Return 3.x um reflectance with the same dtype as the NIR data

pyspectral/near_infrared_reflectance.py

@@ -242,12 +242,12 @@ def reflectance_from_tbs(self, sun_zenith, tb_near_ir, tb_thermal, **kwargs):
 
         # Assume rsr is in microns!!!
         # FIXME!
-        self._rad3x_t11 = self.tb2radiance(tb_therm, lut=lut)['radiance']
-        thermal_emiss_one = self._rad3x_t11 * self.rsr_integral
-
-        l_nir = self.tb2radiance(tb_nir, lut=lut)['radiance']
+        self._rad3x_t11 = self.tb2radiance(tb_therm, lut=lut)['radiance'].astype(tb_nir.dtype)
+        rsr_integral = tb_therm.dtype.type(self.rsr_integral)
+        thermal_emiss_one = self._rad3x_t11 * rsr_integral
+        l_nir = self.tb2radiance(tb_nir, lut=lut)['radiance'].astype(tb_nir.dtype)
         self._rad3x = l_nir.copy()
-        l_nir *= self.rsr_integral
+        l_nir *= rsr_integral
 
         if thermal_emiss_one.ravel().shape[0] < 10:
             LOG.info('thermal_emiss_one = %s', str(thermal_emiss_one))
@@ -268,7 +268,8 @@ def reflectance_from_tbs(self, sun_zenith, tb_near_ir, tb_thermal, **kwargs):
             self.derive_rad39_corr(tb_therm, tbco2)
             LOG.info("CO2 correction applied...")
         else:
-            self._rad3x_correction = 1.0
+            self._rad3x_correction = np.float64(1.0)
+        self._rad3x_correction = self._rad3x_correction.astype(tb_nir.dtype)
 
         corrected_thermal_emiss_one = thermal_emiss_one * self._rad3x_correction
         nomin = l_nir - corrected_thermal_emiss_one

pyspectral/tests/test_reflectance.py

@@ -203,13 +203,25 @@ def test_reflectance(self):
         refl = refl37.reflectance_from_tbs(sunz, tb3, tb4)
         self.assertTrue(hasattr(refl, 'mask'))
 
+        sunz = np.array([80.], dtype=np.float32)
+        tb3 = np.array([295.], dtype=np.float32)
+        tb4 = np.array([282.], dtype=np.float32)
+        refl = refl37.reflectance_from_tbs(sunz, tb3, tb4)
+        np.testing.assert_allclose(refl, np.array([0.45249779], np.float32))
+        assert refl.dtype == np.float32
+
         try:
             import dask.array as da
-            sunz = da.from_array([50.], chunks=10)
-            tb3 = da.from_array([300.], chunks=10)
-            tb4 = da.from_array([285.], chunks=10)
-            refl = refl37.reflectance_from_tbs(sunz, tb3, tb4)
-            self.assertTrue(hasattr(refl, 'compute'))
+            import dask.config
+
+            from pyspectral.tests.unittest_helpers import CustomScheduler
+
+            with dask.config.set(scheduler=CustomScheduler(max_computes=0)):
+                sunz = da.from_array([50.], chunks=10)
+                tb3 = da.from_array([300.], chunks=10)
+                tb4 = da.from_array([285.], chunks=10)
+                refl = refl37.reflectance_from_tbs(sunz, tb3, tb4)
+                self.assertTrue(hasattr(refl, 'compute'))
         except ImportError:
             pass
 

pyspectral/tests/unittest_helpers.py

@@ -28,3 +28,21 @@
         raise AssertionError("Shapes don't match")
     if not np.allclose(self, other):
         raise AssertionError("Elements don't match!")
+
+
+class CustomScheduler(object):
+    """Scheduler raising an exception if data are computed too many times."""
+
+    def __init__(self, max_computes=1):
+        """Set starting and maximum compute counts."""
+        self.max_computes = max_computes
+        self.total_computes = 0
+
+    def __call__(self, dsk, keys, **kwargs):
+        """Compute dask task and keep track of number of times we do so."""
+        import dask
+        self.total_computes += 1
+        if self.total_computes > self.max_computes:
+            raise RuntimeError("Too many dask computations were scheduled: "
+                               "{}".format(self.total_computes))
+        return dask.get(dsk, keys, **kwargs)