Aggregations
OpenSearch provides a full Java API to play with aggregations. See the Aggregations guide.
Use the factory for aggregation builders (AggregationBuilders
) and add each aggregation
you want to compute when querying and add it to your search request:
SearchResponse sr = node.client().prepareSearch()
.setQuery( /* your query */ )
.addAggregation( /* add an aggregation */ )
.execute().actionGet();
Note that you can add more than one aggregation. See Search Java API for details.
To build aggregation requests, use AggregationBuilders
helpers. Just import them
in your class:
import org.opensearch.search.aggregations.AggregationBuilders;
Structuring aggregations
As explained in the Aggregations guide, you can define sub aggregations inside an aggregation.
An aggregation could be a metrics aggregation or a bucket aggregation.
For example, here is a 3 levels aggregation composed of:
-
Terms aggregation (bucket)
-
Date Histogram aggregation (bucket)
-
Average aggregation (metric)
SearchResponse sr = node.client().prepareSearch()
.addAggregation(
AggregationBuilders.terms("by_country").field("country")
.subAggregation(AggregationBuilders.dateHistogram("by_year")
.field("dateOfBirth")
.calendarInterval(DateHistogramInterval.YEAR)
.subAggregation(AggregationBuilders.avg("avg_children").field("children"))
)
)
.execute().actionGet();
Metrics aggregations
Min Aggregation
Here is how you can use Min Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
MinAggregationBuilder aggregation =
AggregationBuilders
.min("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.min.Min;
// sr is here your SearchResponse object
Min agg = sr.getAggregations().get("agg");
double value = agg.getValue();
Max Aggregation
Here is how you can use Max Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
MaxAggregationBuilder aggregation =
AggregationBuilders
.max("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.max.Max;
// sr is here your SearchResponse object
Max agg = sr.getAggregations().get("agg");
double value = agg.getValue();
Sum Aggregation
Here is how you can use Sum Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
SumAggregationBuilder aggregation =
AggregationBuilders
.sum("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.sum.Sum;
// sr is here your SearchResponse object
Sum agg = sr.getAggregations().get("agg");
double value = agg.getValue();
Avg Aggregation
Here is how you can use Avg Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AvgAggregationBuilder aggregation =
AggregationBuilders
.avg("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.avg.Avg;
// sr is here your SearchResponse object
Avg agg = sr.getAggregations().get("agg");
double value = agg.getValue();
Stats Aggregation
Here is how you can use Stats Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
StatsAggregationBuilder aggregation =
AggregationBuilders
.stats("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.stats.Stats;
// sr is here your SearchResponse object
Stats agg = sr.getAggregations().get("agg");
double min = agg.getMin();
double max = agg.getMax();
double avg = agg.getAvg();
double sum = agg.getSum();
long count = agg.getCount();
Extended Stats Aggregation
Here is how you can use Extended Stats Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
ExtendedStatsAggregationBuilder aggregation =
AggregationBuilders
.extendedStats("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.stats.extended.ExtendedStats;
// sr is here your SearchResponse object
ExtendedStats agg = sr.getAggregations().get("agg");
double min = agg.getMin();
double max = agg.getMax();
double avg = agg.getAvg();
double sum = agg.getSum();
long count = agg.getCount();
double stdDeviation = agg.getStdDeviation();
double sumOfSquares = agg.getSumOfSquares();
double variance = agg.getVariance();
Value Count Aggregation
Here is how you can use Value Count Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
ValueCountAggregationBuilder aggregation =
AggregationBuilders
.count("agg")
.field("height");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.valuecount.ValueCount;
// sr is here your SearchResponse object
ValueCount agg = sr.getAggregations().get("agg");
long value = agg.getValue();
Percentile Aggregation
Here is how you can use Percentile Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
PercentilesAggregationBuilder aggregation =
AggregationBuilders
.percentiles("agg")
.field("height");
You can provide your own percentiles instead of using defaults:
PercentilesAggregationBuilder aggregation =
AggregationBuilders
.percentiles("agg")
.field("height")
.percentiles(1.0, 5.0, 10.0, 20.0, 30.0, 75.0, 95.0, 99.0);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.percentiles.Percentile;
import org.opensearch.search.aggregations.metrics.percentiles.Percentiles;
// sr is here your SearchResponse object
Percentiles agg = sr.getAggregations().get("agg");
// For each entry
for (Percentile entry : agg) {
double percent = entry.getPercent(); // Percent
double value = entry.getValue(); // Value
logger.info("percent [{}], value [{}]", percent, value);
}
This will basically produce for the first example:
percent [1.0], value [0.814338896154595]
percent [5.0], value [0.8761912455821302]
percent [25.0], value [1.173346540141847]
percent [50.0], value [1.5432023318692198]
percent [75.0], value [1.923915462033674]
percent [95.0], value [2.2273644908535335]
percent [99.0], value [2.284989339108279]
Percentile Ranks Aggregation
Here is how you can use Percentile Ranks Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
PercentileRanksAggregationBuilder aggregation =
AggregationBuilders
.percentileRanks("agg")
.field("height")
.values(1.24, 1.91, 2.22);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.percentiles.Percentile;
import org.opensearch.search.aggregations.metrics.percentiles.PercentileRanks;
// sr is here your SearchResponse object
PercentileRanks agg = sr.getAggregations().get("agg");
// For each entry
for (Percentile entry : agg) {
double percent = entry.getPercent(); // Percent
double value = entry.getValue(); // Value
logger.info("percent [{}], value [{}]", percent, value);
}
This will basically produce:
percent [29.664353095090945], value [1.24]
percent [73.9335313461868], value [1.91]
percent [94.40095147327283], value [2.22]
Cardinality Aggregation
Here is how you can use Cardinality Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
CardinalityAggregationBuilder aggregation =
AggregationBuilders
.cardinality("agg")
.field("tags");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.cardinality.Cardinality;
// sr is here your SearchResponse object
Cardinality agg = sr.getAggregations().get("agg");
long value = agg.getValue();
Geo Bounds Aggregation
Here is how you can use Geo Bounds Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
GeoBoundsAggregationBuilder aggregation =
GeoBoundsAggregationBuilder
.geoBounds("agg")
.field("address.location")
.wrapLongitude(true);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.metrics.geobounds.GeoBounds;
// sr is here your SearchResponse object
GeoBounds agg = sr.getAggregations().get("agg");
GeoPoint bottomRight = agg.bottomRight();
GeoPoint topLeft = agg.topLeft();
logger.info("bottomRight {}, topLeft {}", bottomRight, topLeft);
This will basically produce:
bottomRight [40.70500764381921, 13.952946866893775], topLeft [53.49603022435221, -4.190029308156676]
Top Hits Aggregation
Here is how you can use Top Hits Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.terms("agg").field("gender")
.subAggregation(
AggregationBuilders.topHits("top")
);
You can use most of the options available for standard search such as from
, size
, sort
, highlight
, explain
…
AggregationBuilder aggregation =
AggregationBuilders
.terms("agg").field("gender")
.subAggregation(
AggregationBuilders.topHits("top")
.explain(true)
.size(1)
.from(10)
);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.terms.Terms;
import org.opensearch.search.aggregations.metrics.tophits.TopHits;
// sr is here your SearchResponse object
Terms agg = sr.getAggregations().get("agg");
// For each entry
for (Terms.Bucket entry : agg.getBuckets()) {
String key = entry.getKey(); // bucket key
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], doc_count [{}]", key, docCount);
// We ask for top_hits for each bucket
TopHits topHits = entry.getAggregations().get("top");
for (SearchHit hit : topHits.getHits().getHits()) {
logger.info(" -> id [{}], _source [{}]", hit.getId(), hit.getSourceAsString());
}
}
This will basically produce for the first example:
key [male], doc_count [5107]
-> id [AUnzSZze9k7PKXtq04x2], _source [{"gender":"male",...}]
-> id [AUnzSZzj9k7PKXtq04x4], _source [{"gender":"male",...}]
-> id [AUnzSZzl9k7PKXtq04x5], _source [{"gender":"male",...}]
key [female], doc_count [4893]
-> id [AUnzSZzM9k7PKXtq04xy], _source [{"gender":"female",...}]
-> id [AUnzSZzp9k7PKXtq04x8], _source [{"gender":"female",...}]
-> id [AUnzSZ0W9k7PKXtq04yS], _source [{"gender":"female",...}]
Scripted Metric Aggregation
Here is how you can use Scripted Metric Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
ScriptedMetricAggregationBuilder aggregation = AggregationBuilders
.scriptedMetric("agg")
.initScript(new Script("state.heights = []"))
.mapScript(new Script("state.heights.add(doc.gender.value == 'male' ? doc.height.value : -1.0 * doc.height.value)"));
You can also specify a combine
script which will be executed on each shard:
ScriptedMetricAggregationBuilder aggregation = AggregationBuilders
.scriptedMetric("agg")
.initScript(new Script("state.heights = []"))
.mapScript(new Script("state.heights.add(doc.gender.value == 'male' ? doc.height.value : -1.0 * doc.height.value)"))
.combineScript(new Script("double heights_sum = 0.0; for (t in state.heights) { heights_sum += t } return heights_sum"));
You can also specify a reduce
script which will be executed on the node which gets the request:
ScriptedMetricAggregationBuilder aggregation = AggregationBuilders
.scriptedMetric("agg")
.initScript(new Script("state.heights = []"))
.mapScript(new Script("state.heights.add(doc.gender.value == 'male' ? doc.height.value : -1.0 * doc.height.value)"))
.combineScript(new Script("double heights_sum = 0.0; for (t in state.heights) { heights_sum += t } return heights_sum"))
.reduceScript(new Script("double heights_sum = 0.0; for (a in states) { heights_sum += a } return heights_sum"));
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.terms.Terms;
import org.opensearch.search.aggregations.metrics.tophits.TopHits;
// sr is here your SearchResponse object
ScriptedMetric agg = sr.getAggregations().get("agg");
Object scriptedResult = agg.aggregation();
logger.info("scriptedResult [{}]", scriptedResult);
Note that the result depends on the script you built. For the first example, this will basically produce:
scriptedResult object [ArrayList]
scriptedResult [ {
"heights" : [ 1.122218480146643, -1.8148918111233887, -1.7626731575142909, ... ]
}, {
"heights" : [ -0.8046067304119863, -2.0785486707864553, -1.9183567430207953, ... ]
}, {
"heights" : [ 2.092635728868694, 1.5697545960886536, 1.8826954461968808, ... ]
}, {
"heights" : [ -2.1863201099468403, 1.6328549117346856, -1.7078288405893842, ... ]
}, {
"heights" : [ 1.6043904836424177, -2.0736538674414025, 0.9898266674373053, ... ]
} ]
The second example will produce:
scriptedResult object [ArrayList]
scriptedResult [-41.279615707402876,
-60.88007362339038,
38.823270659734256,
14.840192739445632,
11.300902755741326]
The last example will produce:
scriptedResult object [Double]
scriptedResult [2.171917696507009]
Bucket aggregations
Global Aggregation
Here is how you can use Global Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilders
.global("agg")
.subAggregation(AggregationBuilders.terms("genders").field("gender"));
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.global.Global;
// sr is here your SearchResponse object
Global agg = sr.getAggregations().get("agg");
agg.getDocCount(); // Doc count
Filter Aggregation
Here is how you can use Filter Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilders
.filter("agg", QueryBuilders.termQuery("gender", "male"));
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.filter.Filter;
// sr is here your SearchResponse object
Filter agg = sr.getAggregations().get("agg");
agg.getDocCount(); // Doc count
Filters Aggregation
Here is how you can use Filters Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.filters("agg",
new FiltersAggregator.KeyedFilter("men", QueryBuilders.termQuery("gender", "male")),
new FiltersAggregator.KeyedFilter("women", QueryBuilders.termQuery("gender", "female")));
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.filters.Filters;
// sr is here your SearchResponse object
Filters agg = sr.getAggregations().get("agg");
// For each entry
for (Filters.Bucket entry : agg.getBuckets()) {
String key = entry.getKeyAsString(); // bucket key
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], doc_count [{}]", key, docCount);
}
This will basically produce:
key [men], doc_count [4982]
key [women], doc_count [5018]
Missing Aggregation
Here is how you can use Missing Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilders.missing("agg").field("gender");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.missing.Missing;
// sr is here your SearchResponse object
Missing agg = sr.getAggregations().get("agg");
agg.getDocCount(); // Doc count
Nested Aggregation
Here is how you can use Nested Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilders
.nested("agg", "resellers");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.nested.Nested;
// sr is here your SearchResponse object
Nested agg = sr.getAggregations().get("agg");
agg.getDocCount(); // Doc count
Reverse Nested Aggregation
Here is how you can use Reverse Nested Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.nested("agg", "resellers")
.subAggregation(
AggregationBuilders
.terms("name").field("resellers.name")
.subAggregation(
AggregationBuilders
.reverseNested("reseller_to_product")
)
);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.nested.Nested;
import org.opensearch.search.aggregations.bucket.nested.ReverseNested;
import org.opensearch.search.aggregations.bucket.terms.Terms;
// sr is here your SearchResponse object
Nested agg = sr.getAggregations().get("agg");
Terms name = agg.getAggregations().get("name");
for (Terms.Bucket bucket : name.getBuckets()) {
ReverseNested resellerToProduct = bucket.getAggregations().get("reseller_to_product");
resellerToProduct.getDocCount(); // Doc count
}
Children Aggregation
Here is how you can use Children Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.children("agg", "reseller"); (1)
-
"agg"
is the name of the aggregation and"reseller"
is the child type
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.join.aggregations.Children;
// sr is here your SearchResponse object
Children agg = sr.getAggregations().get("agg");
agg.getDocCount(); // Doc count
Terms Aggregation
Here is how you can use Terms Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilders
.terms("genders")
.field("gender");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.terms.Terms;
// sr is here your SearchResponse object
Terms genders = sr.getAggregations().get("genders");
// For each entry
for (Terms.Bucket entry : genders.getBuckets()) {
entry.getKey(); // Term
entry.getDocCount(); // Doc count
}
Order
Import bucket ordering strategy classes:
import org.opensearch.search.aggregations.BucketOrder;
Ordering the buckets by their doc_count
in an ascending manner:
AggregationBuilders
.terms("genders")
.field("gender")
.order(BucketOrder.count(true))
Ordering the buckets alphabetically by their terms in an ascending manner:
AggregationBuilders
.terms("genders")
.field("gender")
.order(BucketOrder.key(true))
Ordering the buckets by single value metrics sub-aggregation (identified by the aggregation name):
AggregationBuilders
.terms("genders")
.field("gender")
.order(BucketOrder.aggregation("avg_height", false))
.subAggregation(
AggregationBuilders.avg("avg_height").field("height")
)
Ordering the buckets by multiple criteria:
AggregationBuilders
.terms("genders")
.field("gender")
.order(BucketOrder.compound( // in order of priority:
BucketOrder.aggregation("avg_height", false), // sort by sub-aggregation first
BucketOrder.count(true))) // then bucket count as a tie-breaker
.subAggregation(
AggregationBuilders.avg("avg_height").field("height")
)
Significant Terms Aggregation
Here is how you can use Significant Terms Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.significantTerms("significant_countries")
.field("address.country");
// Let say you search for men only
SearchResponse sr = client.prepareSearch()
.setQuery(QueryBuilders.termQuery("gender", "male"))
.addAggregation(aggregation)
.get();
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.significant.SignificantTerms;
// sr is here your SearchResponse object
SignificantTerms agg = sr.getAggregations().get("significant_countries");
// For each entry
for (SignificantTerms.Bucket entry : agg.getBuckets()) {
entry.getKey(); // Term
entry.getDocCount(); // Doc count
}
Range Aggregation
Here is how you can use Range Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.range("agg")
.field("height")
.addUnboundedTo(1.0f) // from -infinity to 1.0 (excluded)
.addRange(1.0f, 1.5f) // from 1.0 to 1.5 (excluded)
.addUnboundedFrom(1.5f); // from 1.5 to +infinity
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.range.Range;
// sr is here your SearchResponse object
Range agg = sr.getAggregations().get("agg");
// For each entry
for (Range.Bucket entry : agg.getBuckets()) {
String key = entry.getKeyAsString(); // Range as key
Number from = (Number) entry.getFrom(); // Bucket from
Number to = (Number) entry.getTo(); // Bucket to
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], from [{}], to [{}], doc_count [{}]", key, from, to, docCount);
}
This will basically produce for the first example:
key [*-1.0], from [-Infinity], to [1.0], doc_count [9]
key [1.0-1.5], from [1.0], to [1.5], doc_count [21]
key [1.5-*], from [1.5], to [Infinity], doc_count [20]
Date Range Aggregation
Here is how you can use Date Range Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.dateRange("agg")
.field("dateOfBirth")
.format("yyyy")
.addUnboundedTo("1950") // from -infinity to 1950 (excluded)
.addRange("1950", "1960") // from 1950 to 1960 (excluded)
.addUnboundedFrom("1960"); // from 1960 to +infinity
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.range.Range;
// sr is here your SearchResponse object
Range agg = sr.getAggregations().get("agg");
// For each entry
for (Range.Bucket entry : agg.getBuckets()) {
String key = entry.getKeyAsString(); // Date range as key
DateTime fromAsDate = (DateTime) entry.getFrom(); // Date bucket from as a Date
DateTime toAsDate = (DateTime) entry.getTo(); // Date bucket to as a Date
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], from [{}], to [{}], doc_count [{}]", key, fromAsDate, toAsDate, docCount);
}
This will basically produce:
key [*-1950], from [null], to [1950-01-01T00:00:00.000Z], doc_count [8]
key [1950-1960], from [1950-01-01T00:00:00.000Z], to [1960-01-01T00:00:00.000Z], doc_count [5]
key [1960-*], from [1960-01-01T00:00:00.000Z], to [null], doc_count [37]
Ip Range Aggregation
Here is how you can use Ip Range Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder<?> aggregation =
AggregationBuilders
.ipRange("agg")
.field("ip")
.addUnboundedTo("<IP_ADDRESS>") // from -infinity to <IP_ADDRESS> (excluded)
.addRange("<IP_ADDRESS>", "<IP_ADDRESS>") // from <IP_ADDRESS> to <IP_ADDRESS> (excluded)
.addUnboundedFrom("<IP_ADDRESS>"); // from <IP_ADDRESS> to +infinity
Note that you could also use ip masks as ranges:
AggregationBuilder<?> aggregation =
AggregationBuilders
.ipRange("agg")
.field("ip")
.addMaskRange("<IP_ADDRESS>/32")
.addMaskRange("<IP_ADDRESS>/24")
.addMaskRange("<IP_ADDRESS>/16");
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.range.Range;
// sr is here your SearchResponse object
Range agg = sr.getAggregations().get("agg");
// For each entry
for (Range.Bucket entry : agg.getBuckets()) {
String key = entry.getKeyAsString(); // Ip range as key
String fromAsString = entry.getFromAsString(); // Ip bucket from as a String
String toAsString = entry.getToAsString(); // Ip bucket to as a String
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], from [{}], to [{}], doc_count [{}]", key, fromAsString, toAsString, docCount);
}
This will basically produce for the first example:
key [*-<IP_ADDRESS>], from [null], to [<IP_ADDRESS>], doc_count [13]
key [<IP_ADDRESS>-<IP_ADDRESS>], from [<IP_ADDRESS>], to [<IP_ADDRESS>], doc_count [14]
key [<IP_ADDRESS>-*], from [<IP_ADDRESS>], to [null], doc_count [23]
And for the second one (using Ip masks):
key [<IP_ADDRESS>/32], from [<IP_ADDRESS>], to [<IP_ADDRESS>], doc_count [0]
key [<IP_ADDRESS>/24], from [<IP_ADDRESS>], to [<IP_ADDRESS>], doc_count [13]
key [<IP_ADDRESS>/16], from [<IP_ADDRESS>], to [<IP_ADDRESS>], doc_count [50]
Histogram Aggregation
Here is how you can use Histogram Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.histogram("agg")
.field("height")
.interval(1);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.histogram.Histogram;
// sr is here your SearchResponse object
Histogram agg = sr.getAggregations().get("agg");
// For each entry
for (Histogram.Bucket entry : agg.getBuckets()) {
Number key = (Number) entry.getKey(); // Key
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], doc_count [{}]", key, docCount);
}
Order
Supports the same order functionality as the Terms Aggregation
.
Date Histogram Aggregation
Here is how you can use Date Histogram Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.dateHistogram("agg")
.field("dateOfBirth")
.calendarInterval(DateHistogramInterval.YEAR);
Or if you want to set an interval of 10 days:
AggregationBuilder aggregation =
AggregationBuilders
.dateHistogram("agg")
.field("dateOfBirth")
.fixedInterval(DateHistogramInterval.days(10));
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.histogram.Histogram;
// sr is here your SearchResponse object
Histogram agg = sr.getAggregations().get("agg");
// For each entry
for (Histogram.Bucket entry : agg.getBuckets()) {
DateTime key = (DateTime) entry.getKey(); // Key
String keyAsString = entry.getKeyAsString(); // Key as String
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], date [{}], doc_count [{}]", keyAsString, key.getYear(), docCount);
}
This will basically produce for the first example:
key [1942-01-01T00:00:00.000Z], date [1942], doc_count [1]
key [1945-01-01T00:00:00.000Z], date [1945], doc_count [1]
key [1946-01-01T00:00:00.000Z], date [1946], doc_count [1]
...
key [2005-01-01T00:00:00.000Z], date [2005], doc_count [1]
key [2007-01-01T00:00:00.000Z], date [2007], doc_count [2]
key [2008-01-01T00:00:00.000Z], date [2008], doc_count [3]
Order
Supports the same order functionality as the Terms Aggregation
.
Geo Distance Aggregation
Here is how you can use Geo Distance Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.geoDistance("agg", new GeoPoint(48.84237171118314,2.33320027692004))
.field("address.location")
.unit(DistanceUnit.KILOMETERS)
.addUnboundedTo(3.0)
.addRange(3.0, 10.0)
.addRange(10.0, 500.0);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.range.Range;
// sr is here your SearchResponse object
Range agg = sr.getAggregations().get("agg");
// For each entry
for (Range.Bucket entry : agg.getBuckets()) {
String key = entry.getKeyAsString(); // key as String
Number from = (Number) entry.getFrom(); // bucket from value
Number to = (Number) entry.getTo(); // bucket to value
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], from [{}], to [{}], doc_count [{}]", key, from, to, docCount);
}
This will basically produce:
key [*-3.0], from [0.0], to [3.0], doc_count [161]
key [3.0-10.0], from [3.0], to [10.0], doc_count [460]
key [10.0-500.0], from [10.0], to [500.0], doc_count [4925]
Geo Hash Grid Aggregation
Here is how you can use Geo Hash Grid Aggregation with Java API.
Prepare aggregation request
Here is an example on how to create the aggregation request:
AggregationBuilder aggregation =
AggregationBuilders
.geohashGrid("agg")
.field("address.location")
.precision(4);
Use aggregation response
Import Aggregation definition classes:
import org.opensearch.search.aggregations.bucket.geogrid.GeoHashGrid;
// sr is here your SearchResponse object
GeoHashGrid agg = sr.getAggregations().get("agg");
// For each entry
for (GeoHashGrid.Bucket entry : agg.getBuckets()) {
String keyAsString = entry.getKeyAsString(); // key as String
GeoPoint key = (GeoPoint) entry.getKey(); // key as geo point
long docCount = entry.getDocCount(); // Doc count
logger.info("key [{}], point {}, doc_count [{}]", keyAsString, key, docCount);
}
This will basically produce:
key [gbqu], point [47.197265625, -1.58203125], doc_count [1282]
key [gbvn], point [50.361328125, -4.04296875], doc_count [1248]
key [u1j0], point [50.712890625, 7.20703125], doc_count [1156]
key [u0j2], point [45.087890625, 7.55859375], doc_count [1138]
...


Front End Developer
| Company Name | Designation | Tenure |
|---|---|---|
| Freelancer | Sr. UI Developer | May 2017 to Till Date |
| Techmojo Pvt Ltd. | Sr. UI Developer | Sept 2016 to April 2017 |
| Helius Computech Pvt Ltd. | Sr. Front End Engineer | Mar 2015 to April 2016 |
| Oxygen Networks Pvt. Ltd. | Sr. UI Engineer | May 2014 to Dec 2014 |
| IVY Comptech Pvt. Ltd. | Sr. UI Design, Developer | Dec 2007 to April 2014 |
| Oxygen Networks Pvt. Ltd. | UI Developer | Dec 2004 to Nov 2007 |
| Indiainfo.com | Sr. Web Designer | Feb 2000 to Nov 2004 |
| Hyderabad.com (CITI-NET) | Web Designer | Sept 1998 to Jan 2000 |
| Miracle Media | Technical Consultant | Jan 1997 to Sept 1998 |
| Prime Computers | Auto CAD Team Leader | July 1995 to Dec 1996 |


Level of the caudal innervated muscle having grade 3 or better strength where all muscles above are graded 5.
Caudal dermatomal level with normal sensation to pinprick and light touch with all dermatomes above being normal.
Rostral of either the sensory or motor levels
When there is no key muscle immediately rostral to a key muscle graded 3 or 4, use the sensory exam as indicatve of motor function at that more rostral level
If the level of injury is at a site for which there is no key muscle (e.g. C2-C4, T2-L1, S2-S4/5), the motor level is defined by the sensory level.
| A | Complete. No sensory or motor function at S4/S5 |
| B | Sensory Incomplete. Sensory, but no motor function at S4/S5 |
| C | Motor Incomplete. 1) Voluntary anal contraction present or 2) Motor function present more than 3 myotomes below motor level 3) The myotome tested does not have to be a key muscle group, just 4 or more levels below the motor level |
| D | Motor Incomplete. Half or more key muscles below the single neurological level have a grade greater than or equal to 3. |
| E | Normal. All components of International Standards Exam are Normal. |
Before, “5*” was used when full muscle strength could not be achieved due to what the examiner believes to be a non-SCI condition. Now, a general ‘*’-concept is introduced, where all abnormal exam scores can be tagged with a ‘*’ to indicate a non-SCI condition impacts exam results. This general ‘*’-concept is applicable to motor scores of 0-4 and NT as well as to sensory scores of 0, 1 or NT, independent from the level of occurrence (above, at or below the sensory/motor level).
While ‘*’-tagged scores above the sensory/motor level will in most cases be handled as normal during classification, ‘*’-tagged scores at or below the motor/sensory level indicating a non-SCI related impairment superimposed to deficits caused by SCI will typically be handled as not normal.
With this new non-SCI taxonomy, motor and sensory sum scores are always calculated on the basis of the examined scores. As in the past, if key muscles or dermatomes cannot be tested (‘NT’), the sum score is not defined, and should be noted as ‘ND’.
Before, ZPPs were only defined for complete (AIS A) injuries with no sensorimotor function in the most caudal sacral segments.
Motor ZPPs should now be documented in incomplete injuries with absent VAC. Sensory ZPP on each side (L/R) is now defined in the absence of sensory function in S4-5 (LT, PP) on that side as long as DAP is not present. This means that in cases with present DAP, sensory ZPPs on both sides are not defined and should be noted as “not applicable (N/A)”. In cases with absent DAP, a sensory ZPP can be defined on one side (e.g., L) , while it may not necessarily be applicable on the other side (e.g., R).
In complete (AIS A) injuries, the new ZPP definition is still fully compatible with the former definition and does not lead to different classification results
Generally, non-key muscles are not used for the definition of the motor ZPP with one exception: if preserved function of a non-key muscle more than 3 segments below the motor level leads to an AIS C grade of an otherwise AIS B graded subject without VAC, the innervation of this non-key muscle determines the motor ZPP on this side.
-
References
American Spinal Injury Association: International Standards for Neurological Classification of Spinal Cord Injury, revised 2019; Richmond, VA.


{% extends "base.html" %} {% block title %}
Medieval catalogues > REGISTRUM ANGLIE > Union catalogue by author and title, early 14th cent.
{% endif %}59 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Spain coach calls up Dinamo Zagreb midfielder Dani Olmo
MADRID (AP) - Spain coach Robert Moreno called up 21-year-old Dinamo Zagreb attacking midfielder Dani Olmo for the first time on Friday for a pair of European Championship qualifying matches.
Olmo came up through Barcelona’s training academy before joining the Croatian club. He helped Spain win this year’s under-21 European Championship.
Olmo has also scored three goals for Dinamo in qualifying for the group stage of the Champions League, where he has added one more. Dinamo has one win, two draws and a loss in Group C.
Spain has already qualified for Euro 2020. It will host Malta in Cádiz on Nov. 15 and Romania in Madrid three days later.
Moreno also recalled strikers Álvaro Morata and Paco Alcacer, both of whom missed the last squad for games against Norway and Sweden.
Spain:
Goalkeepers: David de Gea (Manchester United), Kepa Arrizabalaga (Chelsea), Pau López (Roma)
Defenders: Sergio Ramos (Real Madrid), Dani Carvajal (Real Madrid), Juan Bernat (Paris Saint-Germain), Jesús Navas (Sevilla), José Gaya (Valencia), Raúl Albiol (Villarreal), Pau Torres (Villarreal), Íñigo Martínez (Athletic Bilbao)
Midfielders: Sergio Busquets (Barcelona), Rodrigo Hernández (Manchester City), Fabián Ruiz (Napoli), Thiago Alcántara (Bayern Munich), Saúl Ñíguez (Atlético Madrid), Santi Cazorla (Villarreal)
Forwards: Mikel Oyarzabal (Real Sociedad), Rodrigo Moreno (Valencia), Gerard Moreno (Villarreal), Dani Olmo (Dinamo Zagreb), Álvaro Morata (Atlético Madrid), Paco Alcacer (Borussia Dortmund)
More AP soccer https://apnews.com/Soccer and https://twitter.com/AP_Sports
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Movies App!
Add a new movie
Movie's title
Movie's author
Movie's genre
Movie's production year
Add a movie
Show List
Hide List
Movies list


Instructions: click on two cards. Try to find matching pairs (they will disappear).
(Also check what happens when you click too fast - the simple JavaScript will fail)


Listed are very basic demos for using Netlify's authentication provider service to send a signed request to GitHub's OAuth server and receive an access token in return. Feel free to look at whichever version is most comfortable for you.
Full setup instructions can be found in the Netlify OAuth provider docs.


GEIESMONADS TRAINING - This page is a complete running implementation of the code presented at
It features a chainable state monad, though


Website architecture¶
The template offers three different architectures.
Default mode¶
With this architecture you don’t have to configure anything. The pages that you will create will be displayed as cards on the first page and links to see the pages.
How to activate¶
This is the default mode you don’t have to do anything. If you want to come back to this mode, in the dashboard, in the menu on the left go to File management -> view/edit templates -> affiliatews -> theme.yml
Set the variable ws_mode to 1 :
ws_mode: 1
Items and categories¶
This architecture requires more configuration as it is based on personalized content types.
The website is organized with items and categories. An item can belong to zero, one or multiple categories.
It is possible to create different kinds of categories.
The template offers the following categories :
- Location
- Period
- Price
Based on these examples, it’s very easy to create new ones that suit exaclty your needs such as brand, size, color, …
Moreover, every item can be tagged and a menu per category can be set to show the categories for a very quick and friendly navigation.
Our content types are available on : https://github.com/symbiodyssey/bolt-affiliate-template/tree/master/contentTypes
The official documentation explains how to use content types in Bolt : https://docs.bolt.cm/3.4/contenttypes/intro
With this architecture if a visitor clicks on an item he/she will be directly redirected to an external website of your choice.
How to activate¶
To activate this mode, in the dashboard, in the menu on the left go to File management -> view/edit templates -> affiliatews -> theme.yml
Set the variable ws_mode to 2 :
ws_mode: 2
Items, categories and reviews¶
This architecture is very similar to the previous one except that clicking on a item will lead first to a review page where one can read a long and detailed article about the item.
How to activate¶
In the dashboard, in the menu on the left go to File management -> view/edit templates -> affiliatews -> theme.yml
Set the variable ws_mode to 3 :
ws_mode: 3


Next: Data-directed Destructuring by Lambda Lists, Previous: Macro Lambda Lists, Up: Macro Lambda Lists
Anywhere in a macro lambda list where a parameter name can appear, and where ordinary lambda list syntax (as described in Ordinary Lambda Lists) does not otherwise allow a list, a destructuring lambda list can appear in place of the parameter name. When this is done, then the argument that would match the parameter is treated as a (possibly dotted) list, to be used as an argument list for satisfying the parameters in the embedded lambda list. This is known as destructuring.
Destructuring is the process of decomposing a compound object into its component parts, using an abbreviated, declarative syntax, rather than writing it out by hand using the primitive component-accessing functions. Each component part is bound to a variable.
A destructuring operation requires an object to be decomposed, a pattern that specifies what components are to be extracted, and the names of the variables whose values are to be the components.


{% extends "layout.html" %} {% block content %}
This tool will help you train a new time-series model, based on the ticker name.
This tool will help you train a new time-series model, please input ticker's name.


Redmine Notifier
Redmine URL (required)
Test connection
API key (required)
Project ID
Fetch interval time
sec (default:
)
Save
Close


Every one of us has that little something which makes our lives complete, something that defines us on many levels and makes us the people that we are. While the human kind revels in habit, we are also creatures of passion which is the drive that helps us pursue our dreams. My passion is represented through a small and simple device with endless possibilities - my camera. I am mesmerized by the notion of a moment. I find it enthralling to be able to capture it, regardless of the fact if it is just a simple glimmer of hope in someones eyes or a complete immortalization of a landscape. I am Lilika Strezoska, wedding photo-maker and truth-teller; situated in Berlin and accessible around the world. If my photographs resound or associate with you in any way- if they influence you to feel things, don't mind to send me a note and I would love to chat more about photographing and colaborating with you.


The integration token to be used to send notifications to Telegram. You can copy this from the settings page within Telegram.
It is possible to override this setting per project.


Function.apply
BiFunction.apply
This allows to extract the composed function after that if necessary.


shapeways
Businessmodel of Shapeways
Customer Segments
Shapeways serves general consumers wishing to make online purchases of 3D printed products, including jewellery, memorabilia, custom parts, games, art and gadgets. While the Company provides its services to customers around the world, serving more than 181,000 customers each month, the US accounts for almost half of the Shapeways website’s traffic. Users from the UK, Germany, Canada and France together account for around 20% of the website’s traffic.
Shapeways also includes designers and independent retailers among its customers, who are able to set up shops through the Shapeways platform and begin selling their own products online and can provide design services through the site.
Value Propositions
Shapeways’ key value is that it makes 3D printed products available to the mass market through a platform as easy to use and familiar as high-profile online retailers. The Company’s marketplace offers a wide variety of products at lower prices than alternative 3D printing channels.
It also allows users to submit custom designs, set up their own online stores and participate in a community events and discussions. Designers are able to enhance their audience and have the opportunity to have their products sold by high-profile third-party online retailers.
Channels
Shapeways sells products through an online marketplace hosted at www.shapeways.com. The Company’s products can also be purchased via a number of third-party online retailers that have developed Shapeways integration.
The Company’s API has also enabled Shapeways technology, pricing and products to be integrated into third-party applications.
Customer Relationships
Shapeways’ online marketplace operates on a self-service basis, with users able to browse and purchase items directly through the Company’s website with no need for registration and no need for interaction with sales representatives. The Shapeways store is full searchable and divided by category so as to be easy to navigate. Shops can also be opened by Shapeways users by completing an online registration form.
Shapeways provides support to its customers through the Help Centre portal hosted by its website. This includes design tutorials, FAQs, information regarding material statuses and shipping details. The Company also oversees hosts its Community Hub, where users can interact with one another and with Company representatives, participate in discussions and organise and attend events and user groups.
Key Activities
Shapeways maintains and operates an online 3D printing marketplace and community, that enables users to create, buy, and sell custom products from a range of materials, including metals, plastics and porcelain. The Company offers products spanning jewellery, household products, gadgets, mechanical parts and memorabilia.
Shapeways’ products are also sold through third-party online retailers and are used in the creation of custom modifications to drones. These products are manufactured both by the Company itself, through facilities in the Netherlands and the US, and through a network of production partners.
Shapeways also provides a platform for customers to set up their own online stores and oversees a community through which customers can share ideas and gain access to new technology.
Key Partners
Shapeways collaborates with a number of companies, including 3D printing and production partners, software developers and online retailers. The Company operates a Global Partner Network comprising fabrication houses, manufacturing facilities and 3D printing shops across the world. This enables the Company to serve customers in more than 130 countries as locally as possible.
Shapeways has released a 3D printing API, allowing third-parties to create and sell 3D products, integrate Shapeways pricing into proprietary product creation apps and add and manage Shapeways products through third-party applications.
The Company has also developed integrated functionality with online retailers, including Neiman Marcus which lists a number of products designed by Shapeways users on its website, and has developed a website in collaboration with toy manufacturer Hasbro through which it sells products inspired by Hasbro brands.
Additionally, Shapeways has partnered with Future Engineers, Made in Space and ASME as part of a series of NASA developed 3D space challenges aimed at students, and has launched a partnership with drone manufacturer DTI to enable designers to create unique 3D printed accessories for drone products.
Key Resources
Shapeways’ ability to print and sell designs through its website are key to its operations. The Company key resources are consequently its online marketplace, its manufacturing facilities and production partners, its personnel and its IT infrastructure.
The Company has protected its technology with patent applications filed with both the US Patent and Trademark Office and IP Australia, including under the titles ‘Weight-based identification of three dimensional printed parts’ and ‘Systems and methods for three dimensional printing’.
Cost Structure
Shapeways primarily incurs costs relating to the maintenance of its IT infrastructure, retention of its personnel and operation of its manufacturing facilities.
The Company has production in New York, Seattle and Eindhoven, which accrue fixed costs relating rental, utilities, equipment maintenance and materials. It also operates three offices that accrue additional rental and utility costs.
Revenue Streams
Shapeways generates revenue through the sale of 3D printed products through its online marketplace, estimated to be around 181,000 each month. The Company earns a variable commission one every product printed and sold through its own facilities and its network of production partners. The rate of this commission depends on the material used, the size of the products and the complexity of the design.
According to reports from 2015, margins on these products can range from a few percent to a few dozen percent. Shapeways’ revenue model is set to rely principally on commission-based payments for the foreseeable future, however, the Company may begin charging small fees for services such as design advice that are currently available free of charge.


MORTICIA Conventions¶
There is a data dictionary for MORTICIA
which defines commonly used variable and data names.
xray.DataArray objects are created routinely, so the constructor is abbreviated to xD.
MORTICIA
often deals with spectral variables, such as spectral transmission, spectral radiance etc.
More often than not, these arrays are multidimensional. The xray package is used in MORTICIA for
purposes of representing N-dimensional arrays (the xray.DataArray class).
However, xray does not (yet) support interpolation when the coordinates of the data hypercube are not the same and two or more xray.DataArray objects must be added, divided or multiplied.
Utility functions that operate on xray.DataArray objects are found in the morticia.tools.xd package. These functions perform axis harmonisation as well as unit checking and conversion.
The axis harmonisation functions for xray.DataArray objects requires that all axes have numeric coordinates.
This applies also to axes that would benefit from having text labels. Text labels would be more appropriate for
axes such as spectral channels and field orientation for MTF data. In the case of spectral channels (with axis label
chn) will be channel numbers. These channel numbers would normally start from 0, but in some cases, such as when
using a sub-range of correlated-k band models (e.g. kato or fu in libRadtran) the channel numbers would be a
range of integers. The convention in MORTICIA
is that the text labels then be placed in an axis attribute called
labels.
This curently applies equally well to timeseries. For incorporation in multi-axis xray.DataArray objects, timestamps must be converted to Julian date. A list of labels for such axes can and should be maintained in the xray.DataArray attributes (attrs).
Units of Measure¶
Tracking of units of measure is not performed automatically in MORTICIA
such as with the use of the pint package.
However, many functions and classes in MORTICIA
expect xray.DataArray instances that provide units of measure in
the metadata. When units are provided in this way, they should be provided in manner consistent with the Python units
package pint (see https://pint.readthedocs.org/ )
Commonly, when a MORTICIA
function or method requires a scalar numeric input, it must be provided as a list
with magnitude and units e.g. [30, ‘mm’]. Unitless quantities are provided as a simple numeric magnitude.
If a variable is named x
, then the units for the variable can be stored as units
in the xray.DataArray
attributes dictionary (actually an OrderedDict). This units string should be recognizable to pint as a valid unit.
When exporting xray.DataArray objects to NetCDF files in xray.Dataset objects, this convention of using the
units attribute is recognised by NetCDF browsers and other NetCDF utilities. Further information on conventional
NetCDF attributes can be found at
UCAR/Unidata.
See the MORTICIA
notebooks for examples of creating xray objects with unit attributes.
The UCAR/Unidata attributes for NetCDF file elements considered as highly recommended are
- units, units expressed as a string - SI units preferred
- long_name, such as may commonly be used to label graph axes
- standard_name, the standard name that may be used in a specific scientific community
and for MORTICIA
, also
- title, such as may be used on the title of a plot of the data
- labels, such as may be used in the legends of a plot
The standard_name should come from the CF (Climate and Forecasting) name glossary, or the project should have its own vocabulary of short, standard and long names for all variables. These are to be found in the morticia.moglo.py module and may be expanded or updated from time to time.
A pint global unit registry is created when morticia or any sub-package is imported. Any other morticia paackages
or modules share a single global unit registry called ureg. Convenience functions Q_
for Quantity
and U_
for
Quantity(1.0, unit_str)
are also defined. Examples of usage are provided in the Jupyter notebooks/tutorials.
Coordinate Systems¶
The general observer/target topocentric, cartesian coordinate system used in MORTICIA is +x towards North, +y towards the East and +z towards the zenith. Zenith angles are polar angles measured from the +z axis. This is a left-handed system and the alternative right-handed sytem is the same, except with +z towards nadir.
The global Earth Centered Earth Fixed (ECEF) coordinate system has the north pole in the +Z direction, the prime meridian (0 deg longitude) in the +X direction and the +Y direction at 90 deg east longitude. This is a right-handed coordinate system.
When dealing with sightlines and camera orientations, several conventions can be used. The Euler angles provide (relative to a given coordinate system, typically topocentric/geodetic) the view azimuth angle (VAZ = \(\alpha\)) and view zenith angle (VZA = \(\beta\)). The camera roll is then the third Euler angle \(\gamma\).
Logging Warnings and Exception Handling¶
As a rule, MORTICIA does not use logging to files. Preferably, if any checking is performed, exceptions are thrown. Informational messages should printed to the terminal using the logging.info() or logging.debug() calls. Warnings that the user should take action on are provided through warnings.warn(). If the warning may relate to the fact that MORTICIA code should be improved or debugged, it should be issued through logging.warning(). MORTICIA internal modules do not define any logging handlers or filters. This is left to the user.
General Terminology and Conventions¶
Radiometry¶
Confusion may arise over the inconsistent use of radiometric terms. In the radiative transfer (RT) community, what is called “flux” (or more helpfully “flux density”) is called “irradiance” in the radiometry community. In the radiometry community, “flux” means optical power (not per unit area). In the RT community, what is called “intensity” is called “radiance” in the radiometry community. In the radiometry community, “intensity” means optical power per unit solid angle (not per unit area). In MORTICIA, the terms “flux” and “irradiance” are used interchangeably. Optical power, usually denoted \(\Phi\), will not be referred to as “flux”, but explicitly as “optical power”. The term “intensity” is avoided as inherently ambiguous and the alternative term “radiance” is preferred. When dealing with irradiance/flux or radiance per unit spectral interval, these are normally explicitly referred to as “spectral irradiance/flux” or “spectral radiance”.
The term “mean intensity” is used in libRadtran/uvspec terminology to mean the actinic flux divided by \(4\pi\) steradians. Actinic flux has the same units as irradiance/flux and could equally well be called actinic irradiance. A preferred term for “mean intensity” could be “actinic sterance”. Actinic flux could also be referred to as “scalar irradiance”.
Camera and Imager¶
A Camera object in MORTICIA does not include a Lens - it is a camera body, including an FPA and a digitisation stage (which may be fully integrated into the FPA chip - a so-called “camera on a chip”). The Imager class incorporates a Camera and a Lens and is capable of producing actual imagery.
Modulation Transfer Function Calculation¶
For image modelling purposes, it is necessary to know the full 2D MTF, whereas the normal situation is that the MTF is described using horizontal and vertical MTF profile functions. Suppose that \(\eta\) and \(\xi\) are the spatial frequencies in the horizontal and vertical directions respectively and the \(M\!T\!F_\eta(\eta)\) and \(M\!T\!F_\xi(\xi)\) are the 1D MTFs in the horizontal and vertical directions. The 2D MTF is then computed as a rotationally weighted mean of the 1D MTFs as
and if the sagittal spatial frequency is defined as \(\rho=\sqrt{\eta^{2}+\xi^{2}}\), then
It is assumed here that the horizontal and vertical MTFs are symmetrical about the origin.


Graduate of University of Wisconsin-Madison's online professional masters for Web Mapping and GIS Development. I am enjoying learning not only about cartography and geospatial technologies but in programming and coding. I look forward to continuing to develop my skills across these technologies as they become more and more relevant to future developments helping to shape our world, decisions, and environment.
I currently live in the Columbus, Ohio area.
University of Wisconsin-Madison, Madison, WI
Professional Online Master’s Degree in GIS & Web Mapping Programming
September 2016 to May 2020
Courses completed: Introduction to GeoComputing; Geospatial Database Design and Development; GIS & Spatial Analysis; Interactive Cartography and Visualization, Graphic Design in Cartography, Geospatial Web and Mobile Programming, Capstone and Practicum
Raritan Valley Community College, Raritan, NJ
Associate of Science, Environmental Science
May 2013
Academic Excellence Award for Environmental Science
Rutgers, the State University of New Jersey, New Brunswick, NJ
Bachelor of Arts, Communication
May 2006
Graduated with High Honors
JPMorgan Chase Columbus, OH
Software Engineer - UI Developer
November 2018 - current
Front end development in a large, mature code base.
Currently acting as Developer Lead for my team while training for additional duties the role requires.
Utilize HTML, CSS, and Javascript to create and maintain 3+ web applications within chase.com for existing bank customers. Requiring front end development along with mobile hybrid integration, analytics and ADA compliance
KNM Resources LLC, Piscataway, NJ (remote)
Operations & Marketing Manager
August 2013-November 2018
Managed day to day operations, marketing and assisted with long term development of a startup boutique recruiting firm established in 2013. Required diverse understanding of business processes and procedures, attention to detail and managing multiple priorities.
New Jersey Department of Environmental Protection, Trenton, NJ
GIS Associate, Natural Heritage Program (part-time)
September 2013 - January 2014
Created shapefiles using submitted data request information for running queries in Natural Heritage Database for rare and endangered animals, plants and ecological communitied in or nearby specified locale. Finalized report for delivery to requestor.
Stephen James Associates, Bridgewater, NJ
Operations Support Associate
June 2010 - July 2012
Supplied administrative support to recruiters for three field offices (remote and local). Regular job postings and email campaigns to local applicants. Manage contractor and internal new hire paperwork, payroll, and background checks. Offices closed by parent company, Allegis Group, in July 2012.


Standard C Date & Time
Display all entries for Standard C Date
& Time on one page, or view entries individually:
| asctime |
a textual version of the time |
| clock |
returns the amount of time that the
program has been running |
| ctime |
returns a specifically formatted
version of the time |
| difftime |
the difference between two
times |
| gmtime |
returns a pointer to the current
Greenwich Mean Time |
| localtime |
returns a pointer to the current
time |
| mktime |
returns the calendar version of a
given time |
| setlocale |
sets the current locale |
| strftime |
returns individual elements of the
date and time |
| time |
returns the current calendar time
of the system |


beats solo 2 magenta
Beats Solo2 headphones have a wide range of dynamic sound for a more immersive, emotional listening experience
Beats Solo2 headphones have a wide range of dynamic sound for a more immersive, emotional listening experience
Beats Solo2 headphones have a wide range of dynamic sound for a more immersive, emotional listening experience
Beats Solo2 headphones have a wide range of dynamic sound for a more immersive, emotional listening experience


{{ partial "metahome.html" . }} {{ partial "common/styles.html" }} {{ partial "common/favicon.html" }}
{{ partial "page_loader.html" }} {{ partial "navbar.html" }} {{ partial "firstfold.html" }} {{ partial "about.html" }} {{ partial "benefits.html" }} {{ partial "testimonials.html" }} {{ partial "team.html" . }} {{ partial "form.html"}} {{ partial "blogpreview.html" . }} {{ partial "footer.html" }} {{ partial "common/scripts.html" }}


neosensory_python package¶
Module contents¶
-
class
neosensory_python.
NeoDevice
(client)¶ Bases:
object
A class for controlling a connected Neosensory device with Bleak
-
async
accept_developer_api_terms
()¶ After successfully calling auth as developer, use the accept command to agree to the Neosensory Developer API License (https://neosensory.com/legal/dev-terms-service/). Successfully calling this unlocks the following commands: audio start, audio stop, motors_clear_queue, motors start, motors_stop, motors vibrate.
-
async
clear_motor_queue
()¶ Clear any vibration commands sitting the device’s motor FIFO queue. This should be called prior to streaming control frames using motors vibrate. This command requires successful developer authorization, otherwise, the command will fail.
-
async
disable_motors
()¶ Clear the motors command queue and shut down the motor drivers. This command requires successful developer authorization, otherwise, the command will fail.
-
async
enable_motors
()¶ Initialize and start the motors interface. The motors can then accept motors vibrate commands. This command requires successful developer authorization, otherwise, the command will fail.
-
async
enable_notifications
(handler)¶ Enable notifications to be sent back to host computer
- Args:
handler: the handler function that processes the received reponse
-
async
get_battery_level
()¶ Obtain the device’s battery level in %. This command does not require developer authorization
-
async
get_device_info
()¶ Obtain various device and firmware information. This command does not require developer authorization.
-
async
pause_device_algorithm
()¶ Pause the running algorithm on the device to accept motor control over the CLI. This command requires successful developer authorization, otherwise, the command will fail.
Request developer authorization. The CLI returns the message “Please type ‘accept’ and hit enter to agree to Neosensory Inc’s Developer Terms and Conditions, which can be viewed at https://neosensory.com/legal/dev-terms-service
-
async
resume_device_algorithm
()¶ (Re)starts the device’s microphone audio acquisition and algorithm. This command requires successful developer authorization, otherwise, the command will fail. This is functionally the same as startAudio()
-
async
send_command
(command)¶ Send a custom API command to Neosensory device
- Args:
command: command string, e.g. “device info”
-
set_client
(new_client)¶ Set the Bleak client object
- Args:
new_client: the Bleak client object
-
async
start_audio
()¶ (Re)starts the device’s microphone audio acquisition. This command requires successful developer authorization, otherwise, the command will fail.
-
async
stop_audio
()¶ Stop the device’s microphone audio acquisition. This should be called prior to transmitting motor vibration data. This command requires successful developer authorization, otherwise, the command will fail.
-
async
stop_motors
()¶ Send a frame that turns off the motors. Note: the API CLI command “motors stop” disables the motor drivers. This command requires successful developer authorization, otherwise, the command will fail. Assumes this is a 4-motor device (e.g. Neosensory Buzz)
-
async
vibrate_motors
(motor_list)¶ - Set the actuators amplitudes on a connected Neosensory device.
Note: actuators will stay vibrating indefinitely on the last frame received until a new control * frame is received
- Args:
- motor_list: a list of motor intensity values on [0,255].
This is API call is designed for Neosensory Buzz–and assumes 4 motors. If the list length is > 4, the Buzz will put subsequent encodings on the queue to be played out on a 16 ms period.
-
async
-
neosensory_python.
get_buzz_illusion_activations
(linear_intensity, location, min_intensity, max_intensity, num_motors)¶ - Apply a haptic illusion across a Neosensory device
to create the sensation of a single arbitrary point around the wrist vibrating.
- Args:
linear_intensity: an intensity value on [0,1]
location: a location around the wrist on [0,1]
- min_intensity: a minimum motor intensity value.
Typically 0 or minimal perceptible motor activation value.
- max_intensity: a maximum motor intensity value.
For Neosensory Buzz, motor values can be on [0,255]
num_motors: number of motors on the device (4 for Neosensory Buzz)
- Returns:
A list of motor encodings representing an illusory point.
-
neosensory_python.
get_motor_intensity
(linear_intensity, min_intensity, max_intensity)¶ - Linearly map a value on [0,1] to a motor
vibration strenght on [min_intensity, max_intensity]
- Args:
linear_intensity: an intensity value on [0,1]
- min_intensity: a minimum motor intensity value.
Typically 0 or minimal perceptible motor activation value.
- max_intensity: a maximum motor intensity value.
For Neosensory Buzz, motor values can be on [0,255]
- Returns:
A linearly mapped value on [min_intensity, max_intensity]


{% extends "fplapp/layout.html" %} {% load fpl %} {% block content %}
| Name | Win | Draw | Loss | Played | Points | Position | Strength | Home Strength | Away Strength | Home Attack Strength | Away Attack Strength | Home Defence Strength | Away Defence Strength |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| {{ team.name }} | {{ team.win }} | {{ team.draw }} | {{ team.loss }} | {{ team.played }} | {{ team.points }} | {{ team.position }} | {{ team.strength }} | {{ team.strength_overall_home }} | {{ team.strength_overall_away }} | {{ team.strength_attack_home }} | {{ team.strength_attack_away }} | {{ team.strength_defence_home }} | {{ team.strength_defence_away }} |


Regression with Stats-Models
rpi.analyticsdojo.com
Regression with Stats-Models¶
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
Scikit-learn vs Stats-Models¶
Scikit-learn provides framework which enables a similar api (way of interacting with codebase) for many different types of machine learning (i.e., predictive) models.
Stats-Models provices a clear set of results for statistical analsyses (understanding relationships) common to scientific (i.e., explanitory) models
#Get a sample dataset
df = sm.datasets.get_rdataset("Guerry", "HistData").data
/Users/jasonkuruzovich/anaconda3/lib/python3.7/site-packages/statsmodels/datasets/utils.py:192: FutureWarning: `item` has been deprecated and will be removed in a future version return dataset_meta["Title"].item()
About the Data¶
See link.
Andre-Michel Guerry (1833) was the first to systematically collect and analyze social data on such things as crime, literacy and suicide with the view to determining social laws and the relations among these variables.
df.columns
Index(['dept', 'Region', 'Department', 'Crime_pers', 'Crime_prop', 'Literacy',
'Donations', 'Infants', 'Suicides', 'MainCity', 'Wealth', 'Commerce',
'Clergy', 'Crime_parents', 'Infanticide', 'Donation_clergy', 'Lottery',
'Desertion', 'Instruction', 'Prostitutes', 'Distance', 'Area',
'Pop1831'],
dtype='object')
df
Predicting Gambling¶
Lottery
Per capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population— Average for the years 1822-1826. (Compte rendus par le ministre des finances)Literacy
Percent Read & Write: Percent of military conscripts who can read and write.Pop1831
Population in 1831, taken from Angeville (1836), Essai sur la Statis- tique de la Population francais, in 1000s.
#Notice this is an R style of Analsysis
results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit()
print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Lottery R-squared: 0.348
Model: OLS Adj. R-squared: 0.333
Method: Least Squares F-statistic: 22.20
Date: Mon, 07 Oct 2019 Prob (F-statistic): 1.90e-08
Time: 14:14:28 Log-Likelihood: -379.82
No. Observations: 86 AIC: 765.6
Df Residuals: 83 BIC: 773.0
Df Model: 2
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 246.4341 35.233 6.995 0.000 176.358 316.510
Literacy -0.4889 0.128 -3.832 0.000 -0.743 -0.235
np.log(Pop1831) -31.3114 5.977 -5.239 0.000 -43.199 -19.424
==============================================================================
Omnibus: 3.713 Durbin-Watson: 2.019
Prob(Omnibus): 0.156 Jarque-Bera (JB): 3.394
Skew: -0.487 Prob(JB): 0.183
Kurtosis: 3.003 Cond. No. 702.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Key Metrics
Link for summary of results.
Challenge: Compare Results¶
Explore another predictor of
Lottery
, was it significant in influecing.Add a train test split to the above analysis and assess overfitting.
Use scikit learn regression to replicate the above analysis.
Then use Stats-models to replicate Boston Housing example below.
!wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/boston_test.csv && wget https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/boston_train.csv
--2019-02-25 16:07:12-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/boston_test.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 451405 (441K) [text/plain]
Saving to: ‘boston_test.csv.1’
boston_test.csv.1 100%[===================>] 440.83K --.-KB/s in 0.06s
2019-02-25 16:07:12 (7.19 MB/s) - ‘boston_test.csv.1’ saved [451405/451405]
--2019-02-25 16:07:12-- https://raw.githubusercontent.com/rpi-techfundamentals/spring2019-materials/master/input/boston_train.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... <IP_ADDRESS>, <IP_ADDRESS>, <IP_ADDRESS>, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|<IP_ADDRESS>|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 460676 (450K) [text/plain]
Saving to: ‘boston_train.csv.1’
boston_train.csv.1 100%[===================>] 449.88K --.-KB/s in 0.06s
2019-02-25 16:07:12 (7.29 MB/s) - ‘boston_train.csv.1’ saved [460676/460676]
train = pd.read_csv("boston_train.csv")
test = pd.read_csv("boston_test.csv")
train.head()
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | ... | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal | 208500 |
| 1 | 2 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal | 181500 |
| 2 | 3 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal | 223500 |
| 3 | 4 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml | 140000 |
| 4 | 5 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal | 250000 |
5 rows × 81 columns
all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],
test.loc[:,'MSSubClass':'SaleCondition']))
Data preprocessing:¶
We’re not going to do anything fancy here:
First I’ll transform the skewed numeric features by taking log(feature + 1) - this will make the features more normal
Create Dummy variables for the categorical features
Replace the numeric missing values (NaN’s) with the mean of their respective columns
#log transform the target:
train["SalePrice"] = np.log1p(train["SalePrice"])
#log transform skewed numeric features:
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index
skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness
skewed_feats = skewed_feats[skewed_feats > 0.75]
skewed_feats = skewed_feats.index
all_data[skewed_feats] = np.log1p(all_data[skewed_feats])
all_data = pd.get_dummies(all_data)
#filling NA's with the mean of the column:
all_data = all_data.fillna(all_data.mean())
#creating matrices for sklearn:
X_train = all_data[:train.shape[0]]
X_test = all_data[train.shape[0]:]
y = train.SalePrice
X_train.shape
(1460, 288)
Models¶
Now we are going to use regularized linear regression models from the scikit learn module. I’m going to try both l_1(Lasso) and l_2(Ridge) regularization. I’ll also define a function that returns the cross-validation rmse error so we can evaluate our models and pick the best tuning par


function [responses, totalReward, rewardVsTime] = runTest(rightAnswers, socIn, socInWeight, momentum, rewardAmount, epsilon, discount) %runTest return a vector of binary responses to the test questions and the %reward garnered. %rightAnswers - Row vector of correct answers. Should be a binary vector. %socIn - Row vector of social input for each question. Should be a binary %vector. %socInWeight - The amount social input is considered. Should be a number %from 0 to 0.9. %momentum - The rate at which the rate of change of the left and right %quality changes. This allows the neural network to handle volitility. %rewardAmount - The reward received per correct answer. %epsilon - The learning rate. numQuestions = size(rightAnswers, 2); rewardVsTime = zeros(2, numQuestions); %for plotting totalReward = 0; QL = 0.5; %quality of left decision qLInc = 0; QR = 0.5; %quality of right decision qRInc = 0; %amount for i = 1:numQuestions SI = socIn(i); RA = rightAnswers(i); %changes the SI to either -1 or 1. Treated as the output of a %linear neuron with weight of 2 connecting it to SI and a bias of %-1. linNeuOut = -1 + 2*SI; %Takes quality of a left decision and gives it a weight of 1, %and takes the quality of a right decision and gives it a weight of %-1, then feeds them both as input to a neuron with a tan activation %function, bounding it between -1 and 1. tanhNeuOut = tanh(QL - QR); %combines the modified social input and the output of the tan %neuron into one value, then feeds it as input into a binary %threshold neuron. binNeuIn = linNeuOut*socInWeight + tanhNeuOut; binNeuOut = (binNeuIn > 0)*1; responses(i) = binNeuOut; %Set the current reward to 0 if none was earned or the amount per %correct answer if the right answer was given, then add it to the %total reward. currentReward = (RA == binNeuOut)*rewardAmount; totalReward = totalReward + currentReward; %reward time plot variable recording. rewardVsTime(1, i) = i; rewardVsTime(2, i) = totalReward; %Update the QBlock which won out, if it failed to be correct the %reward input will be 0, if it was right it will be the set reward %per answer if tanhNeuOut > 0 [QL, qLInc] = updateQBlock(epsilon, momentum, discount, QL, qLInc, currentReward); elseif tanhNeuOut <= 0 [QR, qRInc] = updateQBlock(epsilon, momentum, discount, QR, qRInc, currentReward); end end %commented out the following lines because now graph displays in gui, uncomment to see %graph without gui % figure % hold on % title('Reward Earned by Agent Vs. Trial Number') % ylabel('Reward Earned') % xlabel('Trial Number') % plot(rewardVsTime(1,:), rewardVsTime(2,:)) % hold off end %NEURAL NETWORK DIAGRAM % | OUTPUT % (bin) % /\ % w=socInWeight/ \w=1 % / \ % / \ % (linNeu) (tanhNeu) % /| /\ % / | w=1/ \w=-1 % / |w=2 / \ % -1 | (QL) (QR) % | % | % | % SI INPUT
Error using runTest (line 15) Not enough input arguments.


We are a group of people that seek continuous improvement of quality of people's life through parkour and movement in general. We have traveled around to world to learn, make bonds with movement communities and improve ourselves to offer the best experience to our clients.


Hi everybody. First and foremost I highly recommend everyone make the switch over to "wet ones" for your after toilet business. Once you try them, you'll never go back. Secondly, I am a big supporter of the "extended visits" to the restrooms which allows me to read while I visit. If you're looking for a book to read, do yourself a favor and pick up either "The Name of the Wind" or "The Lies of Locke Lamora".
I am a 36 year old aspiring progammer currently living in the Seattle area. A little while ago I felt it was time for a change in my life and decided to put my GI Bill to use. So now I've started my journey into the world of programming.
Hello! I am an aspiring coder. My goal is to eventually be working behind the scenes, maybe debugging or other data manipulation extraordinaire. I currently live and work in Seattle doing accounting and finance consulting. I'd ideally like to somehow merge my skills and find an awesome workplace to be a part of.
Hello! I am 23 years old and currently live in Seattle, WA. I focus my skills in Full-Stack Javascript programming and am excited to continue my education at Codefellows. I also love the design aspects of web development and plan to continue that as well.


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The mission of the Coalition for the Barbados Association of Central Florida (CBAOCF), otherwise known as the Central Florida Bajans (CFL Bajans), is to encourage networking and to promote kinship among Barbadians (Bajans) living in Central Florida. Our association maintains avenues of communication between Barbadians in Central Florida and their homeland Barbados, and fosters educational, cultural, and business operations, to further the joint interests of Bajans in both regions in an open, friendly environment.
Monthly meetings are normally held the 3rd Saturday of each month beginning at 6:30pm at St. Mary of the Angels, 6316 Matchett Road, Orlando, FL 32809. Catch up on the last meeting here!


The name often given to the Thousand and One Nights, a collection of traditional stories from the Middle East, written in Arabic. Scheherezade marries a king who kills his wives after their first night together. She survives by telling him a different story every night. Many popular films, songs and pantomimes are based on some of these stories, such as Aladdin and Sinbad the Sailor.
This is a list of the stories in Richard Francis Burton's translation of One Thousand and One Nights. Burton's first ten volumes—which he called The Book of the Thousand Nights and a Night—were published in 1885. His Supplemental Nights were published between 1886 and 1888 as six volumes. Later pirate copies split the very large third volume into two volumes. The nights are in the style of stories within stories, and the frame story is The Story Of King Shahryar of Persia and His Brother or The Story Of King Shahryar and Queen Shahrazad, in which Shahrazad tells tales to her husband Shahryar.


Xiaomi TV sells over 200,000 units in 10 minutes - Gizchina.com
by Efe UdinXiaomi TV sells over 200,000 units in 10 minutes
5 (100%) 1 vote
Today, Chinese manufacturing giant, Xiaomi, took to Weibo to announce the sale performance of its TV brand. According to the company, its TV took just 10 minutes and 36 seconds to exceed 200,000 units in the Chinese 11.11 sales. Last year, it took 58 minutes and 17 seconds to hit the same mark. This shows how well the company’s TV brand has grown in just a year.
According to Li Xiaoshuang, general manager of the Xiaomi TV Department, over the last three quarters (Q1 – Q3 2019), Xiaomi TV has shipped about 2.3 million units per quarter.
Xiaomi TV 11.11 sale discount starts with the TV 4S 75 inches which are selling for 4599 yuan ($650). In addition, the TV 4X 65 inches sells for 1999 yuan ($283) while the TV 4X 55 inches is selling for 1399 yuan ($198). Furthermore, the TV 43-inch is selling for 1099 yuan ($155) and the 32-inch version goes for 699 yuan ($99). The Xiaomi full-screen TV 55-inch and TV 4C 40-inch are available for 1699 yuan ($240) and 999 yuan ($141) respectively. These prices will be much lower if there are additional coupons.
You can find these deals on Xiaomi Mall, Xiaomi Youpin, Xiaomi Home, Jingdong, Tmall, Suning Tesco and other major e-commerce platforms. In addition, the general manager of the company’s TV Department revealed that Xiaomi’s full-screen TV Pro 43-inch has hit the market. It comes with a 1499 yuan ($212) price tag.


Laura Poitras is an American director and producer of documentary films. She has recieved numerious awards, like the 2015 Academy Award for Best Documentary Feature for Citizenfour.
Dr. Nadine Chahine is a Lebanese type designer. She students eye movement and legibility studies for Arabic, Latin, and Chinese characters. She has many awards, some of which come from the Type Directors Club in New York.
Manoush Zomorodi is an author, podcaster, and co-founder of Stable Genius Productions. She is the author of Bored and Brilliant: How Spacing Out Can Unlock Your Most Creative Self.
Rebecca Solnit is a writer, historian, and activist, and author of more than 20 books. She has written about feminism, western and indigenous history, social change, and popular power.
Val Head is a speaker, author, and design advocate for Adobe. She specializes web animation. She is also the author of Designing Interface Animation and curates the weekly UI Animation Newsletter.
Zeynep Tufekci is a Turkish writer, academic, and techno-sociologist who is most known for her research on the social implications of emerging technologies.
Zoe Quinn is an American programmer, writer, video game developer, and artist. They are known for their creation of Depression Quest, and interactive video game that was released in 2013.


If you see this page, the default mount point for serving static files is working. Further configuration is required.
$ cnc -h Usage: cnc [options] Options: -m, --mount [<url>:]<path> set the mount point for serving static files (default: /static:static)
$ cnc -m /static:/path/to/your/static/files
The example shown below is a tiny web interface for small size LCD display 320x240 with very limited features.
Visit http://localhost:8000/tinyweb/ after running the command:
$ cnc -m /tinyweb:examples/tinyweb
For online documentation please refer to https://github.com/cheton/cnc.js/wiki.


{{_ "Add members to your organization" }}
- {{_ "Allow your coworkers to manage your organization, too" }}
- {{_ "Change who can edit and who can only see your organization internals: Only managers, founders, and developers can change the organization's data." }}


--- layout: post title: Scientists — ev... date: 2013-01-14 08:34:32.000000000 -05:00 categories: [] tags: - Aaron Swartz - social science status: publish type: post published: true meta: _publicize_pending: '1' author: login: samarthbhaskar email<EMAIL_ADDRESS>display_name: samarthbhaskar first_name: '' last_name: '' ---
Scientists — even the hardest of scientists — fabricate data, fabricate studies, fall prey to fads, and otherwise get things wrong. But more relevantly, they just don’t know that much. We have very little idea of how the body works; the pills we take are made through the bluntest of means. We don’t know how to calculate very simple things, like the dispersion of milk in a cup of coffee. The illusion that social science is ineffective can only be sustained by ignorance of such ineffectiveness of hard science. The upside of all this is that there is hope for social science.
Many people have written and spoken much more poignant words about Aaron Swartz's passing over the weekend. I never knew him, and was only familiar with his work in passing. But, for good reason, I found myself reading a lot about him over the weekend and I found this article from 2005 on his website particularly compelling.
For such a short piece about the nature and state of the social sciences, Swartz really packed a punch. These are thoughts that I've thought and talked about many times before, but this piece is a great articulation of them.
In an effort to not mystify Aaron's work and contributions, I find this sort of thing to be something I can do to help. I can look through his body of work, look up to his example and do my best to find my calling in the quest to bring the social sciences, and specifically the empirical social sciences, to serve society. Rest in piece Aaron.


--- layout: default main_image_path: /assets/mouse-front.jpg ---
{% include img.html src=page.main_image_path alt=page.main_image_description %}
{% endif %}Come and learn how to make funstop frame animations, cartoon or iPad animation movies in these fun, summer workshops - with rigs, Apps & iPads! All work screened on YouTube.
Come and have a play on this fun, hands on one-day Taster animationworkshop! Ever wanted to make a mini Wallace and Gromit, Charlie and Lola- Model or Cut Out Animation? Come and try NOW on this fun, play session.
Thursday 3 August. 9.30 am – 3.30pm. Ages 8-16. Price £ 20.00. 12 people max.
Missed out the first time? Come and play on this fun, hands on one-day minitaster workshop! Ever wanted to make a wacky Lego Movie, Wallace andGromit, Cut Out Animation?
Tuesday 8 August. 9.30 am – 3.30pm. Ages 8-16. Price £ 17.50. 12 people max.
Make a MONSTER of an animation on this fun workshop where you get tomake a monster or bring your own models, toys and drawings to life - usingiPads, and sets. Or, make Godzilla, Kong and T.Rex battle it out on this funhands-on animation workshop. Last one of the summer… Make it SPECIAL!
Thursday 10 August 9.30 am – 3.30pm. Ages 8-16. Price £ 17.50. 12 people max.
 Or email<EMAIL_ADDRESS>Address: North Kidlington Primary School.Benmead Road, Kidlington OX5 2DA
The Animation Station is an inclusive arts organisation which aims to animate young hearts and minds through the use of animation and digital moving image in relation to new technology, artists and the community.
Based on the borders of Northamptonshire, Oxfordshire and Warwickshire in Banbury, it delivers core programmes of activities with schools, community groups, colleges and arts organisations embedding creativity, excellence and aspiration at the heart of its programme.
Animation Station is managed by Clinton Osborne with a professional network of associate artists and support from schools, the community, local authorities and grant funding.


ST-JSON ('ST' because it originated at Streamtech) is a Common Lisp library for encoding and decoding JSON values (as specified on json.org).
This library does mostly the same thing as CL-JSON, but is simpler and more precise about types (distinguishing boolean false, the empty array, and the empty object).
The library is available under a zlib-style license. It can be installed with asdf-install, or you can download the current release manually. The git repository can be checked out with:
git clone http://marijnhaverbeke.nl/git/st-json
The code is also on github.
Mail Marijn Haverbeke with any questions, bug reports, patches, etcetera. I'll set up some mailing list when there is a need.
ST-JSON maps JavaScript numbers to Lisp numbers, strings to
strings, null to the keyword :null
, booleans to the
keywords :true
and :false
, arrays to
lists, and objects to a custom type jso
(JavaScript
object). Since in CL boolean false, the empty list, and nullish
stuff is all usually represented by nil
, you will
sometimes have to selectively transform your values before writing
or after reading them. (The empty array is a truthy value in
JavaScript, so writing out nil
for a boolean is bound
to get you into trouble.)
type json-bool
A type including only the values :true
and :false
. Useful in read-json-as-type
and
similar.
function as-json-bool (value)
→ json-bool
Convert a generalised boolean to a json-bool
.
function from-json-bool (value)
→ bool
Convert a json-bool
to a Lisp boolean.
type json-null
A type containing only :null
.
function jso (&rest fields)
→ jso
jso
names both a function and a type.
This type is used to represent JavaScript objects. It simply wraps
an alist. The function is called with an even number of arguments,
which give the properties of the object and their values.
function getjso (key jso)
→ value
Looks up a value in a JavaScript object, returns a
second value indicating whether the value was found (like
gethash
). Can be used with setf
.
function getjso* (keys jso)
→ value
Takes a key of the form "a.b.c", generates a series of getjso calls that descend into the object and returns 'c'.
function mapjso (function jso)
maphash
equivalent for JavaScript
objects.
method read-json (source &optional junk-allowed-p)
→ value
Reads a JSON value from an input stream or string.
When junk-allowed-p
is nil
, an error will
be raised if any non-whitespace characters are found after the
value. This error, and any other parsing errors, will have the type
json-parse-error
.
function read-json-as-type (source type)
→ value
Like read-json
, but also asserts that
the resulting value is of a given type, and raises an error of type
json-type-error
if not.
function write-json (element stream)
Writes a value to a stream. Raises an error of type
json-write-error
if a value for which no write-json-element
method is defined is encountered.
function write-json-to-string (element)
→ string
Like write-json
, but produces a
string.
method write-json-element (element stream)
Generic function used to serialise values as JSON. You can specialise it for your own types, if needed.
variable *script-tag-hack*
Bind this to t
when writing JSON that
will end up in an HTML document. It prevents
'</script>'
from occurring in strings by escaping
any slash following a '<
' character. Default value
is nil
.
variable *allow-comments*
Bind this to t
to ignore C++ style
end of line comments. Default value is nil
.


Developers are our partners and by pairing their creativity and innovation with our platforms and tools, together we create delightful experiences for billions of people around the world. Listening carefully to their feedback is an important part of how we continue to make Android better with each release and improve how mobile app stores work. In an April 2019 blog post we shared some updates we made to Android APIs and Play Policies based on developer feedback. And today, we wanted to share some additional insights we’ve gained from developer feedback and how we’re taking that input to improve Google Play and Android. Some of the key themes we’ve heard include:
Supporting developers’ ability to choose how they distribute their apps through multiple app stores on different platforms (mobile, PC, and console), each with their own business model competing in a healthy marketplace;
Clarifying our policies regarding who needs to use Google Play’s billing system and who does not;
Ensuring equal treatment for all apps, including first-party and third-party apps, on our platforms;
Allowing developers to connect and communicate directly with their customers;
Enabling innovation and ensuring our policies embrace new technologies that can help drive the consumer experience forward.
The best way to learn is to read and that’s true for a developer as well. If you want to become a better developer, you have to read more code. It’s as simple as that.
Books, blogs, forums are all good to a certain extent but there is nothing that can replace some fully-functional, exhaustive open-source projects where the entire app with all its resources is right in front of you.
All you have to do is sit back, take a cup of coffee and read some kick-ass code. Here in this article, we bring some of the best open-source Android apps from various categories and genres to fulfill all your learning and development needs.
You can try these apps directly from Play Store to have a hands-on experience before diving deep into the code. The difficulty level attached with each app will help you to judge if you should instantly dive into it or put it aside for the time being.
LeafPic
(Github | Play Store | Difficulty: Beginner)
Photo and video gallery apps are one of the most common apps that you can find on any Android device. Ever wondered how they are made? LeafPic is one of the best open-source gallery app you can try and learn from.
It is quite simple, easy to understand and is perfectly suitable for any beginner developer. One of the best things I have found in this app is the way they have implemented dynamic theming. It is something that many Android developers have struggled to implement properly.
Simple Calendar
(Github | Play Store | Difficulty: Beginner)
A simple and easy-to-use calendar app built purely using Kotlin. If you were looking to learn Kotlin, then this is probably one of the best ways to start.
The goal of this app is quite simple which makes it perfect to get your hands dirty learning a completely new language for developing Android apps. Another cool thing you can learn from this project is to make custom desktop widgets for Android.
Amaze File Manager
(Github | Play Store | Difficulty: Intermediate)
Another extremely common Android app that you can find on almost any Android device, is a file manager.
Though building a file manager app might seem straight-forward at first, but it actually is quite difficult to get it right across all Android platforms and all devices.
There are a lot of good things you can learn from this app, especially how to perform proper file handling on SD cards. But I would not recommend you to follow the coding standards used in this project as it is no way up to the mark.
Read More here
Hey, Android Developer. I would like to tell you something new about animations. Google finally made a statement with Material Design: animations are not only for iOS. Part of the new concept is Material motion.
“Motion provides meaning. Objects are presented to the user without breaking the continuity of experience even as they transform and reorganize. Motion in the world of material design is used to describe spatial relationships, functionality, and intention with beauty and fluidity.”
- Material Design guidelines.
In fact, the process of creating animations takes time. It’s sure tempting to just call setVisibility(View.VISIBLE) and move on to business logic of your awesome new feature, given that it’s already past all deadlines. But remember: every time you ignore opportunity to add meaningful UI transition another sad designer appears somewhere in the world.
What if I told you that animations require less effort than you think? Have you ever heard about Transitions API? Yes, it is that thing promoted by Google for fancy animations between Activities. Sadly, it is available only starting from Android 5.0. So no one is actually using it. But imagine that this API can be efficiently used in many different cases and, what’s even more exciting, available on older Android versions.
Let’s start with some history
New animateLayoutChange parameter was introduced for ViewGroup in Android 4.0. But even with calling getLayoutTransition() and configuring some stuff inside it, it was still unstable and not flexible enough. So you couldn’t do much with it.
Version 4.4 Kitkat brings us idea of Scenes and Transitions. Scene is technically the state of all views in our Scene root (layout container). Transition is the set of Animators which will be applied for the view to perform smooth transition from the one scene to another. Will it be more fascinating with the examples? Yeah.
Simple types of Transition
ChangeBounds: It animates changes to view position and size. This one moves the button in our example.
Fade:It extends Visibility class and performs most popular animations — fade in and fade out.In example it’s applied to TextView.
TransitionSet: It’s Transition that is actually a set of another Transitions. They can be started together or sequential, to change it call setOrdering.
AutoTransition: It’s TransitionSet that contains Fade out, ChangeBounds and Fade in in sequential order. At first, views that don’t exist in the second scene are faded out, then change bounds applied for changes of position and size, and, finally, new views appear with fade in. AutoTransition used by default when you don’t specify any transition in the second argument of beginDelayedTransition.


What feature of CSS do we make use of to change the design for different screen sizes?
Answer:
Media Queries


{% extends 'base/base.html' %} {% block exp_info_block %}
{% endblock %} {% block exp_block %}
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04.Write a script that finds the maximal increasing sequence in an array. Example: {3, 2, 3, 4, 2, 2, 4} -> {2, 3, 4}.
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A keystroke or keyboard shortcut allows you to run a command or perform a function in Ameol by pressing a single key or combination of keys. For instance, if you want to post a message to the current forum that you are reading, simply pressing the 'S' key will bring up the New Message dialog box.


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