Merge pull request #484 from BlueBrain/numpy-scaler

New scaler: NrnSegmentSomaDistanceStepScaler

Author: AurelienJaquier

bluepyopt/ephys/create_hoc.py

@@ -105,8 +105,18 @@ def range_exprs_to_hoc(range_params):
     for param in range_params:
         value = param.value_scaler.inst_distribution
         value = re.sub(r'math\.', '', value)
+        value = re.sub(r'\&', '&&', value)
         value = re.sub('{distance}', FLOAT_FORMAT, value)
         value = re.sub('{value}', format_float(param.value), value)
+        if hasattr(param.value_scaler, "step_begin"):
+            value = re.sub(
+                '{step_begin}',
+                format_float(param.value_scaler.step_begin),
+                value
+            )
+            value = re.sub(
+                '{step_end}', format_float(param.value_scaler.step_end), value
+            )
         ret.append(Range(param.location, param.name, value))
     return ret
 

bluepyopt/ephys/parameterscalers/parameterscalers.py

@@ -161,9 +161,8 @@ def inst_distribution(self):
         # Use this special formatting to bypass missing keys
         return string.Formatter().vformat(self.distribution, (), dist_dict)
 
-    def eval_dist(self, values, distance):
-        """Create the final dist string"""
-
+    def scale_dict(self, values, distance):
+        """Create scale dictionary"""
         scale_dict = {}
         if isinstance(values, dict):
             for k, v in values.items():
@@ -172,6 +171,12 @@ def eval_dist(self, values, distance):
             scale_dict["value"] = format_float(values)
         scale_dict["distance"] = format_float(distance)
 
+        return scale_dict
+
+    def eval_dist(self, values, distance):
+        """Create the final dist string"""
+        scale_dict = self.scale_dict(values, distance)
+
         return self.inst_distribution.format(**scale_dict)
 
     def scale(self, values, segment, sim=None):
@@ -261,3 +266,51 @@ def acc_scale_iexpr(self, value, constant_formatter=format_float):
             ArbFileMorphology.region_labels['somatic'].ref
         )
         return generate_acc_scale_iexpr(iexpr, variables, constant_formatter)
+
+
+class NrnSegmentSomaDistanceStepScaler(NrnSegmentSomaDistanceScaler,
+                                       ParameterScaler, DictMixin):
+
+    """Scaler based on distance from soma with a step function"""
+    SERIALIZED_FIELDS = ('name', 'comment', 'distribution', )
+
+    def __init__(
+            self,
+            name=None,
+            distribution=None,
+            comment='',
+            dist_param_names=None,
+            soma_ref_location=0.5,
+            step_begin=None,
+            step_end=None):
+        """Constructor
+        Args:
+            name (str): name of this object
+            distribution (str): distribution of parameter dependent on distance
+                from soma. string can contain `distance` and/or `value` as
+                placeholders for the distance to the soma and parameter value
+                respectively. It can also contain step_begin and step_end.
+            dist_param_names (list): list of names of parameters that
+                parametrise the distribution. These names will become
+                attributes of this object.
+                The distribution string should contain these names, and they
+                will be replaced by values of the corresponding attributes
+            soma_ref_location (float): location along the soma used as origin
+                from which to compute the distances. Expressed as a fraction
+                (between 0.0 and 1.0).
+            step_begin (float): distance at which the step begins
+            step_end (float): distance at which the step ends
+        """
+
+        super(NrnSegmentSomaDistanceStepScaler, self).__init__(
+            name, distribution, comment, dist_param_names,
+            soma_ref_location=soma_ref_location)
+        self.step_begin = step_begin
+        self.step_end = step_end
+
+    def scale_dict(self, values, distance):
+        scale_dict = super().scale_dict(values, distance)
+        scale_dict["step_begin"] = self.step_begin
+        scale_dict["step_end"] = self.step_end
+
+        return scale_dict

bluepyopt/ephys/templates/cell_template.jinja2

@@ -120,7 +120,7 @@ proc distribute_distance(){local x localobj sl
   this.soma[0] distance(0, 0.5)
   sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
   forsec sl for(x, 0) {
-    sprint(stmp, distfunc, secname(), x, distance(x))
+    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))
     execute(stmp)
   }
 }

bluepyopt/tests/test_ephys/test_create_hoc.py

@@ -5,7 +5,9 @@
 import os
 
 from bluepyopt.ephys.acc import ArbLabel
+from bluepyopt.ephys.locations import NrnSomaDistanceCompLocation
 from bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceScaler
+from bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceStepScaler
 
 from . import utils
 from bluepyopt.ephys import create_acc, create_hoc
@@ -151,3 +153,33 @@ def test_range_exprs_to_hoc():
     assert hoc[0].param_name == 'gkbar_hh'
     val_gt = '(-0.8696 + 2.087*exp((%.17g)*0.0031))*0.025000000000000001'
     assert hoc[0].value == val_gt
+
+
+@pytest.mark.unit
+def test_range_exprs_to_hoc_step_scaler():
+    """ephys.create_hoc: Test range_exprs_to_hoc with step scaler"""
+    # apical_region = ArbLabel("region", "apic", "(tag 4)")
+    apical_location = NrnSomaDistanceCompLocation(
+        name='apic100',
+        soma_distance=100,
+        seclist_name='apical',
+    )
+    param_scaler = NrnSegmentSomaDistanceStepScaler(
+        name='soma-distance-step-scaler',
+        distribution='{value} * (0.1 + 0.9 * int('
+                     '({distance} > {step_begin}) & ('
+                     '{distance} < {step_end})))',
+        step_begin=300,
+        step_end=500)
+
+    range_expr = create_hoc.RangeExpr(
+        location=apical_location,
+        name="gCa_LVAstbar_Ca_LVAst",
+        value=1,
+        value_scaler=param_scaler
+    )
+
+    hoc = create_hoc.range_exprs_to_hoc([range_expr])
+    assert hoc[0].param_name == 'gCa_LVAstbar_Ca_LVAst'
+    val_gt = '1 * (0.1 + 0.9 * int((%.17g > 300) && (%.17g < 500)))'
+    assert hoc[0].value == val_gt

bluepyopt/tests/test_ephys/test_parameterscalers.py

@@ -51,6 +51,22 @@ def test_NrnSegmentSectionDistanceScaler_eval_dist_with_dict():
             == '0.5 + (1 - (abs(10 - 8) / 4)) * 1')
 
 
+@pytest.mark.unit
+def test_NrnSegmentSomaDistanceStepScaler_eval_dist_with_dict():
+    """ephys.parameterscalers: eval_dist of NrnSegmentSomaDistanceStepScaler"""
+
+    dist = '{value} * (0.1 + 0.9 * int(' \
+           '({distance} > {step_begin}) & ({distance} < {step_end})))'
+
+    scaler = ephys.parameterscalers.NrnSegmentSomaDistanceStepScaler(
+        distribution=dist, step_begin=300, step_end=500)
+
+    _values = {'value': 1}
+
+    assert (scaler.eval_dist(values=_values, distance=10)
+            == '1 * (0.1 + 0.9 * int((10 > 300) & (10 < 500)))')
+
+
 @pytest.mark.unit
 def test_serialize():
     """ephys.parameterscalers: test serialization"""

examples/stochkv/stochkv3cell.hoc

@@ -98,7 +98,7 @@ proc distribute_distance(){local x localobj sl
   this.soma[0] distance(0, 0.5)
   sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
   forsec sl for(x, 0) {
-    sprint(stmp, distfunc, secname(), x, distance(x))
+    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))
     execute(stmp)
   }
 }

examples/stochkv/stochkv3cell_det.hoc

@@ -98,7 +98,7 @@ proc distribute_distance(){local x localobj sl
   this.soma[0] distance(0, 0.5)
   sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
   forsec sl for(x, 0) {
-    sprint(stmp, distfunc, secname(), x, distance(x))
+    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))
     execute(stmp)
   }
 }

examples/stochkv/stochkvcell.hoc

@@ -98,7 +98,7 @@ proc distribute_distance(){local x localobj sl
   this.soma[0] distance(0, 0.5)
   sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
   forsec sl for(x, 0) {
-    sprint(stmp, distfunc, secname(), x, distance(x))
+    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))
     execute(stmp)
   }
 }

examples/stochkv/stochkvcell_det.hoc

@@ -98,7 +98,7 @@ proc distribute_distance(){local x localobj sl
   this.soma[0] distance(0, 0.5)
   sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc)
   forsec sl for(x, 0) {
-    sprint(stmp, distfunc, secname(), x, distance(x))
+    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))
     execute(stmp)
   }
 }