mantel.test.Rd
Perform correlation tests between pairs of distance matrices. The Mantel
test is different from classical correlation tests (such as those
implemented by cor.test
) in that the null distribution
(and significance level) are obtained through randomisation. The null
distribution is generated by shuffling the locations (matrix rows and
columns) of one of the matrices to calculate an empirical null distribution
for the given data set.
mantel.test(x, y, ...) # S3 method for default mantel.test(x, y, plot = FALSE, method = c("spearman", "kendall", "pearson"), trials = 9999, omitzerodistances = FALSE, ...) # S3 method for formula mantel.test(x, y, groups = NULL, stringdistfun = utils::adist, meaningdistfun = hammingdists, ...) # S3 method for list mantel.test(x, y, plot = FALSE, ...) # S3 method for mantel plot(x, xlab = "generation", ...)
| x | a formula, distance matrix, or list of distance matrices (see below) |
|---|---|
| y | a data frame, distance matrix, or list of distance matrices of the
same length as |
| ... | further arguments which are passed on to the default method (in
particular |
| plot | logical: immediately produce a plot of the test results (default:
|
| method | correlation coefficient to be computed. Passed on to
|
| trials | integer: maximum number of random permutations to be computed (see Details). |
| omitzerodistances | logical: if |
| groups | when |
| stringdistfun | when |
| meaningdistfun | when |
| xlab | the x axis label used when plotting the result of several Mantel tests next to each other |
A dataframe of class mantel
, with one row per Mantel test carried
out, containing the following columns:
method
Character string: type of correlation coefficient used
statistic
The veridical correlation coefficient between the entries in the two distance matrices
rsample
A list of correlation coefficients calculated from the permutations of the input matrices
mean
Average correlation coefficient produced by the permutations
sd
Standard deviation of the sampled correlation coefficients
p.value
Empirical p-value computed from the Mantel
test: let ngreater
be the number of correlation coefficients
in rsample
greater than or equal to statistic
, then
p.value
is (ngreater+1)/(length(rsample)+1
p.approx
The theoretical p-value that would correspond
to the standard z
score as calculated above.
is.unique.max
Logical, TRUE
iff the veridical
correlation coefficient is greater than any of the coefficients
calculated for the permutations. If this is true, then
p.value == 1 / (length(rsample)+1)
Multiple mantel
objects can easily be combined by calling
rbind(test1, test2, ...)
.
If the number of possible permutations of the matrices is reasonably close
to the number of permutations specified by the trials
parameter, a
deterministic enumeration of all the permutations will be carried out
instead of random sampling: such a deterministic test will return an exact
p-value.
plot()
called on a data frame of class mantel
plots a
visualisation of the test results (in particular, the distribution of
the permutated samples against the veridical correlation coefficient). If
the veridical correlation coefficient is plotted in blue it means
that it was higher than all other coefficients generated by random
permutations of the data. When the argument contains the result of more than
one Mantel tests, a side-by-side boxplot visualisation shows the mean and
standard deviation of the randomised samples (see examples). Additional
parameters ...
to plot()
are passed on to
plot.default
.
default
: Perform Mantel correlation test on two distance
matrices. The distance matrices can either be of type
dist
, plain R matrices or any object that can be
interpreted by check.dist
. The order of the two matrices does
not matter unless omitzerodistances = TRUE
, in which case cells with
a 0 in the second matrix are omitted from the calculation of the
correlation coefficient. For consistency it is therefore recommended to
always pass the string distance matrix first, meaning distance matrix second.
formula
: This function can be called with raw experimental
result data frames, distance matrix calculation is taken care of internally.
x
is a formula of the type s ~ m1 + m2 + ...
where s
is the column name of the character strings in data frame or matrix y
,
while m1
etc. are the column names specifying the different meaning
dimensions. To calculate the respective distances, the function
stringdistfun
is applied to the strings, meaningdistfun
to the
meaning columns.
list
: When x
is a list of distance matrices, and
y
is either a single distance matrix or a list of distance matrices
the same length as x
: runs a Mantel test for every pairwise
combination of distance matrices in x
and y
and returns a
mantel
object with as many rows.
Dietz, E. J. 1983 “Permutation Tests for Association Between Two Distance Matrices.” Systematic Biology 32 (1): 21-–26. https://doi.org/10.1093/sysbio/32.1.21.
North, B. V., D. Curtis and P. C. Sham. 2002 “A Note on the Calculation of Empirical P Values from Monte Carlo Procedures.” The American Journal of Human Genetics 71 (2): 439-–41. https://doi.org/10.1086/341527.
# small distance matrix, Mantel test run deterministically mantel.test(dist(1:7), dist(1:7))#>#> Mantel permutation test (method: spearman) #> r = 1, N = 21 #> 5040 permutations, mean = -8.02e-19, sd = 0.224 #> p (exact) = 0.000595 #># run test on smallest distance matrix which requires a random # permutation test, and plot it plot(mantel.test(dist(1:8), dist(1:8), method="kendall"))# 2x2x2x2 design mantel.test(hammingdists(enumerate.meaningcombinations(c(2, 2, 2, 2))), dist(1:16), plot=TRUE)#> Mantel permutation test (method: spearman) #> r = 0.352, N = 120 #> 9999 permutations, mean = 0.00031, sd = 0.0891 #> p (empirical) = 2e-04 #># using the formula interface in combination with a data frame: print(data <- cbind(word=c("aa", "ab", "ba", "bb"), enumerate.meaningcombinations(c(2, 2))))#> word Var2 Var1 #> [1,] "aa" "1" "3" #> [2,] "ab" "1" "4" #> [3,] "ba" "2" "3" #> [4,] "bb" "2" "4"mantel.test(word ~ Var1 + Var2, data)#>#> Mantel permutation test (method: spearman) #> r = 1, N = 6 #> 24 permutations, mean = 0, sd = 0.722 #> p (exact) = 0.36 #># pass a list of distance matrices as the first argument, but just one # distance matrix as the second argument: this runs separate tests on # the pairwise combinations of the first and second argument result <- mantel.test(list(dist(1:8), dist(sample(8:1)), dist(runif(8))), hammingdists(enumerate.meaningcombinations(c(2, 2, 2)))) # print the result of the three independently run permutation tests print(result)#> Group '1': #> Mantel permutation test (method: spearman) #> r = 0.316, N = 28 #> 9999 permutations, mean = -0.00248, sd = 0.191 #> p (empirical) = 0.0477 #> #> Group '2': #> Mantel permutation test (method: spearman) #> r = 0.068, N = 28 #> 9999 permutations, mean = -0.00263, sd = 0.193 #> p (empirical) = 0.365 #> #> Group '3': #> Mantel permutation test (method: spearman) #> r = -0.0931, N = 28 #> 9999 permutations, mean = 0.00111, sd = 0.198 #> p (empirical) = 0.617 #># show the three test results in one plot plot(result, xlab="group")


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You need to prepare the historical data for training before you train the model. If you have prepared the data, build and train the model. In this example, assume the market has only 2 regimes and the market returns follow a Gaussian distribution. Therefore, create a 2-component Hidden Markov Model with Gaussian emissions, which is equivalent to a Gaussian mixture model with 2 means. Follow these steps to create the model:
GaussianHMM
constructor with the number of components, a covariance type, and the number of iterations.model = hmm.GaussianHMM(n_components=2, covariance_type="full", n_iter=100)
fit
method with the training data.model.fit(X)


PyZMQ is the official Python binding for the ZeroMQ Messaging Library
and can be found at https://pypi.python.org/pypi/pyzmq
The project's Home Page is at http://github.com/zeromq/pyzmq
It can be used to run in Python a subscriber or publisher from outside of Mt4,
to receive or send information to either the compiled DLL OTMql4Zmq,
or the Python OTMql4Zmq.
It is the basis for the scripts in the
https://github.com/OpenTrading/OTMql4Zmq/raw/master/bin
directory of the distribution, to publish to (OTZmqPublish.py)
or subscribe from OTZmqSubscribe.py the Zmq running in Mt4,
either the compiled DLL OTMql4Zmq, or the Python OTMql4Zmq.
So even if you are using the compiled DLL OTMql4Zmq, you may still
want a Python with PyZmq installed to run the test scripts in bin.
You will need to have https://pypi.python.org/pypi/pyzmq
installed in your Python. We have used the latest version at the time of writing,
but we expect that any version will work. If you are running the Python(x,y)
distribution, then pyzmq is already included.
If not, and you have pip installed in your Python and are connected to the
Internet, you can simply run pip pyzmq.
Otherwise, download the Python egg:
https://pypi.python.org/packages/2.7/p/pyzmq/pyzmq-14.5.0-py2.7-win32.egg
and easy_install it into your Python's site-packages directory.
Parent: Home


Validating results
It is useful to know how well the Dispatch API approximates NEMDE outputs when presented with historical data. As NEMDE outputs are contained within the NemSpdOutput
section of historical case files it is possible to backtest the Dispatch API's results against these data.
The following sections discuss a feature of the Dispatch API that makes it easy to backtest results.
Imports and authentication
import os
import json
import requests
import pandas as pd
from dotenv import load_dotenv
import matplotlib.pyplot as plt
from jsonpath_ng import jsonpath
from jsonpath_ng.ext import parse
import IPython.display as display
# Load environment variables
load_dotenv()
# Replace with your API token
# E.g. TOKEN = 'd97f0be5-df17-4332-91d8-16c6bbca0b6d'
TOKEN = os.getenv('TOKEN')
# A valid API token must be included when making requests
headers = {'Authorization': f'Token {TOKEN}'}
# Base URL endpoint for the Dispatch API
# base_url = 'https://dispatch.envector.com/api/v1/'
base_url = 'http://dispatch.localhost/api/v1/'
Submit validation job
In the pervious tutorials case files were submitted when creating jobs. Alternatively, the Dispatch API can be backtested against NEMDE by specifying a case ID, and including an option that will append the NEMDE solution to the results returned. This makes it easier to compare the solution returned by the Dispatch API with the 'actual' solution returned by NEMDE.
For example, if we sought to validate the model using data obtained from a case file with case ID<PHONE_NUMBER>1
the request body would be formulated as:
body = {
"case_id": "20201101001",
"options": {
"solution_format": "validation"
}
}
This instructs the Dispatch API to create a job using data from the specified case, and compares the results obtained with those found in the NemSpdOutputs
section of the case file (i.e. the Dispatch API's results are compared against the solution obtained by NEMDE).
def submit_validation_job(base_url, headers, case_id):
"""
Validate Dispatch API results for a given case ID
Parameters
----------
base_url : str
Base URL for the Dispatch API
headers : dict
Contains auth token used to authenticate requests
case_id : str
Case ID for which validation should be performed e.g. '20201101001'.
The format for case_id is as follows: {year}{month}{day}{interval_id}
where interval_id is in [001, ..., 288].
Returns
-------
job_info : dict
Response obtained from API when job is submitted. Includes the job's ID.
"""
# Construct URL request body
url = base_url + 'jobs/create'
# Request body
body = {
'case_id': case_id,
'options':
{'solution_format': 'validation'}
}
# Make request
headers = {'Authorization': f'Token {TOKEN}'}
response = requests.post(url=url, headers=headers, json=body)
# Information pertaining to the submitted job - includes job_id
job_info = response.json()
return job_info
# Submit validation job to the queue
job_info = submit_validation_job(base_url=base_url, headers=headers, case_id='20201101001')
job_info
{'job_id': '4d0a835a-f91c-4db1-96cd-6587a5b8d720',
'created_at': '2021-03-12T06:26:41.249334Z',
'enqueued_at': '2021-03-12T06:26:41.249445Z',
'timeout': 180,
'status': 'queued',
'label': None}
Explore results
First, let's retrieve results from the queue.
def get_job_results(base_url, headers, job_id):
"""Extract job results from the queue"""
url = base_url + f'jobs/{job_id}/results'
response = requests.get(url=url, headers=headers)
return response.json()
# Get job results from the queue
job_id = job_info.get('job_id')
job_results = get_job_results(base_url=base_url, headers=headers, job_id=job_id)
Pandas can be used to observe the difference between results obtained from the Dispatch API and results obtained from NEMDE. Results obtained from the Dispatch API are in the model
column, while results corresponding to the NEMDE solution are in the column titled actual
.
Period solution
This component of the solution provides the value of the objective function, along with aggregate metrics pertaining to constraint violations.
period_solution = job_results.get('results').get('output').get('PeriodSolution')
# Convert to markdown to display results
period_solution_md = pd.DataFrame(period_solution).to_markdown(index=False)
display.Markdown(period_solution_md)
| case_id | intervention | key | model | actual |
|---|---|---|---|---|
|<PHONE_NUMBER>1 | 0 | @TotalObjective | -1.05408e+07 | -1.05408e+07 |
|<PHONE_NUMBER>1 | 0 | @TotalInterconnectorViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalGenericViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalRampRateViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalUnitMWCapacityViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalFastStartViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalMNSPRampRateViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalMNSPOfferViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalMNSPCapacityViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | @TotalUIGFViolation | 0 | 0 |
Region solution
Aggregate generation and load dispatched within each region, along with net power flow out of the region and the wholesale electricity price.
region_solution = job_results.get('results').get('output').get('RegionSolution')
# Convert to markdown to display results
region_solution_md = (pd.DataFrame(region_solution).head(20)
.to_markdown(index=False))
display.Markdown(region_solution_md)
| region_id | intervention | case_id | key | model | actual |
|---|---|---|---|---|---|
| NSW1 | 0 |<PHONE_NUMBER>1 | @DispatchedGeneration | 5818.37 | 5818.37 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @DispatchedLoad | 0 | 0 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @FixedDemand | 5654.29 | 5654.29 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @NetExport | 164.078 | 164.08 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @SurplusGeneration | 0 | 0 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @EnergyPrice | 41.6999 | 41.6997 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @R6Dispatch | 213 | 213 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @R60Dispatch | 196 | 196 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @R5Dispatch | 68 | 52 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @R5RegDispatch | 118.172 | 118.17 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @L6Dispatch | 112.354 | 112.35 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @L60Dispatch | 173 | 173 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @L5Dispatch | 78.968 | 78.97 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @L5RegDispatch | 34 | 34 |
| NSW1 | 0 |<PHONE_NUMBER>1 | @ClearedDemand | 5660.54 | 5660.54 |
| QLD1 | 0 |<PHONE_NUMBER>1 | @DispatchedGeneration | 5232.56 | 5232.56 |
| QLD1 | 0 |<PHONE_NUMBER>1 | @DispatchedLoad | 0 | 0 |
| QLD1 | 0 |<PHONE_NUMBER>1 | @FixedDemand | 5113.69 | 5113.69 |
| QLD1 | 0 |<PHONE_NUMBER>1 | @NetExport | 118.868 | 118.87 |
| QLD1 | 0 |<PHONE_NUMBER>1 | @SurplusGeneration | 0 | 0 |
Trader solution
Dispatch targets for each trade type are reported. Ramp rates and fast start unit parameters are also included for some traders.
trader_solution = job_results.get('results').get('output').get('TraderSolution')
# Convert to markdown to display results
trader_solution_md = (pd.DataFrame(trader_solution).head(20)
.to_markdown(index=False))
display.Markdown(trader_solution_md)
| case_id | intervention | trader_id | key | model | actual |
|---|---|---|---|---|---|
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R6Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R60Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R5Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R5RegTarget | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L6Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L60Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L5Target | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L5RegTarget | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R6Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R60Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R5Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @R5RegViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L6Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L60Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L5Violation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @L5RegViolation | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @EnergyTarget | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @RampUpRate | 720 | 720 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @RampDnRate | 720 | 720 |
|<PHONE_NUMBER>1 | 0 | AGLHAL | @FSTargetMode | 0 | 0 |
Interconnector solution
Power flow over each interconnector along with its associated losses are reported. The amount by which power flow constraints are violated is given by the value corresponding to the @Deficit
key.
interconnector_solution = (job_results.get('results').get('output')
.get('InterconnectorSolution'))
# Convert to markdown to display results
interconnector_solution_md = (pd.DataFrame(interconnector_solution).head(20)
.to_markdown(index=False))
display.Markdown(interconnector_solution_md)
| interconnector_id | case_id | intervention | key | model | actual |
|---|---|---|---|---|---|
| N-Q-MNSP1 |<PHONE_NUMBER>1 | 0 | @Flow | -33 | -33 |
| N-Q-MNSP1 |<PHONE_NUMBER>1 | 0 | @Losses | -0.591673 | -0.59167 |
| N-Q-MNSP1 |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
| NSW1-QLD1 |<PHONE_NUMBER>1 | 0 | @Flow | -86.0011 | -86.0011 |
| NSW1-QLD1 |<PHONE_NUMBER>1 | 0 | @Losses | 0.306671 | 0.30667 |
| NSW1-QLD1 |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
| T-V-MNSP1 |<PHONE_NUMBER>1 | 0 | @Flow | -222.507 | -222.507 |
| T-V-MNSP1 |<PHONE_NUMBER>1 | 0 | @Losses | 6.01955 | 6.01955 |
| T-V-MNSP1 |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
| V-S-MNSP1 |<PHONE_NUMBER>1 | 0 | @Flow | 45 | 45 |
| V-S-MNSP1 |<PHONE_NUMBER>1 | 0 | @Losses | -0.856901 | -0.8569 |
| V-S-MNSP1 |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
| V-SA |<PHONE_NUMBER>1 | 0 | @Flow | 50 | 50 |
| V-SA |<PHONE_NUMBER>1 | 0 | @Losses | 0.60394 | 0.60394 |
| V-SA |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
| VIC1-NSW1 |<PHONE_NUMBER>1 | 0 | @Flow | -276.834 | -276.834 |
| VIC1-NSW1 |<PHONE_NUMBER>1 | 0 | @Losses | 10.8428 | 10.8428 |
| VIC1-NSW1 |<PHONE_NUMBER>1 | 0 | @Deficit | 0 | 0 |
Constraint solution
The right-hand side (RHS) value of each constraint along with the amount by which it is violated, given by @Deficit
, is reported in the constraint solution.
The Dispatch API obtains generic constraint RHS values from the NemSpdOutputs
section of a case file when formulating the mathematical program to be solved. This is a limitation of the Dispatch API. Please the see the caveats section for more details. Consequently, there will be no difference between model
and actual
@RHS
values for all constraints. However, the @Deficit
attribute is obtained from the Dispatch API's solution, so differences may be observed for this key.
constraint_solution = (job_results.get('results').get('output')
.get('ConstraintSolution'))
# Convert to markdown to display results
constraint_solution_md = (pd.DataFrame(constraint_solution)
.head().to_markdown(index=False))
display.Markdown(constraint_solution_md)
| case_id | intervention | constraint_id | key | model | actual |
|---|---|---|---|---|---|
|<PHONE_NUMBER>1 | 0 | #BBTHREE3_E | @RHS | 25 | 25 |
|<PHONE_NUMBER>1 | 0 | #BBTHREE3_E | @Deficit | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | #BULGANA1_E | @RHS | 100 | 100 |
|<PHONE_NUMBER>1 | 0 | #BULGANA1_E | @Deficit | 0 | 0 |
|<PHONE_NUMBER>1 | 0 | #COOPGWF1_E | @RHS | 329 | 329 |
Evaluating the Dispatch API
Let's use a few plots to see how dispatch targets obtained from the Dispatch API compare with results outputted by NEMDE.
def plot_solution(solution, key):
"""Plot 'model' vs 'actual' solution for a given key"""
# Convert to DataFrame
df = pd.DataFrame(solution)
# Initialise figure
fig, ax = plt.subplots()
# Plot results for a given key
ax = df.loc[df['key'] == key, :].plot.scatter(ax=ax, x='model', y='actual')
# Plot line with slope = 1
min_value = df.loc[df['key'] == key, ['model', 'actual']].min().min()
max_value = df.loc[df['key'] == key, ['model', 'actual']].max().max()
line_style = {'color': 'k', 'linestyle': '--', 'alpha': 0.5}
ax.plot([min_value, max_value], [min_value, max_value], **line_style)
# Set title
ax.set_title(key)
ax.set_xlabel('Model (MW)')
ax.set_ylabel('Actual (MW)')
plt.show()
# Check the @EnergyTarget solution for all traders
plot_solution(solution=trader_solution, key='@EnergyTarget')
Each point corresponds to a trader. The horizontal axis corresponds to the target output level obtained from the Dispatch API, while the vertical axis shows targets produced by NEMDE. The dashed line has a slope equal to one, indicating the region over which the Dispatch API's solution equals the NEMDE solution. Points close to the dashed line indicate close correspondence between the Dispatch API's solution and the solution obtained by NEMDE.
Plots can be constructed for the remaining trade types:
keys = ['@R6Target', '@R60Target', '@R5Target', '@R5RegTarget',
'@L6Target', '@L60Target', '@L5Target', '@L5RegTarget']
for i in keys:
plot_solution(solution=trader_solution, key=i)
Objective function value
The value of the objective function at optimality provides a useful heuristic when comparing the solution obtained from the Dispatch API with the results outputted by NEMDE. Small perturbations to input parameters or constraint formulations can manifest as large differences in the objective function's value. It's unlikely there would be close correspondence between these values if the Dispatch API's formulation was a manifestly innaccurate approximation of NEMDE.
Let's examine the objective function values:
# objective = period_solution.loc['@TotalObjective']
objective = [i for i in period_solution if i.get('key') == '@TotalObjective'][0]
objective
{'case_id': '20201101001',
'intervention': '0',
'key': '@TotalObjective',
'model': -10540805.66769616,
'actual': -10540790.07213}
We can also compute the absolute and relative difference.
difference = objective.get('model') - objective.get('actual')
relative_difference = difference / objective.get('actual')
print("Difference:", difference)
print("Relative difference:", relative_difference)
Difference: -15.595566159114242
Relative difference: 1.4795443275498999e-06
The relative difference is very small at approximately 0.000148
%. At this level solver settings, such as tolerances, and floating point precision could be coming into play.
Summary
This notebook examined strategies that can be used to validate model outputs. Reasons for discrepancies between the Dispatch API's solution and results obtained from NEMDE were also discussed, including features of the NEMDE's mathematical formulation that may result in non-unique solutions being returned.
Without the ability to examine the mathematical forumulation used by NEMDE definitive model validation is impossible. Instead, we must adopt a data-driven approach when evaluating the Dispatch API's performance. The model validation section extends the analysis presented above by comparing dispatch outcomes over thousands of dispatch intervals. This process of experimentation and followed by diagnostic analysis helps identify areas in which the Dispatch API's model formulation can be improved.


Just realized that I waited past year (2020) for the perfect blog post. I can tell from the expirence it won't happen unless I write frequently. Now I forgot many ideas for blog posts I had in the past few months.
I need to change my perspective of the blog posts and writing in general. This is not for anyone else to read, this for my future self to read. The best example is the last post I wrote in 2019. I don't remember any of them now. I can get started where I was sinc I documented the process. That's what I need to focus. Write for my future self.
As for the writing, Anne Lamott's advise is a good one.


Before Change
// Inner and outer block shapes are the same.
// We"re using this cache for the "sliced" property, not the "blocked" property.
blockDimsX = { "t" : (1,1),
"z" : (256,256),
"y" : (256,256),
"x" : (10,10),
"c" : (1,1) }
After Change
self.opWatershed.SeedImage.connect( self.opSeedFilter.Output )
else:
self.opWatershed.SeedImage.disconnect()
self.opThreshold.Function.disconnect()
def propagateDirty(self, inputSlot, roi):
// All outputs are directly connected to internal operators
pass


| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | package org.apache.commons.math3.genetics; | |
| 18 | ||
| 19 | import java.util.ArrayList; | |
| 20 | import java.util.List; | |
| 21 | ||
| 22 | import org.apache.commons.math3.exception.DimensionMismatchException; | |
| 23 | import org.apache.commons.math3.exception.MathIllegalArgumentException; | |
| 24 | import org.apache.commons.math3.exception.NotStrictlyPositiveException; | |
| 25 | import org.apache.commons.math3.exception.NumberIsTooLargeException; | |
| 26 | import org.apache.commons.math3.exception.util.LocalizedFormats; | |
| 27 | import org.apache.commons.math3.random.RandomGenerator; | |
| 28 | ||
| 29 | /** | |
| 30 | * N-point crossover policy. For each iteration a random crossover point is | |
| 31 | * selected and the first part from each parent is copied to the corresponding | |
| 32 | * child, and the second parts are copied crosswise. | |
| 33 | * | |
| 34 | * Example (2-point crossover): | |
| 35 | *
 | |
| 36 | * -C- denotes a crossover point | |
| 37 | * -C- -C- -C- -C- | |
| 38 | * p1 = (1 0 | 1 0 0 1 | 0 1 1) X p2 = (0 1 | 1 0 1 0 | 1 1 1) | |
| 39 | * \----/ \-------/ \-----/ \----/ \--------/ \-----/ | |
| 40 | * || (*) || || (**) || | |
| 41 | * VV (**) VV VV (*) VV | |
| 42 | * /----\ /--------\ /-----\ /----\ /--------\ /-----\ | |
| 43 | * c1 = (1 0 | 1 0 1 0 | 0 1 1) X c2 = (0 1 | 1 0 0 1 | 0 1 1) | |
| 44 | *
 | |
| 45 | * | |
| 46 | * This policy works only on {@link AbstractListChromosome}, and therefore it | |
| 47 | * is parameterized by T. Moreover, the chromosomes must have same lengths. | |
| 48 | * | |
| 49 | * @param <T> generic type of the {@link AbstractListChromosome}s for crossover | |
| 50 | * @since 3.1 | |
| 51 | * @version $Id: NPointCrossover.java 1385297 2012-09-16 16:05:57Z tn $ | |
| 52 | */ | |
| 53 | public class NPointCrossover<T> implements CrossoverPolicy { | |
| 54 | ||
| 55 | /** The number of crossover points. */ | |
| 56 | private final int crossoverPoints; | |
| 57 | ||
| 58 | /** | |
| 59 | * Creates a new {@link NPointCrossover} policy using the given number of points. | |
| 60 | *
 | |
| 61 | * Note: the number of crossover points must be < chromosome length - 1. | |
| 62 | * This condition can only be checked at runtime, as the chromosome length is not known in advance. | |
| 63 | * | |
| 64 | * @param crossoverPoints the number of crossover points | |
| 65 | * @throws NotStrictlyPositiveException if the number of {@code crossoverPoints} is not strictly positive | |
| 66 | */ | |
| 67 | public NPointCrossover(final int crossoverPoints) throws NotStrictlyPositiveException { | |
| 68 | 2 | if (crossoverPoints <= 0) { |
| 69 | throw new NotStrictlyPositiveException(crossoverPoints); | |
| 70 | } | |
| 71 | this.crossoverPoints = crossoverPoints; | |
| 72 | } | |
| 73 | ||
| 74 | /** | |
| 75 | * Returns the number of crossover points used by this {@link CrossoverPolicy}. | |
| 76 | * | |
| 77 | * @return the number of crossover points | |
| 78 | */ | |
| 79 | public int getCrossoverPoints() { | |
| 80 | 1 | return crossoverPoints; |
| 81 | } | |
| 82 | ||
| 83 | /** | |
| 84 | * Performs a N-point crossover. N random crossover points are selected and are used | |
| 85 | * to divide the parent chromosomes into segments. The segments are copied in alternate | |
| 86 | * order from the two parents to the corresponding child chromosomes. | |
| 87 | * | |
| 88 | * Example (2-point crossover): | |
| 89 | *
 | |
| 90 | * -C- denotes a crossover point | |
| 91 | * -C- -C- -C- -C- | |
| 92 | * p1 = (1 0 | 1 0 0 1 | 0 1 1) X p2 = (0 1 | 1 0 1 0 | 1 1 1) | |
| 93 | * \----/ \-------/ \-----/ \----/ \--------/ \-----/ | |
| 94 | * || (*) || || (**) || | |
| 95 | * VV (**) VV VV (*) VV | |
| 96 | * /----\ /--------\ /-----\ /----\ /--------\ /-----\ | |
| 97 | * c1 = (1 0 | 1 0 1 0 | 0 1 1) X c2 = (0 1 | 1 0 0 1 | 0 1 1) | |
| 98 | *
 | |
| 99 | * | |
| 100 | * @param first first parent (p1) | |
| 101 | * @param second second parent (p2) | |
| 102 | * @return pair of two children (c1,c2) | |
| 103 | * @throws MathIllegalArgumentException iff one of the chromosomes is | |
| 104 | * not an instance of {@link AbstractListChromosome} | |
| 105 | * @throws DimensionMismatchException if the length of the two chromosomes is different | |
| 106 | */ | |
| 107 | @SuppressWarnings("unchecked") // OK because of instanceof checks | |
| 108 | public ChromosomePair crossover(final Chromosome first, final Chromosome second) | |
| 109 | throws DimensionMismatchException, MathIllegalArgumentException { | |
| 110 | ||
| 111 | 2 | if (!(first instanceof AbstractListChromosome<?> && second instanceof AbstractListChromosome<?>)) { |
| 112 | throw new MathIllegalArgumentException(LocalizedFormats.INVALID_FIXED_LENGTH_CHROMOSOME); | |
| 113 | } | |
| 114 | 1 | return mate((AbstractListChromosome<T>) first, (AbstractListChromosome<T>) second); |
| 115 | } | |
| 116 | ||
| 117 | /** | |
| 118 | * Helper for {@link #crossover(Chromosome, Chromosome)}. Performs the actual crossover. | |
| 119 | * | |
| 120 | * @param first the first chromosome | |
| 121 | * @param second the second chromosome | |
| 122 | * @return the pair of new chromosomes that resulted from the crossover | |
| 123 | * @throws DimensionMismatchException if the length of the two chromosomes is different | |
| 124 | * @throws NumberIsTooLargeException if the number of crossoverPoints is too large for the actual chromosomes | |
| 125 | */ | |
| 126 | private ChromosomePair mate(final AbstractListChromosome<T> first, | |
| 127 | final AbstractListChromosome<T> second) | |
| 128 | throws DimensionMismatchException, NumberIsTooLargeException { | |
| 129 | ||
| 130 | final int length = first.getLength(); | |
| 131 | 1 | if (length != second.getLength()) { |
| 132 | throw new DimensionMismatchException(second.getLength(), length); | |
| 133 | } | |
| 134 | 2 | if (crossoverPoints >= length) { |
| 135 | throw new NumberIsTooLargeException(crossoverPoints, length, false); | |
| 136 | } | |
| 137 | ||
| 138 | // array representations of the parents | |
| 139 | final List<T> parent1Rep = first.getRepresentation(); | |
| 140 | final List<T> parent2Rep = second.getRepresentation(); | |
| 141 | // and of the children | |
| 142 | final ArrayList<T> child1Rep = new ArrayList<T>(first.getLength()); | |
| 143 | final ArrayList<T> child2Rep = new ArrayList<T>(second.getLength()); | |
| 144 | ||
| 145 | final RandomGenerator random = GeneticAlgorithm.getRandomGenerator(); | |
| 146 | ||
| 147 | ArrayList<T> c1 = child1Rep; | |
| 148 | ArrayList<T> c2 = child2Rep; | |
| 149 | ||
| 150 | int remainingPoints = crossoverPoints; | |
| 151 | int lastIndex = 0; | |
| 152 | 4 | for (int i = 0; i < crossoverPoints; i++, remainingPoints--) { |
| 153 | // select the next crossover point at random | |
| 154 | 4 | final int crossoverIndex = 1 + lastIndex + random.nextInt(length - lastIndex - remainingPoints); |
| 155 | ||
| 156 | // copy the current segment | |
| 157 | 3 | for (int j = lastIndex; j < crossoverIndex; j++) { |
| 158 | c1.add(parent1Rep.get(j)); | |
| 159 | c2.add(parent2Rep.get(j)); | |
| 160 | } | |
| 161 | ||
| 162 | // swap the children for the next segment | |
| 163 | ArrayList<T> tmp = c1; | |
| 164 | c1 = c2; | |
| 165 | c2 = tmp; | |
| 166 | ||
| 167 | lastIndex = crossoverIndex; | |
| 168 | } | |
| 169 | ||
| 170 | // copy the last segment | |
| 171 | 3 | for (int j = lastIndex; j < length; j++) { |
| 172 | c1.add(parent1Rep.get(j)); | |
| 173 | c2.add(parent2Rep.get(j)); | |
| 174 | } | |
| 175 | ||
| 176 | 1 | return new ChromosomePair(first.newFixedLengthChromosome(child1Rep), |
| 177 | second.newFixedLengthChromosome(child2Rep)); | |
| 178 | } | |
| 179 | } | |
Mutations | ||
| 68 |
changed conditional boundary : SURVIVED negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testNumberIsTooLargeException(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 80 |
replaced return of integer sized value with (x == 0 ? 1 : 0) : NO_COVERAGE |
|
| 111 |
negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossoverInvalidFixedLengthChromosomeSecond(org.apache.commons.math3.genetics.NPointCrossoverTest) negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossoverInvalidFixedLengthChromosomeFirst(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 114 |
mutated return of Object value for org/apache/commons/math3/genetics/NPointCrossover::crossover to ( if (x != null) null else throw new RuntimeException ) : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 131 |
negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testNumberIsTooLargeException(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 134 |
negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testNumberIsTooLargeException(org.apache.commons.math3.genetics.NPointCrossoverTest) changed conditional boundary : SURVIVED |
|
| 152 |
changed conditional boundary : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Changed increment from -1 to 1 : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Changed increment from 1 to -1 : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 154 |
Replaced integer addition with subtraction : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Replaced integer subtraction with addition : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Replaced integer subtraction with addition : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Replaced integer addition with subtraction : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 157 |
changed conditional boundary : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) Changed increment from 1 to -1 : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 171 |
Changed increment from 1 to -1 : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) negated conditional : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) changed conditional boundary : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |
|
| 176 |
mutated return of Object value for org/apache/commons/math3/genetics/NPointCrossover::mate to ( if (x != null) null else throw new RuntimeException ) : KILLED -> org.apache.commons.math3.genetics.NPointCrossoverTest.testCrossover(org.apache.commons.math3.genetics.NPointCrossoverTest) |


FITSIO Home
Next: 5.7.4 Read and Write
Up: 5.7 ASCII and Binary
Previous: 5.7.2 Column Information Routines
Contents
- 1
- Insert or delete rows in an ASCII or binary table. When inserting rows
all the rows following row FROW are shifted down by NROWS rows; if
FROW = 0 then the blank rows are inserted at the beginning of the
table. Note that it is *not* necessary to insert rows in a table before
writing data to those rows (indeed, it would be inefficient to do so).
Instead one may simply write data to any row of the table, whether that
row of data already exists or not.
The first delete routine deletes NROWS consecutive rows
starting with row FIRSTROW. The second delete routine takes an
input string that lists the rows or row ranges (e.g.,
'5-10,12,20-30'), whereas the third delete routine takes an input
integer array that specifies each individual row to be deleted. In
both latter cases, the input list of rows to delete must be sorted
in ascending order. These routines update the NAXIS2 keyword to
reflect the new number of rows in the
table.
int fits_insert_rows / ffirow
(fitsfile *fptr, LONGLONG firstrow, LONGLONG nrows, > int *status)
int fits_delete_rows / ffdrow
(fitsfile *fptr, LONGLONG firstrow, LONGLONG nrows, > int *status)
int fits_delete_rowrange / ffdrrg
(fitsfile *fptr, char *rangelist, > int *status)
int fits_delete_rowlist / ffdrws
(fitsfile *fptr, long *rowlist, long nrows, > int *status)
int fits_delete_rowlistll / ffdrwsll
(fitsfile *fptr, LONGLONG *rowlist, LONGLONG nrows, > int *status)
- 2
- Insert or delete column(s) in an ASCII or binary
table. When inserting, COLNUM specifies the column number that the
(first) new column should occupy in the table. NCOLS specifies how
many columns are to be inserted. Any existing columns from this
position and higher are shifted over to allow room for the new
column(s). The index number on all the following keywords will be
incremented or decremented if necessary to reflect the new position
of the column(s) in the table: TBCOLn, TFORMn, TTYPEn, TUNITn,
TNULLn, TSCALn, TZEROn, TDISPn, TDIMn, TLMINn, TLMAXn, TDMINn,
TDMAXn, TCTYPn, TCRPXn, TCRVLn, TCDLTn, TCROTn,
and TCUNIn.
int fits_insert_col / fficol
(fitsfile *fptr, int colnum, char *ttype, char *tform,
> int *status)
int fits_insert_cols / fficls
(fitsfile *fptr, int colnum, int ncols, char **ttype,
char **tform, > int *status)
int fits_delete_col / ffdcol(fitsfile *fptr, int colnum, > int *status)
- 3
- Copy a column from one HDU to another (or to the same HDU). If
create_col = TRUE, then a new column will be inserted in the output
table, at position `outcolumn', otherwise the existing output column will
be overwritten (in which case it must have a compatible data type).
If outcolnum is greater than the number of column in the table, then
the new column will be appended to the end of the table.
Note that the first column in a table is at colnum = 1.
The standard indexed keywords that related to the column (e.g., TDISPn,
TUNITn, TCRPXn, TCDLTn, etc.) will also be copied.
int fits_copy_col / ffcpcl
(fitsfile *infptr, fitsfile *outfptr, int incolnum, int outcolnum,
int create_col, > int *status);
- 4
- Copy 'nrows' consecutive rows from one table to another, beginning
with row 'firstrow'. These rows will be appended to any existing
rows in the output table.
Note that the first row in a table is at row = 1.
int fits_copy_rows / ffcprw
(fitsfile *infptr, fitsfile *outfptr, LONGLONG firstrow,
LONGLONG nrows, > int *status);
- 5
- Modify the vector length of a binary table column (e.g.,
change a column from TFORMn = '1E' to '20E'). The vector
length may be increased or decreased from the current value.
int fits_modify_vector_len / ffmvec
(fitsfile *fptr, int colnum, LONGLONG newveclen, > int *status)
FITSIO Home
Next: 5.7.4 Read and Write
Up: 5.7 ASCII and Binary
Previous: 5.7.2 Column Information Routines
Contents


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Global config
The global config, named Danger zone
in Otoroshi, is the place to configure Otoroshi globally.
Warning: In this page, the configuration is really sensitive and affects the global behaviour of Otoroshi.
Misc. Settings
Maintenance mode
: It passes every single service in maintenance mode. If a user calls a service, the maintenance page will be displayedNo OAuth login for BackOffice
: Forces admins to login only with user/password or user/password/u2F deviceAPI Read Only
: Freeze Otoroshi datastore in read only mode. Only people with access to the actual underlying datastore will be able to disable this.Auto link default
: When no group is specified on a service, it will be assigned to default oneUse circuit breakers
: Use circuit breaker on all servicesUse new http client as the default Http client
: All http calls will use the new http client by defaultEnable live metrics
: Enable live metrics in the Otoroshi cluster. Performs a lot of writes in the datastoreDigitus medius
: Use middle finger emoji as a response character for endless HTTP responses (see IP address filtering settings).Limit conc. req.
: Limit the number of concurrent request processed by Otoroshi to a certain amount. Highly recommended for resilienceUse X-Forwarded-* headers for routing
: When evaluating routing of a request, X-Forwarded-* headers will be used if presentsMax conc. req.
: Maximum number of concurrent requests processed by otoroshi.Max HTTP/1.0 resp. size
: Maximum size of an HTTP/1.0 response in bytes. After this limit, response will be cut and sent as is. The best value here should satisfy (maxConcurrentRequests * maxHttp10ResponseSize) < process.memory for worst case scenario.Max local events
: Maximum number of events stored.Lines
: deprecated
IP address filtering settings
IP allowed list
: Only IP addresses that will be able to access Otoroshi exposed servicesIP blocklist
: IP addresses that will be refused to access Otoroshi exposed servicesEndless HTTP Responses
: IP addresses for which each request will return around 128 Gb of 0s
Quotas settings
Global throttling
: The max. number of requests allowed per second globally on OtoroshiThrottling per IP
: The max. number of requests allowed per second per IP address globally on Otoroshi
Analytics: Elastic dashboard datasource (read)
Cluster URI
: Elastic cluster URIIndex
: Elastic indexType
: Event type (not needed for elasticsearch above 6.x)User
: Elastic User (optional)Password
: Elastic password (optional)Version
: Elastic version (optional, if none provided it will be fetched from cluster)Apply template
: Automatically apply index templateCheck Connection
: Button to test the configuration. It will displayed a modal with a connection checklist, if connection is successfull, it will display the found version of the Elasticsearch and the index usedManually apply index template
: try to put the elasticsearch template by calling the api of elasticsearchShow index template
: try to retrieve the current index template present in elasticsearchClient side temporal indexes handling
: When enabled, Otoroshi will manage the creation of indexes over time. When it’s disabled, Otoroshi will push in the same indexOne index per
: When the previous field is enabled, you can choose the interval of time between the creation of a new index in elasticsearchCustom TLS Settings
: Enable the TLS configuration for the communication with ElasticsearchTLS loose
: if enabled, will block all untrustful ssl configsTrustAll
: allows any server certificates even the self-signed onesClient certificates
: list of client certificates used to communicate with elasticsearchTrusted certificates
: list of trusted certificates received from elasticsearch
Statsd settings
Datadog agent
: The StatsD agent is a Datadog agentStatsD agent host
: The host on which StatsD agent is listeningStatsD agent port
: The port on which StatsD agent is listening (default is 8125)
Backoffice auth. settings
Backoffice auth. config
: the authentication module used in front of Otoroshi. It will be used to connect to Otoroshi on the login page
Let’s encrypt settings
Enabled
: when enabled, Otoroshi will have the possiblity to sign certificate from let’s encrypt notably in the SSL/TSL Certificates pageServer URL
: ACME endpoint of let’s encryptEmail addresses
: (optional) list of addresses used to order the certificatesContact URLs
: (optional) list of addresses used to order the certificatesPublic Key
: used to ask a certificate to let’s encrypt, generated by OtoroshiPrivate Key
: used to ask a certificate to let’s encrypt, generated by Otoroshi
CleverCloud settings
Once configured, you can register one clever cloud app of your organization directly as an Otoroshi service.
CleverCloud consumer key
: consumer key of your clever cloud OAuth 1.0 appCleverCloud consumer secret
: consumer secret of your clever cloud OAuth 1.0 appOAuth Token
: oauth token of your clever cloud OAuth 1.0 appOAuth Secret
: oauth token secret of your clever cloud OAuth 1.0 appCleverCloud orga. Id
: id of your clever cloud organization
Global scripts
Global scripts will be deprecated soon, please use global plugins instead (see the next section)!
Global plugins
Enabled
: enable the use of global pluginsPlugins on new Otoroshi engine
: list of plugins used by the new Otoroshi enginePlugins on old Otoroshi engine
: list of plugins used by the old Otoroshi enginePlugin configuration
: the overloaded configuration of plugins
Proxies
In this section, you can add a list of proxies for :
- Proxy for alert emails (mailgun)
- Proxy for alert webhooks
- Proxy for Clever-Cloud API access
- Proxy for services access
- Proxy for auth. access (OAuth, OIDC)
- Proxy for client validators
- Proxy for JWKS access
- Proxy for elastic access
Each proxy has the following fields
Proxy host
: host of proxyProxy port
: port of proxyProxy principal
: user of proxyProxy password
: password of proxyNon proxy host
: IP address that can access the service
Quotas alerting settings
Enable quotas exceeding alerts
: When apikey quotas is almost exceeded, an alert will be sentDaily quotas threshold
: The percentage of daily calls before sending alertsMonthly quotas threshold
: The percentage of monthly calls before sending alerts
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User-Agent extraction
: Allow user-agent details extraction. Can have impact on consumed memory.
Geolocation extraction settings
Extract a geolocation for each call to Otoroshi.
Tls Settings
Use random cert.
: Use the first available cert when none matches the current domainDefault domain
: When the SNI domain cannot be found, this one will be used to find the matching certificateTrust JDK CAs (server)
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: Select the trusted CAs you want for TLS terminaison. Those CAs only will be proposed in the certificate request when performing TLS handshake
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: tags attached to the global configMetadata
: metadata attached to the global config
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: Get the global config at YAML formatEnable Panic Mode
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Following the replacement of the faulty voltage regulator, my system is now up and running. However, I am having some problems with accuracy and calibration.
I am measuring the solar PV generation using a pulse count and this is pretty much spot on as it matches my daily meter reading almost exactly. However the issue is in measuring the power use. I have clipped a CT sensor around the live main supply cable and am also using the AC adapter so that I can determine whether I am importing or exporting. Unfortunately, I cannot plug in the adapter very near to the board so it is on an extension lead totalling about a 5m round trip to the emontx. I have read a lot of the material and tried to get my head around the program but some of it still eludes me! I don't really know whether it is using the voltage to measure power or the ct sensor or a combination of the two!
At night, when I know we are importing it has the power rating far too low. And in the day when the load is unchanged but we are exporting, the measurement of the load on the supply cable (now flowing the other way) is again far too low.
I have read something about air gap in the sensor causing accuracy errors and the ct sensor is not tight around the cable - is this the air gap it talks about? If so how can I reduce this?
If I want to say double the power reading, should I change the 111 to 222?
Re: Accuracy and calibration
Have you clipped the CT around only the line cable? You must not clip it around both line and neutral. (Because the currents in the two cables should be equal but flow in opposite directions, they cancel each other, and you would read almost nothing).
That should make no measurable difference if it is the only thing on that extension cable.
It uses both (unlike some commercial units that measure only current and then estimate power). Power = voltage x current. This page http://openenergymonitor.org/emon/buildingblocks/ac-power-introduction tells you a bit more.
What is the error? Are you displaying voltage, and is that correct? Have you got the correct calibration constants for both your ac adapter and current transformer? Both need to be correct for the units that you have. This page http://openenergymonitor.org/emon/buildingblocks/ct-and-ac-power-adaptor... explains how to calculate the numbers for yourself (apologies if it is hard going), or you can look here: http://openenergymonitor.org/emon/modules/emontx/firmware/calibration if the parts you are using are listed.
The transformer core is the square grey metallic component that is in two halves within the blue plastic case. It is an air gap between the faces of the core that meet when the top is closed down and clipped that you must not have. An air gap around the main cable is perfectly OK.
Post again if you want some more explanation.
Re: Accuracy and calibration
The voltage reading sent by the emontx is about 14500, what units is it supposed to be in? Volts?! If so, how do I correct the reading, I have changed the calibration number to match the AC adapter I am using.
I do not have a calibrated meter so I cannot accurately say how far out the power reading is, but when I turn my 2.75-3kw rated kettled on, it was only increasing by about 1.5kw.
The only other ting on the extension cable is the emontx itself, it appears to make no difference if i power this separately, should it?
Re: Accuracy and calibration
That definitely looks wrong. The published sketches (as far as I know) all output in engineering units, i.e Volts and Amps.
[Edit] ... but the value of voltage is multiplied by 100 and then transmitted over the RF link as an integer - so you are seeing 145.00 V. So if you monitor the serial output of the emonTx, that is what you should see. [/Edit]
(And no, having the emonTx on the same extension lead should not matter).
We need to do some careful and systematic checks.
1. What is your system voltage, i.e what should it be reading, in Volts?
2. Which sketch and library are you using? and where are you reading 14500 ?
3. Unplug the voltage adapter and unclip the CT from the cable, allow the emonTx a few minutes to settle, what do you read then?
Re: Accuracy and calibration
1) I am not sure what the "system voltage" should be. Is it measuring what comes out of the transformer? In which case should it be 9V?
2) I am using the emontx_CT123_Voltage sketch with amendments to also read the optical pulses and to set the voltage calibration to 238.56 rather than 234.26 as I am using that model of adapter. I am reading the 14500 from the serial output of the emonbase
3) With no voltage adapter or CT the power drops to zero and the voltage fluctuates around 800.
Re: Accuracy and calibration
If I read from the emontx board itself, the power reading is 0 and the Vrms fluctuates around 8
Re: Accuracy and calibration
Only difference being that then it is being powered by the FTDI cable instead of the usb cable
Re: Accuracy and calibration
There are 2 values of Vrms aren't there - there is ct1.Vrms which is the one shown on the serial port, and the one it sends to the emonbse (emontx.Vrms) and the 1srt value is multiplied by 100 in the emontx code to give the value to send to emon base - that's why I was reading a value so high from emonbase. However, should it be reading 8V when no connection is made and 145V when the adapter is connected?
Re: Accuracy and calibration
By "system voltage" I meant the nominal mains supply voltage to your installation. In Europe, it is 230 V but in the UK although still nominally 230 V the centre value around which it fluctuates is 240 V.
From the calibration coefficient you give, I deduce that you are in the UK and using the Strontronics adapter.
Check the voltage divider resistors on the emonTx pcb. You might have a wrong value resistor for R13 / R14, or one could be faulty/damaged/cracked when assembled? All properly soldered? If all this looks ok, it is looking very much like your adapter is giving the wrong output. If you have a reasonable a.c. voltmeter, you should read about 11.2 V out of the Mascot adapter. The Sonotronics is likely to give a similar (i.e. higher than 9 V) output.
That looks right. Many people expect an accurate zero but in practice a small number is normal.
That should not affect it.
I strongly advise you to get a reasonable digital multimeter if you are going to do significant electronics work. Something like this http://www.maplin.co.uk/ut50a-manual-ranging-digital-multimeter-46390 should be adequate. [Disclaimer: I don't get a commission - other makes and suppliers are available!].
Re: Accuracy and calibration
I have a multimeter, I just didn't fancy putting it into my mains to see what the voltage reading was!!!! I meant I didn't have a meter where I could measure an accurate power reading for any appliances etc to give the emontx a more accurate calibration coefficient.
The adapter is giving a voltage measurement of 10.9 when switched on.
I checked the resistors and all was fine then I noticed one of my capacitors was incorrectly aligned - oops!! I have now rectified this and will see if it makes a difference to power readings. The Vrms number from the emonbase is now 21300-ish which means on the tx it must be 213V which is making more sense?
Re: Accuracy and calibration
Methinks you've cracked it! Was it C10 the wrong way round? (If the embarrassed answer is yes, then the centre point of the bias chain was probably very near to 0 V, and the wave would have been clipping on the negative half cycle, hence reading low).
I think few do have - it's a fairly specialised piece of kit.
That's totally believable.
A lot more sense, though still somewhat lower than I would have expected. It's possible that you have resistors in the divider chain (R13 & R14) that are 5% out the wrong way, which would mean the reading would be about 10% low, and the correct(?) value was 233.4 V. The mains supply should be 230 V +10% -6%, which gives a low limit of 216.2 V. I think you need to take courage and measure the mains voltage (on the protected side of an RCD if you're really apprehensive - that would protect you but not the meter, just make sure the meter is on the highest ac voltage range first), then adjust the voltage calibration constant to give you that number ( x 100 if you read it as transmitted). When you get the voltage calibration correct, the power readings too should be correct. If not, we'll look for a problem in the current department!
Re: Accuracy and calibration
Do we need a stable supply for the emonTx?
Like others, I checked the Tx calibration using a new pair of batteries for the power supply (3.25V). But what happens when the battery voltage is down to 2.7V? If the ADCs measure the inputs as a proportion of the supply voltage, they will start to read high as the battery voltage reduces. At 2.7V the ADCs will be reading 20% high, so the measured power reading will be over 40% high (being voltage * current).
Have I missed something?
Re: Accuracy and calibration
Indeed you have! (Sorry). The ADC reference voltage is measured internally against a stable reference. It emerges in two registers: ADCL and ADCH, and you'll need to plunder the Atmel data sheet for more about those. These are read by readVcc() in EmonLib.cpp and from there it enters the main calculations as SUPPLYVOLTAGE.
Re: Accuracy and calibration
Thanks!


This sample shows a basic view with ko databinding and view event handling. All events are handled in the view, knockout is used only for templating.


@Composite public abstract class CompositeRecommender extends Recommender
CompositeRecommender
represents the recommendation strategy, called composite recommender
. CompositeRecommender
is combination of other Recommender
algorithms in order to produce the best list of recommended items.
So CompositeRecommender
stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig()
method.| Modifier and Type | Field and Description |
|---|---|
static java.lang.Class<? extends Recommender> |
DEFAULT_RECOMMENDER_CLASS
This constant specifies the class of default internal recommender.
|
static java.lang.String |
INNER_RECOMMENDER
CompositeRecommender stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig() method. |
filterList
config, logger
| Constructor and Description |
|---|
CompositeRecommender()
Default constructor.
|
| Modifier and Type | Method and Description |
|---|---|
DataConfig |
createDefaultConfig()
Creating default configuration,
it is called every time method
Alg.resetConfig() is called |
AlgList |
getInnerRecommenders()
CompositeRecommender stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig() method. |
protected void |
setInnerRecommenders(AlgList recommenders)
CompositeRecommender stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig() method. |
void |
setup(Dataset dataset,
java.lang.Object... params)
Setting up this recommender based on specified dataset and additional parameters
|
void |
unsetup()
Unset this parameter.
|
estimate, finalize, getDataset, getFilterList, getParameters, preprocess, recommend
getConfig, resetConfig, toString
clone, equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
getName, newInstance
public static final java.lang.String INNER_RECOMMENDER
CompositeRecommender
stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig()
method.
Such list has a key specified by this constant.public static final java.lang.Class<? extends Recommender> DEFAULT_RECOMMENDER_CLASS
public DataConfig createDefaultConfig()
Alg
Alg.resetConfig()
is calledcreateDefaultConfig
in interface Alg
createDefaultConfig
in class AbstractAlg
DataConfig
public void setup(Dataset dataset, java.lang.Object... params) throws java.lang.Exception
Recommender
setup
in class Recommender
dataset
- dataset is the main parameter to setup this recommender.params
- Additional parameters to set up this recommender. This parameter is really an array of sub-parameters.java.lang.Exception
- if any error occurs.public void unsetup()
Recommender
Recommender.setup(Dataset, Object...)
is called again.unsetup
in class Recommender
protected void setInnerRecommenders(AlgList recommenders)
CompositeRecommender
stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig()
method.
This method sets such recommender list into the configuration of CompositeRecommender
.recommenders
- specified list of recommender (s).public AlgList getInnerRecommenders()
CompositeRecommender
stores a list of internal recommender (s) in its configuration returned from AbstractAlg.getConfig()
method.
This method gets such recommender list from the configuration.


QuartzForMe
Sample Java DSL for Quartz
Description
This is a simple Domain Specific Language for Quartz(http://quartz-scheduler.org/). It provides both internal and external DSL capabilities and allows users to express simple crons in a more intuitive way.
Internal Domain Specific Language
For each field in the "cron expression", the user can specify single, multiple, range, and/or increments of values. If a specific field is not specified, it will match all possible values by default (equivalent to '*' in quartz)
- Seconds - supports the values from 0 to 59
- setSeconds(first, second, third...) - Run every given second
- setIntervalSeconds(start, end) - Run every second in the given interval
- setIncrementSeconds(start, incrementBy) - Run every second from the initial value and the subsequent increments
- Minutes - supports the values from 0 to 59
- setMinutes(first, second, third...) - Run every given minute
- setIntervalMinutes(start, end) - Run every minute in the given interval
- setIncrementMinutes(start, incrementBy) - Run every minute from the initial value and the subsequent increments
- Hours - supports the values from 0 to 23
- setHours(first, second, third...) - Run every given hour
- setIntervalHours(start, end) - Run every hour in the given interval
- setIncrementHours(start, incrementBy) - Run every hour from the initial value and the subsequent increments
- Day of month - supports the values from 1 to 31
- setDays(first, second, third...) - Run every given day of the month
- setIntervalDays(start, end) - Run every day of the month in the given interval
- setIncrementDays(start, incrementBy) - Run every day from the initial value and the subsequent increments
- Month - supports the values from 1 to 12 and first-three letter representation of the month name e.g. JAN, FEB, MAR
- setMonths(first, second, third...) - Run every given month
- setIntervalMonths(start, end) - Run every month in the given interval
- setIncrementMonths(start, incrementBy) - Run every month from the initial value and the subsequent increments
- Day of week - supports the values from 1 to 7 starting from Sunday and first-three letter representation of the weekday name e.g. MON, TUE, WED
- setDaysOfWeek(first, second, third...) - Run every given weekday
- setIntervalDayOfWeek(start, end) - Run every weekday in the given interval
- setIncrementDayOfWeek(start, incrementBy) - Run every weekday from the initial value and the subsequent increments
- Year - supports values from 1000 to 9999
- setYears(first, second, third...) - Run every given year
- setIntervalYears(start, end) - Run every year in the given interval
- setIncrementYears(start, incrementBy) - Run every year from the initial value and the subsequent increments
Example
String expr = quartzExpression()
.setIncrementSeconds(0,15)
.setSeconds(10)
.setMinutes(10)
.setHours(23)
.setIntervalMonths(FEB-OCT)
.setDaysOfWeek(Monday)
.setYears(2015)
.generate();
External Domain Specific Language
Following a JSON approach, the external DSL requires the user to create an input file with keywords, specifying "cron expression" field and type of values, and the desired values. The two are divided by a colon ":".
- seconds
Example
cron.txt
seconds : 10,20,30


Swagger4j - a simple java API for reading and writing Swagger definitions
See online overview at https://github.com/SmartBear/soapui.


D3 Experiments
Home
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D3 Experiments
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Bubble Sort Example
Play
15
Bubble Sort Example 2
Play
15
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0
Play
Poem


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--- title: BPlanned Pricing | Try now for free heading: Start now to get 14 days free trial description: Bplanned offers a single plan for 3.50 euros per month, and a 14 days free trial with all the features. pricing_table: - name: Personal Plan color: "#414c9f" features: - text: €3.50 per month highlight: true call_to_action: link: # text: Get started faq: - question: What types of payment do you accept? answer: Credit cards including MasterCard, Visa or American Express. - question: Can I cancel my plan at a later time? answer: Yes, you can cancel and downgrade your plan at anytime. - question: Do I get any trial Period? answer: Yes, we offer 14 days free trial with all features. - question: Questions? answer: Contact us for any further questions at<EMAIL_ADDRESS>---


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NO ESCAPE A Film Review by Michael J. Legeros Copyright 1994 Michael J. Legeros
Director Martin Campbell
Writers Michael Gaylin, Joel Gross and based on Richard Herley's book THE PENAL COLONY
Cast Ray Liotta, Lance Henriksen, Stuart Wilson, Kevin Dillon, Ian McNeice, Jack Shepard, Michael Lerner, and Ernie Hudson
MPAA Rating "R" (presumably for v-i-o-l-e-n-c-e)
Running Time 118 Minutes
===
"Prisons have become big business." - Title card explaining imprisonment in the year 2022.
Cross MAD MAX with LORD OF THE FLIES and you've got the idea behind NO ESCAPE, a lousy prison-in-a-jungle pic. from producer-who-knows-better Gale Anne Hurd (ALIENS, THE ABYSS).
The story introduces Robbins (Ray Liotta), a Marine captain con- victed of murder and sent up the river and across the ocean to an island penal colony with no guards and one rule: stay alive. This "resort community" is controlled by two warring factions: the savage Outsiders (led by soft-spoken Stuart Wilson) and the peaceful Insiders (led by softer-spoken Lance Henriksen). But Robbins, who has a genetically traced problem with authority, doesn't want in on either..
For about thirty minutes, NO ESCAPE is the perfect tonic to a sans action spring. The locales are lush, the premise is punchy, and Liotta is the last action hero we'd expect to be engaging. The man can act and his early scenes with Wilson, who plays the lively leader of the Outsiders, have a nice, lazy charm.
Watch a staged fight between Robbins and "Ralph, our director of athletic activities" for a friendly nod to RAIDERS OF THE LOST ARK.
Once our hero goes outside the Outsiders (and inside the Insiders), the plot machinery grinds to a halt and NO ESCAPE becomes just that. Robbins spends the rest of the film seeking salvation in a ramshackle village that looks a little too much like GILLIGAN'S ISLAND to be taken seriously.
There are a couple of brief battles to break up the monotony, but director Martin Campbell doesn't do very well with any more than three people fighting at one time. He's better than, say, Kevin Reynolds, but not the *best* talent to effectively tingle the testosterone.
The gore is pretty good, though. Watch for a dandy beheading and a *great* impaling.
NO ESCAPE stars no women (!) and, considering the number of men shown in close quarters, could use a stronger sexual subtext to be believable. But this is Hollywood and Gale Anne Hurd *already* has a pretty large set of cojones.
BOTTOM LINE: NO ESCAPE is a lousy prison-in-a-jungle pic. that's worth watching for all of thirty minutes.
Grade: D+
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Logged in as ${appState.Username}
${appState.UserGroupLabel} (${appState.IpAddress})
${route.settings.sectionTitle}


A zoning method and a numerical criterion for zoning quality are available in this package. The zoning method is based on a numerical criterion that evaluates the zoning quality. This criterion quantifies simultaneously how zones are heterogeneous on the whole map and how neighbouring zones are similar. This approach allows comparison between maps either with different zones or different labels, which is of importance for zone delineation algorithms aiming at maximizing inter-zone variability. An optimisation procedure provides the user with the best zonings thanks to contour delineation for a given map.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | gstat, RandomFields, sp, maptools, deldir, fields, raster, graphics, stats, utils, grDevices, rgeos, ggplot2 |
| Suggests: | rmarkdown, knitr |
| Published: | 2018-02-08 |
| Author: | Brigitte Charnomordic [aut], Hazaël Jones [aut, cre], Patrice Loisel [aut], Isabelle Sanchez [ctb], Ngoc-Phi Tran [ctr] |
| Maintainer: | Hazaël Jones <hazael.jones at supagro.fr> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | geozoning results |
| Reference manual: | geozoning.pdf |
| Vignettes: |
Geozoning structures Zoning simulated data |
| Package source: | geozoning_1.0.0.tar.gz |
| Windows binaries: | r-devel: geozoning_1.0.0.zip, r-release: geozoning_1.0.0.zip, r-oldrel: geozoning_1.0.0.zip |
| OS X binaries: | r-release: geozoning_1.0.0.tgz, r-oldrel: geozoning_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=geozoning to link to this page.


Railway construction: Army relocates units to Epe
by Tunde AjajaTunde Ajaja with agency report
The Nigerian Army has relocated some of its units and formations located at Yaba to Epe to pave way for the expansion of the railway project in Lagos State.
News Agency of Nigeria reported that the units relocated were the 81 Division Base Ordinance Depot, printing press and tailoring factory.
The Chief of Army Staff, Lt-Gen Tukur Buratai, during the groundbreaking ceremony for the construction on Saturday disclosed that the relocation would lead to enhanced military presence in the area.
He said, “I want to use this opportunity to appreciate the cooperation of the Minister of Transportation (Mr Rotimi Amaechi), in facilitating the prompt approval and release of funds that has made this laudable project a reality.
“The minister was very supportive of the Nigerian army throughout the negotiation for the movement of this unit out of Yaba and the speedy completion of negotiations and of course the funding.”
The army chief also commended Oba Kamorudeen Animashaun of Epe for his continuous support for the army in its operations and the relocation of the unit base to the area.
Copyright PUNCH.
All rights reserved. This material, and other digital content on this website, may not be reproduced, published, broadcast, rewritten or redistributed in whole or in part without prior express written permission from PUNCH.
Contact: theeditor@punchng.com


Offered every semester
Taught by Dr. Wolak every Fall.
Course Content
This course is designed to give you an overview of major themes in the scientific discipline known as ecology. We will study ecological systems from different levels of organization, including individual organisms, populations, communities, and entire ecosystems. At each of the levels, we will focus on selected ecological patterns and the major processes producing such patterns. The course is primarily designed to address basic ecological questions within natural ecosystems (those unaltered by humans), but application of the underlying ecological principles is relevant to ecosystem management and impacts of humans on natural ecosystems. Principles of Ecology is a writing course, meaning that writing assignments will be used to assess your knowledge and ability to apply this knowledge to ecological questions.
Course Description
Students who successfully complete this course will acquire a fundamental knowledge of:
- 1) Abiotic and biotic factors in the environment and their influences on organisms, populations, communities, and ecosystems
- 2) The environmental and evolutionary processes that are ultimately responsible for producing ecological patterns in nature
- 3) Several quantitative approaches that ecologists use to address questions about ecological principles
In addition, students who complete this course will develop a working knowledge of how to:
- 4) Apply the scientific method to studying ecological systems and questions
- 5) Use spreadsheets and basic statistical tests to graphically display, analyze, and interpret field-collected ecological data
- 6) Produce a report written in a scientific format based on ecological data.
Student Learning Objectives
This course introduces or expands on the following Department of Biological Sciences Student Learning Objectives (SLOs):
-
Students will be able to critically review scientific articles in the life sciences. They will be able to recognize the tested hypothesis (or hypotheses) and identify the strengths, weaknesses, and major intellectual contributions of the articles [SLO 1].
-
Students will be able to effectively locate, evaluate, and summarize published information in the life sciences. They will be able to identify appropriate sources for specific information needs and use appropriate search tools and search strategies to access needed information [SLO 2].
-
Students will be able to communicate effectively to the appropriate audience in the written genres common to biological sciences (e.g., lab reports, research articles). Components of successful written communication include the effective organization of scientific content, appropriate use of language, and correct use of scientific citations [SLO 4].
-
Students will be able to apply the scientific method to formulate testable hypotheses, gather data that address the hypotheses, and analyze the data (statistically, graphically) to assess the degree to which their scientific work tests their hypotheses and draw appropriate conclusions from the data [SLO 5].
-
Students will be able to use biological evidence in a comparative framework to explain how the theory of evolution offers a comprehensive scientific explanation for the unity and diversity of life on Earth. They will be able to use specific examples to demonstrate how evolution has shaped organismal morphology, physiology, life history, and behavior [SLO 7].
-
Students will apply broad knowledge of the structure, function, and diversity of organisms to illustrate how ecological and evolutionary processes have shaped organisms at the individual, population, community, and ecosystem levels. Students will have skills related to behavior, physiology, biodiversity, systematics, evolution, ecology, and/or conservation research [SLO 8 Organismal Biology].


RemoveVCProcess: Class to exclude specific parts of the data structure from further processing by removing them,.
More...
Inherits Assimp::BaseProcess.
RemoveVCProcess: Class to exclude specific parts of the data structure from further processing by removing them,.
◆ RemoveVCProcess()
| RemoveVCProcess::RemoveVCProcess |
( |
| ) |
|
The default class constructor.
◆ ~RemoveVCProcess()
| RemoveVCProcess::~RemoveVCProcess |
( |
| ) |
|
◆ Execute()
| void RemoveVCProcess::Execute |
( |
aiScene * |
pScene | ) |
|
|
virtual |
Executes the post processing step on the given imported data.
At the moment a process is not supposed to fail.
- Parameters
-
| pScene | The imported data to work at. |
Implements Assimp::BaseProcess.
◆ ExecuteOnScene()
| void BaseProcess::ExecuteOnScene |
( |
Importer * |
pImp | ) |
|
|
inherited |
Executes the post processing step on the given imported data.
The function deletes the scene if the postprocess step fails ( the object pointer will be set to nullptr).
- Parameters
-
| pImp | Importer instance (pImp->mScene must be valid) |
◆ GetDeleteFlags()
| unsigned int Assimp::RemoveVCProcess::GetDeleteFlags |
( |
| ) |
const |
|
inline |
Query the current configuration.
◆ GetSharedData()
Get the shared data that is assigned to the step.
◆ IsActive()
| bool RemoveVCProcess::IsActive |
( |
unsigned int |
pFlags | ) |
const |
|
virtual |
Returns whether the processing step is present in the given flag field.
- Parameters
-
| pFlags | The processing flags the importer was called with. A bitwise combination of aiPostProcessSteps. |
- Returns
- true if the process is present in this flag fields, false if not.
Implements Assimp::BaseProcess.
◆ ProcessMesh()
| bool RemoveVCProcess::ProcessMesh |
( |
aiMesh * |
pcMesh | ) |
|
|
private |
◆ RequireVerboseFormat()
| bool BaseProcess::RequireVerboseFormat |
( |
| ) |
const |
|
virtualinherited |
Check whether this step expects its input vertex data to be in verbose format.
◆ SetDeleteFlags()
| void Assimp::RemoveVCProcess::SetDeleteFlags |
( |
unsigned int |
f | ) |
|
|
inline |
Manually setup the configuration flags for the step.
- Parameters
-
| Bitwise | combination of the aiComponent enumerated values. |
◆ SetSharedData()
Assign a new SharedPostProcessInfo to the step.
This object allows multiple postprocess steps to share data.
- Parameters
-
◆ SetupProperties()
| void RemoveVCProcess::SetupProperties |
( |
const Importer * |
pImp | ) |
|
|
virtual |
◆ configDeleteFlags
| unsigned int Assimp::RemoveVCProcess::configDeleteFlags |
|
private |
◆ mScene
| aiScene* Assimp::RemoveVCProcess::mScene |
|
private |
The scene we're working with.
◆ progress
Currently active progress handler.
◆ shared
See the doc of #SharedPostProcessInfo for more details.
The documentation for this class was generated from the following files:


int
Img_End(
)
- End the image package.
- See Also
Img_Init
Img_Reinit
mdd_t *
Img_ImageInfoComputeBwdWithDomainVars(
Img_ImageInfo_t * imageInfo,
mdd_t * fromLowerBound,
mdd_t * fromUpperBound,
mdd_t * toCareSet
)
- Computes the backward image of a set expressed in terms of
domain variables. FromLowerBound, fromUpperBound, and toCareSet are
expressed in terms of domain variables. See Img_ImageInfoComputeBwd for more
information.
- See Also
Img_ImageInfoComputeBwd
mdd_t *
Img_ImageInfoComputeBwd(
Img_ImageInfo_t * imageInfo,
mdd_t * fromLowerBound,
mdd_t * fromUpperBound,
mdd_t * toCareSet
)
- Computes the backward image of a set, under the function vector
in imageInfo, using the image computation method specified in imageInfo. The
set for which the backward image is computed is some set containing
fromLowerBound and contained in fromUpperBound. The exact set used is chosen
to simplify the computation. ToCareSet specifies those backward image points
of interest; any points not in this set may or may not belong to the returned
set. The MDDs fromLowerBound and fromUpperBound are defined over the range
variables. The MDD toCareSet and the returned MDD are defined over the
domain variables. If fromLowerBound is zero, then zero will be returned.
- See Also
Img_ImageInfoInitialize
Img_ImageInfoComputeFwd
Img_ImageInfoFree
Img_ImageInfoComputeBwdWithDomainVars
mdd_t *
Img_ImageInfoComputeFwdWithDomainVars(
Img_ImageInfo_t * imageInfo,
mdd_t * fromLowerBound,
mdd_t * fromUpperBound,
mdd_t * toCareSet
)
- Computes the forward image of a set and expresses it in terms
of domain variables. FromLowerBound, fromUpperBound, and toCareSet are
expressed in terms of domain variables. See Img_ImageInfoComputeFwd for more
information.
- See Also
Img_ImageInfoComputeFwd
mdd_t *
Img_ImageInfoComputeFwd(
Img_ImageInfo_t * imageInfo,
mdd_t * fromLowerBound,
mdd_t * fromUpperBound,
mdd_t * toCareSet
)
- Computes the forward image of a set, under the function vector
in imageInfo, using the image computation method specified in imageInfo. The
set for which the forward image is computed is some set containing
fromLowerBound and contained in fromUpperBound. The exact set used is chosen
to simplify the computation. ToCareSet specifies those forward image points
of interest; any points not in this set may or may not belong to the returned
set. The MDDs fromLowerBound and fromUpperBound are defined over the domain
variables. The MDD toCareSet and the returned MDD are defined over the range
variables. If fromLowerBound is zero, then zero will be returned.
- See Also
Img_ImageInfoInitialize
Img_ImageInfoComputeBwd
Img_ImageInfoFree
Img_ImageInfoComputeFwdWithDomainVars
void
Img_ImageInfoFree(
Img_ImageInfo_t * imageInfo
)
- Frees the memory associated with imageInfo.
- See Also
Img_ImageInfoInitialize
Img_ImageInfoComputeFwd
Img_ImageInfoComputeBwd
Img_ImageInfo_t *
Img_ImageInfoInitializeMocha(
mdd_manager * mddManager,
array_t * domainVars,
array_t * rangeVars,
array_t * quantifyVars,
unsigned int methodType,
Img_DirectionType directionType,
void * methodInfo
)
- The function initializes the image info data structure for use
in Mocha. Note that domainVars and rangeVars are arrays of mddIds
of present state variables and next state variables
respectively.
- Side Effects None.
Img_ImageInfo_t *
Img_ImageInfoInitialize(
Img_ImageInfo_t * imageInfo,
mdd_manager * mddManager,
array_t * domainVars,
array_t * rangeVars,
array_t * quantifyVars,
Img_MethodType methodType,
void * methodInfo,
Img_DirectionType directionType
)
- Initializes an imageInfo structure. MethodType specifies which
image computation method to use. If methodType is Img_Default_c, then if
the user-settable flag "image_method" has been set, then this method is
used, otherwise some default is used. DirectionType specifies which types of
image computations will be performed (forward, backward, or both).
Method-specific initialization takes into account the value of relevant
parameters in the global flag table.
MddNetwork is a graph representing the functions to be used. Each vertex of
the graph contains a multi-valued function (MVF) and an MDD id. The MVF
gives the function of the vertex in terms of the MDD ids of the immediate
fanins of the vertex.
Roots is an array of char* specifying the vertices of the graph which
represent those functions for which we want to compute the image (it must
not be empty); for example, for an FSM, roots represent the next state
functions. DomainVars is an array of mddIds; for an FSM, these are the
present state variables. Subsets of the domain are defined over these
variables. RangeVars is an array of mddIds over which the range is
expressed; for an FSM, these are the next state variables. This array must
be in one-to-one correspondence with the array of roots. QuantifyVars is an
array of mddIds, representing variables to be quantified from results of
backward images; for an FSM, these are the input variables. This array may
be empty. No copies are made of any of the input parameters, and thus it is
the application's responsibility to free this data *after* the returned
Img_ImageInfo_t is freed.
- See Also
Img_ImageInfoComputeFwd
Img_ImageInfoComputeBwd
Img_ImageInfoFree
char *
Img_ImageInfoObtainMethodTypeAsString(
Img_ImageInfo_t * imageInfo
)
- Returns a string giving the method type of an imageInfo. It is
the user's responsibility to free this string.
- See Also
Img_ImageInfoInitialize
void
Img_ImageInfoPrintMethodParams(
Img_ImageInfo_t * imageInfo,
FILE * fp
)
- Prints information about the image technique currently used.
- Side Effects None.
mdd_manager *
Img_ImageInfoReadMddManager(
Img_ImageInfo_t * imageInfo
)
- Returns the mdd manager.
- Side Effects None.
int
Img_Init(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- Initialize the image package.
- See Also
Img_Reinit
Img_End
mdd_t*
Img_MultiwayLinearAndSmooth(
mdd_manager * mddManager,
array_t * relationArray,
array_t * smoothVarMddIdArray,
array_t * introducedVarMddIdArray
)
- "relationArray" is an array of mdd's which need to be
multiplied and the variables in the "smoothVarMddIdArray" need to
be quantified out from the product. "introducedVarMddIdArray" is the
array of mddIds of the variables (other than the variables to be
quantified out) in the support of the relations. This array is used
to compute the product order such that the number of new variables
introduced in the product is minimized. However passing an empty
array or an array of mddIds of partial support will not result in any
error (some optimality will be lost though). The computation consists of 2
phases. In phase 1, an ordering of the relations and a schedule of
quantifying variables is found (based on IWLS95) heuristic. In phase
2, the relations are multiplied in order and the quantifying
variables are quantified according to the schedule.
- Side Effects None
int
Img_Reinit(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- Reinitializes the img package.
- See Also
Img_Init
Img_End


Supported Requests :
* GET /rest/sfdcvone
* GET /rest/sfdcvone/{id}
* GET /rest/sfdcvone/search/{where query}
* POST /rest/sfdcvone
* PUT /rest/sfdcvone/{id}
* DELETE /rest/sfdcvone/{id}


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