diff --git a/.gitignore b/.gitignore index 607c39a2..b9198314 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ __pycache__/ .pytest_cache/ .vscode/ -.ipynb_checkpoints/ \ No newline at end of file +.ipynb_checkpoints/ +tests/output \ No newline at end of file diff --git a/documentation/architecture.md b/documentation/architecture.md index 6dc5be83..57dc9f8b 100644 --- a/documentation/architecture.md +++ b/documentation/architecture.md @@ -17,6 +17,6 @@ For the users that would like to use MOSQITO independently, a scripting interfac Each function in the function library shall come with: - a documentation presenting the sources used for the implementation and showing how the implementation is validated (in the [documentation folder](.)) - a tutorial (in the [tutorial folder](../tutorials)) -- a unit test (in the [tests folder](../mosqito/tests)) +- a unit test (in the [tests folder](../tests)) The scripting interface, relies upon an object oriented approach. All operations on audio signals are managed through the Audio class and its methods. diff --git a/documentation/loudness-stationary.md b/documentation/loudness-stationary.md index 5782d507..0620838a 100644 --- a/documentation/loudness-stationary.md +++ b/documentation/loudness-stationary.md @@ -10,33 +10,33 @@ In MOSQITO, the code is based on the BASIC program published in Zwicker and Fast ### Validation of the implementation The ISO 532-1:2017 standard provides a set of synthetic and technical signals covering representative applications to be used to validate any of its implementation. The standards also provides the compliance requirements. -The test signal n°1 (annex B2 of the standard) provides third octave levels to be used as input for stationary loudness calculation. A step by step description of how to use MOSQITO to calculate the loudness and the specific loudness from this input is given in [tutorial n°1](../tutorials/tuto1_Loudness-zwicker-from-3oct.ipynb). +The test signal n°1 (annex B2 of the standard) provides third octave levels to be used as input for stationary loudness calculation. -The test signals n°2 to 5 (annex B3 of the standard) provides .wav files to be used as input for stationary loudness calculation. A step by step description of how to use MOSQITO to calculate the loudness and the specific loudness from signal n°3 is given in [tutorial n°2](../tutorials/tuto2_Loudness-zwicker-from-wav.ipynb). +The test signals n°2 to 5 (annex B3 of the standard) provides .wav files to be used as input for stationary loudness calculation. A step by step description of how to use MOSQITO to calculate the loudness and the specific loudness from signal n°3 is given in [this tutorial](../tutorials/tuto_loudness.ipynb). The plots below compares the MOSQITO loudness calculations for all the tests signals to the compliance requirements of the standards. -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png) *Loudness calculation for ISO 532-1 test signal n°1 (machinery noise in free field)* -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png) *Loudness calculation for ISO 532-1 test signal n°2 (250 Hz tone in free field with a level of 80 dB)* -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png) *Loudness calculation for ISO 532-1 test signal n°3 (1 kHz tone in free field with a level of 60 dB)* -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png) *Loudness calculation for ISO 532-1 test signal n°4 (4 kHz tone in free field with a level of 40 dB)* -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png) *Loudness calculation for ISO 532-1 test signal n°5 (pink noise in free field with an overall level of 60 dB)* -*All the validation plots and scripts can be found in [this folder](../mosqito/validations/loudness_zwicker).* +*All the validation plots and scripts can be found in [this folder](../validations/loudness_zwicker).* ### References diff --git a/documentation/loudness-time-varying.md b/documentation/loudness-time-varying.md index 8829d052..43c589b1 100644 --- a/documentation/loudness-time-varying.md +++ b/documentation/loudness-time-varying.md @@ -7,24 +7,24 @@ The acoustic loudness calculation according to Zwicker method was initially intr In MOSQITO, the code is based on the C++ program published with ISO 532-1:2017. ### Validation of the implementation -The ISO 532-1:2017 standard provides a set of synthetic and technical signals covering representative applications to be used to validate any of its implementation. The standards also provides the compliance requirements. A step by step description of how to use MOSQITO to calculate the loudness and the specific loudness from a .wav file is given in [tutorial n°3](./tuto3_Loudness-zwicker-time-varying.ipynb). +The ISO 532-1:2017 standard provides a set of synthetic and technical signals covering representative applications to be used to validate any of its implementation. The standards also provides the compliance requirements. A step by step description of how to use MOSQITO to calculate the loudness and the specific loudness from a .wav file is given in [this tutorial](../tutorials/tuto_loudness.ipynb). -Annex B4 of the standard provides .wav files of synthetic signals to be used as input for time-varying loudness calculation. The plots below compare the MOSQITO loudness calculations for the test signal n°6 to the compliance requirements of the standards. MOSQITO implementation passes successfully the 8 tests from annex B4 (all compliance plots can be found in the [tests/loudness/output folder](../mosqito/tests/loudness/output)). +Annex B4 of the standard provides .wav files of synthetic signals to be used as input for time-varying loudness calculation. The plots below compare the MOSQITO loudness calculations for the test signal n°6 to the compliance requirements of the standards. MOSQITO implementation passes successfully the 8 tests from annex B4 (all compliance plots can be found in [this folder](../tests/loudness/output)). -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png) -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png) *Loudness calculation for ISO 532-1 test signal n°6 (A 250 Hz tone with a time-varying sound pressure level starting with 30 dB and increasing linearly to 80 dB). Top: overall loudness, Bottom: specific loudness at 2.5 Barks* -Annex B5 of the standard provides .wav files of technical signals to be used as input for time-varying loudness calculation. The plot below compares the MOSQITO loudness calculations for the test signal n°14 to the compliance requirements of the standards. MOSQITO implementation passes successfully 11 tests over the 12 from annex B4 (all compliance plots can be found in [this folder](../mosqito/validations/loudness_zwicker/output)). +Annex B5 of the standard provides .wav files of technical signals to be used as input for time-varying loudness calculation. The plot below compares the MOSQITO loudness calculations for the test signal n°14 to the compliance requirements of the standards. MOSQITO implementation passes successfully 11 tests over the 12 from annex B4 (all compliance plots can be found in [this folder](../tests/loudness/output)). -![](../mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_14_(propeller-driven_airplane)_Loudness.png) +![](../validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_14_(propeller-driven_airplane)_Loudness.png) *Loudness calculation for ISO 532-1 test signal n°14 (Propeller-driven airplane noise)* The test on signal 16 fails because the 5% tolerance limit is exceeded for more than 1% of the time at the end of the signal, during the decay from hairdryer noise to silence (see figure below). This issue is currently under investigation. -![](../mosqito/validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png) +![](../validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png) *Loudness calculation for ISO 532-1 test signal n°14 (Hairdryer noise)* diff --git a/documentation/readme.md b/documentation/readme.md index 40808199..f3eb4492 100644 --- a/documentation/readme.md +++ b/documentation/readme.md @@ -6,6 +6,7 @@ In this folder, you will find the documentation and validation of the different ## Generalities +- [Scope of the project](./scope.md) - [Architecture of the toolbox](./architecture.md) ## Sound Quality metrics diff --git a/documentation/roughness.md b/documentation/roughness.md index e177235e..264c062b 100644 --- a/documentation/roughness.md +++ b/documentation/roughness.md @@ -6,13 +6,13 @@ Several models have been developed to calculate the acoustic roughness, but there is no official standardization yet. In MOSQITO, the code is based on the algorithm described in Daniel and Weber, 1997. -A step by step description of how to use MOSQITO to calculate the roughness is given in [tutorial n°6](../tutorials/tuto6_Roughness-from-wav.ipynb) +A step by step description of how to use MOSQITO to calculate the roughness is given in [this tutorial](../tutorials/tuto_roughness.ipynb) ### Validation of the implementation H.Fastl and E.Zwicker proposed some reference values for the roughness of amplitude-modulated tones in figure 11.2 of Zwicker and Fastl, 2007. -In Mosqito the test AM tones are generated using the '[test_signal_generation](../mosqito/tests/roughness/test_signal_generation.py)' script, in accordance with the equation (1) from the Daniel and Weber article. +In Mosqito the test AM tones are generated using the '[signals_test_generation](../tests/roughness/signals_test_generation.py)' script, in accordance with the equation (1) from the Daniel and Weber article. The plot below compare different roughness implementations' results for amplitude-modulated tones (carrier frequency of 250, 1000, 4000 Hz and modulation frequency from 10 to 400 Hz): - Daniel and Weber implementation described in the 1997 article (results from figure 3) @@ -21,10 +21,10 @@ The plot below compare different roughness implementations' results for amplitud Mosqito implementation give similar results as other implementations. Zwicker and Fastl recommand that any roughness calculation should give results within a +/- 17% range around their reference values. Note that none of the 3 implementations respect this criteria. However, Daniel and Weber algorithm is considered as a reference. Any improvement of the roughness assessment is welcome to be implemented in Mosqito. -![](../mosqito/validations/roughness_danielweber/roughness_implementations_comparison.png) +![](../validations/roughness_danielweber/roughness_implementations_comparison.png) -*All the plots and scripts for more detailed validation can be found in [this folder](../mosqito/validations/roughness_danielweber).* +*All the plots and scripts for more detailed validation can be found in [this folder](../validations/roughness_danielweber).* ### References diff --git a/documentation/scope.md b/documentation/scope.md new file mode 100644 index 00000000..1211f98c --- /dev/null +++ b/documentation/scope.md @@ -0,0 +1,52 @@ +# Scope of the project + +## Sound quality metrics +The scope of the project is to implement the following first set of +metrics: + +| | Reference | Validated | Available | Under dev. | To do | +|:-------------------------------------------------- |:---------------------------------------------------- |:--------------------------------------------------:|:---------------------------------------------:|:----------:|:-----:| +| Loudness for
steady signals
(Zwicker method) | ISO 532B:1975
DIN 45631:1991
ISO 532-1:2017 §5 | [x](./mosqito/validations/loudness_zwicker/output) | [x](./documentation/loudness-stationary.md) | | | +| Loudness for non-stationary
(Zwicker method) | DIN 45631/A1:2010
ISO 532-1:2017 §6 | [x](./mosqito/validations/loudness_zwicker/output) | [x](./documentation/loudness-time-varying.md) | | | +| Loudness for non-stationary
(ECMA-74 method) | ECMA-74:2019, annex F
Sottek, 2016 | | | x | | +| Roughness | Daniel and Weber, 1997 | [x](./mosqito/validations/roughness_danielweber) | [x](./documentation/roughness.md) | | | +| Roughness | ECMA-418-2:2020 | | | | x | +| Fluctuation Strength | To be defined | | | | x | +| Sharpness | DIN 45692:2009 | [x](./mosqito/validations/sharpness/output) | [x](./documentation/sharpness.md) | | | +| Tonality (Hearing model) | ECMA-74:2019 annex G | | | x | | + +As a second priority, the project could address the following metrics: + +| | Reference | Validated | Available | Under dev. | To do | +|:----------------------------------------------------------------------------------- |:------------------------------------- |:---------:|:---------:|:----------:|:-----:| +| Loudness for steady signals
(Moore/Glasberg method) | ISO 532-2:2017 | | | | x | +| Loudness for non-stationary
(Moore/Glasberg method) | Moore, 2014 | | | | x | +| Sharpness (using
Moore/Glasberg loudness) | Hales-Swift
and Gee, 2017 | | | | x | +| Tone-to-noise ratio / Prominence
ratio (occupational noise,
discrete tones) | ECMA-74:2019 annex D
ISO 7719:2018 | | x | | | +| Tone-to-noise ratio
(environmental noise,
automatic tone detection) | DIN 45681 | | | | x | +| Tone-to-noise ratio
(environmental noise) | ISO 1996-2 | | | | x | +| Tone-to-noise ratio
(environmental noise) | ANSI S1.13:2005 | | | | x | + + +## Other SQ tools +In parallel, tools for signal listening and manipulation will be +developed. The objective is to be able to apply some modification to a +signal (filtering, tone removal, etc.) and assess the impact on +different SQ metrics. The integration of tools to define jury tests and +analyze the results is also planned. + +Of course, any other sound quality related implementation by anyone who +wants to contribute is welcome. + +## References + +Daniel, P., and Weber, R. (1997). “Psychoacoustical Roughness: Implementation +of an Optimized Model”, Acta Acustica, Vol. 83: 113-123 + +Hales Swift, S., and Gee, K. L. (2017). “Extending sharpness calculation +for an alternative loudness metric input,” J. Acoust. Soc. Am.142, +EL549. + +Moore, B. C. J. et al. (2016) ‘A Loudness Model for Time-Varying Sounds Incorporating Binaural Inhibition’, Trends in Hearing. [doi: 10.1177/2331216516682698](https://doi.org/10.1177/2331216516682698). + +Sottek, R. (2016) A Hearing Model Approach to Time-Varying Loudness, Acta Acustica united with Acustica, vol. 102, no. 4, pp. 725-744. \ No newline at end of file diff --git a/documentation/sharpness.md b/documentation/sharpness.md index aaac297c..9467cadf 100644 --- a/documentation/sharpness.md +++ b/documentation/sharpness.md @@ -11,11 +11,11 @@ The code is based on the version of the standard published in 2009 and the loudn The DIN 45692:2009 standard provides a set of synthetic and technical signals covering representative applications to be used to validate any of its implementation. The standards also provides the compliance requirements for a set of broad-band noises and narrow-band noises. The sharpness is calculated by mosqito for the 20 broad-band signals and for the 21 narrow-band signals filtered with different center frequencies provided with the standard. The results are compared to the requirements in the figures below. -![](../mosqito/validations/sharpness/output/validation_sharpness_Broad-band_noise.png) +![](../validations/sharpness/output/validation_sharpness_Broad-band_noise.png) -![](../mosqito/validations/sharpness/output/validation_sharpness_Narrow-band_noise.png) +![](../validations/sharpness/output/validation_sharpness_Narrow-band_noise.png) -*The validation plots and scripts can be found in [this folder](../mosqito/validations/sharpness).* +*The validation plots and scripts can be found in [this folder](../validations/sharpness).* ## Sharpness of stationary and time-varying signals (other methods) diff --git a/mosqito/functions/oct3filter/oct3spec.py b/mosqito/functions/oct3filter/oct3spec.py index ec4f1451..a1bbb62c 100644 --- a/mosqito/functions/oct3filter/oct3spec.py +++ b/mosqito/functions/oct3filter/oct3spec.py @@ -11,7 +11,7 @@ from mosqito.functions.oct3filter.oct3level import oct3level -def oct3spec(sig, fs, fc_min=20, fc_max=20000, sig_type="stationary", dec_factor=24): +def oct3spec(sig, fs, fc_min=25, fc_max=12500, sig_type="stationary", dec_factor=24): """Calculate third-octave band spectrum Calculate the rms level of the signal "sig" sampled at freqency "fs" @@ -55,6 +55,8 @@ def oct3spec(sig, fs, fc_min=20, fc_max=20000, sig_type="stationary", dec_factor # Définition of the range of preferred filter center frequency fpref = np.array( [ + 16, + 20, 25, 31.5, 40, @@ -83,11 +85,15 @@ def oct3spec(sig, fs, fc_min=20, fc_max=20000, sig_type="stationary", dec_factor 8000, 10000, 12500, + 16000, + 20000, ] ) fexact = np.array( [ + 15.849, + 19.953, 25.119, 31.623, 39.811, @@ -116,6 +122,8 @@ def oct3spec(sig, fs, fc_min=20, fc_max=20000, sig_type="stationary", dec_factor 7943, 10000, 12589, + 15849, + 19953, ] ) diff --git a/mosqito/functions/shared/load.py b/mosqito/functions/shared/load.py index f5a0fe30..3bff3715 100644 --- a/mosqito/functions/shared/load.py +++ b/mosqito/functions/shared/load.py @@ -50,9 +50,11 @@ def load(is_stationary, file, calib=1, mat_signal="", mat_fs=""): signal = calib * signal / (2 ** 15 - 1) elif isinstance(signal[0], np.int32): signal = calib * signal / (2 ** 31 - 1) + elif isinstance(signal[0], np.float): + signal = calib * signal # load the .uff file content - elif file[-3:] == "uff" or file[-3:] == "UFF": + elif file[-3:].lower() == "uff" or file[-3:].lower() == "unv": uff_file = pyuff.UFF(file) data = uff_file.read_sets() data.keys() diff --git a/mosqito/methods/Audio/comp_3oct_spec.py b/mosqito/methods/Audio/comp_3oct_spec.py index bcf52980..246e3fca 100644 --- a/mosqito/methods/Audio/comp_3oct_spec.py +++ b/mosqito/methods/Audio/comp_3oct_spec.py @@ -11,19 +11,27 @@ from mosqito.functions.oct3filter.oct3spec import oct3spec -def comp_3oct_spec(self, unit="dB"): +def comp_3oct_spec( + self, + fc_min=25, + fc_max=12800, +): """Method to compute third-octave spectrum according to ISO Parameter --------- - unit : string - 'dB' or 'dBA' + fc_min : float + Filter center frequency of the lowest 1/3 oct. band [Hz] + fc_max : float + Filter center frequency of the highest 1/3 oct. band [Hz] """ # Compute third octave band spectrum if self.is_stationary: - third_spec, freq_val = oct3spec(self.signal.values, self.fs) + third_spec, freq_val = oct3spec( + self.signal.values, self.fs, fc_min=fc_min, fc_max=fc_max + ) else: third_spec, freq_val, time_val = calc_third_octave_levels( self.signal.values, self.fs diff --git a/mosqito/tests/Audio/output/test_comp_3oct_spec.png b/mosqito/tests/Audio/output/test_comp_3oct_spec.png deleted file mode 100644 index 50b49253..00000000 Binary files a/mosqito/tests/Audio/output/test_comp_3oct_spec.png and /dev/null differ diff --git a/mosqito/tests/Audio/output/test_compute_loudness.png b/mosqito/tests/Audio/output/test_compute_loudness.png deleted file mode 100644 index 6fc54a07..00000000 Binary files a/mosqito/tests/Audio/output/test_compute_loudness.png and /dev/null differ diff --git a/mosqito/tests/Audio/output/test_compute_loudness_time_1.png b/mosqito/tests/Audio/output/test_compute_loudness_time_1.png deleted file mode 100644 index b138881b..00000000 Binary files a/mosqito/tests/Audio/output/test_compute_loudness_time_1.png and /dev/null differ diff --git a/mosqito/tests/Audio/output/test_compute_loudness_time_2.png b/mosqito/tests/Audio/output/test_compute_loudness_time_2.png deleted file mode 100644 index 186d1417..00000000 Binary files a/mosqito/tests/Audio/output/test_compute_loudness_time_2.png and /dev/null differ diff --git a/mosqito/tests/Audio/output/test_compute_loudness_time_3.png b/mosqito/tests/Audio/output/test_compute_loudness_time_3.png deleted file mode 100644 index 741032cb..00000000 Binary files a/mosqito/tests/Audio/output/test_compute_loudness_time_3.png and /dev/null differ diff --git a/mosqito/tests/Audio/output/test_import_signal.png b/mosqito/tests/Audio/output/test_import_signal.png deleted file mode 100644 index ef05f8ce..00000000 Binary files a/mosqito/tests/Audio/output/test_import_signal.png and /dev/null differ diff --git a/readme.md b/readme.md index feebb872..3b1b31ee 100644 --- a/readme.md +++ b/readme.md @@ -2,7 +2,7 @@ ## Background -Sound quality (SQ) metrics are developed by acoustic engineers and +Sound quality (SQ) metrics are developed by acoustic engineers and researchers to provide an objective assessment of the pleasantness of a sound. Different metrics exist depending on the nature of the sound to be tested. Some of these metrics are already standardized, while some @@ -18,79 +18,28 @@ online, confirming the interest of the engineering and scientific community, but they often use Matlab signal processing commercial toolbox. +Besides the metrics, sound quality studies requires several tool like audio signal filtering or jury testing procedure fore instance. + ## Objectives -The objective of MOSQITO is therefore to provide a unified and modular -development framework of key sound quality metrics with open-source -object-oriented technologies, favoring reproducible science and -efficient shared scripting among engineers, teachers and researchers -community. +The objective of MOSQITO is therefore to provide a unified and modular development framework of key sound quality tools (including key SQ metrics) with open-source object-oriented technologies, favoring reproducible science and efficient shared scripting among engineers, teachers and researchers +community. The development roadmap of the project is presented in more details in the [documentation](./documentation/scope.md). -It is written in Python, one of the most popular free programming -language in the scientific computing community. It is meant to be highly -documented (use of Jupyter notebooks, tutorials) and validated with -reference sound samples and scientific publications. +It is written in Python, one of the most popular free programming language in the scientific computing community. It is meant to be highly documented (use of Jupyter notebooks, tutorials) and validated with reference sound samples and scientific publications. ## Origin of the project -[EOMYS ENGINEERING](https://eomys.com/?lang=en) initiated this open-source project -in 2020 for the study of electric motor sound quality. The project is now -backed by [Green Forge Coop](https://www.linkedin.com/company/greenforgecoop/) non profit organization, -who also supports the development of [Pyleecan](https://www.pyleecan.org) electrical -machine simulation software. +[EOMYS ENGINEERING](https://eomys.com/?lang=en) initiated this open-source project in 2020 for the study of electric motor sound quality. The project is now backed by [Green Forge Coop](https://www.linkedin.com/company/greenforgecoop/) non profit organization, who also supports the development of [Pyleecan](https://www.pyleecan.org) electrical machine simulation software. ## Documentation -Tutorials are available in the [tutorials](./tutorials/) folder. Documentation -and validation of the MOSQITO functions are available in the [documentation](./documentation/) folder. - -## Scope - -The scope of the project is to implement the following first set of -metrics: - -| | Reference | Validated | Available | Under dev. | To do | -|:-------------------------------------------------- |:---------------------------------------------------- |:--------------------------------------------------:|:---------------------------------------------:|:----------:|:-----:| -| Loudness for
steady signals
(Zwicker method) | ISO 532B:1975
DIN 45631:1991
ISO 532-1:2017 §5 | [x](./mosqito/validations/loudness_zwicker/output) | [x](./documentation/loudness-stationary.md) | | | -| Loudness for non-stationary
(Zwicker method) | DIN 45631/A1:2010
ISO 532-1:2017 §6 | [x](./mosqito/validations/loudness_zwicker/output) | [x](./documentation/loudness-time-varying.md) | | | -| Roughness | Daniel and Weber, 1997 | [x](./mosqito/validations/roughness_danielweber) | [x](./documentation/roughness.md) | | | -| Fluctuation Strength | To be defined | | | | x | -| Sharpness | DIN 45692:2009 | [x](./mosqito/validations/sharpness/output) | [x](./documentation/sharpness.md) | | | -| Tonality (Hearing model) | ECMA-74:2019 annex G | | | x | | - -As a second priority, the project could address the following metrics: - -| | Reference | Validated | Available | Under dev. | To do | -|:----------------------------------------------------------------------------------- |:------------------------------------- |:---------:|:---------:|:----------:|:-----:| -| Loudness for steady signals
(Moore/Glasberg method) | ISO 532-2:2017 | | | | x | -| Loudness for non-stationary
(Moore/Glasberg method) | Moore, 2014 | | | | x | -| Sharpness (using
Moore/Glasberg loudness) | Hales-Swift
and Gee, 2017 | | | | x | -| Tone-to-noise ratio / Prominence
ratio (occupational noise,
discrete tones) | ECMA-74:2019 annex D
ISO 7719:2018 | | x | | | -| Tone-to-noise ratio
(environmental noise,
automatic tone detection) | DIN 45681 | | | | x | -| Tone-to-noise ratio
(environmental noise) | ISO 1996-2 | | | | x | -| Tone-to-noise ratio
(environmental noise) | ANSI S1.13:2005 | | | | x | - -In parallel, tools for signal listening and manipulation will be -developed. The objective is to be able to apply some modification to a -signal (filtering, tone removal, etc.) and assess the impact on -different SQ metrics. - -Of course, any other sound quality related implementation by anyone who -wants to contribute is welcome. +Tutorials are available in the [tutorials](./tutorials/) folder. Documentation and validation of the MOSQITO functions are available in the [documentation](./documentation/) folder. ## Contact -You can contact us on Github by opening an issue (to request a feature, -ask a question or report a bug). - -## References - -Daniel, P., and Weber, R. (1997). “Psychoacoustical Roughness: Implementation -of an Optimized Model”, Acta Acustica, Vol. 83: 113-123 +You can contact us on Github by opening an issue (to request a feature, ask a question or report a bug). -Hales Swift, S., and Gee, K. L. (2017). “Extending sharpness calculation -for an alternative loudness metric input,” J. Acoust. Soc. Am.142, -EL549. +## How to cite MOSQITO -Moore, B. C. J. (2014). “Development and Current Status of the -“Cambridge” Loudness Models,” Trends in Hearing, vol. 18: 1-29 +If you use MOSQITO for your research activities and need to cite the software in a publication, please use the following citation: +TODO diff --git a/setup.py b/setup.py index 01987039..18067c85 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ import setuptools # /!\ update before a release -MoSQITo_VERSION = "0.2.1" +MoSQITo_VERSION = "0.3.1" # MoSQITo description with open("README.md", "r", encoding="utf-8") as fh: @@ -16,7 +16,7 @@ requirements ).splitlines() # remove endline in each element -tests_require = ["pytest>=5.4.1","pandas", "openpyxl"] +tests_require = ["pytest>=5.4.1", "pandas", "openpyxl"] setuptools.setup( name="mosqito", @@ -30,7 +30,14 @@ download_url="https://github.com/Eomys/MoSQITo/archive/v{}.tar.gz".format( MoSQITo_VERSION ), - packages=setuptools.find_packages(exclude=["documentation", "tutorials"]), + packages=setuptools.find_packages( + exclude=[ + "documentation", + "tutorials", + "validations", + "tests", + ] + ), include_package_data=True, classifiers=[ "Programming Language :: Python :: 3", @@ -40,7 +47,5 @@ python_requires=python_requires, install_requires=install_requires, tests_require=tests_require, - extras_require={ - 'testing': tests_require - }, + extras_require={"testing": tests_require}, ) diff --git a/mosqito/tests/Audio/__init__.py b/tests/Audio/__init__.py similarity index 100% rename from mosqito/tests/Audio/__init__.py rename to tests/Audio/__init__.py diff --git a/mosqito/tests/Audio/test_Audio.py b/tests/Audio/test_Audio.py similarity index 74% rename from mosqito/tests/Audio/test_Audio.py rename to tests/Audio/test_Audio.py index 006c0c00..38b5c2af 100644 --- a/mosqito/tests/Audio/test_Audio.py +++ b/tests/Audio/test_Audio.py @@ -8,14 +8,14 @@ from mosqito.classes.Audio import Audio from mosqito import COLORS -out_path = "./mosqito/tests/Audio/output/" +out_path = "./tests/output/" is_show_fig = False @pytest.fixture(scope="module") def fixture_import_signal(): audio = Audio( - "./mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav", + "./tests/input/Test signal 3 (1 kHz 60 dB).wav", is_stationary=True, calib=2 * 2 ** 0.5, ) @@ -25,7 +25,7 @@ def fixture_import_signal(): @pytest.fixture(scope="module") def fixture_import_signal_time(): audio = Audio( - "./mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav", + "./tests/input/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav", calib=2 * 2 ** 0.5, ) return audio @@ -34,32 +34,36 @@ def fixture_import_signal_time(): @pytest.mark.audio def test_import_signal(): audio = Audio( - "./mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav", + "./tests/input/Test signal 3 (1 kHz 60 dB).wav", is_stationary=True, calib=2 * 2 ** 0.5, ) audio.signal.plot_2D_Data( "time", is_show_fig=is_show_fig, - save_path=out_path + "test_import_signal.png", + save_path=out_path + "test_Audio_import_signal.png", color_list=COLORS, ) @pytest.mark.audio -def test_comp_3oct_spec(fixture_import_signal): - audio = fixture_import_signal - audio.comp_3oct_spec() - audio.third_spec.plot_2D_Data( +def test_comp_3oct_spec(): + audio1 = Audio( + "./tests/input/Test signal 3 (1 kHz 60 dB).wav", + is_stationary=True, + calib=2 * 2 ** 0.5, + ) + audio1.comp_3oct_spec(fc_min=20, fc_max=20000) + audio1.third_spec.plot_2D_Data( "freqs", - type_plot="curve", + type_plot="octave", is_logscale_x=True, - x_min=20, - y_min=0, - y_max=40, + # x_min=20, + # y_min=0, + # y_max=40, unit="dB", is_show_fig=is_show_fig, - save_path=out_path + "test_comp_3oct_spec.png", + save_path=out_path + "test_Audio_comp_3oct_spec.png", ) @@ -72,7 +76,7 @@ def test_level(fixture_import_signal_time): type_plot="curve", unit="dB", is_show_fig=is_show_fig, - save_path=out_path + "test_level.png", + save_path=out_path + "test_Audio_level.png", ) @@ -84,7 +88,7 @@ def test_compute_loudness(fixture_import_signal): "cr_band", type_plot="curve", is_show_fig=is_show_fig, - save_path=out_path + "test_compute_loudness.png", + save_path=out_path + "test_Audio_compute_loudness.png", color_list=COLORS, ) @@ -97,7 +101,7 @@ def test_compute_loudness_time(fixture_import_signal_time): "time", type_plot="curve", is_show_fig=is_show_fig, - save_path=out_path + "test_compute_loudness_time_1.png", + save_path=out_path + "test_Audio_compute_loudness_time_1.png", color_list=COLORS, ) audio.loudness_zwicker_specific.plot_2D_Data( @@ -105,7 +109,7 @@ def test_compute_loudness_time(fixture_import_signal_time): "cr_band=2.5", type_plot="curve", is_show_fig=is_show_fig, - save_path=out_path + "test_compute_loudness_time_2.png", + save_path=out_path + "test_Audio_compute_loudness_time_2.png", color_list=COLORS, ) audio.loudness_zwicker_specific.plot_3D_Data( @@ -114,7 +118,7 @@ def test_compute_loudness_time(fixture_import_signal_time): is_2D_view=True, is_switch_axes=True, is_show_fig=is_show_fig, - save_path=out_path + "test_compute_loudness_time_3.png", + save_path=out_path + "test_Audio_compute_loudness_time_3.png", ) @@ -132,7 +136,7 @@ def test_compute_sharpness_time(fixture_import_signal_time): "time", type_plot="curve", is_show_fig=is_show_fig, - save_path=out_path + "test_compute_sharpness_time.png", + save_path=out_path + "test_Audio_compute_sharpness_time.png", color_list=COLORS, ) @@ -151,7 +155,7 @@ def test_compute_roughness_time(fixture_import_signal_time): "time", type_plot="curve", is_show_fig=is_show_fig, - save_path=out_path + "test_compute_roughness_time.png", + save_path=out_path + "test_Audio_compute_roughness_time.png", color_list=COLORS, ) @@ -171,5 +175,5 @@ def test_compute_tnr_pr_time(fixture_import_signal_time): "freqs", is_2D_view=True, is_show_fig=is_show_fig, - save_path=out_path + "test_compute_tnr_pr_time.png", - ) + save_path=out_path + "test_Audio_compute_tnr_pr_time.png", + ) \ No newline at end of file diff --git a/mosqito/tests/__init__.py b/tests/__init__.py similarity index 100% rename from mosqito/tests/__init__.py rename to tests/__init__.py diff --git a/mosqito/tests/sharpness/Standard/1KHZ60DB.WAV b/tests/input/1KHZ60DB.WAV similarity index 100% rename from mosqito/tests/sharpness/Standard/1KHZ60DB.WAV rename to tests/input/1KHZ60DB.WAV diff --git a/mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx b/tests/input/Results and tests for synthetic signals (time varying loudness).xlsx similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx rename to tests/input/Results and tests for synthetic signals (time varying loudness).xlsx diff --git a/mosqito/tests/loudness/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav b/tests/input/Test signal 3 (1 kHz 60 dB).wav similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav rename to tests/input/Test signal 3 (1 kHz 60 dB).wav diff --git a/mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav b/tests/input/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav rename to tests/input/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav diff --git a/mosqito/tests/loudness/data/ISO_532-1/test_signal_1.csv b/tests/input/test_signal_1.csv similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/test_signal_1.csv rename to tests/input/test_signal_1.csv diff --git a/mosqito/tests/loudness/data/ISO_532-1/test_signal_3.csv b/tests/input/test_signal_3.csv similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/test_signal_3.csv rename to tests/input/test_signal_3.csv diff --git a/mosqito/tests/tonality_tnr_pr/white_noise_200_2000_Hz_stationary.wav b/tests/input/white_noise_200_2000_Hz_stationary.wav similarity index 100% rename from mosqito/tests/tonality_tnr_pr/white_noise_200_2000_Hz_stationary.wav rename to tests/input/white_noise_200_2000_Hz_stationary.wav diff --git a/mosqito/tests/tonality_tnr_pr/white_noise_442_1768_Hz_stationary.wav b/tests/input/white_noise_442_1768_Hz_stationary.wav similarity index 100% rename from mosqito/tests/tonality_tnr_pr/white_noise_442_1768_Hz_stationary.wav rename to tests/input/white_noise_442_1768_Hz_stationary.wav diff --git a/mosqito/tests/tonality_tnr_pr/white_noise_442_1768_Hz_varying.wav b/tests/input/white_noise_442_1768_Hz_varying.wav similarity index 100% rename from mosqito/tests/tonality_tnr_pr/white_noise_442_1768_Hz_varying.wav rename to tests/input/white_noise_442_1768_Hz_varying.wav diff --git a/mosqito/tests/loudness/__init__.py b/tests/loudness/__init__.py similarity index 100% rename from mosqito/tests/loudness/__init__.py rename to tests/loudness/__init__.py diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav b/tests/loudness/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav rename to tests/loudness/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav diff --git a/mosqito/tests/loudness/data/ISO_532-1/__init__.py b/tests/loudness/data/ISO_532-1/__init__.py similarity index 100% rename from mosqito/tests/loudness/data/ISO_532-1/__init__.py rename to tests/loudness/data/ISO_532-1/__init__.py diff --git a/mosqito/tests/loudness/data/__init__.py b/tests/loudness/data/__init__.py similarity index 100% rename from mosqito/tests/loudness/data/__init__.py rename to tests/loudness/data/__init__.py diff --git a/mosqito/tests/loudness/data/sound-synthesis/__init__.py b/tests/loudness/data/sound-synthesis/__init__.py similarity index 100% rename from mosqito/tests/loudness/data/sound-synthesis/__init__.py rename to tests/loudness/data/sound-synthesis/__init__.py diff --git a/mosqito/tests/loudness/data/sound-synthesis/spectro.xls b/tests/loudness/data/sound-synthesis/spectro.xls similarity index 100% rename from mosqito/tests/loudness/data/sound-synthesis/spectro.xls rename to tests/loudness/data/sound-synthesis/spectro.xls diff --git a/mosqito/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png b/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png similarity index 100% rename from mosqito/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png rename to tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Loudness.png diff --git a/mosqito/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png b/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png similarity index 100% rename from mosqito/tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png rename to tests/loudness/output/test_loudness_zwicker_time_Test_signal_6_(tone_250_Hz_30_dB_-_80_dB)_Specific.png diff --git a/mosqito/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_1.png b/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_1.png similarity index 100% rename from mosqito/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_1.png rename to tests/loudness/output/test_loudness_zwicker_wav_Test_signal_1.png diff --git a/mosqito/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_3_(1_kHz_60_dB).png b/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_3_(1_kHz_60_dB).png similarity index 100% rename from mosqito/tests/loudness/output/test_loudness_zwicker_wav_Test_signal_3_(1_kHz_60_dB).png rename to tests/loudness/output/test_loudness_zwicker_wav_Test_signal_3_(1_kHz_60_dB).png diff --git a/mosqito/tests/loudness/test_loudness_zwicker_stationary.py b/tests/loudness/test_loudness_zwicker_stationary.py similarity index 94% rename from mosqito/tests/loudness/test_loudness_zwicker_stationary.py rename to tests/loudness/test_loudness_zwicker_stationary.py index 022af5aa..21d10d5d 100644 --- a/mosqito/tests/loudness/test_loudness_zwicker_stationary.py +++ b/tests/loudness/test_loudness_zwicker_stationary.py @@ -72,7 +72,7 @@ def test_loudness_zwicker_3oct(): signal = { "data_file": "Test signal 1.txt", "N": 83.296, - "N_specif_file": "mosqito/tests/loudness/data/ISO_532-1/test_signal_1.csv", + "N_specif_file": "tests/input/test_signal_1.csv", } N, N_specific = loudness_zwicker_stationary(test_signal_1) @@ -101,9 +101,9 @@ def test_loudness_zwicker_wav(): # Test signal as input for stationary loudness # (from ISO 532-1 annex B3) signal = { - "data_file": "mosqito/tests/loudness/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav", + "data_file": "tests/input/Test signal 3 (1 kHz 60 dB).wav", "N": 4.019, - "N_specif_file": "mosqito/tests/loudness/data/ISO_532-1/test_signal_3.csv", + "N_specif_file": "tests/input/test_signal_3.csv", } # Load signal and compute third octave band spectrum @@ -211,7 +211,7 @@ def check_compliance(N, N_specific, iso_ref): plt.title("N = " + str(N) + " sone (ISO ref. " + str(N_iso) + " sone)", color=clr) file_name = "_".join(iso_ref["data_file"].split(" ")) plt.savefig( - "mosqito/tests/loudness/output/test_loudness_zwicker_wav_" + "tests/output/test_loudness_zwicker_wav_" + file_name.split("/")[-1][:-4] + ".png", format="png", diff --git a/mosqito/tests/loudness/test_loudness_zwicker_time.py b/tests/loudness/test_loudness_zwicker_time.py similarity index 96% rename from mosqito/tests/loudness/test_loudness_zwicker_time.py rename to tests/loudness/test_loudness_zwicker_time.py index 2e91f19b..0a1bdc40 100644 --- a/mosqito/tests/loudness/test_loudness_zwicker_time.py +++ b/tests/loudness/test_loudness_zwicker_time.py @@ -37,8 +37,8 @@ def test_loudness_zwicker_time(): # Test signal as input for time-varying loudness # (from ISO 532-1 annex B4) signal = { - "data_file": "mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav", - "xls": "mosqito/tests/loudness/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx", + "data_file": "tests/input/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav", + "xls": "tests/input/Results and tests for synthetic signals (time varying loudness).xlsx", "tab": "Test signal 6", "N_specif_bark": 2.5, "field": "free", @@ -270,7 +270,7 @@ def check_compliance(N, N_specific, iso_ref): else: flag = "FAILED_" plt.savefig( - "mosqito/tests/loudness/output/" + "tests/output/" + flag + "test_loudness_zwicker_time_" + file_name.split("/")[-1][:-4] diff --git a/mosqito/tests/roughness/__init__.py b/tests/roughness/__init__.py similarity index 100% rename from mosqito/tests/roughness/__init__.py rename to tests/roughness/__init__.py diff --git a/mosqito/tests/roughness/signals_test_generation.py b/tests/roughness/signals_test_generation.py similarity index 100% rename from mosqito/tests/roughness/signals_test_generation.py rename to tests/roughness/signals_test_generation.py diff --git a/mosqito/tests/roughness/test_roughness.py b/tests/roughness/test_roughness.py similarity index 96% rename from mosqito/tests/roughness/test_roughness.py rename to tests/roughness/test_roughness.py index 556493d9..6cc9cbf2 100644 --- a/mosqito/tests/roughness/test_roughness.py +++ b/tests/roughness/test_roughness.py @@ -10,7 +10,7 @@ # Local application imports from mosqito.functions.roughness_danielweber.comp_roughness import comp_roughness -from mosqito.tests.roughness.signals_test_generation import signal_test +from tests.roughness.signals_test_generation import signal_test @pytest.mark.roughness_dw # to skip or run only Daniel and Weber roughness tests diff --git a/mosqito/tests/sharpness/__init__.py b/tests/sharpness/__init__.py similarity index 100% rename from mosqito/tests/sharpness/__init__.py rename to tests/sharpness/__init__.py diff --git a/mosqito/tests/sharpness/test_sharpness.py b/tests/sharpness/test_sharpness.py similarity index 95% rename from mosqito/tests/sharpness/test_sharpness.py rename to tests/sharpness/test_sharpness.py index 1d4637de..aae6f925 100644 --- a/mosqito/tests/sharpness/test_sharpness.py +++ b/tests/sharpness/test_sharpness.py @@ -33,7 +33,7 @@ def test_sharpness(): """ # Input signal from DIN 45692_2009E - signal = {"data_file": r"mosqito\tests\sharpness\Standard\1KHZ60DB.wav", "S": 1} + signal = {"data_file": r"tests\input\1KHZ60DB.wav", "S": 1} # Load signal sig, fs = load(True, signal["data_file"], calib=1) diff --git a/mosqito/tests/tonality_tnr_pr/__init__.py b/tests/tonality_tnr_pr/__init__.py similarity index 100% rename from mosqito/tests/tonality_tnr_pr/__init__.py rename to tests/tonality_tnr_pr/__init__.py diff --git a/mosqito/tests/tonality_tnr_pr/test_pr.py b/tests/tonality_tnr_pr/test_pr.py similarity index 86% rename from mosqito/tests/tonality_tnr_pr/test_pr.py rename to tests/tonality_tnr_pr/test_pr.py index 7a96b77f..18334e8a 100644 --- a/mosqito/tests/tonality_tnr_pr/test_pr.py +++ b/tests/tonality_tnr_pr/test_pr.py @@ -35,14 +35,14 @@ def test_pr(): signal.append( { "is_stationary": True, - "data_file": r"mosqito\tests\tonality_tnr_pr\white_noise_442_1768_Hz_stationary.wav", + "data_file": r"tests\input\white_noise_442_1768_Hz_stationary.wav", } ) signal.append( { "is_stationary": False, - "data_file": r"mosqito\tests\tonality_tnr_pr\white_noise_442_1768_Hz_varying.wav", + "data_file": r"tests\input\white_noise_442_1768_Hz_varying.wav", } ) diff --git a/mosqito/tests/tonality_tnr_pr/test_tnr.py b/tests/tonality_tnr_pr/test_tnr.py similarity index 86% rename from mosqito/tests/tonality_tnr_pr/test_tnr.py rename to tests/tonality_tnr_pr/test_tnr.py index edd01634..4b2a5d95 100644 --- a/mosqito/tests/tonality_tnr_pr/test_tnr.py +++ b/tests/tonality_tnr_pr/test_tnr.py @@ -36,14 +36,14 @@ def test_tnr(): { "is_stationary": True, "tones freq": [200, 2000], - "data_file": r"mosqito\tests\tonality_tnr_pr\white_noise_442_1768_Hz_stationary.wav", + "data_file": r"tests\input\white_noise_442_1768_Hz_stationary.wav", } ) signal.append( { "is_stationary": False, - "data_file": r"mosqito\tests\tonality_tnr_pr\white_noise_442_1768_Hz_varying.wav", + "data_file": r"tests\input\white_noise_442_1768_Hz_varying.wav", } ) diff --git a/tutorials/tuto_loudness.ipynb b/tutorials/tuto_loudness.ipynb index 963b045a..957b8740 100644 --- a/tutorials/tuto_loudness.ipynb +++ b/tutorials/tuto_loudness.ipynb @@ -20,7 +20,9 @@ "\n", "## Using the scripting interface\n", "### Time varying signal\n", - "An Audio object is first created by importing an audio file. In this example, the signal is imported from a .wav file. The tutorial [Audio signal basic operations](./signal_basic_operations.ipynb) gives more information about the syntax of the import and the other supported file types. It also explains how to plot the time signal, compute and plot its 1/3 octave band spectrum, compute its overall level, etc." + "In this tutorial, the signal is imported from a .wav file. The tutorial [Audio signal basic operations](./signal_basic_operations.ipynb) gives more information about the syntax of the import and the other supported file types. It also explains how to plot the time signal, compute and plot its 1/3 octave band spectrum, compute its overall level, etc. You can use any .wav file to perform the tutorial or you can download the [signal from MOSQITO](../validations/loudness_zwicker/data/ISO_532-1/Annex%20B.5/Test%20signal%2024%20(woodpecker).wav) that is used here.\n", + "\n", + "An Audio object is first created by importing the audio file. " ] }, { @@ -39,9 +41,13 @@ "# Import Audio class\n", "from mosqito.classes.Audio import Audio\n", "\n", + "# Define path to the .wav file\n", + "# To be replaced by your own path\n", + "path = \"../validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav\"\n", + "\n", "# Create an Audio object\n", "woodpecker = Audio(\n", - " \"../mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav\",\n", + " path,\n", " calib=2 * 2 ** 0.5,\n", ")" ] @@ -78,13 +84,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:359: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:388: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -119,13 +125,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:359: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:388: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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ZRQmOiCRddDbxXCrBgWM9qXapmkok49Jy9zGziJndYWZLzKzWzGb2eP1jZvacmS0zs3eGy8zMdobr15rZN9MRq4gM3bGB/nItwQkaGu9RQ2ORjEvXVA3XAMXufp6ZnQvcBlwNYGZVwE3AWUAx8IKZ/Ro4EVjp7lelKUYRSYJOdxwwcq+IWD2pRLKHeRomojOz7wLL3H1h+Hynu0+JeT3f3TvMbBbwR3efZWbvAj4HNABHgU+7+0u97PsG4AaA6urqeQsXLkx6/M3NzZSVlSV9vxIfnf/MS+QadAD35xeS5847OttTG1iWOYjxaH4B5e68OcnvXZ+DzNL5z7ye12DBggUr3L2mr/XTVYIzmiBRieqMJjUAYXLzceArwH+F6+wGvunuvzazC4CfAa/ruWN3vxO4E6Cmpsbnz5+f9OBra2tJxX4lPjr/mZfINTjS0cH9S5dRmJ/P/AvPT21gWaats5M/L3mWw2acf9FFFCSxik6fg8zS+c+8RK9BukqQG4Hy2ONGk5sod/8+MAm4yMwWAMuB34WvPQVMNsuhLhkiw1R7jra/gZieVMA+9aQSyah03YGeBq4ECNvgrIu+YGZzzOz+MHlpB1qBLuDLwKfCdeYC2z0d9WkiMiS5NpN4T9F2OOpJJZJZ6aqiegC4zMyeIWh7eL2ZfQbY5O4PmtkaYAngwCJ3f9LM1gI/M7O3EFTrfyBNsYrIEHR3Ec+haRpiTRpVwtoDamgskmlpSXDcvQu4scfiDTGvf4Wg/U3sNgeBt6Q+OhFJJpXgqCeVSDbIzZ9YIpIy3dM05GwJTjTBURWVSCbl5h1IRFLm2ESbuXl7qS4pxggaGbdrTiqRjMnNO5CIpMyxiTZzs4oqtieV5qQSyRwlOCKSVNE2OAU5WkUFqqYSyQa5ewcSkZRo99xuZAzHJt1UQ2ORzFGCIyJJdayKKndvL+pJJZJ5uXsHEpGU6O4mrioqVVGJZFDu3oFEJCW6B/rL4SqqiaNK1JNKJMOU4IhIUnXk8FxUUQWRCOPVk0oko3L3DiQiKXGsiip3S3AAJqqaSiSjlOCISFIdq6LK7duLelKJZFZu34FEJOlUghOYrJ5UIhmlBEdEkkolOAFVUYlkVm7fgUQk6VSCE1BPKpHMUoIjIknVrl5UwF/3pNqrnlQiaZfbdyARSbpOVVF1m1waVFPtPHw4w5GI5B7dgUQkqbqnasjxKiqAaaWlAOxoVoIjkm5KcEQkqTpcVVRRU8vCBOewelKJpFta7kBmFjGzO8xsiZnVmtnMHq9/zMyeM7NlZvbOcFmJmf3GzBab2R/NbHw6YhWRoTk2krFKcKZGS3AOH8bDqjsRSY90/cS6Bih29/OAzwO3RV8wsyrgJuANwBuB28zMwmXr3P1C4F7g5jTFKiJDEK2iKsjhyTajKgsLKc3P53BHBwdb2zIdjkhOSdcd6ALgYQB3XwrURF9w93rgTHdvByYCLR781OneBlgEXJqmWEVkCNpVRdXNzJhWdqwUR0TSJz9NxxkNNMQ87zSzfHfvAHD3DjP7OPAV4L962aYJqOhtx2Z2A3ADQHV1NbW1tUkPvrm5OSX7lfjo/GdeItfgQF4+WIR1q1ezG1XLeCQPInksXr+eAz748XD0Ocgsnf/MS/QapCvBaQTKY55HoslNlLt/38zuBBaZ2YIe25QDh3rbsbvfCdwJUFNT4/Pnz09u5EBtbS2p2K/ER+c/8xK5BktWrWH/4cO8bt48jisvS21gw8Coun28tPFl8idMYP7JJw16P/ocZJbOf+Yleg3SVYb8NHAlgJmdC6yLvmBmc8zs/rDdTTvQCnTFbgO8GVicplhFZAiO9aJSI2OIaWisruIiaZWuEpwHgMvM7BnAgOvN7DPAJnd/0MzWAEsABxa5+5Nm9hzwv2b2FNAGXJumWEVkCLrHwVEbHACqR5VQEImwv7WVIx0djMpP121XJLel5ZPm7l3AjT0Wb4h5/SsE7W9itzkC/F3qoxORZOouwdFAfwDkmTFl1Ci2NDezrbmZkyorMx2SSE7QTywRSSqV4LzWjLAt0uam5gxHIpI7dAcSkaSKDvRXoASn24zyoL/ElqamDEcikjt0BxKRpOpwzUXV0/HdCU6zRjQWSRMlOCKSNO4eM1WDbi9RVcVFlOXn09TezoHW1kyHI5ITdAcSkaTpcscJbiwRleB0M7PuMYHUDkckPZTgiEjStLsaGPfleLXDEUkrDcggkgWOdnSwsaGRrhS1zyjKy2NOZQV5KS5VUfVU36INjVWCI5IeSnBEssDCV17luX31KT3Gu048nosnTUrpMbq7iKt66jWiXcW3NzfT0dWlJFAkxZTgiGSBg61tAMwcPZrSguR+LBvb2tjc1MzGQw0pT3A6NU1Dn0bl51NdUsLeo0fZcfhwd4nOYLk7+1paaO8a/ASeiSjKy6OquDgtxxJJBiU4IlkgOvrvNTOO44TRQ/vi62n3kSN8deXqtFSNHCvBUelEb44vL2Pv0aNsaWoecoJTu3sPv351c5Iii891s07kvOrqtB5TZLCU4Ihkgc7u0X+TX/JRXVJCSV4eh9raONTaSmVRUdKPEdXuaoPTnxnl5Syt28eWJCSb0cbKY4oKKclL7a28pbOTA62trD9wUAmODBtKcESyQCrnb4qEXZQ3HGpgc1MzZ6UwwTnWyFhVVL05NmXD0HtSNba1A/CemTM5ZUzlkPfXn3SWAooki35miWSBaNVOXopKPtI1VYCqqPo3ZdQoCiIR9rW00NzePqR9NbYH7bZGFxYkI7R+9SwFFBkOBrwLmdnYOP5VpiFWkRGrM8UzcB+fpkHmOtTIuF95kQjTy0oB2DrEa9EQluBUFBYOOa6BRDRQoQxD8VRR7Qr/9XfHygOmJyUikRyU6hm4oyU425qb6XRP2Xg4mkl8YDPKy3mlsYnNTU2cOnbMoPbR3tXFkY4OIkBpfnpaGhxfXs6GQw1saWrirKpxaTmmyFDE88l40d3P6m8FM1uVpHhEclJnOMBfqhKP8oICxhUVsb+1ld2HjzA1LEVItu42OKqi6lO0NG0oDY2bwtKb8sLCtE2JMSMJcYukUzx3ofMAzOxrPV8ws7zYdURkcNKRGBwfdj9/4dChlB2jeyZxVVH1qbs9VPPgZxZvCNvfVKSh/U1UNO6tYSmgSLYb8G7q7i3hwylmdm10uZlNAP7cYx0RGYRoYpCXwsTgdeOrAKjdtbs7oUo2leAMbExhIRWFBRzp6KCuZXC3zmgPqtEFqW9/E1VeUEBVcRFtXV3sPnwkbccVGaxE7kIfAT5sZq83s9cBjwPfiWdDM4uY2R1mtsTMas1sZo/XP21mz4b/vhwuMzPbGa5fa2bfTCBWkWHD3VNeRQVw6pgxTBpVwqG2tpRNCxFtZFygEpw+mRkzyobWq62hLf0lOBBb+qQJQyX7DdgGx8zuBVYCq4CPAb8AOoBr3H1TnMe5Bih29/PM7FzgNuDqcP8nAO8BzgG6gKfM7AHgCLDS3a9K6B2JDDOxyU0q21NEzLhsyhTufXkTj+7YyaRRJXFvewCL68u47mhQIqFGxv2bUV7GmgMH2NzUzDkTJiS8fWNbtIt4+kpwAGaUlbF8Xz2bG5u5YGJaDy2SsHgaGd8DzAWuB84AZgDPAe81s/Xu/n9x7OMC4GEAd19qZjUxr20HrnD3TgAzKwBagHkE1WJPAEeBT7v7S/G8KZHhpLvdShoai9aMr+LBrdvYc/Qo316zLv4N8wv4cwLrK8Hp3/FDHJeosT1aRZXeEpxoOy6V4MhwYIk2cjOzfOBkgqRnrrv/cxzb/Aj4jbsvCp9vA05w946YdQy4FSh394+Y2UVAtbv/2swuAL7n7q/rZd83ADcAVFdXz1u4cGFC7ycezc3NlJWVJX2/Ep+Rfv5bgd/lF1LozjWdQxv8LR47zdgQySORVjhdnV1E8uJLWvKBMzs7GYMaovalHfhtXpCc/E1ne1y/NGM/B09F8tkVifCGznamprHBbyfwQF4BXQRxpze9yqyRfh8aDnpegwULFqxw95q+1o9rAAUziwCfd/dvhEnJuvDfz+KMqxGInVku0iO5KQbuBpqAj4aLlxNUheHuT5nZZDMz75GRufudwJ0ANTU1Pn/+/DhDil9tbS2p2K/EZ6Sf/4a2Nn63bDnFhYXMP+f8TIfTq9raWuZfeFGmwxhRnl25ml1HjnD82Wdz4ujRA64f+zlYtnotNDdz3llnxbVtMq1YvZYtzc1MO/NMTqqsTOuxM2mk34eGg0SvQVw/ydy9CxhKW5ingSsBwjY43WXdYcnN74A17v6RaFUV8GXgU+E6c4HtPZMbkZFA8zflpmPVVImPK3Osm3h62+DAsYbGGtFYsl0iQ2CuCXs4fTVMeBLxAHCZmT1DMCLy9Wb2GWATwSjIFwNFZvbmcP0vAP8O/MzM3kJQkvOBBI8pMiwca2Ssdiu5ZEZ5GU/v3ZvwxJvuHtNNPP2VRMeXl1G7O/XzmokMVSIJzliCROQmM3sWWAusdfdfD7RhmBDd2GPxhpjHxX1s+pYE4hMZllSCk5uiIxq/2thEl3vcPegOd3TQ6U5xXh6FeXkDb5BkM2JKntwdS9NIyiKJivsno7u/091PBo4DvkJQ+nJOqgITyRUdKsHJSRNHjaKysJBDbW2sP3Aw7u0a0zjJZm+qiosoy8+nqb2d/ZpZXLJY3HdUMxtnZjcB1xKU/Nzn7v+UsshEckRnV/q6iUv2iJjxximTAfjTjp1xT9vQGLa/yUT1FIQDFXa3w1E1lWSvRH4yPgCMB75B0J27wcxeTElUIjkkOvqvxo7JPedPrKY0P5/NTU280hhfstCQ4RIcgBPD8XBebmjMWAwiA0mkDU65u/+bmb3d3S82s3cQjIUjIkOQjmkaJDsV5+Vx8aSJ/HH7Dn7wwosU99OmpjWvgEeWLae1M+hoOjrN0zTEml1ZAVth46GGjMUgMpBEfjJGZ4VrNbMSd/8NcHkKYhLJKe1qZJzT5k+eRHlBAS2dnRxqa+vz31Gz4P/OTgzSPv5NrOllZRTn5VHX0sIhtcORLJVICc53zGws8Cvg7rDLd2VKohLJIdE2OGpknJvKCgr4as3ZNHd09Lve0iVLOPe88wAojEQoy1AbHAhKG08cXc7zBw/xUkMj50wYn7FYRPoSd4ITltgAfNfM3gecBrw9JVGJ5JDuuahUgpOzCvPyGDtAl+9RwNiiovQEFIfZFRU8f/AQGxsalOBIVopnNvF8gkk2N7p7M4C735vqwERyRWe0kbFKcGQYmVNZAagdjmSveEpw7gNOBzCz64EvEfSmehT4kru39LOtiAygI1pFpRIcGUamlpZSkpfH/tZWfrThpbgHKoxHvhmXT53CxFGjkrZPyT3xJDinA7OBWcAy4OMEoxi/H/hO+FxEBqm7m7h6UckwEjHj5DGVrKzfz8r6/Unff2tnFx8+eU7S9yu5I54Epymc5HKjme1y958BmNk/ESQ8IjIEamQsw9W1M0/krHHj6EriPMitXZ38YtOrvHDoIO1dXRRofCgZpHgSnIlho+LVQFt0obu7me7IIkOlRsYyXI3Kz2fe+Kqk7/cvu/ew4/ARNjY0cOqYMUnfv+SGeBKcW4DXAR8CpprZ88ALwIsEbXFEZAg6uzSSsUisM8aOZcfhI6zdf0AJjgxaPHfUdcAn3P1id68C3gTcDRwG/pLK4ERyQXcJjtrgiABwxrixAKw9cDCp1V+SW+IpwXkf8D9mthF4GHjY3RcBi1IamUiO6AhLcDRVg0hgWmlp90zr25qbuyf3FEnEgCU47n6Tu59NUFU1BrjHzJaY2TfM7CIz6390KhHpV2d3GxxVUYlAMGP53LAUZ83+AxmORoaruO+o7r7B3b/n7lcAlwBPAX8HPJuq4ERygaqoRF7rzHHjAFhVvx9XNZUMQtxTNZjZLOALwBF3/zjwx/CfSMK2Nx/mV6+8SlN7+4DrHs0r4InlK/t8fU5lBdfOPDGZ4aVVtJFxnkpwRLrNrBhNeUEBdS0t7Dx8hKllpZkOSYaZRCbb/CnwFeBbAGZ2GvBZd39fKgKTkaulo5MfbXiJfS1xDoJtRnM/6+7b08KCyZOYNExHPW3vUgmOSE95Zpw5biyL9+xlRX29EhxJWCIJTsTdF5nZNwDcfX2Y5AwoHC/nB8BcoBX4B3ffFPP6p4F3h0//6O5fMbMS4GfABKAJeL+770sgXkmRLnf2Hj066O3/tH0n+1pamFo6ig/NmcNA3+vPPvss55xzTq+vPbR1O8vr61lVv59J04dnghOdi0pTNYj8tbOrqli8Zy+r6vfztuOmY/oRIAlIJMHZZWbHAw5gwV9aSZzbXgMUu/t5ZnYucBtwdbifE4D3AOcAXcBTZvYAcCmwzt1vMbN3AzcDn0wgXkmRH2/YyKr9QxuavSAS4fo5s6keNfCfUDkwoaT39c6ZMJ7l9fWsrN/PldOnDSmmTDnWBkdVVCKxZsVUU+04fIRpKsWRBFi8jbfMbAbwI+AU4IvAFQSlOn8Xx7bfBZa5+8Lw+U53nxI+LgAq3L0+fL4MeC/w78C33X2pmVUAz7j7qb3s+wbgBoDq6up5CxcujOv9JKK5uZmysrKk73e4+mNeAc1mlLnH30o9RgTn1K5OpsT5t9ff+e8Efp9XQJsZV3S0MXoQ8WTa4kg+uyMRLuhsZ3KWNqbUZyDzcvUarIjk8Uokj5O7Ojm9qzNjceTq+c8mPa/BggULVrh7TV/rx12C4+5bzOwKgtKYucCTBAP+xWM00BDzvNPM8t29w93bgfqwROhWYJW7bzSz2G2agIo+4roTuBOgpqbG58+fH+9bilttbS2p2O9w9adly6GtjS+8voYxRUUpP95A53/Xxk0sqasj/4QTmT8MS3HWrn+e3YcaOPOMuZwypjLT4fRKn4HMy9VrMOlQA/+5/nnqR5Vy8byzMlZNlavnP5skeg3i/gFuZpcAPwTOA14l6B4e78/NRoKahu7juntHzL6LgZ+H63y0l23KgUPxxiqp1dYZ/IoqysuOIZDOHh90J11RX5/hSAanQ42MRfoUrabaF1ZTicQrkRqGu4HfA0uBE4AvAc/Hue3TwJUAYRucddEXwpKb3wFr3P0j7t7ZcxvgzcDiBGKVFGoLuzVnyyy/J1VUMCo/n91HjrL7yPC7AaqRsUjfImFvKoCVw/RHjGRGIo2Mt7r7b8PHv07wOA8Al5nZM4AB15vZZ4BNQB5wMVBkZm8O1/8CcDvwv2b2FMEs5tcmeExJgc6uLjrDtjfZUuKQF4kwd+xYltTVsbJ+P28ZZr2poiU42ZIwimSbaG+qlepNJQlIJMH5S9id+z88wWEl3b0LuLHH4g0xj4v72HTABsySXtHSm8K8vKy6yZw9flyY4NTzlmHWDic6VYPmohLpXc9qKvWmkngk8pPxFOAmYLeZ/cHMvm5mSkByTGtnmOBkWWnDcK6mik62qW7iIr1TNZUMRiK9qN4BEA7AdwpwOnAuiVdXyRCsrK/n91u30zXE7sQRM66YNoVzJkxIaLu2ruxqYByVF4kwd9xYluwdftVU0XFw1AZHpG+qppJEJTIX1Vjg0wQjC78A3Ovu96QoLunD0r37hjSKcKw/bd+ZeILTmV0NjGOdXTUuTHCGVzVVtJFxtrRpEslGf11NdZhpGpNGBpBIG5yFwJ8JuoefTjDi8PXuviwlkUmv2sPqjOtmzeTE0eUDrN07d/j31WvYc/Qoh1rbqCwqjHvb1u4SnOxLcGKrqXYdPsLk0uFRitPdTTwLk0aRbBGJmZtqZf1+JTgyoEQSnPHu/u3w8UNm9ivgFwTVVJImHeGv/arioj6nL4jHzIrRPH/wEC81NHDOhPFxbxdNsAoj2VVFBT2rqeqZXDo90yHFRY2MReIzT9VUkoBEfjIeMLPTo0/c/VVgePxEHkGS9Wt/TkUwMPRLhw4ltF13I+MsLMEBmFcVDPo31Lmy0qm7kbFKcET6NbNiNKNjqqlE+pPIHfVjwC/N7HYz+6iZfR94JUVxSR+S1eNmTmUlAC81NJBIr//uRsZZWIIDQeJWGlNNle3c/VgjY/0aFelXxIy5YW+qFfXD50eMZEbc35LuvgE4G3iCoKHxGuDvUxSX9KF75ukh9riZUjqKsvx8Dra2UdfSEvd23Y2Ms7QEJ1pNBcOjO2m0N1yE4OYtIv2bV1UFwKr6/Qn9OJPck9C3lLu3uft97n6Lu9/l7vF/M0pSJKsEJ2LG7MpoNVXDAGsfEx3oryiLq1PODm+AT++to6Uzc7MPx+NYwpq951Mkm6iaSuKVyGSbfwln+MbMbjSzT5lZ/N1vJCmSVYIDcFJl4u1wWsOEoTDLxsGJdVJlBceVldHQ1sbD23dkOpx+RRNWVU+JxCdixplV0Ql2VU0lfUukF1WFuzea2Tzgw8BDwF3A+1MSmfQqmQ1S51RUArCxoZEu97iqSLqnasjiEoeIGe868XhuXbOOx3bu4oTyckoLEvlTP6aysJBxxX3NJDJ0KsERSdzZ48bxl917WFW/n6vVm0r6kMhdv93M8oH3Ad9y9/vMbHmK4pI+RLsUJ2NQuKriIsYWFXGgtZUdhw8zPY5xJdo6s3Mk455mlJfzhuoJPL23jjte3DDwBn3IM+Nfzz5zSF3y+9PZpQbGIomKrabaHue9S3JPIgnOfxE0LC4GPh8u019VmiWzBMfMmFNZwZK9dbx0qCG+BKcre0cy7unqGcfR3N5BU3v7oLY/2NbKwdY2lu+r58oUjYwcHddoOJxPkWwRrab6y+49rKzfrwRHepXIXFT3mtn9QKe7HzWzmcCS1IUmPaWiS/FJFUGCs+FQA5dNnTLg+tFeVNk4knFPZQUFfOSUkwa9/fMHDvI/L7zIyvr9KUtwNMifyOBEq6lW1termkp6lWgvqmZ3Pxo+3uTu16cmLOlNbHKTrC7F0Z5Umxobu0cp7k90qoZsHMk42eZUVlCSl8euI0dSNkP5sRI53ZxFEhGtpqpvaWW7elNJLwZMcMxsZTLWkaHr7EpeD6qoisJCJo8aRXtXF5ubmgZcvy3LRzJOpvyYMXVWpai3xrGkdeSfT5Fkiu1NtVK9qaQX8dxVTzaztf38WwdUpTpQOdZeY6hj4PQ0J4HxcLJ9JONkiw4qlqobaCqSVpFccXZ3glOvQf/kNeJpgxNPI4bsHk1thEhVdcYplZU8sWs36w8c5Krj+p+gMpdKcCBI/kbl57PryBF2Hj7MlNLSpO4/VUmrSC6YOfpYNdXW5mZmlJdnOiTJIgPeVd19axz/+h1NzcwiZnaHmS0xs9qwgXLPdcab2UYzKw6fm5ntDNevNbNvDv5tjgzdY6Yk+ctwdmUFRZEI2w8f5kBLa7/rDodxcJIpPxLpnsBzyd66pO+/u5u4SnBEEhYx43Xjg1LWVHw+ZXgb1LeUmX3ZzKoT2OQaoNjdzyPoYn5bj/29CXgEmBiz+ERgpbvPD/99YTCxjiSpmnW6IBLh5DGVAKw7cKDfdbtHMs6RKiqAN1QHf+rL6vZ1X4Nkae8uwVGCIzIY51VPAOC5ffXd43SJwCATHOBu4CYzu83Mzo5j/QuAhwHcfSlQ0+P1LuBSIPbbdR4wxcyeMLM/mtmcQcY6YnSkcFC4M8YGjWnXHjjY73rdJTg5UkUFML2slMmjRtHc0cG6Ac5Poo4N9Jc751MkmSaXljKjrIyWzk5W7e//B5rkFhtMwywzuxVoBUqBee5+0QDr/wj4jbsvCp9vA05w944e620BTnL3FjO7CKh291+b2QXA99z9db3s+wbgBoDq6up5CxcuTPj9DKS5uZmyLBhIaj/GY/kFjPEuLuvsGHiDBLQCD+YVYMDVne0U9LHe/+UV0GXG2zvaEholciiy4fxvtAir8/KZ1NXFhV3JO/dbLcKzeflM7+rk3K7s/fWZDdcg1+ka9O0Vi7AiL58JXV3MT+LnM5bOf+b1vAYLFixY4e49C0y6DfY76mVgNvA7d/90HOs3ArGtvyI9k5teLAc6ANz9KTObbGbmPTIyd78TuBOgpqbG58+fH+dbiF9tbS2p2G+iNjU08ti69YytqGD+Gacnff/Pr13PpsZGxp56GvPGv7ZjXJc79z29BAPeePHFaRtYKxvO/7z2dtYtW86eSITTX39u0uanembPXp7d9ApTJk5i/uzXNE3LGtlwDXKdrkHfzunoYO2y5dQBJ9W8nomjRiX9GDr/mZfoNRhsufiLwOPAh8zsuTjWfxq4EsDMzgXWxbHNl4FPhdvMBbb3TG5yTap73JwxdgwQdLnsTWwPqlwbNbS8oIB5VVU48NjO3Unbb/c4OGpkLDJoJfn5nDNhPACP7dyV4WgkWwz2m3IGkAf8EPhAHOs/ALSY2TPA94BPm9lnzOxt/Wzz78DFZvYk8N04jzOidaS4x03N+PEYsO7AQQ73Mn9TLo1i3JtLp04G4Jm9e2ke5PxWPXU3HM+xhFEk2d44ZTIGPFu3j4a2tkyHI1lgsAnO5cCf3f0pYOdAK7t7l7vf6O5vcPfz3H2Du3/X3R/ssd4Md28JHx9097e4+8Xu/kZ3H/yU0CPEsS/D1JTgVBYVclJlJR3urOhlYLv2HOsi3tPU0lJOqaykrauLxbv3JGWf3XNR5eg5FUmW6pISzhg3lg53anclr5RVhq/B3lUNuN3MSoDPJDEe6Uf3ODgprM6IFvM+W7fvNa91dxHPoR5UPUUnJH1i9+6kdEk9NraRSnBEhuryKcHn8y+793C0IzWNjWX4GOw31WbgFuB2gp5UkgapLsEBmDtuLEV5ETY3NVF39OhfvdY9k3iOVlEBzK4YzbTSUprbO3pNAhPVmaKxjURy0fGjy5lVMZqjnZ1qiyODTnDudPctBEnOFUmLRvqVjhKcorw8zhoXjNzb8ws8F8fA6cnMuktx/rxzF11DbPceO0O8iAzdVdOD6WYe37U7aW3lZHjq95vKzK4xs//pOcieu28P/9/i7qemMkA5JlUjGfd0zoRgZNBldfv+6gs8F0cx7s1ZVeMYV1TEvpYW1gxxYDE1MhZJrpkVozm5spKWzk7ue3UzS/bu7fGvjoOt/U9JIyNDv+PguPtvw9nCLzezi8MxZyRD0vVlOKtiNGOKCtnf2sorjY3MqghmG1cJTiDPjDdOmcx9r27mTzt2cOa4sYPuNq9GxiLJd9Vx03jx0CGW76tn+b7XDnsxZdQo/uWsuTk33EWuiWegvw53vz3lkciAjlVRpfbLMGLG68eP5087dvJs3b5jCU53CY6+jM+rnsDD23ewrfkwy/bVdzfOTlS063+BxsERSZoZ5eX8/YknsLmp6TWvrT94kJ1HjrD+4EFOD6eokZEpngRnkZlNADYAawkG6VsLrHP3xlQGJ38tndUZ50wIEpyV9ft55wnHU5iXR2t4/KK83K6iguAcXD3jOH768iZ+u2ULc8eOpTg/8fPSGQ7eqLmoRJLrwkkTuXDSxNcs//OOndy/ZSuP7tilBGeEGzDBcfdTzKwIOAU4HTgDuBo4w8xa3f34FMcooXSV4ABMHDWKGWVlbGluZu2Bg9SMr6K9SyU4sc6ZMJ7Fe/awpamZ/1z/PGOKChPex5amZkBtcETS5fyJ1SzavoNNjY1sbmzi+NHlA28kw1Jcc1G5eyuwysw2AUeBKoK5qNamMDbpId0NUs+ZMJ4tzc08W1dHzfgqWrunalAJDgRVee884XhuXbOOrc3NbG0e/L4qB5EciUjiSvLzuWjSRP60YyeP7NjJR045KdMhSYoMmOCEPajeArwVGA88CvwcuMHdNR52GqWzBAdg3vgq/m/zFl44eIiGtrZjjYxVgtNtRnk5nzvzDPa3DL5XRnlBASfqV6RI2iyYPInHdu5i7YED7DlyJCWTc0rmxVOC8yKwCvgWwezh6l+XIccGhUtPCU5ZQQGnjhnD2gMHWL6v/lgj4xzvRdXT9LIyppeVZToMEYnT6MJCzq2ewFN79vLozl1cN2tmpkOSFIjnm+om4BngY8B2M3vRzO4zs381s2tSGp38lfau6LD+6Uswjk3dUNddRZXLIxmLyMhwaTg557K6fRzSuDgj0oDflO7+Q3f/x3DSywnAZcBPgDbgHakOUI7p8PQP63/a2DGMys9nx+EjbGpsAFSCIyLD34SSEs4cN45Odx7X5JwjUsLfVO6+w90Xufu33P26VAQlvevoSv/EjAWRCBeHXS0PtAZNrnJ9JGMRGRkuD6ddeWrPXo5ocs4RRz/Fh5HuMVPSPCjcW6dP423HTSd61JJBjPciIpJtjisvY05FBS2dnSzevSfT4UiSxdVNXLLDsVFv05uXmhlXTJvKjPIyNjc1c3y5evyIyMhw2dQpvNTQwBO7dnPJlMlpv79K6uhKDiPdbXAyNOrtSZWVvHnaVCIalE5ERoiTKyuYVlpKY3s7z9bVZTocSSIlOMNIe5q7iYuIjHRmxmVhW5xHd+yiKxxvTIa/tCQ4ZhYxszvMbImZ1ZrZawYdMLPxZrbRzIrD5yVm9hszW2xmfzSzwc1mOIJEq6g0b5GISPKcVTWOcUVF7GtpYfX+/ZkOR5IkXd+U1wDF7n4e8HngttgXzexNwCNA7MxoNxFM6HkhcC9wc3pCzV6d3SMZqwRHRCRZ8sy4dMpkAB7ZsRNXKc6IkK4E5wLgYQB3XwrU9Hi9C7gUONDbNsCi8PWcFp2LSo3gRESS67zqCZTl57Ot+TAvNTRkOhxJAktHpmpmPwJ+4+6LwufbgBPcvaPHeluAk9y9xcz+DPyju79oZhFgm7tP7WXfNwA3AFRXV89buHBh0uNvbm6mLAuG4n8wr4AWM97a0UYuzZySLec/l+kaZJ6uQeq9YBHW5+VT6s7lne0UxLym8595Pa/BggULVrh7zwKTbunqJt4IxPYtjvRMbgbYphw41NtK7n4ncCdATU2Nz58/f0iB9qa2tpZU7DdRf1i6DDo6uPD88ykvKBh4gxEiW85/LtM1yDxdg9Q7v6uL76xZx/bDh9k+cTLXz5mFhb1Gdf4zL9FrkK66jqeBKwHM7FxgXSLbAG8GFqcmtOHjWDdxtcEREUm2gkiED540m6JIhOX19SzZq27jw1m6EpwHgBYzewb4HvBpM/uMmb2tn21uB041s6cIqqC+koY4s1r3VA1qgyMikhLVJSW8e+YJAPzq1c3sPnIkwxHJYKWlisrdu4Abeyze0Mt6M2IeHwH+LrWRDR9d7sd6UakER0QkZc6ZMIENhxp4tm4fP96wkc/OPT3TIckgqChgmIgmN3lm3XXCIiKSGu868QQmFBez68gRfrN5S6bDkUFQgjNMdGgUYxGRtCnOy+NDJ80m34zFe/ayQz8shx0lOMNER3f1lC6ZiEg6TCsr42+OnwHAc5F89re0ZDYgSYi+LYeJYyU4umQiIukyf9JETh87hnYz7n7pZTrDe7FkP31bDhPdPahUTCoikjZmxnWzZlLizuamJh7atj3TIUmclOAME91j4KgER0QkrcoKCji3swMjmKtqw6FDmQ5J4qBvy2FCJTgiIpkzHuct06fhwD0vvUxjW1umQ5IBKMEZJlSCIyKSWVdMm8qs0aNpbG/n3o2b6NKs41lN35bDhLqJi4hkVsSM6+fMojQ/nxcOHeKxnbsyHZL0QwnOMHGsikqXTEQkUyqLinjf7JkA/G7rNjY3NWU4IumLvi2HiWNVVCrBERHJpNPHjuWSyZPocufHG15i1+HDmQ5JepGWuahk6FSCIyKSPa6ecRyvNjWxpamZb61Zx4LJkyjOy+t+/bQxY5haVprBCEUJzjARLcHJUwmOiEjGFUQifPK0U7nvlc0sqavjkR07/+r1P27bzmfPPIOppUpyMkUJzjChEhwRkexSlJfHdbNnMnfcWF6NaYuzrbmZDYcauHvDRj535hkUxZTsSPoowRkmOsMSnAKV4IiIZJUzxo3ljHFju5+3dnbyrdVr2XP0KL9+dTPvnTUzg9HlLhUHDBPt0RIcjYMjIpLVimJmIn9mbx3P7duX6ZBykr4th4noODh5GslYRCTrTSkt5W9POB6AX256lX1HNRN5uinBGSY6XCU4IiLDyYUTqzlr3FhaOjv58Usvdf9QlfTQt+Uw0RkdyVglOCIiw4KZ8Z5ZMxlbVMS25sP8buu2TIeUU9KW4JhZxMzuMLMlZlZrZjN7vP5hM1tuZkvN7K3hsrFmVh+uX2tmn0xXvNlGJTgiIsPPqPx8PjhnNhHgsZ27WH/gYKZDyhnp/La8Bih29/OAzwO3RV8ws4nAJ4DzgTcB3zSzIuBs4JfuPj/8959pjDerdKgER0RkWDphdDlXHTcdgHs3vsyhVs1Eng7maZoN1cy+Cyxz94Xh853uPiV8/DbgSne/MXz+APAN4BLgaqAdqAM+4e67e+z3BuAGgOrq6nkLFy5MeuzNzc2UlZUlfb+JWBHJ45VIHmd1djDLc6seNxvOf67TNcg8XYPMGur5d+AvkXz2RiJM6Orioq4OtRFJUM9rsGDBghXuXtPX+ukcB2c00BDzvNPM8t29o5fXmoAKYAOwwt3/bGbvAf4b+NvYnbr7ncCdADU1NT5//vykB15bW0sy9vvUnr1sb24e1LaHGxrh6FFOnjOHCyZWDzmW4SRZ518GT9cg83QNMisZ5//stja+sWoNde3ttM44kTdPn5ac4HJEotcgnQlOI1Ae8zwSJje9vVYOHAKeBY6Eyx4A/i3FMaZMQ1sbv9j0ypD3M7qgIAnRiIhIulUUFvL+2bP4/vMv8NC27cyqqGBmxehMhzVipTPBeRq4CrjPzM4F1sW8tgz4upkVA0XAycB64H+B3wD3AW8EVqQx3qRqbAvqXCsLC3nTtCmD2kdZfgGnjh2TzLBERCSNThlTyeVTp/DIjp3c/dJGvnjWXEr1wzUl0pngPABcZmbPAAZcb2afATa5+4Nm9l/AYoKGz1909xYz+zxwt5l9FDgM/EMa402qpvagsKq6pISLJ03KcDQiIpIpV02fxsaGBrY0NfPTl1/hIyfPwdSBJOnSluC4exdwY4/FG2Jevwu4q8c2m4EFqY8u9Q63twNQVqDpv0REclleJMIH58zmm6vWsPbAAZ7cvYf5k/XDN9nUiDtNmroTHBVFiojkuqriYt4z60QA7t+8hVcbmwbYQhKlBCdNDncEVVRKcEREBODsqioumFhNhzv/uf55nqvTpJzJpAQnTbpLcPJVRSUiIoF3nnA851dX097VxU82vsyPNrxE3dGjmQ5rRNC3bZocVhWViIj0kB+JcO3ME5haOor7t2xlZf1+Vu8/wIUTq7ly2lTKCwszHeKwpQQnTZrbVUUlIiKvZWZcPHkSZ4wby0Nbt7O0ro4nd+/h2bp9XDZ1CpdMnkRRXl6mwxx2VEWVJs0dQQlOqXpRiYhIL8YUFXHd7Jn8y1lzOXVMJS2dnfx+6zZuWbGSp/fspTNNUyuNFEpw0iRaglOuEhwREenHlNJSPnbqKXzytFOZXlZKQ1s7P9/0Cl9fuZq1+w+QrjkkhzslOGnQ5d7dBqdUjYxFRCQOcyor+OzcM/jgnNmMKypiz9Gj3PHiBr637nk2N6lb+UD0bZsGRzs66AJK8vLIjyinFBGR+ETMqBlfxZnjxrJ4z17+uG07mxobuXXNOs4aN46rZ0xnQklJpsPMSkpw0qBZY+CIiMgQ5EciLJg8iXMnjOeRHTt5fNduVu3fz5oD6nHVFyU4adDcrgbGIiIydCX5+Vw94zgumjSRh7ZtZ+neoMfV0ro6LpsyhUumTKZYPa4AtcFJi+4u4vkqwRERkaEbU1TEdbNm8sWz5nLamDG0dnbx0Lbt3LJ8JU/t2aMeVyjBSYtmTbQpIiIpMLm0lI+eejKfOv1Ujisro7G9nV9sepWvrVzNmhzvcaUEJw2aNYqxiIik0OyKCj4793Q+NGc2VcVF7D16lB++uIHvrlufsxN5qkghDZTgiIhIqpkZ88ZXMTfscbVo23ZeaWziO2vXcea4sVw94ziqc6jHlRKcNIj2oipXFZWIiKRYbI+rR3fu4rGdu1i9/wBr9x/g/InVvGX6NEbnQI8rfeOmQXcvKjUyFhGRNCnJz+dtx03noonV/GHbdp7ZW8fiPXtZVrePS6dO4Y0jvMeV2uCkgSbaFBGRTKksKuI9s2Zy89lncvrYMbR2dfGHsMfVX3bvobOrK9MhpkTaEhwzi5jZHWa2xMxqzWxmj9c/bGbLzWypmb01XFZlZo+Y2WIz+5WZjUpXvMmkXlQiIpJpk0aN4qZTTubTp5/KjLDH1cJXXuWrq1azqn4/XSOsx1U6v3GvAYrd/TwzOxe4DbgawMwmAp8AaoBi4CkzexT4EvALd7/HzD4PfAT4XhpjBuAQxnN1+wa9fZMaGYuISJaYVVHBP889nVX79/O7LduoO9rCXRteYkxRIXPHjqM4P/5qq8JIhPKCAgr7mIZoTFERMytGJyv0hKQzwbkAeBjA3ZeaWU3Ma68Hnnb3VqDVzDYBZ4TbfCNcZ1H4OO0Jzo5IhEc2vjykfRREIpSM4LpOEREZPsyMs6uqOGPsWJ7as5fHdu5if2srtbt3J/U4Z40blxMJzmigIeZ5p5nlu3tHL681ARU9lkeX/RUzuwG4AaC6upra2tqkB17U2sr0kqHVjk3s7ODJJ59MUkS5pbm5OSXXVeKna5B5ugaZNdLP/yXAPox6s7i3caDDjFagr1Y8XreX2r3JSZoSvQbpTHAagfKY55EwuenttXLgUMzyozHL/oq73wncCVBTU+Pz589PcthAbS0fvujC5O9X4lJbW0tKrqvETdcg83QNMkvnP/MSvQbp7EX1NHAlQNgGZ13Ma8uAC82s2MwqgJOB9bHbAG8GFqcvXBERERmu0lmC8wBwmZk9AxhwvZl9Btjk7g+a2X8RJDAR4Ivu3mJmXwP+18w+DNQD16YxXhERERmm0pbguHsXcGOPxRtiXr8LuKvHNnuBK1IfnYiIiIwkGuhPRERERhwlOCIiIjLiKMERERGREUcJjoiIiIw4SnBERERkxDEfQZNrmdk+YGsKdl1F0E1dMkPnP/N0DTJP1yCzdP4zr+c1OM7dx/e18ohKcFLFzJa7e83Aa0oq6Pxnnq5B5ukaZJbOf+Yleg1URSUiIiIjjhIcERERGXGU4MTnzkwHkON0/jNP1yDzdA0yS+c/8xK6BmqDIyIiIiOOSnBERERkxFGCIyIiIiOOEpw+mFnEzO4wsyVmVmtmMzMdUy4ys5Xh+a81s59kOp5cYmbnmFlt+HimmT1lZovN7HYz070jxXqc/7PMbGfMZ+FdGQ5vRDOzAjP7afj3vszM3qbPQHr1cQ0S+hzkpyvYYegaoNjdzzOzc4HbgKszG1JuMbNignZi8zMdS64xs88C1wGHw0XfBW5291ozu4Pgs/BApuIb6Xo5//OA77r7bZmLKqe8F9jv7teZ2VhgdfhPn4H06e0a/BsJfA6UgfbtAuBhAHdfCmiAp/SbC4wys0fM7PEw0ZT0eAV4e8zzecCT4eNFwKVpjyi39Hb+32JmfzGzH5tZeYbiyhW/Bv41fGxAB/oMpFtf1yDuz4ESnL6NBhpinneamUq80usI8B3gTcCNwM91DdLD3X8DtMcsMj/W5bIJqEh/VLmjl/O/DPhnd78IeBX4ckYCyxHu3uzuTeEX6P8BN6PPQFr1cQ0S+hwowelbIxCbHUbcvSNTweSojcDPPLAR2A9MynBMuaor5nE5cChDceSqB9x9RfQxcFYmg8kFZjYNeAL4qbv/An0G0q6Xa5DQ50AJTt+eBq4ECKtG1mU2nJz0QYK2T5jZZIJStd0ZjSh3rTKz+eHjNwOLMxdKTvqTmb0+fPxGYEV/K8vQmFk18AjwOXe/O1ysz0Aa9XENEvocaKC/PoQt5H8AnEFQ/3e9u2/IbFS5xcwKgXuA6YAT/KE/k9GgcoiZzQAWuvu5ZjYbuAsoBF4EPuzunZmMb6Trcf7PBv6boNpqD3CDuzdmMr6RzMz+E3gXEHvP/yTwX+gzkBZ9XIMvAt8mzs+BEhwREREZcVRFJSIiIiOOEhwREREZcZTgiIiIyIijBEdERERGHCU4IiIiMuIowRHJQWZ2qpn9wcyeMLPnzOwrZma9rLfQzArNbLqZXRUu+w8zm57g8baEc4v1u2wozGxp2LU60e1ON7OL4lz3HjNba2aXhJP9LYv5/z8SOOZ8M1vYY9nXzGyPmV2R4FsQkV4owRHJMWZWCSwEPuXuC4BzgdOBj/Rc193f7e5twCXA+eGyT7n7tvRFnHLvAE5JYP3Puvvj4eP3hZPBngPUmNmg56xz95sJ578TkaHTvD4iuedq4HF3fxnA3TvN7H1AWzhS67eANuBO4KvAqcDnCSY+fQb4DMHcYPuB/wUqCQbDfB9wFLgdKCaYVuNmd/9tP7H8MCx12Qu8HygAfhTuczLwP+5+u5nVEswmfBrBiNZ/5+5bzezrwBXAdqCq587D7eqAsQSJzF2x+wYeBD4QvveVQAnwdaCTYMLLj7h7e8/99qKIYAC4A2aWB/wQmBaegwfd/WYzuwcYF/67NYxvFPAbgilJfh7HcUQkTirBEck9kwkmqusWTmzXFj4tdvcL3f2n4fNO4N+BX7j7gzGb3Uzw5f0G4P8BrwdOAm5z98uAG4CPDRDL7e5+MbAF+DAwk2D03suBywmSqahl7n4p8Cjw92FpyUXA6wiSq75mFv5luN2JPfft7jsJRsv+LvAcQQL09jCmnQTJT3/uDZOojQRzE+0gSGyWuvubwnNyY8z6j4fn6yBQBvw+PAdKbkSSTCU4IrlnK3B27AIzO57gixngpTj3Mwe4GyCcQuMZMzsVuNnMPkQwvUZBP9u3ufvS8PEzwGUEpRmfMrO3E0x4G7v9qvD/7cBEYDaw3N27gEYz62u+uOj72dvPvgHGE5S43Bc2RyohSKb68z533xBO7XI38FmC4fxfZ2YLwuMU9RILwMUEc9zFvi4iSaISHJHc8xBwhZmdCGBmBQQlGKeFr3f1sk0Xr71fvEhQeoKZXWRm3yKo0rrX3a8jmAX4NQ2XYxSa2Znh4wuB9QQlQUvc/b3Ar3ts33NemReA15tZxMxK6bsdTfT99LXv6HurJyiBuTpsV/N14HHiECZZOwmqqT4AHHL39xBMFjsqpgF37Ln9A/A3wNfDyWRFJIlUgiOSY9y90czeD9wVljyUE1aVEJQq9GYd8MWwnUrUN4C7zey9BMnHhwga237HzL5AkCy8pl1MjFbgH81sFkGp0ueBC4D/NrN3E1T5dJhZryUc7r7azBYRVC3tImhr05/f97HvFQRtYl4kmFDxD+F5aSSo+urPvWZ2JHx8BHgvQSnQL8zsvPA9vkxQLdjbe9hrZl8GfqLeUyLJpck2RUTiFDYUXujuKentlOr9i+QSVVGJiCTm22Z2SbJ3amZfI+gRJiJJoBIcERERGXFUgiMiIiIjjhIcERERGXGU4IiIiMiIowRHRERERhwlOCIiIjLi/H8wqmcuojPD5gAAAABJRU5ErkJggg==\n", 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" ] @@ -161,13 +167,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:359: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:388: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -198,13 +204,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:359: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:388: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -229,12 +235,12 @@ "metadata": {}, "source": [ "### Steady signal\n", - "For a steady signal, the syntax is almost equivalent, see below." + "For a steady signal, the syntax is almost equivalent, see below (you can download the [signal](../validations/loudness_zwicker/data/ISO_532-1/Test%20signal%205%20(pinknoise%2060%20dB).wav) that is used in the following." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -248,13 +254,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:359: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + "d:\\scripts\\github_svn\\scidatatool_martin\\SciDataTool\\Functions\\Plot\\plot_2D.py:388: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -266,9 +272,13 @@ } ], "source": [ + "# Define path to the .wav file\n", + "# To be replaced by your own path\n", + "path = \"../validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav\"\n", + "\n", "# Create an Audio object\n", "pinknoise = Audio(\n", - " \"../mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav\",\n", + " path,\n", " calib=2 * 2 ** 0.5,\n", " is_stationary=True,\n", ")\n", @@ -296,12 +306,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -321,10 +331,14 @@ "from mosqito.functions.shared.load import load\n", "from mosqito.functions.loudness_zwicker.comp_loudness import comp_loudness\n", "\n", + "# Define path to the .wav file\n", + "# To be replaced by your own path\n", + "path = \"../validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav\"\n", + "\n", "# Load signal \n", "signal, fs = load(\n", " False,\n", - " \"../mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav\", \n", + " path, \n", " calib = 2 * 2**0.5 \n", ")\n", "\n", @@ -351,12 +365,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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2fuViZ0emPNiUCZpKmmSqf/THhp0UzOw8YKu7/y6CeBIhlxRKeODpzCNaeN8pc7nl0ZeHvW/KnZ8+sYUHP/XGCCIrDbs6MlfJTh6vlkKSKSn8sZG0FE4GjjazKnc/p9ABJUF2TKGyhJMCwBfPP4ovnn/UsPf76r1rue6X6+juTavraQC79gcthQYlhSTLdh9lFtoRGEFScPe/jyKQJMmOKZR4ThixuZPHk3a0jOEgdrZnWwrqPkqyaQ21AGxr02oAWWFKZ/8fM2sIbn/WzH5kZsdFH1p8POg+qhxDYwrFNG9yZsbShl37Y44kuXZmWwoaaE60mqoKJo+vYWvbwbhDSYwwbf9/cPd2MzsNOAP4L+Cb0YYVr0Ozj5QU+jN3cqZ18NJOJYWB7GzvZnxNpea+jwHTm2rZuq8z7jASI0xSyF5S+2fA9e7+M6Ck28TZMYWxNPuomKZMqGF8TSUbdh2IO5TE2rW/S+MJY8T0xlq2qvsoJ0xS2Gxm3wIuBFaY2biQ+41Zh2YfxRxIQpkZcyeP5yV1Hw1oZ0eXxhPGiJamWra1qaWQFebD/QIy6ym8xd33As3AJ6MMKm7ZpFDqs49GY96Uel5SS2FAuzq6NZ4wRkxvrGX3/m66eoeuM1YOwsw+mgH8zN27zOx04BjgpiiDipvGFIY2d/J47luzjW8/uH5E+1dVGu88YXZuvepSs7Oji+NUynxMmN6YmYG0va1LZV8IlxTuAJaa2SIyZbLvBG4G/jTKwOLkGlMY0vGtk+hNO19aEa60d3/aDvZyxRnx15Zyd17Z10k6O5g0Sml3du/vZqquZh4TpjdlksLWtk4lBcIlhbS795rZO4Cvu/vXzezxqAOL01isfVRsZx7RwpovnE3KR/ZB+hffXcmdqzdz+ZsXxV6y+MaHN/DFu9YU/LgtwYeNJFs2KWzRDCQgXFLoMbOLgPdzaH2F0mzzB9K6TiGU0Uy3fPtxs7jyR0/x1OZ9HDN7YuGCGoE7V29mScsELn39woIds7rSOPOIloIdT6LTEnQfbVNSAMIlhQ8AHwG+5O4vmtl84L/DHNzMzgb+HagEbnD3Lw+w3TuB24ET3X1VqMgjlE0KcX+DLWXnHD2Dq+58hvfe8LsRLyBz+IxGrvvz46mtHnly2rTnAE9u2seV5xzGu06YPeLjyNjVWFtFXXUlWzUDCQiRFNx9jZl9GmgN7r8I/PNQ+5lZJXAdcCawCVhpZsvdfU2f7RrILOuZmAJ75VL7KE5NddV88fwjWbVhz4j2706luXP1K3z2J0/zvlPmjjiOu5/eCsA5R00f8TFkbDMzZjTVsmWfrmqGEEnBzN4GfIXMBWvzzexY4Gp3P3eIXU8C1rn7+uA4twLnAX07b79IJskkZpqrxhSK48ITW7nwxNYR7z9rYh3/+cAL3P7YplHFcdSsxtxV2lKeZjfXs2mPkgKE6z76PJkP+AcA3H21mS0Isd8sYGPe/U1kKqzmmNnxwBx3/5mZDZgUzOxS4FKA1taRf4iENRbXUyhHnzjrNbxhyVT2d/eO6jiHz2gsUEQyVs2ZVMeTm/bGHUYihBpodvd9ffrX06N9YjOrAK4FLhlqW3e/nsx0WJYuXVqYeYODSAdnp+sUkq2iwjh5QTTrY0t5aW2uZ++BHto6e0r22pmwwlzR/IyZvQeoNLPFZvZ14JEQ+20G5uTdnx08ltUAHAU8YGYbgFOA5Wa2NFTkETo0+yjmQESkKLLXJ2zcrav0w3zsfQw4EugCbgHagI+H2G8lsNjM5ptZDfBuYHn2l+6+z92nuPs8d58H/BY4Nwmzj1KafSRSVlpzSUHjCmFmHx0APhP8hBZc8HYZmbpJlcCN7v6MmV0NrHL35YMfIT6u2kciZWXOJLUUssLMPloCfAKYl7+9u79pqH3dfQWwos9jVw2w7elDHa9YUhpTECkrTfXVNNZW8bKSQqiB5tvILKpzA4fWVihph2YfxRyIiBTNnOZ6Nu5RUgiTFHrd/RuRR5Ig2cJo6j4SKR+tzfU8u6UNdy/r8cQwSeGnZvZ/gR+TGWwGwN13RxZVzLTymkj5OXl+M3c/vZVP3v4kyxYNPdW5urKCMw5vGVWZlSQKkxQuDv7Nv7jMgTAXsI1JKdd6CiLl5uJT57GtvYtvDOMq+X96x9FcdFL0F9QWU5jZR/OLEUiSaDlOkfJjZnz67MO4+HXz6OwZfPjUgbf824Ns2Fl6S9IOmBSC9RMG5O4/Knw4yZAbU1BWECk700OugzF7Ul1JzlYarKWQXTthGnAq8Ivg/hvJXNFcukkhO6ag7iMRGUBric5WGjApuPsHAMzsXuAId98S3J8BfLco0cVEazSLyFDmTKrn9y+NrPR7koWZiT8nmxAC2wjWVihVuk5BRIbS2lxPW2cv+w70xB1KQYWZfXS/md1Dpu4RwIXA/0YXUvzSKnMhIkOY01wHwMY9B2iqb4o5msIZ8ruwu18GfAt4bfBzvbt/LOrA4pTtPirnC1hEZHDZyqqlNtgcanHcYKZRyQ4s9+XZ5Tg1+0hEBlCq5bbDFMRrJzMtFzJLclYD+929ZJer0nKcIjKUxtpqJtZXl19Lwd0bsrct059yHpkFcUpWWlc0i0gIrc31JZcUhjW/xjN+ArwlmnCSQWs0i0gYcybVs2lPaS3ME6b7KP/K5gpgKdAZWUQJkL14TbOPRGQwc5rruXfNVlJpL5kxyDADzW/Lu90LbCDThVSyNKYgImHMaa6jJ+Vsa+tk5sS6uMMpiDBjCh8oRiBJ4uo+EpEQWvOmpZZKUhhyTMHMZpvZj81se/Bzh5nNLkZwcVGZCxEJI7u2cykNNocZaP4OsByYGfz8NHisZGlMQUTCmDmxjgqDTWWWFKa6+3fcvTf4+S4wNeK4YpWdfWSqfSQig6ipqmBGU2mV0A7zsbfLzN5rZpXBz3uBXVEHFifVPhKRsOY017GxhKalhkkKHwQuALYCW4B3AZdEGFPsUunMvxpTEJGhtDbX89KuMmopuPtL7n6uu09192nufj5wRfShxUels0UkrEXTJrCzo4vd+7vjDqUgRvqxd0FBo0iYtGYfiUhIh8/IlIF7dktbzJEUxkiTQkl/Wmr2kYiEVWpJYcCL18yseaBfUeJJIZWdfVTSZykihTBlwjimNYxjzSslnhSAx8iUzO7vo7E0Os8G4O5UmBbZEZFwDp/RyJpSbym4+/xiBpIkqbRrPEFEQjt8RiOPvLCTfQd7qKnsv1d+XFXFmCidE2rltXKTdtU9EpHwjpzZSE/Kee0X7h1wm9MWTeF//vLkIkY1MkoK/UgH3UciImGceUQLn3/bEXT2pvv9/a/W7mDVS7tx98R3Sysp9COdds08EpHQaqsruWTZwD3u9TWV/Gb9Lna0dzGtsbaIkQ1fmCqpp5hZ/pKcjWaW/DbQKKRcYwoiUjjZEtsvjYEaSWGuU/gG0JF3vyN4rGS5xhREpIBy6y6MgXIYYZKCeXbVGcDd05R4t1Nm9lHcUYhIqZg9qR6z0mkprDezy82sOvi5Algf5uBmdraZrTWzdWZ2ZT+//4iZPWVmq83sITM7YrgnEIW0l856qyISv5qqCmY21bGxRJLCR4BTgc3AJuBk4NKhdjKzSuA64BzgCOCifj70b3b3o939WOAa4NrwoUcnPQZmCIjI2JKppro/7jCGFGaN5u3Au0dw7JOAde6+HsDMbgXOA9bkHTv/EsDxZK6gjl06rbpHIlJYrc313P+H7XGHMaTBah99yt2vMbOv08+HtbtfPsSxZwEb8+5nWxl9n+ejwN8ANcCbBojlUoLWSWtr6xBPO3opXacgIgXWOrmenR1d7O/qZfy45A7LDtZ9lP1Gv4pMHaS+PwXh7te5+0Lg08BnB9jmendf6u5Lp06NfiXQtLtmH4lIQc2bPB6AF3cmuwtpsHR1IXAXMNHd/30Ex94MzMm7Pzt4bCC3kpCprmnVPhKRAnvN9AkAPLetnaNmNcUczcAGaymcYGYzgQ+a2SQza87/CXHslcBiM5tvZjVkxiWW529gZovz7v4Z8PxwTyAKaUezj0SkoOZOHk91pfHcto6hN47RYC2FbwL3AwvIdBflf0p68PiA3L3XzC4D7gEqgRvd/RkzuxpY5e7LgcvM7AygB9gDXDziMymglLvWUhCRgqqurGDBlAk8t6097lAGNVjp7K8BXzOzb7j7X43k4O6+AljR57Gr8m4ncq1nd9U+EpHCWzK9gcdf3hN3GIMasPvIzBqDm5/p23UUsvtozEqldfGaiBTekmkT2LTnIPu7euMOZUCDdR/dDLyV/ldgG7L7aCxLu1ZdE5HCWzI9U1v0+e0dHDtnYrzBDGCw7qO3Bv+W3Qps6bQzwOJJIiIjtqQlkxSe29qe2KQQpnT2282sKe/+RDM7P9KoYpZW6WwRiUBrcz3jqioSPdgc5vvw59x9X/aOu+8FPhdZRAmQcpQURKTgKiuMRdMm8Nz25E5LDZMU+tsmuddoF4CrzIWIRGRJSwPPbR3bLYVVZnatmS0Mfq6lgGUukkizj0QkKktaGtja1sm+gz1xh9KvMEnhY0A38AMypSg6gY9GGVTcVDpbRKKypCVT7uL5hI4rhCmdvR+40szGB7dLXjqtMhciEo3cDKRtHSydl7xLvsLMPjrVzNYAzwb3X2tm/xl5ZDFKuVOhKakiEoFZE+uor6lM7AykMB99/wq8BdgF4O5PAK+PMqi4aUqqiESlosJY3NIwppMC7r6xz0OpCGJJDJXOFpEoLZk2IbHVUsMkhY1mdirgZlZtZp8g6EoqVSqdLSJRWtLSwM6OLnbv7447lD8SJil8hMxso1nAK8CxlPjso1Ra1ymISHSyNZCS2IUUZvbRTuDPixBLYmhMQUSilJ2W+ty2dk5ZMDnmaF4tzOyjBWb2UzPbYWbbzexOMyvZCqmgpCAi0ZreWEtDbVUiWwphuo9uBn4IzABmArcBt0QZVNw0piAiUTKzTLmLBA42h0kK9e7+3+7eG/z8D1AbdWBxSqe1HKeIRGtJS2ZpTnePO5RXCZMU7jazK81snpnNNbNPAStKeQW2tKv2kYhEa0lLA3sP9LCjoyvuUF4lTLXTC4J/P9zn8XdToiuwpTSmICIRO7TgTgfTGpLT+RJm9lEZrrym9RREJFqHaiC1c9riKTFHc8iA3UdmdqKZTc+7//5g5tHXSrXbKCut9RREJGJTJtQwqb6a57cnawbSYGMK3yJTMhszez3wZeAmYB9wffShxUdjCiIStewMpLUJW3BnsKRQ6e67g9sXAte7+x3u/g/AouhDi08qjdZTEJHILWlp4PltHYmagTRoUjCz7JjDm4Ff5P2u5JfjrFTpbBGJ2JKWCbR39bJlX2fcoeQM9uF+C/ArM9sJHAR+DWBmi8h0IZUszT4SkWI4bEYjAM9uaWPmxLqYo8kY8Puwu38J+Fvgu8Bpfqh9U0Fmic6SpdLZIlIMR8xoxAye3twWdyg5g3YDuftv+3nsuejCSYa0a0qqiERv/LgqFkwZz1Obk9P5op7zfqQ1piAiRXL0rCaeVlJItpS6j0SkSI6a1cTWtk52tCej3IWSQj/cM+uoiohE7ahZTQA8/UoyWgtKCv1I6YpmESmSI2dmBpuf2Lg37lAAJYV+pd2pVPeRiBRBQ201R85s5JEXdsUdCqCk8EfcHXdd0SwixbNs4RQef3kPB7p74w5FSaGvdHA1hmofiUixnLpoCj0pZ+WGPXGHEm1SMLOzzWytma0zsyv7+f3fmNkaM3vSzO43s7lRxhNGKsgKygkiUiwnzptEdaXxyLqdcYcSXVIws0rgOuAc4AjgIjM7os9mjwNL3f0Y4HbgmqjiCSsdXLit2UciUiz1NVUc3zqJB9buiDuUSFsKJwHr3H29u3cDtwLn5W/g7r909wPB3d8CsyOMJ5RcUtCYgogU0TlHTWfttnbWxby+QpRJYRawMe/+puCxgfwFcHd/vzCzS81slZmt2rEj2kyaG1NQUhCRIjrn6BmYwV1Pbok1jkQMNJvZe4GlwL/093t3v97dl7r70qlTp0YaS3ZMQTlBRIqppbGWE+c1c9uqTXzt/udp7+yJJY4ok8JmYE7e/dnBY69iZmcAnwHOdffYr/POFoPV7CMRKbY/P7mVHR1dXHvfc/xk9SuxxBBlUlgJLDaz+WZWA7wbWJ6/gZkdR2bZz3PdfXuEsYR2aPaRkoKIFNd5x85i7RfPZmZTbWwzkSJLCu7eC1wG3AM8C/zQ3Z8xs6vN7Nxgs38BJgC3mdlqM1s+wOGKJjumoNlHIhIHM+PURVP4zfpdpNPFX6Yz0mU13X0FsKLPY1fl3T4jyucfiUOzj2IORETK1rJFk7n9sU2s2dKWK5hXLIkYaE6SbFLQ7CMRicupC6cA8HAMXUhKCn3kxhTUVBCRmLQ01rJo2gQeUlKIX3Ylag00i0icli2czMoNu+nqTRX1eZUU+si2FLQcp4jEadmiKXT2pHn85b1FfV599PWhMhcikgQnL5hMhVH0qalKCn0oKYhIEjTVVXP07Ik8XOTFd5QU+khrTEFEEmLZwsk8sXEvHV3FW3xHSaEPjSmISFIsWzSF3rTz6IvFay3oo6+PbPeRluMUkbidMHcSNVUVPLxOSSE26XTmX128JiJxq62uZOncSUW9iE1JoY9UbuW1mAMRESHThfSHre3s7ChOEWl99PWh2UcikiTLFmVKXjxSpFlISgp9pFU6W0QS5OhZTTTUVhXtegUlhT5yy3Gq9pGIJEBlhXHKgsk8/IKSQiy0HKeIJM2yhZPZuPsgG3cfiPy5lBT6cJXOFpGEyY4rFGMWUqSL7CTJD1du5Nu/Xj/kdge6MxUJVTpbRJJi0bQJfPbPDufE+c2RP1fZJIWJ9dUsbpkQattliyZzxIzGiCMSEQnHzPjLP1lQlOcqm6Rw1pHTOevI6XGHISKSaBpTEBGRHCUFERHJUVIQEZEcJQUREclRUhARkRwlBRERyVFSEBGRHCUFERHJsWytn7HCzHYAL41w9ylA8ZYwSp5yPv9yPnco7/PXuWfMdfepQ+0w5pLCaJjZKndfGncccSnn8y/nc4fyPn+d+/DOXd1HIiKSo6QgIiI55ZYUro87gJiV8/mX87lDeZ+/zn0YympMQUREBlduLQURERmEkoKIiOSUTVIws7PNbK2ZrTOzK+OOp5jMbIOZPWVmq81sVdzxRM3MbjSz7Wb2dN5jzWZ2n5k9H/w7Kc4YozLAuX/ezDYHr/9qM/vTOGOMipnNMbNfmtkaM3vGzK4IHi+X136g8x/W618WYwpmVgk8B5wJbAJWAhe5+5pYAysSM9sALHX3sriAx8xeD3QAN7n7UcFj1wC73f3LwZeCSe7+6TjjjMIA5/55oMPdvxJnbFEzsxnADHf/vZk1AI8B5wOXUB6v/UDnfwHDeP3LpaVwErDO3de7ezdwK3BezDFJRNz9QWB3n4fPA74X3P4emT+WkjPAuZcFd9/i7r8PbrcDzwKzKJ/XfqDzH5ZySQqzgI159zcxgv+sMcyBe83sMTO7NO5gYtLi7luC21uBljiDicFlZvZk0L1Ukt0n+cxsHnAc8DvK8LXvc/4wjNe/XJJCuTvN3Y8HzgE+GnQxlC3P9JmWfr/pId8AFgLHAluAr8YaTcTMbAJwB/Bxd2/L/105vPb9nP+wXv9ySQqbgTl592cHj5UFd98c/Lsd+DGZ7rRysy3oc832vW6POZ6icfdt7p5y9zTwbUr49TezajIfiN939x8FD5fNa9/f+Q/39S+XpLASWGxm882sBng3sDzmmIrCzMYHg06Y2XjgLODpwfcqScuBi4PbFwN3xhhLUWU/EANvp0RffzMz4L+AZ9392rxflcVrP9D5D/f1L4vZRwDBNKx/AyqBG939S/FGVBxmtoBM6wCgCri51M/dzG4BTidTNngb8DngJ8APgVYypdcvcPeSG5Ad4NxPJ9N14MAG4MN5fewlw8xOA34NPAWkg4f/nky/ejm89gOd/0UM4/Uvm6QgIiJDK5fuIxERCUFJQUREcpQUREQkR0lBRERylBRERCRHSUHGBDNL5VV5XB1cxj/mmdklZrbDzG7Iu/8fozzmBjOb0uexuuD/rbvv70TyVcUdgEhIB9392P5+EVy0Y8EVm2PRD9z9skIcKKgI/Efc/SBwbFAxV2RAainImGRm84L1MW4ic4XmHDP7pJmtDAp/fSFv28+Y2XNm9pCZ3WJmnwgef8DMlga3p2Q/MM2s0sz+Je9YHw4ePz3Y53Yz+4OZfT9ISJjZiWb2iJk9YWaPmlmDmT1oZsfmxfGQmb02xOnNCZ7neTP7XN7+PwmKGj6TX9jQzDrM7Ktm9gTwurzH68zsbjP70Ej+j6U8qaUgY0Wdma0Obr8I/DWwGLjY3X9rZmcF908CDFgeFP7bT6asybFk3u+/J1NnfjB/Aexz9xPNbBzwsJndG/zuOOBI4BXgYWCZmT0K/AC40N1XmlkjcJBMyYFLgI+b2RKg1t2fCHGuJwFHAQeAlWb2M3dfBXzQ3XebWV3w+B3uvgsYD/zO3f8WIMhTE8iUiL/J3W8K8ZwigJKCjB2v6j4KxhRecvffBg+dFfw8HtyfQCZJNAA/dvcDwX5hal6dBRxjZu8K7jcFx+oGHnX3TcGxVgPzgH3AFndfCZCtzGlmtwH/YGafBD4IfDfkud4XfNhjZj8CTgNWAZeb2duDbeYEMe0CUmSKoOW7E7jG3b8f8jlFACUFGdv259024J/c/Vv5G5jZxwfZv5dDXai1fY71MXe/p8+xTge68h5KMcjfkLsfMLP7yCzycgFwwiCxvGrXvveD5z4DeF1w3AfyYu5091SffR4Gzjazm121bGQYNKYgpeIe4INBLXnMbJaZTQMeBM4P+tcbgLfl7bOBQx/U7+pzrL8KyhBjZkuCCrMDWQvMMLMTg+0bzCybLG4AvgasdPc9Ic/lTMusK1xHZpWwh8m0VvYECeEw4JQhjnEVsAe4LuRzigBKClIi3P1e4GbgN2b2FHA70BAsT/gD4AngbjJl1LO+QubD/3EyVUWzbgDWAL83s6eBbzF4i6AbuBD4ejDYex/Bt3h3fwxoA74zjNN5lEx30JPAHcF4ws+BKjN7Fvgy8NtB9s+6gsxYzDXDeG4pc6qSKmXFiryIvZnNBB4ADutvyqyZXQIsLdSU1BDxbAieb2cxnk/GHrUURCJiZu8nU8v/M4NcQ3EQOCd78VqEsWRnb1VzqNa+yB9RS0FERHLUUhARkRwlBRERyVFSEBGRHCUFERHJUVIQEZGc/w+patlRfMX1SQAAAABJRU5ErkJggg==\n", 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" ] @@ -368,10 +382,15 @@ } ], "source": [ + "# Define path to the .wav file\n", + "# To be replaced by your own path\n", + "path = \"../validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav\"\n", + "\n", + "\n", "# Load signal \n", "signal, fs = load(\n", " True,\n", - " \"../mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav\", \n", + " path, \n", " calib = 2 * 2**0.5 \n", ")\n", "\n", @@ -411,7 +430,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/tutorials/tuto_roughness.ipynb b/tutorials/tuto_roughness.ipynb index 64c30199..a0513473 100644 --- a/tutorials/tuto_roughness.ipynb +++ b/tutorials/tuto_roughness.ipynb @@ -273,7 +273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Results and tests for synthetic signals (time varying loudness).xlsx diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 10 (tone pulse 1 kHz 10 ms 70 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 10 (tone pulse 1 kHz 10 ms 70 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 10 (tone pulse 1 kHz 10 ms 70 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 10 (tone pulse 1 kHz 10 ms 70 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 12 (tone pulse 1 kHz 500 ms 70 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 12 (tone pulse 1 kHz 500 ms 70 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 12 (tone pulse 1 kHz 500 ms 70 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 12 (tone pulse 1 kHz 500 ms 70 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 13 (combined tone pulses 1 kHz).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 13 (combined tone pulses 1 kHz).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 13 (combined tone pulses 1 kHz).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 13 (combined tone pulses 1 kHz).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 7 (tone 1 kHz 30 dB - 80 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 7 (tone 1 kHz 30 dB - 80 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 7 (tone 1 kHz 30 dB - 80 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 7 (tone 1 kHz 30 dB - 80 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 8 (tone 4 kHz 30 dB - 80 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 8 (tone 4 kHz 30 dB - 80 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 8 (tone 4 kHz 30 dB - 80 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 8 (tone 4 kHz 30 dB - 80 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 9 (pink noise 0 dB - 50 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 9 (pink noise 0 dB - 50 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 9 (pink noise 0 dB - 50 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.4/Test signal 9 (pink noise 0 dB - 50 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Results and tests for technical signals (time varying loudness).xlsx b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Results and tests for technical signals (time varying loudness).xlsx similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Results and tests for technical signals (time varying loudness).xlsx rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Results and tests for technical signals (time varying loudness).xlsx diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 14 (propeller-driven airplane).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 14 (propeller-driven airplane).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 14 (propeller-driven airplane).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 14 (propeller-driven airplane).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 15 (vehicle interior 40 kmh).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 15 (vehicle interior 40 kmh).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 15 (vehicle interior 40 kmh).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 15 (vehicle interior 40 kmh).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 16 (hairdryer).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 16 (hairdryer).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 16 (hairdryer).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 16 (hairdryer).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 17 (machine gun).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 17 (machine gun).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 17 (machine gun).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 17 (machine gun).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 18 (hammer).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 18 (hammer).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 18 (hammer).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 18 (hammer).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 19 (door creak).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 19 (door creak).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 19 (door creak).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 19 (door creak).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 20 (shaking coins).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 20 (shaking coins).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 20 (shaking coins).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 20 (shaking coins).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 21 (jackhammer).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 21 (jackhammer).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 21 (jackhammer).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 21 (jackhammer).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 22 (ratchet wheel (large)).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 22 (ratchet wheel (large)).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 22 (ratchet wheel (large)).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 22 (ratchet wheel (large)).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 23 (typewriter).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 23 (typewriter).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 23 (typewriter).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 23 (typewriter).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 24 (woodpecker).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 25 (full can rattle).wav b/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 25 (full can rattle).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 25 (full can rattle).wav rename to validations/loudness_zwicker/data/ISO_532-1/Annex B.5/Test signal 25 (full can rattle).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/PinkNoise_40dBpHz@1000Hz.wav b/validations/loudness_zwicker/data/ISO_532-1/PinkNoise_40dBpHz@1000Hz.wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/PinkNoise_40dBpHz@1000Hz.wav rename to validations/loudness_zwicker/data/ISO_532-1/PinkNoise_40dBpHz@1000Hz.wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 11 (tone pulse 1 kHz 50 ms 70 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 13 (combined tone pulses 1 kHz).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 13 (combined tone pulses 1 kHz).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 13 (combined tone pulses 1 kHz).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 13 (combined tone pulses 1 kHz).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 2 (250 Hz 80 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 2 (250 Hz 80 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 2 (250 Hz 80 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 2 (250 Hz 80 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 4 (4 kHz 40 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 4 (4 kHz 40 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 4 (4 kHz 40 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 4 (4 kHz 40 dB).wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav rename to validations/loudness_zwicker/data/ISO_532-1/Test signal 5 (pinknoise 60 dB).wav diff --git a/validations/loudness_zwicker/data/ISO_532-1/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav b/validations/loudness_zwicker/data/ISO_532-1/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav new file mode 100644 index 00000000..6f48284e Binary files /dev/null and b/validations/loudness_zwicker/data/ISO_532-1/Test signal 6 (tone 250 Hz 30 dB - 80 dB).wav differ diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/sinus_1000Hz_60dBSPL.wav b/validations/loudness_zwicker/data/ISO_532-1/sinus_1000Hz_60dBSPL.wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/sinus_1000Hz_60dBSPL.wav rename to validations/loudness_zwicker/data/ISO_532-1/sinus_1000Hz_60dBSPL.wav diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_1.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_1.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_1.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_1.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_13.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_13.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_13.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_13.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_13_spec.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_13_spec.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_13_spec.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_13_spec.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_2.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_2.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_2.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_2.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_3.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_3.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_3.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_3.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_4.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_4.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_4.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_4.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_5.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_5.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_5.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_5.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_6.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_6.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_6.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_6.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_6_spec.csv b/validations/loudness_zwicker/data/ISO_532-1/test_signal_6_spec.csv similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/test_signal_6_spec.csv rename to validations/loudness_zwicker/data/ISO_532-1/test_signal_6_spec.csv diff --git a/mosqito/validations/loudness_zwicker/data/ISO_532-1/trafic.wav b/validations/loudness_zwicker/data/ISO_532-1/trafic.wav similarity index 100% rename from mosqito/validations/loudness_zwicker/data/ISO_532-1/trafic.wav rename to validations/loudness_zwicker/data/ISO_532-1/trafic.wav diff --git a/mosqito/validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png b/validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png rename to validations/loudness_zwicker/output/FAILED_validation_loudness_zwicker_time_Test_signal_16_(hairdryer)_Loudness.png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png b/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_1.png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png b/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_2_(250_Hz_80_dB).png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png b/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_3_(1_kHz_60_dB).png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png b/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_4_(4_kHz_40_dB).png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png b/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_stationary_Test_signal_5_(pinknoise_60_dB).png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_10_(tone_pulse_1_kHz_10_ms_70_dB)_Loudness.png b/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_10_(tone_pulse_1_kHz_10_ms_70_dB)_Loudness.png similarity index 100% rename from mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_10_(tone_pulse_1_kHz_10_ms_70_dB)_Loudness.png rename to validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_10_(tone_pulse_1_kHz_10_ms_70_dB)_Loudness.png diff --git a/mosqito/validations/loudness_zwicker/output/validation_loudness_zwicker_time_Test_signal_10_(tone_pulse_1_kHz_10_ms_70_dB)_Specific.png 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