Merge pull request #296 from grlee77/052_tmp2

backporting bug fixes from master to 0.5.2

Author: rgommers

.travis.yml

@@ -16,28 +16,37 @@ matrix:
         - PYFLAKES=1
         - PEP8=1
         - NUMPYSPEC=numpy
+        - MPLSPEC=matplotlib
       before_install:
         - pip install pep8==1.5.1
         - pip install pyflakes
       script:
         - PYFLAKES_NODOCTEST=1 pyflakes pywt demo | grep -E -v 'unable to detect undefined names|assigned to but never used|imported but unused|redefinition of unused' > test.out; cat test.out; test \! -s test.out
         - pep8 pywt demo
-
     - python: 3.5
       env:
         - NUMPYSPEC=numpy
-    - python: 3.4
+        - MPLSPEC=matplotlib
+        - USE_WHEEL=1
+    - os: linux
+      python: 3.4
       env:
         - NUMPYSPEC=numpy
-    - python: 2.6
+        - MPLSPEC=matplotlib
+        - USE_SDIST=1
+    - os: linux
+      python: 2.6
       env:
         - NUMPYSPEC="numpy==1.9.3"
+        - MPLSPEC="matplotlib<2"
     - python: 2.7
       env:
         - NUMPYSPEC=numpy
+        - MPLSPEC=matplotlib
     - python: 3.5
       env:
         - NUMPYSPEC=numpy
+        - MPLSPEC=matplotlib
         - REFGUIDE_CHECK=1  # run doctests only
 
 cache: pip
@@ -52,8 +61,9 @@ before_install:
   - pip install --upgrade wheel
   # Set numpy version first, other packages link against it
   - pip install $NUMPYSPEC
-  - pip install Cython matplotlib nose coverage codecov
+  - pip install Cython $MPLSPEC nose coverage codecov futures
   - set -o pipefail
+  - if [ "${USE_WHEEL}" == "1" ]; then pip install wheel; fi
   - |
     if [ "${REFGUIDE_CHECK}" == "1" ]; then
         pip install sphinx numpydoc
@@ -62,7 +72,21 @@ before_install:
 script:
   # Define a fixed build dir so next step works
   - |
-    if [ "${REFGUIDE_CHECK}" == "1" ]; then
+    if [ "${USE_WHEEL}" == "1" ]; then
+        # Need verbose output or TravisCI will terminate after 10 minutes
+        pip wheel . -v
+        pip install PyWavelets*.whl -v
+        pushd demo
+        nosetests pywt
+        popd
+    elif [ "${USE_SDIST}" == "1" ]; then
+        python setup.py sdist
+        # Move out of source directory to avoid finding local pywt
+        pushd dist
+        pip install PyWavelets* -v
+        nosetests pywt
+        popd
+    elif [ "${REFGUIDE_CHECK}" == "1" ]; then
         pip install -e . -v
         python util/refguide_check.py --doctests
     else

appveyor.yml

@@ -23,7 +23,7 @@ install:
   - "util\\appveyor\\build.cmd %PYTHON%\\python.exe -m pip install
      numpy --cache-dir c:\\tmp\\pip-cache"
   - "util\\appveyor\\build.cmd %PYTHON%\\python.exe -m pip install
-     Cython nose coverage matplotlib --cache-dir c:\\tmp\\pip-cache"
+     Cython nose coverage matplotlib futures --cache-dir c:\\tmp\\pip-cache"
 
 test_script:
   - "util\\appveyor\\build.cmd %PYTHON%\\python.exe setup.py build --build-lib build\\lib\\"

demo/wp_scalogram.py

@@ -40,7 +40,8 @@
 # Show spectrogram and wavelet packet coefficients
 fig2 = plt.figure()
 ax2 = fig2.add_subplot(211)
-ax2.specgram(data, NFFT=64, noverlap=32, cmap=cmap)
+ax2.specgram(data, NFFT=64, noverlap=32, Fs=2, cmap=cmap,
+             interpolation='bilinear')
 ax2.set_title("Spectrogram of signal")
 ax3 = fig2.add_subplot(212)
 ax3.imshow(values, origin='upper', extent=[-1, 1, -1, 1],

pywt/_extensions/_swt.pyx

@@ -102,8 +102,7 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
     # memory-views do not support n-dimensional arrays, use np.ndarray instead
     cdef common.ArrayInfo data_info, output_info
     cdef np.ndarray cD, cA
-    # Explicit input_shape necessary to prevent memory leak
-    cdef size_t[::1] input_shape, output_shape
+    cdef size_t[::1] output_shape
     cdef size_t end_level = start_level + level
     cdef int i, retval
 
@@ -122,28 +121,23 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
         raise ValueError(msg)
 
     data = data.astype(_check_dtype(data), copy=False)
-
-    input_shape = <size_t [:data.ndim]> <size_t *> data.shape
-    output_shape = input_shape.copy()
-    output_shape[axis] = common.swt_buffer_length(data.shape[axis])
-    if output_shape[axis] != input_shape[axis]:
-        raise RuntimeError("swt_axis assumes output_shape is the same as "
-                           "input_shape")
+    # For SWT, the output matches the shape of the input
+    output_shape = <size_t [:data.ndim]> <size_t *> data.shape
 
     data_info.ndim = data.ndim
     data_info.strides = <pywt_index_t *> data.strides
     data_info.shape = <size_t *> data.shape
 
-    cA = np.empty(output_shape, data.dtype)
-    output_info.ndim = cA.ndim
-    output_info.strides = <pywt_index_t *> cA.strides
-    output_info.shape = <size_t *> cA.shape
+    output_info.ndim = data.ndim
 
     ret = []
     for i in range(start_level+1, end_level+1):
-
+        cA = np.empty(output_shape, dtype=data.dtype)
+        cD = np.empty(output_shape, dtype=data.dtype)
+        # strides won't match data_info.strides if data is not C-contiguous
+        output_info.strides = <pywt_index_t *> cA.strides
+        output_info.shape = <size_t *> cA.shape
         if data.dtype == np.float64:
-            cA = np.zeros(output_shape, dtype=np.float64)
             with nogil:
                 retval = c_wt.double_downcoef_axis(
                     <double *> data.data, data_info,
@@ -152,8 +146,8 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
                     common.COEF_APPROX, common.MODE_PERIODIZATION,
                     i, common.SWT_TRANSFORM)
             if retval:
-                raise RuntimeError("C wavelet transform failed")
-            cD = np.zeros(output_shape, dtype=np.float64)
+                raise RuntimeError(
+                    "C wavelet transform failed with error code %d" % retval)
             with nogil:
                 retval = c_wt.double_downcoef_axis(
                     <double *> data.data, data_info,
@@ -162,9 +156,9 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
                     common.COEF_DETAIL, common.MODE_PERIODIZATION,
                     i, common.SWT_TRANSFORM)
             if retval:
-                raise RuntimeError("C wavelet transform failed")
+                raise RuntimeError(
+                    "C wavelet transform failed with error code %d" % retval)
         elif data.dtype == np.float32:
-            cA = np.zeros(output_shape, dtype=np.float32)
             with nogil:
                 retval = c_wt.float_downcoef_axis(
                     <float *> data.data, data_info,
@@ -173,8 +167,8 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
                     common.COEF_APPROX, common.MODE_PERIODIZATION,
                     i, common.SWT_TRANSFORM)
             if retval:
-                raise RuntimeError("C wavelet transform failed")
-            cD = np.zeros(output_shape, dtype=np.float32)
+                raise RuntimeError(
+                    "C wavelet transform failed with error code %d" % retval)
             with nogil:
                 retval = c_wt.float_downcoef_axis(
                     <float *> data.data, data_info,
@@ -183,15 +177,18 @@ cpdef swt_axis(np.ndarray data, Wavelet wavelet, size_t level,
                     common.COEF_DETAIL, common.MODE_PERIODIZATION,
                     i, common.SWT_TRANSFORM)
             if retval:
-                raise RuntimeError("C wavelet transform failed")
+                raise RuntimeError(
+                    "C wavelet transform failed with error code %d" % retval)
         else:
             raise TypeError("Array must be floating point, not {}"
                             .format(data.dtype))
         ret.append((cA, cD))
 
         # previous approx coeffs are the data for the next level
         data = cA
-        data_info = output_info
+        # update data_info to match the new data array
+        data_info.strides = <pywt_index_t *> data.strides
+        data_info.shape = <size_t *> data.shape
 
     ret.reverse()
     return ret

pywt/_extensions/c/wt.template.c

@@ -36,25 +36,25 @@ int CAT(TYPE, _downcoef_axis)(const TYPE * const restrict input, const ArrayInfo
     if (input_info.ndim != output_info.ndim)
         return 1;
     if (axis >= input_info.ndim)
-        return 1;
+        return 2;
 
     for (i = 0; i < input_info.ndim; ++i){
         if (i == axis){
             switch (transform) {
             case DWT_TRANSFORM:
                 if (dwt_buffer_length(input_info.shape[i], wavelet->dec_len,
                                       dwt_mode) != output_info.shape[i])
-                    return 1;
+                    return 3;
                 break;
             case SWT_TRANSFORM:
                 if (swt_buffer_length(input_info.shape[i])
                     != output_info.shape[i])
-                    return 1;
+                    return 4;
                 break;
             }
         } else {
             if (input_info.shape[i] != output_info.shape[i])
-                return 1;
+                return 5;
         }
     }
 
@@ -160,7 +160,7 @@ int CAT(TYPE, _downcoef_axis)(const TYPE * const restrict input, const ArrayInfo
  cleanup:
     free(temp_input);
     free(temp_output);
-    return 2;
+    return 6;
 }
 
 

pywt/_multidim.py

@@ -79,7 +79,8 @@ def idwt2(coeffs, wavelet, mode='symmetric', axes=(-2, -1)):
     ----------
     coeffs : tuple
         (cA, (cH, cV, cD)) A tuple with approximation coefficients and three
-        details coefficients 2D arrays like from `dwt2()`
+        details coefficients 2D arrays like from `dwt2`. If any of these
+        components are set to ``None``, it will be treated as zeros.
     wavelet : Wavelet object or name string
         Wavelet to use
     mode : str, optional
@@ -106,10 +107,6 @@ def idwt2(coeffs, wavelet, mode='symmetric', axes=(-2, -1)):
         raise ValueError("Expected 2 axes")
 
     coeffs = {'aa': LL, 'da': HL, 'ad': LH, 'dd': HH}
-
-    # drop the keys corresponding to value = None
-    coeffs = dict((k, v) for k, v in coeffs.items() if v is not None)
-
     return idwtn(coeffs, wavelet, mode, axes)
 
 
@@ -215,8 +212,8 @@ def idwtn(coeffs, wavelet, mode='symmetric', axes=None):
     Parameters
     ----------
     coeffs: dict
-        Dictionary as in output of `dwtn`. Missing or None items
-        will be treated as zeroes.
+        Dictionary as in output of `dwtn`. Missing or ``None`` items
+        will be treated as zeros.
     wavelet : Wavelet object or name string
         Wavelet to use
     mode : str, optional
@@ -240,6 +237,9 @@ def idwtn(coeffs, wavelet, mode='symmetric', axes=None):
         wavelet = Wavelet(wavelet)
     mode = Modes.from_object(mode)
 
+    # drop the keys corresponding to value = None
+    coeffs = dict((k, v) for k, v in coeffs.items() if v is not None)
+
     # Raise error for invalid key combinations
     coeffs = _fix_coeffs(coeffs)
 

pywt/_multilevel.py

@@ -142,8 +142,14 @@ def waverec(coeffs, wavelet, mode='symmetric', axis=-1):
     a, ds = coeffs[0], coeffs[1:]
 
     for d in ds:
-        if (a is not None) and (d is not None) and (len(a) == len(d) + 1):
-            a = a[:-1]
+        if (a is not None) and (d is not None):
+            try:
+                if a.shape[axis] == d.shape[axis] + 1:
+                    a = a[[slice(s) for s in d.shape]]
+                elif a.shape[axis] != d.shape[axis]:
+                    raise ValueError("coefficient shape mismatch")
+            except IndexError:
+                raise ValueError("Axis greater than coefficient dimensions")
         a = idwt(a, d, wavelet, mode, axis)
 
     return a

pywt/data/_readers.py

@@ -174,10 +174,10 @@ def nino():
     sst_csv = np.load(fname)['sst_csv']
     # sst_csv = pd.read_csv("http://www.cpc.ncep.noaa.gov/data/indices/ersst4.nino.mth.81-10.ascii", sep=' ', skipinitialspace=True)
     # take only full years
-    n = np.floor(sst_csv.shape[0]/12.)*12.
+    n = int(np.floor(sst_csv.shape[0]/12.)*12.)
     # Building the mean of three mounth
     # the 4. column is nino 3
-    sst = np.mean(np.reshape(np.array(sst_csv)[:n,4],(n/3,-1)),axis=1)
+    sst = np.mean(np.reshape(np.array(sst_csv)[:n, 4], (n//3, -1)), axis=1)
     sst = (sst - np.mean(sst)) / np.std(sst, ddof=1)
 
     dt = 0.25