Slope | A simpler way to implement gradients on iOS | iOS library

Chris W. is 100% correct, using a vector editor like Adobe Illustrator, Inkscape, or Affinity Designer will make your life much easier when working with complex shapes in SVG. However, for simple shapes like this it doesn't hurt to understand the inner-workings of SVG curves. Not only will it help you malke mathematically perfect shapes but your code will also (usually) be much smaller than what an editor will produce.

The example I'm showing here is only one possible approach out of many to accomplishing this shape. I'll explain the procedure and series of commands briefly but I've also included a second copy of your shape with comments and additional shapes to highlight what the control points are doing (this helps me visualize SVG code).

First it moves to a point at x0, y 100 and draws a relative cubic curve (c) whose first control point is right 100px from the start point with no vertical change and whose second control point is right 180px and up 90px from the start point. The following s curve assumes that it will reflect the previous control point of the c curve before it so it only needs it's second control point and end point specified both of which are designated relative to the end point of the c curve and mirror the previous control points of the c curve. The rest is an absolute vertical line (V) to the bottom of the SVG, an absolute horizontal line to the bottom left corner (H) and a Z to close the path. SVG is awesome, hope this helps you.

1. It's not necessary to use .groupby because there is only 1 record per day.
2. Don't use .reset_index(0, drop=True) because this is dropping the date index. When you drop the index from the calculation, it no longer matches the index of df, so the data is added as NaN.
   • df['Close'].rolling(window=days_back, min_periods=days_back).apply(get_slope, raw=True) creates a pandas.Series. When assigning a pandas.Series to a pandas.DataFrame as a new column, the indices must match.

Here's an approach using tidyverse and broom, based on the tutorial here.

First, I reshape the data to be longer, to more easily apply the same approach to each column of data. Then I nest the time and value data, map to lm, and use broom::tidy to extract the coefficients. The rest is filtering / reshaping the output.

Let's continue with @wwnde solution with some changes in it:

You can use pd.merge_asof to perform this type of merge quickly. However there is some preprocessing you'll need to do to your data.

• reshape df1 to match the format of the expected output (e.g. where "slope", "x", and "y" are columns instead of rows
• drop NaNs from the merge keys AND sort both df1 and df2 by their merge keys (this is a requirement of pd.merge_asof that we need to do explicitly). Merge keys are going to be the "slope" and "optimal cost" columns.
• Ensure that the merge keys are of the same dtype (in this case they should both be floats, meaning we'll need to convert "optimal cost" to a float type instead of int.
• perform the merge operation

Try this:

(I’m assuming your 3 first columns are actually index, as they seem to be displayed. If not, you first need to do df.set_index(['Name', 'Segment', 'Axis']))

You can easily access each coordinate with .xs − here we’re basically saying « access the data labeled 'x' in the index level Axis) »:

The Rescale Intercept (0028,1052) and the Rescale Slope (0028,1053) are standard DICOM tags.

As you already said in question, the Scale Slope (probably (2005,100E)) is a Private Tag - specific to the Equipment Manufacturer. So, only manufacturer may say something reliable about this.

Private tags do not have standard names; generally, manufacturers mention the details of private tags they created in their DICOM Conformance Statement or other similar document. They may also name the tag in that document. Please refer to this answer for more details.

Now, to your question -- what is the difference?
Considering what I said above, it is hard to answer this question. Meaning and usage of standard tags can be found in standards. It is not the case with private tags. You have to go through vendor specific documents to understand it in details (if they mention it in details).

Even so, a quick googling give me this and this. One of the post in thread discusses about usage of Scale Slope in a mathematical formula.

If you open a PAR/REC header you will see the Phiips description of these values
//# === PIXEL VALUES =============================================================
//# PV = pixel value in REC file, FP = floating point value, DV = displayed value on console
//# RS = rescale slope, RI = rescale intercept, SS = scale slope
//# DV = PV * RS + RI FP = DV / (RS * SS)

and

Inputs:
R = raw stored value of voxel in DICOM without scaling
WS = RealWorldValue slope (0040,9225) "PhilipsRWVSlope"
WI = RealWorldValue intercept (0040,9224) "PhilipsRWVIntercept"
RS = rescale slope (0028,1053) "PhilipsRescaleSlope"
RI = rescale intercept (0028,1052) "PhilipsRescaleIntercept"
SS = scale slope (2005,100E) "PhilipsScaleSlope"
Outputs:
W = real world value
P = precise value
D = displayed value
Formulas:
W = R * WS + WI
D = R * RS + RI
P = D / (RS * SS)