diff --git a/.gitignore b/.gitignore
index 6b6df68954ed5b19ff346032bcb27c1b79c3a41d..2f5618bf415a8f7bb407be742983ced6b553eb99 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,3 +23,4 @@ src/tests/test_io/gzip_data_files/
*.gz
\.dmypy\.json
+TestCSV*
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index a252257727a34f6f84c5b90d1befdabd73394249..7f42e9f9a015df9223a43e428cd6c9b8b44e9b3e 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -53,9 +53,23 @@ test:
- src/tests/htmlcov
expire_in: 30 days
+.codequality:
+ image: lasnq/openqlab
+ stage: test
+ allow_failure: true
+ before_script:
+ - pip install pycodestyle
+ script:
+ - pycodestyle src/openqlab
+ cache:
+ key: openqlab:pep8
+ paths:
+ - .pip/
+
pylint:
image: lasnq/openqlab
stage: test
+ allow_failure: true
before_script:
script:
- make pylint
@@ -123,3 +137,37 @@ update rrz tag:
- git push $PUBLIC_REPO $CI_COMMIT_TAG
only:
- tag
+
+update nqlab version:
+ image: alpine
+ stage: deploy
+ before_script:
+ - apk add git gawk
+ - sh bin/enable_ssh
+ - git config --global user.email "lasnq-ci@physnet"
+ - git config --global user.name "LasNQ CI"
+ script:
+ - export VERSION=`git tag | tail -1 | sed 's/v//g'`
+
+ # clone git repo
+ - git clone git@git.physnet.uni-hamburg.de:las-nq/nqlab.git
+ - cd nqlab
+ - git checkout master
+
+ # change openqlab version in requirements
+ - sed -i '/openqlab.*/d' requirements.txt
+ - echo "openqlab>=$VERSION" >> requirements.txt
+
+ # push changes
+ - git add requirements.txt
+ - git commit -m "openqlab version $VERSION"
+ - git push
+
+ # make nqlab version
+ - export old_version=`git tag | tail -1`
+ - export new_version=`gawk -f ../bin/increment_version.sh $old_version`
+ - echo $new_version
+ - git tag $new_version -m 'Autoupdated version from openqlab'
+ - git push --tags
+ only:
+ - tag
diff --git a/.pylintrc b/.pylintrc
index 1956d202bbdfce181fc1e2500058b1375c1fad74..23f1840a33df21bf5c73c721ea8640611182f302 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -234,7 +234,7 @@ const-naming-style=UPPER_CASE
# Minimum line length for functions/classes that require docstrings, shorter
# ones are exempt.
-docstring-min-length=-1
+docstring-min-length=10
# Naming style matching correct function names.
function-naming-style=snake_case
diff --git a/Dockerfile b/Dockerfile
index 34bb8299cd88ac992cd8544f3be7cb66d173d90d..ab0b5cd7bad2fb5b1618aef80f06210d31240364 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -8,5 +8,5 @@ WORKDIR /code
RUN apt-get update -y && apt-get install -y $BUILD_PACKAGES
RUN pip install --upgrade pip
-ADD requirements.txt /code/
-RUN pip install --upgrade -r requirements.txt
+ADD requirements.txt requirements-devel.txt /code/
+RUN pip install --upgrade -r requirements.txt -r requirements-devel.txt
diff --git a/Makefile b/Makefile
index b5b569e89f46bc52badacd39793c4ed5692b14e0..713d88c8c619f0fa6ab498c52b94f483b64071f0 100644
--- a/Makefile
+++ b/Makefile
@@ -6,6 +6,12 @@ requirements:
requirements-user:
@pip3 install --user -r requirements.txt
+requirements-devel:
+ @pip3 install -r requirements-devel.txt
+
+requirements-devel-user:
+ @pip3 install --user -r requirements-devel.txt
+
doc:
@export PYTHONPATH=`pwd`/src:$(PYTHONPATH); cd doc; make html
@@ -22,7 +28,7 @@ mypy:
@export PYTHONPATH=`pwd`/src:$(PYTHONPATH); mypy src
pylint:
- @export PYTHONPATH=`pwd`/src:$(PYTHONPATH); pylint --exit-zero --disable=C src/openqlab
+ @export PYTHONPATH=`pwd`/src:$(PYTHONPATH); pylint --disable=C src/openqlab
all-tests: mypy test pylint
diff --git a/bin/codeclimate-pep8 b/bin/codeclimate-pep8
new file mode 100755
index 0000000000000000000000000000000000000000..9bdb8d89fe5091f9c3bc19e1a6dbfac12a993666
--- /dev/null
+++ b/bin/codeclimate-pep8
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+
+import json
+import os.path
+from pycodestyle import BaseReport, StyleGuide
+import sys
+
+class CodeClimateReport(BaseReport):
+ """Print results of the checks in Code Climate format."""
+
+ def error(self, line_number, offset, text, check):
+ """Print an error in Code Climate format."""
+
+ code = super(CodeClimateReport, self).error(line_number, offset,
+ text, check)
+ if code:
+ issue = {
+ 'type': 'issue',
+ 'check_name': code,
+ 'categories': ['Style'],
+ 'description': text[5:].capitalize(),
+ 'remediation_points': 50000,
+ 'location': {
+ 'path': os.path.normpath(self.filename),
+ 'positions': {
+ 'begin': {
+ 'line': line_number,
+ 'column': offset + 1
+ },
+ 'end': {
+ 'line': line_number,
+ 'column': offset + 1
+ }
+ }
+ },
+ 'content': {
+ 'body': check.__doc__.strip()
+ }
+ }
+ print(json.dumps(issue)+'\0')
+
+ return code
+
+
+def run(paths):
+ """Parse options and run checks on Python source."""
+ import signal
+
+ # Handle "Broken pipe" gracefully
+ try:
+ signal.signal(signal.SIGPIPE, lambda signum, frame: sys.exit(1))
+ except AttributeError:
+ pass # not supported on Windows
+
+ style_guide = StyleGuide(
+ paths=paths,
+ reporter=CodeClimateReport)
+ options = style_guide.options
+
+ report = style_guide.check_files()
+
+ if report.total_errors:
+ if options.count:
+ sys.stderr.write(str(report.total_errors) + '\n')
+ sys.exit(1)
+
+include_paths = ["."]
+
+if os.path.exists("/config.json"):
+ contents = open("/config.json").read()
+ config = json.loads(contents)
+
+ i_paths = config.get("include_paths")
+ if type(i_paths) == list:
+ python_paths = []
+ for i in i_paths:
+ ext = os.path.splitext(i)[1]
+ if os.path.isdir(i) or ext == ".py":
+ python_paths.append(i)
+ include_paths = python_paths
+
+
+if len(include_paths) > 0:
+ args = " ".join(include_paths)
+ sys.stderr.write(args)
+ run(include_paths)
+ #os.system("/usr/src/app/pep8.py --codeclimate {0}".format(args))
diff --git a/bin/enable_ssh b/bin/enable_ssh
index 1740c8f98e5d28830877fff73ceb87bd6bd36404..56f4ecdb1c9b4b51c6cf0edfb02974475d85852c 100755
--- a/bin/enable_ssh
+++ b/bin/enable_ssh
@@ -6,4 +6,6 @@ mkdir -p ~/.ssh
chmod 700 ~/.ssh
echo "$SSH_PRIVATE_KEY" | tr -d '\r' > ~/.ssh/id_rsa
echo "gitlab.rrz.uni-hamburg.de,134.100.32.38 ecdsa-sha2-nistp256 AAAAE2VjZHNhLXNoYTItbmlzdHAyNTYAAAAIbmlzdHAyNTYAAABBBKSK3dLzO0LTDNqjV7xH9713WXTCyhDUoxqNLrD/gUdK3m+dDfIXzZ46FeQB/xjmzz6kRk/n7VrujZM5NkISk8Y=" > ~/.ssh/known_hosts
+echo "git.physnet.uni-hamburg.de,134.100.109.28 ecdsa-sha2-nistp256 AAAAE2VjZHNhLXNoYTItbmlzdHAyNTYAAAAIbmlzdHAyNTYAAABBBB1c6yDYUhd5zYspAYSVYmdVah0HhxoeviJvNxpe98Z9ywFYMLDoSoRAYR7tqVQV23Unmf7lxiLOMOl5DEZ2Xgs=" >> ~/.ssh/known_hosts
+
chmod 600 ~/.ssh/id_rsa
diff --git a/bin/increment_version.sh b/bin/increment_version.sh
new file mode 100755
index 0000000000000000000000000000000000000000..35e05868a103636f20fae039eb968e3646baa64d
--- /dev/null
+++ b/bin/increment_version.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/gawk -f
+
+BEGIN{
+ var = ARGV[1]
+ printf("%s\n", inc(var))
+}
+
+
+function inc(s, a, len1, len2, len3, head, tail)
+{
+ echo s
+ split(s, a, ".")
+
+ len1 = length(a)
+ if(len1==0)
+ return -1
+ else if(len1==1)
+ return s+1
+
+ len2 = length(a[len1])
+ len3 = length(a[len1]+1)
+
+ head = join(a, 1, len1-1)
+ tail = sprintf("%0*d", len2, (a[len1]+1)%(10^len2))
+
+ if(len2==len3)
+ return head "." tail
+ else
+ return inc(head) "." tail
+}
+
+function join(a, x, y, s)
+{
+ for(i=x; i<y; i++)
+ s = s a[i] "."
+ return s a[y]
+}
diff --git a/doc/analysis.rst b/doc/analysis.rst
index 69e3443d0d56f3d664cf88c3619e4632cfa8cf08..6892ea02639829f0f684c09020768dc3014b3c06 100644
--- a/doc/analysis.rst
+++ b/doc/analysis.rst
@@ -16,6 +16,14 @@ Fit beam data obtained from a beam analyzer to the gaussian beam model using non
.. automodule:: openqlab.analysis.gaussian_beam
:members:
+Cavity
+------
+
+Cavity calculations
+
+.. automodule:: openqlab.analysis.cavity
+ :members:
+
Phase
-----
diff --git a/doc/install.md b/doc/install.md
index f27828432fa6efa96c598fb6a9d662dc7209ec8e..33e053e89d59a0404318d9c57f8a3172c4f75e41 100644
--- a/doc/install.md
+++ b/doc/install.md
@@ -46,3 +46,14 @@ If you want to install it for all users on the PC, run:
```bash
sudo python3 setup.py install
```
+
+## Development
+
+If you want to take part in the development install the additional requirements from `requirements-devel.txt`:
+
+```python
+make requirements-devel
+
+# If you want to install at user level:
+make requirements-devel-user
+```
diff --git a/requirements-devel.txt b/requirements-devel.txt
new file mode 100644
index 0000000000000000000000000000000000000000..326d2b46090a34234680d8417df6186a51c6742c
--- /dev/null
+++ b/requirements-devel.txt
@@ -0,0 +1,11 @@
+Sphinx
+recommonmark
+sphinx-markdown-tables
+wheel
+pytest
+pytest-cov>=2.5.1
+sphinx_rtd_theme
+setuptools
+twine
+mypy
+pylint
diff --git a/requirements.txt b/requirements.txt
index 0c7e8332f5eaa7a86600b632e4fafb4cba8f272c..492052c138c21d297f7c1475dfec951283528394 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,16 +1,9 @@
pyserial
-Sphinx
numpy
scipy>=1.1.0
pandas>=0.23.3
matplotlib
-sphinx_rtd_theme
tabulate
-recommonmark
-sphinx-markdown-tables
-wheel
-pytest<4.0
-pytest-cov>=2.5.1
pytimeparse
pytz
pathlib
@@ -24,7 +17,3 @@ requests
six>=1.12.0
python-dateutil>=2.7.5
eml_parser>=1.11
-setuptools
-twine
-mypy
-pylint
diff --git a/setup.cfg b/setup.cfg
index b8ca5609d14922c31be711b8d8479256337d51ab..687c5173905b909ddf346a501be9ea7cefb6b405 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -87,3 +87,8 @@ exclude =
# This will be used when updating. Do not change!
version = 3.0.3
package = openqlab
+
+[pycodestyle]
+max-line-length = 100
+statistics = True
+exclude = OldImporters
diff --git a/src/openqlab/analysis/cavity.py b/src/openqlab/analysis/cavity.py
new file mode 100644
index 0000000000000000000000000000000000000000..a66887618e6b8ded9b2db574f0f36965e3233abb
--- /dev/null
+++ b/src/openqlab/analysis/cavity.py
@@ -0,0 +1,192 @@
+"""Automatically calculate cavity parameters with data taken from an oscilloscope."""
+
+from typing import Optional, Tuple, List
+import logging as log
+from pandas import Series
+from scipy.signal import find_peaks, peak_widths
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def modematching(data: Series, # pylint: disable=invalid-name, too-many-arguments
+ plot: bool = False,
+ U_max: Optional[float] = None,
+ offset: Optional[float] = None,
+ rel_prominence: float = .02,
+ without_main_peaks: bool = False,
+ ) -> float:
+ """Calculate the mode matching.
+
+ It assumes a cavity scan bounded by two peaks of the main mode.
+ The method looks for the smaller peaks where the detection threshold
+ can be adjusted with :obj:`rel_prominence`.
+
+ Offset
+ The default method to find out the offset is by calculating the mean value.
+ If you have measured it more precisely, use the parameter :obj:`offset`.
+
+ Improve precision
+ To get a better resolution for small peaks there is an option to take data
+ with a clipped main mode. Use the parameter :obj:`U_max` to manually set
+ the measured maximum value.
+
+ Parameters
+ ----------
+
+ data : Series
+ Measured data (just one column).
+ plot : bool
+ Make a plot to see, if the correct peaks where detected.
+ U_max : Optional[float]
+ U_max is the parameter to set the peak voltage of the clipped main peak.
+ rel_prominence : float
+ rel_prominence is the parameter to adjust the threshold for the detection
+ of small peaks.
+ without_main_peaks : bool
+ Takes all peaks as minor peaks, if the main peaks are clipped.
+ This requires the parameter :obj:`U_max` to be set.
+
+ Returns
+ -------
+
+ float
+ Calculated mode matching value.
+ """
+ if len(data.shape) != 1:
+ raise ValueError('The DataFrame should only contain one single column.')
+ if without_main_peaks and not U_max:
+ raise ValueError('without_main_peaks can not be used without U_max.')
+
+ data = data.dropna()
+
+ # Adjust offset
+ if offset is None:
+ offset = np.median(data)
+ data -= offset
+
+ # Make peaks positive if necessary
+ _adjust_peak_sign(data)
+
+ # Find highest value
+ if U_max is None:
+ U_max = np.max(data)
+ else:
+ U_max = abs(U_max - offset)
+
+ peaks, main_mode = _find_peaks(data, rel_prominence, U_max)
+
+ if not without_main_peaks:
+ if len(main_mode) != 2:
+ raise ValueError('The main mode must occur exactly two times for the algorithm to work,'
+ f' but it found {len(main_mode)} main modes.')
+
+ # Main peak voltage
+ log.info(f'U_max: {U_max}')
+ # Sum of all different modes (excluding 2nd main mode)
+ if without_main_peaks:
+ U_sum = sum(data.iloc[peaks], U_max) # pylint: disable=invalid-name
+ else:
+ U_sum = sum(data.iloc[peaks[main_mode[0]+1:main_mode[1]]], U_max) # pylint: disable=invalid-name
+ # This version with U_max makes it possible to manually
+ # include a clipped value for the main peak
+ log.info(f'U_sum: {U_sum}')
+
+ # Mode matching
+ mode_matching = U_max / U_sum
+
+ # Plotting
+ if plot:
+ _main_plot(data, peaks=peaks, main_peaks=peaks[main_mode])
+
+ if not without_main_peaks:
+ index_first, index_last = peaks[main_mode]
+ plt.axvline(x=data.index[index_first], color='gray')
+ plt.axvline(x=data.index[index_last], color='gray')
+ plt.axvspan(data.index[0], data.index[index_first], color='gray', alpha=0.5)
+ plt.axvspan(data.index[index_last], data.index[-1], color='gray', alpha=0.5)
+
+ print(f'Mode matching: {round(mode_matching*100, 2)}%')
+
+ return mode_matching
+
+
+def _main_plot(data: Series,
+ peaks: Optional[np.ndarray] = None,
+ main_peaks: Optional[np.ndarray] = None):
+ axes = data.plot()
+ axes.set_xlim(data.index[0], data.index[-1])
+ if peaks is not None:
+ data.iloc[peaks].plot(style='.')
+ if main_peaks is not None:
+ data.iloc[main_peaks].plot(style='o')
+
+
+def _find_peaks(data: Series,
+ rel_prominence: float,
+ max_value: Optional[float] = None
+ ) -> Tuple[np.ndarray, np.ndarray]:
+ if max_value is None:
+ max_value = np.max(data)
+
+ # Find peaks
+ peaks, peak_dict = find_peaks(data, prominence=max_value*rel_prominence)
+ # Find occurences of the main mode.
+ main_mode = np.where(peak_dict['prominences'] >= np.max(data)*.9)[0]
+
+ return peaks, main_mode
+
+
+def finesse(data: Series, plot: bool = False) -> List[float]:
+ """Finesse calculation using a cavity scan.
+
+ Parameters
+ ----------
+
+ data : Series
+ data is the amplitude column with two main modes.
+ plot : bool
+ plot is giving out a plot to make shure the algorithm has found the correct points.
+
+ Returns
+ -------
+
+ list(float)
+ Calculated finesse for both peaks.
+ """
+ if len(data.shape) != 1:
+ raise ValueError('The DataFrame should only contain one single column.')
+ data = data.dropna()
+ _adjust_peak_sign(data)
+
+ peaks, main_mode = _find_peaks(data, .9)
+ result = _calculate_finesse(data, peaks, main_mode, plot)
+ print(f'Finesse first peak: {round(result[0], 2)}, second peak: {round(result[1], 2)}')
+ return result
+
+
+def _calculate_finesse(data: Series,
+ peaks: np.ndarray,
+ main_mode: np.ndarray,
+ plot: bool = False
+ ) -> List[float]:
+ peak_data = peak_widths(data, peaks)
+ peak_fwhm = peak_data[0]
+ peaks_left = peak_data[2]
+ peaks_right = peak_data[3]
+ main_width = peak_fwhm[main_mode]
+ fsr = peaks[main_mode[1]] - peaks[main_mode[0]]
+
+ if plot:
+ _main_plot(data, main_peaks=peaks[main_mode])
+ for x in np.concatenate([peaks_left[main_mode], peaks_right[main_mode]]):
+ plt.axvline(x=data.index[int(x)], ls=':', color='green')
+
+ return [fsr / main_width[0], fsr / main_width[1]]
+
+
+def _adjust_peak_sign(data: Series):
+ minimum = np.min(data)
+ maximum = np.max(data)
+
+ if abs(minimum) > abs(maximum):
+ data *= -1
diff --git a/src/openqlab/analysis/phase.py b/src/openqlab/analysis/phase.py
index a1a91ca5086c67555cb3455e5a4e2b1a6fbae27e..f141eede977385249de4f953fdf36ef78fe65164 100644
--- a/src/openqlab/analysis/phase.py
+++ b/src/openqlab/analysis/phase.py
@@ -26,3 +26,10 @@ def accumulated_phase(data: Series, limit: float = 340) -> None:
for i in range(1, len(data)):
if abs(data.iloc[i-1] - data.iloc[i]) > limit:
data.iloc[i:] += 360 * sign(data.iloc[i-1] - data.iloc[i])
+
+
+def clamp_phase(phase):
+ """
+ Returns phase with all values mapped to between +/- 180 degrees.
+ """
+ return (phase + 180.0) % 360.0 - 180.0
diff --git a/src/openqlab/analysis/servo_design.py b/src/openqlab/analysis/servo_design.py
index d72b7e1b9b9cfa60bd7c8e75bf58216004f8e97c..a48661968eb96d67ca020abebb1d0b79878939fa 100644
--- a/src/openqlab/analysis/servo_design.py
+++ b/src/openqlab/analysis/servo_design.py
@@ -4,13 +4,12 @@ import numpy as np
import pandas as pd
from scipy import signal
from tabulate import tabulate
+from warnings import warn
from ..conversion import db
from .. import plots
from ..io import DataContainer
from ..conversion.utils import human_readable
-# TODO update documentation in servodesign.rst`
-
def _handle_keysight_files(df):
value = df.copy()
@@ -203,14 +202,14 @@ class Differentiator(Filter):
cF: :obj:`float`
The corner frequency.
sF: :obj:`float`, optional
- Frequency were the ~f slope stops, defaults to 1000 * `cF`.
+ Frequency were the ~f slope stops, defaults to 10 * `cF`.
"""
def calculate(self):
z = -self.cF
if self.sF is None:
- self._second_parameter = self.cF * 1000
+ self._second_parameter = self.cF * 10
p = -self.sF
k = self.sF / self.cF
return z, p, k
@@ -310,9 +309,15 @@ class ServoDesign:
Current purpose is mainly filter handling and should be used as follows:
- The object itself holds a set of (currently maximum) 5 filters. Filter types can be defined as new subclasses to the Filter class.
+ The object itself holds a set of (currently maximum) 5 filters.
+ Filter types can be defined as new subclasses to the Filter class.
- The FILTER UTILITY section contains all methods handling filter operations, including clear and read. Filters should be added using either 'add' or 'addIndexed'. Normal 'add' will simply append the filter to the list and fail when the list is fullself. 'addIndexed' however will overwrite the the filter at the current position and fail in case an index out of range was specified.
+ The FILTER UTILITY section contains all methods handling filter operations,
+ including clear and read.
+ Filters should be added using either 'add' or 'addIndexed'.
+ Normal 'add' will simply append the filter to the list and fail when the list is fullself.
+ 'addIndexed' however will overwrite the the filter at the current position
+ and fail in case an index out of range was specified.
"""
MAX_FILTERS = 5
@@ -569,7 +574,7 @@ class ServoDesign:
df['Servo+TF P'] = phase + p
return df
- def plot(self, freq=None, plot=True, correct_latency=False):
+ def plot(self, freq=None, plot=True, correct_latency=False, **kwargs):
"""
Plot the servo response over the frequencies given in `freq`.
@@ -588,6 +593,8 @@ class ServoDesign:
correct_latency: :obj:`bool` or :obj:`float`
If the data has been taken piping through ADwin an extra phase has been added.
This can be corrected by giving ADwins sample rate (Default 200 kHz).
+ **kwargs
+ Parameters are passed to the :obj:`pandas.DataFrame.plot` method
Returns
-------
@@ -599,8 +606,8 @@ class ServoDesign:
if self.plant is None:
df = self._apply(freq)
- dfA = df['Servo A']
- dfP = df['Servo P']
+ df_amplitude = df['Servo A']
+ df_phase = df['Servo P']
else:
df = self._apply(self.plant.index,
self.plant.iloc[:, 0], self.plant.iloc[:, 1])
@@ -610,15 +617,15 @@ class ServoDesign:
correct_latency = self.SAMPLING_RATE
print(type(correct_latency))
df['Servo+TF P'] = df['Servo+TF P'] + 360 * df.index / correct_latency
- dfA = df[['Servo A', 'Servo+TF A']]
- dfP = df[['Servo P', 'Servo+TF P']]
+ df_amplitude = df[['Servo A', 'Servo+TF A']]
+ df_phase = df[['Servo P', 'Servo+TF P']]
if not plot:
return df
# Plotting
- plt = plots.amplitude_phase(dfA, dfP)
+ plt = plots.amplitude_phase(df_amplitude, df_phase, **kwargs)
# add 0dB and -135deg markers
plt.axes[0].hlines(0, *plt.axes[0].get_xlim(),
colors=(0.6, 0.6, 0.6), linestyles='dashed')
@@ -626,33 +633,38 @@ class ServoDesign:
colors=(0.6, 0.6, 0.6), linestyles='dashed')
return plt
- def discrete_form(self, fs=SAMPLING_RATE, filename=None):
+ def discrete_form(self, sampling_frequency=SAMPLING_RATE, # pylint: disable=invalid-name
+ filename=None,
+ fs=None):
"""
- Convert the servo and its filters to a digital, discrete-time representation in terms of second-order sections at a sampling frequency of `fs`.
+ Convert the servo and its filters to a digital,
+ discrete-time representation in terms of second-order sections
+ at a sampling frequency of `sampling_frequency`.
If `filename` is given, write the filter coefficients in
machine-readable format into this file.
- .. todo:: this should probably return a usable form of this filter for
- plotting the discrete-time response (using signal.dbode)
-
Returns
-------
:obj:`dict`
a dictionary containing sample rate, gain and SOS coefficients for
each filter
"""
+ if fs is not None:
+ warn('fs is deprecated. use sampling_frequency.', DeprecationWarning)
+ sampling_frequency = fs
+
coeffs = []
for f in self._filters:
if f is not None:
- coeffs.append(f.discrete_SOS(fs).flatten())
+ coeffs.append(f.discrete_SOS(sampling_frequency).flatten())
filters = {}
for f, d in zip(self._filters, coeffs):
if f is not None:
filters[f.description] = d
data = {
- 'fs': fs,
+ 'fs': sampling_frequency,
'gain': self.gain,
'filters': filters
}
diff --git a/src/openqlab/io/data_container.py b/src/openqlab/io/data_container.py
index cefab8fd22f03168d10b6e7a96c77b7e69ed04a5..297823c101051b1778ba8368fb42a7b1dbdf04d9 100644
--- a/src/openqlab/io/data_container.py
+++ b/src/openqlab/io/data_container.py
@@ -118,7 +118,7 @@ class DataContainer(pd.DataFrame, metaclass=MetaDataContainer):
self._header = {}
self._type = type
-
+
if header:
if isinstance(header, dict):
self.header = header
@@ -156,6 +156,9 @@ class DataContainer(pd.DataFrame, metaclass=MetaDataContainer):
return string
+ def __repr__(self):
+ return super().__repr__()
+
def __getitem__(self, key):
header = self.header
output = super().__getitem__(key)
@@ -214,9 +217,13 @@ class DataContainer(pd.DataFrame, metaclass=MetaDataContainer):
@staticmethod
def from_csv(file, *args, index_col=0, **kwargs) -> 'DataContainer':
- with open(file, 'r') as f:
- header_dict = DataContainer._json_to_header(f)
- output = DataContainer(pd.read_csv(f, *args, index_col=index_col, **kwargs), header=header_dict)
+ try:
+ with open(file, 'r') as f:
+ header_dict = DataContainer._json_to_header(f)
+ output = DataContainer(pd.read_csv(f, *args, index_col=index_col, **kwargs), header=header_dict)
+ except TypeError:
+ header_dict = DataContainer._json_to_header(file)
+ output = DataContainer(pd.read_csv(file, *args, index_col=index_col, **kwargs), header=header_dict)
return output
def to_json(self, path_or_buf=None, *args, orient='split', **kwargs):
diff --git a/src/openqlab/io/importers/data_container_csv.py b/src/openqlab/io/importers/data_container_csv.py
index 4b4f6f5d3af18272f5352110aa0603d1b4970c75..fc6546c653745f89fca5724756aef2a6a11b8e2f 100644
--- a/src/openqlab/io/importers/data_container_csv.py
+++ b/src/openqlab/io/importers/data_container_csv.py
@@ -6,10 +6,10 @@ from openqlab.io.importers import utils
class DataContainerCSV(StreamImporter):
NAME = 'DataContainerCSV'
AUTOIMPORTER = True
- STARTING_LINES = ['^' + DataContainer.json_prefix]
+ STARTING_LINES = [DataContainer.json_prefix]
def read(self):
- self._check_header()
+ self._stream.seek(0)
output = DataContainer.from_csv(self._stream, parse_dates=True)
if output.empty:
raise utils.ImportFailed(
diff --git a/src/openqlab/plots/frequency_domain.py b/src/openqlab/plots/frequency_domain.py
index 1069d97dee429c8a0d355c38b9c75f1780d207fa..2941edaa8210a69b8b9c15d0af6fcb9500889c07 100644
--- a/src/openqlab/plots/frequency_domain.py
+++ b/src/openqlab/plots/frequency_domain.py
@@ -1,6 +1,9 @@
+"""Plotting scripts in frequency domain."""
from typing import Optional
+from warnings import warn
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
+from openqlab import analysis
from ..conversion import db
from ..io import DataContainer
@@ -9,11 +12,18 @@ def _clamp_phase(phase):
"""
Returns phase with all values mapped to between +/- 180 degrees.
"""
- return (phase + 180.0) % 360.0 - 180.0
-
-
-def amplitude_phase(amplitude, phase, logf=True, bodeplot=True, clamp_phase=True,
- dbunits=True, title='Transfer Function'):
+ warn('DEPRECATED: _clamp_phase is deprecated, \
+ use openqlab.analysis.phase.clamp_phase.', DeprecationWarning)
+ return analysis.phase.clamp_phase(phase)
+
+
+def amplitude_phase(amplitude, phase, # pylint: disable=too-many-arguments
+ logf=True,
+ bodeplot=True,
+ clamp_phase=True,
+ dbunits=True,
+ title='Transfer Function',
+ **kwargs):
"""
Create an amplitude-phase plot to display transfer function measurements.
@@ -35,6 +45,8 @@ def amplitude_phase(amplitude, phase, logf=True, bodeplot=True, clamp_phase=True
Use dB units for display. Otherwise convert to linear units.
title: :obj:`str`, optional
The figure title.
+ **kwargs:
+ Parameters are passed to the :obj:`pandas.DataFrame.plot` method
Returns
-------
@@ -51,17 +63,17 @@ def amplitude_phase(amplitude, phase, logf=True, bodeplot=True, clamp_phase=True
if dbunits:
ampl = amplitude
else:
- ampl = db.to_lin(amplitude / 2.0) # the /2.0 accounts for power/amplitude
+ ampl: DataContainer = db.to_lin(amplitude / 2.0) # the /2.0 accounts for power/amplitude
- ampl.plot(ax=ax, title=title, legend=False, logx=logf, logy=(not dbunits))
+ ampl.plot(ax=ax, title=title, legend=False, logx=logf, logy=(not dbunits), **kwargs)
if not bodeplot:
- for ii in range(len(ax.lines) + 1):
+ for _ in range(len(ax.lines) + 1):
# HACKHACK: advance color cycler so we get continuous colours across
# the two plots. May break in future versions of matplotlib.
next(ax2._get_lines.prop_cycler)
if clamp_phase:
- phase = _clamp_phase(phase)
+ phase = analysis.phase.clamp_phase(phase)
phase.plot(ax=ax2, legend=False, logx=logf)
ax.set_ylabel('Amplitude (dB)')
ax2.set_ylabel(u'Phase (ยบ)')
@@ -96,7 +108,6 @@ def power_spectrum(data, normalize_to=None, logf=True, title='Power Spectrum'):
A handle to the created matplotlib figure.
"""
- # TODO: implement linear/power spectral density (/Hz) plots
fig, ax = plt.subplots()
if normalize_to is not None:
@@ -113,13 +124,13 @@ def power_spectrum(data, normalize_to=None, logf=True, title='Power Spectrum'):
return fig
-def relative_input_noise(data: DataContainer,
- volt_dc: float,
- logf: bool = True,
- logy: bool = True,
- title: Optional[str] = None,
- ylabel: str = 'RIN ($1/\sqrt{\mathrm{Hz}}$)',
- **kwargs) -> Axes:
+def relative_input_noise(data: DataContainer, # pylint: disable=too-many-arguments
+ volt_dc: float,
+ logf: bool = True,
+ logy: bool = True,
+ title: Optional[str] = None,
+ ylabel: str = r'RIN ($1/\sqrt{\mathrm{Hz}}$)',
+ **kwargs) -> Axes:
"""Create a plot for relative input noise.
The values of data are devided by the constant voltage :obj:`volt_dc`
diff --git a/src/openqlab/plots/time_domain.py b/src/openqlab/plots/time_domain.py
index e2f6af4539ea51868cdc36d015c042c850b4a2ee..92cc615de97b1cf6d316e1d9e69b7e06f599e48f 100644
--- a/src/openqlab/plots/time_domain.py
+++ b/src/openqlab/plots/time_domain.py
@@ -93,7 +93,7 @@ def scope(traces, title='Oscilloscope View'):
line, = host.plot(traces.index, traces.iloc[:, 0], label=traces.columns[0])
host.set_ylabel(traces.columns[0])
- host.set_xlabel(traces.index.NAME)
+ host.set_xlabel(traces.index.name)
host.axis['left'].label.set_color(line.get_color())
for ii in range(1, Ntraces):
diff --git a/src/tests/modematching/opo_scan.csv b/src/tests/modematching/opo_scan.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3277c43d11233e12397b73286ab9b259cde6721e
--- /dev/null
+++ b/src/tests/modematching/opo_scan.csv
@@ -0,0 +1,2002 @@
+x-axis,1,2,3,4
+second,Volt,Volt,Volt,Volt
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diff --git a/src/tests/modematching/opo_scan_zoom.csv b/src/tests/modematching/opo_scan_zoom.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b2e500e1c242c9ffba3b844ace7d744060a0f15b
--- /dev/null
+++ b/src/tests/modematching/opo_scan_zoom.csv
@@ -0,0 +1,2002 @@
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++16.404000E-03,+221.206028E-03,-468.178421E-03,+3.327889413E+00,+8.806146E-03
++16.412000E-03,+221.206028E-03,-476.218622E-03,+3.327889413E+00,+8.426587E-03
++16.420000E-03,+301.608037E-03,-484.258823E-03,+3.200457977E+00,+8.407732E-03
++16.428000E-03,+221.206028E-03,-484.258823E-03,+3.327889413E+00,+8.137549E-03
++16.436000E-03,+235.034301E-03,-484.258823E-03,+3.161455068E+00,+8.397095E-03
++16.444000E-03,+296.864159E-03,-476.693010E-03,+3.138744080E+00,+8.713568E-03
++16.452000E-03,,,,
diff --git a/src/tests/test_cavity.py b/src/tests/test_cavity.py
new file mode 100644
index 0000000000000000000000000000000000000000..a920c4258eb36337a7dac000b23e06afc5e8e67f
--- /dev/null
+++ b/src/tests/test_cavity.py
@@ -0,0 +1,66 @@
+import unittest
+import matplotlib.pyplot as plt
+from openqlab import analysis, io
+from openqlab.analysis.cavity import modematching, finesse
+
+
+class TestModematching(unittest.TestCase):
+ def setUp(self):
+ self.data = io.read('modematching/opo_scan.csv', importer='KeysightCSV')
+ plt.figure()
+
+ def test_default_inverted(self):
+ result = modematching(self.data['opo_scan_2'], plot=True)
+ # plt.show()
+ self.assertAlmostEqual(result, .955, places=2)
+
+ def test_default_not_inverted(self):
+ self.data *= -1
+ result = modematching(self.data['opo_scan_2'], plot=True)
+ self.assertAlmostEqual(result, .955, places=2)
+
+ def test_to_many_columns(self):
+ with self.assertRaisesRegex(ValueError, 'only contain one single'):
+ modematching(self.data)
+
+ # @unittest.skip
+ def test_with_clipped_data(self):
+ self.data.clip(lower=-1, upper=1, inplace=True)
+ result = modematching(self.data['opo_scan_2'], U_max=6.668)
+ self.assertAlmostEqual(result, .955, places=2)
+
+ def test_fail_with_only_one_peak(self):
+ with self.assertRaises(ValueError) as err:
+ modematching(self.data['opo_scan_2'].loc[:.01])
+ self.assertEqual(err.exception.args[0],
+ 'The main mode must occur exactly two times for the algorithm to work, '\
+ 'but it found 1 main modes.')
+
+ def test_with_much_offset(self):
+ self.data *= -1
+ self.data -= 2
+ result = modematching(self.data['opo_scan_2'], U_max=6.668-2, offset=-1.98, plot=True)
+ self.assertAlmostEqual(result, .955, places=2)
+
+ def test_impossible_parameter_combinations(self):
+ with self.assertRaises(ValueError):
+ modematching(self.data['opo_scan_2'], without_main_peaks=True)
+
+ def test_cropped_main_peak(self):
+ data = io.read('modematching/opo_scan_zoom.csv', importer='KeysightCSV')
+ mm = modematching(data['opo_scan_zoom_2'].loc[.0015:.0155], offset=-.015,
+ plot=True, U_max=9.27, rel_prominence=.003, without_main_peaks=True)
+ self.assertAlmostEqual(mm, .92, places=2)
+
+class TestFinesse(unittest.TestCase):
+ def setUp(self):
+ self.data = io.read('modematching/opo_scan.csv', importer='KeysightCSV')
+ plt.figure()
+
+ def test_finesse_calculation(self):
+ result = finesse(self.data['opo_scan_2'])
+ self.assertAlmostEqual(result[0], 54.27, places=1)
+ self.assertAlmostEqual(result[1], 67.45, places=1)
+
+ def test_finesse_plot(self):
+ finesse(self.data['opo_scan_2'], plot=True)
diff --git a/src/tests/test_importers/data_files/DataContainerCSV/data_container_csv.csv b/src/tests/test_importers/data_files/DataContainerCSV/data_container_csv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e8ac8e69e4cb102f0aa01fb4de393c805696aca2
--- /dev/null
+++ b/src/tests/test_importers/data_files/DataContainerCSV/data_container_csv.csv
@@ -0,0 +1,7 @@
+-----DataContainerHeader
+{"test": 1}
+-----DataContainerData
+,0
+1,1
+2,2
+3,3
diff --git a/src/tests/test_importers/test_data_container_csv/data_container_csv.csv b/src/tests/test_importers/test_data_container_csv/data_container_csv.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e8ac8e69e4cb102f0aa01fb4de393c805696aca2
--- /dev/null
+++ b/src/tests/test_importers/test_data_container_csv/data_container_csv.csv
@@ -0,0 +1,7 @@
+-----DataContainerHeader
+{"test": 1}
+-----DataContainerData
+,0
+1,1
+2,2
+3,3
diff --git a/src/tests/test_importers/test_data_container_csv/test_data_container_csv.py b/src/tests/test_importers/test_data_container_csv/test_data_container_csv.py
new file mode 100644
index 0000000000000000000000000000000000000000..9116c2315c8848654975ae50399384aa1351b8a5
--- /dev/null
+++ b/src/tests/test_importers/test_data_container_csv/test_data_container_csv.py
@@ -0,0 +1,62 @@
+import unittest
+from pandas.errors import EmptyDataError
+from openqlab.io.importers.data_container_csv import DataContainerCSV
+from openqlab.io.data_container import DataContainer
+from openqlab.io.importers.utils import UnknownFileType, ImportFailed
+from pathlib import Path
+
+
+class TestDataContainerCSV(unittest.TestCase):
+ importer = DataContainerCSV
+ files_path = Path(r'./test_importers/data_files')
+ supported_files_path = Path(r'./test_importers/data_files/DataContainerCSV')
+
+ def read_file(self, file):
+ try:
+ data = self.importer(file).read()
+ self.assertTrue(isinstance(data, DataContainer) and not data.empty)
+ self.assertTrue(data._is_numeric_mixed_type)
+ for column in data.columns:
+ self.assertFalse(data[column].isna().values.all())
+ return data
+ except Exception as e:
+ print(f'{file}')
+ raise type(e)(f'"{file}" raised Exception: {e}') from e
+
+ @classmethod
+ def setUpClass(cls):
+ cls.supported_files = list(cls.supported_files_path.glob('*'))
+ cls.supported_files = [file for file in cls.supported_files if not file.stem.startswith('.')]
+ cls.test_files = list(cls.files_path.glob('*/*'))
+ cls.unsupported_files = [file for file in cls.test_files if file not in cls.supported_files]
+ assert cls.supported_files
+ assert cls.unsupported_files
+
+ def test_supported_files(self):
+ for file in self.supported_files:
+ self.read_file(file)
+
+ def test_unsupported_files(self):
+ for file in self.unsupported_files:
+ try:
+ with self.assertRaises(UnknownFileType):
+ self.read_file(file)
+ except AssertionError as e:
+ raise AssertionError(f'{file} did not raise {UnknownFileType}') from e
+
+ def test_correct_import(self):
+ file = self.supported_files_path / 'data_container_csv.csv'
+ data = self.read_file(file)
+
+ # header
+ self.assertEqual(1, data.header['test'])
+
+ # columns and index
+ self.assertEqual(['0'], list(data.columns))
+
+ # data
+ self.assertEqual(1, data.index[0])
+ self.assertEqual(2, data.index[1])
+
+ self.assertAlmostEqual(1, data.iloc[0, 0])
+ self.assertAlmostEqual(2, data.iloc[1, 0])
diff --git a/src/tests/test_plots.py b/src/tests/test_plots.py
index fd9e8d6a349d74b221609531ff8d7035e0e17473..20bee8302bd8cc71917c192da92a1dee626e95c7 100644
--- a/src/tests/test_plots.py
+++ b/src/tests/test_plots.py
@@ -4,7 +4,7 @@ import matplotlib
import matplotlib.pyplot as plt
from numpy import array_equal
from openqlab import io, plots
-from openqlab.plots.frequency_domain import relative_input_noise
+from openqlab.plots.frequency_domain import relative_input_noise, amplitude_phase
class TestRIN(unittest.TestCase):
@@ -17,7 +17,7 @@ class TestRIN(unittest.TestCase):
def test_default_values(self):
plot = relative_input_noise(self.data, 6)
self.assertIsInstance(plot, matplotlib.axes.Axes)
- self.assertEqual(plot.get_ylabel(), 'RIN ($1/\sqrt{\mathrm{Hz}}$)')
+ self.assertEqual(plot.get_ylabel(), r'RIN ($1/\sqrt{\mathrm{Hz}}$)')
self.assertEqual(plot.get_xlabel(), 'Frequency (Hz)')
self.assertEqual(plot.get_title(), '')
self.assertEqual(plot.get_xscale(), 'log')
@@ -25,10 +25,10 @@ class TestRIN(unittest.TestCase):
def test_different_parameters(self):
plot = relative_input_noise(self.data, 6.5,
- logf=False,
- logy=False,
- title='testtitle',
- ylabel='testylabel')
+ logf=False,
+ logy=False,
+ title='testtitle',
+ ylabel='testylabel')
self.assertEqual(plot.get_ylabel(), 'testylabel')
self.assertEqual(plot.get_title(), 'testtitle')
self.assertEqual(plot.get_xscale(), 'linear')
@@ -37,3 +37,17 @@ class TestRIN(unittest.TestCase):
def test_kwargs(self):
plot = relative_input_noise(self.data, 6.5, figsize=(4, 3))
self.assertTrue(array_equal(plot.figure.get_size_inches(), [4, 3]))
+
+
+class TestAmplitudePhase(unittest.TestCase):
+ def setUp(self):
+ self.data = io.read(
+ r'./ContainerTestData/SuccedingImport/Oscilloscope/KeysightFra/scope_0.csv')
+
+ def test_default_plot(self):
+ plot = amplitude_phase(self.data['Gain (dB)'], self.data['Phase (deg)'])
+ self.assertIsInstance(plot, matplotlib.figure.Figure)
+
+ def test_clamp_phase_warns(self):
+ with self.assertWarns(DeprecationWarning):
+ plots.frequency_domain._clamp_phase(4)
diff --git a/src/tests/test_servo_design.py b/src/tests/test_servo_design.py
index 4ff8abef369b054169655acb26863508e81842d4..b841db0f7ef69b8ea26007d6670066397a20aee0 100644
--- a/src/tests/test_servo_design.py
+++ b/src/tests/test_servo_design.py
@@ -3,7 +3,8 @@ import unittest
from openqlab.analysis.servo_design import ServoDesign, Filter, Integrator, Differentiator, Notch, Lowpass
from openqlab import io
import numpy as np
-import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib as mp
import logging as log
import jsonpickle
from pandas import DataFrame
@@ -33,8 +34,8 @@ class TestFilter(unittest.TestCase):
i.sF = None
np.testing.assert_allclose(i.zeros, -cF)
- np.testing.assert_allclose(i.poles, -cF * 1000)
- np.testing.assert_allclose(i.gain, 1000)
+ np.testing.assert_allclose(i.poles, -cF * 10)
+ np.testing.assert_allclose(i.gain, 10)
def test_lowpass(self):
cF = 840
@@ -181,7 +182,7 @@ class TestServoDesign(unittest.TestCase):
np.testing.assert_allclose(zpk[i], zpk_expected[i])
self.sd.integrator(500)
- self.sd.differentiator(1000)
+ self.sd.differentiator(1000, 1000*1000)
self.sd.notch(900, Q=200)
zpk_expected = (
np.array([-500., -1000., 0. + 900.j, -0. - 900.j]),
@@ -210,7 +211,7 @@ class TestServoDesign(unittest.TestCase):
def test_simple_plot(self):
if self.display_available:
plt = self.sd.plot()
- self.assertIsInstance(plt, matplotlib.figure.Figure)
+ self.assertIsInstance(plt, mp.figure.Figure)
ax = plt.axes[0]
self.assertLessEqual(ax.get_xlim()[1], 1e6)
self.assertGreaterEqual(ax.get_xlim()[1], 1e5)
@@ -220,7 +221,11 @@ class TestServoDesign(unittest.TestCase):
self.sd.integrator(500)
self.sd.integrator(500)
plt = self.sd.plot()
- self.assertIsInstance(plt, matplotlib.figure.Figure)
+ self.assertIsInstance(plt, mp.figure.Figure)
+
+ def test_kwargs_plot(self):
+ if self.display_available:
+ plot = self.sd.plot(color='black')
def test_plot_with_frequencies(self):
if self.display_available:
@@ -234,7 +239,7 @@ class TestServoDesign(unittest.TestCase):
self.sd.differentiator(390)
self.sd.plant = io.read('servo_design/fra_3.csv')
plt = self.sd.plot()
- self.assertIsInstance(plt, matplotlib.figure.Figure)
+ self.assertIsInstance(plt, mp.figure.Figure)
ax = plt.axes[0]
self.assertLessEqual(ax.get_xlim()[1], 3e4)
self.assertGreaterEqual(ax.get_xlim()[1], 5e3)
@@ -286,7 +291,7 @@ class TestServoDesign(unittest.TestCase):
'Notch 900Hz, Q=200': np.array([0.99985872, -1.996521, 0.99985872, 1, -1.996521, 0.99971745])
}
}
- discrete = self.sd.discrete_form(fs=100e3)
+ discrete = self.sd.discrete_form(sampling_frequency=100e3)
self.assertEqual(discrete['fs'], discrete_orig['fs'])
self.assertEqual(discrete['gain'], discrete_orig['gain'])
np.testing.assert_allclose(discrete['filters']['Int 500Hz'], discrete_orig['filters']['Int 500Hz'])
@@ -308,7 +313,7 @@ class TestServoDesign(unittest.TestCase):
np.testing.assert_allclose(zpk[i], zpk_expected[i])
self.sd.integrator(500)
- self.sd.differentiator(1000)
+ self.sd.differentiator(1000, 1000*1000)
self.sd.notch(900, Q=200)
self.sd.notch(1400, Q=200, enabled=False)
self.sd.differentiator(3000, enabled=False)
@@ -334,7 +339,7 @@ class TestServoDesign(unittest.TestCase):
'Notch 900Hz, Q=200': np.array([0.99985872, -1.996521, 0.99985872, 1, -1.996521, 0.99971745])
}
}
- discrete = self.sd.discrete_form(fs=100e3)
+ discrete = self.sd.discrete_form(sampling_frequency=100e3)
self.assertEqual(discrete['fs'], discrete_orig['fs'])
self.assertEqual(discrete['gain'], discrete_orig['gain'])
np.testing.assert_allclose(discrete['filters']['Int 500Hz'], discrete_orig['filters']['Int 500Hz'])
diff --git a/watch_tests.local.sh b/watch_tests.local.sh
index 4c1f40ec6f38507a94bd0cbc3423991cf949c2b6..8222f4432111b6002f4a72082dc2b5722d3730b9 100755
--- a/watch_tests.local.sh
+++ b/watch_tests.local.sh
@@ -2,5 +2,5 @@
while true
do
inotifywait -e modify src/**/*.py
- make mypy && make test && make pylint
+ make mypy && make test
done