InfluxDB: Points are Missing - influxdb

When running the following piece of code:
!pip install influxdb-client
from influxdb_client import InfluxDBClient, BucketRetentionRules,WritePrecision
import influxdb_client
import time
client = InfluxDBClient(url="http://localhost:8086", token= "my_password", org="primary")
write_api = client.write_api()
query_api =client.query_api()
write_api.write("myFirstBucket", "primary", ["myMeasurement,location=coyote_creek water_level=1"],write_precision=WritePrecision.MS)
time.sleep(1)
write_api.write("myFirstBucket", "primary", ["myMeasurement,location=coyote_creek water_level=2"],write_precision=WritePrecision.MS)
time.sleep(1)
write_api.write("myFirstBucket", "primary", ["myMeasurement,location=coyote_creek water_level=3"],write_precision=WritePrecision.MS)
time.sleep(1)
write_api.write("myFirstBucket", "primary", ["myMeasurement,location=coyote_creek water_level=4"],write_precision=WritePrecision.MS)
time.sleep(1)
tables=query_api.query('from(bucket:"myFirstBucket") |> range(start: -15s)') #last 15 seconds
for table in tables:
print(table)
for row in table.records:
print (row.values)
I get:
FluxTable() columns: 9, records: 2
{'result': '_result', 'table': 0, '_start': datetime.datetime(2022, 3, 2, 11, 32, 18, 591779, tzinfo=tzutc()), '_stop': datetime.datetime(2022, 3, 2, 11, 32, 33, 591779, tzinfo=tzutc()), '_time': datetime.datetime(2022, 3, 2, 11, 32, 30, 595000, tzinfo=tzutc()), '_value': 2.0, '_field': 'water_level', '_measurement': 'myMeasurement', 'location': 'coyote_creek'}
{'result': '_result', 'table': 0, '_start': datetime.datetime(2022, 3, 2, 11, 32, 18, 591779, tzinfo=tzutc()), '_stop': datetime.datetime(2022, 3, 2, 11, 32, 33, 591779, tzinfo=tzutc()), '_time': datetime.datetime(2022, 3, 2, 11, 32, 32, 590000, tzinfo=tzutc()), '_value': 3.0, '_field': 'water_level', '_measurement': 'myMeasurement', 'location': 'coyote_creek'}
Why do I get only two records? I would expect four records: one record for each write command!

Related

Implementing WeightedRandomSampler on imbalanced data set: RuntimeError: invalid multinomial distribution

I am trying to implement a weighted sampler for a very imbalanced data set. There are 182 different classes. Here is an array of the bin counts per class:
array([69487, 5770, 5753, 138, 4308, 10, 1161, 29, 5611,
350, 7, 183, 218, 4, 3, 3872, 5, 950,
33, 3, 443, 16, 20, 330, 4353, 186, 19,
122, 546, 6, 44, 6, 3561, 2186, 3, 48,
8440, 338, 9, 610, 74, 236, 160, 449, 72,
6, 37, 1729, 2255, 1392, 12, 1, 3426, 513,
44, 3, 28, 12, 9, 27, 5, 75, 15,
3, 21, 549, 7, 25, 871, 240, 128, 28,
253, 62, 55, 12, 8, 57, 16, 99, 6,
5, 150, 7, 110, 8, 2, 1296, 70, 1927,
470, 1, 1, 511, 2, 620, 946, 36, 19,
21, 39, 6, 101, 15, 7, 1, 90, 29,
40, 14, 1, 4, 330, 1099, 1248, 1146, 7414,
934, 156, 80, 755, 3, 6, 6, 9, 21,
70, 219, 3, 3, 15, 15, 12, 69, 21,
15, 3, 101, 9, 9, 11, 6, 32, 6,
32, 4422, 16282, 12408, 2959, 3352, 146, 1329, 1300,
3795, 90, 1109, 120, 48, 23, 9, 1, 6,
2, 1, 11, 5, 27, 3, 7, 1, 3,
70, 1598, 254, 90, 20, 120, 380, 230, 180,
10, 10])
In some classes, instances are as low as 1. I am trying to implement a Weighted random sampler from torch for this dataset. However, as the class imbalance is so large, when I calculate weights using
count_occr = np.bincount(dataset.y)
lbl_weights = 1. / count_occr
weights = np.array(lbl_weights)
weights = torch.from_numpy(weights)
sampler = WeightedRandomSampler(weights.type('torch.DoubleTensor'), len(weights*2))
I get two error messages:
RuntimeWarning: divide by zero encountered in true_divide
and
RuntimeError: invalid multinomial distribution (encountering probability entry = infinity or NaN)
Does anyone have a work around for this ? I was considering multiplying the lbl_weights by some scalar however I am not sure if this is a viable option.

Spliting tables into 9's

I would like to know how can I split my table into subtables of 9's.
Example:
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }
Code shall return:
{ {1, 2, 3, 4, 5, 6, 7, 8, 9}, {10, 11, 12, 13, 14, 15, 16, 17, 18}, { 19, 20} }
How do you think is this done?

Your code seems over complicated. The task is to create a subtable every 9 elements. The code below does that:
a={ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }
b={}
j=0
k=9
for i=1,#a do
if k==9 then j=j+1; b[j]={}; k=0 end
k=k+1
b[j][k]=a[i]
end
Here, j tracks the number of subtables created and k tracks the number of elements added to a subtable. When k becomes 9, a new subtable is created. k starts at 9 to signal that.

How do i append another list within list comprehension

I am trying to find out unique elements and avoid duplicates , between the lists (using list comprehension)
`a = [10 , 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89,9]`
`b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]`
dup_list = []
This works , but with duplicates
`final_list = [uniq for uniq in a if a not in dup_list if uniq in b ]`
when i try to append the my dup_list in the last line of the comprehension statement it says invalid syntax
i.e this doesn't work.
"final_list = [uniq for uniq in a if a not in dup_list if uniq in b dup_list.append(uniq) "
I am a newbie in python so apologize for any missed out basic facts .

a = [10, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 9]
b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
nodupes = sorted([item for item in a if item not in b] +
[item for item in b if item not in a])
That yields [4, 6, 7, 11, 12, 21, 34, 55, 89]
Which is the same answer I get using sets:
seta = set(a)
setb = set(b)
nodupes = sorted(list(seta ^ setb))

Improve precision algorithm to detect facial expression using LBP

I'm developping a simple algorithm to detect several facial expressions (happiness, sadness, anger...). I'm based on this paper to do that. I'm preprocessing before to apply LBP uniform operator dividing the normalized image into 6x6 regions as shown in the example below:
By applying uniform LBP 59 feats are extracted for each region, so finally I have 2124 feats by image (6x6x59). I think it's a too large number of feats when I have about 700 images to train a model. I have read that's not good to get a good precission. My question is how can I reduce the dimension of the feats or another technique to improve the precision of the algorithm.

A straightforward way to reduce feature dimensionality - and increase robustness at the same time - would be using rotation-invariant uniform patterns. For a circular neighbourhood of radius and formed by pixels, the texture descriptor represents each region through 10 features. Thus dimensionality is reduced from 2124 to 6 × 6 × 10 = 360.

PCA can help to reduce the size of descriptor without loosing important information. Just google "opencv pca example".
Another helpful thing is to add rotation invariance to your uniform lbp features. This will improve the precision as well as dramatically decrease size of descriptor from 59 to 10.
static cv::Mat rotate_table = (cv::Mat_<uchar>(1, 256) <<
0, 1, 1, 3, 1, 5, 3, 7, 1, 9, 5, 11, 3, 13, 7, 15, 1, 17, 9, 19, 5, 21, 11, 23,
3, 25, 13, 27, 7, 29, 15, 31, 1, 33, 17, 35, 9, 37, 19, 39, 5, 41, 21, 43, 11,
45, 23, 47, 3, 49, 25, 51, 13, 53, 27, 55, 7, 57, 29, 59, 15, 61, 31, 63, 1,
65, 33, 67, 17, 69, 35, 71, 9, 73, 37, 75, 19, 77, 39, 79, 5, 81, 41, 83, 21,
85, 43, 87, 11, 89, 45, 91, 23, 93, 47, 95, 3, 97, 49, 99, 25, 101, 51, 103,
13, 105, 53, 107, 27, 109, 55, 111, 7, 113, 57, 115, 29, 117, 59, 119, 15, 121,
61, 123, 31, 125, 63, 127, 1, 3, 65, 7, 33, 97, 67, 15, 17, 49, 69, 113, 35,
99, 71, 31, 9, 25, 73, 57, 37, 101, 75, 121, 19, 51, 77, 115, 39, 103, 79, 63,
5, 13, 81, 29, 41, 105, 83, 61, 21, 53, 85, 117, 43, 107, 87, 125, 11, 27, 89,
59, 45, 109, 91, 123, 23, 55, 93, 119, 47, 111, 95, 127, 3, 7, 97, 15, 49, 113,
99, 31, 25, 57, 101, 121, 51, 115, 103, 63, 13, 29, 105, 61, 53, 117, 107, 125,
27, 59, 109, 123, 55, 119, 111, 127, 7, 15, 113, 31, 57, 121, 115, 63, 29, 61,
117, 125, 59, 123, 119, 127, 15, 31, 121, 63, 61, 125, 123, 127, 31, 63, 125,
127, 63, 127, 127, 255
);
// the well known original uniform2 pattern
static cv::Mat uniform_table = (cv::Mat_<uchar>(1, 256) <<
0,1,2,3,4,58,5,6,7,58,58,58,8,58,9,10,11,58,58,58,58,58,58,58,12,58,58,58,13,58,
14,15,16,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,17,58,58,58,58,58,58,58,18,
58,58,58,19,58,20,21,22,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,58,58,58,58,58,58,58,58,58,58,58,23,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,58,24,58,58,58,58,58,58,58,25,58,58,58,26,58,27,28,29,30,58,31,58,58,58,32,58,
58,58,58,58,58,58,33,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,34,58,58,58,58,
58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,35,36,37,58,38,58,58,58,39,58,58,58,58,58,58,58,40,58,58,58,58,58,58,58,58,58,
58,58,58,58,58,58,41,42,43,58,44,58,58,58,45,58,58,58,58,58,58,58,46,47,48,58,49,
58,58,58,50,51,52,58,53,54,55,56,57
);
static cv::Mat rotuni_table = (cv::Mat_<uchar>(1, 256) <<
0, 1, 1, 2, 1, 9, 2, 3, 1, 9, 9, 9, 2, 9, 3, 4, 1, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9,
3, 9, 4, 5, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9,
3, 9, 9, 9, 4, 9, 5, 6, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
3, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 5, 9, 6, 7, 1, 2, 9, 3, 9, 9, 9, 4, 9, 9, 9, 9,
9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7, 2, 3, 9, 4,
9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7,
3, 4, 9, 5, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 7, 4, 5, 9, 6, 9, 9, 9, 7, 5, 6, 9, 7,
6, 7, 7, 8
);
static void hist_patch_uniform(const Mat_<uchar> &fI, Mat &histo,
int histSize, bool norm, bool rotinv)
{
cv::Mat ufI, h, n;
if (rotinv) {
cv::Mat r8;
// rotation invariant transform
cv::LUT(fI, rotate_table, r8);
// uniformity for rotation invariant
cv::LUT(r8, rotuni_table, ufI);
// histSize is max 10 bins
} else {
cv::LUT(fI, uniform_table, ufI);
}
// the upper boundary is exclusive
float range[] = {0, (float)histSize};
const float *histRange = {range};
cv::calcHist(&ufI, 1, 0, Mat(), h, 1, &histSize, &histRange, true, false);
if (norm)
normalize(h, n);
else
n = h;
histo.push_back(n.reshape(1, 1));
}
The input is your CV_8U grey-scaled patch (one of those rects). The out is the rotation invariant, uniform, normalized reshaped histogram (1 line). Then you concat your patches histograms into the face descriptor. You will have 6*6*10 = 360. This is good by itself but with pca you can make it 300 or less without loosing important information and even improving the quality of detection because removed dimensions (let's say with variances less than 5%) not just occupy space but also contain mostly the noise (coming from, for example, gaussian noise from the sensor).
Then you can compare this concat histogram with the bank of faces or using svm (rbf kernel fits better). If you do it correctly, then predict for one face should not take more than 1-15ms (5 ms on my iphone7).
Hope this helps.

Issue with Propel Criteria::IN

I'm trying as add following condition in my query
AND momento_distribution.MOMENTO_IDMEMBER IN ( 5, 1, 3, 10, 11, 12, 18, 32, 51, 6 )
For that, I'm having following code
$friendCsv=Friend::getFriendIdAsCsv($member); //returning string 5, 1, 3, 10, 11, 12, 18, 32, 51, 6
//code
$c->add(MomentoDistributionPeer::MOMENTO_IDMEMBER, $friendCsv, Criteria::IN);
Query is failing because it is generating
AND momento_distribution.MOMENTO_IDMEMBER IN ( '5, 1, 3, 10, 11, 12, 18, 32, 51, 6' )
Adding a single quote on string. If I remove that single quote manually, query runs successfully.
IS there any way to force propel not to put single quotes in values?

try that
$friendCsv=Friend::getFriendIdAsCsv($member); //returning string 5, 1, 3, 10, 11, 12, 18, 32, 51, 6
$friendArr= explode(',', $friendCsv);
//code
$c->add(MomentoDistributionPeer::MOMENTO_IDMEMBER, $friendArr, Criteria::IN);
Criteria::IN should be used with array not CSV.

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