Daimler CEO says electric cars need incentives to become mainstream
FRANKFURT (Reuters) - Daimler Chief Executive Ola Kaellenius on Wednesday said the resulting drop in sales of electric and hybrid vehicles in China after incentives were curbed shows that they are still needed.
The China Association of Automobile Manufacturers (CAAM), said sales of new energy vehicles (NEVs) fell 45.6% in October from year-ago levels, following a 33% decline in September.
“When China pulled back incentives for new energy vehicles the market was down. It went into freefall. It is important to have an incentive structure during the transformation phase,” Kaellenius said, commenting on the shift away from combustion engines.
CAAM said last week that fewer NEV’s could be sold in China this year than in 2018 as customers hold back purchases following the government’s decision this year to cut back subsidies.


Note
Click here to download the full example code
08: Further Analysis¶
Analyze results from fitting power spectrum models.
Exploring Power Spectrum Model Results¶
So far we have explored how to parameterize neural power spectra as a method to extract parameters of interest from data - in particular measuring aperiodic and periodic activity.
These measured parameters can then be examined within or between groups of interest, and/or fed into further analysis to examine if, for example, these parameters predict other behavioural or physiological features of interest. Largely, it is up to you what to do after fitting power spectrum models, as it depends on your questions of interest.
Here, we briefly introduce some analysis utilities that are included in the module, and explore some simple analyses that can be done with model parameters.
To start, we will load and fit some example data, as well as simulate a group of power spectra to fit with power spectrum models.
# General imports
import numpy as np
# Import the FOOOF and FOOOFGroup objects
from fooof import FOOOF, FOOOFGroup
# Import the Bands object, which is used to define frequency bands
from fooof.bands import Bands
# Import simulation code and utilities
from fooof.sim.params import param_sampler
from fooof.sim.gen import gen_group_power_spectra
from fooof.sim.utils import set_random_seed
# Import some analysis functions
from fooof.analysis import get_band_peak_fm, get_band_peak_fg
# Import utility to download and load example data
from fooof.utils.download import load_fooof_data
Load and Fit Example Data¶
# Load examples data files needed for this example
freqs = load_fooof_data('freqs.npy', folder='data')
spectrum = load_fooof_data('spectrum.npy', folder='data')
Simulate and Fit Group Data¶
# Set random seed, for consistency generating simulated data
set_random_seed(21)
# Generate some simulated power spectra
freqs, spectra = gen_group_power_spectra(n_spectra=10,
freq_range=[3, 40],
aperiodic_params=param_sampler([[20, 2], [35, 1.5]]),
periodic_params=param_sampler([[], [10, 0.5, 2]]))
# Initialize a FOOOFGroup object with desired settings
fg = FOOOFGroup(peak_width_limits=[1, 8], min_peak_height=0.05,
max_n_peaks=6, verbose=False)
# Fit power spectrum models across the group of simulated power spectra
fg.fit(freqs, spectra)
Analysis Utilities¶
The FOOOF module includes some analysis functions. Note that these utilities are generally relatively simple utilities that assist in accessing and investigating the model parameters.
In depth analysis of power spectrum model results is typically idiosyncratic to the goals of the project, and so we consider that this will typically require custom code, and seek here to offer the most general utilities, and not support all possible applications. Here we demonstrate some of these utility functions covering very general use cases.
Analyzing Periodic Components¶
We will start by analyzing the periodic components. In particular, these utilities mostly serve to help organize and extract periodic components, for example extracting peaks that fall within defined frequency bands.
This also includes using the Bands
object, that is provided
to store band definitions.
Extracting peaks from FOOOF Objects¶
The get_band_peak_fm()
function takes in a
FOOOF
object and extracts peak(s) from a requested frequency range.
You can optionally specify:
whether to return one peak from the specified band, in which case the highest peak is returned, or whether to return all peaks from within the band
whether to apply a minimum threshold to extract peaks, for example, to extract peaks only above some minimum power threshold
# Extract any alpha band peaks from the power spectrum model
alpha = get_band_peak_fm(fm, bands.alpha)
print(alpha)
Out:
[9.48375519 0.91782334 2. ]
Extracting peaks from FOOOFGroup Objects¶
Similarly, the get_band_peak_fg()
function can be used
to select peaks within specific frequency ranges, from FOOOFGroup
objects.
Note that you can also apply a threshold to extract group peaks but, as discussed below, this approach will only extract one peak per individual model in the FOOOFGroup object.
# Get all alpha peaks from a group of power spectrum models
alphas = get_band_peak_fg(fg, bands.alpha)
# Check out some of the alpha data
print(alphas[0:5, :])
Out:
[[10.03969821 0.48627642 3.78825898]
[10.03394582 0.4880723 3.82623256]
[ nan nan nan]
[10.04095459 0.49087164 3.81810626]
[ nan nan nan]]
When selecting peaks from a group of model fits, we want to retain information about which model each peak comes from.
To do so, the output of get_band_peak_fg()
is organized
such that each row corresponds to a specific model fit. This means that returned array
has the shape [n_models, 3], and so the index of each row corresponds to the index of the
model from the FOOOFGroup object.
For this to work, at most 1 peak is extracted for each model fit within the specified band. If more than 1 peak are found within the band, the peak with the highest power is extracted. If no peaks are found, that row is filled with ‘nan’.
# Check descriptive statistics of extracted peak data
print('Alpha CF : {:1.2f}'.format(np.nanmean(alphas[:, 0])))
print('Alpha PW : {:1.2f}'.format(np.nanmean(alphas[:, 1])))
print('Alpha BW : {:1.2f}'.format(np.nanmean(alphas[:, 2])))
Out:
Alpha CF : 10.03
Alpha PW : 0.49
Alpha BW : 3.81
Customizing Peak Extraction¶
If you want to do more customized extraction of peaks, for example, extracting all peaks in a frequency band from each model in a FOOOFGroup object, you may need to use the underlying functions that operate on arrays of peak parameters. To explore these functions, check the listing in the API page.
A Note on Frequency Ranges¶
A benefit of fitting power spectrum models is that you do not have to define a priori frequency ranges from which to extract peaks.
Nevertheless, it may still be useful to group extracted peaks into ‘bands’ of interest, which is why the aforementioned functions are offered.
Since this frequency-range selection can be done after model fitting, we do recommend checking the model results, for example by checking a histogram of the center frequencies extracted across a group, in order to ensure the frequency ranges you choose reflect the characteristics of the data under study.
Analyzing the Aperiodic Component¶
Typically, for analyzing the aperiodic component of the data, aperiodic parameters just need to be extracted from FOOOF objects and fit into analyses of interest.
# Plot from the FOOOFGroup, to visualize the parameters
fg.plot()
# Extract aperiodic exponent data from group results
exps = fg.get_params('aperiodic_params', 'exponent')
# Check out the aperiodic exponent results
print(exps)
Out:
[1.5174819 1.5180664 1.99827884 1.51563434 2.00559925 2.01700177
2.00089211 2.02010911 1.51575116 1.99955831]
Example Analyses¶
Once you have extracted the parameters you can analyze them by, for example:
Characterizing periodic & aperiodic properties, and analyzing spatial topographies, across demographics, modalities, and tasks
Comparing peaks within and between subjects across different tasks of interest
Predicting disease state based on power spectrum model parameters
Fitting power spectrum models in a trial-by-trial approach to try and decode task properties, and behavioral states
So far we have only introduced the basic utilities to help with selecting and examining power spectrum model parameters.
To further explore some of these specific analyses, and explore other utilities that may be useful, check out the examples page.
Conclusion¶
This is the end of the main tutorial materials!
If you are having any troubles, please submit an issue on Github here, and/or get in contact with us at<EMAIL_ADDRESS>Total running time of the script: ( 0 minutes 1.039 seconds)


This document outlines the changes made in Vision release 5.9.5. This
release contains a number of miscellaneous cleanups to various executables
and new and corrected methods for the core classes and some tool kits.
| IMPORTANT: | Solaris users must have installed Patch-ID# 103612-33 for SunOS 5.5.1 prior to running the 5.9.5 version of batchvision. |
/vision/bin/batchvision /vision/bin/format /vision/bin/loadtape /vision/bin/paginate /vision/dbs/compustat2 /vision/dbs/inc. /vision/dbs/normalize /vision/dbs/preproc2 /vision/dbs/shuffle /vision/dbs/transpose /vision/dbs/valueline /vision/tools/checksum /vision/tools/dbconvert /vision/tools/makemvscript /vision/tools/ndftool /vision/tools/onck /vision/tools/onprof /vision/tools/rollback /vision/tools/viewndf /vision/tools/viewseg /vision/tools/vquery /vision/tools/xndfosdsThe rel.5.9.5 directory contains Vision protocol that is specific to this release and modifications or additions to existing administrative procedures. From this directory, the script Install can be used to install the necessary scripts and update the Schema. It assumes the scripts are located in the rel5.9.5 directory and reads in install.5.9.5 which installs the following files containing Vision code:
| FIXcore.5.9.5 | Modified protocol for core classes |
| EXTcore.5.9.5 | New protocol for core classes |
| CHadmin.5.9.5 | New and modified protocol for the AdminTools ToolKit |
| CHov.5.9.5 | New and modified protocol for the OpenVision ToolKit |
| CHschema.5.9.5 | New and modified protocol for the Schema ToolKit |
| CHiface.5.9.5 | New and modified protocol for Interface and FormatTools |
Generalized access to Global Reclamation Manager (GRM) --
HPUX only.
See the main page for syslogd(1M) for a general explanation of the syslog facility. The executables listed in the next section can be configured to log to a number of different facilities -- local0, local1, local2, local3, local4, local5, local6, local7, daemon. For most of the executables the means of configuration is the environment variable VisionGRMlogFacility. In the case of dbconvert, a command line option (-l) is also provided. dbconvert cannot rely on the environment being properly initialized when it is invoked in server mode via the inetd.conf file.
GRM traces previously enabled by the environment variable VisionNetOpenTrace and by the debugger expression, '#M segmentScanTrace on' are now enabled by this new variable only.
Utility define:'restartSession' toBe: 3 asPrimitive;
Running this primitive will replace the current session with a fresh session started with the same options as the original. The pid will be the same. The input and output channels will be the same. All uncommitted modifications will be lost. Because the NDF will be reread, the latest version of the network will be accessed after this primitive is executed (unless batchvision was invoked with the -v option. In that case, the same version of the object network will be accessed.)
This definition is included as part of the CHcore.5.9.5 script.
Addition of TCP_NODELAY option to OpenVision Channels
This release exposes the TCP_NODELAY flag for OpenVision socket channels. In previous releases TCP_NODELAY was hardcoded to be off. In certain uses (e.g. numerous short messages being sent between a client and server) setting TCP_NODELAY can improve performance. The default value for the flag is to be off.
TCP_NODELAY can be turned on via an option in the specification string used at channel creation or via a message sent to the channel after it has been created.
By including the 'n' character in the option portion of the specification string, a service or stream channel can be created with TCP_NODELAY turned on. For other types of open vision channels (e.g. files), the 'n' option is silently ignored.
!nd_spec <- "inet/stream,pn:9004"; !nd_service <- nd_spec asOpenVisionChannel;
!nd_spec <- "inet/stream,p:9004"; !nd_service <- nd_spec asOpenVisionChannel; nd_service turnTCPNoDelayOn;
nd_service turnTCPNoDelayOff;
!noDelaySetting <- nd_service tcpNoDelay;
Bug Fix: (Identity vs. Scalar)
[] send:[^self = asSelf]
5.9.4 result:
>>> Error Trapped By Read-Eval-Print <<< * Utility Primitive Function Services [1246] * Unknown UVector RType * TestForIdentity: Unrecognized Reference Pointer R-Type RTYPE_C_RefUV
TRUE
The 5.9.4 build for the HPUX9 platform could not run the OpenDialog package ... 5.9.5 can.
Reimplementation of the Rebuild function
The old Rebuild feature of batchvision would create an NDF from an object space directory. This is a useful feature in that it allows recovery from the case of a deleted or corrupted NDF file. The ability to make a new NDF turns out to have another practical use. Over time, the NDF can grow quite large and rebuilding it to contain only the latest version provides a means of managing the size of the NDF.
A fair amount of care was required to use this feature. Any segments present in an object space which might have been leftover from a previous rollback would result in the rebuilt NDF containing an inconsistent view of the network. This inconvenience is a necessary one when recovering from a corrupted NDF, but is an unnecessary nuisance when simply trying to manage NDF size.
A second shortcoming of the previous rebuild implementation is that it neglected to record segment information in the NDF. Utilities such as dbconvert make use of the segment information and can't run if it is not present. Therefore, on a system where dbconvert is being used to replicate the object network, the old rebuilder could not be used.
A final problem with the previous implementation was the obsolete, currently meaningless timestamp parameter which was required as input.
The new rebuilder operates in two modes. It can create an NDF given an object space directory, as before. It can also create a single version NDF given an already existing NDF. The first mode would be used to recover from the case of a corrupted or lost NDF. The second mode would be used to manage the size of the NDF. The NDF output by either mode will contain the segment information required by the dbconvert utility.
The value following the -R option now refers to the rebuild mode. Valid values are fromOSD and fromNDF. To invoke the Rebuilder in the first mode, use the following command line:
batchvision -R fromOSD -o <OSD Path Name> -n <NDF Path Name>
This mode will create a new NDF as specified using the object spaces found in the Object Space Directory (OSD) as specified. If a file by the same name as <NDF Path Name> exists, it is first removed and then replaced by the rebuilt NDF.
To invoke the Rebuilder in the second mode, use the following command line:
batchvision -R fromNDF -n <NDF Path Name>The -n option can be omitted if the environment variable, NDFPathName, has been set, or if the default value, /vision/network/NDF is desired.
Invoked this way, the rebuilder simply writes out the latest version contained in <NDF Path Name> to a new file: <NDF Path Name>.new . If <NDF Path Name>.new already exists, it is first removed.
By specifying the -v option to batchvision, a version other than the latest version can be chosen. This provides an alternative to the rollback utility.
Beginning with this release, batchvision and onprof/onck will verify that a segment belongs to the NDF which is pointing to it. On occasion, an NDF has been copied to provide access to a particular version of a network without having to use the -v option. If someone using such a copied NDF updates the network, it is likely that an existing segment created via the original NDF will be overwritten. The network as described by the original NDF is therefore corrupted. This feature will catch this error early, and identify it via an explicit error message.
Cleanup / Alignment augmentation
This release builds on the container table based cleanup strategy introduced in release 5.9.4.
Several omissions have been discovered and closed. Two new methods have been added to the AdminTools ObjectSpace class. These are cleanDictionaries and cleanDescriptors. They are implemented in terms of two new primitives added to the AdminTools _POP class -- cleanDictionaryViaPOP and cleanDescriptorViaPOP. These POP based cleaners have been added because not all descriptors and dictionaries are accessible for cleanup via Vision objects. cleanDictionaries and cleanDescriptors have been added to the AdminTools ObjectSpace cleanSpace method. In addition, the cleanTables method has been modified so that it no longer cleans dictionaries.
Message definitions and changes are included as part of the CHadmin.5.9.5 script.
The compustat2 data base update utility has been modified to use the EBCDIC flag in the layout file header (line 1, field 4). If the flag is set, compustat2 converts from EBCDIC to ASCII. If the flag is 0, no conversion is performed. In prior releases, compustat2 ignored the EBCDIC flag and always performed the EBCDIC to ASCII conversion.
compustat2 also has been modified to deal correctly with industrial annual tapes that contain header records.
NDF Mapping Coherency on HP (dbconvert)
A problem was noticed under HPUX where dbconvert would attempt to use an out-of-date version of the NDF which was mapped into memory, even though the current version existed on disk. This release forces dbconvert to discard any previously mapped version and use the latest version found on disk.
General documentation about the dbconvert utility has been added to the insytenet web site under the Administration/Tools section.
Starting with the 5.9.5 Release, the following tools are no longer included in the build.
- checkseg5.7 / fixseg5.7 - copyndf - makeomitlist - xndfvts
onck and onprof are now links to the same executable, the difference between them being that onprof presents RTYPE statistics whereas onck does not.
In addition, because error messages sometimes get lost in the volume of output, these programs now print an explicit pass/fail message upon exit.
The AIX platform has been built using the C++ compiler.
A number of changes have been made to the vquery program. Some of these were released in the web site version prior to 5.9.5 but are redocumented here for completeness.
Interface HtmlAccess get: "url-path" usingQuery: "url-query" for: "client-host-name" at: "client-host-address" ;Earlier versions were defined to use the UserInterface HyperWS class. You have the ability to override the default class in the vquery.cfg file. The recipient Vision class can be overridden for all destinations in a configuration file using the default class directive. The recipient Vision class can be overridden for a specific destination using the class
default class UserInterface HyperWS server default pugsley:7001 server test pugsley:8010 server demo pugsley:7200 class demo Interface HtmlAccessAs expected, when both a class
server analyzer pugsley:7010 server analyzer lurch:7010 class analyzer UserInterface HyperWSapplies to both analyzer servers.
The entire source for the vquery program resides in a single file which is available on our web site under the Interface/Downloads section for users who wish to modify it for their own purposes. Instructions for compiling it on different systems are supplied at the top of vquery.cpp.
Two new commands have been added to the set of commands recognized in a vquery.cfg file. The command:
default expression aVisionExpressionSpecificationestablishes a Vision expression to use by default for any service destination that does not supply its own specification. Any service that requires a different expression can override the default expression using the directive:
expression aServiceName aVisionExpressionSpecificationBoth the default expression and expression directives must be included on a single input line. THERE IS NO PROVISION FOR ESCAPING A LONG EXPRESSION ACROSS MULTIPLE LINES.
If a default expression directive does not appear in a vquery.cfg file, vquery behaves as if the following default expression were supplied:
default expression $$class get: $$path usingQuery: $$query for: $REMOTE_HOST at: $REMOTE_ADDR
The substitution rules are straightforward. Except for the $$class substitution, all substitutions are formatted as Vision strings with embedded " and \ characters suitably quoted to keep Vision happy. All substitutions begin with a $ followed by a symbol that names an environment variable or special substitution. A substitution that begins with a single $ is assumed to name an environment variable. If the environment variable exists, its value is substituted as a string into the expression generated and sent to Vision. The $REMOTE_HOST and $REMOTE_ADDR substitutions above are examples of environment variable substitutions. If the environment variable does not exist, an empty string is substituted. As vquery is currently implemented, there is no way to distinguish between a non-existent environment variable and one that is defined to have an empty value.
In addition to environment variable substitution, vquery recognizes four special substitutions:
Container Table Overflow checking
An infinitely recursive Vision method can result in filling up all available slots in the container table for the scratch pad space. In earlier versions, the result of a container table overflow was undefined, usually some sort of Read-Eval-Print error. This version includes an explicit check for container table overflow, and generates a Read-Eval-Print error if overflow would occur. On most systems, the infinitely recursive Vision method would run into a malloc error before container table overflow would occur.

The algorithm to reclaim unreferenced PToken chains was changed from recursive to iterative. The same change was applied to the reclamation of virtual machine fragments. If either chain was long enough, an internal run-time stack overflow could occur.
Solaris only: Segmentation Faults and Bus Error Signals generate
a stack dump.
A stack dump is generated in the current working directory with a name of the form: pstack.ppp.n where ppp is the process id of the batchvision session, and n is a counter which is incremented by each stack dump generated by that session.
This behavior can be disabled by setting an environment variable prior to starting the vision session:
setenv VisionStackDump 0Setting this variable to any non-zero numeric value, or
unsetenv VisionStackDumpwill turn stack dumping back on for subsequent invocations of batchvision.
VisionAddressThreshold environment variable
This variable is used to influence when a segment reclamation scan is done. If a map segment operation returns a process address less than this threshold, a reclamation is attempted. This variable applies primarily to the Solaris platform. Its purpose is to reserve the address space lower than the threshold for private memory allocation. Judicious use of this tuning parameter can help an application avoid a memory allocation error. The default value for this variable is zero, meaning that, by default, this threshold will trigger no rescans. If the value is set too high, there will be a performance impact, but if a scan of the segments does not yield enough address space, the segment will be mapped below the threshold. For example, if the value is set to 0xf0000000, a segment scan would be performed upon every segment access since, under Solaris, no segment can be mapped at an address higher than 0xf0000000.
Environmental Variables that accept Octal Input
The following environment variables now accept octal or hexadecimal input. In earlier releases, only decimal input was valid. Octal values are denoted with a leading '0'. Hexadecimal values are denoted with a leading 0x or 0X.
VisionLargeContainerSize VisionMappingLimit VisionAddressThreshold VisionNSyncRetries VisionSSyncRetries VisionMaxSegSize VisionMaxCompSegs VisionSOpenRetries VisionSORetryDelay VisionNetOpenTrace VisionCheckCheckSM VisionStackDump LargeTaskSize
The persistent garbage collector (Utility collectGarbage) now rebuilds the container table free list in the opposite direction so that lower table indices are allocated first. This anticipates a feature in a future release that will allow container tables to be reduced in size.
New Messages
********************
FIXcore.5.9.5
********************
This script contains messages that have been modified in the core classes as
part of release 5.9.5.
Class Number
Message pctChangeTo:
Class Collection Message collectListElementsFrom: block
Class Object Message createIncorporatorClass:
******************** EXTcore.5.9.5 ********************This script contains messages that have been added to the core classes as part of release 5.9.5.
Class Object Message ifDefault: object
Class Object Message ifDefault: block1 else: block2
date ifDefault: [ "initial" ] else: [ formatUsingShortName ]
date isDefault ifTrue: [ "initial" ] ifFalse: [ date formatUsingShortName ]
Class String Message convertToCaseInsensitiveRegEx
"app" convertToCaseInsensitiveRegEx
"Apple" contains: "app"
"Apple" contains: "app" convertToCaseInsensitiveRegEx
Class Utility Message restartSession
******************** CHadmin.5.9.5 ********************This script contains modifications and additions related to the AdminTools ToolKit and cannot be installed in releases prior to release 5.9.5.
Class AdminTools _POP Message cleanDictionaryViaPOP cleanDescriptorViaPOP
Class AdminTools ClusterType Message isDictionary isDescriptor
Class AdminTools ObjectSpace Message cleanDictionaries
Class AdminTools ObjectSpace Message cleanDescriptors
Class AdminTools ObjectSpace Message cleanSpace
Class AdminTools ObjectSpace Message cleanTables
******************** CHov.5.9.5 ********************This script contains modifications and additions related to the OpenVision ToolKit and requires release 5.9.5.
Class OpenVisionConstant Message nameInstances:
******************** CHschema.5.9.5 ********************This script contains modifications and additions related to the Schema ToolKit added as a supplement to release 5.9.5. These changes assume that this ToolKit has been installed.
Class Schema MID Message cleanupDeletedObject
Class Schema CD Message clearDefaultValueUsing: mid
Class Schema Message setDefaultValueOf:at:to:
Class Schema MID Message setDefaultValueTo:
Class Object Message initializeDefaults
Class Object Message showMessageSummaryFor:
Class Schema CD Message showInheritance
Class Schema CD Message getMessagesReturningClass
Class Schema CD Message getMessagesReturningSuperClass
******************** CHiface.5.9.5 ********************This script contains modifications and additions related to the Interface and FormatTools ToolKits added as a supplement to release 5.9.5. These changes assume that these ToolKits have been installed. The web site documents have been updated to reflect these changes.
Summary of major changes:
The class FormatTools was originally defined as the name of a class at CoreWorkspace and the method at Object that selects the correct context if redefined at ^local; this led to incorrect behavior when FormatTools was directly referenced since the version at ^global would bypass the version at ^local; these changes remove this inconsistency:
A number of changes have been installed to support the correct encoding and decoding of strings that will be passed through a cgi-bin program such as vquery.
The following FormatTools messages have been modified:
The following FormatTools messages have been added:
The methods GetValue, EntityHelper, and Profile have been modifed at Interface ApplicationWS as follows:
EntityHelper
Profile
GetValue
The code used to set the Global currency for Interface-based applications has been modified to allow the default to imply the local currency, instead of Named Currency Default.
| Copyright © 1998 Innovative Systems Techniques, Inc. | Updated December 23, 1998 |


|
M4521 BSP V3.01.000
The Board Support Package for M4521
|
CMSIS Cortex-M4 Core Peripheral Access Layer Source File for M4521. More...
#include "M4521.h"
Go to the source code of this file.
Functions | |
| void | SystemCoreClockUpdate (void) |
| Updates the SystemCoreClock with current core Clock retrieved from cpu registers. More... | |
| void | SystemInit (void) |
| Setup the microcontroller system. Initialize the System. More... | |
Variables | |
| uint32_t | SystemCoreClock = __SYSTEM_CLOCK |
| uint32_t | CyclesPerUs = (__HSI / 1000000) |
| uint32_t | PllClock = __HSI |
| uint32_t | gau32ClkSrcTbl [] = {__HXT, __LXT, 0, __LIRC, 0, 0, 0, __HIRC} |
CMSIS Cortex-M4 Core Peripheral Access Layer Source File for M4521.
Definition in file system_M4521.c.
| void SystemCoreClockUpdate | ( | void | ) |
Updates the SystemCoreClock with current core Clock retrieved from cpu registers.
Update SystemCoreClock variable
| None |
Definition at line 32 of file system_M4521.c.
| void SystemInit | ( | void | ) |
Setup the microcontroller system. Initialize the System.
Setup the microcontroller system. Initialize the System and update the SystemCoreClock variable.
Initialize the system
| None |
Definition at line 76 of file system_M4521.c.
| uint32_t CyclesPerUs = (__HSI / 1000000) |
Cycles per micro second
Definition at line 25 of file system_M4521.c.
Definition at line 27 of file system_M4521.c.
| uint32_t PllClock = __HSI |
PLL Output Clock Frequency
Definition at line 26 of file system_M4521.c.
| uint32_t SystemCoreClock = __SYSTEM_CLOCK |
System Clock Frequency (Core Clock)
Definition at line 24 of file system_M4521.c.


A Collaborative Leader
Technology Enthusiasts
& Web Developer
to reveal our creativity through team work!


--- layout: example.html title: Min/Max resolution shortdesc: Example of minResolution/maxResolution on Google layers. docs: > In this example, the three layers are only visible in two zoom levels, thanks to the minResolution and maxResolution layer attributes. ---


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Cuisine on the road, pt. 1: Kamchatka
eugene.kaspersky.com
Some time ago I was asked about what I pack in my suitcase on my frequent long trips round the globe – to kinda come up with a list of essential ...
-
Ok, shits real. Its in the wild... src:<IP_ADDRESS>
gist.github.com
You can clone with HTTPS or SSH .
-
When the Bug Bashes you
business.kaspersky.com
A new nasty bug discovered in Bourne again shell set the infosec on fire, Heartbleed-style. Is it as dangerous as the notorious OpenSSL flaw? It ...
-
David Jacoby on Hacking His Home
threatpost.com
Dennis Fisher talks with David Jacoby of Kaspersky Lab about the research he did on the security of electronics gear in his home, including his smart ...
-
Bash Exploit Reported, First Round of Patches Incomplete
threatpost.com
Reports of the first in-the-wild exploits targeting the Bash vulnerability have surfaced, as have complaints the first patches for the bug are ...
-
September's 3x CON: Part 1
securelist.com
What, Where & When: the 4th edition of 44CON, an annual IT Security Conference organized by Sense/Net Ltd, took place on 10-12 September in London, ...
-
Securing virtual machines still a dark art, says Kaspersky Lab
www.computerweekly.com
Five common myths about virtualisation security is preventing businesses getting the greatest benefit with the least risk, says Kaspersky Lab
-
Hungry for hotness in Hungary.
eugene.kaspersky.com
I'd heard plenty of good stuff about Hungarian spas and hot springs. And I'd been to the country many times since first visiting it in 1998. But ...
-
Estonia takes the plunge
www.economist.com
THE founders of the internet were academics who took users’ identities on trust. When only research co-operation was at stake, this was reasonable. ...
-
Suitcase astray – not ok.
eugene.kaspersky.com
There's an unusual feeling you get… when you sit by the window on a plane waiting for the end of boarding and your flight to begin, observing the ...
-
Like sardines… ‘United’ in a can.
eugene.kaspersky.com
At first I was going to call this post something like ‘cattle wagon’. Then I had a think, and figured that’d be too malicious. Kinda like, ‘Shock, ...
-
Scammers' delivery service: exclusively dangerous
securelist.com
Well-known companies and brands are favorite targets for fraudsters. After all, it is much easier to get people's attention with the use of a popular ...
-
Good Gets Better: KIS 2015
blog.kaspersky.com
Like it or not, your children are going to be on the web. Kaspersky’s award-winning parental controls let you monitor their activity and block them ...
-
NSA Director Rogers Urges Cyber-Resiliency
threatpost.com
NSA Director and Commander of U.S. Cyber Command, Admiral Mike Rogers, urged listeners at the Billington Cybersecurity Summit that cyber-resilience ...
-
The biggest sources of stolen banking information: online retailers?
business.kaspersky.com
Kaspersky Lab's recent survey surprisingly found that e-commerce, online retailers and financial service providers are not just the biggest sources ...
-
The Kurils: Why, where, how.
eugene.kaspersky.com
So, where on earth did the idea of a cruise, not around tourist-friendly tropical islands, but around mostly uninhabited – for a reason – polar-esque ...
-
Same Security Threats, Different Devices: Wearables and Watchables
blog.kaspersky.com
New mobile and wearable devices offer users a robust set of innovative features and utilities but they often face the same traditional threats as old ...


{% extends "layout_with_help.html" %} {% block pageTitle %} NRS Diagnostic Prototype {% endblock %} {% block header %} {{ govukHeader() }} {% endblock %} {% block beforeContent %}
{% endblock %} {% block content %}The best way for you to aquire new or improve existing skills is to get a qualification. To help you with this, below are a selection of free online courses for you to work through right away.
Once completed, you will be in a stronger position to obtain your qualification and apply for your desired job.
This course helps you to develop practical maths skills appled to work and other real-life situations. It is equivalent to GCSE Maths.


[SYMBOL]
[A]
[B]
[C]
[D]
[E]
[F]
[G]
[H]
[I]
[J]
[K]
[L]
[M]
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[U]
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[X]
Native POSIX Thread Library (NPTL)
NDB storage engine and cluster
Network Appliance filers
Network Attached Storage (NAS) and MySQL
network load-balancing products
2nd
Network Operation Center's (NOC) user account
networks capacity utilization
impact on server performance
network ports
security and SSH tunneling
next-connection algorithms
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nonreplicated dependencies, problems caused by
nonunique server IDs, problems caused by
NPTL (Native POSIX Thread Library)
NULLs
primary keys and
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{% extends "layout.html" %} {% block body %}
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Dear {{FNAME}} {{LNAME}},
A gracious “thank you” for agreeing to participating within our upcoming {{CLASS}} class!
Here is some information we want to share about you. We know that you are {{TAG1}} and as well {{TAG2}}. Further,{% if TAG1 == "NEW" %} thank you for being a new user! {% else %} welcome back to another informative class! {% endif %}
{{FNAME}}, once again, thank you for your kind support!
Kind Regards,
Organization Communication Manager


You can configure the format for TODO comments in Settings | Editor | TODO. Since syntax highlighting for TODO comments is already provided, this inspection is intended for use in batch mode or on a continuous integration server.


UseJava¶
Use Module for Java
This file provides functions for Java. It is assumed that FindJava.cmake has already been loaded. See FindJava.cmake for information on how to load Java into your CMake project.
add_jar(target_name
[SOURCES] source1 [source2 ...] [resource1 ...]
[INCLUDE_JARS jar1 [jar2 ...]]
[ENTRY_POINT entry]
[VERSION version]
[OUTPUT_NAME name]
[OUTPUT_DIR dir]
)
This command creates a <target_name>.jar. It compiles the given source files (source) and adds the given resource files (resource) to the jar file. If only resource files are given then just a jar file is created. The list of include jars are added to the classpath when compiling the java sources and also to the dependencies of the target. INCLUDE_JARS also accepts other target names created by add_jar. For backwards compatibility, jar files listed as sources are ignored (as they have been since the first version of this module).
The default OUTPUT_DIR can also be changed by setting the variable CMAKE_JAVA_TARGET_OUTPUT_DIR.
Additional instructions:
To add compile flags to the target you can set these flags with
the following variable:
set(CMAKE_JAVA_COMPILE_FLAGS -nowarn)
To add a path or a jar file to the class path you can do this
with the CMAKE_JAVA_INCLUDE_PATH variable.
set(CMAKE_JAVA_INCLUDE_PATH /usr/share/java/shibboleet.jar)
To use a different output name for the target you can set it with:
add_jar(foobar foobar.java OUTPUT_NAME shibboleet.jar)
To use a different output directory than CMAKE_CURRENT_BINARY_DIR
you can set it with:
add_jar(foobar foobar.java OUTPUT_DIR ${PROJECT_BINARY_DIR}/bin)
To define an entry point in your jar you can set it with the ENTRY_POINT
named argument:
add_jar(example ENTRY_POINT com/examples/MyProject/Main)
To define a custom manifest for the jar, you can set it with the manifest
named argument:
add_jar(example MANIFEST /path/to/manifest)
To add a VERSION to the target output name you can set it using
the VERSION named argument to add_jar. This will create a jar file with the
name shibboleet-1.0.0.jar and will create a symlink shibboleet.jar
pointing to the jar with the version information.
add_jar(shibboleet shibbotleet.java VERSION 1.2.0)
If the target is a JNI library, utilize the following commands to
create a JNI symbolic link:
set(CMAKE_JNI_TARGET TRUE)
add_jar(shibboleet shibbotleet.java VERSION 1.2.0)
install_jar(shibboleet ${LIB_INSTALL_DIR}/shibboleet)
install_jni_symlink(shibboleet ${JAVA_LIB_INSTALL_DIR})
If a single target needs to produce more than one jar from its
java source code, to prevent the accumulation of duplicate class
files in subsequent jars, set/reset CMAKE_JAR_CLASSES_PREFIX prior
to calling the add_jar() function:
set(CMAKE_JAR_CLASSES_PREFIX com/redhat/foo)
add_jar(foo foo.java)
set(CMAKE_JAR_CLASSES_PREFIX com/redhat/bar)
add_jar(bar bar.java)
Target Properties:
The add_jar() functions sets some target properties. You can get these
properties with the
get_property(TARGET <target_name> PROPERTY <propery_name>)
command.
INSTALL_FILES The files which should be installed. This is used by
install_jar().
JNI_SYMLINK The JNI symlink which should be installed.
This is used by install_jni_symlink().
JAR_FILE The location of the jar file so that you can include
it.
CLASS_DIR The directory where the class files can be found. For
example to use them with javah.
find_jar(<VAR>
name | NAMES name1 [name2 ...]
[PATHS path1 [path2 ... ENV var]]
[VERSIONS version1 [version2]]
[DOC "cache documentation string"]
)
This command is used to find a full path to the named jar. A cache entry named by <VAR> is created to stor the result of this command. If the full path to a jar is found the result is stored in the variable and the search will not repeated unless the variable is cleared. If nothing is found, the result will be <VAR>-NOTFOUND, and the search will be attempted again next time find_jar is invoked with the same variable. The name of the full path to a file that is searched for is specified by the names listed after NAMES argument. Additional search locations can be specified after the PATHS argument. If you require special a version of a jar file you can specify it with the VERSIONS argument. The argument after DOC will be used for the documentation string in the cache.
install_jar(TARGET_NAME DESTINATION)
This command installs the TARGET_NAME files to the given DESTINATION. It should be called in the same scope as add_jar() or it will fail.
install_jni_symlink(TARGET_NAME DESTINATION)
This command installs the TARGET_NAME JNI symlinks to the given DESTINATION. It should be called in the same scope as add_jar() or it will fail.
create_javadoc(<VAR>
PACKAGES pkg1 [pkg2 ...]
[SOURCEPATH <sourcepath>]
[CLASSPATH <classpath>]
[INSTALLPATH <install path>]
[DOCTITLE "the documentation title"]
[WINDOWTITLE "the title of the document"]
[AUTHOR TRUE|FALSE]
[USE TRUE|FALSE]
[VERSION TRUE|FALSE]
)
Create java documentation based on files or packages. For more details please read the javadoc manpage.
There are two main signatures for create_javadoc. The first signature works with package names on a path with source files:
Example:
create_javadoc(my_example_doc
PACKAGES com.exmaple.foo com.example.bar
SOURCEPATH "${CMAKE_CURRENT_SOURCE_DIR}"
CLASSPATH ${CMAKE_JAVA_INCLUDE_PATH}
WINDOWTITLE "My example"
DOCTITLE "
My example
"
AUTHOR TRUE
USE TRUE
VERSION TRUE
)
The second signature for create_javadoc works on a given list of files.
create_javadoc(<VAR>
FILES file1 [file2 ...]
[CLASSPATH <classpath>]
[INSTALLPATH <install path>]
[DOCTITLE "the documentation title"]
[WINDOWTITLE "the title of the document"]
[AUTHOR TRUE|FALSE]
[USE TRUE|FALSE]
[VERSION TRUE|FALSE]
)
Example:
create_javadoc(my_example_doc
FILES ${example_SRCS}
CLASSPATH ${CMAKE_JAVA_INCLUDE_PATH}
WINDOWTITLE "My example"
DOCTITLE "
My example
"
AUTHOR TRUE
USE TRUE
VERSION TRUE
)
Both signatures share most of the options. These options are the same as what you can find in the javadoc manpage. Please look at the manpage for CLASSPATH, DOCTITLE, WINDOWTITLE, AUTHOR, USE and VERSION.
The documentation will be by default installed to
${CMAKE_INSTALL_PREFIX}/share/javadoc/<VAR>
if you don’t set the INSTALLPATH.


Functions |
| EvalBuilder * | eval_builder_create (int n_vectors, int *vector_size, int *vector_times, int n_features, int n_games) |
| void | eval_builder_destroy (EvalBuilder *eval) |
| void | eval_builder_set_ply (EvalBuilder *eval, int ply) |
| void | eval_builder_read (EvalBuilder *eval, const char *file) |
| void | eval_builder_write (EvalBuilder *eval, const char *file) |
| void | eval_builder_logistello_get_features (const Board *b, int *X) |
| void | eval_builder_get_corner5x2_features (const Board *b, int *X) |
| void | eval_builder_get_corner3x3_features (const Board *b, int *X) |
| void | eval_builder_get_edge_features (const Board *b, int *X) |
| void | eval_builder_get_edge_X_features (const Board *b, int *X) |
| void | eval_builder_get_edge_C_features (const Board *b, int *X) |
| void | eval_builder_get_edge_CX_features (const Board *b, int *X) |
| void | eval_builder_get_ABFG_features (const Board *b, int *X) |
| void | eval_builder_get_edge_FG_features (const Board *b, int *X) |
| void | eval_builder_get_CC_features (const Board *b, int *X) |
| void | eval_builder_get_AA_features (const Board *b, int *X) |
| void | eval_builder_get_BB_features (const Board *b, int *X) |
| void | eval_builder_get_d8_features (const Board *b, int *X) |
| void | eval_builder_get_d7_features (const Board *b, int *X) |
| void | eval_builder_get_d6_features (const Board *b, int *X) |
| void | eval_builder_get_d5_features (const Board *b, int *X) |
| void | eval_builder_get_d4_features (const Board *b, int *X) |
| void | eval_builder_get_d3_features (const Board *b, int *X) |
| EvalBuilder * | eval_builder_create_edax3c (int n_games) |
| EvalBuilder * | eval_builder_create_edax3d (int n_games) |
| EvalBuilder * | eval_builder_create_logistello (int n_games) |
| EvalBuilder * | eval_builder_create_feature (int n_games, int feature) |
| void | eval_builder_build_features (EvalBuilder *eval, Gamebase *base, int ply) |
| void | eval_builder_equalize (EvalBuilder *eval, double *w) |
| void | eval_builder_zero (EvalBuilder *eval, double *w, int *N, int N_min) |
| void | eval_builder_eval (EvalBuilder *eval, int ply, double *x, double *y) |
| int | eval_builder_count_features (EvalBuilder *eval, int ply) |
| int | eval_builder_count_significant_coefficients (EvalBuilder *eval, int ply) |
| void | eval_builder_get_feature_frequency (EvalBuilder *eval, int *N) |
| void | eval_builder_get_coefficient (EvalBuilder *eval, double *w) |
| void | eval_builder_get_score (EvalBuilder *eval, double *y) |
| void | eval_builder_set_coefficient (EvalBuilder *eval, double *w) |
| double | eval_builder_get_abs_error (EvalBuilder *eval, double *w, double *e) |
| void | eval_builder_get_abs_error_gradient (EvalBuilder *eval, double *e, double *g, int *N, int N_min) |
| double | eval_builder_get_squared_error (EvalBuilder *eval, double *w, double *e) |
| void | eval_builder_get_squared_error_gradient (EvalBuilder *eval, double *e, double *g, int *N, int N_min) |
| double | eval_builder_get_dir_squared_error (EvalBuilder *eval, double *w, double *d, double l) |
| double | eval_builder_minimize_dir_abs_error (EvalBuilder *eval, double *w, double *d) |
| double | eval_builder_minimize_dir_squared_error (EvalBuilder *eval, double *w, double *d) |
| double | eval_builder_minimize_dir_squared_error_using_brent (EvalBuilder *eval, double *w, double *d, double accuracy) |
| int | eval_builder_conjugate_gradient (EvalBuilder *eval, int ply, EvalOption *option) |
| void | eval_builder_build (EvalBuilder *eval, Gamebase *base, EvalOption *option) |
| void | eval_builder_temporal_filter (EvalBuilder *eval, Gamebase *base, int max_iter, double accuracy) |
| void | print_feature (int n, int i) |
| void | eval_builder_get_feature (int n, int offset, int *feature) |
| void | eval_builder_get_sub_features (int n, int offset, int feature, int **subfeature, int *P, int *L) |
| void | eval_builder_spatial_filter (EvalBuilder *eval, Gamebase *base, int max_iter, double accuracy) |
| void | eval_builder_equalize_all (EvalBuilder *eval) |
| void | eval_builder_zero_rare_features (EvalBuilder *eval, Gamebase *base) |
| void | eval_builder_merge (EvalBuilder *eval_1, EvalBuilder *eval_2, int split) |
| void | eval_builder_unbias (EvalBuilder *eval, Gamebase *base, int error_type) |
| void | eval_builder_stat (EvalBuilder *eval, Gamebase *base) |
| void | eval_builder_diff (EvalBuilder *eval_1, EvalBuilder *eval_2) |
| void | eval_builder_plot (EvalBuilder *eval, Gamebase *base, const char *plot_file) |
| void | eval_builder_show_feature_weights (EvalBuilder *eval, int type, const char *feature) |
| void | print_version (void) |
| void | print_usage (void) |
| int | main (int argc, char **argv) |
Set of tools to build evaluation functions This code is not used. This or similar code has been used to build current evaluation function. It will be reused to build a new evaluation function for Edax version 5.0
- Date:
- 1998 - 201?
- Author:
- Richard Delorme
- Version:
- 5.0


DET
: determiner
This document is a placeholder for the language-specific documentation
for DET
.
Treebank Statistics (UD_Sanskrit)
There are 14 DET
lemmas (2%), 35 DET
types (4%) and 58 DET
tokens (5%).
Out of 13 observed tags, the rank of DET
is: 8 in number of lemmas, 6 in number of types and 5 in number of tokens.
The 10 most frequent DET
lemmas: तद्, इदम्, एतद्, यद्, क, एन, अदस्, एव, कदाचित्, कधाचिद्
The 10 most frequent DET
types: तत्, तया, तस्य, तेन, स, अयम्, इदम्, एनम्, तम्, ते
The 10 most frequent ambiguous lemmas: तद् (PRON 46, DET 31, ADV 9, NOUN 1, X 1), इदम् (PRON 8, DET 5), एतद् (PRON 7, DET 5), यद् (PRON 14, DET 4), क (PRON 11, DET 3), एन (PRON 2, DET 2), अदस् (PRON 3, DET 1), एव (ADV 3, DET 1), त्वद् (PRON 10, DET 1), सर्व (ADJ 2, NOUN 1, DET 1)
The 10 most frequent ambiguous types: तत् (DET 11, PRON 8, ADV 7), तया (DET 3, PRON 1), तस्य (PRON 9, DET 3), तेन (DET 3, PRON 1), स (PRON 5, DET 3, NOUN 1), अयम् (PRON 4, DET 2), इदम् (PRON 3, DET 2, NOUN 1), एनम् (DET 2, PRON 1), तम् (PRON 2, DET 2), ते (DET 2, PRON 2)
- तत्
- तया
- तस्य
- तेन
- स
- अयम्
- इदम्
- एनम्
- तम्
- ते
Morphology
The form / lemma ratio of DET
is 2.500000 (the average of all parts of speech is 1.219298).
The 1st highest number of forms (11) was observed with the lemma “तद्”: तत्, तम्, तया, तस्मात्, तस्मै, तस्य, तान्, ते, तेन, स, सः.
The 2nd highest number of forms (5) was observed with the lemma “एतद्”: एतत्, एतस्मिन्, एताम्, एते, एषा.
The 3rd highest number of forms (4) was observed with the lemma “यद्”: यः, यत्, यस्मिन्, ये.
DET
occurs with 6 features: sa-feat/PronType (56; 97% instances), sa-feat/Case (55; 95% instances), sa-feat/Number (55; 95% instances), sa-feat/Gender (54; 93% instances), sa-feat/Person (3; 5% instances), sa-feat/Poss (3; 5% instances)
DET
occurs with 20 feature-value pairs: Case=Abl
, Case=Acc
, Case=Dat
, Case=Gen
, Case=Ins
, Case=Loc
, Case=Nom
, Gender=Fem
, Gender=Masc
, Gender=Neut
, Number=Dual
, Number=Plur
, Number=Sing
, Person=2
, Person=3
, Poss=Yes
, PronType=Dem
, PronType=Int
, PronType=Prs
, PronType=Rel
DET
occurs with 30 feature combinations.
The most frequent feature combination is Case=Acc|Gender=Neut|Number=Sing|PronType=Dem
(9 tokens).
Examples: तत्, असौ, इदम्, एषा, तम्
Relations
DET
nodes are attached to their parents using 7 different relations: sa-dep/det (42; 72% instances), sa-dep/obj (7; 12% instances), sa-dep/nsubj (3; 5% instances), sa-dep/obl (3; 5% instances), sa-dep/nmod (1; 2% instances), sa-dep/nsubj:pass (1; 2% instances), sa-dep/root (1; 2% instances)
Parents of DET
nodes belong to 5 different parts of speech: NOUN (41; 71% instances), VERB (12; 21% instances), PROPN (2; 3% instances), X (2; 3% instances), ROOT (1; 2% instances)
57 (98%) DET
nodes are leaves.
1 (2%) DET
nodes have one child.
The highest child degree of a DET
node is 1.
Children of DET
nodes are attached using 1 different relations: sa-dep/nsubj (1; 100% instances)
Children of DET
nodes belong to 1 different parts of speech: NOUN (1; 100% instances)
DET in other languages: [am] [ar] [bg] [bxr] [ca] [ckb] [cop] [cs] [cu] [da] [de] [el] [en] [es] [et] [eu] [fa] [fi] [fo] [fr] [ga] [gl] [got] [grc] [he] [hi] [hr] [hu] [id] [it] [ja] [kk] [kmr] [ko] [la] [lv] [mr] [nl] [no] [pl] [pt] [ro] [ru] [sa] [sk] [sla] [sl] [so] [sr] [sv] [swl] [ta] [tr] [ug] [uk] [u] [urj] [ur] [vi] [yue] [zh]


Getting started¶
Python environment:
1. Create a new python environment by executing make environment. This creates a new python virtual environment (or Conda environment if Conda is installed) with the same name as the project.
2. Activate the new environment in the command line. If you have conda installed this will be conda activate phone-sentiment-analysis.
3. Install need packages by executing make requirements. This will install all the packages described in requirements.txt to the currently active environment (be sure that the right one is active!).
Environmental Variables:
Create a file called .env in the root directory and add write variables S3_BUCKET, EMR_JOB, SUBNET_ID there. E.g. S3_BUCKET=”some-bucket”. These environmental variables are used to create the compute instance in AWS.
Configuring AWS Client:
Execute aws configure in the shell and give your aws account information if you have not done so already (if you have aws client previously installed).
Creating the data with EMR process:
1. Create json job step descriptions by executing make job_json. This will break down the Common Crawl WET-paths to chunks used as processing steps.
2. Execute make create_bucket. This will create a S3 bucket to hold all necessary data and scripts for the EMR jobs.
3-5. Execute make run_job1-3 in shell. Each of these will start a separate EMR job with specific steps dictated by different json files. Although it is possible to put all the three scripts running at once, it is probably not actually feasible as the the limit of computing instances in AWS is by default 12 and each job takes up 5 instances.
BEWARE! Using Amazon EMS is not free! Processing the data will cost around 25 euros (21.10.2019 pricing)
6. Download and pre-process the data by executing make download_data. The data will appear in data/raw folder. With two separate files. With one having the actual data and one containing the urls of the websites where the data comes from.
Processing the Data for Analysis:
Execute make data in the shell. This will do all the preprocessing and cleaning outlined in src/make_dataset.py


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