis=0, mode='wrap'), axis=0)
if self._combine_startegy == 'strat1':
offset = np.random.choice(segment_len, 1)
for i in range(self._segments):
start = (i * segment_len) + offset
end = (i + 1) * segment_len + offset
seq = np.arange(start, end)
flow_segments[i, :] = np.mean(
np.take(flow_features, seq, axis=0, mode='wrap'),
axis=0)
rgb_segments[i, :] = np.mean(
np.take(rgb_features, seq, axis=0, mode='wrap'),
axis=0)
if self._combine_startegy == 'strat2':
for i in range(self._segments):
start = (i * segment_len)
end = (i + 1) * segment_len
if segment_len > 1:
seq = np.random.choice(segment_len, int(segment_len * 0.8)) + start
else:
seq = np.arange(start, end)
flow_segments[i, :] = np.mean(
np.take(flow_features, seq, axis=0, mode='wrap'),
axis=0)
rgb_segments[i, :] = np.mean(
np.take(rgb_features, seq, axis=0, mode='wrap'),
axis=0)
if self._combine_startegy == 'strat3':
for i in range(self._segments):
sample_range = total_segments
while sample_range < self._segments+1:
sample_range = sample_range + total_segments
sampledN = np.round(np.linspace(0, sample_range, self._segments+1)).astype(np.int32)
#import pdb;pdb.set_trace()
differences = sampledN[1:] - sampledN[0:-1]
randoms = np.random.rand(self._segments)
K = sampledN[0:-1] + np.round(randoms*differences).astype(np.int)
K = np.mod(K, np.ones(K.shape)*total_segments).astype(np.int)
flow_segments = flow_features[K, :]
rgb_segments = rgb_features[K, :]
if self._combine_startegy == 'strat3_test':
for i in range(self._segments):
sample_range = total_segments
if sample_range < self._segments:
sample_range = 400
#import pdb;pdb.set_trace()
sampledN = np.round(np.linspace(0, sample_range, self._segments+1)).astype(np.int32)
K = sampledN[0:-1] #+ np.round(randoms*differences).astype(np.int)
K = np.mod(K, np.ones(K.shape)*total_segments).astype(np.int)
flow_segments = flow_features[K, :]
rgb_segments = rgb_features[K, :]
## rgb_feautes are of the T depending on the length of the video.
# Each segment has 1024 dimensional feature.
flow_segments = Variable(torch.from_numpy(flow_segments).cuda())
rgb_segments = Variable(torch.from_numpy(rgb_segments).cuda())
numInputs = Variable(torch.from_numpy(numInputs))
return flow_segments, rgb_segments, numInputs
class UCF101Temporal(Dataset):
def __init__(self, dataset_name, video_names, opts):
self._ucf_dir = opts.ucf_dir
self._video_names = video_names
self._feature_size = opts.feature_size
self._file_names = []
self._labels = []
self.class_labels(dataset_name, opts.labels_dir)
self._labels = []
self._video2segment_label = dict()
for file in os.listdir(self._ucf_dir):
if file in self._video_names:
self._file_names.append(file)
video_index = self.video2index[file]
self._labels.append(self.video_labels[video_index])
with open(os.path.join(opts.labels_dir, 'time_stamps.txt')) as f:
for line in f:
splits = line.strip().split(';')
s = []
for p in splits[1:]:
s.append((int(float(p.split(',')[0])), int(float(p.split(',')[1])), int(p.split(',')[2])))
self._video2segment_label[splits[0]] = s
self._segment_positions_and_labels = [] # (start, end, label)
for f in self._file_names:
self._segment_positions_and_labels.append(self._video2segment_label[f])
self._labels = np.stack(self._labels)
self._num_classes = opts.num_classes
self._combine_startegy = opts.combine_strategy
self._segments = opts.segments
self._labels = torch.from_numpy(self._labels).float()
def __len__(self):
return len(self._file_names)
def __getitem__(self, item):
## returns the feature vector for the video
flow_features, rgb_features, labels, filename = self.forward_video_as_segments(item)
data = dict()
data['flow'] = flow_features
data['rgb'] = rgb_features
data['label'] = labels
data['video_name'] = filename
return data
def forward_video_as_segments(self, index):
filename = self._file_names[index]
flow_file = osp.join(self._ucf_dir, filename, 'features_flow.npy')
rgb_file = osp.join(self._ucf_dir, filename, 'features_rgb.npy')
flow_features = np.load(open(flow_file, 'r'))
rgb_features = np.load(open(rgb_file, 'r'))
flow_segments = []
rgb_segments = []
labels = []
segments = self._segment_positions_and_labels[index]
for s in segments:
if s[0] < rgb_features.shape[0]:
flow_segments.append(torch.from_numpy(np.mean(flow_features[s[0]:max(s[1], s[1] + 1), :], axis=0)))
rgb_segments.append(
torch.from_numpy(np.mean(rgb_features[s[0]:max(s[1], s[1] + 1), :], axis=0)))
labels.append(torch.Tensor([s[2]]))
return (flow_segments, rgb_segments, labels, filename)
def class_labels(self, name, labels_dir):
class2index_file = osp.join(labels_dir, 'class_dict.csv')
video2index_file = osp.join(labels_dir, 'video_indices_{}.csv'.format(name))
video2labels_file = osp.join(labels_dir, 'class_labels_{}.npy'.format(name))
self.class2index = dict()
self.video2index = dict()
self.video_labels = None
with open(class2index_file, 'r') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
self.class2index[row[0]] = int(row[1])
with open(video2index_file, 'r') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
self.video2index[row[0]] = int(row[1])
self.video_labels = np.load(video2labels_file)
def forward_label(self, index):
return Variable(self._labels[index]).cuda()
def split(data_dir):
all_files = []
for file in os.listdir(data_dir):
all_files.append(file)
return all_files
|
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|
# Plots a chirp signal, it's discrete fourier transform, and it's spectrogram.
import numpy as np
from scipy.signal import chirp
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
time = np.linspace(0.0, 0.01, 2000)
chirp = chirp(time, f0=65.0e3, f1=35.0e3, t1=0.01, method='linear')
samples = len(chirp)
frequencies = np.fft.fftfreq(2000, d = 1 / 2000.0e3)
chirp_transform = np.fft.fft(chirp)
def sum_of_squares(function):
return np.sum(function ** 2)
chirp_sum = sum_of_squares(chirp)
fourier_sum = sum_of_squares(np.abs(chirp_transform)) / samples
print(chirp_sum)
print(fourier_sum)
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_subplot(3, 1, 1)
ax2 = fig.add_subplot(3, 1, 2)
ax3 = fig.add_subplot(3, 1, 3)
ax1.plot(time, chirp)
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Amplitude')
ax2.plot(frequencies / 10000, np.abs(chirp_transform) / samples)
ax2.set_xlabel('Frequency (khz)')
ax2.set_ylabel('Power')
ax3.specgram(chirp, NFFT=200, noverlap=0, Fs = 200.0e3,
window=mlab.window_none)
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('Frequency (hz)')
plt.show()
|
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|
import numpy as np
import contextlib
from collections import deque
from spirl.utils.general_utils import listdict2dictlist, AttrDict, ParamDict, obj2np
from spirl.modules.variational_inference import MultivariateGaussian
from spirl.rl.utils.reward_fcns import sparse_threshold
class Sampler:
"""Collects rollouts from the environment using the given agent."""
def __init__(self, config, env, agent, logger, max_episode_len):
self._hp = self._default_hparams().overwrite(config)
self._env = env
self._agent = agent
self._logger = logger
self._max_episode_len = max_episode_len
self._obs = None
self._episode_step, self._episode_reward = 0, 0
def _default_hparams(self):
return ParamDict({})
def init(self, is_train):
"""Starts a new rollout. Render indicates whether output should contain image."""
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
self._episode_reset()
def sample_action(self, obs):
return self._agent.act(obs)
def sample_batch(self, batch_size, is_train=True, global_step=None):
"""Samples an experience batch of the required size."""
experience_batch = []
step = 0
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while step < batch_size:
# perform one rollout step
agent_output = self.sample_action(self._obs)
if agent_output.action is None:
self._episode_reset(global_step)
continue
agent_output = self._postprocess_agent_output(agent_output)
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
experience_batch.append(AttrDict(
observation=self._obs,
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs,
))
# update stored observation
self._obs = obs
step += 1; self._episode_step += 1; self._episode_reward += reward
# reset if episode ends
if done or self._episode_step >= self._max_episode_len:
if not done: # force done to be True for timeout
experience_batch[-1].done = True
self._episode_reset(global_step)
return listdict2dictlist(experience_batch), step
def sample_episode(self, is_train, render=False):
"""Samples one episode from the environment."""
self.init(is_train)
episode, done = [], False
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while not done and self._episode_step < self._max_episode_len:
# perform one rollout step
agent_output = self.sample_action(self._obs)
if agent_output.action is None:
break
agent_output = self._postprocess_agent_output(agent_output)
if render:
render_obs = self._env.render()
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
episode.append(AttrDict(
observation=self._obs,
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs,
info=obj2np(info),
))
if render:
episode[-1].update(AttrDict(image=render_obs))
# update stored observation
self._obs = obs
self._episode_step += 1
episode[-1].done = True # make sure episode is marked as done at final time step
return listdict2dictlist(episode)
def get_episode_info(self):
episode_info = AttrDict(episode_reward=self._episode_reward,
episode_length=self._episode_step,)
if hasattr(self._env, "get_episode_info"):
episode_info.update(self._env.get_episode_info())
return episode_info
def _episode_reset(self, global_step=None):
"""Resets sampler at the end of an episode."""
if global_step is not None and self._logger is not None: # logger is none in non-master threads
self._logger.log_scalar_dict(self.get_episode_info(),
prefix='train' if self._agent._is_train else 'val',
step=global_step)
self._episode_step, self._episode_reward = 0, 0.
self._obs = self._postprocess_obs(self._reset_env())
self._agent.reset()
def _reset_env(self):
return self._env.reset()
def _postprocess_obs(self, obs):
"""Optionally post-process observation."""
return obs
def _postprocess_agent_output(self, agent_output):
"""Optionally post-process / store agent output."""
return agent_output
class HierarchicalSampler(Sampler):
"""Collects experience batches by rolling out a hierarchical agent. Aggregates low-level batches into HL batch."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.last_hl_obs, self.last_hl_action = None, None # stores observation when last hl action was taken
self.reward_since_last_hl = 0 # accumulates the reward since the last HL step for HL transition
def sample_batch(self, batch_size, is_train=True, global_step=None, store_ll=True):
"""Samples the required number of high-level transitions. Number of LL transitions can be higher."""
hl_experience_batch, ll_experience_batch = [], []
env_steps, hl_step = 0, 0
with self._env.val_mode() if not is_train else contextlib.suppress():
with self._agent.val_mode() if not is_train else contextlib.suppress():
with self._agent.rollout_mode():
while hl_step < batch_size or len(ll_experience_batch) <= 1:
# perform one rollout step
agent_output = self.sample_action(self._obs)
agent_output = self._postprocess_agent_output(agent_output)
obs, reward, done, info = self._env.step(agent_output.action)
obs = self._postprocess_obs(obs)
# update last step's 'observation_next' with HL action
if store_ll:
if ll_experience_batch:
ll_experience_batch[-1].observation_next = \
self._agent.make_ll_obs(ll_experience_batch[-1].observation_next, agent_output.hl_action)
# store current step in ll_experience_batch
ll_experience_batch.append(AttrDict(
observation=self._agent.make_ll_obs(self._obs, agent_output.hl_action),
reward=reward,
done=done,
action=agent_output.action,
observation_next=obs, # this will get updated in the next step
))
# store HL experience batch if this was HL action or episode is done
if agent_output.is_hl_step or (done or self._episode_step >= self._max_episode_len-1):
if self.last_hl_obs is not None and self.last_hl_action is not None:
hl_experience_batch.append(AttrDict(
observation=self.last_hl_obs,
reward=self.reward_since_last_hl,
done=done,
action=self.last_hl_action,
observation_next=obs,
))
hl_step += 1
if hl_step % 1000 == 0:
print("Sample step {}".format(hl_step))
self.last_hl_obs = self._obs
self.last_hl_action = agent_output.hl_action
self.reward_since_last_hl = 0
# update stored observation
self._obs = obs
env_steps += 1; self._episode_step += 1; self._episode_reward += reward
self.reward_since_last_hl += reward
# reset if episode ends
if done or self._episode_step >= self._max_episode_len:
if not done: # force done to be True for timeout
ll_experience_batch[-1].done = True
if hl_experience_batch: # can potentially be empty
hl_experience_batch[-1].done = True
self._episode_reset(global_step)
return AttrDict(
hl_batch=listdict2dictlist(hl_experience_batch),
ll_batch=listdict2dictlist(ll_experience_batch[:-1]), # last element does not have updated obs_next!
), env_steps
def _episode_reset(self, global_step=None):
super()._episode_reset(global_step)
self.last_hl_obs, self.last_hl_action = None, None
self.reward_since_last_hl = 0
class ImageAugmentedSampler(Sampler):
"""Appends image rendering to raw observation."""
def _postprocess_obs(self, obs):
img = self._env.render().transpose(2, 0, 1) * 2. - 1.0
return np.concatenate((obs, img.flatten()))
class MultiImageAugmentedSampler(Sampler):
"""Appends multiple past images to current observation."""
def _episode_reset(self, global_step=None):
self._past_frames = deque(maxlen=self._hp.n_frames) # build ring-buffer of past images
super()._episode_reset(global_step)
def _postprocess_obs(self, obs):
img = self._env.render().transpose(2, 0, 1) * 2. - 1.0
if not self._past_frames: # initialize past frames with N copies of current frame
[self._past_frames.append(img) for _ in range(self._hp.n_frames - 1)]
self._past_frames.append(img)
stacked_img = np.concatenate(list(self._past_frames), axis=0)
return np.concatenate((obs, stacked_img.flatten()))
class ACImageAugmentedSampler(ImageAugmentedSampler):
"""Adds no-op renders to make sure agent-centric camera reaches agent."""
def _reset_env(self):
obs = super()._reset_env()
for _ in range(100): # so that camera can "reach" agent
self._env.render(mode='rgb_array')
return obs
class ACMultiImageAugmentedSampler(MultiImageAugmentedSampler, ACImageAugmentedSampler):
def _reset_env(self):
return ACImageAugmentedSampler._reset_env(self)
class ImageAugmentedHierarchicalSampler(HierarchicalSampler, ImageAugmentedSampler):
def _postprocess_obs(self, *args, **kwargs):
return ImageAugmentedSampler._postprocess_obs(self, *args, **kwargs)
class MultiImageAugmentedHierarchicalSampler(HierarchicalSampler, MultiImageAugmentedSampler):
def _postprocess_obs(self, *args, **kwargs):
return MultiImageAugmentedSampler._postprocess_obs(self, *args, **kwargs)
def _episode_reset(self, *args, **kwargs):
return MultiImageAugmentedSampler._episode_reset(self, *args, **kwargs)
class ACImageAugmentedHierarchicalSampler(ImageAugmentedHierarchicalSampler, ACImageAugmentedSampler):
def _reset_env(self):
return ACImageAugmentedSampler._reset_env(self)
class ACMultiImageAugmentedHierarchicalSampler(MultiImageAugmentedHierarchicalSampler,
ACImageAugmentedHierarchicalSampler):
def _reset_env(self):
return ACImageAugmentedHierarchicalSampler._reset_env(self)
|
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|
[STATEMENT]
lemma list_encode_eq: "list_encode x = list_encode y \<longleftrightarrow> x = y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (list_encode x = list_encode y) = (x = y)
[PROOF STEP]
by (rule inj_list_encode [THEN inj_eq])
|
{"llama_tokens": 96, "file": null, "length": 1}
|
import pickle
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from sklearn.model_selection import StratifiedKFold
from torch.utils.data import Dataset, DataLoader
from Functions import *
import matplotlib.pyplot as plt
tokens='ACGU().BEHIMSX'
#eterna,'nupack','rnastructure','vienna_2','contrafold_2',
class RNADataset(Dataset):
def __init__(self,seqs,labels,ids,ew,bpp_path,transform=None,training=True):
self.transform=transform
self.seqs=seqs#.transpose(1,0,2,3)
#print(self.data.shape)
self.data=[]
self.labels=labels.astype('float32')
self.bpp_path=bpp_path
self.ids=ids
self.training=training
self.bpps=[]
dm=get_distance_mask(len(seqs[0]))#.reshape(1,bpps.shape[-1],bpps.shape[-1])
# print(dm.shape)
# exit()
for i,id in tqdm(enumerate(self.ids)):
bpps=np.load(os.path.join(self.bpp_path,'train_test_bpps',id+'_bpp.npy'))
dms=np.asarray([dm for i in range(bpps.shape[0])])
bpps=np.concatenate([bpps.reshape(bpps.shape[0],1,bpps.shape[1],bpps.shape[2]),dms],1)
with open(os.path.join(self.bpp_path,'train_test_bpps',id+'_struc.p'),'rb') as f:
structures=pickle.load(f)
with open(os.path.join(self.bpp_path,'train_test_bpps',id+'_loop.p'),'rb') as f:
loops=pickle.load(f)
seq=self.seqs[i]
# print(seq)
# exit()
input=[]
for j in range(bpps.shape[0]):
input_seq=np.asarray([tokens.index(s) for s in seq])
input_structure=np.asarray([tokens.index(s) for s in structures[j]])
input_loop=np.asarray([tokens.index(s) for s in loops[j]])
input.append(np.stack([input_seq,input_structure,input_loop],-1))
input=np.asarray(input).astype('int')
#print(input.shape)
self.data.append(input)
#exit()
#print(np.stack([input_seq,input_structure,input_loop],-1).shape)
#exit()
# plt.subplot(1,4,1)
# for _ in range(4):
# plt.subplot(1,4,_+1)
# plt.imshow(bpps[0,_])
# plt.show()
# exit()
self.bpps.append(np.clip(bpps,0,1).astype('float32'))
self.data=np.asarray(self.data)
self.ew=ew
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
#sample = {'data': self.data[idx], 'labels': self.labels[idx]}
if self.training:
bpp_selection=np.random.randint(self.bpps[idx].shape[0])
#print(self.bpps[idx].shape[0])
sample = {'data': self.data[idx][bpp_selection], 'labels': self.labels[idx], 'bpp': self.bpps[idx][bpp_selection],
'ew': self.ew[idx],'id':self.ids[idx]}
else:
sample = {'data': self.data[idx], 'labels': self.labels[idx], 'bpp': self.bpps[idx],
'ew': self.ew[idx],'id':self.ids[idx]}
if self.transform:
sample=self.transform(sample)
return sample
|
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|
import cv2
import numpy as np
import pyautogui
from pynput.keyboard import Key, Controller
import time
SCREEN_SIZE = (1920, 1200)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
keyboard = Controller()
keyboard.press(Key.up)
keyboard.release(Key.up)
while True:
img = pyautogui.screenshot(region=(815,71, 75,180))
frame1 = np.array(img)
frame1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2RGB)
frame = cv2.cvtColor(frame1, cv2.COLOR_RGB2GRAY)
bias = 35
h = 154
h2 = 135
place1 = frame[h, 35 - bias]
place2 = frame[h, 39 - bias]
place3 = frame[h, 43 - bias]
place4 = frame[h, 47 - bias]
place5 = frame[h, 51 - bias]
place10 = frame[h2, 35 - bias]
place20 = frame[h2, 39 - bias]
place30 = frame[h2, 43 - bias]
place40 = frame[h2, 47 - bias]
place50 = frame[h2, 51 - bias]
lookingat = (int(place1) + int(place2) + int(place3) + int(place4) + int(place5)) / 5
fly = (int(place10) + int(place20) + int(place30) + int(place40) + int(place50)) / 5
if (lookingat > 250):
# print(lookingat)
pass
elif (lookingat < 250):
keyboard.press(Key.up)
time.sleep(0.1)
keyboard.release(Key.up)
if (fly < 250):
if (lookingat > 250):
keyboard.press(Key.down)
time.sleep(0.5)
keyboard.release(Key.down)
if cv2.waitKey(1) == ord("q"):
break
frame1[h, 35 - bias] = np.array([0, 0, 255])
frame1[h, 39 - bias] = np.array([0, 0, 255])
frame1[h, 43 - bias] = np.array([0, 0, 255])
frame1[h, 47 - bias] = np.array([0, 0, 255])
frame1[h, 51 - bias] = np.array([0, 0, 255])
frame1[h2, 35 - bias] = np.array([255, 0, 0])
frame1[h2, 39 - bias] = np.array([255, 0, 0])
frame1[h2, 43 - bias] = np.array([255, 0, 0])
frame1[h2, 47 - bias] = np.array([255, 0, 0])
frame1[h2, 51 - bias] = np.array([255, 0, 0])
cv2.imshow("thing", frame1)
cv2.destroyAllWindows()
|
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|
// Copyright (c) 2014-2017 The Dash Core developers
// Copyright (c) 2017-2018 The NIX Core developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "activeghostnode.h"
#include "darksend.h"
#include "ghostnode-payments.h"
#include "ghostnode-sync.h"
#include "ghostnodeman.h"
#include "netfulfilledman.h"
#include "spork.h"
#include "util.h"
#include "netmessagemaker.h"
#include <boost/lexical_cast.hpp>
/** Object for who's going to get paid on which blocks */
CGhostnodePayments mnpayments;
CCriticalSection cs_vecPayees;
CCriticalSection cs_mapGhostnodeBlocks;
CCriticalSection cs_mapGhostnodePaymentVotes;
/**
* IsBlockValueValid
*
* Determine if coinbase outgoing created money is the correct value
*
* Why is this needed?
* - In Dash some blocks are superblocks, which output much higher amounts of coins
* - Otherblocks are 10% lower in outgoing value, so in total, no extra coins are created
* - When non-superblocks are detected, the normal schedule should be maintained
*/
bool IsBlockValueValid(const CBlock &block, int nBlockHeight, CAmount blockReward, std::string &strErrorRet) {
strErrorRet = "";
bool isBlockRewardValueMet = block.vtx[0]->IsCoinStake() ? true : (block.vtx[0]->GetValueOut() <= blockReward);
//LogPrintf("IsBlockValueValid(): value-out=%llf, block-reward=%llf \n", block.vtx[0]->GetValueOut(), blockReward);
//if (fDebug) //LogPrint("block.vtx[0].GetValueOut() %lld <= blockReward %lld\n", block.vtx[0]->GetValueOut(), blockReward);
if (!ghostnodeSync.IsSynced(chainActive.Height())) {
if (!isBlockRewardValueMet) {
strErrorRet = strprintf("coinbase pays too much at height %d (actual=%d vs limit=%d), exceeded block reward, only regular blocks are allowed at this height",
nBlockHeight, block.vtx[0]->GetValueOut(), blockReward);
}
// it MUST be a regular block otherwise
return isBlockRewardValueMet;
}
// we are synced, let's try to check as much data as we can
if (sporkManager.IsSporkActive(SPORK_9_SUPERBLOCKS_ENABLED)) {
} else {
// // should NOT allow superblocks at all, when superblocks are disabled
//LogPrint("gobject", "IsBlockValueValid -- Superblocks are disabled, no superblocks allowed\n");
if (!isBlockRewardValueMet) {
strErrorRet = strprintf("coinbase pays too much at height %d (actual=%d vs limit=%d), exceeded block reward, superblocks are disabled",
nBlockHeight, block.vtx[0]->GetValueOut(), blockReward);
}
}
// it MUST be a regular block
return isBlockRewardValueMet;
}
bool IsBlockPayeeValid(const CTransaction &txNew, int nBlockHeight, CAmount blockReward) {
// we can only check ghostnode payment /
const Consensus::Params &consensusParams = Params().GetConsensus();
if (nBlockHeight < consensusParams.nGhostnodePaymentsStartBlock) {
//there is no budget data to use to check anything, let's just accept the longest chain
//if (fDebug) //LogPrint("IsBlockPayeeValid -- ghostnode isn't start\n");
return true;
}
if (!ghostnodeSync.IsSynced(chainActive.Height())) {
//there is no budget data to use to check anything, let's just accept the longest chain
//if (fDebug) //LogPrint("IsBlockPayeeValid -- WARNING: Client not synced, skipping block payee checks\n");
return true;
}
//check for ghostnode payee
if (mnpayments.IsTransactionValid(txNew, nBlockHeight)) {
//LogPrint("mnpayments", "IsBlockPayeeValid -- Valid ghostnode payment at height %d: %s", nBlockHeight, txNew.ToString());
return true;
} else {
if(sporkManager.IsSporkActive(SPORK_8_GHOSTNODE_PAYMENT_ENFORCEMENT)){
return false;
} else {
//LogPrint("GhostNode payment enforcement is disabled, accepting block\n");
return true;
}
}
}
void FillBlockPayments(CMutableTransaction &txNew, int nBlockHeight, CAmount ghostnodePayment, CTxOut &txoutGhostnodeRet, std::vector <CTxOut> &voutSuperblockRet) {
// FILL BLOCK PAYEE WITH GHOSTNODE PAYMENT OTHERWISE
mnpayments.FillBlockPayee(txNew, nBlockHeight, ghostnodePayment, txoutGhostnodeRet);
//LogPrint("mnpayments", "FillBlockPayments -- nBlockHeight %d ghostnodePayment %lld txoutGhostnodeRet %s txNew %s",
//nBlockHeight, ghostnodePayment, txoutGhostnodeRet.ToString(), txNew.ToString());
}
std::string GetRequiredPaymentsString(int nBlockHeight) {
// IF WE HAVE A ACTIVATED TRIGGER FOR THIS HEIGHT - IT IS A SUPERBLOCK, GET THE REQUIRED PAYEES
// if(CSuperblockManager::IsSuperblockTriggered(nBlockHeight)) {
// return CSuperblockManager::GetRequiredPaymentsString(nBlockHeight);
// }
// OTHERWISE, PAY GHOSTNODE
return mnpayments.GetRequiredPaymentsString(nBlockHeight);
}
void CGhostnodePayments::Clear() {
LOCK2(cs_mapGhostnodeBlocks, cs_mapGhostnodePaymentVotes);
mapGhostnodeBlocks.clear();
mapGhostnodePaymentVotes.clear();
}
bool CGhostnodePayments::CanVote(COutPoint outGhostnode, int nBlockHeight) {
LOCK(cs_mapGhostnodePaymentVotes);
if (mapGhostnodesLastVote.count(outGhostnode) && mapGhostnodesLastVote[outGhostnode] == nBlockHeight) {
return false;
}
//record this ghostnode voted
mapGhostnodesLastVote[outGhostnode] = nBlockHeight;
return true;
}
std::string CGhostnodePayee::ToString() const {
CTxDestination address1;
ExtractDestination(scriptPubKey, address1);
CBitcoinAddress address2(address1);
std::string str;
str += "(address: ";
str += address2.ToString();
str += ")\n";
return str;
}
/**
* FillBlockPayee
*
* Fill Ghostnode ONLY payment block
*/
void CGhostnodePayments::FillBlockPayee(CMutableTransaction &txNew, int nBlockHeight, CAmount ghostnodePayment, CTxOut &txoutGhostnodeRet) {
// make sure it's not filled yet
txoutGhostnodeRet = CTxOut();
CScript payee;
bool foundMaxVotedPayee = true;
if (!mnpayments.GetBlockPayee(nBlockHeight, payee)) {
// no ghostnode detected...
// //LogPrint("no ghostnode detected...\n");
foundMaxVotedPayee = false;
int nCount = 0;
CGhostnode *winningNode = mnodeman.GetNextGhostnodeInQueueForPayment(nBlockHeight, true, nCount);
if (!winningNode) {
// ...and we can't calculate it on our own
//LogPrint("CGhostnodePayments::FillBlockPayee -- Failed to detect ghostnode to pay\n");
return;
}
// fill payee with locally calculated winner and hope for the best
payee = GetScriptForDestination(winningNode->pubKeyCollateralAddress.GetID());
//LogPrint("payee=%s\n", winningNode->ToString());
}
txoutGhostnodeRet = CTxOut(ghostnodePayment, payee);
txNew.vout.push_back(txoutGhostnodeRet);
CTxDestination address1;
ExtractDestination(payee, address1);
CBitcoinAddress address2(address1);
if (foundMaxVotedPayee) {
//LogPrint("CGhostnodePayments::FillBlockPayee::foundMaxVotedPayee -- Ghostnode payment %lld to %s\n", ghostnodePayment, address2.ToString());
} else {
//LogPrint("CGhostnodePayments::FillBlockPayee -- Ghostnode payment %lld to %s\n", ghostnodePayment, address2.ToString());
}
}
int CGhostnodePayments::GetMinGhostnodePaymentsProto() {
return sporkManager.IsSporkActive(SPORK_10_GHOSTNODE_PAY_UPDATED_NODES)
? MIN_GHOSTNODE_PAYMENT_PROTO_VERSION_2
: MIN_GHOSTNODE_PAYMENT_PROTO_VERSION_1;
}
void CGhostnodePayments::ProcessMessage(CNode *pfrom, std::string &strCommand, CDataStream &vRecv) {
//LogPrintf("CGhostnodePayments::ProcessMessage strCommand=%s\n", strCommand);
// Ignore any payments messages until ghostnode list is synced
if (!ghostnodeSync.IsGhostnodeListSynced()) return;
if (fLiteMode) return; // disable all Dash specific functionality
if (strCommand == NetMsgType::GHOSTNODEPAYMENTSYNC) { //Ghostnode Payments Request Sync
// Ignore such requests until we are fully synced.
// We could start processing this after ghostnode list is synced
// but this is a heavy one so it's better to finish sync first.
if (!ghostnodeSync.IsSynced(chainActive.Height())) return;
int nCountNeeded;
vRecv >> nCountNeeded;
if (netfulfilledman.HasFulfilledRequest(pfrom->addr, NetMsgType::GHOSTNODEPAYMENTSYNC)) {
// Asking for the payments list multiple times in a short period of time is no good
//LogPrintf("GHOSTNODEPAYMENTSYNC -- peer already asked me for the list\n");
Misbehaving(pfrom->GetId(), 20);
return;
}
netfulfilledman.AddFulfilledRequest(pfrom->addr, NetMsgType::GHOSTNODEPAYMENTSYNC);
Sync(pfrom);
//LogPrintf("mnpayments GHOSTNODEPAYMENTSYNC -- Sent Ghostnode payment votes to peer \n");
} else if (strCommand == NetMsgType::GHOSTNODEPAYMENTVOTE) { // Ghostnode Payments Vote for the Winner
CGhostnodePaymentVote vote;
vRecv >> vote;
if (pfrom->nVersion < GetMinGhostnodePaymentsProto()) return;
if (!pCurrentBlockIndex) return;
uint256 nHash = vote.GetHash();
pfrom->setAskFor.erase(nHash);
{
LOCK(cs_mapGhostnodePaymentVotes);
if (mapGhostnodePaymentVotes.count(nHash)) {
//LogPrintf("mnpayments GHOSTNODEPAYMENTVOTE -- nHeight=%d seen\n", pCurrentBlockIndex->nHeight);
return;
}
// Avoid processing same vote multiple times
mapGhostnodePaymentVotes[nHash] = vote;
// but first mark vote as non-verified,
// AddPaymentVote() below should take care of it if vote is actually ok
mapGhostnodePaymentVotes[nHash].MarkAsNotVerified();
}
int nFirstBlock = pCurrentBlockIndex->nHeight - GetStorageLimit();
if (vote.nBlockHeight < nFirstBlock || vote.nBlockHeight > pCurrentBlockIndex->nHeight + 20) {
//LogPrintf("mnpaymentsGHOSTNODEPAYMENTVOTE -- vote out of range: nFirstBlock=%d, nBlockHeight=%d, nHeight=%d\n", nFirstBlock, vote.nBlockHeight, pCurrentBlockIndex->nHeight);
return;
}
std::string strError = "";
if (!vote.IsValid(pfrom, pCurrentBlockIndex->nHeight, strError)) {
//LogPrintf("mnpayments GHOSTNODEPAYMENTVOTE -- invalid message, error: %s\n", strError);
return;
}
if (!CanVote(vote.vinGhostnode.prevout, vote.nBlockHeight)) {
//LogPrintf("GHOSTNODEPAYMENTVOTE -- ghostnode already voted, ghostnode\n");
return;
}
ghostnode_info_t mnInfo = mnodeman.GetGhostnodeInfo(vote.vinGhostnode);
if (!mnInfo.fInfoValid) {
// mn was not found, so we can't check vote, some info is probably missing
//LogPrintf("GHOSTNODEPAYMENTVOTE -- ghostnode is missing \n");
mnodeman.AskForMN(pfrom, vote.vinGhostnode);
return;
}
int nDos = 0;
if (!vote.CheckSignature(mnInfo.pubKeyGhostnode, pCurrentBlockIndex->nHeight, nDos)) {
if (nDos) {
//LogPrintf("GHOSTNODEPAYMENTVOTE -- ERROR: invalid signature\n");
Misbehaving(pfrom->GetId(), nDos);
} else {
// only warn about anything non-critical (i.e. nDos == 0) in debug mode
//LogPrintf("mnpayments GHOSTNODEPAYMENTVOTE -- WARNING: invalid signature\n");
}
// Either our info or vote info could be outdated.
// In case our info is outdated, ask for an update,
mnodeman.AskForMN(pfrom, vote.vinGhostnode);
// but there is nothing we can do if vote info itself is outdated
// (i.e. it was signed by a mn which changed its key),
// so just quit here.
return;
}
CTxDestination address1;
ExtractDestination(vote.payee, address1);
CBitcoinAddress address2(address1);
//LogPrintf("mnpayments GHOSTNODEPAYMENTVOTE -- vote: address=%s, nBlockHeight=%d, nHeight=%d, prevout=%s\n", address2.ToString(), vote.nBlockHeight, pCurrentBlockIndex->nHeight, vote.vinGhostnode.prevout.ToStringShort());
if (AddPaymentVote(vote)) {
vote.Relay();
ghostnodeSync.AddedPaymentVote();
}
}
}
bool CGhostnodePaymentVote::Sign() {
std::string strError;
std::string strMessage = vinGhostnode.prevout.ToStringShort() +
boost::lexical_cast<std::string>(nBlockHeight) +
ScriptToAsmStr(payee);
if (!darkSendSigner.SignMessage(strMessage, vchSig, activeGhostnode.keyGhostnode)) {
//LogPrint("CGhostnodePaymentVote::Sign -- SignMessage() failed\n");
return false;
}
if (!darkSendSigner.VerifyMessage(activeGhostnode.pubKeyGhostnode, vchSig, strMessage, strError)) {
//LogPrint("CGhostnodePaymentVote::Sign -- VerifyMessage() failed, error: %s\n", strError);
return false;
}
return true;
}
bool CGhostnodePayments::GetBlockPayee(int nBlockHeight, CScript &payee) {
if (mapGhostnodeBlocks.count(nBlockHeight)) {
return mapGhostnodeBlocks[nBlockHeight].GetBestPayee(payee);
}
return false;
}
// Is this ghostnode scheduled to get paid soon?
// -- Only look ahead up to 8 blocks to allow for propagation of the latest 2 blocks of votes
bool CGhostnodePayments::IsScheduled(CGhostnode &mn, int nNotBlockHeight) {
LOCK(cs_mapGhostnodeBlocks);
if (!pCurrentBlockIndex) return false;
CScript mnpayee;
mnpayee = GetScriptForDestination(mn.pubKeyCollateralAddress.GetID());
CScript payee;
for (int64_t h = pCurrentBlockIndex->nHeight; h <= pCurrentBlockIndex->nHeight + 8; h++) {
if (h == nNotBlockHeight) continue;
if (mapGhostnodeBlocks.count(h) && mapGhostnodeBlocks[h].GetBestPayee(payee) && mnpayee == payee) {
return true;
}
}
return false;
}
bool CGhostnodePayments::AddPaymentVote(const CGhostnodePaymentVote &vote) {
//LogPrintf("\nghostnode-payments CGhostnodePayments::AddPaymentVote\n");
uint256 blockHash = uint256();
if (!GetBlockHash(blockHash, vote.nBlockHeight - 100)){
LogPrintf("\nghostnode-payments CGhostnodePayments::Invalid Hash\n");
return false;
}
if (HasVerifiedPaymentVote(vote.GetHash())) return false;
LOCK2(cs_mapGhostnodeBlocks, cs_mapGhostnodePaymentVotes);
mapGhostnodePaymentVotes[vote.GetHash()] = vote;
if (!mapGhostnodeBlocks.count(vote.nBlockHeight)) {
CGhostnodeBlockPayees blockPayees(vote.nBlockHeight);
mapGhostnodeBlocks[vote.nBlockHeight] = blockPayees;
}
mapGhostnodeBlocks[vote.nBlockHeight].AddPayee(vote);
return true;
}
bool CGhostnodePayments::HasVerifiedPaymentVote(uint256 hashIn) {
LOCK(cs_mapGhostnodePaymentVotes);
std::map<uint256, CGhostnodePaymentVote>::iterator it = mapGhostnodePaymentVotes.find(hashIn);
return it != mapGhostnodePaymentVotes.end() && it->second.IsVerified();
}
void CGhostnodeBlockPayees::AddPayee(const CGhostnodePaymentVote &vote) {
LOCK(cs_vecPayees);
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
if (payee.GetPayee() == vote.payee) {
payee.AddVoteHash(vote.GetHash());
return;
}
}
CGhostnodePayee payeeNew(vote.payee, vote.GetHash());
vecPayees.push_back(payeeNew);
}
bool CGhostnodeBlockPayees::GetBestPayee(CScript &payeeRet) {
LOCK(cs_vecPayees);
//LogPrint("mnpayments", "CGhostnodeBlockPayees::GetBestPayee, vecPayees.size()=%s\n", vecPayees.size());
if (!vecPayees.size()) {
//LogPrint("mnpayments", "CGhostnodeBlockPayees::GetBestPayee -- ERROR: couldn't find any payee\n");
return false;
}
int nVotes = -1;
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
if (payee.GetVoteCount() > nVotes) {
payeeRet = payee.GetPayee();
nVotes = payee.GetVoteCount();
}
}
return (nVotes > -1);
}
bool CGhostnodeBlockPayees::HasPayeeWithVotes(CScript payeeIn, int nVotesReq) {
LOCK(cs_vecPayees);
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
if (payee.GetVoteCount() >= nVotesReq && payee.GetPayee() == payeeIn) {
return true;
}
}
// //LogPrint("mnpayments", "CGhostnodeBlockPayees::HasPayeeWithVotes -- ERROR: couldn't find any payee with %d+ votes\n", nVotesReq);
return false;
}
bool CGhostnodeBlockPayees::IsTransactionValid(const CTransaction &txNew) {
LOCK(cs_vecPayees);
int nMaxSignatures = 0;
std::string strPayeesPossible = "";
CAmount nGhostnodePayment = GetGhostnodePayment(nBlockHeight, txNew.GetValueOut());
//require at least MNPAYMENTS_SIGNATURES_REQUIRED signatures
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
if (payee.GetVoteCount() >= nMaxSignatures) {
nMaxSignatures = payee.GetVoteCount();
}
}
// if we don't have at least MNPAYMENTS_SIGNATURES_REQUIRED signatures on a payee, approve whichever is the longest chain
if (nMaxSignatures < MNPAYMENTS_SIGNATURES_REQUIRED) return true;
bool hasValidPayee = false;
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
if (payee.GetVoteCount() >= MNPAYMENTS_SIGNATURES_REQUIRED) {
hasValidPayee = true;
BOOST_FOREACH(CTxOut txout, txNew.vout) {
if (payee.GetPayee() == txout.scriptPubKey && nGhostnodePayment == txout.nValue) {
//LogPrint("mnpayments", "CGhostnodeBlockPayees::IsTransactionValid -- Found required payment\n");
return true;
}
}
CTxDestination address1;
ExtractDestination(payee.GetPayee(), address1);
CBitcoinAddress address2(address1);
if (strPayeesPossible == "") {
strPayeesPossible = address2.ToString();
} else {
strPayeesPossible += "," + address2.ToString();
}
}
}
if (!hasValidPayee) return true;
//LogPrint("CGhostnodeBlockPayees::IsTransactionValid -- ERROR: Missing required payment, possible payees: '%s', amount: %f NIX\n", strPayeesPossible, (float) nGhostnodePayment / COIN);
return false;
}
std::string CGhostnodeBlockPayees::GetRequiredPaymentsString() {
LOCK(cs_vecPayees);
std::string strRequiredPayments = "Unknown";
BOOST_FOREACH(CGhostnodePayee & payee, vecPayees)
{
CTxDestination address1;
ExtractDestination(payee.GetPayee(), address1);
CBitcoinAddress address2(address1);
if (strRequiredPayments != "Unknown") {
strRequiredPayments += ", " + address2.ToString() + ":" + boost::lexical_cast<std::string>(payee.GetVoteCount());
} else {
strRequiredPayments = address2.ToString() + ":" + boost::lexical_cast<std::string>(payee.GetVoteCount());
}
}
return strRequiredPayments;
}
std::string CGhostnodePayments::GetRequiredPaymentsString(int nBlockHeight) {
LOCK(cs_mapGhostnodeBlocks);
if (mapGhostnodeBlocks.count(nBlockHeight)) {
return mapGhostnodeBlocks[nBlockHeight].GetRequiredPaymentsString();
}
return "Unknown";
}
bool CGhostnodePayments::IsTransactionValid(const CTransaction &txNew, int nBlockHeight) {
LOCK(cs_mapGhostnodeBlocks);
if (mapGhostnodeBlocks.count(nBlockHeight)) {
return mapGhostnodeBlocks[nBlockHeight].IsTransactionValid(txNew);
}
return true;
}
void CGhostnodePayments::CheckAndRemove() {
if (!pCurrentBlockIndex) return;
LOCK2(cs_mapGhostnodeBlocks, cs_mapGhostnodePaymentVotes);
int nLimit = GetStorageLimit();
std::map<uint256, CGhostnodePaymentVote>::iterator it = mapGhostnodePaymentVotes.begin();
while (it != mapGhostnodePaymentVotes.end()) {
CGhostnodePaymentVote vote = (*it).second;
if (pCurrentBlockIndex->nHeight - vote.nBlockHeight > nLimit) {
//LogPrint("mnpayments", "CGhostnodePayments::CheckAndRemove -- Removing old Ghostnode payment: nBlockHeight=%d\n", vote.nBlockHeight);
mapGhostnodePaymentVotes.erase(it++);
mapGhostnodeBlocks.erase(vote.nBlockHeight);
} else {
++it;
}
}
//LogPrint("CGhostnodePayments::CheckAndRemove -- %s\n", ToString());
}
bool CGhostnodePaymentVote::IsValid(CNode *pnode, int nValidationHeight, std::string &strError) {
CGhostnode *pmn = mnodeman.Find(vinGhostnode);
if (!pmn) {
strError = strprintf("Unknown Ghostnode: prevout=%s", vinGhostnode.prevout.ToStringShort());
// Only ask if we are already synced and still have no idea about that Ghostnode
if (ghostnodeSync.IsGhostnodeListSynced()) {
mnodeman.AskForMN(pnode, vinGhostnode);
}
return false;
}
int nMinRequiredProtocol;
if (nBlockHeight >= nValidationHeight) {
// new votes must comply SPORK_10_GHOSTNODE_PAY_UPDATED_NODES rules
nMinRequiredProtocol = mnpayments.GetMinGhostnodePaymentsProto();
} else {
// allow non-updated ghostnodes for old blocks
nMinRequiredProtocol = MIN_GHOSTNODE_PAYMENT_PROTO_VERSION_1;
}
if (pmn->nProtocolVersion < nMinRequiredProtocol) {
strError = strprintf("Ghostnode protocol is too old: nProtocolVersion=%d, nMinRequiredProtocol=%d", pmn->nProtocolVersion, nMinRequiredProtocol);
return false;
}
// Only ghostnodes should try to check ghostnode rank for old votes - they need to pick the right winner for future blocks.
// Regular clients (miners included) need to verify ghostnode rank for future block votes only.
if (!fGhostNode && nBlockHeight < nValidationHeight) return true;
int nRank = mnodeman.GetGhostnodeRank(vinGhostnode, nBlockHeight - 100, nMinRequiredProtocol, false);
if (nRank == -1) {
//LogPrint("mnpayments", "CGhostnodePaymentVote::IsValid -- Can't calculate rank for ghostnode %s\n",
//vinGhostnode.prevout.ToStringShort());
return false;
}
if (nRank > MNPAYMENTS_SIGNATURES_TOTAL) {
// It's common to have ghostnodes mistakenly think they are in the top 10
// We don't want to print all of these messages in normal mode, debug mode should print though
strError = strprintf("Ghostnode is not in the top %d (%d)", MNPAYMENTS_SIGNATURES_TOTAL, nRank);
// Only ban for new mnw which is out of bounds, for old mnw MN list itself might be way too much off
if (nRank > MNPAYMENTS_SIGNATURES_TOTAL * 2 && nBlockHeight > nValidationHeight) {
strError = strprintf("Ghostnode is not in the top %d (%d)", MNPAYMENTS_SIGNATURES_TOTAL * 2, nRank);
//LogPrint("CGhostnodePaymentVote::IsValid -- Error: %s\n", strError);
Misbehaving(pnode->GetId(), 20);
}
// Still invalid however
return false;
}
return true;
}
bool CGhostnodePayments::ProcessBlock(int nBlockHeight) {
// DETERMINE IF WE SHOULD BE VOTING FOR THE NEXT PAYEE
if (fLiteMode || !fGhostNode) {
return false;
}
// We have little chances to pick the right winner if winners list is out of sync
// but we have no choice, so we'll try. However it doesn't make sense to even try to do so
// if we have not enough data about ghostnodes.
if (!ghostnodeSync.IsGhostnodeListSynced()) {
return false;
}
int nRank = mnodeman.GetGhostnodeRank(activeGhostnode.vin, nBlockHeight - 100, GetMinGhostnodePaymentsProto(), false);
if (nRank == -1) {
LogPrintf("mnpayments CGhostnodePayments::ProcessBlock -- Unknown Ghostnode\n");
return false;
}
if (nRank > MNPAYMENTS_SIGNATURES_TOTAL) {
LogPrintf("mnpayments CGhostnodePayments::ProcessBlock -- Ghostnode not in the top %d (%d)\n", MNPAYMENTS_SIGNATURES_TOTAL, nRank);
return false;
}
// LOCATE THE NEXT GHOSTNODE WHICH SHOULD BE PAID
//LogPrintf("CGhostnodePayments::ProcessBlock -- Start: nBlockHeight=%d, ghostnode=%s\n", nBlockHeight, activeGhostnode.vin.prevout.ToStringShort());
// pay to the oldest MN that still had no payment but its input is old enough and it was active long enough
int nCount = 0;
CGhostnode *pmn = mnodeman.GetNextGhostnodeInQueueForPayment(nBlockHeight, true, nCount);
if (pmn == NULL) {
LogPrintf("CGhostnodePayments::ProcessBlock -- ERROR: Failed to find ghostnode to pay\n");
return false;
}
//LogPrintf("CGhostnodePayments::ProcessBlock -- Ghostnode found by GetNextGhostnodeInQueueForPayment(): %s\n", pmn->vin.prevout.ToStringShort());
CScript payee = GetScriptForDestination(pmn->pubKeyCollateralAddress.GetID());
CGhostnodePaymentVote voteNew(activeGhostnode.vin, nBlockHeight, payee);
CTxDestination address1;
ExtractDestination(payee, address1);
CBitcoinAddress address2(address1);
// SIGN MESSAGE TO NETWORK WITH OUR GHOSTNODE KEYS
//LogPrintf("ProcessBlock -- vote: address=%s, nBlockHeight=%d, nHeight=%d, prevout=%s\n", address2.ToString(), voteNew.nBlockHeight, pCurrentBlockIndex->nHeight, voteNew.vinGhostnode.prevout.ToStringShort());
if (voteNew.Sign()) {
if (AddPaymentVote(voteNew)) {
voteNew.Relay();
return true;
}
}
return false;
}
void CGhostnodePaymentVote::Relay() {
// do not relay until synced
if (!ghostnodeSync.IsWinnersListSynced()) {
//LogPrint("CGhostnodePaymentVote::Relay - ghostnodeSync.IsWinnersListSynced() not sync\n");
return;
}
CInv inv(MSG_GHOSTNODE_PAYMENT_VOTE, GetHash());
g_connman->RelayInv(inv);
}
bool CGhostnodePaymentVote::CheckSignature(const CPubKey &pubKeyGhostnode, int nValidationHeight, int &nDos) {
// do not ban by default
nDos = 0;
std::string strMessage = vinGhostnode.prevout.ToStringShort() +
boost::lexical_cast<std::string>(nBlockHeight) +
ScriptToAsmStr(payee);
std::string strError = "";
if (!darkSendSigner.VerifyMessage(pubKeyGhostnode, vchSig, strMessage, strError)) {
// Only ban for future block vote when we are already synced.
// Otherwise it could be the case when MN which signed this vote is using another key now
// and we have no idea about the old one.
if (ghostnodeSync.IsGhostnodeListSynced() && nBlockHeight > nValidationHeight) {
nDos = 20;
}
return error("CGhostnodePaymentVote::CheckSignature -- Got bad Ghostnode payment signature, ghostnode=%s, error: %s", vinGhostnode.prevout.ToStringShort().c_str(), strError);
}
return true;
}
std::string CGhostnodePaymentVote::ToString() const {
std::ostringstream info;
info << vinGhostnode.prevout.ToStringShort() <<
", " << nBlockHeight <<
", " << ScriptToAsmStr(payee) <<
", " << (int) vchSig.size();
return info.str();
}
// Send only votes for future blocks, node should request every other missing payment block individually
void CGhostnodePayments::Sync(CNode *pnode) {
LOCK(cs_mapGhostnodeBlocks);
if (!pCurrentBlockIndex) return;
int nInvCount = 0;
for (int h = pCurrentBlockIndex->nHeight; h < pCurrentBlockIndex->nHeight + 20; h++) {
if (mapGhostnodeBlocks.count(h)) {
BOOST_FOREACH(CGhostnodePayee & payee, mapGhostnodeBlocks[h].vecPayees)
{
std::vector <uint256> vecVoteHashes = payee.GetVoteHashes();
BOOST_FOREACH(uint256 & hash, vecVoteHashes)
{
if (!HasVerifiedPaymentVote(hash)) continue;
pnode->PushInventory(CInv(MSG_GHOSTNODE_PAYMENT_VOTE, hash));
nInvCount++;
}
}
}
}
//LogPrint("CGhostnodePayments::Sync -- Sent %d votes to peer %d\n", nInvCount, pnode->GetId());
const CNetMsgMaker msgMaker(pnode->GetSendVersion());
g_connman->PushMessage(pnode, msgMaker.Make(NetMsgType::SYNCSTATUSCOUNT, GHOSTNODE_SYNC_MNW, nInvCount));
}
// Request low data/unknown payment blocks in batches directly from some node instead of/after preliminary Sync.
void CGhostnodePayments::RequestLowDataPaymentBlocks(CNode *pnode) {
if (!pCurrentBlockIndex) return;
LOCK2(cs_main, cs_mapGhostnodeBlocks);
std::vector <CInv> vToFetch;
int nLimit = GetStorageLimit();
const CBlockIndex *pindex = pCurrentBlockIndex;
while (pCurrentBlockIndex->nHeight - pindex->nHeight < nLimit) {
if (!mapGhostnodeBlocks.count(pindex->nHeight)) {
// We have no idea about this block height, let's ask
vToFetch.push_back(CInv(MSG_GHOSTNODE_PAYMENT_BLOCK, pindex->GetBlockHash()));
// We should not violate GETDATA rules
if (vToFetch.size() == MAX_INV_SZ) {
//LogPrint("CGhostnodePayments::SyncLowDataPaymentBlocks -- asking peer %d for %d blocks\n", pnode->GetId(), MAX_INV_SZ);
const CNetMsgMaker msgMaker(pnode->GetSendVersion());
g_connman->PushMessage(pnode, msgMaker.Make(NetMsgType::GETDATA, vToFetch));
// Start filling new batch
vToFetch.clear();
}
}
if (!pindex->pprev) break;
pindex = pindex->pprev;
}
std::map<int, CGhostnodeBlockPayees>::iterator it = mapGhostnodeBlocks.begin();
while (it != mapGhostnodeBlocks.end()) {
int nTotalVotes = 0;
bool fFound = false;
BOOST_FOREACH(CGhostnodePayee & payee, it->second.vecPayees)
{
if (payee.GetVoteCount() >= MNPAYMENTS_SIGNATURES_REQUIRED) {
fFound = true;
break;
}
nTotalVotes += payee.GetVoteCount();
}
// A clear winner (MNPAYMENTS_SIGNATURES_REQUIRED+ votes) was found
// or no clear winner was found but there are at least avg number of votes
if (fFound || nTotalVotes >= (MNPAYMENTS_SIGNATURES_TOTAL + MNPAYMENTS_SIGNATURES_REQUIRED) / 2) {
// so just move to the next block
++it;
continue;
}
// DEBUG
// DBG (
// // Let's see why this failed
// BOOST_FOREACH(CGhostnodePayee& payee, it->second.vecPayees) {
// CTxDestination address1;
// ExtractDestination(payee.GetPayee(), address1);
// CBitcoinAddress address2(address1);
// printf("payee %s votes %d\n", address2.ToString().c_str(), payee.GetVoteCount());
// }
// printf("block %d votes total %d\n", it->first, nTotalVotes);
// )
// END DEBUG
// Low data block found, let's try to sync it
uint256 hash;
if (GetBlockHash(hash, it->first)) {
vToFetch.push_back(CInv(MSG_GHOSTNODE_PAYMENT_BLOCK, hash));
}
// We should not violate GETDATA rules
if (vToFetch.size() == MAX_INV_SZ) {
//LogPrint("CGhostnodePayments::SyncLowDataPaymentBlocks -- asking peer %d for %d payment blocks\n", pnode->GetId(), MAX_INV_SZ);
// Start filling new batch
const CNetMsgMaker msgMaker(pnode->GetSendVersion());
g_connman->PushMessage(pnode, msgMaker.Make(NetMsgType::GETDATA, vToFetch));
vToFetch.clear();
}
++it;
}
// Ask for the rest of it
if (!vToFetch.empty()) {
//LogPrint("CGhostnodePayments::SyncLowDataPaymentBlocks -- asking peer %d for %d payment blocks\n", pnode->GetId(), vToFetch.size());
const CNetMsgMaker msgMaker(pnode->GetSendVersion());
g_connman->PushMessage(pnode, msgMaker.Make(NetMsgType::GETDATA, vToFetch));
}
}
std::string CGhostnodePayments::ToString() const {
std::ostringstream info;
info << "Votes: " << (int) mapGhostnodePaymentVotes.size() <<
", Blocks: " << (int) mapGhostnodeBlocks.size();
return info.str();
}
bool CGhostnodePayments::IsEnoughData() {
float nAverageVotes = (MNPAYMENTS_SIGNATURES_TOTAL + MNPAYMENTS_SIGNATURES_REQUIRED) / 2;
int nStorageLimit = GetStorageLimit();
return GetBlockCount() > nStorageLimit && GetVoteCount() > nStorageLimit * nAverageVotes;
}
int CGhostnodePayments::GetStorageLimit() {
return std::max(int(mnodeman.size() * nStorageCoeff), nMinBlocksToStore);
}
void CGhostnodePayments::UpdatedBlockTip(const CBlockIndex *pindex) {
pCurrentBlockIndex = pindex;
//LogPrint("mnpayments", "CGhostnodePayments::UpdatedBlockTip -- pCurrentBlockIndex->nHeight=%d\n", pCurrentBlockIndex->nHeight);
ProcessBlock(pindex->nHeight + 5);
}
|
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// Copyright (c) 2016 Samsung Electronics Co., Ltd All Rights Reserved
// Use of this source code is governed by a apache 2.0 license that can be
// found in the LICENSE file.
#include "common/plugins/plugin_list_parser.h"
#include <boost/algorithm/string/classification.hpp>
#include <boost/algorithm/string/split.hpp>
namespace common_installer {
PluginInfo::PluginInfo(int flag, const std::string& type,
const std::string& name,
const boost::filesystem::path& path)
: flag_(flag),
type_(std::move(type)),
name_(std::move(name)),
path_(std::move(path)) {}
int PluginInfo::flag() const { return flag_; }
const std::string& PluginInfo::type() const { return type_; }
const std::string& PluginInfo::name() const { return name_; }
const boost::filesystem::path& PluginInfo::path() const { return path_; }
// class PluginsListParser
bool PluginsListParser::ValidType(const std::string& type) {
if (type.empty()) {
LOG(ERROR) << "Type is empty (valid function)";
return false;
}
std::regex re_valid(R"((tag|metadata|category))");
return std::regex_search(type, re_valid);
}
bool PluginsListParser::ValidFlag(const std::string& flag) {
if (flag.empty()) {
LOG(ERROR) << "Flag is empty (valid function)";
return false;
}
std::regex re_valid(R"(0x[01248]+)");
return std::regex_match(flag, re_valid);
}
bool PluginsListParser::ValidName(const std::string& name) {
if (name.empty()) {
LOG(ERROR) << "Name is empty (valid function)";
return false;
}
return true;
}
bool PluginsListParser::ValidPath(const std::string& path) {
if (path.empty()) {
LOG(ERROR) << "Path is empty (valid function)";
return false;
}
std::smatch match;
std::regex re_extension(R"((\.so[^/]+)?$)");
if (!std::regex_search(path, match, re_extension)) {
return false;
}
// if no matched group
if (match.size() != 2) {
LOG(ERROR) << "Path not included extension lib file";
return false;
}
return true;
}
std::string PluginsListParser::ExtractRaw(const std::string& data,
const std::regex& re_extract) {
std::smatch match;
std::regex_search(data, match, re_extract);
// 2 mean matched group
if (match.size() != 2) {
LOG(ERROR) << "Could not find data during extracting parameter";
return {};
}
return match[1];
}
std::string PluginsListParser::ExtractFlag(const std::string& flag) {
std::regex re_extract(R"(flag\s*\=\s*\"(.*)\")");
return ExtractRaw(flag, re_extract);
}
std::string PluginsListParser::ExtractName(const std::string& type) {
std::regex re_extract(R"(name\s*\=\s*\"(.*)\")");
return ExtractRaw(type, re_extract);
}
std::string PluginsListParser::ExtractType(const std::string& type) {
std::regex re_extract(R"(type\s*\=\s*\"(.*)\")");
return ExtractRaw(type, re_extract);
}
std::string PluginsListParser::ExtractPath(const std::string& path) {
std::regex re_extract(R"(path\s*\=\s*\"(.*)\")");
return ExtractRaw(path, re_extract);
}
bool PluginsListParser::Parse() {
std::vector<std::string> lines;
if (!ReadLinesFromFile(&lines)) {
LOG(ERROR) << "No read lines from file";
return false;
}
if (!ParsePluginsRawData(lines)) {
LOG(ERROR) << "No parse data from lines";
return false;
}
return true;
}
const PluginsListParser::PluginList& PluginsListParser::PluginInfoList() const {
return plugin_info_list_;
}
bool PluginsListParser::ReadLinesFromFile(std::vector<std::string>* lines) {
std::ifstream plugins_file;
plugins_file.open(path_);
if (!plugins_file.is_open()) {
LOG(ERROR) << "File " << path_ << " no open";
return false;
}
LOG(INFO) << "Plugin list path: " << path_;
std::string line;
while (plugins_file >> line) {
lines->push_back(line);
}
plugins_file.close();
if (lines->empty()) {
LOG(ERROR) << "No data in file " << path_;
return false;
}
return true;
}
bool PluginsListParser::ParsePluginsRawData(
const std::vector<std::string>& lines) {
plugin_info_list_.clear();
std::vector<int> flag_container;
for (const std::string& line : lines) {
std::vector<std::string> parts;
if (!SplitPluginLine(line, &parts)) {
LOG(ERROR) << "Invalid split plugin line";
return false;
}
std::string flag = ExtractFlag(parts.at(Flag));
std::string type = ExtractType(parts.at(Type));
std::string name = ExtractName(parts.at(Name));
std::string path = ExtractPath(parts.at(Path));
if (!ValidFlag(flag)) {
LOG(ERROR) << "Invalid flag: " << flag;
return false;
}
const int kConvertStringBase = 16;
int _flag = std::strtoul(flag.c_str(), nullptr, kConvertStringBase);
// flag should be unique
if (std::find(flag_container.begin(), flag_container.end(), _flag) !=
flag_container.end()) {
LOG(ERROR) << "Flag isn't unique, flag: " << _flag;
return false;
} else {
flag_container.push_back(_flag);
}
if (!ValidType(type)) {
LOG(ERROR) << "Invalid type: " << type;
return false;
}
if (!ValidName(name)) {
LOG(ERROR) << "Invalid name: " << name;
return false;
}
if (!ValidPath(path)) {
LOG(ERROR) << "Invalid path: " << path;
return false;
}
boost::filesystem::path _path = boost::filesystem::path(path);
plugin_info_list_.push_back(
std::make_shared<PluginInfo>(_flag, type, name, _path));
}
return true;
}
bool PluginsListParser::SplitPluginLine(const std::string& line,
std::vector<std::string>* parts) {
static const char kPartsInLine = 4;
std::vector<std::string> _parts;
boost::algorithm::split(_parts, line, boost::algorithm::is_any_of(";"));
if (_parts.size() != kPartsInLine) {
LOG(ERROR) << "Invalid number of parts";
return false;
}
parts->assign(_parts.begin(), _parts.end());
return true;
}
} // namespace common_installer
|
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|
subroutine z_turclo(j ,nmmaxj ,nmmax ,kmax ,ltur , &
& icx ,icy ,tkemod , &
& kcs ,kfu ,kfv ,kfs ,kfuz1 , &
& kfvz1 ,kfsz1 ,kfumin ,kfumax ,kfvmin , &
& kfvmax ,kfsmin ,kfsmax ,s1 ,dps , &
& hu ,hv ,u1 ,v1 ,rtur1 , &
& thick ,sig ,rho ,vicuv ,vicww , &
& dicuv ,dicww ,windsu ,windsv ,z0urou , &
& z0vrou ,bruvai ,rich ,dudz ,dvdz , &
& dzu1 ,dzv1 ,dzs1 ,zk ,ueul , &
& veul ,gdp )
!----- GPL ---------------------------------------------------------------------
!
! Copyright (C) Stichting Deltares, 2011-2016.
!
! This program is free software: you can redistribute it and/or modify
! it under the terms of the GNU General Public License as published by
! the Free Software Foundation version 3.
!
! This program is distributed in the hope that it will be useful,
! but WITHOUT ANY WARRANTY; without even the implied warranty of
! MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
! GNU General Public License for more details.
!
! You should have received a copy of the GNU General Public License
! along with this program. If not, see <http://www.gnu.org/licenses/>.
!
! contact: delft3d.support@deltares.nl
! Stichting Deltares
! P.O. Box 177
! 2600 MH Delft, The Netherlands
!
! All indications and logos of, and references to, "Delft3D" and "Deltares"
! are registered trademarks of Stichting Deltares, and remain the property of
! Stichting Deltares. All rights reserved.
!
!-------------------------------------------------------------------------------
! $Id: z_turclo.f90 5717 2016-01-12 11:35:24Z mourits $
! $HeadURL: https://svn.oss.deltares.nl/repos/delft3d/tags/6686/src/engines_gpl/flow2d3d/packages/kernel/src/compute/z_turclo.f90 $
!!--description-----------------------------------------------------------------
!
! Function: Computes eddy viscosity and eddy diffusivity.
! dependent of closure model (ltur).
! ltur=0 algebraic model
! ltur=1 k-L model
! ltur=2 k-epsilon model (SANCTUM-model)
! For ltur=1,2 transport equations are solved.
! - For tkemod = 'Constant ' user input is used
!
! Method used: Reference: R.E. Uittenbogaard, J.A.Th.M. van
! Kester, G.S. Stelling, Implementation of three
! turbulence models in 3D-FLOW for rectangular
! grids, Deltares report Z81, april 1992)
! Fixed Layer Approach
!
!!--pseudo code and references--------------------------------------------------
! NONE
!!--declarations----------------------------------------------------------------
use precision
use dfparall
!
use globaldata
!
implicit none
!
type(globdat),target :: gdp
!
! The following list of pointer parameters is used to point inside the gdp structure
!
real(fp) , pointer :: rhow
real(fp) , pointer :: ag
real(fp) , pointer :: z0
real(fp) , pointer :: z0v
real(fp) , pointer :: vonkar
real(fp) , pointer :: vicmol
real(fp) , pointer :: vicoww
real(fp) , pointer :: dicoww
real(fp) , pointer :: cmukl
real(fp) , pointer :: cmukep
real(fp) , pointer :: zwi
integer , pointer :: inpzw
!
! Global variables
!
integer , intent(in) :: icx !! Increment in the X-dir., if ICX= NMAX
!! then computation proceeds in the X-
!! dir. If icx=1 then computation pro-
!! ceeds in the Y-dir.
integer , intent(in) :: icy !! Increment in the Y-dir. (see ICX)
integer :: j !! Begin pointer for arrays which have
!! been transformed into 1D arrays.
!! Due to the shift in the 2nd (M-)
!! index, J = -2*NMAX + 1
integer , intent(in) :: kmax ! Description and declaration in esm_alloc_int.f90
integer , intent(in) :: ltur ! Description and declaration in esm_alloc_int.f90
integer , intent(in) :: nmmax ! Description and declaration in dimens.igs
integer :: nmmaxj ! Description and declaration in dimens.igs
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kcs ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfs ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfsmax ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfsmin ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfu ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfumax ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfumin ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfv ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfvmax ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: kfvmin ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub, kmax) :: kfsz1 ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: kfuz1 ! Description and declaration in esm_alloc_int.f90
integer, dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: kfvz1 ! Description and declaration in esm_alloc_int.f90
real(prec), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: dps ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) :: hu ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) :: hv ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: s1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: windsu ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: windsv ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: z0urou ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub) , intent(in) :: z0vrou ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: bruvai ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: dicww ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: dudz ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: dvdz ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: rich ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax) :: vicww ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, 0:kmax, ltur), intent(in) :: rtur1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax + 2) :: dicuv ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax + 2) :: vicuv ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: dzs1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: dzu1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: dzv1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: rho ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: u1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: ueul !! Eulerian velocity in X-direction (including Stokes drift)
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: v1 ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(gdp%d%nmlb:gdp%d%nmub, kmax) , intent(in) :: veul !! Eulerian velocity in Y-direction (including Stokes drift)
real(fp), dimension(kmax) :: sig ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(kmax) :: thick ! Description and declaration in esm_alloc_real.f90
real(fp), dimension(0:kmax) :: zk
character(12) , intent(in) :: tkemod ! Description and declaration in tricom.igs
!
! Local variables
!
integer :: kbg ! Denotes the k-index of vicuv/dicuv containing the background values
integer :: khtur ! Denotes the k-index of vicuv/dicuv containing the HLES values
integer :: k
integer :: kmin
integer :: kup
integer :: maskval
integer :: ndm
integer :: nm
integer :: nmd
integer :: nm_pos ! Indicating the array to be exchanged has nm index at the 2nd place, e.g., dbodsd(lsedtot,nm)
real(fp) :: aa
real(fp) :: bb
real(fp) :: difiwe
real(fp) :: drhodz
real(fp) :: dz
real(fp) :: ee
real(fp) :: epsd ! Underbound denominator
real(fp) :: fl
real(fp) :: fs
real(fp) :: h0
real(fp) :: reldik
real(fp) :: rl
real(fp) :: rz
real(fp) :: shear
real(fp) :: sqrtbv ! Square root of Brunt Vaisly frequency BRUVAI(NM,K)
real(fp) :: tke
real(fp) :: tkewin
real(fp) :: ustbe
real(fp) :: ustbot
real(fp) :: ustwi
real(fp) :: ustwin
real(fp) :: ustwkw
real(fp) :: utot
real(fp) :: uuu
real(fp) :: vvv
real(fp) :: zcord
real(fp) :: zw
real(fp) :: zwc
!
data epsd/1.e-20/
!
!! executable statements -------------------------------------------------------
!
cmukl => gdp%gdturcoe%cmukl
cmukep => gdp%gdturcoe%cmukep
zwi => gdp%gdturcoe%zwi
inpzw => gdp%gdturcoe%inpzw
rhow => gdp%gdphysco%rhow
ag => gdp%gdphysco%ag
z0 => gdp%gdphysco%z0
z0v => gdp%gdphysco%z0v
vonkar => gdp%gdphysco%vonkar
vicmol => gdp%gdphysco%vicmol
vicoww => gdp%gdphysco%vicoww
dicoww => gdp%gdphysco%dicoww
!
! Initialization
!
kbg = kmax + 1
khtur = kmax + 2
ee = exp(1.0)
nm_pos = 1
!
! Copy the computed HLES eddy viscosity to the index that will be
! used by the program
! This part of routine is only of interest in case KMAX = 1
! Copying of HLES eddy viscosity for 3D see below)
!
if (kmax==1) then
do nm = 1, nmmax
vicuv(nm, 1) = vicuv(nm, kbg)
dicuv(nm, 1) = dicuv(nm, kbg)
enddo
goto 2000
endif
!
! Free surface moving through grid, set VICWW, DICWW, BRUVAI and RICH zero
!
do nm = 1, nmmax
do k = 1, kmax
vicww (nm, k) = 0.0
dicww (nm, k) = 0.0
bruvai(nm, k) = 0.0
rich (nm, k) = 0.0
enddo
enddo
!
if (tkemod=='Constant ') then
!=======================================================================
!
! Vertical eddy viscosities/diffusivities at layer interfaces
!
! Constant values for eddy viscosity & diffusivity
!
do nm = 1, nmmax
if (kfsmin(nm)/=0) then
do k = kfsmin(nm) - 1, kfsmax(nm)
vicww(nm, k) = vicoww
dicww(nm, k) = dicoww
enddo
endif
enddo
else
!=======================================================================
!
! Other turbulence models
!
!=======================================================================
!
! Use DICWW as scratch array to compute Z0 in water-level points
!
nmd = -icx
ndm = -icy
do nm = 1, nmmax
vicww(nm, kmax) = 0.0
nmd = nmd + 1
ndm = ndm + 1
if (kfs(nm)==1) then
dicww(nm, kmax) = (kfu(nm)*z0urou(nm) + kfu(nmd)*z0urou(nmd) &
& + kfv(nm)*z0vrou(nm) + kfv(ndm)*z0vrou(ndm)) &
& /(kfu(nm) + kfu(nmd) + kfv(nm) + kfv(ndm))
endif
enddo
!
! Production and buoyancy term (only vertical gradients)
!
do nm = 1, nmmax
if (kfu(nm) == 1) then
do k = 1, kmax
dudz(nm, k) = 0.0
if (k>=kfumin(nm) .and. k<=kfumax(nm) - 1) then
kup = k + 1
dz = 0.5_fp*(dzu1(nm, k) + dzu1(nm, kup))
dudz(nm, k) = (ueul(nm, kup) - ueul(nm, k))/dz
endif
enddo
endif
enddo
do nm = 1, nmmax
if (kfv(nm) == 1) then
do k = 1, kmax
dvdz(nm, k) = 0.0
if (k>=kfvmin(nm) .and. k<=kfvmax(nm) - 1) then
kup = k + 1
dz = 0.5_fp*(dzv1(nm, k) + dzv1(nm, kup))
dvdz(nm, k) = (veul(nm, kup) - veul(nm, k))/dz
endif
enddo
endif
enddo
!
! Delft3D-16494: NOT NECESSARY?
!
! parallel case: exchange arrays in the overlapping cells
!
call dfexchg(dudz, 0, kmax, dfloat, nm_pos, gdp)
call dfexchg(dvdz, 0, kmax, dfloat, nm_pos, gdp)
!
do nm = 1, nmmax
do k = kfsmin(nm), kfsmax(nm) - 1
kup = k + 1
nmd = nm - icx
ndm = nm - icy
if (kfs(nm)==1) then
h0 = max(s1(nm) + real(dps(nm),fp), 0.01_fp)
dz = 0.5_fp*(dzs1(nm, k) + dzs1(nm, kup))
drhodz = (rho(nm, kup) - rho(nm, k))/dz
if (kcs(nm)==3) then
maskval = kcs(nm) - 2
else
maskval = kcs(nm)
endif
shear = maskval*0.5_fp*( dudz(nm, k)**2 + dudz(nmd, k)**2 &
& + dvdz(nm, k)**2 + dvdz(ndm, k)**2)
if (shear<1E-8) then
shear = 1E-8
endif
bruvai(nm, k) = -ag*drhodz/rho(nm, k)
rich (nm, k) = bruvai(nm, k)/shear
endif
enddo
enddo
!=======================================================================
!
! Algebraic turbulence model (LTUR = 0)
!
!=======================================================================
if (ltur==0) then
do nm = 1, nmmax
nmd = nm - icx
ndm = nm - icy
zcord = 0.
if (kfs(nm)==1) then
do k = kfsmin(nm), kfsmax(nm)
uuu = (ueul(nmd, k) + ueul(nm, k)) &
& /max(1, kfuz1(nmd, k) + kfuz1(nm, k))
vvv = (veul(ndm, k) + veul(nm, k)) &
& /max(1, kfvz1(ndm, k) + kfvz1(nm, k))
utot = sqrt(uuu*uuu + vvv*vvv)
!
! Bottom is assumed at Z0
!
if (k==kfsmin(nm)) then
zcord = zcord + .5*dzs1(nm, k)
else
zcord = zcord + .5*(dzs1(nm, k - 1) + dzs1(nm, k))
endif
rz = 1.0 + zcord/dicww(nm, kmax)
vicww(nm, kmax) = vicww(nm, kmax) &
& + utot*vonkar/(log(rz)*(kfsmax(nm) &
& - kfsmin(nm) + 1))
enddo
endif
enddo
!
do nm = 1, nmmax
nmd = nm - icx
ndm = nm - icy
if (kfs(nm)==1) then
kmin = kfsmin(nm)
if (kfsmin(nm)/=kfsmax(nm)) then
uuu = (ueul(nmd, kmin + 1) + ueul(nm, kmin + 1)) &
& /max(1, kfuz1(nmd, kmin + 1) + kfuz1(nm, kmin + 1))
vvv = (veul(ndm, kmin + 1) + veul(nm, kmin + 1)) &
& /max(1, kfvz1(ndm, kmin + 1) + kfvz1(nm, kmin + 1))
dz = dzs1(nm, kmin) + 0.5_fp*dzs1(nm, kmin + 1)
else
uuu = (ueul(nmd, kmin) + ueul(nm, kmin)) &
& /max(1, kfuz1(nmd, kmin) + kfuz1(nm, kmin))
vvv = (veul(ndm, kmin) + veul(nm, kmin)) &
& /max(1, kfvz1(ndm, kmin) + kfvz1(nm, kmin))
dz = dzs1(nm, kmin)/ee
endif
utot = sqrt(uuu*uuu + vvv*vvv)
!
! Bottom is assumed at Z0
!
rz = 1.0_fp + dz/dicww(nm, kmax)
ustbot = utot*vonkar/log(rz)
vicww(nm, kmax) = max(vicww(nm, kmax), ustbot)
endif
enddo
!
! Kolmogorov-Prandtl mixing length model
!
do nm = 1, nmmax
reldik = 0.
do k = kfsmin(nm), kfsmax(nm) - 1
nmd = nm - icx
ndm = nm - icy
if (kfs(nm)==1) then
h0 = max(0.01_fp, real(dps(nm),fp) + s1(nm))
!
! Damping function mixing length (FL)
! Damping function diffusivity (FS)
!
if (rich(nm, k)>=0.0) then
fl = exp( - 2.3*min(rich(nm, k), 30.0_fp))
aa = (1. + 3.33*rich(nm, k))
aa = aa*sqrt(aa)
bb = (1. + 10.0*rich(nm, k))
bb = sqrt(bb)
fs = aa/bb
else
fl = (1. - 14.*rich(nm, k))**0.25
fs = 1.
endif
!
! Mixing length
!
reldik = reldik + dzs1(nm, k)/h0
rl = vonkar*h0*reldik*sqrt(1.0 - reldik)*fl
!
! Algebraic eddy viscosity (Uittenbogaard '91 )
!
tkewin = sqrt(windsu(nm)**2 + windsv(nm)**2)
ustwi = sqrt(tkewin/rhow)
ustbe = vicww(nm, kmax)
tke = ustbe*ustbe*(1. - reldik)/sqrt(cmukep) &
& + ustwi*ustwi*(reldik)/sqrt(cmukep)
vicww(nm, k) = cmukl*rl*sqrt(tke)
!
! Eddy viscosity is maximum Uittenbogaard/Kolmogorov-Prandtl
!
if (kcs(nm)==3) then
maskval = kcs(nm) - 2
else
maskval = kcs(nm)
endif
shear = 0.5*maskval*(dudz(nm, k)**2 + dudz(nmd, k) &
& **2 + dvdz(nm, k)**2 + dvdz(ndm, k)**2)
shear = max(epsd, shear)
vicww(nm, k) = max(vicww(nm, k), rl*rl*sqrt(shear))
dicww(nm, k) = vicww(nm, k)/fs
endif
enddo
enddo
!=======================================================================
!
! K-L model (LTUR = 1)
!
!=======================================================================
elseif (ltur==1) then
do nm = 1, nmmax
reldik = 0.
do k = kfsmin(nm), kfsmax(nm) - 1
if (kfs(nm)==1) then
h0 = max(0.01_fp, real(dps(nm),fp) + s1(nm))
!
! Damping function mixing length (FL)
! Damping function diffusivity (FS)
!
if (rich(nm, k)>=0.0) then
fl = exp( - 2.3*min(rich(nm, k), 30.0_fp))
else
fl = (1. - 14.*rich(nm, k))**0.25
endif
fs = 1.
reldik = reldik + dzs1(nm, k)/h0
rl = vonkar*h0*reldik*sqrt(1.0 - reldik)*fl
vicww(nm, k) = cmukl*rl*sqrt(rtur1(nm, k, 1))
dicww(nm, k) = cmukl*rl*sqrt(rtur1(nm, k, 1))/fs
endif
enddo
enddo
!=======================================================================
!
! K-EPS model (LTUR = 2)
!
!=======================================================================
else
do nm = 1, nmmax
h0 = real(dps(nm),fp) + s1(nm)
if (kfs(nm)==1 .and. h0>0.01_fp) then
do k = kfsmin(nm), kfsmax(nm) - 1
vicww(nm, k) = cmukep*rtur1(nm, k, 1) &
& **2/max(rtur1(nm, k, 2), epsd)
dicww(nm, k) = vicww(nm, k)
enddo
else
!
! Make vicww and dicww small for points
! that are dry or almost dry
! Actually, it would be more consistent to use
! dicww(nm, k) = vicmol/sigmol
! but sigmol depends on l and is not available here
! So, just use
! dicww(nm, k) = vicmol
!
do k = kfsmin(nm), kfsmax(nm) - 1
vicww(nm, k) = vicmol
dicww(nm, k) = vicmol
enddo
endif
enddo
endif
!
! Bottom and free surface (for all turbulence models except constant)
!
do nm = 1, nmmax
nmd = nm - icx
ndm = nm - icy
h0 = real(dps(nm),fp) + s1(nm)
if (kfs(nm)==1 .and. h0>0.01_fp) then
!
!
! Bottom is assumed at Z0 (for all turbulence models except constant)
!
kmin = kfsmin(nm)
if (kfsmin(nm)/=kfsmax(nm)) then
uuu = (ueul(nmd, kmin + 1) + ueul(nm, kmin + 1)) &
& /max(1, kfuz1(nmd, kmin + 1) + kfuz1(nm, kmin + 1))
vvv = (veul(ndm, kmin + 1) + veul(nm, kmin + 1)) &
& /max(1, kfvz1(ndm, kmin + 1) + kfvz1(nm, kmin + 1))
dz = dzs1(nm, kmin) + 0.5*dzs1(nm, kmin + 1)
else
uuu = (ueul(nmd, kmin) + ueul(nm, kmin)) &
& /max(1, kfuz1(nmd, kmin) + kfuz1(nm, kmin))
vvv = (veul(ndm, kmin) + veul(nm, kmin)) &
& /max(1, kfvz1(ndm, kmin) + kfvz1(nm, kmin))
dz = dzs1(nm, kmin)/ee
endif
utot = sqrt(uuu*uuu + vvv*vvv)
rz = 1.0 + dz/dicww(nm, kmax)
ustbot = abs(utot)*vonkar/log(rz)
!
vicww(nm, kmin - 1) = vonkar*ustbot*dicww(nm, kmax)
dicww(nm, kmin - 1) = vicww(nm, kmin - 1)
if (kfsmax(nm)/=kfsmin(nm)) then
!
! Viscosity/difusivity reduction depending on stratification.
! The so-called Munk-Anderson (1948) damping function is applied.
! The stratified flows formulation of Busch (1972) is applied.
! See Delft3D-FLOW manual section 9.5 "Turbulence".
! See also [Journal of Marine Res., Vol 1] for related formulas.
!
if (rich(nm, kmin) >= 0.0_fp) then
fl = exp( - 2.3_fp * min(rich(nm, kmin), 30.0_fp))
aa = (1.0_fp + 3.33_fp*rich(nm, kmin))
aa = aa*sqrt(aa)
bb = (1.0_fp + 10.0_fp*rich(nm, kmin))
bb = sqrt(bb)
fs = aa/bb
else
fl = (1.0_fp - 14.0_fp*rich(nm, kmin))**0.25_fp
fs = 1.0_fp
endif
vicww(nm, kmin) = vonkar*ustbot*dzs1(nm, kmin)*fl
dicww(nm, kmin) = vicww(nm,kmin)/fs
endif
vicww(nm, kmax) = 0.0
dicww(nm, kmax) = 0.0
zwc = .5*dzs1(nm, kfsmax(nm))
zw = inpzw*zwi + (1 - inpzw)*zwc
ustwkw = sqrt(windsu(nm)**2 + windsv(nm)**2)/rhow
ustwin = sqrt(ustwkw)
vicww(nm, kfsmax(nm)) = vonkar*zw*ustwin
dicww(nm, kfsmax(nm)) = vonkar*zw*ustwin
!
! RTUR1 can be used to determine viscosity at surface
! Only for k-eps model
!
if (ltur == 2) then
vicww(nm, kfsmax(nm)) = cmukep*rtur1(nm, kfsmax(nm), 1)**2 &
& /max(rtur1(nm, kfsmax(nm), 2), epsd)
dicww(nm, kfsmax(nm)) = vicww(nm, kfsmax(nm))
endif
else
!
! Make vicww and dicww small for points
! that are dry or almost dry (for k=kfsmin and k=kfsmax)
! Actually, it would be more consistent to use
! dicww(nm, k) = vicmol/sigmol
! but sigmol depends on l and is not available here
! So, just use
! dicww(nm, k) = vicmol
!
! KFSMAX may be uninitialized (-1)
!
vicww(nm, kfsmin(nm)) = vicmol
dicww(nm, kfsmin(nm)) = vicmol
vicww(nm, max(kfsmax(nm),kfsmin(nm))) = vicmol
dicww(nm, max(kfsmax(nm),kfsmin(nm))) = vicmol
endif
enddo
!
! The following code is moved to UZD, CUCNP:
! vicww(nm,k) = max(vicww(nm,k), vicoww)
! vicww(nm,k) = min(vicww(nm,k), 10)
! The following code is moved to DIFU, DIFUVL:
! dicww(nm,k) = max(dicww(nm,k), dicoww)
! dicww(nm,k) = min(dicww(nm,k), 10)
!
endif
!
! Delft3D-16494: NOT NECESSARY?
! parallel case: exchange arrays in the overlapping cells
!
call dfexchg(vicww, 0, kmax, dfloat, nm_pos, gdp)
call dfexchg(dicww, 0, kmax, dfloat, nm_pos, gdp)
!
! Horizontal eddy viscosities and diffusivities in density points
!
do nm = 1, nmmax
if (kfsmax(nm)>kfsmin(nm)) then
do k = kfsmin(nm), kfsmax(nm)
if (kfs(nm)==1) then
vicuv(nm, k) = 0.5 * (vicww(nm, k) + vicww(nm, k-1)) &
& + vicuv(nm, kbg) + vicuv(nm, khtur)
dicuv(nm, k) = 0.5 * (dicww(nm, k) + dicww(nm, k-1)) &
& + dicuv(nm, kbg) + dicuv(nm, khtur)
endif
enddo
endif
enddo
2000 continue
end subroutine z_turclo
|
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|
from PIL import Image
from numpy import asarray
from mtcnn.mtcnn import MTCNN
def extract_single_face_facenet(file, size=(160,160)):
# extract single face from given image
image = Image.open(file)
# convert to RGB if required
image = image.convert('RGB')
# convert to numpp array
pixel_array = asarray(image)
# create our detector, uses default weights
detector = MTCNN()
# detect face in the image
result = detector.detect_faces(pixels)
# extract the bounding box from face
x1, y1, width, height = result[0]['box']
x2, y2 = x1 + width, y1 + width
# extract face
face = pixel_array[y1:y2, x1:x2]
image = Image.fromarray(face)
image = image.resize(size)
face_array = asarray(image)
return face_array
|
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|
C @(#)kmpvltdif.f 20.3 2/13/96
C****************************************************************
C
C File: kmpvltdif.f
C
C Purpose: Routine to compares kdiff(p) with kdiff(q)
c
c "key" denotes the interpretation of kdiff(*,*)
c 1 = interpret as bus indices.
c 2 = interpret as branch indices.
C
C Author: Walt Powell Date: 14 December 1992
C Called by: p_report.f
C
C****************************************************************
integer function kmpvltdif (p, q)
integer p, q
include 'ipfinc/parametr.inc'
include 'ipfinc/qksrt.inc'
include 'ipfinc/bus.inc'
include 'ipfinc/branch.inc'
include 'ipfinc/alt_case.inc'
common /scratch/ numdiff, ixref(MAXBUS), kdiff(2,MAXBUS),
& fdiff(6,MAXBUS), cdiff(2,MAXBUS)
character cdiff * 1
integer p1, p2, op1, op2
if (p .eq. q) then
kmpvltdif = 0
else
i = ixref(p)
j = ixref(q)
idv1 = fdiff(1,i)
idv2 = fdiff(1,j)
kmpvltdif = idv2 - idv1 ! Sort dv high to low
if (kmpvltdif .eq. 0) then
p1 = kdiff(1,i)
p2 = kdiff(1,j)
if (key .eq. 1) then
op1 = kdiff(2,i)
op2 = kdiff(2,j)
if (p1 .gt. 0 .and. p2 .gt. 0) then
kmpvltdif = kompr (bus(p1), bus(p2), junk)
if (kmpvltdif .eq. 0) then
vltdif = 100.0 * (base(p1) - base(p2))
kmpvltdif = int (vltdif)
endif
else if (p1 .gt. 0 .and. p2 .eq. 0) then
kmpvltdif = kompr (bus(p1), oldbus(op2), junk)
if (kmpvltdif .eq. 0) then
vltdif = 100.0 * (base(p1) - oldbase(op2))
kmpvltdif = int (vltdif)
endif
else if (p1 .eq. 0 .and. p2 .gt. 0) then
kmpvltdif = kompr (oldbus(op1), bus(p2), junk)
if (kmpvltdif .eq. 0) then
vltdif = 100.0 * (oldbase(op1) - base(p2))
kmpvltdif = int (vltdif)
endif
else
kmpvltdif = kompr (oldbus(op1), oldbus(op2), junk)
if (kmpvltdif .eq. 0) then
vltdif = 100.0 * (oldbase(op1) - oldbase(op2))
kmpvltdif = int (vltdif)
endif
endif
else
op1 = kdiff(2,i)
op2 = kdiff(2,j)
if (p1 .gt. 0 .and. p2 .gt. 0) then
kmpvltdif = kompr (bus(kx(p1)), bus(kx(p2)), junk)
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (bus(ky(p1)), bus(ky(p2)),
& junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (brid(p1), brid(p2), junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = brsect(p1) - brsect(p2)
endif
else if (p1 .gt. 0 .and. p2 .eq. 0) then
kmpvltdif = kompr (bus(kx(p1)), oldbus(okx(op2)),
& junk)
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (bus(ky(p1)), oldbus(oky(op2)),
& junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (brid(p1), obrid(op2), junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = brsect(p1) - obrsect(op2)
endif
else if (p1 .eq. 0 .and. p2 .gt. 0) then
kmpvltdif = kompr (oldbus(okx(op1)), bus(kx(p2)),
& junk)
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (oldbus(oky(op1)), bus(ky(p2)),
& junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (obrid(op1), brid(p2), junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = obrsect(op1) - brsect(p2)
endif
else
kmpvltdif = kompr (oldbus(okx(op1)),
& oldbus(okx(op2)), junk)
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (oldbus(oky(op1)),
& oldbus(oky(op2)),
& junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = kompr (obrid(op1), obrid(op2), junk)
endif
if (kmpvltdif .eq. 0) then
kmpvltdif = obrsect(op1) - obrsect(op2)
endif
endif
endif
endif
endif
return
end
|
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|
# pass_args.py
import numpy as np
import _scalar_args
print _scalar_args.scalar_args.__doc__
# these are simple python scalars.
int_in = 1.0
real_in = 10.0
# since these are intent(inout) variables, these must be arrays
int_inout = np.zeros((1,), dtype = np.int32)
real_inout = np.zeros((1,), dtype = np.float32)
# all intent(out) variables are returned in a tuple, so they aren't passed as
# arguments.
int_out, real_out = _scalar_args.scalar_args(int_in, real_in, int_inout, real_inout)
for name in ('int_inout', 'real_inout', 'int_out', 'real_out'):
print '%s == %s' % (name, locals()[name])
|
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|
[STATEMENT]
lemma zero_less_Limit: "Limit \<beta> \<Longrightarrow> 0 < \<beta>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Limit \<beta> \<Longrightarrow> 0 < \<beta>
[PROOF STEP]
by (simp add: Limit_def OrdmemD)
|
{"llama_tokens": 86, "file": "ZFC_in_HOL_ZFC_in_HOL", "length": 1}
|
-- Inverso_del_inverso_en_grupos.lean
-- Inverso del inverso en grupos
-- José A. Alonso Jiménez
-- Sevilla, 7 de julio de 2021
-- ---------------------------------------------------------------------
-- ---------------------------------------------------------------------
-- Sea G un grupo y a ∈ G. Demostrar que
-- (a⁻¹)⁻¹ = a
-- ---------------------------------------------------------------------
import algebra.group.basic
universe u
variables {G : Type u} [group G]
variables {a b : G}
-- 1ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a :=
calc (a⁻¹)⁻¹
= (a⁻¹)⁻¹ * 1 : (mul_one (a⁻¹)⁻¹).symm
... = (a⁻¹)⁻¹ * (a⁻¹ * a) : congr_arg ((*) (a⁻¹)⁻¹) (inv_mul_self a).symm
... = ((a⁻¹)⁻¹ * a⁻¹) * a : (mul_assoc _ _ _).symm
... = 1 * a : congr_arg (* a) (inv_mul_self a⁻¹)
... = a : one_mul a
-- 2ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a :=
calc (a⁻¹)⁻¹
= (a⁻¹)⁻¹ * 1 : by simp only [mul_one]
... = (a⁻¹)⁻¹ * (a⁻¹ * a) : by simp only [inv_mul_self]
... = ((a⁻¹)⁻¹ * a⁻¹) * a : by simp only [mul_assoc]
... = 1 * a : by simp only [inv_mul_self]
... = a : by simp only [one_mul]
-- 3ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a :=
calc (a⁻¹)⁻¹
= (a⁻¹)⁻¹ * 1 : by simp
... = (a⁻¹)⁻¹ * (a⁻¹ * a) : by simp
... = ((a⁻¹)⁻¹ * a⁻¹) * a : by simp
... = 1 * a : by simp
... = a : by simp
-- 4ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a :=
begin
apply mul_eq_one_iff_inv_eq.mp,
exact mul_left_inv a,
end
-- 5ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a :=
mul_eq_one_iff_inv_eq.mp (mul_left_inv a)
-- 6ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a:=
inv_inv a
-- 7ª demostración
-- ===============
example : (a⁻¹)⁻¹ = a:=
by simp
-- Referencia
-- ==========
-- Propiedad 3.20 del libro "Abstract algebra: Theory and applications"
-- de Thomas W. Judson.
-- http://abstract.ups.edu/download/aata-20200730.pdf#page=49
|
{"author": "jaalonso", "repo": "Calculemus", "sha": "0fb664ab298c0e90b4b8034729a2cdad20503e18", "save_path": "github-repos/lean/jaalonso-Calculemus", "path": "github-repos/lean/jaalonso-Calculemus/Calculemus-0fb664ab298c0e90b4b8034729a2cdad20503e18/src/Inverso_del_inverso_en_grupos.lean"}
|
This editor can edit this entry and tell us a bit about themselves by clicking the Edit icon.
20080905 14:48:13 nbsp Welcome to the Wiki Howdy, Ms. or Mr. 139, and Welcome to the Wiki! You might want to check out the importance of using your RealName, just so we can get to know you (or not: its your choice, but people were pretty friendly here). My names Evan, pleased to meet you! Thanks for all the reviews. It would be great if you could explain what you did with the Summer Sessions Summer Abroad program and why it was so great. In fact, if you know much about the program, it might make sense to just go ahead and make a Summer Abroad entry for it! Anyway, welcome to the wiki! Users/JabberWokky Evan JabberWokky Edwards
|
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|
// test_thread_clock.cpp ----------------------------------------------------------//
// Copyright 2009 Vicente J. Botet Escriba
// Distributed under the Boost Software License, Version 1.0.
// See http://www.boost.org/LICENSE_1_0.txt
#include <boost/chrono/thread_clock.hpp>
#include <boost/type_traits.hpp>
#include <iostream>
void test_thread_clock()
{
#if defined(BOOST_CHRONO_HAS_THREAD_CLOCK)
using namespace boost::chrono;
std::cout << "thread_clock test" << std::endl;
thread_clock::duration delay = milliseconds(5);
thread_clock::time_point start = thread_clock::now();
while (thread_clock::now() - start <= delay)
;
thread_clock::time_point stop = thread_clock::now();
thread_clock::duration elapsed = stop - start;
std::cout << "paused " << nanoseconds(elapsed).count() << " nanoseconds\n";
start = thread_clock::now();
stop = thread_clock::now();
std::cout << "thread_clock resolution estimate: " << nanoseconds(stop-start).count() << " nanoseconds\n";
#else
std::cout << "thread_clock not available\n";
#endif
}
int main()
{
test_thread_clock();
return 0;
}
|
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|
Require Import Coq.Strings.String Coq.Lists.List.
Require Export Fiat.Common.Coq__8_4__8_5__Compat.
Set Implicit Arguments.
Local Open Scope list_scope.
Local Open Scope string_scope.
Fixpoint list_of_string (s : string) : list Ascii.ascii
:= match s with
| "" => nil
| String ch s' => ch :: list_of_string s'
end.
Fixpoint string_of_list (ls : list Ascii.ascii) : string
:= match ls with
| nil => ""
| ch :: ls' => String ch (string_of_list ls')
end.
Fixpoint string_copy (n : nat) (ch : Ascii.ascii)
:= match n with
| 0 => EmptyString
| S n' => String.String ch (string_copy n' ch)
end.
|
{"author": "mit-plv", "repo": "fiat", "sha": "4c78284c3a88db32051bdba79202f40c645ffb7f", "save_path": "github-repos/coq/mit-plv-fiat", "path": "github-repos/coq/mit-plv-fiat/fiat-4c78284c3a88db32051bdba79202f40c645ffb7f/src/Common/StringOperations.v"}
|
# -*- coding: utf-8 -*-
"""TimeDelayingRidge class."""
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Ross Maddox <ross.maddox@rochester.edu>
#
# License: BSD (3-clause)
import numpy as np
from .base import BaseEstimator
from ..cuda import _setup_cuda_fft_multiply_repeated
from ..filter import next_fast_len
from ..fixes import jit
from ..parallel import check_n_jobs
from ..utils import warn, ProgressBar, logger
def _compute_corrs(X, y, smin, smax, n_jobs=1, fit_intercept=False,
edge_correction=True):
"""Compute auto- and cross-correlations."""
if fit_intercept:
# We could do this in the Fourier domain, too, but it should
# be a bit cleaner numerically to do it here.
X_offset = np.mean(X, axis=0)
y_offset = np.mean(y, axis=0)
if X.ndim == 3:
X_offset = X_offset.mean(axis=0)
y_offset = np.mean(y_offset, axis=0)
X = X - X_offset
y = y - y_offset
else:
X_offset = y_offset = 0.
if X.ndim == 2:
assert y.ndim == 2
X = X[:, np.newaxis, :]
y = y[:, np.newaxis, :]
assert X.shape[:2] == y.shape[:2]
len_trf = smax - smin
len_x, n_epochs, n_ch_x = X.shape
len_y, n_epcohs, n_ch_y = y.shape
assert len_x == len_y
n_fft = next_fast_len(2 * X.shape[0] - 1)
n_jobs, cuda_dict = _setup_cuda_fft_multiply_repeated(
n_jobs, [1.], n_fft, 'correlation calculations')
# create our Toeplitz indexer
ij = np.empty((len_trf, len_trf), int)
for ii in range(len_trf):
ij[ii, ii:] = np.arange(len_trf - ii)
x = np.arange(n_fft - 1, n_fft - len_trf + ii, -1)
ij[ii + 1:, ii] = x
x_xt = np.zeros([n_ch_x * len_trf] * 2)
x_y = np.zeros((len_trf, n_ch_x, n_ch_y), order='F')
n = n_epochs * (n_ch_x * (n_ch_x + 1) // 2 + n_ch_x)
logger.info('Fitting %d epochs, %d channels' % (n_epochs, n_ch_x))
pb = ProgressBar(n, mesg='Sample')
count = 0
pb.update(count)
for ei in range(n_epochs):
this_X = X[:, ei, :]
# XXX maybe this is what we should parallelize over CPUs at some point
X_fft = cuda_dict['rfft'](this_X, n=n_fft, axis=0)
X_fft_conj = X_fft.conj()
y_fft = cuda_dict['rfft'](y[:, ei, :], n=n_fft, axis=0)
for ch0 in range(n_ch_x):
for oi, ch1 in enumerate(range(ch0, n_ch_x)):
this_result = cuda_dict['irfft'](
X_fft[:, ch0] * X_fft_conj[:, ch1], n=n_fft, axis=0)
# Our autocorrelation structure is a Toeplitz matrix, but
# it's faster to create the Toeplitz ourselves than use
# linalg.toeplitz.
this_result = this_result[ij]
# However, we need to adjust for coeffs that are cut off,
# i.e. the non-zero delays should not have the same AC value
# as the zero-delay ones (because they actually have fewer
# coefficients).
#
# These adjustments also follow a Toeplitz structure, so we
# construct a matrix of what has been left off, compute their
# inner products, and remove them.
if edge_correction:
_edge_correct(this_result, this_X, smax, smin, ch0, ch1)
# Store the results in our output matrix
x_xt[ch0 * len_trf:(ch0 + 1) * len_trf,
ch1 * len_trf:(ch1 + 1) * len_trf] += this_result
if ch0 != ch1:
x_xt[ch1 * len_trf:(ch1 + 1) * len_trf,
ch0 * len_trf:(ch0 + 1) * len_trf] += this_result.T
count += 1
pb.update(count)
# compute the crosscorrelations
cc_temp = cuda_dict['irfft'](
y_fft * X_fft_conj[:, slice(ch0, ch0 + 1)], n=n_fft, axis=0)
if smin < 0 and smax >= 0:
x_y[:-smin, ch0] += cc_temp[smin:]
x_y[len_trf - smax:, ch0] += cc_temp[:smax]
else:
x_y[:, ch0] += cc_temp[smin:smax]
count += 1
pb.update(count)
x_y = np.reshape(x_y, (n_ch_x * len_trf, n_ch_y), order='F')
return x_xt, x_y, n_ch_x, X_offset, y_offset
@jit()
def _edge_correct(this_result, this_X, smax, smin, ch0, ch1):
if smax > 0:
tail = _toeplitz_dot(this_X[-1:-smax:-1, ch0],
this_X[-1:-smax:-1, ch1])
if smin > 0:
tail = tail[smin - 1:, smin - 1:]
this_result[max(-smin + 1, 0):, max(-smin + 1, 0):] -= tail
if smin < 0:
head = _toeplitz_dot(this_X[:-smin, ch0],
this_X[:-smin, ch1])[::-1, ::-1]
if smax < 0:
head = head[:smax, :smax]
this_result[:-smin, :-smin] -= head
@jit()
def _toeplitz_dot(a, b):
"""Create upper triangular Toeplitz matrices & compute the dot product."""
# This is equivalent to:
# a = linalg.toeplitz(a)
# b = linalg.toeplitz(b)
# a[np.triu_indices(len(a), 1)] = 0
# b[np.triu_indices(len(a), 1)] = 0
# out = np.dot(a.T, b)
assert a.shape == b.shape and a.ndim == 1
out = np.outer(a, b)
for ii in range(1, len(a)):
out[ii, ii:] += out[ii - 1, ii - 1:-1]
out[ii + 1:, ii] += out[ii:-1, ii - 1]
return out
def _compute_reg_neighbors(n_ch_x, n_delays, reg_type, method='direct',
normed=False):
"""Compute regularization parameter from neighbors."""
from scipy import linalg
from scipy.sparse.csgraph import laplacian
known_types = ('ridge', 'laplacian')
if isinstance(reg_type, str):
reg_type = (reg_type,) * 2
if len(reg_type) != 2:
raise ValueError('reg_type must have two elements, got %s'
% (len(reg_type),))
for r in reg_type:
if r not in known_types:
raise ValueError('reg_type entries must be one of %s, got %s'
% (known_types, r))
reg_time = (reg_type[0] == 'laplacian' and n_delays > 1)
reg_chs = (reg_type[1] == 'laplacian' and n_ch_x > 1)
if not reg_time and not reg_chs:
return np.eye(n_ch_x * n_delays)
# regularize time
if reg_time:
reg = np.eye(n_delays)
stride = n_delays + 1
reg.flat[1::stride] += -1
reg.flat[n_delays::stride] += -1
reg.flat[n_delays + 1:-n_delays - 1:stride] += 1
args = [reg] * n_ch_x
reg = linalg.block_diag(*args)
else:
reg = np.zeros((n_delays * n_ch_x,) * 2)
# regularize features
if reg_chs:
block = n_delays * n_delays
row_offset = block * n_ch_x
stride = n_delays * n_ch_x + 1
reg.flat[n_delays:-row_offset:stride] += -1
reg.flat[n_delays + row_offset::stride] += 1
reg.flat[row_offset:-n_delays:stride] += -1
reg.flat[:-(n_delays + row_offset):stride] += 1
assert np.array_equal(reg[::-1, ::-1], reg)
if method == 'direct':
if normed:
norm = np.sqrt(np.diag(reg))
reg /= norm
reg /= norm[:, np.newaxis]
return reg
else:
# Use csgraph. Note that our -1's above are really the neighbors!
# If we ever want to allow arbitrary adjacency matrices, this is how
# we'd want to do it.
reg = laplacian(-reg, normed=normed)
return reg
def _fit_corrs(x_xt, x_y, n_ch_x, reg_type, alpha, n_ch_in):
"""Fit the model using correlation matrices."""
# do the regularized solving
from scipy import linalg
n_ch_out = x_y.shape[1]
assert x_y.shape[0] % n_ch_x == 0
n_delays = x_y.shape[0] // n_ch_x
reg = _compute_reg_neighbors(n_ch_x, n_delays, reg_type)
mat = x_xt + alpha * reg
# From sklearn
try:
# Note: we must use overwrite_a=False in order to be able to
# use the fall-back solution below in case a LinAlgError
# is raised
w = linalg.solve(mat, x_y, sym_pos=True, overwrite_a=False)
except np.linalg.LinAlgError:
warn('Singular matrix in solving dual problem. Using '
'least-squares solution instead.')
w = linalg.lstsq(mat, x_y, lapack_driver='gelsy')[0]
w = w.T.reshape([n_ch_out, n_ch_in, n_delays])
return w
class TimeDelayingRidge(BaseEstimator):
"""Ridge regression of data with time delays.
Parameters
----------
tmin : int | float
The starting lag, in seconds (or samples if ``sfreq`` == 1).
Negative values correspond to times in the past.
tmax : int | float
The ending lag, in seconds (or samples if ``sfreq`` == 1).
Positive values correspond to times in the future.
Must be >= tmin.
sfreq : float
The sampling frequency used to convert times into samples.
alpha : float
The ridge (or laplacian) regularization factor.
reg_type : str | list
Can be "ridge" (default) or "laplacian".
Can also be a 2-element list specifying how to regularize in time
and across adjacent features.
fit_intercept : bool
If True (default), the sample mean is removed before fitting.
n_jobs : int | str
The number of jobs to use. Can be an int (default 1) or ``'cuda'``.
.. versionadded:: 0.18
edge_correction : bool
If True (default), correct the autocorrelation coefficients for
non-zero delays for the fact that fewer samples are available.
Disabling this speeds up performance at the cost of accuracy
depending on the relationship between epoch length and model
duration. Only used if ``estimator`` is float or None.
.. versionadded:: 0.18
See Also
--------
mne.decoding.ReceptiveField
Notes
-----
This class is meant to be used with :class:`mne.decoding.ReceptiveField`
by only implicitly doing the time delaying. For reasonable receptive
field and input signal sizes, it should be more CPU and memory
efficient by using frequency-domain methods (FFTs) to compute the
auto- and cross-correlations.
"""
_estimator_type = "regressor"
def __init__(self, tmin, tmax, sfreq, alpha=0., reg_type='ridge',
fit_intercept=True, n_jobs=1, edge_correction=True):
if tmin > tmax:
raise ValueError('tmin must be <= tmax, got %s and %s'
% (tmin, tmax))
self.tmin = float(tmin)
self.tmax = float(tmax)
self.sfreq = float(sfreq)
self.alpha = float(alpha)
self.reg_type = reg_type
self.fit_intercept = fit_intercept
self.edge_correction = edge_correction
self.n_jobs = n_jobs
@property
def _smin(self):
return int(round(self.tmin * self.sfreq))
@property
def _smax(self):
return int(round(self.tmax * self.sfreq)) + 1
def fit(self, X, y):
"""Estimate the coefficients of the linear model.
Parameters
----------
X : array, shape (n_samples[, n_epochs], n_features)
The training input samples to estimate the linear coefficients.
y : array, shape (n_samples[, n_epochs], n_outputs)
The target values.
Returns
-------
self : instance of TimeDelayingRidge
Returns the modified instance.
"""
if X.ndim == 3:
assert y.ndim == 3
assert X.shape[:2] == y.shape[:2]
else:
assert X.ndim == 2 and y.ndim == 2
assert X.shape[0] == y.shape[0]
n_jobs = check_n_jobs(self.n_jobs, allow_cuda=True)
# These are split into two functions because it's possible that we
# might want to allow people to do them separately (e.g., to test
# different regularization parameters).
self.cov_, x_y_, n_ch_x, X_offset, y_offset = _compute_corrs(
X, y, self._smin, self._smax, n_jobs, self.fit_intercept,
self.edge_correction)
self.coef_ = _fit_corrs(self.cov_, x_y_, n_ch_x,
self.reg_type, self.alpha, n_ch_x)
# This is the sklearn formula from LinearModel (will be 0. for no fit)
if self.fit_intercept:
self.intercept_ = y_offset - np.dot(X_offset, self.coef_.sum(-1).T)
else:
self.intercept_ = 0.
return self
def predict(self, X):
"""Predict the output.
Parameters
----------
X : array, shape (n_samples[, n_epochs], n_features)
The data.
Returns
-------
X : ndarray
The predicted response.
"""
from scipy.signal import fftconvolve
if X.ndim == 2:
X = X[:, np.newaxis, :]
singleton = True
else:
singleton = False
out = np.zeros(X.shape[:2] + (self.coef_.shape[0],))
smin = self._smin
offset = max(smin, 0)
for ei in range(X.shape[1]):
for oi in range(self.coef_.shape[0]):
for fi in range(self.coef_.shape[1]):
temp = fftconvolve(X[:, ei, fi], self.coef_[oi, fi])
temp = temp[max(-smin, 0):][:len(out) - offset]
out[offset:len(temp) + offset, ei, oi] += temp
out += self.intercept_
if singleton:
out = out[:, 0, :]
return out
|
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|
//---------------------------------------------------------------------------//
// Copyright (c) 2018-2021 Mikhail Komarov <nemo@nil.foundation>
// Copyright (c) 2020-2021 Nikita Kaskov <nbering@nil.foundation>
//
// MIT License
//
// 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.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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.
//---------------------------------------------------------------------------//
#define BOOST_TEST_MODULE marshalling_types_test
#include <boost/test/unit_test.hpp>
#include <algorithm>
#include <cstdint>
#include <iostream>
#include <iterator>
#include <limits>
#include <memory>
#include <type_traits>
#include <nil/marshalling/types/integral.hpp>
#include <nil/marshalling/types/bitmask_value.hpp>
#include <nil/marshalling/types/enumeration.hpp>
#include <nil/marshalling/types/array_list.hpp>
#include <nil/marshalling/types/string.hpp>
#include <nil/marshalling/types/bitfield.hpp>
#include <nil/marshalling/types/optional.hpp>
#include <nil/marshalling/types/bundle.hpp>
#include <nil/marshalling/types/float_value.hpp>
#include <nil/marshalling/types/no_value.hpp>
#include <nil/marshalling/types/variant.hpp>
#include <nil/marshalling/compile_control.hpp>
#include <nil/marshalling/units.hpp>
#include <nil/marshalling/version.hpp>
#include <nil/marshalling/algorithms/pack.hpp>
#include <nil/marshalling/container/array_view.hpp>
#include <nil/marshalling/container/static_vector.hpp>
#include <nil/marshalling/container/static_string.hpp>
#include <nil/marshalling/container/string_view.hpp>
#include <nil/marshalling/container/type_traits.hpp>
using namespace nil::marshalling;
static_assert(has_member_function_clear<std::string>::value, "Invalid function presence detection");
static_assert(has_member_function_clear<std::vector<std::uint8_t>>::value, "Invalid function presence detection");
static_assert(has_member_function_clear<container::static_string<5>>::value, "Invalid function presence detection");
static_assert(has_member_function_clear<container::static_vector<std::uint8_t, 5>>::value,
"Invalid function presence detection");
static_assert(!has_member_function_clear<container::string_view>::value, "Invalid function presence detection");
static_assert(!has_member_function_clear<container::array_view<std::uint8_t>>::value,
"Invalid function presence detection");
static_assert(has_member_function_resize<std::string>::value, "Invalid function presence detection");
static_assert(has_member_function_resize<std::vector<std::uint8_t>>::value, "Invalid function presence detection");
static_assert(has_member_function_resize<container::static_string<5>>::value, "Invalid function presence detection");
static_assert(has_member_function_resize<container::static_vector<std::uint8_t, 5>>::value,
"Invalid function presence detection");
static_assert(!has_member_function_resize<container::string_view>::value, "Invalid function presence detection");
static_assert(!has_member_function_resize<container::array_view<std::uint8_t>>::value,
"Invalid function presence detection");
static_assert(has_member_function_reserve<std::string>::value, "Invalid function presence detection");
static_assert(has_member_function_reserve<std::vector<std::uint8_t>>::value, "Invalid function presence detection");
static_assert(has_member_function_reserve<container::static_string<5>>::value, "Invalid function presence detection");
static_assert(has_member_function_reserve<container::static_vector<std::uint8_t, 5>>::value,
"Invalid function presence detection");
static_assert(!has_member_function_reserve<container::string_view>::value, "Invalid function presence detection");
static_assert(!has_member_function_reserve<container::array_view<std::uint8_t>>::value,
"Invalid function presence detection");
struct types_fixture {
typedef option::big_endian BigEndianOpt;
typedef option::little_endian LittleEndianOpt;
enum Enum1 { Enum1_Value1, Enum1_Value2, Enum1_Value3, Enum1_NumOfValues };
enum class Enum2 : unsigned { Value1, Value2, Value3, Value4, NumOfValues };
template<typename TField, typename OutputIterator>
void write_read_field(const TField &field, const OutputIterator expectedBuf, std::size_t size,
status_type expectedStatus = status_type::success) {
std::vector<char> outDataBuf(size);
pack<TField>(field, outDataBuf.begin(), expectedStatus);
bool bufAsExpected = std::equal(expectedBuf, expectedBuf + size, outDataBuf.begin());
if (!bufAsExpected) {
std::cout << "Expected buffer: " << std::hex;
std::copy_n(expectedBuf, size, std::ostream_iterator<unsigned>(std::cout, " "));
std::cout << "\nActual buffer: ";
std::copy_n(&outDataBuf[0], size, std::ostream_iterator<unsigned>(std::cout, " "));
std::cout << std::dec << std::endl;
}
BOOST_CHECK(bufAsExpected);
TField newField = pack<TField>(outDataBuf.begin(), outDataBuf.begin() + size, expectedStatus);
BOOST_CHECK(field == newField);
BOOST_CHECK(field.value() == newField.value());
}
template<typename TFP>
bool fpEquals(TFP value1, TFP value2) {
return (std::abs(value1 - value2) < std::numeric_limits<TFP>::epsilon());
}
};
BOOST_FIXTURE_TEST_SUITE(types_accumulator_test_suite, types_fixture)
BOOST_AUTO_TEST_CASE(types_accumulator_test_minus1) {
using big_endian_array_type = types::array_list<field_type<option::big_endian>, std::uint32_t>;
static const std::vector<std::uint8_t> Buf
= {0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f, 0x10};
big_endian_array_type be_array = pack<big_endian_array_type>(Buf.begin(), Buf.end());
std::vector<std::uint32_t> v = be_array.value();
BOOST_CHECK_EQUAL(v[0], 0x01020304);
BOOST_CHECK_EQUAL(v[1], 0x05060708);
BOOST_CHECK_EQUAL(v[2], 0x090a0b0c);
BOOST_CHECK_EQUAL(v[3], 0x0d0e0f10);
BOOST_CHECK(be_array.valid());
BOOST_CHECK(!be_array.set_version(5));
}
BOOST_AUTO_TEST_CASE(types_accumulator_test_minus2) {
using little_endian_array_type = types::array_list<field_type<option::little_endian>, std::uint32_t>;
static const std::vector<std::uint8_t> Buf
= {0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f, 0x10};
little_endian_array_type le_array = pack<little_endian_array_type>(Buf.begin(), Buf.end());
std::vector<std::uint32_t> v = le_array.value();
BOOST_CHECK_EQUAL(v[0], 0x04030201);
BOOST_CHECK_EQUAL(v[1], 0x08070605);
BOOST_CHECK_EQUAL(v[2], 0x0c0b0a09);
BOOST_CHECK_EQUAL(v[3], 0x100f0e0d);
BOOST_CHECK(le_array.valid());
BOOST_CHECK(!le_array.set_version(5));
}
BOOST_AUTO_TEST_CASE(types_accumulator_test_minus3) {
using big_endian_array_type =
types::array_list<
field_type<option::big_endian>,
types::integral<
field_type<option::big_endian>,
std::uint32_t>,
option::sequence_fixed_size<5>
>;
static const std::vector<std::uint8_t> Buf = {0x01, 0x02, 0x03, 0x04,
0x05, 0x06, 0x07, 0x08,
0x09, 0x0a, 0x0b, 0x0c,
0x0d, 0x0e, 0x0f, 0x10,
0x11, 0x12, 0x13, 0x14,
0x15, 0x16, 0x17, 0x18,
0x19, 0x1a, 0x1b, 0x1c,
0x1d, 0x1e, 0x1f, 0x20};
big_endian_array_type be_array = pack<big_endian_array_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL((be_array.value())[0].value(), 0x01020304);
BOOST_CHECK_EQUAL((be_array.value())[1].value(), 0x05060708);
BOOST_CHECK_EQUAL((be_array.value())[2].value(), 0x090a0b0c);
BOOST_CHECK_EQUAL((be_array.value())[3].value(), 0x0d0e0f10);
BOOST_CHECK_EQUAL((be_array.value())[4].value(), 0x11121314);
BOOST_CHECK(be_array.valid());
BOOST_CHECK(!be_array.set_version(5));
}
BOOST_AUTO_TEST_CASE(types_accumulator_test1) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<std::uint8_t> Buf = {0x01, 0x02, 0x03, 0x04};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), sizeof(std::uint32_t));
BOOST_CHECK_EQUAL(field.value(), 0x01020304);
BOOST_CHECK(field.valid());
BOOST_CHECK(!field.set_version(5));
}
BOOST_AUTO_TEST_CASE(types_accumulator_test2) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::fixed_length<3>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<std::uint8_t> Buf = {0x01, 0x02, 0x03, 0x04};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), 3);
BOOST_CHECK_EQUAL(field.value(), 0x010203);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test3) {
typedef types::integral<field_type<option::big_endian>, std::int16_t> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<std::uint8_t> Buf = {0x01, 0x02};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), sizeof(std::int16_t));
BOOST_CHECK_EQUAL(field.value(), static_cast<std::int16_t>(0x0102));
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test4) {
typedef types::integral<field_type<option::big_endian>, std::int16_t> testing_type;
static const std::vector<char> Buf = {(char)0xff, (char)0xff};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), sizeof(std::int16_t));
BOOST_CHECK_EQUAL(field.value(), -1);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test5) {
typedef types::integral<field_type<option::little_endian>, std::int16_t> testing_type;
static const std::vector<char> Buf = {0x0, (char)0x80};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), sizeof(std::int16_t));
BOOST_CHECK_EQUAL(field.value(), std::numeric_limits<std::int16_t>::min());
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test6) {
typedef types::integral<field_type<option::big_endian>, std::int16_t, option::fixed_length<1>> testing_type;
static const std::vector<char> Buf = {(char)0xff, 0x00};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), 1);
BOOST_CHECK_EQUAL(field.value(), -1);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test7) {
typedef types::integral<field_type<option::big_endian>, std::int16_t, option::fixed_length<1>,
option::num_value_ser_offset<-2000>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<char> Buf = {13};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 1);
BOOST_CHECK(field.value() == 2013);
BOOST_CHECK(field.valid());
field.value() = 2000;
static const std::vector<char> ExpectedBuf = {0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = 2000 + 0x7f;
static const std::vector<char> ExpectedBuf2 = {(char)0x7f};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test8) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::fixed_length<3>,
option::valid_num_value_range<0, 0x010200>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value() == 0U);
field.value() = 0x010200;
BOOST_CHECK(field.value() == 0x010200);
BOOST_CHECK(field.valid());
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf = {0x01, 0x02, 0x03, 0x04};
field = pack<testing_type>(Buf.begin(), Buf.end(), acc);
BOOST_CHECK(field.length() == 3);
BOOST_CHECK(field.value() == 0x010203);
BOOST_CHECK(!field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test9) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 10>,
#ifndef CC_COMPILER_GCC47
option::valid_num_value_range<20, 30>,
#endif
option::default_num_value<100>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 100);
BOOST_CHECK(!field.valid());
field.value() = 5U;
BOOST_CHECK(field.valid());
field.value() = 15U;
BOOST_CHECK(!field.valid());
#ifndef CC_COMPILER_GCC47
field.value() = 25U;
BOOST_CHECK(field.valid());
#endif
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf = {0x05, 0x02};
field = pack<testing_type>(Buf.begin(), Buf.end(), acc);
BOOST_CHECK(field.length() == 1);
BOOST_CHECK(field.value() == 0x05);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test10) {
typedef types::bitmask_value<field_type<option::big_endian>, option::fixed_length<2>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value() == 0U);
static const std::vector<char> Buf = {
(char)0xde,
(char)0xad,
};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 2);
BOOST_CHECK(field.value() == 0xdead);
BOOST_CHECK(field.get_bit_value(0U) == true);
BOOST_CHECK(field.get_bit_value(1U) == false);
BOOST_CHECK(field.get_bit_value(2U) == true);
BOOST_CHECK(field.get_bit_value(3U) == true);
BOOST_CHECK(field.get_bit_value(4U) == false);
BOOST_CHECK(field.get_bit_value(5U) == true);
BOOST_CHECK(field.get_bit_value(6U) == false);
BOOST_CHECK(field.get_bit_value(7U) == true);
BOOST_CHECK(field.get_bit_value(8U) == false);
BOOST_CHECK(field.get_bit_value(9U) == true);
BOOST_CHECK(field.get_bit_value(10U) == true);
BOOST_CHECK(field.get_bit_value(11U) == true);
BOOST_CHECK(field.get_bit_value(12U) == true);
BOOST_CHECK(field.get_bit_value(13U) == false);
BOOST_CHECK(field.get_bit_value(14U) == true);
BOOST_CHECK(field.get_bit_value(15U) == true);
field.set_bit_value(1U, true);
BOOST_CHECK(field.value() == 0xdeaf);
field.set_bits(0x2);
BOOST_CHECK(field.value() == 0xdeaf);
BOOST_CHECK(field.valid());
static const std::vector<char> ExpectedBuf = {(char)0xde, (char)0xaf};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test11) {
typedef types::bitmask_value<field_type<option::little_endian>, option::fixed_length<3>,
option::default_num_value<0xffffff>, option::bitmask_reserved_bits<0xff0000, 0>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value() == 0xffffff);
static const std::vector<char> Buf = {(char)0xde, (char)0xad, (char)0x00, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 3);
BOOST_CHECK(field.value() == 0xadde);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.get_bit_value(0U) == false);
BOOST_CHECK(field.get_bit_value(1U) == true);
BOOST_CHECK(field.get_bit_value(2U) == true);
BOOST_CHECK(field.get_bit_value(3U) == true);
BOOST_CHECK(field.get_bit_value(4U) == true);
BOOST_CHECK(field.get_bit_value(5U) == false);
BOOST_CHECK(field.get_bit_value(6U) == true);
BOOST_CHECK(field.get_bit_value(7U) == true);
BOOST_CHECK(field.get_bit_value(8U) == true);
BOOST_CHECK(field.get_bit_value(9U) == false);
BOOST_CHECK(field.get_bit_value(10U) == true);
BOOST_CHECK(field.get_bit_value(11U) == true);
BOOST_CHECK(field.get_bit_value(12U) == false);
BOOST_CHECK(field.get_bit_value(13U) == true);
BOOST_CHECK(field.get_bit_value(14U) == false);
BOOST_CHECK(field.get_bit_value(15U) == true);
BOOST_CHECK(field.get_bit_value(16U) == false);
BOOST_CHECK(field.get_bit_value(17U) == false);
BOOST_CHECK(field.get_bit_value(18U) == false);
BOOST_CHECK(field.get_bit_value(19U) == false);
BOOST_CHECK(field.get_bit_value(20U) == false);
BOOST_CHECK(field.get_bit_value(21U) == false);
BOOST_CHECK(field.get_bit_value(22U) == false);
BOOST_CHECK(field.get_bit_value(23U) == false);
field.set_bits(0x10000);
BOOST_CHECK(field.value() == 0x1adde);
BOOST_CHECK(!field.valid());
field.set_bit_value(0U, true);
BOOST_CHECK(field.value() == 0x1addf);
field.set_bit_value(16U, false);
BOOST_CHECK(field.value() == 0xaddf);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test12) {
typedef types::enumeration<field_type<option::big_endian>, Enum1, option::fixed_length<1>,
option::valid_num_value_range<0, Enum1_NumOfValues - 1>,
option::default_num_value<Enum1_NumOfValues>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value() == Enum1_NumOfValues);
static const std::vector<char> Buf = {(char)Enum1_Value1, (char)0x3f};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 1);
BOOST_CHECK(field.value() == Enum1_Value1);
BOOST_CHECK(field.valid());
field.value() = Enum1_NumOfValues;
BOOST_CHECK(!field.valid());
field.value() = Enum1_Value2;
static const std::vector<char> ExpectedBuf = {(char)Enum1_Value2};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test13) {
typedef types::enumeration<field_type<option::big_endian>, Enum2, option::fixed_length<2>,
option::valid_num_value_range<0, (int)(Enum2::NumOfValues)-1>,
option::default_num_value<(int)Enum2::NumOfValues>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value() == Enum2::NumOfValues);
static const std::vector<char> Buf = {0x0, (char)Enum2::Value4, (char)0x3f};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 2);
BOOST_CHECK(field.value() == Enum2::Value4);
BOOST_CHECK(field.valid());
field.value() = Enum2::NumOfValues;
BOOST_CHECK(!field.valid());
field.value() = Enum2::Value3;
static const std::vector<char> ExpectedBuf = {0x0, (char)Enum2::Value3};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test14) {
typedef types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint8_t>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
BOOST_CHECK(!field.refresh());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test15) {
typedef types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint8_t>,
option::fixed_size_storage<32>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
static const std::vector<char> Buf2 = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9, 0xa, 0xb, 0xc};
field = pack<testing_type>(Buf2.begin(), Buf2.end());
BOOST_CHECK(field.length() == Buf2.size());
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test16) {
struct SizeField : public types::integral<field_type<option::big_endian>, std::uint8_t> { };
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::fixed_size_storage<256>>
StaticStorageField;
static_assert(!StaticStorageField::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
StaticStorageField staticStorageField;
BOOST_CHECK(staticStorageField.valid());
BOOST_CHECK(staticStorageField.value().empty());
static const std::vector<char> ExpectedBuf = {0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
write_read_field(staticStorageField, ExpectedBuf.begin(), ExpectedBuf.size());
static const std::vector<char> Buf = {0x5, 'h', 'e', 'l', 'l', 'o', 'g', 'a', 'r'};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value().size() == static_cast<std::size_t>(Buf[0]));
BOOST_CHECK(field.length() == field.value().size() + 1U);
BOOST_CHECK(field.valid());
staticStorageField = pack<StaticStorageField>(Buf.begin(), Buf.end());
BOOST_CHECK(staticStorageField.value().size() == static_cast<std::size_t>(Buf[0]));
BOOST_CHECK(staticStorageField.length() == staticStorageField.value().size() + 1U);
BOOST_CHECK(staticStorageField.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test17) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 4>> SizeField;
static_assert(!SizeField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == SizeField::max_length());
BOOST_CHECK(testing_type::max_length() == SizeField::max_length() + std::numeric_limits<std::uint16_t>::max());
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::fixed_size_storage<256>>
StaticStorageField;
static_assert(!StaticStorageField::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(StaticStorageField::min_length() == SizeField::max_length());
BOOST_CHECK(StaticStorageField::max_length() == SizeField::max_length() + 255);
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
StaticStorageField staticStorageField;
BOOST_CHECK(staticStorageField.valid());
BOOST_CHECK(staticStorageField.value().empty());
static const std::vector<char> Buf = {0x5, 'h', 'e', 'l', 'l', 'o', 'g', 'a', 'r'};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value().size() == static_cast<std::size_t>(Buf[0]));
BOOST_CHECK(field.length() == field.value().size() + 1U);
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value() == "hello");
staticStorageField = pack<StaticStorageField>(Buf.begin(), Buf.end());
BOOST_CHECK(staticStorageField.value().size() == static_cast<std::size_t>(Buf[0]));
BOOST_CHECK(staticStorageField.length() == field.value().size() + 1U);
BOOST_CHECK(!staticStorageField.valid());
BOOST_CHECK(std::string(staticStorageField.value().c_str()) == std::string("hello"));
}
struct HelloInitialiser {
template<typename TField>
void operator()(TField &&field) {
field.value() = "hello";
}
};
BOOST_AUTO_TEST_CASE(types_accumulator_test18) {
typedef types::integral<field_type<option::big_endian>, std::uint16_t> SizeField;
static_assert(!SizeField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::default_value_initializer<HelloInitialiser>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::default_value_initializer<HelloInitialiser>, option::fixed_size_storage<64>>
StaticStorageField;
static_assert(!StaticStorageField::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(!field.value().empty());
BOOST_CHECK(field.value() == "hello");
field.value().clear();
BOOST_CHECK(field.value().empty());
field.value() = "bla";
BOOST_CHECK(field.value() == "bla");
BOOST_CHECK(field.value().size() == 3);
BOOST_CHECK(field.length() == 5);
StaticStorageField staticStorageField;
BOOST_CHECK(staticStorageField.valid());
BOOST_CHECK(!staticStorageField.value().empty());
BOOST_CHECK(std::string(staticStorageField.value().c_str()) == std::string("hello"));
staticStorageField.value().clear();
BOOST_CHECK(staticStorageField.value().empty());
staticStorageField.value() = "bla";
BOOST_CHECK(std::string(staticStorageField.value().c_str()) == std::string("bla"));
BOOST_CHECK(staticStorageField.value().size() == 3);
BOOST_CHECK(staticStorageField.length() == 5);
static const std::vector<char> ExpectedBuf = {0x0, 0x3, 'b', 'l', 'a'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
write_read_field(staticStorageField, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test19) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t> SizeField;
static_assert(!SizeField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::fixed_size_storage<64>>
StaticStorageField;
static_assert(!StaticStorageField::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
auto &fieldStr = field.value();
BOOST_CHECK(field.valid());
BOOST_CHECK(fieldStr.empty());
StaticStorageField staticStorageField;
auto &staticStorageFieldStr = staticStorageField.value();
BOOST_CHECK(staticStorageField.valid());
BOOST_CHECK(staticStorageFieldStr.empty());
static const std::string Str("hello");
std::copy(Str.begin(), Str.end(), std::back_inserter(fieldStr));
BOOST_CHECK(!fieldStr.empty());
BOOST_CHECK(fieldStr.size() == Str.size());
BOOST_CHECK(fieldStr == Str);
std::copy(Str.begin(), Str.end(), std::back_inserter(staticStorageFieldStr));
BOOST_CHECK(!staticStorageFieldStr.empty());
BOOST_CHECK(staticStorageFieldStr.size() == Str.size());
BOOST_CHECK(std::string(staticStorageFieldStr.c_str()) == std::string(Str.c_str()));
static const std::vector<char> ExpectedBuf = {0x5, 'h', 'e', 'l', 'l', 'o'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
write_read_field(staticStorageField, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test20) {
typedef types::integral<field_type<option::little_endian>, std::uint16_t, option::var_length<1, 2>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<char> Buf = {(char)0x81, 0x01};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), 2U);
BOOST_CHECK_EQUAL(field.value(), static_cast<std::uint16_t>(0x81));
BOOST_CHECK(field.valid());
field.value() = 0x7ff;
BOOST_CHECK_EQUAL(field.length(), 2U);
static const std::vector<char> ExpectedBuf = {(char)0xff, 0x0f};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test21) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 3>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<char> Buf = {(char)0x83, 0x0f};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK_EQUAL(field.length(), 2U);
BOOST_CHECK_EQUAL(field.value(), static_cast<std::uint32_t>(0x18f));
BOOST_CHECK(field.valid());
field.value() = 0x7ff;
BOOST_CHECK_EQUAL(field.length(), 2U);
static const std::vector<char> ExpectedBuf = {(char)0x8f, (char)0x7f};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = 0x7f;
BOOST_CHECK_EQUAL(field.length(), 1U);
BOOST_CHECK_EQUAL(field.value(), 0x7f);
static const std::vector<char> ExpectedBuf2 = {(char)0x7f};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
static const std::vector<char> Buf2 = {(char)0x91, (char)0xc2, (char)0x3f, (char)0xff};
field = pack<testing_type>(Buf2.begin(), Buf2.end());
BOOST_CHECK_EQUAL(field.length(), 3U);
BOOST_CHECK_EQUAL(field.value(), static_cast<std::uint32_t>(0x4613f));
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test22) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 3>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<char> Buf = {(char)0x83, (char)0x8f, (char)0x8c, (char)0x3f, (char)0xff};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end(), status_type::protocol_error);
static_cast<void>(field);
}
BOOST_AUTO_TEST_CASE(types_accumulator_test23) {
typedef types::integral<field_type<option::little_endian>, std::int16_t, option::var_length<1, 2>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = static_cast<int16_t>(0xe000);
BOOST_CHECK_EQUAL(field.length(), 2U);
static const std::vector<char> ExpectedMinValueBuf = {(char)0x80, (char)0x40};
write_read_field(field, ExpectedMinValueBuf.begin(), ExpectedMinValueBuf.size());
field.value() = 0x1fff;
BOOST_CHECK_EQUAL(field.length(), 2U);
static const std::vector<char> ExpectedMaxValueBuf = {(char)0xff, (char)0x3f};
write_read_field(field, ExpectedMaxValueBuf.begin(), ExpectedMaxValueBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test24) {
typedef types::integral<field_type<option::big_endian>, unsigned, option::fixed_length<2>,
option::num_value_ser_offset<2>, option::valid_num_value_range<0, 2>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::vector<char> Buf = {0x00, 0x02};
testing_type field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 2);
BOOST_CHECK(field.value() == 0x0);
BOOST_CHECK(field.valid());
field.value() = 3;
BOOST_CHECK(!field.valid());
static const std::vector<char> ExpectedBuf = {0x00, 0x05};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(types_accumulator_test25) {
typedef std::tuple<
types::integral<field_type<option::big_endian>, std::uint8_t, option::fixed_bit_length<2>>,
types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1>, option::fixed_bit_length<6>>>
BitfileMembers;
typedef types::bitfield<field_type<option::big_endian>, BitfileMembers> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
static_cast<void>(field);
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(field.member_bit_length<0>() == 2U);
BOOST_CHECK(field.member_bit_length<1>() == 6U);
static const std::vector<char> Buf = {(char)0x41, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &members = field.value();
auto &mem1 = std::get<0>(members);
BOOST_CHECK(mem1.value() == 0x1);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem2.value() == 0x10);
}
BOOST_AUTO_TEST_CASE(test26) {
typedef std::tuple<
types::integral<field_type<option::big_endian>, std::uint8_t, option::fixed_bit_length<3>>,
types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1>, option::fixed_bit_length<5>>>
BitfileMembers;
typedef types::bitfield<field_type<option::big_endian>, BitfileMembers> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
static_cast<void>(field);
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(field.member_bit_length<0>() == 3U);
BOOST_CHECK(field.member_bit_length<1>() == 5U);
static const std::vector<char> Buf = {(char)0x09, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &members = field.value();
auto &mem1 = std::get<0>(members);
BOOST_CHECK(mem1.value() == 0x1);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem2.value() == 0x1);
}
using Test27_FieldBase = field_type<option::big_endian>;
typedef std::tuple<types::integral<Test27_FieldBase, std::uint8_t, option::fixed_bit_length<4>>,
types::bitmask_value<Test27_FieldBase, option::fixed_length<1>, option::fixed_bit_length<8>>,
types::enumeration<Test27_FieldBase, types_fixture::Enum1, option::fixed_bit_length<4>>>
Test27_BitfildMembers;
template<typename... TExtraOpts>
class Test27_Field : public types::bitfield<Test27_FieldBase, Test27_BitfildMembers, TExtraOpts...> {
using Base = types::bitfield<Test27_FieldBase, Test27_BitfildMembers, TExtraOpts...>;
public:
MARSHALLING_FIELD_MEMBERS_ACCESS(mem1, mem2, mem3);
};
BOOST_AUTO_TEST_CASE(test27) {
using testing_type = Test27_Field<>;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.member_bit_length<testing_type::FieldIdx_mem1>() == 4U);
BOOST_CHECK(field.member_bit_length<testing_type::FieldIdx_mem2>() == 8U);
BOOST_CHECK(field.member_bit_length<testing_type::FieldIdx_mem3>() == 4U);
static const std::vector<char> Buf = {(char)0x4f, (char)0xa1, (char)0xaa};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &mem1 = field.field_mem1();
BOOST_CHECK(mem1.value() == 0x1);
auto &mem2 = field.field_mem2();
BOOST_CHECK(mem2.value() == 0xfa);
auto &mem3 = field.field_mem3();
BOOST_CHECK(mem3.value() == 0x4);
}
BOOST_AUTO_TEST_CASE(test28) {
typedef types::array_list<
field_type<option::big_endian>,
types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 5>>,
option::sequence_size_field_prefix<types::integral<field_type<option::big_endian>, std::uint16_t>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == sizeof(std::uint16_t));
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf = {0x0, 0xa, 0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value().size() == 10U);
field.value().resize(5);
static const std::vector<char> ExpectedBuf = {0x0, 0x5, 0x0, 0x1, 0x2, 0x3, 0x4};
BOOST_CHECK(field.valid());
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test29) {
typedef types::enumeration<field_type<option::big_endian>, Enum1, option::fixed_length<2>,
option::valid_num_value_range<0, Enum1_NumOfValues - 1>,
option::default_num_value<Enum1_Value2>,
option::fail_on_invalid<status_type::protocol_error>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value() == Enum1_Value2);
static const std::vector<char> Buf = {0x0, (char)Enum1_Value1, (char)0x3f};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 2);
BOOST_CHECK(field.value() == Enum1_Value1);
BOOST_CHECK(field.valid());
static const std::vector<char> Buf2 = {0x0, (char)Enum1_NumOfValues, (char)0x3f};
field = pack<testing_type>(Buf2.begin(), Buf2.end(), status_type::protocol_error);
field.value() = Enum1_Value3;
BOOST_CHECK(field.valid());
static const std::vector<char> ExpectedBuf = {0x0, (char)Enum1_Value3};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test30) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::default_num_value<0x2>,
option::valid_num_value_range<0x2, 0x2>, option::ignore_invalid>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value() == 0x2);
static const std::vector<char> Buf = {0x0f};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value() == 0x2);
BOOST_CHECK(field.valid());
static const std::vector<char> Buf2 = {0x00, 0x02, (char)0xff};
field = pack<testing_type>(Buf2.begin(), Buf2.end());
BOOST_CHECK(field.value() == 0x2);
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(test31) {
typedef types::optional<
types::integral<field_type<option::big_endian>, std::uint16_t, option::valid_num_value_range<0, 10>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
typedef testing_type::Mode Mode;
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.field().value() == 0U);
BOOST_CHECK(field.get_mode() == Mode::tentative);
static const std::vector<char> Buf = {0x0f, (char)0xf0};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.field().value() == 0xff0);
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.get_mode() == Mode::exists);
field.set_mode(Mode::missing);
}
BOOST_AUTO_TEST_CASE(test32) {
typedef types::bundle<
field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint16_t, option::valid_num_value_range<0, 10>,
option::default_num_value<5>>,
types::enumeration<field_type<option::big_endian>, Enum1, option::fixed_length<1>,
option::valid_num_value_range<0, Enum1_NumOfValues - 1>,
option::default_num_value<Enum1_Value2>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 3U, "Invalid min_length");
static_assert(testing_type::min_length_from<1>() == 1U, "Invalid min_length");
static_assert(testing_type::min_length_until<1>() == 2U, "Invalid min_length");
static_assert(testing_type::max_length() == 3U, "Invalid max_length");
static_assert(testing_type::max_length_from<1>() == 1U, "Invalid min_length");
static_assert(testing_type::max_length_until<1>() == 2U, "Invalid min_length");
testing_type field;
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.length_from<1>() == 1U);
BOOST_CHECK(field.length_until<1>() == 2U);
BOOST_CHECK(field.valid());
auto &intValField = std::get<0>(field.value());
auto &enumValField = std::get<1>(field.value());
BOOST_CHECK(intValField.value() == 5U);
BOOST_CHECK(enumValField.value() == Enum1_Value2);
intValField.value() = 50U;
BOOST_CHECK(!field.valid());
intValField.value() = 1U;
BOOST_CHECK(field.valid());
enumValField.value() = Enum1_NumOfValues;
BOOST_CHECK(!field.valid());
static const std::vector<char> Buf = {0x00, 0x3, Enum1_Value3, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.valid());
BOOST_CHECK(intValField.value() == 3U);
BOOST_CHECK(enumValField.value() == Enum1_Value3);
intValField.value() = 0xabcd;
enumValField.value() = Enum1_Value1;
static const std::vector<char> ExpectedBuf = {(char)0xab, (char)0xcd, (char)Enum1_Value1};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
testing_type fieldTmp;
auto readIter = &ExpectedBuf[0];
status_type es = fieldTmp.read_from_until<0, 2>(readIter, ExpectedBuf.size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(fieldTmp == field);
fieldTmp = testing_type();
BOOST_CHECK(fieldTmp != field);
readIter = &ExpectedBuf[0];
es = fieldTmp.read_until<1>(readIter, 2);
BOOST_CHECK(es == status_type::success);
es = fieldTmp.read_from<1>(readIter, 1);
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(fieldTmp == field);
std::vector<std::uint8_t> outBuf;
auto writeIter = std::back_inserter(outBuf);
es = fieldTmp.write_from_until<0, 2>(writeIter, outBuf.max_size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(outBuf.size() == ExpectedBuf.size());
BOOST_CHECK(std::equal(outBuf.begin(), outBuf.end(), (const std::uint8_t *)&ExpectedBuf[0]));
outBuf.clear();
writeIter = std::back_inserter(outBuf);
es = fieldTmp.write_until<1>(writeIter, outBuf.max_size());
BOOST_CHECK(es == status_type::success);
es = fieldTmp.write_from<1>(writeIter, outBuf.max_size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(outBuf.size() == ExpectedBuf.size());
BOOST_CHECK(std::equal(outBuf.begin(), outBuf.end(), (const std::uint8_t *)&ExpectedBuf[0]));
}
BOOST_AUTO_TEST_CASE(test33) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t> SizeField;
static_assert(!SizeField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>> StringField;
static_assert(!StringField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::array_list<field_type<option::big_endian>, StringField> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == 0U);
BOOST_CHECK(testing_type::max_length() == 0xffff * StringField::max_length());
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
static const std::vector<char> Buf = {0x05, 'h', 'e', 'l', 'l', 'o', 0x03, 'b', 'l', 'a'};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value()[0].value() == "hello");
BOOST_CHECK(field.value()[1].value() == "bla");
}
BOOST_AUTO_TEST_CASE(test34) {
typedef types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint8_t>,
option::sequence_size_forcing_enabled>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
static const std::size_t MaxCount = 5;
field.force_read_elem_count(MaxCount);
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end(), acc);
BOOST_CHECK(field.length() == MaxCount);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == MaxCount);
}
BOOST_AUTO_TEST_CASE(test35) {
typedef types::float_value<field_type<option::big_endian>, float> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(fpEquals(field.value(), 0.0f));
field.value() = 1.23f;
BOOST_CHECK(fpEquals(field.value(), 1.23f));
std::vector<std::uint8_t> buf;
auto writeIter = std::back_inserter(buf);
status_type es = field.write(writeIter, buf.max_size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(buf.size() == sizeof(float));
field = testing_type();
BOOST_CHECK(fpEquals(field.value(), 0.0f));
const auto *readIter = &buf[0];
field = pack<testing_type>(buf.begin(), buf.end());
BOOST_CHECK(fpEquals(field.value(), 1.23f));
}
BOOST_AUTO_TEST_CASE(test36) {
typedef types::array_list<field_type<option::big_endian>, std::uint8_t, option::sequence_fixed_size<5>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 5U, "Invalid min length");
static_assert(testing_type::max_length() == 5U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(testing_type::min_length() == 5U);
BOOST_CHECK(testing_type::max_length() == 5U);
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == Buf.size());
BOOST_CHECK(!field.refresh());
}
BOOST_AUTO_TEST_CASE(test37) {
typedef types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint16_t>,
option::sequence_fixed_size<3>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 6U, "Invalid min length");
static_assert(testing_type::max_length() == 6U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 6U);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 3U);
BOOST_CHECK((field.value())[0].value() == 0x1);
BOOST_CHECK((field.value())[1].value() == 0x203);
BOOST_CHECK((field.value())[2].value() == 0x405);
}
BOOST_AUTO_TEST_CASE(test38) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 0>> TrailField;
static_assert(!TrailField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_fixed_size<5>,
option::sequence_trailing_field_suffix<TrailField>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(std::is_same<testing_type::value_type, std::string>::value, "Invalid storage assumption assumption");
static_assert(testing_type::min_length() == 6U, "Invalid min length");
static_assert(testing_type::max_length() == 6U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 6U);
field.value() = "hello";
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o', 0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = "foo";
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf2 = {'f', 'o', 'o', 0x0, 0x0, 0x0};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field = pack<testing_type>(ExpectedBuf2.begin(), ExpectedBuf2.end());
BOOST_CHECK(field.value() == "foo");
}
BOOST_AUTO_TEST_CASE(test39) {
typedef types::float_value<field_type<option::big_endian>, float, option::valid_num_value_range<5, 10>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(fpEquals(field.value(), 0.0f));
BOOST_CHECK(!field.valid());
field.value() = 4.999999f;
BOOST_CHECK(fpEquals(field.value(), 4.999999f));
BOOST_CHECK(!field.valid());
field.value() = 5.00001f;
BOOST_CHECK(fpEquals(field.value(), 5.00001f));
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(test40) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 100>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 0U);
BOOST_CHECK(field.scale_as<double>() == 0.0);
field.set_scaled(0.15);
BOOST_CHECK(field.value() == 15U);
static const std::vector<char> Buf = {115};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value() == 115);
BOOST_CHECK(fpEquals(field.scale_as<float>(), 1.15f));
}
BOOST_AUTO_TEST_CASE(test41) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 0>> TermField;
static_assert(!TermField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_termination_field_suffix<TermField>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 1U);
field.value() = "hello";
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o', 0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
static const std::vector<char> InputBuf = {'f', 'o', 'o', 0x0, 'b', 'l', 'a'};
field = pack<testing_type>(InputBuf.begin(), InputBuf.end());
BOOST_CHECK(field.value() == "foo");
BOOST_CHECK(field.value().size() == 3U);
}
BOOST_AUTO_TEST_CASE(test42) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 4>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 0U);
BOOST_CHECK(field.length() == 1U);
field.value() = 127U;
BOOST_CHECK(field.length() == 1U);
static const std::vector<char> ExpectedBuf1 = {(char)0x7f};
write_read_field(field, ExpectedBuf1.begin(), ExpectedBuf1.size());
field.value() = 128U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf2 = {(char)0x81, 0x00};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field.value() = 0x3fff;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf3 = {(char)0xff, (char)0x7f};
write_read_field(field, ExpectedBuf3.begin(), ExpectedBuf3.size());
field.value() = 0x4000;
BOOST_CHECK(field.length() == 3U);
static const std::vector<char> ExpectedBuf4 = {(char)0x81, (char)0x80, (char)0x00};
write_read_field(field, ExpectedBuf4.begin(), ExpectedBuf4.size());
field.value() = 0x1fffff;
BOOST_CHECK(field.length() == 3U);
static const std::vector<char> ExpectedBuf5 = {(char)0xff, (char)0xff, (char)0x7f};
write_read_field(field, ExpectedBuf5.begin(), ExpectedBuf5.size());
field.value() = 0x200000;
BOOST_CHECK(field.length() == 4U);
static const std::vector<char> ExpectedBuf6 = {(char)0x81, (char)0x80, (char)0x80, (char)0x00};
write_read_field(field, ExpectedBuf6.begin(), ExpectedBuf6.size());
}
BOOST_AUTO_TEST_CASE(test43) {
typedef types::integral<field_type<option::little_endian>, std::uint32_t, option::var_length<1, 4>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 0U);
BOOST_CHECK(field.length() == 1U);
field.value() = 127U;
BOOST_CHECK(field.length() == 1U);
static const std::vector<char> ExpectedBuf1 = {(char)0x7f};
write_read_field(field, ExpectedBuf1.begin(), ExpectedBuf1.size());
field.value() = 128U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf2 = {(char)0x80, 0x01};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field.value() = 0x3fff;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf3 = {(char)0xff, (char)0x7f};
write_read_field(field, ExpectedBuf3.begin(), ExpectedBuf3.size());
field.value() = 0x4000;
BOOST_CHECK(field.length() == 3U);
static const std::vector<char> ExpectedBuf4 = {(char)0x80, (char)0x80, (char)0x01};
write_read_field(field, ExpectedBuf4.begin(), ExpectedBuf4.size());
field.value() = 0x1fffff;
BOOST_CHECK(field.length() == 3U);
static const std::vector<char> ExpectedBuf5 = {(char)0xff, (char)0xff, (char)0x7f};
write_read_field(field, ExpectedBuf5.begin(), ExpectedBuf5.size());
field.value() = 0x200000;
BOOST_CHECK(field.length() == 4U);
static const std::vector<char> ExpectedBuf6 = {(char)0x80, (char)0x80, (char)0x80, (char)0x01};
write_read_field(field, ExpectedBuf6.begin(), ExpectedBuf6.size());
}
BOOST_AUTO_TEST_CASE(test44) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<2, 4>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 0U);
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf1 = {(char)0x80, 0x00};
write_read_field(field, ExpectedBuf1.begin(), ExpectedBuf1.size());
field.value() = 127U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf2 = {(char)0x80, 0x7f};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field.value() = 128U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf3 = {(char)0x81, 0x00};
write_read_field(field, ExpectedBuf3.begin(), ExpectedBuf3.size());
}
BOOST_AUTO_TEST_CASE(test45) {
typedef types::integral<field_type<option::little_endian>, std::uint32_t, option::var_length<2, 4>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 0U);
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf1 = {(char)0x80, 0x00};
write_read_field(field, ExpectedBuf1.begin(), ExpectedBuf1.size());
field.value() = 127U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf2 = {(char)0xff, 0x00};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field.value() = 128U;
BOOST_CHECK(field.length() == 2U);
static const std::vector<char> ExpectedBuf3 = {(char)0x80, 0x01};
write_read_field(field, ExpectedBuf3.begin(), ExpectedBuf3.size());
}
BOOST_AUTO_TEST_CASE(test46) {
typedef types::no_value<field_type<option::big_endian>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
static const std::vector<char> ExpectedBuf = {0};
write_read_field(field, ExpectedBuf.begin(), 0);
}
struct BundleInitialiserTest47 {
template<typename TField>
void operator()(TField &field) const {
auto &members = field.value();
auto &first = std::get<0>(members);
auto &second = std::get<1>(members);
first.value() = 1;
second.value() = 2;
}
};
BOOST_AUTO_TEST_CASE(test47) {
typedef types::bundle<field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint16_t>,
types::integral<field_type<option::big_endian>, std::uint8_t>>,
option::default_value_initializer<BundleInitialiserTest47>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == 3U);
BOOST_CHECK(testing_type::max_length() == 3U);
testing_type field;
static const std::vector<char> ExpectedBuf = {(char)0x0, (char)0x1, (char)0x2};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test48) {
typedef types::optional<types::integral<field_type<option::big_endian>, std::uint16_t>,
option::default_optional_mode<types::optional_mode::exists>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
typedef testing_type::Mode Mode;
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.field().value() == 0U);
BOOST_CHECK(field.get_mode() == Mode::exists);
field.field().value() = 0xff0;
static const std::vector<char> Buf = {0x0f, (char)0xf0};
write_read_field(field, Buf.begin(), Buf.size());
}
struct BundleCustomReaderTest49 {
template<typename TField, typename TIter>
status_type operator()(TField &field, TIter &iter, std::size_t len) const {
auto &members = field.value();
auto &first = std::get<0>(members);
auto &second = std::get<1>(members);
status_type es = first.read(iter, len);
if (es != status_type::success) {
return es;
}
if (first.value() != 0) {
second.set_mode(types::optional_mode::missing);
} else {
second.set_mode(types::optional_mode::exists);
}
return second.read(iter, len - first.length());
}
};
BOOST_AUTO_TEST_CASE(test49) {
typedef types::bundle<field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t>,
types::optional<types::integral<field_type<option::big_endian>, std::uint16_t>>>,
option::custom_value_reader<BundleCustomReaderTest49>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 1U, "Invalid min_length");
static_assert(testing_type::max_length() == 3U, "Invalid max_length");
static_assert(testing_type::min_length_until<1>() == 1U, "Invalid min_length");
static_assert(testing_type::max_length_until<1>() == 1U, "Invalid max_length");
static_assert(testing_type::min_length_from<1>() == 0U, "Invalid min_length");
static_assert(testing_type::max_length_from<1>() == 2U, "Invalid max_length");
testing_type field;
BOOST_CHECK(field.valid());
auto &mem1 = std::get<0>(field.value());
auto &mem2 = std::get<1>(field.value());
static const std::vector<char> Buf = {0x00, 0x10, 0x20, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(mem1.value() == 0U);
BOOST_CHECK(mem2.field().value() == 0x1020);
BOOST_CHECK(mem2.get_mode() == types::optional_mode::exists);
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf2 = {0x01, 0x10, 0x20, (char)0xff};
field = pack<testing_type>(Buf2.begin(), Buf2.end(), acc);
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(mem1.value() == 1U);
BOOST_CHECK(mem2.get_mode() == types::optional_mode::missing);
}
struct Test50_Field : public types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1>> {
MARSHALLING_BITMASK_BITS(first, second, third, fourth, sixth = 5, seventh, eighth);
MARSHALLING_BITMASK_BITS_ACCESS_NOTEMPLATE(first, second, third, fourth, sixth, seventh, eighth);
};
template<typename... TExtraOpts>
class Test50_Field2
: public types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1>, TExtraOpts...> {
using Base = types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1>, TExtraOpts...>;
public:
MARSHALLING_BITMASK_BITS_SEQ(first, second, third, fourth, fifth, sixth, seventh, eighth);
};
BOOST_AUTO_TEST_CASE(test50) {
using testing_type = Test50_Field;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 0xaa;
BOOST_CHECK(field.getBitValue_first() == false);
BOOST_CHECK(field.getBitValue_second() == true);
BOOST_CHECK(field.getBitValue_third() == false);
BOOST_CHECK(field.getBitValue_fourth() == true);
BOOST_CHECK(field.getBitValue_sixth() == true);
BOOST_CHECK(field.getBitValue_seventh() == false);
BOOST_CHECK(field.getBitValue_eighth() == true);
field.set_bit_value_first(true);
field.set_bit_value_second(false);
field.set_bit_value_third(true);
field.set_bit_value_fourth(false);
field.set_bit_value_sixth(false);
field.set_bit_value_seventh(true);
field.set_bit_value_eighth(false);
BOOST_CHECK(field.value() == 0x45);
using Field2 = Test50_Field2<>;
Field2 field2;
static_cast<void>(field2);
static_assert(!Field2::is_version_dependent(), "Invalid version dependency assumption");
}
class Field_51
: public types::bitfield<field_type<types_fixture::BigEndianOpt>,
std::tuple<types::integral<field_type<types_fixture::BigEndianOpt>, std::uint8_t,
option::fixed_bit_length<2>>,
types::bitmask_value<field_type<types_fixture::BigEndianOpt>,
option::fixed_length<1>, option::fixed_bit_length<6>>>> {
public:
MARSHALLING_FIELD_MEMBERS_ACCESS_NOTEMPLATE(name1, name2)
};
BOOST_AUTO_TEST_CASE(test51) {
typedef Field_51 testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(field.member_bit_length<testing_type::FieldIdx_name1>() == 2U);
BOOST_CHECK(field.member_bit_length<testing_type::FieldIdx_name2>() == 6U);
static const std::vector<char> Buf = {(char)0x41, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &mem1 = field.field_name1();
BOOST_CHECK(mem1.value() == 0x1);
auto &mem2 = field.field_name2();
BOOST_CHECK(mem2.value() == 0x10);
}
BOOST_AUTO_TEST_CASE(test52) {
typedef std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t, option::fixed_bit_length<8>>,
types::integral<field_type<option::big_endian>, std::int8_t, option::fixed_bit_length<8>>>
BitfildMembers;
typedef types::bitfield<field_type<option::big_endian>, BitfildMembers> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
static_cast<void>(field);
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.member_bit_length<0>() == 8U);
BOOST_CHECK(field.member_bit_length<1>() == 8U);
static const std::vector<char> Buf = {(char)0xff, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &members = field.value();
auto &mem1 = std::get<0>(members);
BOOST_CHECK(mem1.value() == 255);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem2.value() == -1);
}
BOOST_AUTO_TEST_CASE(test53) {
typedef types::integral<field_type<option::little_endian>, std::int32_t, option::fixed_bit_length<23>,
option::scaling_ratio<180, 0x800000>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field(std::numeric_limits<std::int32_t>::max());
static const double ExpVal1 = (static_cast<double>(std::numeric_limits<std::int32_t>::max()) * 180) / 0x800000;
BOOST_CHECK(field.scale_as<double>() == ExpVal1);
}
BOOST_AUTO_TEST_CASE(test54) {
typedef types::integral<field_type<option::big_endian>, std::int8_t, option::scaling_ratio<100, 1>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field(1);
BOOST_CHECK(field.value() == 1);
BOOST_CHECK(field.scale_as<int>() == 100);
field.set_scaled(1000);
BOOST_CHECK(field.value() == 10);
field.set_scaled(260.38);
BOOST_CHECK(field.value() == 2);
field.set_scaled(-200.00);
BOOST_CHECK(field.value() == -2);
}
BOOST_AUTO_TEST_CASE(test55) {
typedef types::integral<field_type<option::big_endian>, std::int16_t, option::scaling_ratio<1, 100>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.set_scaled(-0.1);
BOOST_CHECK(field.value() == -10);
field.value() = -123;
BOOST_CHECK(field.scale_as<float>() == -1.23f);
}
BOOST_AUTO_TEST_CASE(test56) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 0>> TrailField;
static_assert(!TrailField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_fixed_size<5>, option::fixed_size_storage<5>,
option::sequence_trailing_field_suffix<TrailField>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 6U, "Invalid min length");
static_assert(testing_type::max_length() == 6U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 6U);
field.value() = "hello";
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o', 0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = "foo";
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf2 = {'f', 'o', 'o', 0x0, 0x0, 0x0};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field = pack<testing_type>(ExpectedBuf2.begin(), ExpectedBuf2.end());
BOOST_CHECK(field.value() == "foo");
}
BOOST_AUTO_TEST_CASE(test57) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::scaling_ratio<1, 10>,
option::units_milliseconds>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static const std::uint32_t InitVal = 600000;
testing_type field;
field.value() = InitVal;
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == InitVal / 10);
BOOST_CHECK(units::get_microseconds<unsigned long>(field) == (InitVal * 1000L) / 10);
BOOST_CHECK(units::get_nanoseconds<unsigned long long>(field) == (InitVal * 1000ULL * 1000) / 10);
BOOST_CHECK(units::get_seconds<unsigned>(field) == InitVal / (10 * 1000));
BOOST_CHECK(units::get_minutes<unsigned>(field) == InitVal / (10 * 60 * 1000));
BOOST_CHECK(units::get_hours<double>(field) == (double)InitVal / (10 * 60 * 60 * 1000));
BOOST_CHECK(units::get_days<double>(field) == (double)InitVal / (10 * 24L * 60 * 60 * 1000));
BOOST_CHECK(units::get_weeks<double>(field) == (double)InitVal / (10 * 7ULL * 24 * 60 * 60 * 1000));
units::set_nanoseconds(field, 500000U);
BOOST_CHECK(units::get_nanoseconds<unsigned>(field) == 500000U);
BOOST_CHECK(field.value() == 5);
units::set_microseconds(field, 300U);
BOOST_CHECK(units::get_microseconds<unsigned>(field) == 300U);
BOOST_CHECK(field.value() == 3);
units::set_milliseconds(field, 100U);
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 100U);
BOOST_CHECK(std::abs(units::get_seconds<float>(field) - 0.1f) <= std::numeric_limits<float>::epsilon());
BOOST_CHECK(field.value() == 1000);
units::set_seconds(field, 1.2);
BOOST_CHECK(std::abs(units::get_seconds<float>(field) - 1.2f) <= std::numeric_limits<float>::epsilon());
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 1200U);
BOOST_CHECK(field.value() == 12000);
units::set_minutes(field, (double)1 / 3);
BOOST_CHECK(std::abs(units::get_minutes<double>(field) - (double)1 / 3) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::get_hours<double>(field) - (double)1 / (3 * 60))
<= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_seconds<unsigned>(field) == 20U);
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 20000U);
BOOST_CHECK(field.value() == 200000);
units::set_hours(field, 0.5f);
BOOST_CHECK(std::abs(units::get_hours<double>(field) - 0.5) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_minutes<unsigned>(field) == 30U);
BOOST_CHECK(units::get_seconds<unsigned>(field) == 30U * 60U);
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 30U * 60U * 1000U);
BOOST_CHECK(field.value() == 30U * 60U * 1000U * 10U);
units::set_days(field, (float)1 / 3);
BOOST_CHECK(std::abs(units::get_days<double>(field) - (double)1 / 3) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_hours<unsigned>(field) == 8U);
BOOST_CHECK(units::get_minutes<unsigned>(field) == 8U * 60);
BOOST_CHECK(units::get_seconds<unsigned>(field) == 8U * 60U * 60U);
BOOST_CHECK(units::get_milliseconds<unsigned long>(field) == 8UL * 60U * 60U * 1000U);
BOOST_CHECK(field.value() == 8UL * 60U * 60U * 1000U * 10U);
units::set_weeks(field, (double)2 / 7);
BOOST_CHECK(std::abs(units::get_weeks<double>(field) - (double)2 / 7) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_days<unsigned>(field) == 2U);
BOOST_CHECK(units::get_hours<unsigned>(field) == 2U * 24U);
BOOST_CHECK(units::get_minutes<unsigned>(field) == 2U * 24 * 60);
BOOST_CHECK(units::get_seconds<unsigned long>(field) == 2UL * 24U * 60U * 60U);
BOOST_CHECK(units::get_milliseconds<unsigned long>(field) == 2UL * 24U * 60U * 60U * 1000U);
BOOST_CHECK(field.value() == 2UL * 24U * 60U * 60U * 1000U * 10U);
}
BOOST_AUTO_TEST_CASE(test58) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_nanoseconds>
Field1;
static_assert(!Field1::is_version_dependent(), "Invalid version dependency assumption");
do {
Field1 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::get_nanoseconds<unsigned>(field) == 100U);
BOOST_CHECK(std::abs(units::get_microseconds<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_microseconds>
Field2;
static_assert(!Field2::is_version_dependent(), "Invalid version dependency assumption");
do {
Field2 field(5U);
BOOST_CHECK(field.value() == 5U);
BOOST_CHECK(units::get_microseconds<unsigned>(field) == 500U);
BOOST_CHECK(std::abs(units::get_milliseconds<double>(field) - 0.5) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_milliseconds> Field3;
static_assert(!Field3::is_version_dependent(), "Invalid version dependency assumption");
do {
Field3 field(200U);
BOOST_CHECK(field.value() == 200U);
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 200U);
BOOST_CHECK(std::abs(units::get_seconds<double>(field) - 0.2) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_seconds>
Field4;
static_assert(!Field4::is_version_dependent(), "Invalid version dependency assumption");
do {
Field4 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::get_seconds<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 100U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_minutes>
Field5;
static_assert(!Field5::is_version_dependent(), "Invalid version dependency assumption");
do {
Field5 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::get_minutes<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_seconds<unsigned>(field) == 6U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>, option::units_hours>
Field6;
static_assert(!Field6::is_version_dependent(), "Invalid version dependency assumption");
do {
Field6 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::get_hours<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_seconds<unsigned>(field) == 6U * 60U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 12>, option::units_days>
Field7;
static_assert(!Field7::is_version_dependent(), "Invalid version dependency assumption");
do {
Field7 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::get_days<double>(field) - (double)1 / 12)
<= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_hours<unsigned>(field) == 2U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_weeks> Field8;
static_assert(!Field8::is_version_dependent(), "Invalid version dependency assumption");
do {
Field8 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::get_weeks<unsigned>(field) == 1U);
BOOST_CHECK(units::get_hours<unsigned>(field) == 24U * 7U);
} while (false);
}
BOOST_AUTO_TEST_CASE(test59) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::units_millimeters> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 345U;
BOOST_CHECK(units::get_nanometers<unsigned long long>(field) == 345000000UL);
BOOST_CHECK(units::get_micrometers<unsigned>(field) == 345000U);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 345U);
BOOST_CHECK(std::abs(units::get_centimeters<double>(field) - 34.5) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getMeters<double>(field) - 0.345) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getKilometers<double>(field) - 0.000345) <= std::numeric_limits<double>::epsilon());
units::set_nanometers(field, 100000000UL);
BOOST_CHECK(field.value() == 100U);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 100U);
units::set_micrometers(field, 222000UL);
BOOST_CHECK(field.value() == 222U);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 222U);
units::set_millimeters(field, 400);
BOOST_CHECK(field.value() == 400U);
BOOST_CHECK(units::get_micrometers<unsigned>(field) == 400000U);
units::setCentimeters(field, 10);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 100U);
units::setMeters(field, 0.025);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 25U);
units::setKilometers(field, 0.025);
BOOST_CHECK(units::getMeters<unsigned>(field) == 25U);
}
BOOST_AUTO_TEST_CASE(test60) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_nanometers>
Field1;
static_assert(!Field1::is_version_dependent(), "Invalid version dependency assumption");
do {
Field1 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::get_nanometers<unsigned>(field) == 100U);
BOOST_CHECK(std::abs(units::get_micrometers<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_micrometers>
Field2;
static_assert(!Field2::is_version_dependent(), "Invalid version dependency assumption");
do {
Field2 field(5U);
BOOST_CHECK(field.value() == 5U);
BOOST_CHECK(units::get_micrometers<unsigned>(field) == 500U);
BOOST_CHECK(std::abs(units::get_millimeters<double>(field) - 0.5) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_millimeters> Field3;
static_assert(!Field3::is_version_dependent(), "Invalid version dependency assumption");
do {
Field3 field(200U);
BOOST_CHECK(field.value() == 200U);
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 200U);
BOOST_CHECK(std::abs(units::getMeters<double>(field) - 0.2) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>, option::units_meters>
Field4;
static_assert(!Field4::is_version_dependent(), "Invalid version dependency assumption");
do {
Field4 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::getMeters<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 100U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_centimeters>
Field5;
static_assert(!Field5::is_version_dependent(), "Invalid version dependency assumption");
do {
Field5 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::get_centimeters<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::get_millimeters<unsigned>(field) == 1U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_kilometers>
Field6;
static_assert(!Field6::is_version_dependent(), "Invalid version dependency assumption");
do {
Field6 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::getKilometers<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::getMeters<unsigned>(field) == 100U);
} while (false);
}
BOOST_AUTO_TEST_CASE(test61) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::units_centimeters_per_second>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 10U;
BOOST_CHECK(units::getNanometersPerSecond<unsigned long long>(field) == 100000000UL);
BOOST_CHECK(units::getMicrometersPerSecond<unsigned>(field) == 100000U);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 100U);
BOOST_CHECK(units::getCentimetersPerSecond<unsigned>(field) == 10U);
BOOST_CHECK(std::abs(units::getMetersPerSecond<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getKilometersPerSecond<double>(field) - 0.0001)
<= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getKilometersPerHour<double>(field) - (0.1 * 3600) / 1000)
<= std::numeric_limits<double>::epsilon());
units::setNanometersPerSecond(field, 50000000UL);
BOOST_CHECK(field.value() == 5U);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 50U);
units::setMicrometersPerSecond(field, 10000UL);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 10U);
units::setMillimetersPerSecond(field, 400);
BOOST_CHECK(field.value() == 40U);
BOOST_CHECK(units::getCentimetersPerSecond<unsigned>(field) == 40U);
units::setCentimetersPerSecond(field, 10);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 100U);
units::setMetersPerSecond(field, 0.02);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 20U);
units::setKilometersPerSecond(field, 0.00002);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 20U);
units::setKilometersPerHour(field, 36);
BOOST_CHECK(units::getMetersPerSecond<unsigned>(field) == 10U);
}
BOOST_AUTO_TEST_CASE(test62) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_nanometers_per_second>
Field1;
static_assert(!Field1::is_version_dependent(), "Invalid version dependency assumption");
do {
Field1 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::getNanometersPerSecond<unsigned>(field) == 100U);
BOOST_CHECK(std::abs(units::getMicrometersPerSecond<double>(field) - 0.1)
<= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<100, 1>,
option::units_micrometers_per_second>
Field2;
static_assert(!Field2::is_version_dependent(), "Invalid version dependency assumption");
do {
Field2 field(5U);
BOOST_CHECK(field.value() == 5U);
BOOST_CHECK(units::getMicrometersPerSecond<unsigned>(field) == 500U);
BOOST_CHECK(std::abs(units::getMillimetersPerSecond<double>(field) - 0.5)
<= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_millimeters_per_second> Field3;
static_assert(!Field3::is_version_dependent(), "Invalid version dependency assumption");
do {
Field3 field(200U);
BOOST_CHECK(field.value() == 200U);
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 200U);
BOOST_CHECK(std::abs(units::getMetersPerSecond<double>(field) - 0.2) <= std::numeric_limits<double>::epsilon());
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_meters_per_second>
Field4;
static_assert(!Field4::is_version_dependent(), "Invalid version dependency assumption");
do {
Field4 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::getMetersPerSecond<double>(field) - 0.1) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 100U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::scaling_ratio<1, 10>,
option::units_centimeters_per_second>
Field5;
static_assert(!Field5::is_version_dependent(), "Invalid version dependency assumption");
do {
Field5 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(std::abs(units::getCentimetersPerSecond<double>(field) - 0.1)
<= std::numeric_limits<double>::epsilon());
BOOST_CHECK(units::getMillimetersPerSecond<unsigned>(field) == 1U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_kilometers_per_hour> Field6;
static_assert(!Field6::is_version_dependent(), "Invalid version dependency assumption");
do {
Field6 field(36U);
BOOST_CHECK(field.value() == 36U);
BOOST_CHECK(units::getMetersPerSecond<unsigned>(field) == 10U);
} while (false);
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::units_kilometers_per_second> Field7;
static_assert(!Field7::is_version_dependent(), "Invalid version dependency assumption");
do {
Field7 field(1U);
BOOST_CHECK(field.value() == 1U);
BOOST_CHECK(units::getMetersPerSecond<unsigned>(field) == 1000U);
} while (false);
}
BOOST_AUTO_TEST_CASE(test63) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::units_kilohertz> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 10U;
BOOST_CHECK(units::getHertz<unsigned long>(field) == 10000UL);
BOOST_CHECK(units::getKilohertz<unsigned>(field) == 10U);
BOOST_CHECK(std::abs(units::getMegahertz<double>(field) - 0.01) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getGigahertz<double>(field) - 0.00001) <= std::numeric_limits<double>::epsilon());
units::setHertz(field, 20000U);
BOOST_CHECK(units::getKilohertz<unsigned>(field) == 20U);
units::setKilohertz(field, 1);
BOOST_CHECK(units::getHertz<unsigned long>(field) == 1000L);
units::setMegahertz(field, 2);
BOOST_CHECK(units::getHertz<unsigned long>(field) == 2000000UL);
units::setGigahertz(field, 3);
BOOST_CHECK(units::getKilohertz<unsigned long>(field) == 3000000UL);
}
BOOST_AUTO_TEST_CASE(test64) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::scaling_ratio<1, 10>,
option::units_degrees>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 300U;
BOOST_CHECK(units::getDegrees<unsigned>(field) == 30U);
BOOST_CHECK(std::abs(units::getRadians<double>(field) - 0.523599) <= 0.000001);
units::setDegrees(field, 50U);
BOOST_CHECK(field.value() == 500U);
BOOST_CHECK(units::getDegrees<unsigned>(field) == 50U);
BOOST_CHECK(std::abs(units::getRadians<double>(field) - 0.872665) <= 0.000001);
units::setRadians(field, 1.04719);
BOOST_CHECK(units::getDegrees<unsigned>(field) == 60U);
BOOST_CHECK(field.value() == 600U);
BOOST_CHECK(std::abs(units::getRadians<double>(field) - 1.04719) <= 0.00001);
}
BOOST_AUTO_TEST_CASE(test65) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::scaling_ratio<1, 100>,
option::units_radians>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 100U;
BOOST_CHECK(units::getRadians<unsigned>(field) == 1U);
BOOST_CHECK(std::abs(units::getDegrees<double>(field) - 57.2958) <= 0.0001);
units::setRadians(field, 0.5);
BOOST_CHECK(field.value() == 50U);
BOOST_CHECK(std::abs(units::getRadians<double>(field) - 0.5) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getDegrees<double>(field) - 28.6479) <= 0.0001);
units::setDegrees(field, 114.592);
BOOST_CHECK(units::getRadians<unsigned>(field) == 2U);
BOOST_CHECK(field.value() == 200U);
BOOST_CHECK(std::abs(units::getDegrees<double>(field) - 114.592) <= 0.001);
}
BOOST_AUTO_TEST_CASE(test66) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::units_milliamps> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 345U;
BOOST_CHECK(units::getNanoamps<unsigned long long>(field) == 345000000UL);
BOOST_CHECK(units::getMicroamps<unsigned>(field) == 345000U);
BOOST_CHECK(units::getMilliamps<unsigned>(field) == 345U);
BOOST_CHECK(std::abs(units::getAmps<double>(field) - 0.345) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getKiloamps<double>(field) - 0.000345) <= std::numeric_limits<double>::epsilon());
units::setNanoamps(field, 100000000UL);
BOOST_CHECK(field.value() == 100U);
BOOST_CHECK(units::getMilliamps<unsigned>(field) == 100U);
units::setMicroamps(field, 222000UL);
BOOST_CHECK(field.value() == 222U);
BOOST_CHECK(units::getMilliamps<unsigned>(field) == 222U);
units::setMilliamps(field, 400);
BOOST_CHECK(field.value() == 400U);
BOOST_CHECK(units::getMicroamps<unsigned>(field) == 400000U);
units::setAmps(field, 0.025);
BOOST_CHECK(units::getMilliamps<unsigned>(field) == 25U);
units::setKiloamps(field, 0.025);
BOOST_CHECK(units::getAmps<unsigned>(field) == 25U);
}
BOOST_AUTO_TEST_CASE(test67) {
typedef types::integral<field_type<option::big_endian>, std::uint32_t, option::units_millivolts> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 345U;
BOOST_CHECK(units::getNanovolts<unsigned long long>(field) == 345000000UL);
BOOST_CHECK(units::getMicrovolts<unsigned>(field) == 345000U);
BOOST_CHECK(units::getMillivolts<unsigned>(field) == 345U);
BOOST_CHECK(std::abs(units::getVolts<double>(field) - 0.345) <= std::numeric_limits<double>::epsilon());
BOOST_CHECK(std::abs(units::getKilovolts<double>(field) - 0.000345) <= std::numeric_limits<double>::epsilon());
units::setNanovolts(field, 100000000UL);
BOOST_CHECK(field.value() == 100U);
BOOST_CHECK(units::getMillivolts<unsigned>(field) == 100U);
units::setMicrovolts(field, 222000UL);
BOOST_CHECK(field.value() == 222U);
BOOST_CHECK(units::getMillivolts<unsigned>(field) == 222U);
units::setMillivolts(field, 400);
BOOST_CHECK(field.value() == 400U);
BOOST_CHECK(units::getMicrovolts<unsigned>(field) == 400000U);
units::setVolts(field, 0.025);
BOOST_CHECK(units::getMillivolts<unsigned>(field) == 25U);
units::setKilovolts(field, 0.025);
BOOST_CHECK(units::getVolts<unsigned>(field) == 25U);
}
BOOST_AUTO_TEST_CASE(test68) {
typedef types::float_value<field_type<option::big_endian>, float, option::units_seconds> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value() = 1.345f;
BOOST_CHECK(std::abs(field.value() - 1.345f) <= std::numeric_limits<float>::epsilon());
BOOST_CHECK(units::get_microseconds<unsigned>(field) == 1345000U);
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 1345U);
BOOST_CHECK(std::abs(units::get_seconds<float>(field) - 1.345f) <= std::numeric_limits<float>::epsilon());
units::set_milliseconds(field, 500U);
BOOST_CHECK(std::abs(field.value() - 0.5f) <= std::numeric_limits<float>::epsilon());
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 500U);
BOOST_CHECK(std::abs(units::get_seconds<float>(field) - 0.5f) <= std::numeric_limits<float>::epsilon());
units::set_minutes(field, (float)1 / 180);
BOOST_CHECK(std::abs(units::get_seconds<float>(field) - (float)1 / 3) <= std::numeric_limits<float>::epsilon());
BOOST_CHECK(units::get_milliseconds<unsigned>(field) == 333U);
BOOST_CHECK(std::abs(units::get_milliseconds<float>(field) - (333 + (float)1 / 3))
<= std::numeric_limits<float>::epsilon());
}
BOOST_AUTO_TEST_CASE(test69) {
struct LenField : public types::integral<field_type<option::big_endian>, std::uint8_t> { };
static_assert(!LenField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint16_t>,
option::sequence_ser_length_field_prefix<LenField>>
testing_type;
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
static const std::vector<char> ExpectedBuf = {0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
static const std::vector<char> Buf = {0x8, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value().size() == static_cast<std::size_t>(Buf[0]) / 2U);
BOOST_CHECK(field.length() == (field.value().size() * 2) + 1U);
BOOST_CHECK(field.value()[0].value() == 0x0102);
BOOST_CHECK(field.value()[1].value() == 0x0304);
BOOST_CHECK(field.value()[2].value() == 0x0506);
BOOST_CHECK(field.value()[3].value() == 0x0708);
static const std::vector<char> Buf2 = {0x7, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8};
field = pack<testing_type>(Buf2.begin(), Buf2.end(), status_type::invalid_msg_data);
static const std::vector<char> Buf3 = {0x4, 0xa, 0xb, 0xc, 0xd, 0xe, 0xf};
field = pack<testing_type>(Buf3.begin(), Buf3.end());
BOOST_CHECK(field.value().size() == static_cast<std::size_t>(Buf3[0]) / 2U);
BOOST_CHECK(field.length() == (field.value().size() * 2) + 1U);
BOOST_CHECK(field.value()[0].value() == 0x0a0b);
BOOST_CHECK(field.value()[1].value() == 0x0c0d);
}
using Test70_FieldBase = field_type<option::big_endian>;
template<std::uint8_t TVal>
using Test70_IntKeyField = types::integral<Test70_FieldBase, std::uint8_t, option::default_num_value<TVal>,
option::valid_num_value_range<TVal, TVal>, option::fail_on_invalid<>>;
using Test70_Mem1 = types::bundle<Test70_FieldBase,
std::tuple<Test70_IntKeyField<1>, types::integral<Test70_FieldBase, std::uint16_t>>>;
using Test70_Mem2 = types::bundle<Test70_FieldBase,
std::tuple<Test70_IntKeyField<2>, types::integral<Test70_FieldBase, std::uint32_t>>>;
template<typename... TExtra>
class Test70_Field : public types::variant<Test70_FieldBase, std::tuple<Test70_Mem1, Test70_Mem2>, TExtra...> {
using Base = types::variant<Test70_FieldBase, std::tuple<Test70_Mem1, Test70_Mem2>, TExtra...>;
public:
MARSHALLING_VARIANT_MEMBERS_ACCESS(mem1, mem2);
};
class Test70_LengthRetriever {
public:
Test70_LengthRetriever(std::size_t &val) : val_(val) {
}
template<std::size_t TIdx, typename TField>
void operator()(const TField &field) {
val_ = field.length();
}
private:
std::size_t &val_;
};
BOOST_AUTO_TEST_CASE(test70) {
using testing_type = Test70_Field<>;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.length() == 0U);
BOOST_CHECK(field.current_field() == std::tuple_size<testing_type::members_type>::value);
auto &mem1 = field.initField_mem1();
std::get<1>(field.accessField_mem1().value()).value() = 0x0a0b;
BOOST_CHECK(std::get<1>(mem1.value()).value() == 0x0a0b);
BOOST_CHECK(field.current_field() == 0U);
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.valid());
testing_type field2(field);
BOOST_CHECK(field2 == field);
testing_type field3(std::move(field2));
BOOST_CHECK(field3 == field);
auto &mem2 = field.initField_mem2();
std::get<1>(field.accessField_mem2().value()).value() = 0x0c0c0c0c;
BOOST_CHECK(std::get<1>(mem2.value()).value() == 0x0c0c0c0c);
BOOST_CHECK(field.current_field() == 1U);
BOOST_CHECK(field.length() == 5U);
BOOST_CHECK(field.valid());
field.reset();
BOOST_CHECK(!field.current_field_valid());
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.length() == 0U);
BOOST_CHECK(field.current_field() == std::tuple_size<testing_type::members_type>::value);
static const std::vector<char> Buf = {0x1, 0x2, 0x3};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.current_field() == 0U);
static const std::vector<char> Buf2 = {0x2, 0x3, 0x4};
field = pack<testing_type>(Buf2.begin(), Buf2.end(), status_type::not_enough_data);
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.length() == 0U);
BOOST_CHECK(field.current_field() == std::tuple_size<testing_type::members_type>::value);
static const std::vector<char> Buf3 = {0x2, 0x3, 0x4, 0x5, 0x6};
field = pack<testing_type>(Buf3.begin(), Buf3.end());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 5U);
BOOST_CHECK(field.current_field() == 1U);
std::size_t len1 = 0U;
field.current_field_exec(Test70_LengthRetriever(len1));
BOOST_CHECK(field.length() == len1);
std::size_t len2 = 0U;
Test70_LengthRetriever lenRetriever(len2);
field.current_field_exec(lenRetriever);
BOOST_CHECK(len2 == len1);
std::size_t len3 = 0U;
static_cast<const testing_type &>(field).current_field_exec(Test70_LengthRetriever(len3));
BOOST_CHECK(len3 == len1);
field.initField_mem1();
std::get<1>(field.accessField_mem1().value()).value() = 0x0a0b;
BOOST_CHECK(field3 == field);
using InitialisedField = Test70_Field<option::default_variant_index<0>>;
InitialisedField iniField;
BOOST_CHECK(iniField.valid());
BOOST_CHECK(iniField.length() == 3U);
BOOST_CHECK(iniField.current_field() == 0);
auto &iniMem1 = iniField.initField_mem1();
BOOST_CHECK(std::get<0>(iniMem1.value()).value() == 1U);
BOOST_CHECK(std::get<1>(iniMem1.value()).value() == 0U);
BOOST_CHECK(field.current_field() == 0U);
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.valid());
std::size_t len4 = 0U;
field.current_field_exec(Test70_LengthRetriever(len4));
BOOST_CHECK(field.length() == len4);
}
struct Test71_Field
: public types::bundle<field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t>,
types::optional<types::integral<field_type<option::big_endian>, std::uint8_t>,
option::default_optional_mode<types::optional_mode::missing>>>,
option::has_custom_read, option::has_custom_refresh> {
MARSHALLING_FIELD_MEMBERS_ACCESS_NOTEMPLATE(mask, val);
template<typename TIter>
status_type read(TIter &iter, std::size_t len) {
status_type es = field_mask().read(iter, len);
if (es != status_type::success) {
return es;
}
if (field_mask().value() == 0) {
field_val().set_missing();
} else {
field_val().set_exists();
}
len -= field_mask().length();
return field_val().read(iter, len);
}
bool refresh() {
bool exists = (field_mask().value() != 0);
if (exists == field_val().does_exist()) {
return false;
}
if (exists) {
field_val().set_exists();
} else {
field_val().set_missing();
}
return true;
}
};
BOOST_AUTO_TEST_CASE(test71) {
using testing_type = Test71_Field;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(field.field_val().is_missing());
field.field_mask().value() = 1;
bool result = field.refresh();
BOOST_CHECK(result);
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(!field.refresh());
field.field_mask().value() = 0;
BOOST_CHECK(field.refresh());
BOOST_CHECK(field.length() == 1U);
static const std::vector<char> Buf = {0, 0, 0};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 1U);
BOOST_CHECK(field.field_val().is_missing());
static const std::vector<char> Buf2 = {1, 5, 0};
field = pack<testing_type>(Buf2.begin(), Buf2.end());
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.field_val().does_exist());
BOOST_CHECK(field.field_val().field().value() == (unsigned)Buf2[1]);
}
BOOST_AUTO_TEST_CASE(test72) {
static_assert(!types::detail::string_has_push_back<container::string_view>::value,
"string_view doesn't have push_back");
typedef types::integral<field_type<option::big_endian>, std::uint8_t> SizeField;
static_assert(!SizeField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<SizeField>,
option::orig_data_view>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
static const std::vector<char> Buf = {0x5, 'h', 'e', 'l', 'l', 'o', 'g', 'a', 'r'};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.value().size() == static_cast<std::size_t>(Buf[0]));
BOOST_CHECK(field.length() == field.value().size() + 1U);
BOOST_CHECK(field.valid());
BOOST_CHECK(&(*field.value().begin()) == &Buf[1]);
static const std::string Str("blabla");
field.value() = testing_type::value_type(Str.c_str(), Str.size());
BOOST_CHECK(&(*field.value().begin()) == &Str[0]);
static const std::vector<char> ExpectedBuf = {0x6, 'b', 'l', 'a', 'b', 'l', 'a'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test73) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 0>> TermField;
static_assert(!TermField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_termination_field_suffix<TermField>,
option::orig_data_view>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 1U);
static const char *HelloStr = "hello";
field.value() = HelloStr;
BOOST_CHECK(&(*field.value().begin()) == HelloStr);
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o', 0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
static const std::vector<char> InputBuf = {'f', 'o', 'o', 0x0, 'b', 'l', 'a'};
field = pack<testing_type>(InputBuf.begin(), InputBuf.end());
BOOST_CHECK(field.value() == "foo");
BOOST_CHECK(field.value().size() == 3U);
}
BOOST_AUTO_TEST_CASE(test74) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 0>> TrailField;
static_assert(!TrailField::is_version_dependent(), "Invalid version dependency assumption");
typedef types::string<field_type<option::big_endian>, option::sequence_fixed_size<5>,
option::sequence_trailing_field_suffix<TrailField>, option::orig_data_view>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 6U, "Invalid min length");
static_assert(testing_type::max_length() == 6U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 6U);
static const char *HelloStr = "hello";
field.value() = HelloStr;
BOOST_CHECK(field.value().size() == 5U);
BOOST_CHECK(field.length() == 6U);
BOOST_CHECK(&(*field.value().begin()) == HelloStr);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o', 0x0};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = "foo";
BOOST_CHECK(field.value().size() == 3U);
BOOST_CHECK(std::string(field.value().data()) == "foo");
BOOST_CHECK(field.value() == container::string_view("foo"));
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf2 = {'f', 'o', 'o', 0x0, 0x0, 0x0};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field = pack<testing_type>(ExpectedBuf2.begin(), ExpectedBuf2.end());
BOOST_CHECK(field.value() == "foo");
}
BOOST_AUTO_TEST_CASE(test75) {
typedef types::array_list<field_type<option::big_endian>, std::uint8_t, option::orig_data_view> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(std::is_same<testing_type::value_type, container::array_view<std::uint8_t>>::value,
"Expected to be array view");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().empty());
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
auto &view = field.value();
auto *viewStart = reinterpret_cast<const char *>(&(*view.begin()));
BOOST_CHECK(viewStart == &Buf[0]);
BOOST_CHECK(!field.refresh());
}
BOOST_AUTO_TEST_CASE(test76) {
typedef types::array_list<
field_type<option::big_endian>, std::uint8_t,
option::sequence_size_field_prefix<types::integral<field_type<option::big_endian>, std::uint16_t>>,
option::orig_data_view>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == sizeof(std::uint16_t));
testing_type field;
BOOST_CHECK(field.value().size() == 0U);
BOOST_CHECK(field.value().empty());
static const std::vector<char> Buf = {0x0, 0xa, 0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9, 0xf, 0xf};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 12);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 10U);
BOOST_CHECK(&(*field.value().begin()) == reinterpret_cast<const std::uint8_t *>(&Buf[2]));
field.value().remove_suffix(5);
BOOST_CHECK(field.valid());
static const std::vector<char> ExpectedBuf = {0x0, 0x5, 0x0, 0x1, 0x2, 0x3, 0x4};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test77) {
typedef types::array_list<field_type<option::big_endian>, std::uint8_t, option::sequence_fixed_size<6>,
option::orig_data_view>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 6U, "Invalid min length");
static_assert(testing_type::max_length() == 6U, "Invalid max length");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
BOOST_CHECK(field.value().empty());
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 6U);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 6U);
BOOST_CHECK((field.value())[0] == 0x0);
BOOST_CHECK((field.value())[1] == 0x1);
BOOST_CHECK(&(*field.value().begin()) == reinterpret_cast<const std::uint8_t *>(&Buf[0]));
field.value().remove_prefix(3);
BOOST_CHECK(field.value().size() == 3U);
BOOST_CHECK((field.value())[0] == 0x3);
BOOST_CHECK((field.value())[1] == 0x4);
BOOST_CHECK(&(*field.value().begin()) == reinterpret_cast<const std::uint8_t *>(&Buf[3]));
BOOST_CHECK(field.length() == 6U);
static const std::vector<char> ExpectedBuf = {0x3, 0x4, 0x5, 0x0, 0x0, 0x0};
std::vector<char> outDataBuf(ExpectedBuf.size());
pack<testing_type>(field, outDataBuf.begin());
bool bufAsExpected = std::equal(ExpectedBuf.begin(), ExpectedBuf.end(), outDataBuf.begin());
BOOST_CHECK(bufAsExpected);
}
class Test78_Field : public types::variant<Test70_FieldBase, std::tuple<Test70_Mem1, Test70_Mem2>> {
public:
MARSHALLING_VARIANT_MEMBERS_ACCESS_NOTEMPLATE(mem1, mem2);
};
BOOST_AUTO_TEST_CASE(test78) {
Test78_Field field;
static_assert(!Test78_Field::is_version_dependent(), "Invalid version dependency assumption");
auto &mem1_1 = field.initField_mem1();
static_cast<void>(mem1_1);
auto &mem1_2 = field.accessField_mem1();
static_cast<void>(mem1_2);
auto &mem2_1 = field.initField_mem2();
static_cast<void>(mem2_1);
auto &mem2_2 = field.accessField_mem2();
static_cast<void>(mem2_2);
}
BOOST_AUTO_TEST_CASE(test79) {
class testing_type
: public types::array_list<field_type<option::big_endian>, types::integral<field_type<option::big_endian>, std::uint8_t>,
option::sequence_elem_length_forcing_enabled, option::sequence_fixed_size<3>> {
public:
testing_type() {
force_read_elem_length(2U);
}
};
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.valid());
static_assert(testing_type::min_length() == 3U, "Min length is incorrect");
static_assert(3U < testing_type::max_length(), "Max length is incorrect");
static const std::vector<char> Buf
= {0x1, 0x0, 0x2, 0x0, 0x3, 0x0, 0x4, 0x0, 0x5, 0x0, 0x6, 0x0, 0x7, 0x0, 0x8, 0x0};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 6U);
BOOST_CHECK(field.value().size() == 3U);
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value()[0].value() == 0x1);
BOOST_CHECK(field.value()[1].value() == 0x2);
BOOST_CHECK(field.value()[2].value() == 0x3);
}
BOOST_AUTO_TEST_CASE(test80) {
typedef types::bundle<
field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint16_t, option::valid_num_value_range<0, 10>,
option::default_num_value<5>>,
types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<100, 100>,
option::default_num_value<100>, option::empty_serialization>,
types::enumeration<field_type<option::big_endian>, Enum1, option::fixed_length<1>,
option::valid_num_value_range<0, Enum1_NumOfValues - 1>,
option::default_num_value<Enum1_Value2>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 3U, "Invalid min_length");
static_assert(testing_type::max_length() == 3U, "Invalid max_length");
static_assert(testing_type::min_length_from_until<1, 2>() == 0U, "Invalid min_length");
static_assert(testing_type::max_length_from_until<1, 2>() == 0U, "Invalid max_length");
static_assert(testing_type::min_length_from<1>() == 1U, "Invalid min_length");
static_assert(testing_type::max_length_from<1>() == 1U, "Invalid max_length");
testing_type field;
BOOST_CHECK(field.valid());
auto &intValField = std::get<0>(field.value());
auto &constValField = std::get<1>(field.value());
auto &enumValField = std::get<2>(field.value());
BOOST_CHECK(intValField.value() == 5U);
BOOST_CHECK(constValField.value() == 100U);
BOOST_CHECK(enumValField.value() == Enum1_Value2);
intValField.value() = 50U;
BOOST_CHECK(!field.valid());
intValField.value() = 1U;
BOOST_CHECK(field.valid());
enumValField.value() = Enum1_NumOfValues;
BOOST_CHECK(!field.valid());
enumValField.value() = Enum1_Value1;
BOOST_CHECK(field.valid());
constValField.value() = 10;
BOOST_CHECK(!field.valid());
constValField.value() = 100;
BOOST_CHECK(field.valid());
static const std::vector<char> Buf = {0x00, 0x3, Enum1_Value3, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.valid());
BOOST_CHECK(intValField.value() == 3U);
BOOST_CHECK(constValField.value() == 100U);
BOOST_CHECK(enumValField.value() == Enum1_Value3);
intValField.value() = 0xabcd;
enumValField.value() = Enum1_Value1;
static const std::vector<char> ExpectedBuf = {(char)0xab, (char)0xcd, (char)Enum1_Value1};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test81) {
using testing_type = types::integral<
field_type<option::big_endian>, std::uint64_t,
option::valid_big_unsigned_num_value_range<0xffffffff, std::numeric_limits<std::uintmax_t>::max() - 1>,
option::default_big_unsigned_num_value<std::numeric_limits<std::uintmax_t>::max()>>;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value() == std::numeric_limits<std::uintmax_t>::max());
}
BOOST_AUTO_TEST_CASE(test82) {
typedef types::bundle<
field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint16_t, option::valid_num_value_range<0, 10>,
option::default_num_value<5>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 2U, "Invalid min_length");
static_assert(testing_type::min_length_from<0>() == 2U, "Invalid min_length");
static_assert(testing_type::min_length_until<1>() == 2U, "Invalid min_length");
static_assert(testing_type::max_length() == 2U, "Invalid max_length");
static_assert(testing_type::max_length_from<0>() == 2U, "Invalid min_length");
static_assert(testing_type::max_length_until<1>() == 2U, "Invalid min_length");
testing_type field;
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.length_from<0>() == 2U);
BOOST_CHECK(field.length_until<1>() == 2U);
BOOST_CHECK(field.valid());
auto &intValField = std::get<0>(field.value());
BOOST_CHECK(intValField.value() == 5U);
intValField.value() = 50U;
BOOST_CHECK(!field.valid());
intValField.value() = 1U;
BOOST_CHECK(field.valid());
static const std::vector<char> Buf = {0x00, 0x3, (char)0xff};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.valid());
BOOST_CHECK(intValField.value() == 3U);
intValField.value() = 0xabcd;
static const std::vector<char> ExpectedBuf = {(char)0xab, (char)0xcd};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
testing_type fieldTmp;
auto readIter = &ExpectedBuf[0];
status_type es = fieldTmp.read_from_until<0, 1>(readIter, ExpectedBuf.size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(fieldTmp == field);
}
BOOST_AUTO_TEST_CASE(test83) {
typedef types::array_list<field_type<option::big_endian>, std::uint8_t, option::sequence_fixed_size<5>,
option::sequence_fixed_size_use_fixed_size_storage>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 5U, "Invalid min length");
static_assert(testing_type::max_length() == 5U, "Invalid max length");
static_assert(container::is_static_vector<testing_type::value_type>(), "The storage typ is incorrect");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(testing_type::min_length() == 5U);
BOOST_CHECK(testing_type::max_length() == 5U);
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == Buf.size());
}
BOOST_AUTO_TEST_CASE(test84) {
typedef types::string<field_type<option::big_endian>, option::sequence_fixed_size<5>,
option::sequence_fixed_size_use_fixed_size_storage>
testing_type;
static_assert(testing_type::min_length() == 5U, "Invalid min length");
static_assert(testing_type::max_length() == 5U, "Invalid max length");
static_assert(container::is_static_string<testing_type::value_type>(), "Invalid storage type");
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.length() == 5U);
static const char *HelloStr = "hello";
field.value() = HelloStr;
BOOST_CHECK(field.value().size() == 5U);
BOOST_CHECK(field.length() == 5U);
// BOOST_CHECK(&(*field.value().begin()) == HelloStr);
static const std::vector<char> ExpectedBuf = {'h', 'e', 'l', 'l', 'o'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value() = "foo";
BOOST_CHECK(field.value().size() == 3U);
BOOST_CHECK(std::string(field.value().data()) == "foo");
BOOST_CHECK(field.length() == 5U);
static const std::vector<char> ExpectedBuf2 = {'f', 'o', 'o', 0x0, 0x0};
write_read_field(field, ExpectedBuf2.begin(), ExpectedBuf2.size());
field = pack<testing_type>(ExpectedBuf2.begin(), ExpectedBuf2.end());
BOOST_CHECK(field.value() == "foo");
}
BOOST_AUTO_TEST_CASE(test85) {
typedef types::string<field_type<option::big_endian>, option::sequence_fixed_size<5>> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
static_assert(testing_type::min_length() == 5U, "Invalid min length");
static_assert(testing_type::max_length() == 5U, "Invalid max length");
testing_type field;
field.value() = "blabla";
BOOST_CHECK(field.value().size() == 6U);
BOOST_CHECK(field.length() == 5U);
static const std::vector<char> ExpectedBuf = {'b', 'l', 'a', 'b', 'l'};
std::vector<std::uint8_t> outBuf;
auto writeIter = std::back_inserter(outBuf);
status_type es = field.write(writeIter, outBuf.max_size());
BOOST_CHECK(es == status_type::success);
BOOST_CHECK(outBuf.size() == ExpectedBuf.size());
BOOST_CHECK(std::equal(outBuf.begin(), outBuf.end(), std::begin(ExpectedBuf)));
}
BOOST_AUTO_TEST_CASE(test86) {
typedef types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range_override<0, 10>,
option::valid_num_value_range<20, 30>, option::default_num_value<20>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.value() == 20);
BOOST_CHECK(!field.valid());
field.value() = 5U;
BOOST_CHECK(field.valid());
}
BOOST_AUTO_TEST_CASE(test87) {
typedef types::array_list<
field_type<option::big_endian>,
types::integral<field_type<option::big_endian>, std::uint8_t, option::valid_num_value_range<0, 5>>,
option::sequence_size_field_prefix<types::integral<field_type<option::big_endian>, std::uint16_t>>,
option::sequence_elem_ser_length_field_prefix<types::integral<field_type<option::big_endian>, std::uint8_t>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == sizeof(std::uint16_t));
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf = {0x0, 0x4, 0x1, 0x0, 0x1, 0x1, 0x1, 0x2, 0x1, 0x3};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(field.length() == Buf.size());
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 4U);
field.value().resize(5);
static const std::vector<char> ExpectedBuf = {0x0, 0x5, 0x1, 0x0, 0x1, 0x1, 0x1, 0x2, 0x1, 0x3, 0x1, 0x0};
BOOST_CHECK(field.valid());
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
static const std::vector<char> Buf2 = {0x0, 0x4, 0x2, 0x0, 0x1, 0x2, 0x3, 0x4, 0x2, 0x5, 0x6, 0x2, 0x7, 0x8};
field = pack<testing_type>(Buf2.begin(), Buf2.end());
BOOST_CHECK(field.length() == Buf2.size() - 4U);
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.value().size() == 4U);
BOOST_CHECK(field.value()[0].value() == 0x0);
BOOST_CHECK(field.value()[1].value() == 0x3);
BOOST_CHECK(field.value()[2].value() == 0x5);
BOOST_CHECK(field.value()[3].value() == 0x7);
}
BOOST_AUTO_TEST_CASE(test88) {
typedef types::array_list<
field_type<option::big_endian>,
types::bundle<
field_type<option::big_endian>,
std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t>,
types::string<field_type<option::big_endian>, option::sequence_size_field_prefix<types::integral<
field_type<option::big_endian>, std::uint8_t>>>>>,
option::sequence_size_field_prefix<types::integral<field_type<option::big_endian>, std::uint8_t>>,
option::sequence_elem_ser_length_field_prefix<
types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 4>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == sizeof(std::uint8_t));
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf
= {0x2, 0x9, 0x1, 0x5, 'h', 'e', 'l', 'l', 'o', 0xa, 0xb, 0x7, 0x2, 0x3, 'b', 'l', 'a', 0xc, 0xd};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &vec = field.value();
BOOST_CHECK(vec.size() == 2U);
auto &bundle0 = vec[0];
auto &bundle1 = vec[1];
BOOST_CHECK(std::get<0>(bundle0.value()).value() == 1U);
BOOST_CHECK(std::get<1>(bundle0.value()).value() == "hello");
BOOST_CHECK(std::get<0>(bundle1.value()).value() == 2U);
BOOST_CHECK(std::get<1>(bundle1.value()).value() == "bla");
static const std::vector<char> ExpectedBuf
= {0x2, 0x7, 0x1, 0x5, 'h', 'e', 'l', 'l', 'o', 0x5, 0x2, 0x3, 'b', 'l', 'a'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value().resize(1);
auto &intField = std::get<0>(field.value()[0].value());
intField.value() = 4U;
auto &stringField = std::get<1>(field.value()[0].value());
stringField.value().clear();
for (auto idx = 0; idx < 128; ++idx) {
stringField.value().push_back('a');
}
std::vector<char> expBuf;
expBuf.push_back(0x1); // count
expBuf.push_back(0x81); // high byte of length
expBuf.push_back(0x02); // low byte of length
expBuf.push_back(0x4); // value of first integral byte
expBuf.push_back((char)128); // length of string
for (auto idx = 0; idx < 128; ++idx) {
expBuf.push_back('a'); // string itself
}
write_read_field(field, &expBuf[0], expBuf.size());
}
BOOST_AUTO_TEST_CASE(test89) {
typedef types::array_list<
field_type<option::big_endian>,
types::bundle<
field_type<option::little_endian>,
std::tuple<types::integral<field_type<option::little_endian>, std::uint32_t, option::var_length<1, 4>>,
types::string<field_type<option::little_endian>,
option::sequence_size_field_prefix<types::integral<
field_type<option::little_endian>, std::uint16_t, option::var_length<1, 2>>>>>>,
option::sequence_ser_length_field_prefix<
types::integral<field_type<option::little_endian>, std::uint32_t, option::var_length<1, 4>>>,
option::sequence_elem_ser_length_field_prefix<
types::integral<field_type<option::little_endian>, std::uint32_t, option::var_length<1, 4>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == sizeof(std::uint8_t));
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf
= {18, 0x9, 0x1, 0x5, 'h', 'e', 'l', 'l', 'o', 0xa, 0xb, 0x7, 0x2, 0x3, 'b', 'l', 'a', 0xc, 0xd};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &vec = field.value();
BOOST_CHECK(vec.size() == 2U);
auto &bundle0 = vec[0];
auto &bundle1 = vec[1];
BOOST_CHECK(std::get<0>(bundle0.value()).value() == 1U);
BOOST_CHECK(std::get<1>(bundle0.value()).value() == "hello");
BOOST_CHECK(std::get<0>(bundle1.value()).value() == 2U);
BOOST_CHECK(std::get<1>(bundle1.value()).value() == "bla");
static const std::vector<char> ExpectedBuf
= {14, 0x7, 0x1, 0x5, 'h', 'e', 'l', 'l', 'o', 0x5, 0x2, 0x3, 'b', 'l', 'a'};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value().resize(1);
auto &intField = std::get<0>(field.value()[0].value());
intField.value() = 0x4000;
auto &stringField = std::get<1>(field.value()[0].value());
stringField.value().clear();
for (auto idx = 0; idx < 128; ++idx) {
stringField.value().push_back('a');
}
auto expTotalLength = 2 + 2 + 3 + 2 + 128;
BOOST_CHECK(field.length() == expTotalLength);
std::vector<char> expBuf;
static const std::vector<char> totalLenEncoding = {(char)0x87, 0x1};
static const std::vector<char> elemLenEncoding = {(char)0x85, 0x1};
static const std::vector<char> intEncoding = {(char)0x80, (char)0x80, 0x1};
static const std::vector<char> stringLenEncoding = {(char)0x80, 0x1};
expBuf.insert(expBuf.end(), totalLenEncoding.begin(), totalLenEncoding.end());
expBuf.insert(expBuf.end(), elemLenEncoding.begin(), elemLenEncoding.end());
expBuf.insert(expBuf.end(), intEncoding.begin(), intEncoding.end());
expBuf.insert(expBuf.end(), stringLenEncoding.begin(), stringLenEncoding.end());
for (auto idx = 0; idx < 128; ++idx) {
expBuf.push_back('a'); // string itself
}
write_read_field(field, &expBuf[0], expBuf.size());
}
BOOST_AUTO_TEST_CASE(test90) {
typedef types::array_list<
field_type<option::big_endian>,
types::bundle<field_type<option::big_endian>, std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t>,
types::integral<field_type<option::big_endian>, std::uint16_t>>>,
option::sequence_size_field_prefix<types::integral<field_type<option::big_endian>, std::uint8_t>>,
option::sequence_elem_fixed_ser_length_field_prefix<
types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 4>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == 2U);
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf = {0x2, 0x4, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &vec = field.value();
BOOST_CHECK(vec.size() == 2U);
auto &bundle0 = vec[0];
auto &bundle1 = vec[1];
BOOST_CHECK(std::get<0>(bundle0.value()).value() == 0x1);
BOOST_CHECK(std::get<1>(bundle0.value()).value() == 0x0203);
BOOST_CHECK(std::get<0>(bundle1.value()).value() == 0x5);
BOOST_CHECK(std::get<1>(bundle1.value()).value() == 0x0607);
static const std::vector<char> ExpectedBuf = {0x2, 0x3, 0x1, 0x2, 0x3, 0x5, 0x6, 0x7};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
field.value().clear();
static const std::vector<char> EmptyExpectedBuf = {0x0};
write_read_field(field, EmptyExpectedBuf.begin(), EmptyExpectedBuf.size());
BOOST_CHECK(field.length() == 1U);
}
BOOST_AUTO_TEST_CASE(test91) {
typedef types::array_list<
field_type<option::big_endian>,
types::bundle<field_type<option::big_endian>, std::tuple<types::integral<field_type<option::big_endian>, std::uint8_t>,
types::integral<field_type<option::big_endian>, std::uint16_t>>>,
option::sequence_fixed_size<2>,
option::sequence_elem_fixed_ser_length_field_prefix<
types::integral<field_type<option::big_endian>, std::uint32_t, option::var_length<1, 4>>>>
testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
BOOST_CHECK(testing_type::min_length() == 7U);
testing_type field;
BOOST_CHECK(field.valid());
BOOST_CHECK(field.value().size() == 0U);
static const std::vector<char> Buf = {0x4, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &vec = field.value();
BOOST_CHECK(vec.size() == 2U);
auto &bundle0 = vec[0];
auto &bundle1 = vec[1];
BOOST_CHECK(std::get<0>(bundle0.value()).value() == 0x1);
BOOST_CHECK(std::get<1>(bundle0.value()).value() == 0x0203);
BOOST_CHECK(std::get<0>(bundle1.value()).value() == 0x5);
BOOST_CHECK(std::get<1>(bundle1.value()).value() == 0x0607);
static const std::vector<char> ExpectedBuf = {0x3, 0x1, 0x2, 0x3, 0x5, 0x6, 0x7};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test92) {
typedef std::tuple<types::integral<field_type<option::little_endian>, std::uint8_t>,
types::integral<field_type<option::little_endian>, std::uint8_t>,
types::integral<field_type<option::little_endian>, std::uint8_t>>
BitfileMembers;
typedef types::bitfield<field_type<option::little_endian>, BitfileMembers> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(!field.set_version(5U));
static_cast<void>(field);
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.member_bit_length<0>() == 8U);
BOOST_CHECK(field.member_bit_length<1>() == 8U);
BOOST_CHECK(field.member_bit_length<2>() == 8U);
static const std::vector<char> Buf = {(char)0x1, (char)0x2, (char)0x3};
field = pack<testing_type>(Buf.begin(), Buf.end());
auto &members = field.value();
auto &mem1 = std::get<0>(members);
BOOST_CHECK(mem1.value() == 0x1);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem2.value() == 0x2);
auto &mem3 = std::get<2>(members);
BOOST_CHECK(mem3.value() == 0x3);
}
BOOST_AUTO_TEST_CASE(test93) {
typedef std::tuple<types::integral<field_type<option::little_endian>, std::uint8_t, option::fixed_bit_length<4>,
option::default_num_value<0xf>>,
types::integral<field_type<option::little_endian>, std::int16_t, option::default_num_value<2016>,
option::num_value_ser_offset<-2000>, option::fixed_bit_length<8>>,
types::integral<field_type<option::little_endian>, std::uint16_t, option::fixed_bit_length<12>,
option::default_num_value<0x801>>>
BitfileMembers;
typedef types::bitfield<field_type<option::little_endian>, BitfileMembers> testing_type;
static_assert(!testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
static_cast<void>(field);
BOOST_CHECK(field.length() == 3U);
BOOST_CHECK(field.member_bit_length<0>() == 4U);
BOOST_CHECK(field.member_bit_length<1>() == 8U);
BOOST_CHECK(field.member_bit_length<2>() == 12U);
auto &members = field.value();
auto &mem1 = std::get<0>(members);
BOOST_CHECK(mem1.value() == 0xf);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem2.value() == 2016);
auto &mem3 = std::get<2>(members);
BOOST_CHECK(mem3.value() == 0x801);
static const std::vector<char> ExpectedBuf = {0x0f, 0x11, (char)0x80};
write_read_field(field, ExpectedBuf.begin(), ExpectedBuf.size());
}
BOOST_AUTO_TEST_CASE(test94) {
using Mem1 = types::integral<field_type<option::big_endian>, std::uint16_t>;
struct Mem2 : public types::integral<field_type<option::big_endian>, std::uint16_t, option::has_custom_version_update> {
bool set_version(unsigned) {
return true;
}
};
typedef types::bundle<field_type<option::big_endian>, std::tuple<Mem1, Mem2>> testing_type;
static_assert(testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.set_version(5U));
}
BOOST_AUTO_TEST_CASE(test95) {
using Mem1 = types::integral<field_type<option::big_endian>, std::uint16_t>;
using Mem2 = types::optional<Mem1, option::exists_since_version<5>, option::exists_by_default>;
typedef types::bundle<field_type<option::big_endian>, std::tuple<Mem1, Mem2>> testing_type;
static_assert(testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.length() == 4U);
BOOST_CHECK(!field.set_version(5U));
BOOST_CHECK(field.length() == 4U);
BOOST_CHECK(field.set_version(4U));
BOOST_CHECK(field.length() == 2U);
BOOST_CHECK(field.set_version(15U));
BOOST_CHECK(field.length() == 4U);
}
BOOST_AUTO_TEST_CASE(test96) {
using Mem1 = types::integral<field_type<option::big_endian>, std::uint8_t, option::fixed_bit_length<4>>;
struct Mem2 : public types::integral<field_type<option::big_endian>, std::uint8_t, option::has_custom_version_update,
option::fixed_bit_length<4>> {
bool set_version(unsigned) {
return true;
}
};
typedef types::bitfield<field_type<option::big_endian>, std::tuple<Mem1, Mem2>> testing_type;
static_assert(testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
BOOST_CHECK(field.set_version(5U));
}
BOOST_AUTO_TEST_CASE(test97) {
using Mem1 = types::integral<field_type<option::big_endian>, std::uint16_t>;
using Mem2 = types::optional<Mem1, option::exists_since_version<5>, option::exists_by_default>;
using ListElem = types::bundle<field_type<option::big_endian>, std::tuple<Mem1, Mem2>>;
static_assert(ListElem::is_version_dependent(), "Invalid version dependency assumption");
using testing_type = types::array_list<field_type<option::big_endian>, ListElem>;
static_assert(testing_type::is_version_dependent(), "Invalid version dependency assumption");
testing_type field;
field.value().resize(1);
BOOST_CHECK(field.length() == 4U);
BOOST_CHECK(field.set_version(1U));
BOOST_CHECK(field.length() == 2U);
do {
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf1 = {(char)0x01, (char)0x02};
field = pack<testing_type>(Buf1.begin(), Buf1.end(), acc);
BOOST_CHECK(field.value().size() == 1U);
auto &members = field.value()[0].value();
auto &mem1 = std::get<0>(members);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(mem1.value() == 0x102);
BOOST_CHECK(mem2.is_missing());
BOOST_CHECK(field.set_version(15U));
BOOST_CHECK(mem2.does_exist());
BOOST_CHECK(field.length() == 4U);
} while (false);
do {
accumulator_set<testing_type> acc = accumulator_set<testing_type>(field);
static const std::vector<char> Buf2 = {(char)0x03, (char)0x04, (char)0x05, (char)0x06};
field = pack<testing_type>(Buf2.begin(), Buf2.end(), acc);
BOOST_CHECK(field.value().size() == 1U);
auto &members = field.value()[0].value();
auto &mem1 = std::get<0>(members);
auto &mem2 = std::get<1>(members);
BOOST_CHECK(field.length() == 4U);
BOOST_CHECK(mem2.does_exist());
BOOST_CHECK(mem1.value() == 0x304);
BOOST_CHECK(mem2.field().value() == 0x506);
} while (false);
}
BOOST_AUTO_TEST_CASE(test98) {
using testing_type
= types::integral<field_type<option::big_endian>, std::uint8_t, option::invalid_by_default, option::version_storage>;
testing_type field;
BOOST_CHECK(!field.valid());
BOOST_CHECK(field.get_version() == 0U);
BOOST_CHECK(field.set_version(5U));
BOOST_CHECK(field.get_version() == 5U);
using Field2
= types::bitmask_value<field_type<option::big_endian>, option::fixed_length<1U>, option::default_num_value<0x6U>,
option::version_storage, option::bitmask_reserved_bits<0xc2U, 0x2U>>;
Field2 field2;
BOOST_CHECK(field2.get_version() == 0U);
BOOST_CHECK(field2.set_version(5U));
BOOST_CHECK(field2.get_version() == 5U);
}
BOOST_AUTO_TEST_CASE(test99) {
typedef types::array_list<field_type<option::big_endian>, std::uint8_t, option::sequence_length_forcing_enabled> Field1;
static_assert(!Field1::is_version_dependent(), "Invalid version dependency assumption");
Field1 field1;
BOOST_CHECK(field1.valid());
field1.force_read_length(4U);
static const std::vector<char> Buf = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9};
accumulator_set<Field1> acc1 = accumulator_set<Field1>(field1);
field1 = pack<Field1>(Buf.begin(), Buf.end(), acc1);
BOOST_CHECK(field1.value().size() == 4U);
BOOST_CHECK(field1.length() == 4U);
BOOST_CHECK(field1.valid());
field1.clear_read_length_forcing();
typedef types::string<field_type<option::big_endian>, option::sequence_length_forcing_enabled> Field2;
static_assert(!Field2::is_version_dependent(), "Invalid version dependency assumption");
Field2 field2;
BOOST_CHECK(field2.valid());
field2.force_read_length(5U);
static const std::vector<char> Buf2 = {'h', 'e', 'l', 'l', 'o', 'a', 'b', 'c', 'd'};
accumulator_set<Field2> acc2 = accumulator_set<Field2>(field2);
field2 = pack<Field2>(Buf2.begin(), Buf2.end(), acc2);
BOOST_CHECK(field2.value() == "hello");
BOOST_CHECK(field2.valid());
field2.clear_read_length_forcing();
}
BOOST_AUTO_TEST_CASE(test100) {
typedef types::integral<field_type<option::big_endian>, std::int64_t, option::fixed_length<5U, false>,
option::num_value_ser_offset<0x492559f64fLL>, option::scaling_ratio<1, 0x174878e800LL>>
testing_type;
testing_type field;
static const std::vector<char> Buf = {(char)0x87, (char)0x54, (char)0xa2, (char)0x03, (char)0xb9};
field = pack<testing_type>(Buf.begin(), Buf.end());
BOOST_CHECK(std::abs(field.get_scaled<double>() - 2.67) < 0.1);
}
BOOST_AUTO_TEST_SUITE_END()
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|
#!/usr/bin/env python
# coding: utf-8
# Supplementary codes for:
# #Potential severity and control of Omicron waves depending on pre-existing immunity and immune evasion
#
# Ferenc A. Bartha, Péter Boldog, Tamás Tekeli, Zsolt Vizi, Attila Dénes and Gergely Röst
#
#
#
# ---
# In[ ]:
use_colab = False
if use_colab:
from google.colab import files
from typing import Union
from ipywidgets import interact
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
from scipy.integrate import odeint
# ## Parametrization
# ### Epidemiological Parameters
# In[ ]:
# Delta variant
# basic reproduction number of the Delta variant
# (relevant for fully susceptible population with no interventions in place)
r0_delta_glob = 6.0
# In[ ]:
# Observations from South Africa (Laboratory Country)
# ratio of the immunized population
p_south_africa = 0.85
# ratio of the effective reproduction numbers - as observed:
# R_t^{Omicron} / R_t^{Delta}
ratio_omicron_per_delta_south_africa = 4
# In[ ]:
# Assumptions on Omicron
# latent period (days): 2-chain L1->L2
omicron_latent_period = 2.5
# infectious period (days): 4-chain I1->I2->I3
omicron_infectious_period = 5.
# hospital evasion with pre-existing immunity (probability of evasion)
omicron_hospital_evasion = 0.85
# In[ ]:
# Deriving model parameters from the above assumptions
# alpha
alpha_glob = 1. / omicron_latent_period
# gamma
gamma_glob = 1. / omicron_infectious_period
# ### Technical Parameters
# In[ ]:
# Region for immune evasion (e) and local pre-existing immunity (p_loc)
# immune evasion (e)
e_vals = np.linspace(0, 1, 100)
# local pre-existing immunity (p_loc)
p_loc_vals = np.linspace(0, 1, 100)
# In[ ]:
# ODE solver
# integration timespan and resolution (t)
t_glob = np.linspace(0, 500, 5000)
# Model compartments
comps = ["s", "l1_s", "l2_s", "i1_s", "i2_s", "i3_s", "i4_s", "r_s",
"p", "l1_p", "l2_p", "i1_p", "i2_p", "i3_p", "i4_p", "r_p"]
# In[ ]:
# Figures
# resolution
figures_dpi = 250
# auto download
figures_autodownload = True
# ## Methods
# ### Contour relation: pre-existing immunity vs immune evasion
# In[ ]:
def r0_omicron_from_contour_relation(
e: Union[float, np.ndarray],
p: float = p_south_africa,
r0_delta: float = r0_delta_glob,
ratio_omicron_per_delta: float = ratio_omicron_per_delta_south_africa
) -> float:
"""
Approximates the basic reproduction number (R0) of the Omicron variant
:param float e: immune evasion of Omicron, i.e. ratio of individuals with
immunity against Delta who are susceptible to Omicron
:param float p: pre-existing immunized fraction of the population
:param float r0_delta: basic reproduction number of the Delta variant
:param float ratio_omicron_per_delta: ratio of effective reproduction numbers
for Omicron and Delta variants
:return float: basic reproduction number of the Omicron variant
"""
num = r0_delta * ratio_omicron_per_delta
denom = 1 + (0 if p == 1 else e * p / (1 - p))
return num / denom
# ### Level of non-pharmaceutical interventions (NPI) required to suppress an epidemic
# In[ ]:
def calculate_suppressing_npi(
r0: float,
p: float,
goal: float = 1
) -> float:
"""
Calculate the necessary contact rate reduction to achieve the <goal> rep. number
:param float r0: basic reproduction number
:param float p: pre-existing immunity
:param float goal: desired reproduction number (<= 1)
:return float: NPI
"""
return 0 if (p == 1) else 1 - np.min((1.0, goal / (r0 * (1 - p))))
# ### Compartmental ODE modeling of the Omicron variant
# In[ ]:
def omicron_model(
xs: np.ndarray,
ts: np.ndarray,
params: dict
) -> np.ndarray:
"""
SL_2I_4R model with dual immunity
:param np.ndarray xs: actual array of states
:param np.ndarray ts: time values
:param dict params: dictionary of parameters
:return np.ndarray
"""
# get parameters
alpha = params["alpha"]
beta = params["beta"]
gamma = params["gamma"]
npi = params["npi"]
# get all states
# _s: individuals susceptible to both Omicron and Delta
# _p: individuals susceptible to Omicron but immune to Delta
s, l1_s, l2_s, i1_s, i2_s, i3_s, i4_s, r_s, \
p, l1_p, l2_p, i1_p, i2_p, i3_p, i4_p, r_p = xs
# total count of infectious individuals
i_sum = i1_s + i2_s + i3_s + i4_s + i1_p + i2_p + i3_p + i4_p
# compartmental model
ds = - beta * (1 - npi) * s * i_sum
dl1_s = beta * (1 - npi) * s * i_sum - 2 * alpha * l1_s
dl2_s = 2 * alpha * l1_s - 2 * alpha * l2_s
di1_s = 2 * alpha * l2_s - 4 * gamma * i1_s
di2_s = 4 * gamma * i1_s - 4 * gamma * i2_s
di3_s = 4 * gamma * i2_s - 4 * gamma * i3_s
di4_s = 4 * gamma * i3_s - 4 * gamma * i4_s
dr_s = 4 * gamma * i4_s
dp = - beta * (1 - npi) * p * i_sum
dl1_p = beta * (1 - npi) * p * i_sum - 2 * alpha * l1_p
dl2_p = 2 * alpha * l1_p - 2 * alpha * l2_p
di1_p = 2 * alpha * l2_p - 4 * gamma * i1_p
di2_p = 4 * gamma * i1_p - 4 * gamma * i2_p
di3_p = 4 * gamma * i2_p - 4 * gamma * i3_p
di4_p = 4 * gamma * i3_p - 4 * gamma * i4_p
dr_p = 4 * gamma * i4_p
return np.array([ds, dl1_s, dl2_s, di1_s, di2_s, di3_s, di4_s, dr_s,
dp, dl1_p, dl2_p, di1_p, di2_p, di3_p, di4_p, dr_p])
# In[ ]:
def calculate_beta(
r0: float,
params: dict
) -> float:
"""
Calculate beta from R0 and other parameters
:param float r0: basic reproduction number
:param dict params: dictionary of parameters
:return float: calculated beta
"""
return r0 * params["gamma"]
# In[ ]:
def solve_omicron_model(
r0_omicron: float,
e: Union[float, np.ndarray],
p_loc: float,
npi_loc: float,
initial_l1: float,
t: np.ndarray = t_glob
) -> np.ndarray:
"""
Calculate peak and final sizes
:param float r0_omicron: basic reproduction number of the Omicron variant
:param float e: immune evasion of Omicron
:param float p_loc: pre-existing immunity in the model country
:param float npi_loc: npi in effect in the model country
:param float initial_l1: initially infected (L1_s + L1_p, symmetric)
:param np.ndarray t: timespan and resolution of the numerical solution
:return np.ndarray: numerical solution to the omicron model
"""
# initial values
s_0 = 1 - p_loc
l1_s_0 = initial_l1 / 2.
l2_s_0 = 0.0
i1_s_0 = 0.0
i2_s_0 = 0.0
i3_s_0 = 0.0
i4_s_0 = 0.0
r_s_0 = 0.0
p_0 = e * p_loc
l1_p_0 = initial_l1 / 2.
l2_p_0 = 0.0
i1_p_0 = 0.0
i2_p_0 = 0.0
i3_p_0 = 0.0
i4_p_0 = 0.0
r_p_0 = 0.0
iv = [s_0, l1_s_0, l2_s_0, i1_s_0, i2_s_0, i3_s_0, i4_s_0, r_s_0,
p_0, l1_p_0, l2_p_0, i1_p_0, i2_p_0, i3_p_0, i4_p_0, r_p_0]
# set readily known parameters
params = {
"alpha": alpha_glob,
"gamma": gamma_glob,
"npi": npi_loc
}
# calculate beta
beta = calculate_beta(
r0=r0_omicron,
params=params
)
params["beta"] = beta
# compute the numerical solution
sol = odeint(
func=omicron_model,
y0=iv,
t=t,
args=(params, )
)
return sol
# In[ ]:
def calculate_peak_and_final_size(
sol: np.ndarray,
severity: float = 1,
relative_severity: float = (1 - omicron_hospital_evasion)
) -> list:
"""
Calculate peak and final sizes
:param np.ndarray sol: solution of the numerical simulation
:param float severity: common weight of _s and _p compartments
:param float relative_severity: additional weight of _p compartments
:return list: peak and final size
"""
# unwrap the ODE solution
sol_d = {comps[i]: sol[:, i] for i in range(len(comps))}
# plug-in weights
r = severity * (sol_d["r_s"] + relative_severity * sol_d["r_p"])
i = severity * (
sol_d["i1_s"] + sol_d["i2_s"] + sol_d["i3_s"] + sol_d["i4_s"] +
relative_severity * (sol_d["i1_p"] + sol_d["i2_p"] + sol_d["i3_p"] + sol_d["i4_p"])
)
# peak size
peak_size = np.max(i)
# final size
final_size = r[-1]
return [peak_size, final_size]
# ## Results
# ### Contours: R0 of Omicron vs immune evasion
# #### Code
# In[ ]:
def plot_r0_omicron_vs_immune_evasion(
es: np.ndarray,
ps: Union[np.ndarray, list],
save_this_figure: bool = False
) -> None:
"""
Plot R0 of Omicron depending on its immune evasion
:param np.ndarray es: immune evasion values for the horizontal axis (resultion)
:param np.ndarray ps: pre-existing immunity values (number of curves)
:param bool save_this_figure: if True then the figure is saved
:return None
"""
# ensure proper fontsize
plt.rcParams.update({'font.size': 10})
# setup the coloring scheme
magic_color_count = round(1.5 * len(ps))
colors = plt.cm.winter(np.linspace(0, 1, magic_color_count + 1))
# setup the figure
fig, ax = plt.subplots(
dpi=figures_dpi if save_this_figure else 180,
figsize=(5, 3)
)
# plot a contour for each p \in ps
for idx, p in enumerate(ps):
r0_omicron_vals = r0_omicron_from_contour_relation(
e=es,
p=p,
r0_delta=r0_delta_glob,
ratio_omicron_per_delta=ratio_omicron_per_delta_south_africa
)
ax.plot(es, r0_omicron_vals,
label=str(round(p, 2)),
color=colors[magic_color_count - idx])
lgd = ax.legend(loc='right', bbox_to_anchor=(1.6, 0.5),
title='Pre-existing immunity\nin South Africa\n(fraction of population)')
ax.set_xlim(0, 1)
ax.set_ylim(0, r0_delta_glob * ratio_omicron_per_delta_south_africa)
ax.set_yticks(range(0, int(r0_delta_glob * ratio_omicron_per_delta_south_africa) + 1, 4))
ax.set_xlabel('immune evasion')
ax.set_ylabel('$R_0$ of Omicron')
if not save_this_figure:
ax.set_title('Immune Evasion vs $R_0$ of Omicron')
else:
my_file_name = "contourRelation.pdf"
plt.savefig(my_file_name, dpi=figures_dpi,
bbox_extra_artists=(lgd,), bbox_inches='tight')
if figures_autodownload and use_colab:
files.download(my_file_name)
# In[ ]:
def heatmap_r0_omicron_vs_immune_evasion(
es: np.ndarray,
ps: Union[np.ndarray, list],
r0s: list,
save_this_figure: bool = False
) -> None:
"""
Heatmap for R0 of Omicron depending wrt. pre-existing immunity and immune evasion
:param np.ndarray es: immune evasion values for the vertical axis (resultion)
:param np.ndarray ps: pre-existing immunity values for the horizontal axis (resultion)
:param list r0s: R0-contours to be highlighted
:param bool save_this_figure: if True then the figure is saved
:return None
"""
# compute data
reproduction_numbers = []
for e in es:
reproduction_numbers.append([
r0_omicron_from_contour_relation(e=e, p=p_sa)
for p_sa in ps
])
# setup the coloring scheme
my_levels = np.arange(0, np.ceil(r0_delta_glob * ratio_omicron_per_delta_south_africa) + 1, 1)
magic_color_count = round(1.1 * len(my_levels))
colors = plt.cm.winter(np.linspace(0, 1, magic_color_count + 1))
# ensure proper fontsize
plt.rcParams.update({'font.size': 10})
fig, ax = plt.subplots(
dpi=figures_dpi if save_this_figure else 200,
figsize=(4, 4)
)
ax.contourf(
ps, es, reproduction_numbers,
levels=my_levels,
colors=colors, alpha=1)
contours = ax.contour(
ps, es, reproduction_numbers, r0s,
colors='#e0e0e0', linewidths=1.2)
ax.clabel(contours, inline=True, fmt=str, fontsize=7)
ax.set_ylabel("immune evasion")
ax.set_xlabel("pre-existing immunity in South Africa")
ax.margins(0)
plt.tight_layout()
if not save_this_figure:
ax.set_title('$R_0$ of Omicron')
else:
my_file_name = "contourRelationHeatmap.pdf"
plt.savefig(my_file_name, dpi=figures_dpi)
if figures_autodownload and use_colab:
files.download(my_file_name)
# #### Figure
# In[ ]:
interact(
lambda production: plot_r0_omicron_vs_immune_evasion(
es=e_vals,
ps=[0.75, 0.8, 0.85, 0.9, 0.95],
save_this_figure=production
),
production=False
)
# In[ ]:
interact(
lambda production: heatmap_r0_omicron_vs_immune_evasion(
es=np.linspace(0, 1, 100),
ps=np.linspace(0, 0.98, 100),
r0s=[3, 6, 9, 12, 15, 18, 20, 22, 23, 23.5, 23.8],
save_this_figure=production
),
production=False
)
# ### Level of non-pharmaceutical interventions (NPI) required to suppress Delta
# #### Code
# In[ ]:
def plot_omicron_suppressing_npi(
ps: Union[np.ndarray, list],
es: Union[np.ndarray, list],
p_sa: float = p_south_africa,
r0_delta: float = r0_delta_glob,
save_this_figure: bool = True
) -> None:
"""
Plot of Omicron suppressing suppressing NPIs compared to the NPI suppressing Delta
:param list ps: pre-existing immunity values for the horizontal axis (resultion)
:param list es: immune evasion values (number of curves)
:param float p_sa: pre-existing immunity in South Africa
:param float r0_delta: R0 of the Delta variant
:param bool save_this_figure: if True then the figure is saved
:return None
"""
# compute the npi suppressing Delta for all model locations (ps)
npi_suppressing_delta = np.array([
calculate_suppressing_npi(
r0=r0_delta,
p=p
)
for p in ps
])
# ensure proper fontsize
plt.rcParams.update({'font.size': 10})
# setup the coloring scheme
magic_color_count = round(1.5 * len(es))
colors = plt.cm.winter(np.linspace(0, 1, magic_color_count + 1))
# setup the figure
plt.figure(
dpi=figures_dpi if save_this_figure else 150,
figsize=(5, 3)
)
# plot a curve for each e \in es
for idx, e in enumerate(es):
# Get R0 of the Omicron variant
r0_omicron = r0_omicron_from_contour_relation(
p=p_sa,
e=e
)
# compute the npi suppressing Delta for all model locations (ps)
npi_suppressing_omicron = np.array([
calculate_suppressing_npi(
r0=r0_omicron,
p=p * (1 - e)
)
for p in ps
])
plt.plot(ps, npi_suppressing_omicron,
label=str(round(e, 1)),
color=colors[magic_color_count - idx])
# plot a curve for Delta suppression
plt.plot(ps, npi_suppressing_delta, 'r--',
linewidth=3,
label="suppression of $\Delta$")
lgd = plt.legend(loc='right', bbox_to_anchor=(1.55, 0.5),
title='Immune evasion\nof the Omicron variant')
plt.xlim(ps[0], ps[-1])
plt.ylim(0, 1)
plt.xlabel('pre-existing immunity')
plt.ylabel('reduction of transmission by NPIs')
if not save_this_figure:
plt.title('NPI requirement for controlling Omicron')
else:
my_file_name = "npiRequirementPlot.pdf"
plt.savefig(my_file_name, dpi=figures_dpi,
bbox_extra_artists=(lgd,), bbox_inches='tight')
if figures_autodownload and use_colab:
files.download(my_file_name)
# #### Figures
# In[ ]:
interact(
lambda p_sa=p_south_africa, production=False: plot_omicron_suppressing_npi(
ps=np.linspace(0.4, 1, 1000),
es=np.arange(0.2, 0.8, 0.1),
p_sa=p_sa,
save_this_figure=production
),
p_sa=(0, 1, 0.01),
production=False
)
# ### Timeplots of the Omicron model
# #### Code
# In[ ]:
def plot_omicron_model_on_axes(
ax,
p_loc: float,
e: Union[list, np.ndarray],
t: np.ndarray,
y_range: int = 100,
title_prefix: str = '',
title_r0: bool = False
) -> None:
"""
Plot omicron model on input axes
:param title_r0:
:param ax: axes of the figure
:param float p_loc: pre-existing immunity of the model country
:param Union[list, np.ndarray] e: immune evasion ratio of Omicron
:param np.ndarray t: time range and resolution
:param int y_range: sets the y-range of the main plot (I-plot)
:param str title_prefix: prepends title
:return: None
"""
# local npi
npi_loc = calculate_suppressing_npi(
r0=r0_delta_glob,
p=p_loc
)
# r0 omicron
r0_omicron = r0_omicron_from_contour_relation(e=e)
# Get model solution
sol = solve_omicron_model(
r0_omicron=r0_omicron,
e=e,
p_loc=p_loc,
npi_loc=npi_loc,
initial_l1=0.00001,
t=t
)
sol_d = {comps[i]: sol[:, i] for i in range(len(comps))}
# get the timeseries for compartments
s = sol_d["s"]
l_s = sol_d["l1_s"] + sol_d["l2_s"]
i_s = sol_d["i1_s"] + sol_d["i2_s"] + sol_d["i3_s"] + sol_d["i4_s"]
r_s = sol_d["r_s"]
p = sol_d["p"]
l_p = sol_d["l1_p"] + sol_d["l2_p"]
i_p = sol_d["i1_p"] + sol_d["i2_p"] + sol_d["i3_p"] + sol_d["i4_p"]
r_p = sol_d["r_p"]
# main plot
color_map = ["#ff6666", "#ffaaaa"]
plt.rcParams.update({'font.size': 7})
ax.stackplot(t, i_s * 100., i_p * 100., colors=color_map)
ax.set_xlabel("time (days)")
ax.set_ylabel("infected (%)")
title = title_prefix + "p=" + str(p_loc) + ", e=" + str(e) + ", npi=" + "{:.2f}".format(npi_loc)
if title_r0:
title = title + ", $R_0$=" + "{:.2f}".format(r0_omicron) + \
", $R_{t^*}$=" + "{:.2f}".format(r0_omicron * (1 - npi_loc) * (1 - p_loc + e * p_loc))
ax.set_title(title)
ax.set_xlim([0, t[-1]])
ax.set_ylim([0, y_range])
# create the inset
left, bottom, width, height = [0.55, 0.55, 0.40, 0.40]
ax2 = ax.inset_axes([left, bottom, width, height])
color_map_inset = color_map + ["#ffffff", "#dfdfdf", "#d0d0d0"]
ax2.stackplot(
t,
r_s * 100,
r_p * 100,
(s + l_s + i_s + l_p + i_p) * 100,
p * 100,
np.full(r_s.shape, (1 - e) * p_loc * 100),
colors=color_map_inset)
ax2.set_ylabel("affected (%)")
ax2.set_xlim([0, t[-1]])
ax2.set_ylim([0, 100])
# In[ ]:
def plot_omicron_model(
p_loc: Union[int, float] = 0.5,
e: Union[float, np.ndarray] = 0.5,
T: Union[int, float] = 200,
y_range: Union[int, float] = 20,
title_prefix: str = '',
title_r0: bool = True,
save_this_figure: bool = False
) -> None:
"""
Plot omicron model
:param title_r0: adds R_0, R_t of Omicron to title
:param Union[int, float] p_loc: pre-existing immunity of the model country
:param Union[float, np.ndarray] e: immune evasion ratio of Omicron
:param Union[int, float] T: final simulation time
:param Union[int, float] y_range: sets the y-range of the main plot (I-plot)
:param str title_prefix: prepends title
:param bool save_this_figure: if True then the figure is saved
:return:
"""
fig = plt.figure(
dpi=figures_dpi if save_this_figure else 150,
figsize=(4, 4))
ax = plt.gca()
plt.rcParams.update({'font.size': 7})
plot_omicron_model_on_axes(
ax=ax,
p_loc=p_loc,
e=e,
t=np.linspace(0, T, 200),
y_range=y_range,
title_prefix=title_prefix,
title_r0=title_r0
)
if save_this_figure:
my_file_name = "singleTimeplot.pdf"
plt.savefig(my_file_name, dpi=figures_dpi)
if figures_autodownload and use_colab:
files.download(my_file_name)
# In[ ]:
def multiplot_omicron_model(
ps: Union[list, np.ndarray],
es: Union[list, np.ndarray, float],
title_prefixes: list,
T: Union[float, int],
y_range: Union[float, int],
title_r0: bool = False,
save_this_figure: bool = False
) -> None:
"""
4 timeplots of Omicron spread (4 scenarios)
:param list ps: pre-existing immunity levels of model countries (4-list)
:param list es: immune evasion ratios of Omicron (4-list)
:param list title_prefixes: prefixes to titles (4-list)
:param float T: final simulation time (common)
:param float y_range: sets the y-range of the main I-plots (common)
:param bool title_r0: adds R_0, R_t of Omicron to titles (common)
:param bool save_this_figure: if True then the figure is saved
:return None
"""
fig = plt.figure(
dpi=figures_dpi if save_this_figure else 110,
figsize=(7, 7)
)
plt.rcParams.update({'font.size': 7})
if not isinstance(title_prefixes, list):
title_prefixes = ['', '', '', '']
t = np.linspace(0, T, 1000)
ax = fig.add_subplot(221)
plot_omicron_model_on_axes(
ax=ax, p_loc=ps[0],
e=es[0], t=t,
title_prefix=title_prefixes[0],
y_range=y_range,
title_r0=title_r0)
ax = fig.add_subplot(222)
plot_omicron_model_on_axes(
ax=ax, p_loc=ps[1], e=es[1], t=t,
title_prefix=title_prefixes[1],
y_range=y_range, title_r0=title_r0)
ax = fig.add_subplot(223)
plot_omicron_model_on_axes(
ax=ax, p_loc=ps[2], e=es[2], t=t,
title_prefix=title_prefixes[2],
y_range=y_range, title_r0=title_r0)
ax = fig.add_subplot(224)
plot_omicron_model_on_axes(
ax=ax, p_loc=ps[3], e=es[3], t=t,
title_prefix=title_prefixes[3],
y_range=y_range, title_r0=title_r0)
fig.tight_layout()
if save_this_figure:
my_file_name = "fourTimeplots.pdf"
plt.savefig(my_file_name, dpi=figures_dpi)
if figures_autodownload and use_colab:
files.download(my_file_name)
# #### Figures
# In[ ]:
interact(
plot_omicron_model,
p_loc=(0, 1, 0.01),
e=(0, 1, 0.01),
T=(0, 500, 1),
y_range=(0, 100, 1)
)
# In[ ]:
interact(
lambda production=False: multiplot_omicron_model(
ps=[0.1, 0.75, 0.9, 0.96],
es=[0.03, 0.08, 0.47, 0.68],
title_prefixes=['a) ', 'b) ', 'c) ', 'd) '],
T=75,
y_range=60,
title_r0=True,
save_this_figure=production
),
production=False
)
# In[ ]:
interact(
lambda production=False: multiplot_omicron_model(
ps=[0.6, 0.9, 0.6, 0.9],
es=[0.8, 0.8, 0.5, 0.5],
title_prefixes=['a) ', 'b) ', 'c) ', 'd) '],
T=175,
y_range=60,
title_r0=False,
save_this_figure=production
),
production=False
)
# ### Analysis of peak and final size
# #### Code
# ##### Data generators
# In[ ]:
def calculate_for_fixed_e_all_peak_and_final_sizes(
e: Union[list, np.ndarray, float],
ps: Union[list, np.ndarray],
p_sa: float = p_south_africa,
severity: float = 1,
relative_severity: float = (1 - omicron_hospital_evasion)
) -> list:
"""
Interactive plot for relationship between peak and final size
:param Union[list, np.ndarray, float] ps: pre-existing immunity values for the horiztonal axis
:param Union[list, np.ndarray] e: immune evasion of Omicron
:param float p_sa: pre-existing immunity in South Africa
:param float severity: common weight of _s and _p compartments
:param float relative_severity: additional weight of _p compartments
:return list: list of peak sizes and list of final sizes
"""
# r0 omicron
r0_omicron = r0_omicron_from_contour_relation(
e=e,
p=p_sa
)
peak_sizes = []
final_sizes = []
for p_loc in ps:
# local npi
npi_loc = calculate_suppressing_npi(
r0 = r0_delta_glob,
p = p_loc
)
# Get model solution
sol = solve_omicron_model(
r0_omicron=r0_omicron,
e=e,
p_loc=p_loc,
npi_loc=npi_loc,
initial_l1=0.00001,
t=t_glob
)
peak_size, final_size = calculate_peak_and_final_size(
sol=sol,
severity=severity,
relative_severity=relative_severity)
peak_sizes.append(peak_size)
final_sizes.append(final_size)
return [peak_sizes, final_sizes]
# In[ ]:
def generate_heatmap_data(
severity: float = 1,
relative_severity: float = (1-omicron_hospital_evasion),
) -> tuple:
"""
Generates data for heatmaps
:param float severity: common weight of _s and _p compartments
:param float relative_severity: additional weight of _p compartments
:return tuple: tuple containing final sizes, peak sizes and reproduction numbers
"""
peak_sizes = []
final_sizes = []
reproduction_numbers = []
for e in e_vals:
peaks, finals = calculate_for_fixed_e_all_peak_and_final_sizes(
e=e,
ps=p_loc_vals,
severity=severity,
relative_severity=relative_severity)
peak_sizes.append(peaks)
final_sizes.append(finals)
# r0 omicron
r0_omicron = r0_omicron_from_contour_relation(
e=e,
p=p_south_africa
)
reproduction_numbers.append(
[r0_omicron * (1 - calculate_suppressing_npi(r0=r0_delta_glob,
p=p_loc)) * (1 - p_loc + e * p_loc)
for p_loc in p_loc_vals
])
return np.array(peak_sizes), np.array(final_sizes), np.array(reproduction_numbers)
# ##### Figure generators
# In[ ]:
def plot_peak_and_final_size(
e: float,
p_sa: float = p_south_africa,
severity: float = 1,
relative_severity: float = (1 - omicron_hospital_evasion),
y_limit_peak: list = 1.,
y_limit_final: list = 1.,
save_this_figure: bool = False
) -> None:
"""
Plot of peak and final size wrt. pre-existing immunity in model country (p_loc)
:param float e:
:param float p_sa: pre-existing immunity in South Africa
:param float severity: common weight of _s and _p compartments
:param float relative_severity: additional weight of _p compartments
:param list y_limit_peak: ymax for the peak size
:param list y_limit_final: ymax for the final size
:param bool save_this_figure: if True then the figure is saved
:return None
"""
peak_sizes, final_sizes = calculate_for_fixed_e_all_peak_and_final_sizes(
e=e,
ps=p_loc_vals,
p_sa=p_sa,
severity=severity,
relative_severity=relative_severity
)
fig = plt.figure(
dpi=figures_dpi if save_this_figure else 110,
figsize=(5, 3))
plt.rcParams.update({'font.size': 7})
ax = fig.add_subplot(121)
# peak sizes
ax.plot(p_loc_vals, peak_sizes)
ax.set_xlabel("pre-existing immunity")
ax.set_title("peak size")
ax.set_ylim(0.0, y_limit_peak)
ax = fig.add_subplot(122)
# final sizes
ax.plot(p_loc_vals, final_sizes)
ax.set_xlabel("pre-existing immunity")
ax.set_title("final size")
ax.set_ylim(0.0, y_limit_final)
# finalize
fig.tight_layout()
if save_this_figure:
my_file_name = "peakAndFinalSize.pdf"
plt.savefig(my_file_name, dpi=figures_dpi)
if figures_autodownload and use_colab:
files.download(my_file_name)
# In[ ]:
def plot_heatmap(
data: np.ndarray,
typ: str = "final",
add_frame: dict = None,
add_npi_plot: bool = True,
save_this_figure: bool = False
) -> None:
"""
Generate heatmap of given type from the data
:param np.ndarray data: data given as [[data(p, e) for p_loc_vals] for e_vals]
:param str typ: final, peak, reproduction_number
:param dict add_frame: None or dictionary describing a highlighted frame
:param bool add_npi_plot: adding a plot of Delta suppressing npis
:param bool save_this_figure: if True then the figure is saved
:return None
"""
this_figure_dpi = figures_dpi if save_this_figure else 100
if add_npi_plot:
fig, (ax1, ax) = plt.subplots(
2, sharex=True, dpi=this_figure_dpi,
figsize=(5, 7.5),
gridspec_kw={'height_ratios': [1, 2]})
# NPI plot
ax1.plot(
p_loc_vals,
[calculate_suppressing_npi(r0=r0_delta_glob, p=p_loc)
for p_loc in p_loc_vals])
ax1.set_ylabel("NPI controlling Delta")
ax1.set_ylim(0, 1.)
ax1.margins(0)
else:
fig, ax = plt.subplots(dpi=this_figure_dpi,
figsize=(7, 7))
plt.rcParams.update({'font.size': 11})
# final size
if typ == "final":
frame_color = "white"
marker_color = "white"
levels = [0.0001, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
colormap = 'Reds'
curves = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
curve_color = '#4a4a4a'
title = 'final size'
# peak size
elif typ == "peak":
frame_color = "#6e6e6e"
marker_color = "#6e6e6e"
levels = [0.0001, 0.001, 0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4]
colormap = 'Oranges'
curves = [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4]
curve_color = '#5e5e5e'
title = 'peak size'
# reproduction number
else:
frame_color = "black"
marker_color = "black"
levels = [1, 1.2, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6]
colormap = 'Purples'
curves = [1, 1.2, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6]
curve_color = '#5e5e5e'
title = 'control reproduction number'
# generate the main heatmap
ax.contourf(
p_loc_vals, e_vals, data,
levels=levels,
cmap=colormap, alpha=1)
contours = ax.contour(
p_loc_vals, e_vals, data,
curves,
colors=curve_color, linewidths=1)
ax.clabel(contours, inline=True, fmt=str, fontsize=8)
ax.set_ylabel("immune evasion")
ax.set_xlabel("pre-existing immunity")
if not save_this_figure:
ax.set_title(title, fontsize=15)
ax.margins(0)
# label axes with %
positions = [0, 0.25, 0.5, 0.75, 1]
labels = ["0%", "25%", "50%", "75%", "100%"]
ax.yaxis.set_major_locator(ticker.FixedLocator(positions))
ax.yaxis.set_major_formatter(ticker.FixedFormatter(labels))
ax.xaxis.set_major_locator(ticker.FixedLocator(positions))
ax.xaxis.set_major_formatter(ticker.FixedFormatter(labels))
# add highlighting frame
if add_frame is not None:
frame_p = add_frame["frame_p"]
frame_e = add_frame["frame_e"]
markers = add_frame["markers"]
highlighted_area = Rectangle(
(frame_p[0], frame_e[0]),
frame_p[1] - frame_p[0], frame_e[1] - frame_e[0],
fc='none',
ec=frame_color,
lw=5,
alpha=0.5)
ax.add_patch(highlighted_area)
for marker in markers:
ax.text(marker["p"] + 0.01, marker["e"] + 0.01, s=marker["name"], fontsize=12, color=marker_color)
ax.plot(marker["p"], marker["e"], "x", color=marker_color)
# finalize
fig.tight_layout()
if save_this_figure:
my_file_name = "heatmap-" + typ + ".pdf"
plt.savefig(my_file_name, dpi=figures_dpi)
if figures_autodownload and use_colab:
files.download(my_file_name)
# #### Figures
# ##### Plot of peak and final sizes for fixed immune evasion
# In[ ]:
# TODO: does not work
interact(
plot_peak_and_final_size,
e=(0.2, 1, 0.01),
p_sa=(0, 1, 0.01),
severity=(0, 1, 0.01),
relative_severity=(0, 1, 0.01),
y_limit_peak=(0, 1, 0.01),
y_limit_final=(0, 1, 0.01)
)
# ##### Heatmaps for peak size, final size, and control reproduction number of Omicron
# ###### Data generation [slow ~ 2 x 1m 30s]
# In[ ]:
# generate data considering the population not immune to Omicron
peak_sizes_to_plot, final_sizes_to_plot, reproduction_numbers_to_plot = generate_heatmap_data(
severity=1,
relative_severity=1)
# In[ ]:
# generate data considering the population not immune to Delta
peak_sizes_s_only, final_sizes_s_only, reproduction_numbers_to_plot = generate_heatmap_data(
severity=1,
relative_severity=0)
# ###### Heatmaps
# In[ ]:
frame_to_add = {
"frame_p": [0.3, 0.95],
"frame_e": [0.4, 0.95],
"markers": [
{"p": 0.6, "e": 0.8, "name": "a"},
{"p": 0.9, "e": 0.8, "name": "b"},
{"p": 0.6, "e": 0.5, "name": "c"},
{"p": 0.9, "e": 0.5, "name": "d"}
]
}
print('CONTROL REPRODUCTION NUMBER')
interact(
lambda add_npi_plot, add_frame, production: plot_heatmap(
data=reproduction_numbers_to_plot,
typ="reproduction_number",
add_frame=(frame_to_add if add_frame else None),
add_npi_plot=add_npi_plot,
save_this_figure=production
),
add_npi_plot=True,
add_frame=True,
production=False
)
# In[ ]:
frame_to_add = {
"frame_p": [0.3, 0.95],
"frame_e": [0.4, 0.95],
"markers": [
{"p": 0.6, "e": 0.8, "name": "a"},
{"p": 0.9, "e": 0.8, "name": "b"},
{"p": 0.6, "e": 0.5, "name": "c"},
{"p": 0.9, "e": 0.5, "name": "d"}
]
}
print('PEAK SIZE FOR SEVERITY = 1, RELATIVE SEVERITY = 1')
interact(
lambda add_npi_plot, add_frame, production: plot_heatmap(
data=peak_sizes_to_plot,
typ="peak",
add_frame=(frame_to_add if add_frame else None),
add_npi_plot=add_npi_plot,
save_this_figure=production
),
add_npi_plot=True,
add_frame=True,
production=False
)
# In[ ]:
frame_to_add = {
"frame_p": [0.3, 0.95],
"frame_e": [0.4, 0.95],
"markers": [
{"p": 0.6, "e": 0.8, "name": "a"},
{"p": 0.9, "e": 0.8, "name": "b"},
{"p": 0.6, "e": 0.5, "name": "c"},
{"p": 0.9, "e": 0.5, "name": "d"}
]
}
print('FINAL SIZE FOR SEVERITY = 1, RELATIVE SEVERITY = 1')
interact(
lambda add_npi_plot, add_frame, production: plot_heatmap(
data=final_sizes_to_plot,
typ="final",
add_frame=(frame_to_add if add_frame else None),
add_npi_plot=add_npi_plot,
save_this_figure=production
),
add_npi_plot=True,
add_frame=True,
production=False
)
# In[ ]:
frame_to_add = {
"frame_p": [0.3, 0.95],
"frame_e": [0.4, 0.95],
"markers": [
{"p": 0.6, "e": 0.8, "name": "a"},
{"p": 0.9, "e": 0.8, "name": "b"},
{"p": 0.6, "e": 0.5, "name": "c"},
{"p": 0.9, "e": 0.5, "name": "d"}
]
}
print('PEAK SIZE FOR SEVERITY = 1, RELATIVE SEVERITY = 0')
interact(
lambda add_npi_plot, add_frame, production : plot_heatmap(
data=peak_sizes_s_only,
typ="peak",
add_frame=(frame_to_add if add_frame else None),
add_npi_plot=add_npi_plot,
save_this_figure=production
),
add_npi_plot=True,
add_frame=True,
production=False
)
# In[ ]:
frame_to_add = {
"frame_p": [0.3, 0.95],
"frame_e": [0.4, 0.95],
"markers": [
{"p": 0.6, "e": 0.8, "name": "a"},
{"p": 0.9, "e": 0.8, "name": "b"},
{"p": 0.6, "e": 0.5, "name": "c"},
{"p": 0.9, "e": 0.5, "name": "d"}
]
}
print('FINAL SIZE FOR SEVERITY = 1, RELATIVE SEVERITY = 0')
interact(
lambda add_npi_plot, add_frame, production: plot_heatmap(
data=final_sizes_s_only,
typ="final",
add_frame=(frame_to_add if add_frame else None),
add_npi_plot=add_npi_plot,
save_this_figure=production
),
add_npi_plot=True,
add_frame=True,
production=False
)
|
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|
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 28 18:41:27 2020
@author: Administrator
"""
import cv2
import paddlehub as hub
import os
import CVTools
import time
import numpy as np
from tqdm import tqdm
from tqdm._tqdm import trange
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def filesInFolder(rootdir, fileType='.jpg'):
pathList = []
nameList = []
filelist = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
for i in range(len(filelist)):
if filelist[i][-4:] == fileType:
pathList.append(os.path.join(rootdir, filelist[i]))
nameList.append(filelist[i])
return pathList, nameList
# 模型加载
# use_gpu：是否使用GPU进行预测
# model = hub.Module(name='animegan_v2_hayao_99', use_gpu=True)
modelName = []
modelName.append('animegan_v2_shinkai_53')
modelName.append('animegan_v2_shinkai_33')
modelName.append('animegan_v2_paprika_98')
modelName.append('animegan_v2_paprika_97')
modelName.append('animegan_v2_paprika_74')
modelName.append('animegan_v2_paprika_54')
modelName.append('animegan_v2_hayao_99')
modelName.append('animegan_v2_hayao_64')
modelName.append('animegan_v1_hayao_60')
modelName.append('UGATIT_92w')
modelName.append('UGATIT_83w')
modelName.append('UGATIT_100w')
modelName.append('U2Net_Portrait')
modelName.append('Photo2Cartoon')
# 模型预测
# t1 = time.time()
# folderPath = 'E://DATA//XULIEZHEN'
# folderPath = 'E://DLresult//mopi9'
#
# # folderPath='E://DLresult//p1//pa97-2in1'
# outPath = 'E://DLresult//p1//sh53-2in1MOPI'
# outPath='E://DLresult//p1//pa97-8in1'
# os.makedirs(outPath)
# pathList, nameList = filesInFolder(folderPath)
def rename(pathList):
# 外循环遍历所有文件名，内循环遍历每个文件名的每个字符
for index, path in enumerate(pathList):
if 'result' in path:
# newpath=path.replace('zudui','组队')
newpath = path.replace('组队', 'zudui')
os.renames(path, newpath)
# rename()
# pathList, nameList = filesInFolder(folderPath)
# print(nameList)
##https://www.paddlepaddle.org.cn/hubdetail?name=animegan_v2_paprika_97&en_category=GANs
# model.style_transfer(paths=pathList[0:1],visualization=True,output_dir=outPath)
# bgpic = cv2.imread('bg1.jpg')
# img = cv2.imread('./test/mopi.jpg')
#
# img = cv2.imread('./thin 3.jpg')
# contenx = int(img.shape[1] / resizeIndex)
# conteny = int(img.shape[0] / resizeIndex)
# #
#
# image = bgpic.copy()
class cartoon_face():
def __init__(self,faceLandmarker):
self.faceLandmarker=faceLandmarker
self.perspect_size=256
# resizeIndex = 2
modelN = modelName[-7]
print('modelN', modelN)
self.cartoon_model = hub.Module(name=modelN, use_gpu=True)
def process(self,roi_img):
roi_img = cv2.medianBlur(roi_img, 3)
return self.cartoon_model.style_transfer(images=[roi_img], visualization=False)[0]
def run(self,user_bot,emotion_flag=0):
img_path=user_bot.imgPath
print('emotion_flag',emotion_flag)
print('img_path',img_path)
img=[]
img=cv2.imread(img_path)[:,:,:3]
if len(img.shape)==0:return []
landmarks=self.faceLandmarker.run(img)
if len(landmarks)>0:
roi_img=CVTools.roiChoice(landmarks, img, self.perspect_size)
user_bot.roiImg=roi_img
user_bot.roiCartoon=self.process(roi_img)
# roi_img = cv2.medianBlur(roi_img, 3)
# user_bot.roiCartoon =self.cartoon_model.style_transfer(images=[roi_img], visualization=False)[0]
return user_bot
else:
return user_bot
if __name__=='__main__':
from landmarkModule import landmarker
la=landmarker()
cf=cartoon_face(la)
import botClass
bot=botClass.bot()
bot.imgPath='pic/24451810641815485231.jpg'
out=cf.run(bot,2)
# print('in',image.shape,'out',out.shape)
cv2.imwrite('../cartoon.jpg', out.roiCartoon)
# print('time', time.time() - t1)
|
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|
import numpy as np
class GeneticOperations:
"""
GeneticOperations implements crossover and two types of mutation
"""
@staticmethod
def simpleCrossover(pro1, pro2):
""" Two point crossover """
fracStart1 = np.random.randint(len(pro1.seq))
fracEnd1 = fracStart1 + np.random.randint(len(pro1.seq) - fracStart1)
fracStart2 = np.random.randint(len(pro2.seq))
fracEnd2 = fracStart2 + np.random.randint(len(pro2.seq) - fracStart2)
frag1 = []
frag2 = []
for _ in range(fracStart1, fracEnd1):
frag1.append(pro1.seq.pop(fracStart1))
for _ in range(fracStart2, fracEnd2):
frag2.append(pro2.seq.pop(fracStart2))
while frag2:
pro1.seq.insert(fracStart1, frag2.pop(0))
fracStart1 += 1
while frag1:
pro2.seq.insert(fracStart2, frag1.pop(0))
fracStart2 += 1
return pro1, pro2
@staticmethod
def macroMutation(prog, pInsert, maxProgLength, minProgLength, randomInstr):
""" MacroMutation mutate a whole Instruction """
choose = np.random.random_sample()
if (len(prog.seq) < maxProgLength) and ((choose < pInsert) or len(prog.seq) == minProgLength):
insertPos = np.random.randint(len(prog.seq))
prog.seq.insert(insertPos, randomInstr)
elif (len(prog.seq) > minProgLength) and ((choose >= pInsert) or len(prog.seq) == maxProgLength):
deletePos = np.random.randint(len(prog.seq))
del prog.seq[deletePos]
@staticmethod
def microMutation(prog, pRegMut, pConst, numberOfVariable,
numberOfInput, numberOfOperation, numberOfConstant):
""" MicroMutation mutate registers inside a Instruction """
mutPos = np.random.randint(len(prog.seq))
mutInstr = prog.seq[mutPos]
if prog.seq[mutPos].isBranch: # mutation of branch
# print("branch mutation")
regIndex = np.random.randint(1, 3) # 1 or 2
if regIndex == 1:
GeneticOperations.__mutateRegister1(mutInstr, numberOfVariable, numberOfInput, numberOfConstant, pConst)
elif regIndex == 2:
GeneticOperations.__mutateRegister2(mutInstr, numberOfVariable, numberOfInput, numberOfConstant, pConst)
# calculation mutation
else:
if np.random.random_sample() < pRegMut: # register mutation
regIndex = np.random.randint(3) # 3 register mutation situations
if regIndex == 0: # mutate return register
# print("mutate return register")
rt = np.random.randint(numberOfVariable)
while mutInstr.returnRegIndex == rt:
rt = np.random.randint(numberOfVariable)
mutInstr.returnRegIndex = rt
elif regIndex == 1: # mutate register1
# print("mutate register1")
GeneticOperations.__mutateRegister1(mutInstr, numberOfVariable, numberOfInput, numberOfConstant,
pConst)
elif regIndex == 2: # mutate register 2
GeneticOperations.__mutateRegister2(mutInstr, numberOfVariable, numberOfInput, numberOfConstant,
pConst)
else: # operator mutation
# print("operator mutation")
index = mutInstr.operIndex
while mutInstr.operIndex == index:
index = np.random.randint(numberOfOperation)
mutInstr.operIndex = index
@staticmethod
def __mutateRegister1(mutInstr, numberOfVariable, numberOfInput, numberOfConstant, pConst):
""" Helper for MicroMutation """
# print("mutate register1")
index = mutInstr.reg1Index
if mutInstr.reg2Index < numberOfVariable + numberOfInput: # reg2 is a variable or input
flip = np.random.random_sample()
if flip >= pConst: # reg1 will be a variable
while mutInstr.reg1Index == index:
index = np.random.randint(numberOfVariable + numberOfInput)
mutInstr.reg1Index = index
else: # reg1 will be a constant
while mutInstr.reg1Index == index:
index = np.random.randint(numberOfConstant)
mutInstr.reg1Index = numberOfVariable + numberOfInput + index
else: # reg2 is a constant then reg1 must be a variable
while mutInstr.reg1Index == index:
index = np.random.randint(numberOfVariable + numberOfInput)
mutInstr.reg1Index = index
@staticmethod
def __mutateRegister2(mutInstr, numberOfVariable, numberOfInput, numberOfConstant, pConst):
""" Helper for MicroMutation """
# print("mutate register 2")
index = mutInstr.reg2Index
if mutInstr.reg1Index < numberOfVariable + numberOfInput: # reg1 is a variable or input
flip = np.random.random_sample()
if flip >= pConst: # reg1 will be a variable
while mutInstr.reg2Index == index:
index = np.random.randint(numberOfVariable + numberOfInput)
mutInstr.reg2Index = index
else: # reg1 will be a constant
while mutInstr.reg2Index == index:
index = np.random.randint(numberOfConstant)
mutInstr.reg2Index = numberOfVariable + numberOfInput + index
else: # reg2 is a constant then reg1 must be a variable
while mutInstr.reg2Index == index:
index = np.random.randint(numberOfVariable + numberOfInput)
mutInstr.reg2Index = index
|
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|
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