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# Interpolating with multiple y-values

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## Interpolating values in R (and avoiding negative interpolated values)

Tag : r , By : µilad
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
``````