Version 1.1 (#20)

* Updated Gitignore

* Big Documentation Update

* New doc test github action

* Update sphinx packages

* Update github action

* Another githuh action fix

* Update ETH AWG for new software

* Added rise time to linear chirp pulses

* F236 update

* Updated F236 Interface

* GaussPulse Improvements

* Added tau to tune mode + bug fixes

* Bug Fixes

* Better NUS handling of parameter rounding

* Removed div 0 warnings

* Patch13 dev (#19)

- Added `AmplifierLinearityAnalysis` class for characterizing amplifier non-linearity.
- Added `T1InversionRecovery` sequence.
- Added rise time to linear chirp pulses.
- Added right based arithmatic.
- Fixed Version Detection Bug
- Fixed Dependency issues
- Improved Documentation
- Removed Numba dependency

* Update doc dependecies

* Updated main deps

Author: HKaras

docsrc/API_docs.rst

@@ -36,6 +36,7 @@ Sequences
    pyepr.sequences.CarrPurcellSequence
    pyepr.sequences.ResonatorProfileSequence
    pyepr.sequences.TWTProfileSequence
+   pyepr.sequences.T1InversionRecoverySequence
 
 Pulses
 ~~~~~~
@@ -90,6 +91,7 @@ I/O
     pyepr.dataset.create_dataset_from_sequence
     pyepr.dataset.create_dataset_from_axes
     pyepr.dataset.create_dataset_from_bruker
+    pyepr.dataset.downconvert_dataset
 
 Utilities
 ~~~~~~~~~

docsrc/conf.py

@@ -11,6 +11,8 @@
 from pyepr import __version__, __copyright__
 sys.path.insert(0, os.path.abspath('..'))
 
+import matplotlib
+matplotlib.use('Agg')  # Use non-interactive backend
 
 project = 'PyEPR'
 copyright = __copyright__
@@ -30,7 +32,10 @@
               'sphinx_copybutton',
               'numpydoc',
               'sphinx_favicon',
-              'sphinx_gallery.gen_gallery']
+              'sphinx_gallery.gen_gallery',
+              'matplotlib.sphinxext.plot_directive',
+              'sphinx.ext.imgmath'
+]
 
 templates_path = ['_templates']
 exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
@@ -41,6 +46,10 @@
 autodoc_typehints = "description"
 autoapi_template_dir = "_templates/autoapi"
 
+plot_include_source = True
+plot_html_show_source_code = False
+plot_formats = [('png', 100)]
+
 autoapi_keep_files = True
 autoapi_add_toctree_entry = False
 autoapi_python_class_content= "both"

docsrc/install.rst

@@ -38,5 +38,4 @@ PyEPR requires:
     - h5netcdf
     - toml
     - deerlab (https://github.com/JeschkeLab/DeerLab)
-    - numba
     - psutil

docsrc/releasenotes.rst

@@ -1,6 +1,19 @@
 Release Notes
 =============
 
+Version 1.1 (2026-05-27):
+++++++++++++++++++++++++++++
+- Added `AmplifierLinearityAnalysis` class for characterizing amplifier non-linearity.
+- Added `T1InversionRecovery` sequence.
+- Added rise time to linear chirp pulses.
+- Added right based arithmatic.
+- Fixed Version Detection Bug
+- Fixed Dependency issues
+- Improved Documentation
+- Removed Numba dependency
+
+
+
 Version 1.0.0 (2025-09-12):
 ++++++++++++++++++++++++++++
 - All references to `LO` have been changed to `freq` in the frequency object and related.

docsrc/tutorial_sequencer.md

@@ -1,6 +1,6 @@
 # Pulse Sequencer
 
-PyEPR provides an intuitive object-oriented pulse programmer allowing the user to design pulsesequences in a hardware-agnostic manner. Additionally, several common EPR experiments are pre-defined and can be easily instantiated and modified.
+PyEPR provides an intuitive object-oriented pulse programmer allowing the user to design pulse sequences in a hardware-agnostic manner. Additionally, several common EPR experiments are pre-defined and can be easily instantiated and modified.
 
 PyEPR uses ns, GHz and G as the default time, frequency and field units. Very occasionally, other units such as µs or MHz are used, in which case it will be explicitly mentioned.
 
@@ -31,33 +31,33 @@ These pulses will eventually need a scale (amplitude), before the sequence can b
 A Detection window is also created
 ```python
 p90 = epr.RectPulse(tp=16,
-                    freq=0, # Frequency offset in MHz, w.r.t the sequence frequency,
+                    freq=0, # Frequency offset in GHz, w.r.t the sequence frequency,
                     flipangle=np.pi/2, # Flip angle in degrees
                     pcyc = {"phases":[0, np.pi], "dets":[1,-1]}
                     )
 p180 = epr.RectPulse(tp=32,
-                    freq=0, # Frequency offset in MHz, w.r.t the sequence frequency,
+                    freq=0, # Frequency offset in GHz, w.r.t the sequence frequency,
                     flipangle=np.pi, # Flip angle in degrees
                     )
-det = epr.Detetction(tp=32,
-                    freq=0, # Frequency offset in MHz, w.r.t the sequence frequency,
+det = epr.Detection(tp=32,
+                    freq=0, # Frequency offset in GHz, w.r.t the sequence frequency,
                     )
 ```                  
 We now need a time axis for our sequence and to add them to the sequence object.
-When a pulse is copied into the sequence using the `add_pulse` method, parameters can be modified allowing the same pulse can be used multiple times with different timings or amplitudes.
+When a pulse is copied into the sequence using the `addPulse` method, parameters can be modified allowing the same pulse can be used multiple times with different timings or amplitudes.
 ```python
 t = epr.Parameter(name='Interpulse Delay',
                   value=400, # Initial interpulse delay in ns
                   step=8,  # Step size in ns
-                  dim=1024  # Number of points,
-                  unit='ns'  # Unit of the parameter
+                  dim=1024,  # Number of points,
+                  unit='ns',  # Unit of the parameter
                   description='Interpulse delay between the pi/2 and pi pulse'
                   )
 
 # Adding the pulses to the sequence
-seq.add_pulse(p90.copy(t=0))
-seq.add_pulse(p180.copy(t=t))
-seq.add_pulse(det.copy(t=2*t))  
+seq.addPulse(p90.copy(t=0))
+seq.addPulse(p180.copy(t=t))
+seq.addPulse(det.copy(t=2*t))  
 
 # Defining the evolution
 seq.evolution([t])
@@ -81,7 +81,7 @@ HE_Seq = epr.HahnEchoRelaxationSequence(
     shots = 20, # Number of shots per point
     start = 400, # Initial interpulse delay in ns
     step = 8,  # Step size in ns
-    dim = 1024  # Number of points
+    dim = 1024,  # Number of points
     pi2_pulse = p90,  # The 90 degree pulse
     pi_pulse = p180  # The 180 degree pulse
 )

pyepr/classes.py

@@ -23,11 +23,19 @@ class Interface:
     """
 
     def __init__(self,config_file:dict=None,log=None) -> None:
+        """
+        Parameters
+        ----------
+        config_file : dict or str or Path, optional
+            The configuration file or dict for the spectrometer interface, by default None. If None, a default configuration will be used.
+        log : logging.Logger, optional  
+            The logger to be used, by default None. If None, a default logger will be created.
+        """
         if isinstance(config_file, (str,Path)):
             with open(config_file, 'r') as f:
                 config_file = yaml.safe_load(f)
 
-        self.config = config_file if isinstance(config_file, dict) else {}
+        self.config = config_file if isinstance(config_file, dict) else {"Spectrometer":{"Bridge":{}}}
 
         self.pulses = {}
         self.savefolder = str(Path.home())
@@ -37,7 +45,10 @@ def __init__(self,config_file:dict=None,log=None) -> None:
         else:
             self.log = log
         self.resonator = None
-        self.amp_nonlinearity = self.config["Spectrometer"]["Bridge"].get('Amplifier Non-Linearity',None)
+        if self.config != {}:
+            self.amp_nonlinearity = self.config["Spectrometer"]["Bridge"].get('Amplifier Non-Linearity',None)
+        else:
+            self.amp_nonlinearity = None
         pass
 
     def connect(self) -> None:
@@ -153,7 +164,8 @@ def terminate_at(self, criterion, test_interval=2, keep_running=True, verbosity=
             data = self.acquire_dataset()
             if autosave:
                 self.log.debug(f"Autosaving to {os.path.join(self.savefolder,self.savename)}")
-                data.to_netcdf(os.path.join(self.savefolder,self.savename),engine='h5netcdf',invalid_netcdf=True)
+                # data.to_netcdf(os.path.join(self.savefolder,self.savename),engine='h5netcdf',invalid_netcdf=True)
+                data.epr.save(os.path.join(self.savefolder,self.savename))
 
             try:
                 # nAvgs = data.num_scans.value
@@ -278,7 +290,7 @@ def __init__(self, name, value, unit="", description="", virtual=False,
             self.value = value
             self.NUS = False # uniform sampling
         elif isinstance(value, np.ndarray):
-            self.value = np.median(value)
+            self.value = value[0]
             axis = value - self.value
             self.NUS = True # non-uniform sampling
         elif value is None:
@@ -382,17 +394,22 @@ def adjust_step(self, waveform_precision, keep_dim=True):
             current_step =old_axis[1] - old_axis[0]
             # test if uniformally sampled
             if not np.allclose(np.diff(self.axis[i]["axis"]), current_step):
-                raise ValueError("This only works for uniformaly sampled data at the moment")
-            new_step = round_step(current_step, waveform_precision)
-
-            if new_step == 0:
-                new_step = waveform_precision
-            
-            if keep_dim:
-                dim = old_axis.shape[0]
-                new_axis = np.arange(self.axis[i]["axis"][0], self.axis[i]["axis"][0]+new_step*dim, new_step)
+                tolerance = 1e-9
+                new_axis = copy.deepcopy(old_axis)
+                remainders = np.abs(new_axis % waveform_precision)
+                not_multiples = ~(np.isclose(remainders, 0, atol=1e-9) | np.isclose(remainders, waveform_precision, atol=1e-9))
+                new_axis[not_multiples] = np.round(new_axis[not_multiples] / waveform_precision) * waveform_precision
             else:
-                new_axis = np.arange(self.axis[i]["axis"][0], self.axis[i]["axis"][-1]+new_step, new_step)
+                new_step = round_step(current_step, waveform_precision)
+
+                if new_step == 0:
+                    new_step = waveform_precision
+                
+                if keep_dim:
+                    dim = old_axis.shape[0]
+                    new_axis = np.arange(self.axis[i]["axis"][0], self.axis[i]["axis"][0]+new_step*dim, new_step)
+                else:
+                    new_axis = np.arange(self.axis[i]["axis"][0], self.axis[i]["axis"][-1]+new_step, new_step)
             self.axis[i]["axis"] = new_axis
 
         if isinstance(self.value, numbers.Number):
@@ -496,6 +513,10 @@ def __add__(self, __o:object):
                 raise RuntimeError(
                     "Both parameters axis and the array must have the same shape")
 
+    def __radd__(self, __o:object):
+        return self.__add__(__o) 
+
+
     def __sub__(self, __o:object):
 
         if type(__o) is Parameter:
@@ -564,6 +585,9 @@ def __sub__(self, __o:object):
                 raise RuntimeError(
                     "Both parameters axis and the array must have the same shape")
 
+    def __rsub__(self, __o:object):
+        return self.__sub__(__o)
+
     def __mul__(self, __o:object):
         if type(__o) is Parameter:
             if self.unit != __o.unit: