Offer to run all implemented algos at once

dimep/api.py

@@ -1,6 +1,8 @@
 """Main Access Point for DiMEP Algorithms"""
 from dimep.algo import *
 from dimep.version import version
+from numpy import ndarray
+from typing import Dict
 
 
 def available() -> None:
@@ -9,3 +11,28 @@ def available() -> None:
 
     for algo in __all__:
         print(algo)
+
+
+def all(trace: ndarray, tms_sampleidx: int) -> Dict[str, float]:
+    """Estimate the iMEP amplitude in the given trace with all implemented algorithms
+    
+    args
+    ----
+    trace:ndarray
+        the one-dimensional (samples,) EMG signal
+    tms_sampleidx: int
+        the sample at which the TMS pulse was applied
+    
+
+    returns
+    -------
+    estimtes: Dict[str, float]
+        a dictionary of estimates, with the algorithm name as key and the estimate as value
+    
+    """
+    from dimep.algo import __all__
+
+    out = dict()
+    for algo in __all__:
+        out[str(algo)] = eval(algo)(trace, tms_sampleidx=tms_sampleidx)
+    return out

dimep/tools.py

@@ -45,7 +45,7 @@ def bw_boundaries(bools: ndarray) -> ndarray:
 
     """
     bools = np.asarray_chkfinite(bools)
-    L: ndarray = np.zeros(bools.shape[0], dtype=np.int)  # type: ignore
+    L: ndarray = np.zeros(bools.shape[0], dtype=int)  # type: ignore
     i: int = 0
     counter: int = 0
     # all values are initially zero, i.e. belong to no cluster

dimep/version.py

@@ -1 +1 @@
-version = "0.1.0"
+version = "0.2.0"

docs/source/usage.rst

@@ -9,6 +9,43 @@ List available algorithms
    from dimep.api import available
    available()
 
+Mock a trace
+++++++++++++
+
+.. code-block::
+
+   # mock a single-channel EMG signal
+   from numpy import random
+   random.seed(0)
+   trace = random.randn(1000)
+   print(trace.shape)
+   # >>> (1000,)
+
+and subsequently call one (or all) of the algorithms on this mock trace, where the trace is the single-channel EMG recording, tms_sampleidx marks the onset of the TMS pulse and fs is the sampling rate. 
+
+Call all implemented algorithms at once
++++++++++++++++++++++++++++++++++++++++
+
+.. code-block::
+
+   from dimep.api import all
+   all(trace, tms_sampleidx=500)
+   # >>> {'chen': 0.0,
+   #       'bawa': 5.805498168821509,
+   #       'bradnam': 0.0,
+   #       'guggenberger': 0.11904591308664515,
+   #       'lewis': 0.0,
+   #       'loyda': 0.0,
+   #       'odergren': 0.0,
+   #       'rotenberg': 26.662225635355707,
+   #       'summers': 0.349919002236347,
+   #       'wassermann': 0.7782071535040253,
+   #       'zewdie': 0.0,
+   #       'ziemann': 0.0}
+
+
+
+
 
 Access a specific algorithm
 +++++++++++++++++++++++++++
@@ -21,16 +58,6 @@ Access a specific algorithm
 Call a specific algorithm
 +++++++++++++++++++++++++
 
-.. code-block::
-
-   # mock a single-channel EMG signal
-   from numpy import random
-   random.seed(0)
-   trace = random.randn(1000)
-   print(trace.shape)
-   # >>> (1000,)
-
-and subsequently call one of the algorithms on this mock trace, where the trace is the single-channel EMG recording, tms_sampleidx marks the onset of the TMS pulse and fs is the sampling rate. 
 
 For example, the threshold based approach by Lewis (2007)