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Interpolating with multiple y-values


Interpolating with multiple y-values

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Interpolating with multiple y-values
Tag : python , By : Jojo
Date : November 25 2020, 01:01 AM


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Interpolating values in R (and avoiding negative interpolated values)


Tag : r , By : ┬Áilad
Date : March 29 2020, 07:55 AM
will be helpful for those in need Is there any interpolation approach implemented in R where you can avoid interpolating missing values with negative value? , You could interpolate over log(d):
library(zoo)
d.interpolation <- exp(na.spline(log(d)))
d.interpolation
#  [1]      1.86    282.86   5000.00  17782.00  22424.08  19122.70  21450.00
#  [8]  42320.00  59826.52  58724.79  52900.00  54170.00  60600.00  69000.00
# [15]  78000.00  87000.00  96900.00  96900.00 122000.00 132700.00 145000.00
# [22] 171500.00 198900.00 213400.00 229600.00 250200.00 272000.00 291600.00
# [29] 318000.00 343000.00 367000.00 419200.00 445000.00 495000.00 540000.00

Interpolating a value between two values


Tag : cpp , By : Arun Thomas
Date : March 29 2020, 07:55 AM
will be helpful for those in need I'm looking for a function that interpolates a value between two values following a soft curve. , As I said in comment, exponent fits quite well:
double mBase = 5; // higher = more "curvy"
double minY = pow(mBase, mMin - mBase);
double maxY = pow(mBase, mMax - mBase);
double scale = (mMax - mMin) / (maxY - minY);
double shift = mMin - minY;
return pow(mBase, mFactor - mBase) * scale + shift;

Programmatically interpolating among multiple column values in PostgreSQL


Tag : postgresql , By : nickthecook
Date : March 29 2020, 07:55 AM
like below fixes the issue Since your interpolation is simple (you know beforehand where the interpolation points are located on the year dimension), the query is rather straightforward:
SELECT id,
       veh_1995,
       (veh_1995 + (veh_2000 - veh_1995) * 0.2)::int AS veh_1996,
       (veh_1995 + (veh_2000 - veh_1995) * 0.4)::int AS veh_1997,
       (veh_1995 + (veh_2000 - veh_1995) * 0.6)::int AS veh_1998,
       (veh_1995 + (veh_2000 - veh_1995) * 0.8)::int AS veh_1999,
       veh_2000,
       ... -- add similar lines to interpolate other years
       veh_2015
FROM my_table;

Interpolating missing values in SQL


Tag : sql , By : 40a
Date : March 29 2020, 07:55 AM
this one helps. My solution needs a helper function that calculates the length of an interval in minutes:
CREATE OR REPLACE FUNCTION int_minutes(interval) RETURNS double precision
   LANGUAGE sql STRICT IMMUTABLE AS
'SELECT EXTRACT (minutes FROM $1)
       + 60 * extract (hours FROM $1)
       + 1440 * extract (days FROM $1)';
SELECT id, timestamp, connection, bat_level,
       greatest(bat_level
                - int_minutes(timestamp
                              - conn_ts_arr[cardinality(conn_ts_arr)]
                  ) * 0.05,
                0.0
       ) AS theoretical_bat_level
FROM (SELECT id, timestamp, connection, bat_level,
             array_agg(timestamp)
                FILTER (WHERE connection)
                OVER (PARTITION BY id
                      ORDER BY timestamp) AS conn_ts_arr
      FROM cellbat) AS s1
ORDER BY id, timestamp;

Interpolating missing values for time series based on the values of the same period from a different year


Tag : python , By : RichAA
Date : March 29 2020, 07:55 AM
Does that help If you are OK with pandas library One option is to find the week number from date and fill NaN values.
df['week'] = pd.to_datetime(df['date'], format='%Y-%m-%d').dt.strftime("%V")
df2 = df.sort_values(['week']).fillna(method='bfill').sort_values(['date'])
df2
    date    value   week
0   2017-08-27  564.285714  34
1   2017-09-03  28.857143   35
2   2017-09-10  288.714286  36
3   2017-09-17  274.000000  37
4   2017-09-24  248.142857  38
5   2017-10-01  236.857143  39
6   2018-09-02  345.142857  35
7   2018-09-09  288.714286  36
8   2018-09-16  274.000000  37
9   2018-09-23  248.142857  38
10  2018-09-30  166.428571  39
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