From 693c30a2fc430b119f0b94ec0f93a1087bd43ea7 Mon Sep 17 00:00:00 2001 From: John Garrett Date: Mon, 11 Mar 2019 10:53:59 +0000 Subject: [PATCH] Minor changes to documentation [ci skip] --- examples/analyze-experimental-data.ipynb | 18 ++++--- qmix/circuit.py | 22 ++++---- qmix/exp/parameters.py | 62 ++++++++++++---------- qmix/respfn.py | 67 ++++++++++++++---------- 4 files changed, 97 insertions(+), 72 deletions(-) diff --git a/examples/analyze-experimental-data.ipynb b/examples/analyze-experimental-data.ipynb index f7e8798..ab42208 100644 --- a/examples/analyze-experimental-data.ipynb +++ b/examples/analyze-experimental-data.ipynb @@ -39,7 +39,7 @@ "\n", "This is the data collected from the SIS device with **no local-oscillator (LO) illumination.**\n", "\n", - "### Properties of the experimental data\n", + "### Properties of the experimental DC data\n", "\n", "The DC experimental data is located in two different files:\n", "\n", @@ -417,9 +417,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Importing the experimental pumped data\n", + "### Importing the experimental data\n", + "\n", + "The ``RawData`` class will automatically load, filter and analyze the data. This includes calculating the noise temperature and the properties of the embedding circuit.\n", "\n", - "The ``RawData`` class will automatically load, filter and analyze the data. This includes calculating the noise temperature and the properties of the embedding circuit." + "**Note:** that this experimental data is imported in the form of Numpy arrays. Each array has two columns with one for voltage and one for current or IF power (depending on the file)." ] }, { @@ -526,9 +528,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": { @@ -539,8 +541,10 @@ ], "source": [ "# Plot the error surface\n", - "fig, ax = plt.subplots(figsize=(7,5))\n", - "pump.plot_error_surface(ax=ax);" + "fig, ax = plt.subplots(figsize=(7,5.5))\n", + "ax = pump.plot_error_surface(ax=ax)\n", + "ax.set_xlabel(r'Thevenin Resistance ($\\Omega$)')\n", + "ax.set_ylabel(r'Thevenin Reactance ($\\Omega$)');" ] }, { diff --git a/qmix/circuit.py b/qmix/circuit.py index f0b1e21..af454bb 100644 --- a/qmix/circuit.py +++ b/qmix/circuit.py @@ -8,16 +8,16 @@ all of the components in these embedding circuits are **linear**, the embedding circuit can be reduced to a **Thevenin equivalent circuit**, with one for **each tone and harmonic.** - + To fully describe the embedding circuit, 3 bits of information are needed for each signal that is applied to the junction: - + 1. the frequency of the applied signal, 2. the Thevenin voltage of the embedding circuit at this freq., and 3. the Thevenin impedance of the embedding circuit at this freq. - The main class in this module (``EmbeddingCircuit``) allows the user to - build an embedding circuit in the proper format. +The main class in this module (``EmbeddingCircuit``) allows the user to +build an embedding circuit in the proper format. """ @@ -114,15 +114,15 @@ class EmbeddingCircuit(object): (tones) applied to the junction. num_p (int, optional, default is 1): Number of harmonics included for each tone. - vb_min (float, optional, default is 0): Minimum bias voltage for array. - vb_max (float, optional, default is 2): Maximum bias voltage for array. + vb_min (float, optional, default is 0): Minimum bias voltage. + vb_max (float, optional, default is 2): Maximum bias voltage. vb_npts (int, optional, default is 201): Number of points in bias voltage sweep. fgap (float, optional): Gap frequency of the junction in units [Hz]. This is equal to ``e*Vgap/h``, where ``e`` is the charge of an electron, ``Vgap`` is the gap voltage, and ``h`` is the Planck constant. ``fgap`` is used to normalize and de-normalize frequency - values (that't it). + values (that't it!). vgap (float, optional): Gap voltage of the junction in units [V]. This is the voltage where the sharp non-linearity in the DC I-V curve occurs (a.k.a., the transition voltage). @@ -135,11 +135,11 @@ class EmbeddingCircuit(object): Attributes: vph (numpy.ndarray): An array for the photon voltage normalized to the gap voltage. This is a 1-dimensional array of real numbers. It - contains all of the photon voltages for the fundamental tones that + contains photon voltages for all of the fundamental tones that are applied to the junction. The photon voltage is defined as ``hf/e``, where ``h`` is the Planck constant, ``f`` is the frequency of the fundamental tone, and ``e`` is the charge of an - electron. However, since this value is normalized to the gap + electron. Since this value is normalized to the gap voltage, the photon voltage is also equal to the normalized frequency, ``f/fgap``, where ``fgap`` is the gap frequency. Note that this array is 1-based, meaning that the photon voltage of the @@ -188,10 +188,12 @@ class EmbeddingCircuit(object): vb (numpy.ndarray): Array for the DC bias voltage sweep. This value is normalized to the gap voltage. vb_npts (int): Number of points in the bias voltage sweep. + name (str): A name to describe this instance of the embedding circuit + class. comment (list): A list of comments to describe the different signals. For example, to describe tone 1/harmonic 1 as the local-oscillator signal, you might use ``cct.comment[1][1] = "LO"``. This has to be - done after the initialization of the EmbeddingCircuit class. + set after the initialization of the EmbeddingCircuit class. """ diff --git a/qmix/exp/parameters.py b/qmix/exp/parameters.py index db61cdc..d7581b9 100644 --- a/qmix/exp/parameters.py +++ b/qmix/exp/parameters.py @@ -1,34 +1,34 @@ """ This module contains a dictionary of parameters (``params``) that is -used by the ``qmix.exp.RawData`` and ``qmix.exp.RawData0`` classes to control +used by ``qmix.exp.RawData`` and ``qmix.exp.RawData0`` to control how experimental data is loaded and analyzed. Note: This dictionary just contains the default values. You can overwrite these values by passing keyword arguments to ``RawData`` or ``RawData0``. For - example, the default value for voltage units is ``"mV"``. - You can change this parameter to be ``"uV"`` by passing - ``v_fmt="uV"`` to the ``RawData`` or ``RawData0`` class. + example, the default value for voltage units is millivolts (``"mV"``). + You can change this parameter to be microvolts (``"uV"``) by passing + ``v_fmt="uV"`` to ``RawData`` or ``RawData0``. + + Also note that experimental data can be passed to ``RawData0`` and + ``RawData`` either as CSV data files or as Numpy arrays. In both + cases, the data should have two columns: one for voltage and one + for current or IF power, depending on the file. See Example #3 on the + QMix website for more information. All of the different parameters are described below along with their default values. **Parameters:** - - Experimental data data: - - **Note:** Experimental data can be passed to ``RawData0`` and - ``RawData`` either as CSV data files or as Numpy arrays. The CSV - files should have two columns: one for voltage and one for current - or IF power. The Numpy arrays should also have two columns. - CSV files: - - **Note:** All of the experimental data is expected to be stored in - CSV data files. These parameters control how the data is read in - from these files. + - **Note:** If you are using CSV files, these parameters control how + the data is loaded from the CSV files. - ``delimiter = ","`` : The delimiter used by the CSV data files. - ``usecols = (0,1)`` : Which columns to import from the CSV data files. - ``skip_header = 1`` : Number of rows to skip at the beginning CSV - data files. Used to skip the header. + data files. (Used to skip the header.) - Units: - ``v_fmt = "mV"`` : Units for imported voltage data. The options are: ``"uV"``, ``"mV"`` and ``"V"``. @@ -41,7 +41,8 @@ interpolation. - ``debug = False`` : If set to ``True``, this will plot each step of the I-V data loading and analysis procedure. Note: This will - display 4 individual plots for each I-V curve that is loaded. + display 4 individual plots for each I-V curve that is loaded, so do + not use this if you are looping through multiple files. - Correcting voltage/current offsets: - **Note:** Sometimes there is an offset in the I-V data. The parameters below can be used to correct this. If you know the @@ -50,9 +51,10 @@ find the offset on its own. This is done by taking the derivative of the DC I-V curve, and then finding the maximum derivative value between ``-voffset_range`` and ``+voffset_range``. - - ``ioffset = None`` : Offset in DC tunneling current data in units - [A]. - - ``voffset = None`` : Offset in DC bias voltage data in units [V]. + - ``ioffset = None`` : Offset of the DC tunneling current data in + units [A]. + - ``voffset = None`` : Offset of the DC bias voltage data in units + [V]. - ``voffset_range = 3e-4`` : Voltage range over which to look for the voltage offset in units [V]. The ``RawData0`` class will look from ``-voffset_range`` to ``+voffset_range`` for the voltage offset. @@ -84,8 +86,8 @@ - ``filter_nwind = 21`` : Width of the Savitzky-Golay filter. - ``filter_npoly = 3`` : Order of the Savitzky-Golay filter. - Analyzing the DC I-V curve: - - ``vgap_threshold = 105e-6`` : Threshold current at which to measure - the gap voltage, in units [A]. (Note: the gap voltage is defined + - ``vgap_threshold = 105e-6`` : Threshold current, in units [A], at + which to measure the gap voltage. (Note: the gap voltage is defined here as the voltage at which the DC I-V curve crosses this current value.) - ``rn_vmin = 3.5e-3`` : Lower range over which to calculate normal @@ -100,17 +102,17 @@ - ``analyze_iv = True`` : Analyze the pumped I-V data? This involves a procedure to recover the embedding circuit. - ``cut_low = 0.25`` : Fit interval for impedance recovery, lower end, - normalized to photon step width. + normalized to the photon voltage. - ``cut_high = 0.2`` : Fit interval for impedance recovery, upper end, - normalized to photon step width. + normalized to the photon voltage. - ``remb_range = (0, 1)`` : Range of embedding resistances to test, - normalized to normal resistance. + normalized to the normal resistance. - ``xemb_range = (-1, 1)`` : Range of embedding reactances to test, - normalized to normal resistance. - - ``alpha_max = 1.5`` : Maximum drive level (alpha) for initial guess + normalized to the normal resistance. + - ``alpha_max = 1.5`` : Initial guess for the drive level (alpha) during impedance recovery. - ``num_b = 20`` : Maximum number of Bessel functions to include when - calculating tunneling currents. + calculating the tunneling currents. - Importing IF data: - ``ifdata_vmax = 2.25`` : Maximum IF voltage to import (normalized to vgap). Used in case the IF data is saturated beyond a certain bias @@ -122,6 +124,10 @@ convolving it with a Gaussian distribution. This is the standard deviation, in number of data points. - Analyzing the DC IF data: + - **Note:** DC IF data (IF power with no LO injection) is used to + measure the IF noise contribution and convert the power units into + units of temperature [K]. This is done by fitting a linear trend + to the shot noise present in the DC IF data. - ``vshot = None`` : Range of voltages over which to fit a linear trend line to the shot noise. This is a list of lists, in units [V]. For example, to fit the shot noise slope from 4-5 mV and from @@ -141,8 +147,8 @@ class will determine the best bias voltage automatically. - Response function: - **Note:** The ``RawData0`` class generates a response function based on the imported DC I-V data (using ``qmix.respfn.RespFn``). - It also generates a second response function that is smeared - slightly. This smeared response function is useful for simulations + It also generates a second response function that is slightly + smeared. This smeared response function is useful for simulations because it simulates a small amount of heating. - ``v_smear = 0.020`` : Voltage smear of the "smeared" response function. @@ -155,7 +161,7 @@ class will determine the best bias voltage automatically. [GHz]. - Miscellaneous: - ``comment = ""`` : Add a comment to describe this instance. - - ``verbose = True`` : Print information to the terminal? + - ``verbose = True`` : Print information to the terminal. """ diff --git a/qmix/respfn.py b/qmix/respfn.py index 033d702..d230faf 100644 --- a/qmix/respfn.py +++ b/qmix/respfn.py @@ -33,14 +33,14 @@ - ``RespFnPolynomial``: This class will generate a response function based on the polynomial model from Kennedy (1999). The order of the - polynomial controls how sharp the transition is, so this class can be - used to simulate the effect of the transition's sharpness (i.e., how - do the results change when the gap is more or less sharp). + polynomial controls the sharpness of the transition, so this class can + be used to simulate the effect of the transition's sharpness (e.g., + how does the gain change when the gap is more or less sharp?). - ``RespFnExponential``: This class will generate a response function - based on the exponential model from Rashid et al. (2016). This model is - very similar to ``RespFnPolynomial``, except that you can include a - constant subgap resistance. + based on the exponential model from Rashid *et al.* (2016). This model + is very similar to ``RespFnPolynomial``, except that you can include a + leakage current (i.e., a finite subgap resistance). Upon initialization, these classes will: @@ -118,7 +118,13 @@ class RespFn(object): """Generate the response function from pre-processed I-V data. - This is a class to contain, interpolate and plot the response function. + This class expects "pre-processed" I-V data, meaning that there are more + data points around the curvier regions of the I-V curve and fewer points + in the linear regions. Sampling in this way helps the interpolation run + as quick as possible. + + If you have not "pre-processed" your data, please use + ``RespFnFromIVData``. Args: voltage (ndarray): normalized DC bias voltage @@ -192,6 +198,9 @@ def __init__(self, voltage, current, **params): def plot_interpolation(self, fig_name=None, ax=None): # pragma: no cover """Plot the interpolation of the response function. + This can be used to check the interpolation of the response function. + (Mostly just a sanity check.) + Note: If ``fig_name`` is provided, this method will save the plot to the specified folder and then close the plot. This means that the Matplotlib axis object will not be returned in this @@ -246,6 +255,9 @@ def plot_interpolation(self, fig_name=None, ax=None): # pragma: no cover def show_current(self, fig_name=None, ax=None): # pragma: no cover """Plot the interpolation of the response function. + This can be used to check the interpolation of the response function. + (Mostly just a sanity check.) + Note: If ``fig_name`` is provided, this method will save the plot to the specified folder and then close the plot. This means that the Matplotlib axis object will not be returned in this @@ -312,7 +324,7 @@ def didc(self, vbias): This method is not used directly by QMix, but it can be useful if you are calculating the tunneling currents using Tucker theory - (Tucker and Feldman, 1985). + (see: Tucker and Feldman, 1985). Args: vbias (ndarray): Bias voltage (normalized) @@ -335,7 +347,7 @@ def dikk(self, vbias): This method is not used directly by QMix, but it can be useful if you are calculating the tunneling currents using Tucker theory - (Tucker and Feldman, 1985). + (see: Tucker and Feldman, 1985). Args: vbias (ndarray): Bias voltage (normalized) @@ -361,14 +373,13 @@ def resp(self, vbias): return self._f_ikk(vbias) + 1j * self._f_idc(vbias) def resp_conj(self, vbias): - """Interpolate the complex conjugate of the response function - . + """Interpolate the complex conjugate of the response function. Note: This method is not used directly by QMix, but it can be useful if you are calculating the tunneling currents using Tucker theory - (Tucker and Feldman, 1985). + (see: Tucker and Feldman, 1985). This method is included because it might be *slightly* faster than ``np.conj(resp.resp(vb))`` where ``resp`` is an instance of this @@ -386,18 +397,18 @@ def resp_conj(self, vbias): def resp_swap(self, vbias): """Interpolate the response function, with the real and imaginary - components swapped,. + components swapped. Note: This method is not used directly by QMix, but it can be useful if you are calculating the tunneling currents using Tucker theory - (Tucker and Feldman, 1985). + (see: Tucker and Feldman, 1985). This method is included because it might be *slightly* faster than - ``1j * np.conj(resp.resp(vb))`` where ``resp`` is an instance of - this class (which is the normal way that you would swap the real - and imaginary components). + ``1j*np.conj(resp.resp(vb))`` where ``resp`` is an instance of + this class. This is the normal way that you would swap the real + and imaginary components. Args: vbias (ndarray): Bias voltage (normalized) @@ -416,8 +427,6 @@ def resp_swap(self, vbias): class RespFnFromIVData(RespFn): """Generate the response function from I-V data. - This is a class to contain, interpolate and plot the response function. - Unlike ``RespFn``, this class will resample the I-V data to optimize the interpolation. @@ -476,7 +485,10 @@ def __init__(self, voltage, current, **kwargs): class RespFnPolynomial(RespFn): """Response function based on the polynomial I-V curve model. - Class to contain, interpolate and plot the response function. + This model is from Kennedy (1999). The order of the polynomial + (``p_order``) controls the sharpness of the non-linearity. + + See ``qmix.mathfn.ivcurve_models.polynomial`` for the model. Args: p_order (int): Order of the polynomial @@ -511,13 +523,10 @@ def __init__(self, p_order=50, **kwargs): class RespFnExponential(RespFn): """Response function based on the exponential I-V curve model. - Class to contain, interpolate and plot the response function. - - Reference: + This model is from Rashid *et al.* (2016). Through this model you can + set the sharpness of the non-linearity *and* the subgap resistance. - H. Rashid, et al., "Harmonic and reactive behavior of the - quasiparticle tunnel current in SIS junctions," AIP Advances, - vol. 6, 2016. + See ``qmix.mathfn.ivcurve_models.exponential`` for the model. Args: vgap (float): Gap voltage (un-normalized) @@ -555,7 +564,11 @@ def __init__(self, vgap=2.8e-3, rn=14, rsg=1000, agap=4e4, **kwargs): class RespFnPerfect(RespFn): """Response function based on the perfect I-V curve model. - Class to contain, interpolate and plot the response function. + The perfect I-V curve has zero subgap current below the transition, and + a current exactly equal to ``vb * Rn``, where ``vb`` is the bias voltage + and ``Rn`` is the normal resistance, above the transition. + + See ``qmix.mathfn.ivcurve_models.perfect`` for the model. Keyword Args: verbose (bool, default is True): print info to terminal?