SJW Group

SJW Group ("SJW") is a water utility processed, distribution, wholesale and retail company that is based in San Jose, California. It served 228,000 connections that serves over 1 million residents in regions of California, and approximately 17,000 connections, which serves about 60,000 people in Texas.[2][3] The enterprise value (As of February 2014) is $916.35 Million.[1]

SJW Group
Public
Traded asNYSE: SJW
Russell 2000 Component
Industrywater utilities
Founded1866 (1866)
Headquarters
Area served
North America
Revenue$272.06 Million (As of 2013)[1]
Number of employees
385
Websitewww.sjwgroup.com

Operations

The company has four subsidiaries, comprising San Jose Water Company, SJWTX, Inc., SJW Land Company as well as Texas Water Alliance Limited (TWA).[3] TWA is engaged in operating the essential water supply activities in Texas.[4] The company's diverse utility property include land held in fee, impounding reservoirs, diversion facilities, wells, distribution storage, and all water facilities, equipment, office buildings, etc.[5] In 2012, the revenue generated by purchased water made up for about 40% to 45% of the company’s annual.[4]

The company attended the industry’s conference, Water Utility Conference of 2013, as one of an influential water utility in US.[6]

In October 2018, it was announced that SJW was acquiring Connecticut Water Service, Inc., a water utility company serving communities in Connecticut and Maine, pending federal approval.[7] [8][9]

gollark: It allocates memory and doesn't consider it a side effect.
gollark: I didn't do any horrible homoglyph hacks with THAT.
gollark: It uses the function, yes.
gollark: So, I finished that to highly dubious demand. I'd like to know how #11 and such work.
gollark: > `x = _(int(0, e), int(e, е))`You may note that this would produce slices of 0 size. However, one of the `e`s is a homoglyph; it contains `2 * e`.`return Result[0][0], x, m@set({int(e, 0), int(е, e)}), w`From this, it's fairly obvious what `strassen` *really* does - partition `m1` into 4 block matrices of half (rounded up to the nearest power of 2) size.> `E = typing(lookup[2])`I forgot what this is meant to contain. It probably isn't important.> `def exponentiate(m1, m2):`This is the actual multiplication bit.> `if m1.n == 1: return Mаtrix([[m1.bigData[0] * m2.bigData[0]]])`Recursion base case. 1-sized matrices are merely multiplied scalarly.> `aa, ab, ac, ad = strassen(m1)`> `аa, аb, аc, аd = strassen(m2)`More use of homoglyph confusion here. The matrices are quartered.> `m = m1.subtract(exponentiate(aa, аa) ** exponentiate(ab, аc), exponentiate(aa, аb) ** exponentiate(ab, аd), exponentiate(ac, аa) ** exponentiate(ad, аc), exponentiate(ac, аb) ** exponentiate(ad, аd)) @ [-0j, int.abs(m2.n * 3, m1.n)]`This does matrix multiplication in an inefficient *recursive* way; the Strassen algorithm could save one of eight multiplications here, which is more efficient (on big matrices). It also removes the zero padding.> `m = exponentiate(Mаtrix(m1), Mаtrix(m2)) @ (0j * math.sin(math.asin(math.sin(math.asin(math.sin(math.e))))), int(len(m1), len(m1)))`This multiples them and I think also removes the zero padding again, as we want it to be really very removed.> `i += 1`This was added as a counter used to ensure that it was usably performant during development.> `math.factorial = math.sinh`Unfortunately, Python's factorial function has really rather restrictive size limits.> `for row in range(m.n):`This converts back into the 2D array format.> `for performance in sorted(dir(gc)): getattr(gc, performance)()`Do random fun things to the GC.

References

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