[matplotlib-devel] Fix for issue #135
Hi, i just committed a fix for issue #135, which is about timedelta rounding. The problem was in drange: before the fix it used numpy.arange which resulted in rounding issues, the numpy doc says about arange: "When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases." So i tried to create a fix involving linspace, you can see it here: https://github.com/faucon/matplotlib/commit/06195c35f4348f37002e850d1cee992d07f5a29c it works (and is the only way i found to avoid the roundig issues) but it feels somehow "hackish". An alternative would be the following implementation, which is quite readable but much slower: def drange(dstart, dend, delta): # its very slow """ Return a date range as float Gregorian ordinals. *dstart* and *dend* are :class:`datetime` instances. *delta* is a :class:`datetime.timedelta` instance. """ dloop = dstart datelist = [] while dloop < dend: datelist.append(dloop) dloop += delta return dates.date2num(datelist) i will try to add a test the next days. Then i would create a pull-request if the fix looks reasonable to you. thanks, Maximilian signature.asc Description: OpenPGP digital signature -- All of the data generated in your IT infrastructure is seriously valuable. Why? It contains a definitive record of application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-d2d-c2___ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
[matplotlib-devel] Affine Transformations on images
Dear list,
I was just trying to shear an image to be plotted with
matplotlib (to get some snazzy 3D effect) and realized
that it's apparently not possible. Investigating further,
I realized that the underlying agg library indeed supports
shearing, as it simply uses an affine matrix for its
transforms (very much like the rest of matplotlib),
but it does not export that feature to matplotlib.
So, I just quickly added some code to actually
access the transformation matrix in the C++ code,
so that one can use it from within python. The next
step would be to hook that up to the usual code used
in matplotlib.
A patch is attached at the end of this post.
Greetings
Martin
--- patch follows ---
commit b7d0d23d90460ee790f1e94f387070a69be661c8
Author: Martin Teichmann
Date: Wed Jul 6 14:04:44 2011 +0200
Make affine transformations work for images
The current code only supports scaling and maybe rotation of
images, but not all affine transformations, although it is
already prepared to do so.
This patch adds a method set_matrix to the C++ image handling code,
so that one can set (and thus perform) arbitrary affine transformations.
It also fixes a little bug which introduced weird side effects
if images were resized more than once.
diff --git a/src/_image.cpp b/src/_image.cpp
index 3278b6c..7d40664 100644
--- a/src/_image.cpp
+++ b/src/_image.cpp
@@ -92,7 +92,6 @@ Image::apply_rotation(const Py::Tuple& args)
agg::trans_affine M = agg::trans_affine_rotation(r * agg::pi / 180.0);
srcMatrix *= M;
-imageMatrix *= M;
return Py::Object();
}
@@ -156,7 +155,6 @@ Image::apply_scaling(const Py::Tuple& args)
//printf("applying scaling %1.2f, %1.2f\n", sx, sy);
agg::trans_affine M = agg::trans_affine_scaling(sx, sy);
srcMatrix *= M;
-imageMatrix *= M;
return Py::Object();
}
@@ -179,7 +177,6 @@ Image::apply_translation(const Py::Tuple& args)
//printf("applying translation %1.2f, %1.2f\n", tx, ty);
agg::trans_affine M = agg::trans_affine_translation(tx, ty);
srcMatrix *= M;
-imageMatrix *= M;
return Py::Object();
}
@@ -285,7 +282,6 @@ Image::reset_matrix(const Py::Tuple& args)
args.verify_length(0);
srcMatrix.reset();
-imageMatrix.reset();
return Py::Object();
}
@@ -316,6 +312,31 @@ Image::get_matrix(const Py::Tuple& args)
return ret;
}
+char Image::set_matrix__doc__[] =
+"set_matrix(m11,m21,m12,m22,m13,m23)\n"
+"\n"
+"Set the affine transformation matrix\n"
+" /m11,m12,m13\\\n"
+" /m21,m22,m23|\n"
+" \\ 0 , 0 , 1 /"
+;
+
+Py::Object
+Image::set_matrix(const Py::Tuple& args)
+{
+_VERBOSE("Image::set_matrix");
+
+args.verify_length(6);
+
+double m[6];
+for (int i = 0;i < 6;i++)
+{
+ m[i] = Py::Float(args[i]);
+}
+srcMatrix.load_from(m);
+return Py::Object();
+}
+
char Image::resize__doc__[] =
"resize(width, height, norm=1, radius=4.0)\n"
"\n"
@@ -376,8 +397,7 @@ Image::resize(const Py::Tuple& args, const Py::Dict& kwargs)
ras.clip_box(0, 0, numcols, numrows);
-//srcMatrix *= resizingMatrix;
-//imageMatrix *= resizingMatrix;
+imageMatrix = srcMatrix;
imageMatrix.invert();
interpolator_type interpolator(imageMatrix);
@@ -733,6 +753,7 @@ Image::init_type()
add_varargs_method("get_size_out", &Image::get_size_out,
Image::get_size_out__doc__);
add_varargs_method("reset_matrix", &Image::reset_matrix,
Image::reset_matrix__doc__);
add_varargs_method("get_matrix", &Image::get_matrix,
Image::get_matrix__doc__);
+add_varargs_method("set_matrix", &Image::set_matrix,
Image::set_matrix__doc__);
add_keyword_method("resize", &Image::resize, Image::resize__doc__);
add_varargs_method("set_interpolation",
&Image::set_interpolation, Image::set_interpolation__doc__);
add_varargs_method("set_resample", &Image::set_resample,
Image::set_resample__doc__);
diff --git a/src/_image.h b/src/_image.h
index 8a3be54..89a923c 100644
--- a/src/_image.h
+++ b/src/_image.h
@@ -34,6 +34,7 @@ public:
Py::Object buffer_rgba(const Py::Tuple& args);
Py::Object reset_matrix(const Py::Tuple& args);
Py::Object get_matrix(const Py::Tuple& args);
+Py::Object set_matrix(const Py::Tuple& args);
Py::Object resize(const Py::Tuple& args, const Py::Dict& kwargs);
Py::Object get_aspect(const Py::Tuple& args);
Py::Object get_size(const Py::Tuple& args);
@@ -105,6 +106,7 @@ private:
static char buffer_rgba__doc__[];
static char reset_matrix__doc__[];
static char get_matrix__doc__[];
+static char set_matrix__doc__[];
static char resize__doc__[];
static char get_aspect__doc__[];
static char get_size__doc__[];
--
All of the data generated in your IT infrastructure is seriously valuable.
Why? It contains a definitive record of appl
Re: [matplotlib-devel] Fix for issue #135
On Wednesday, July 6, 2011, Maximilian Trescher wrote: > Hi, > > i just committed a fix for issue #135, which is about timedelta rounding. > > The problem was in drange: > before the fix it used numpy.arange which resulted in rounding issues, > the numpy doc says about arange: > "When using a non-integer step, such as 0.1, the results will often not > be consistent. It is better to use linspace for these cases." > > So i tried to create a fix involving linspace, > you can see it here: > https://github.com/faucon/matplotlib/commit/06195c35f4348f37002e850d1cee992d07f5a29c > > it works (and is the only way i found to avoid the roundig issues) but > it feels somehow "hackish". > > An alternative would be the following implementation, which is quite > readable but much slower: > > def drange(dstart, dend, delta): # its very slow > """ > Return a date range as float Gregorian ordinals. *dstart* and > *dend* are :class:`datetime` instances. *delta* is a > :class:`datetime.timedelta` instance. > """ > dloop = dstart > datelist = [] > while dloop < dend: > datelist.append(dloop) > dloop += delta > return dates.date2num(datelist) > > > i will try to add a test the next days. Then i would create a > pull-request if the fix looks reasonable to you. > > thanks, > > Maximilian > > Thanks for patching. I haven't tested it yet, but the linspace solution is probably the best way to go. I would avoid drange because I believe numpy will soon be implementing such a function and I wouldn't want the possible confusion that comes from that. Ben Root -- All of the data generated in your IT infrastructure is seriously valuable. Why? It contains a definitive record of application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-d2d-c2 ___ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
Re: [matplotlib-devel] Affine Transformations on images
Looks good. Does matplotlib still pass all regression tests with this
change?
(See here for information on running the regression tests:
http://matplotlib.sourceforge.net/devel/coding_guide.html?highlight=nosetests#testing).
Cheers,
Mike
On 07/06/2011 08:33 AM, Martin Teichmann wrote:
> Dear list,
>
> I was just trying to shear an image to be plotted with
> matplotlib (to get some snazzy 3D effect) and realized
> that it's apparently not possible. Investigating further,
> I realized that the underlying agg library indeed supports
> shearing, as it simply uses an affine matrix for its
> transforms (very much like the rest of matplotlib),
> but it does not export that feature to matplotlib.
>
> So, I just quickly added some code to actually
> access the transformation matrix in the C++ code,
> so that one can use it from within python. The next
> step would be to hook that up to the usual code used
> in matplotlib.
>
> A patch is attached at the end of this post.
>
> Greetings
>
> Martin
>
> --- patch follows ---
>
> commit b7d0d23d90460ee790f1e94f387070a69be661c8
> Author: Martin Teichmann
> Date: Wed Jul 6 14:04:44 2011 +0200
>
> Make affine transformations work for images
>
> The current code only supports scaling and maybe rotation of
> images, but not all affine transformations, although it is
> already prepared to do so.
>
> This patch adds a method set_matrix to the C++ image handling code,
> so that one can set (and thus perform) arbitrary affine transformations.
>
> It also fixes a little bug which introduced weird side effects
> if images were resized more than once.
>
> diff --git a/src/_image.cpp b/src/_image.cpp
> index 3278b6c..7d40664 100644
> --- a/src/_image.cpp
> +++ b/src/_image.cpp
> @@ -92,7 +92,6 @@ Image::apply_rotation(const Py::Tuple& args)
>
> agg::trans_affine M = agg::trans_affine_rotation(r * agg::pi / 180.0);
> srcMatrix *= M;
> -imageMatrix *= M;
> return Py::Object();
> }
>
> @@ -156,7 +155,6 @@ Image::apply_scaling(const Py::Tuple& args)
> //printf("applying scaling %1.2f, %1.2f\n", sx, sy);
> agg::trans_affine M = agg::trans_affine_scaling(sx, sy);
> srcMatrix *= M;
> -imageMatrix *= M;
>
> return Py::Object();
> }
> @@ -179,7 +177,6 @@ Image::apply_translation(const Py::Tuple& args)
> //printf("applying translation %1.2f, %1.2f\n", tx, ty);
> agg::trans_affine M = agg::trans_affine_translation(tx, ty);
> srcMatrix *= M;
> -imageMatrix *= M;
>
> return Py::Object();
> }
> @@ -285,7 +282,6 @@ Image::reset_matrix(const Py::Tuple& args)
>
> args.verify_length(0);
> srcMatrix.reset();
> -imageMatrix.reset();
>
> return Py::Object();
> }
> @@ -316,6 +312,31 @@ Image::get_matrix(const Py::Tuple& args)
> return ret;
> }
>
> +char Image::set_matrix__doc__[] =
> +"set_matrix(m11,m21,m12,m22,m13,m23)\n"
> +"\n"
> +"Set the affine transformation matrix\n"
> +" /m11,m12,m13\\\n"
> +" /m21,m22,m23|\n"
> +" \\ 0 , 0 , 1 /"
> +;
> +
> +Py::Object
> +Image::set_matrix(const Py::Tuple& args)
> +{
> +_VERBOSE("Image::set_matrix");
> +
> +args.verify_length(6);
> +
> +double m[6];
> +for (int i = 0;i< 6;i++)
> +{
> + m[i] = Py::Float(args[i]);
> +}
> +srcMatrix.load_from(m);
> +return Py::Object();
> +}
> +
> char Image::resize__doc__[] =
> "resize(width, height, norm=1, radius=4.0)\n"
> "\n"
> @@ -376,8 +397,7 @@ Image::resize(const Py::Tuple& args, const Py::Dict&
> kwargs)
>
> ras.clip_box(0, 0, numcols, numrows);
>
> -//srcMatrix *= resizingMatrix;
> -//imageMatrix *= resizingMatrix;
> +imageMatrix = srcMatrix;
> imageMatrix.invert();
> interpolator_type interpolator(imageMatrix);
>
> @@ -733,6 +753,7 @@ Image::init_type()
> add_varargs_method("get_size_out",&Image::get_size_out,
> Image::get_size_out__doc__);
> add_varargs_method("reset_matrix",&Image::reset_matrix,
> Image::reset_matrix__doc__);
> add_varargs_method("get_matrix",&Image::get_matrix,
> Image::get_matrix__doc__);
> +add_varargs_method("set_matrix",&Image::set_matrix,
> Image::set_matrix__doc__);
> add_keyword_method("resize",&Image::resize, Image::resize__doc__);
> add_varargs_method("set_interpolation",
> &Image::set_interpolation, Image::set_interpolation__doc__);
> add_varargs_method("set_resample",&Image::set_resample,
> Image::set_resample__doc__);
> diff --git a/src/_image.h b/src/_image.h
> index 8a3be54..89a923c 100644
> --- a/src/_image.h
> +++ b/src/_image.h
> @@ -34,6 +34,7 @@ public:
> Py::Object buffer_rgba(const Py::Tuple& args);
> Py::Object reset_matrix(const Py::Tuple& args);
> Py::Object get_matrix(const Py::Tuple& args);
> +Py::Object set_matrix(const Py::Tuple& args);
> Py::Object resize(const Py::Tuple& args, const Py:
