Octave axes zoom redraw - octave

I am facing an issue in Octave. When I set custom tick labels of y axis, labels are not updated correctly when zoomed. It is easy to solve in Matlab:
plot(1:10);
ax = gca;
ax.YAxis.TickLabelFormat = '%,.1f';
My code with faulty y labels when zoomed:
ax2 = gca;
ytick = get (ax2, "ytick");
yticklabel = strsplit (sprintf ("%9.0f\n", ytick), "\n", true);
set (ax2, "yticklabel", yticklabel);
The above code formats y tick labels properly, but labels does not match plot when zoomed. There is a screenshot of my issue: nonzoomed vs zoomed.
I am using W10 64bit, Octave version 4.0.3.. Octave was configured for "i686-w64-mingw32".
Any ideas?
I have decided to add minimal code example to be more clear about the issue:
x=1:length(inv);
figure
hax1 = subplot(2,1,1);
stairs(x,inv);
hax2 = subplot(2,1,2);
x=1:length(mon);
% big numbers here, need to format to get rid of scientific notation
stairs(x,mon);
ax2 = gca;
ytick = get (ax2, "ytick");
yticklabel = strsplit (sprintf ("%9.0f\n", ytick), "\n", true);
set (ax2, "yticklabel", yticklabel);
linkaxes([hax1 hax2],'x');

Related

Sequence to Sequence Loss

I'm trying to figure out how sequence to sequence loss is calculated. I am using the huggingface transformers library in this case, but this might actually be relevant to other DL libraries.
So to get the required data we can do:
from transformers import EncoderDecoderModel, BertTokenizer
import torch
import torch.nn.functional as F
torch.manual_seed(42)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
MAX_LEN = 128
tokenize = lambda x: tokenizer(x, max_length=MAX_LEN, truncation=True, padding=True, return_tensors="pt")
model = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert from pre-trained checkpoints
input_seq = ["Hello, my dog is cute", "my cat cute"]
output_seq = ["Yes it is", "ok"]
input_tokens = tokenize(input_seq)
output_tokens = tokenize(output_seq)
outputs = model(
input_ids=input_tokens["input_ids"],
attention_mask=input_tokens["attention_mask"],
decoder_input_ids=output_tokens["input_ids"],
decoder_attention_mask=output_tokens["attention_mask"],
labels=output_tokens["input_ids"],
return_dict=True)
idx = output_tokens["input_ids"]
logits = F.log_softmax(outputs["logits"], dim=-1)
mask = output_tokens["attention_mask"]
Edit 1
Thanks to #cronoik I was able to replicate the loss calculated by huggingface as being:
output_logits = logits[:,:-1,:]
output_mask = mask[:,:-1]
label_tokens = output_tokens["input_ids"][:, 1:].unsqueeze(-1)
select_logits = torch.gather(output_logits, -1, label_tokens).squeeze()
huggingface_loss = -select_logits.mean()
However, since the last two tokens of the second input is just padding, shouldn't we calculate the loss to be:
seq_loss = (select_logits * output_mask).sum(dim=-1, keepdims=True) / output_mask.sum(dim=-1, keepdims=True)
seq_loss = -seq_loss.mean()
^This takes into account the length of the sequence of each row of outputs, and the padding by masking it out. Think this is especially useful when we have batches of varying length outputs.

ok I found out where I was making the mistakes. This is all thanks to this thread in the HuggingFace forum.
The output labels need to have -100 for the masked version. The transoformers library does not do it for you.
One silly mistake I made was with the mask. It should have been output_mask = mask[:, 1:] instead of :-1.
1. Using Model
We need to set the masks of output to -100. It is important to use clone as shown below:
labels = output_tokens["input_ids"].clone()
labels[output_tokens["attention_mask"]==0] = -100
outputs = model(
input_ids=input_tokens["input_ids"],
attention_mask=input_tokens["attention_mask"],
decoder_input_ids=output_tokens["input_ids"],
decoder_attention_mask=output_tokens["attention_mask"],
labels=labels,
return_dict=True)
2. Calculating Loss
So the final way to replicate it is as follows:
idx = output_tokens["input_ids"]
logits = F.log_softmax(outputs["logits"], dim=-1)
mask = output_tokens["attention_mask"]
# shift things
output_logits = logits[:,:-1,:]
label_tokens = idx[:, 1:].unsqueeze(-1)
output_mask = mask[:,1:]
# gather the logits and mask
select_logits = torch.gather(output_logits, -1, label_tokens).squeeze()
-select_logits[output_mask==1].mean(), outputs["loss"]
The above however ignores the fact that this comes from two different lines. So an alternate way of calculating loss could be:
seq_loss = (select_logits * output_mask).sum(dim=-1, keepdims=True) / output_mask.sum(dim=-1, keepdims=True)
seq_loss.mean()

thanks for sharing. However, the new version of transformers as of today actually does not "shift" anymore. The following is not needed.
#shift things
output_logits = logits[:,:-1,:]
label_tokens = idx[:, 1:].unsqueeze(-1)
output_mask = mask[:,1:

How to effectively adjust graph margin or padding in dash plotly

I have plotted two graphs using plotly dash. But when the y-axis / x-axis tick size is more it gets cut off.
Y-axis :
Code :
data = [go.Scatter(x = df[df['S2PName-Category']==category]['S2BillDate'],
y = df[df['S2PName-Category']==category]['totSale'],
mode = 'markers+lines',
name = category) for category in df['S2PName-Category'].unique()]
layout = go.Layout(title='Category Trend',
xaxis = dict(title = 'Time Frame', tickformat = '%d-%b-%y'),
yaxis = dict(tickprefix= '₹', tickformat=',.2f',type='log'),
hovermode = 'closest',
plot_bgcolor = colors['background'],
paper_bgcolor = colors['background'],
font = dict(color = colors['text'])
)
X-Axis :
Code :
data = [go.Scatter(x = df[df['S2PName']==item]['S2BillDate'],
y = df[df['S2PName']==item]['totSale'],
mode = 'markers+lines',
name = item) for item in items]
layout = go.Layout(title='Category Trend',
xaxis = dict(title = 'Time Frame' , tickformat = '%d-%b'),
yaxis = dict(tickprefix= '₹', tickformat=',.2f',type='log',autorange = True),
hovermode = 'closest',
plot_bgcolor = colors['background'],
paper_bgcolor = colors['background'],
font = dict(color = colors['text'])
)
In the above 2 graphs , as the length of the tick value increases, it gets cut off . Is there a better way to handle this ?

Credit for #Flavia Giammarino in comments for the reference to the docs. I'm posting the answer for completeness.
https://plotly.com/python/setting-graph-size/
From that link the example below shows how to set margin:
fig.update_layout(
margin=dict(l=20, r=20, t=20, b=20),
)
Where l r t b correspond to left, right, top, bottom.

I had a similar problem with some Dash/Plotly charts and long y axis labels being truncated or hidden. There didn't seem to be much information or documentation on this issue, so it took a while to solve.
Solution: add this code to the layout settings to prevent truncation of the y axes labels:
fig.update_layout(
yaxis=dict(
automargin=True
)
)
or you can update the yaxes setting specifically:
fig.update_yaxes(automargin=True)
Update: I tried another version of Plotly (5.10 or above) which mentions setting the automargin setting to any combination of automargin=['left+top+right+bottom'] with similar results. This still seems a bit unstable and doesn't solve all possible scenarios or corner cases, but works fine in most cases, especially when the browser window is maximized.

Why do I get odd 0,0 point in Octave trisurf

I am trying to draw a surface from a file on disk (shown below). But I get an odd additional point at co-ords (0,0).
The file appears to be in correct shape to me.
I draw the chart from my C# application with a call to Octave .Net. Here is the Octave part of the script:
figure (1,'name','Map');
colormap('hot');
t = dlmread('C:\Map3D.csv');
# idx = find(t(:,4) == 4.0);t2 = t(idx,:);
tx =t(:,1);ty=t(:,2);tz=t(:,3);
tri = delaunay(tx,ty);
handle = trisurf(tri,tx,ty,tz);xlabel('Floor');ylabel('HurdleF');zlabel('Sharpe');
waitfor(handle);
This script is called from my C# app, with the following , very simple, code snippet:
using (var octave = new OctaveContext())
{
octave.Execute(script, int.MaxValue);
}
Can anyone explain if my Octave script is wrong, or the way I have structured the file.
Floor,HurdleF,Sharpe,Model
1.40000000,15.00000000,-0.44,xxx1.40_Hrd_15.00
1.40000000,14.00000000,-0.49,xxx1.40_Hrd_14.00
1.40000000,13.00000000,-0.19,xxx1.40_Hrd_13.00
1.40000000,12.00000000,-0.41,xxx1.40_Hrd_12.00
1.40000000,11.00000000,0.42,xxx1.40_Hrd_11.00
1.40000000,10.00000000,0.17,xxx1.40_Hrd_10.00
1.40000000,9.00000000,0.28,xxx1.40_Hrd_9.00
1.40000000,8.00000000,0.49,xxx1.40_Hrd_8.00
1.40000000,7.00000000,0.45,xxx1.40_Hrd_7.00
1.40000000,6.00000000,0.79,xxx1.40_Hrd_6.00
1.40000000,5.00000000,0.56,xxx1.40_Hrd_5.00
1.40000000,4.00000000,1.76,xxx1.40_Hrd_4.00
1.30000000,15.00000000,-0.46,xxx1.30_Hrd_15.00
1.30000000,14.00000000,-0.55,xxx1.30_Hrd_14.00
1.30000000,13.00000000,-0.24,xxx1.30_Hrd_13.00
1.30000000,12.00000000,0.35,xxx1.30_Hrd_12.00
1.30000000,11.00000000,0.08,xxx1.30_Hrd_11.00
1.30000000,10.00000000,0.63,xxx1.30_Hrd_10.00
1.30000000,9.00000000,0.83,xxx1.30_Hrd_9.00
1.30000000,8.00000000,0.21,xxx1.30_Hrd_8.00
1.30000000,7.00000000,0.55,xxx1.30_Hrd_7.00
1.30000000,6.00000000,0.63,xxx1.30_Hrd_6.00
1.30000000,5.00000000,0.93,xxx1.30_Hrd_5.00
1.30000000,4.00000000,2.50,xxx1.30_Hrd_4.00
1.20000000,15.00000000,-0.40,xxx1.20_Hrd_15.00
1.20000000,14.00000000,-0.69,xxx1.20_Hrd_14.00
1.20000000,13.00000000,0.23,xxx1.20_Hrd_13.00
1.20000000,12.00000000,0.56,xxx1.20_Hrd_12.00
1.20000000,11.00000000,0.22,xxx1.20_Hrd_11.00
1.20000000,10.00000000,0.56,xxx1.20_Hrd_10.00
1.20000000,9.00000000,0.79,xxx1.20_Hrd_9.00
1.20000000,8.00000000,0.20,xxx1.20_Hrd_8.00
1.20000000,7.00000000,1.09,xxx1.20_Hrd_7.00
1.20000000,6.00000000,0.99,xxx1.20_Hrd_6.00
1.20000000,5.00000000,1.66,xxx1.20_Hrd_5.00
1.20000000,4.00000000,2.23,xxx1.20_Hrd_4.00
1.10000000,15.00000000,-0.31,xxx1.10_Hrd_15.00
1.10000000,14.00000000,-0.18,xxx1.10_Hrd_14.00
1.10000000,13.00000000,0.24,xxx1.10_Hrd_13.00
1.10000000,12.00000000,0.70,xxx1.10_Hrd_12.00
1.10000000,11.00000000,0.31,xxx1.10_Hrd_11.00
1.10000000,10.00000000,0.76,xxx1.10_Hrd_10.00
1.10000000,9.00000000,1.24,xxx1.10_Hrd_9.00
1.10000000,8.00000000,0.94,xxx1.10_Hrd_8.00
1.10000000,7.00000000,1.09,xxx1.10_Hrd_7.00
1.10000000,6.00000000,1.53,xxx1.10_Hrd_6.00
1.10000000,5.00000000,2.41,xxx1.10_Hrd_5.00
1.10000000,4.00000000,2.16,xxx1.10_Hrd_4.00
1.00000000,15.00000000,-0.41,xxx1.00_Hrd_15.00
1.00000000,14.00000000,-0.24,xxx1.00_Hrd_14.00
1.00000000,13.00000000,0.33,xxx1.00_Hrd_13.00
1.00000000,12.00000000,0.18,xxx1.00_Hrd_12.00
1.00000000,11.00000000,0.61,xxx1.00_Hrd_11.00
1.00000000,10.00000000,0.96,xxx1.00_Hrd_10.00
1.00000000,9.00000000,1.75,xxx1.00_Hrd_9.00
1.00000000,8.00000000,0.74,xxx1.00_Hrd_8.00
1.00000000,7.00000000,1.63,xxx1.00_Hrd_7.00
1.00000000,6.00000000,2.12,xxx1.00_Hrd_6.00
1.00000000,5.00000000,2.73,xxx1.00_Hrd_5.00
1.00000000,4.00000000,2.03,xxx1.00_Hrd_4.00
0.90000000,15.00000000,-0.42,xxx0.90_Hrd_15.00
0.90000000,14.00000000,-0.37,xxx0.90_Hrd_14.00
0.90000000,13.00000000,0.58,xxx0.90_Hrd_13.00
0.90000000,12.00000000,0.03,xxx0.90_Hrd_12.00
0.90000000,11.00000000,0.68,xxx0.90_Hrd_11.00
0.90000000,10.00000000,0.79,xxx0.90_Hrd_10.00
0.90000000,9.00000000,1.54,xxx0.90_Hrd_9.00
0.90000000,8.00000000,0.82,xxx0.90_Hrd_8.00
0.90000000,7.00000000,1.81,xxx0.90_Hrd_7.00
0.90000000,6.00000000,2.33,xxx0.90_Hrd_6.00
0.90000000,5.00000000,2.99,xxx0.90_Hrd_5.00
0.90000000,4.00000000,1.71,xxx0.90_Hrd_4.00
0.80000000,15.00000000,-0.46,xxx0.80_Hrd_15.00
0.80000000,14.00000000,-0.26,xxx0.80_Hrd_14.00
0.80000000,13.00000000,0.55,xxx0.80_Hrd_13.00
0.80000000,12.00000000,0.07,xxx0.80_Hrd_12.00
0.80000000,11.00000000,0.65,xxx0.80_Hrd_11.00
0.80000000,10.00000000,1.08,xxx0.80_Hrd_10.00
0.80000000,9.00000000,1.27,xxx0.80_Hrd_9.00
0.80000000,8.00000000,1.12,xxx0.80_Hrd_8.00
0.80000000,7.00000000,1.98,xxx0.80_Hrd_7.00
0.80000000,6.00000000,2.62,xxx0.80_Hrd_6.00
0.80000000,5.00000000,3.35,xxx0.80_Hrd_5.00
0.80000000,4.00000000,1.27,xxx0.80_Hrd_4.00
0.70000000,15.00000000,-0.56,xxx0.70_Hrd_15.00
0.70000000,14.00000000,-0.33,xxx0.70_Hrd_14.00
0.70000000,13.00000000,0.24,xxx0.70_Hrd_13.00
0.70000000,12.00000000,-0.22,xxx0.70_Hrd_12.00
0.70000000,11.00000000,0.74,xxx0.70_Hrd_11.00
0.70000000,10.00000000,1.19,xxx0.70_Hrd_10.00
0.70000000,9.00000000,1.24,xxx0.70_Hrd_9.00
0.70000000,8.00000000,1.14,xxx0.70_Hrd_8.00
0.70000000,7.00000000,2.26,xxx0.70_Hrd_7.00
0.70000000,6.00000000,2.70,xxx0.70_Hrd_6.00
0.70000000,5.00000000,3.52,xxx0.70_Hrd_5.00
0.70000000,4.00000000,1.05,xxx0.70_Hrd_4.00
0.60000000,15.00000000,-0.50,xxx0.60_Hrd_15.00
0.60000000,14.00000000,-0.60,xxx0.60_Hrd_14.00
0.60000000,13.00000000,0.11,xxx0.60_Hrd_13.00
0.60000000,12.00000000,-0.16,xxx0.60_Hrd_12.00
0.60000000,11.00000000,0.73,xxx0.60_Hrd_11.00
0.60000000,10.00000000,1.08,xxx0.60_Hrd_10.00
0.60000000,9.00000000,1.31,xxx0.60_Hrd_9.00
0.60000000,8.00000000,1.38,xxx0.60_Hrd_8.00
0.60000000,7.00000000,2.24,xxx0.60_Hrd_7.00
0.60000000,6.00000000,2.89,xxx0.60_Hrd_6.00
0.60000000,5.00000000,3.50,xxx0.60_Hrd_5.00
0.60000000,4.00000000,1.11,xxx0.60_Hrd_4.00
0.50000000,15.00000000,-0.40,xxx0.50_Hrd_15.00
0.50000000,14.00000000,-0.37,xxx0.50_Hrd_14.00
0.50000000,13.00000000,0.13,xxx0.50_Hrd_13.00
0.50000000,12.00000000,-0.11,xxx0.50_Hrd_12.00
0.50000000,11.00000000,0.61,xxx0.50_Hrd_11.00
0.50000000,10.00000000,0.92,xxx0.50_Hrd_10.00
0.50000000,9.00000000,1.41,xxx0.50_Hrd_9.00
0.50000000,8.00000000,1.39,xxx0.50_Hrd_8.00
0.50000000,7.00000000,2.19,xxx0.50_Hrd_7.00
0.50000000,6.00000000,2.80,xxx0.50_Hrd_6.00
0.50000000,5.00000000,3.41,xxx0.50_Hrd_5.00
0.50000000,4.00000000,1.05,xxx0.50_Hrd_4.00
0.40000000,15.00000000,-0.25,xxx0.40_Hrd_15.00
0.40000000,14.00000000,-0.44,xxx0.40_Hrd_14.00
0.40000000,13.00000000,0.02,xxx0.40_Hrd_13.00
0.40000000,12.00000000,0.00,xxx0.40_Hrd_12.00
0.40000000,11.00000000,0.69,xxx0.40_Hrd_11.00
0.40000000,10.00000000,0.67,xxx0.40_Hrd_10.00
0.40000000,9.00000000,1.02,xxx0.40_Hrd_9.00
0.40000000,8.00000000,1.29,xxx0.40_Hrd_8.00
0.40000000,7.00000000,2.17,xxx0.40_Hrd_7.00
0.40000000,6.00000000,2.88,xxx0.40_Hrd_6.00
0.40000000,5.00000000,3.19,xxx0.40_Hrd_5.00
0.40000000,4.00000000,0.98,xxx0.40_Hrd_4.00
0.30000000,15.00000000,-0.02,xxx0.30_Hrd_15.00
0.30000000,14.00000000,-0.36,xxx0.30_Hrd_14.00
0.30000000,13.00000000,-0.26,xxx0.30_Hrd_13.00
0.30000000,12.00000000,-0.11,xxx0.30_Hrd_12.00
0.30000000,11.00000000,0.50,xxx0.30_Hrd_11.00
0.30000000,10.00000000,0.50,xxx0.30_Hrd_10.00
0.30000000,9.00000000,1.01,xxx0.30_Hrd_9.00
0.30000000,8.00000000,1.28,xxx0.30_Hrd_8.00
0.30000000,7.00000000,2.11,xxx0.30_Hrd_7.00
0.30000000,6.00000000,2.89,xxx0.30_Hrd_6.00
0.30000000,5.00000000,3.16,xxx0.30_Hrd_5.00
0.30000000,4.00000000,0.95,xxx0.30_Hrd_4.00

What's happening
dlmread() is reading the file in as numeric data and returning a numeric matrix. It doesn't recognize the text in your header line, so it silently converts that row to all zeros. (IMHO this is a design flaw in dlmread.) Remove the header line.
How to debug this
So, you've got some zeros in your plot that you didn't expect to be there? Check for zeros in your input data:
ixZerosX = find(tx == 0)
ixZerosY = find(ty == 0)
ixZerosZ = find(tz == 0)
The semicolons are omitted intentionally there to get Octave to automatically display the results.
Better yet, since doubles are an approximate type, and the values might be close to but not actually zero, do a "near zero" search:
threshold = 0.1;
ixZerosX = find(abs(tx) < threshold)
ixZerosY = find(abs(ty) < threshold)
ixZerosZ = find(abs(tz) < threshold)

Oxyplot showing HeatMapSeries behind ScatterSeries

I am trying to combine LineSeries, ScatterSeries and HeatmapSeries on a single OxyPlot instance. I am able to show the first two just fine on the same plot and it looks like the following:
The axes for this are generated by the following code:
var xAxis = new DateTimeAxis();
xAxis.Key = "X";
xAxis.Position = AxisPosition.Bottom;
xAxis.AbsoluteMinimum = DateTimeAxis.ToDouble(CurrentPass.AOS);
xAxis.AbsoluteMaximum = DateTimeAxis.ToDouble(CurrentPass.LOS);
xAxis.AxislineColor = xAxis.TextColor = xAxis.TicklineColor = xAxis.MajorGridlineColor = OxyColors.DarkGray;
var yAxis = new LinearAxis();
yAxis.Key = "Y";
yAxis.Position = AxisPosition.Left;
yAxis.AbsoluteMinimum = 0.0;
yAxis.AbsoluteMaximum = MaximumFrequency;
yAxis.Maximum = MaximumFrequency;
yAxis.AxislineColor = yAxis.TextColor = yAxis.TicklineColor = yAxis.MajorGridlineColor = OxyColors.DarkGray;
If I add in a third axes for the Heatmap (not even a HeatMapSeries yet), I get the following:
The extra code here for the axis for the HeatMap is:
var heatmapAxis = new LinearColorAxis();
heatmapAxis.AbsoluteMinimum = 0.0;
heatmapAxis.AbsoluteMaximum = MaximumFrequency;
heatmapAxis.Palette = OxyPalettes.Gray(1024);
heatmapAxis.Key = "HeatMap";
I am not sure what's going on here. The line series still shows. And all the ScatterPoints from the scatterseries I have are definitely there - the tracker shows up and I can interact with the points (hover, click etc.). But the points don't show. If I add a HeatMapSeries, the HeatMapSeries data does show up as expected, the LineSeries data still shows up but no ScatterSeries data.
Again, the HeatMap data and the Scatter Series data show up individually, but never together.
Has anyone encountered this before? Are there workarounds?
Thanks,
Aditya

Sorry this is a bit late but just stumbled upon this. I had something similar with a line not showing up on a Heatmap and after some experimenting i realized the order in which the series' are added to the plot model make a big difference. Make sure you add your ScatterSeries after you've added your Heatmap to the plot model.

Octave and multiple Bode plots

I'm teaching myself Octave and as a motivational exercise am attempting to create some Bode plots. I'd like to create a plot that has multiple curves for different values of a parameter in a transfer function, for example the time constant of a simple RC filter. I'm trying to do it as follows:
tau = [1,2,3]
for i = tau
g(i) = tf(1,[tau(i),1])
endfor
bode(g(1),g(2),g(3))
But it doesn't work, I get the error
error: octave_base_value::imag (): wrong type argument `struct'
However, it works fine if there are not multiple arguments to the bode command and the last line is simply:
bode(g(1))
Any advice as to where I've gone wrong would be appreciated - is there a better way to do what I want to do?

I was able to do it with the following sequence (with octave 3.2.4 on debian):
bode(g(1))
set (findobj (gcf, "type", "axes"), "nextplot", "add")
bode(g(2))
bode(g(3))
The second command is similar to hold on but it works when there are subplots; I found it here.

Using your own code:
subplot(211), hold on
subplot(212), hold on
tau = [1,2,3]
for i = 1:length(tau),
g(i) = tf(1,[tau(i),1]);
bode(g(i))
endfor
The problem with this solution is that you cannot identify a specific plot. You cannot access figure properties through bode() function directly.
Here then a plausible solution to bring you colorful plots:
colorsplot = ["b","m","g"]
tau = [1,2,3]
g = tf(1,[tau(1),1]);
[mag, ph, w] = bode(g);
subplot(211), semilogx(w,20*log(mag)), hold on
subplot(212), semilogx(w,ph), hold on
for i = 2:length(tau),
g = tf(1,[tau(i),1]);
[mag, ph, waux] = bode(g,w);
subplot(211), semilogx(w,20*log(mag),colorsplot(i))
subplot(212), semilogx(w,ph,colorsplot(i))
endfor