Remove unused API-calling code (will be covered by #14)

kymata/entities/expression.py

@@ -39,7 +39,7 @@
 BLOCK_SCALP = "scalp"
 
 # Column names
-COL_LOGP_VALUE = "log-p-value"
+COL_LOGP_VALUE = "log p-value"
 
 
 class ExpressionSet(ABC):

kymata/gridsearch/plain.py

@@ -9,11 +9,7 @@
 from kymata.entities.transform import Transform
 from kymata.math.combinatorics import generate_derangement
 from kymata.math.vector import normalize, get_stds
-from kymata.entities.expression import (
-    ExpressionSet,
-    SensorExpressionSet,
-    HexelExpressionSet,
-)
+from kymata.entities.expression import ExpressionSet, SensorExpressionSet, HexelExpressionSet
 from kymata.math.p_values import log_base, p_to_logp
 from kymata.plot.plot import plot_top_five_channels_of_gridsearch
 

kymata/ippm/data_tools.py

@@ -2,7 +2,6 @@
 
 from kymata.entities.constants import HEMI_LEFT, HEMI_RIGHT
 from kymata.entities.expression import HexelExpressionSet, DIM_TRANSFORM, DIM_LATENCY, COL_LOGP_VALUE
-from kymata.io.atlas import fetch_data_dict
 from kymata.math.p_values import logp_to_p
 
 
@@ -60,23 +59,6 @@ def add_pairing(self, hemi: str, expr_timing: ExpressionPairing):
 SpikeDict = dict[str, IPPMSpike]
 
 
-def fetch_spike_dict(api: str) -> SpikeDict:
-    """
-    Fetches data from Kymata API and converts it into a dictionary of transform names as keys
-    and spike objects as values. Advantage of dict is O(1) look-up and spike object is readable
-    access to attributes.
-
-    Params
-    ------
-        api : URL of the API from which to fetch data
-
-    Returns
-    -------
-        Dictionary containing data in the format [transform name, spike]
-    """
-    return build_spike_dict_from_api_response(fetch_data_dict(api))
-
-
 def build_spike_dict_from_expression_set(expression_set: HexelExpressionSet) -> SpikeDict:
     """
     Builds the dictionary from an ExpressionSet. This function builds a new dictionary
@@ -102,34 +84,6 @@ def build_spike_dict_from_expression_set(expression_set: HexelExpressionSet) ->
     return spikes
 
 
-def build_spike_dict_from_api_response(dict_: dict) -> SpikeDict:
-    """
-    Builds the dictionary from response dictionary. Response dictionary has unneccesary
-    keys and does not have transform names as keys. This function builds a new dictionary
-    which has transform names (fast look-up) and only necessary data.
-
-    Params
-    ------
-        dict_ : JSON dictionary of HTTP GET response object.
-
-    Returns
-    -------
-        Dict of the format [transform name, spike(trans_name, id, left_timings, right_timings)]
-    """
-    spikes = {}
-    for hemi in [HEMI_LEFT, HEMI_RIGHT]:
-        for _, latency, pval, trans in dict_[hemi]:
-            # we have id, latency (ms), pvalue (log_10), transform name.
-            # discard id as it conveys no useful information
-            if trans not in spikes:
-                # first time seeing transform, so create key and spike object.
-                spikes[trans] = IPPMSpike(trans)
-
-            spikes[trans].add_pairing(hemi, ExpressionPairing(latency, pow(10, pval)))
-
-    return spikes
-
-
 def remove_excess_transforms(to_retain: list[str], spikes: SpikeDict) -> SpikeDict:
     """
     Utility function to distill the spikes down to a subset of transforms. Use this to visualise a subset of transforms

kymata/math/p_values.py

@@ -14,3 +14,23 @@ def p_to_logp(arraylike: ArrayLike) -> ArrayLike:
 def logp_to_p(arraylike: ArrayLike) -> ArrayLike:
     """The one-stop-shop for converting from log p-values to p-values."""
     return float(10) ** arraylike
+
+
+def p_to_surprisal(arraylike: ArrayLike) -> ArrayLike:
+    """Converts p-values to surprisal values."""
+    return logp_to_surprisal(p_to_logp(arraylike))
+
+
+def logp_to_surprisal(arraylike: ArrayLike) -> ArrayLike:
+    """Converts logp-values to surprisal values."""
+    return -1 * arraylike
+
+
+def surprisal_to_logp(arraylike: ArrayLike) -> ArrayLike:
+    """Converts surprisal values to logp values."""
+    return -1 * arraylike
+
+
+def surprisal_to_p(arraylike: ArrayLike) -> ArrayLike:
+    """Converts surprisal values to p-values."""
+    return logp_to_p(surprisal_to_logp(arraylike))

tests/test_ippm_data_tools.py

@@ -3,10 +3,7 @@
 """
 
 from kymata.entities.constants import HEMI_RIGHT, HEMI_LEFT
-from kymata.ippm.data_tools import (
-    IPPMSpike, build_spike_dict_from_api_response,
-    copy_hemisphere, remove_excess_transforms,
-)
+from kymata.ippm.data_tools import IPPMSpike, copy_hemisphere, remove_excess_transforms
 
 
 def test_hexel():
@@ -24,24 +21,6 @@ def test_hexel():
     assert hexel.right_best_pairings == [(20, 10e-3), (50, 0.000012), (611, 0.00053)]
 
 
-def test_build_spike_dict():
-    test_dict = {
-        HEMI_LEFT: [[2, 1, 0.012, "left1"], [2, 14, 0.213, "left1"]],
-        HEMI_RIGHT: [[3, 51, 0.1244, "left1"], [4, 345, 0.557, "right1"]],
-    }
-
-    hexels = build_spike_dict_from_api_response(test_dict)
-
-    # check transforms are saved correctly
-    assert list(hexels.keys()) == ["left1", "right1"]
-    # check p value is stored and calculated correctly
-    assert hexels["left1"].left_best_pairings == [
-        (1, pow(10, 0.012)),
-        (14, pow(10, 0.213)),
-    ]
-    assert hexels["left1"].right_best_pairings == [(51, pow(10, 0.1244))]
-
-
 def test_Should_removeExcessFuncs_When_validInput():
     hexels = {"f1": IPPMSpike("f1"), "f2": IPPMSpike("f2"), "f3": IPPMSpike("f3")}
     to_retain = ["f2"]