diff --git a/README.md b/README.md
index 17ef4da..41d68fe 100644
--- a/README.md
+++ b/README.md
@@ -8,10 +8,10 @@ pip install .
 Note that on some systems the command is `pip3` instead of `pip`.
 
 ## Requirements
-This library requires Python 3.6 or newer, and both `numpy` and `matplotlib`.
+This library requires Python 3.6 or newer. For plotting configurations with the commandline tool `plotcamillaconf`, it also requires `numpy` and `matplotlib`. These are not required for using only with the web interface.
 
 These are the names of the packages needed:
-| Distribution | python | numpy | matplotlib |
+| Distribution | python | numpy (optional) | matplotlib (optional) |
 |--------------|--------|-------|------------|
 | Fedora | python3 | python3-numpy | python3-matplotlib |
 | Debian/Raspbian | python3 | python3-numpy | python3-matplotlib |
@@ -30,8 +30,11 @@ On macOS use either Anaconda or Homebrew. The Anaconda procedure is the same as
 
 For Homebrew, install Python with `brew install python`, after which you can install the needed packages with pip, `pip3 install numpy` etc.
 
-## Plotting a configuration
-This library provides the console command `plotcamillaconf`. Once the library is installed, the command should be available in your terminal.
+
+
+
+## Plotting a configuration from the command line
+This library provides the console command `plotcamillaconf`. Once the library is installed, togehter with the optional dependencies numpy and matplotlib, the command should be available in your terminal.
 To use it type:
 ```sh
 plotcamillaconf /path/to/some/config.yml
@@ -40,7 +43,7 @@ plotcamillaconf /path/to/some/config.yml
 This will plot the frequency response of all the defined filters, and show a block diagram of the pipeline.
 
 
-## Evaluating filters
+## Plotting filters
 To plot the frequency response of a filter, use the function `plot_filter`. This is mostly meant for internal use by the `plotcamillaconf` command.
 ```python
 plot_filter(filterconf, name=None, samplerate=44100, npoints=1000, toimage=False)
@@ -51,7 +54,7 @@ It's also possible to plot the combined frequency response of a Filter step in t
 ```python
 plot_filterstep(conf, pipelineindex, name="filterstep", npoints=1000, toimage=False)
 ```
-This command takes the full configuration as `conf` must be provided. This will plot the step with index `pipelineindex` in the pipeline where 0 is the first step. The `name` is used for the plot title. The number of points in the plot is set with `npoints`. If `toimage` is set to True, then it will instead return the plot as an svg image.
+This command takes a full configuration as `conf`. It will then plot the step with index `pipelineindex` in the pipeline where 0 is the first step. The `name` is used for the plot title. The number of points in the plot is set with `npoints`. If `toimage` is set to True, then it will instead return the plot as an svg image.
 
 ## Plotting the pipeline
 To plot a block diagram of the pipeline, use the function `plot_pipeline`. This is mostly meant for internal use by the `plotcamillaconf` command.
@@ -60,3 +63,16 @@ plot_pipeline(conf, toimage=False)
 ```
 This takes a full CamillaDSP configuration, `conf`. It will then plot the pipeline using PyPlot. If `toimage` is set to True, then it will instead return the plot as an svg image.
 
+## Evaluating filters
+To evaluate the frequency response of a filter, use the function `eval_filter`. This is mostly meant for internal use by the `plotcamillaconf` command as well as the web gui.
+```python
+eval_filter(filterconf, name=None, samplerate=44100, npoints=1000)
+```
+This will evaluate the filter and return the result as a dictionary. The filter configuration `filterconf` must be provided. The `samplerate` defaults to 44100 if not given. The filter `name` is used for labels. The number of points in the plot is set with `npoints`. The contents of the returned dictionary depends on the filter type. A Biquad returns `name`, `samplerate`, `f`, `magnitude` and `phase`. A Conv filter additionally return the impulse response in `time` and `impulse`.
+
+It's also possible to evaluate the combined frequency response of a Filter step in the pipeline.
+```python
+eval_filterstep(conf, pipelineindex, name="filterstep", npoints=1000)
+```
+This command takes a full configuration as `conf`. It will evaluate the step with index `pipelineindex` in the pipeline where 0 is the first step. As for eval_filter, the result is returned as a dictionary with the same fields as for a Biquad.
+
diff --git a/camilladsp_plot/eval_filterconfig.py b/camilladsp_plot/eval_filterconfig.py
index e58e1d2..f86b141 100644
--- a/camilladsp_plot/eval_filterconfig.py
+++ b/camilladsp_plot/eval_filterconfig.py
@@ -12,9 +12,9 @@ def logspace(minval, maxval, npoints):
 
 def eval_filter(filterconf, name=None, samplerate=44100, npoints=1000):
     fvect = logspace(1.0, samplerate*0.95/2.0, npoints)
-    result = {"name": name, "samplerate": samplerate, "f": fvect }
     if name is None:
         name = "unnamed {}".format(filterconf['type'])
+    result = {"name": name, "samplerate": samplerate, "f": fvect }
     if filterconf['type'] in ('Biquad', 'DiffEq', 'BiquadCombo'):
         if filterconf['type'] == 'DiffEq':
             currfilt = DiffEq(filterconf['parameters'], samplerate)
diff --git a/setup.py b/setup.py
index d1355d9..f269cb6 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="camilladsp_plot",
-    version="0.3.0",
+    version="0.4.0",
     author="Henrik Enquist",
     author_email="henrik.enquist@gmail.com",
     description="A library for plotting configs and filters for CamillaDSP",
diff --git a/tests/test_filtereval.py b/tests/test_filtereval.py
index 59eaf4a..51feb94 100644
--- a/tests/test_filtereval.py
+++ b/tests/test_filtereval.py
@@ -1,12 +1,9 @@
 import pytest
-from camilladsp_plot import filter_eval
+from camilladsp_plot import filters
 import numpy as np
 
-def test_24bit():
-    filt = filter_eval.Conv(None, 44100)
-    vect = np.asarray([1, 0, 0, 255, 0, 0, 0, 0, 1], dtype=np.uint8)
-    expected = np.array([2**16, -2**16, 1])
-    new_vect = filt._repack_24bit(vect)
-    assert abs(np.sum(new_vect-expected))<1e-15 
+def test_conv():
+    filt = filters.Conv(None, 44100)
+    # TODO make proper tests