MAINT: replace progressbar with tqdm

.gitignore

@@ -32,7 +32,7 @@ pip-delete-this-directory.txt
 # Unit test / coverage reports
 htmlcov/
 .tox/
-.coverage
+.coverage*
 .cache
 nosetests.xml
 coverage.xml

.travis.yml

@@ -38,7 +38,7 @@ install:
     - eval "$(conda shell.bash hook)"
     - conda activate test-env
     - travis_retry conda install -c conda-forge python=$TRAVIS_PYTHON_VERSION
-    - travis_retry conda install -c conda-forge numpy scipy python-igraph h5netcdf
+    - travis_retry conda install -c conda-forge numpy scipy python-igraph h5netcdf tqdm
     - travis_retry conda update  -c conda-forge --all
 
     # testing dependencies

CONTRIBUTIONS.rst

@@ -59,9 +59,6 @@
     - `Numpy <http://www.numpy.org/>`_
     - `Cython <http://cython.org/>`_
 
-**Included**
-    - `progressbar <http://pypi.python.org/pypi/progressbar/>`_
-
 **To Do**
   - A lot - See current product backlog.
 

pyproject.toml

@@ -18,9 +18,9 @@ build-backend = "setuptools.build_meta"
 
 [tool.pylint.main]
 ignore = [
-    "CVS", ".cache", ".tox", ".ropeproject", "build", "mpi.py", "progressbar"
+    "CVS", ".cache", ".tox", ".ropeproject", "build", "mpi.py"
 ]
-ignore-patterns = ["navigator", "progressbar", "numerics"]
+ignore-patterns = ["navigator", "numerics"]
 persistent = false
 jobs = 0
 

setup.cfg

@@ -47,6 +47,7 @@ install_requires =
     scipy >= 1.10
     igraph >= 0.10
     h5netcdf >= 1.1
+    tqdm >= 4.66
 python_requires = >=3.8
 packages = find:
 package_dir =
@@ -125,7 +126,7 @@ commands =
 
 [flake8]
 extend-exclude =
-    .git, .cache, .tox, .ropeproject, build, progressbar,
+    .git, .cache, .tox, .ropeproject, build,
     docs/source/conf.py
 extend-ignore =
     E121, E123, E126, E226, E24, E704, E731, F401, F403, F405, F812, F841, W503
@@ -139,6 +140,6 @@ testpaths =
 python_files =
     test*.py Test*.py
 norecursedirs =
-    .git .cache .tox .ropeproject build progressbar
+    .git .cache .tox .ropeproject build
 addopts =
     -v -r a -n auto

src/pyunicorn/climate/havlin.py

@@ -23,8 +23,8 @@
 #  Import NumPy for the array object and fast numerics
 import numpy as np
 
-#  Import progress bar for easy progress bar handling
-from ..utils import progressbar
+#  Import tqdm for easy progress bar handling
+from tqdm import trange
 
 #  Import cnNetwork for Network base class
 from .climate_network import ClimateNetwork
@@ -172,19 +172,13 @@ def _calculate_correlation_strength(self, anomaly, max_delay, gamma=0.2):
         correlation_strength = np.empty((N, N))
         max_lag_matrix = np.empty((N, N))
 
-        #  Initialize progress bar
-        if self.silence_level <= 1:
-            progress = progressbar.ProgressBar(maxval=N).start()
-
         #  Calculate the inverse Fourier transform of all time series
         ifft = np.fft.ifft(anomaly, axis=0)
 
-        for i in range(N):
-            # Update progress bar every 10 steps
-            if self.silence_level <= 1:
-                if (i % 10) == 0:
-                    progress.update(i)
+        #  toggle progress bar
+        silence = self.silence_level > 1
 
+        for i in trange(N, disable=silence, leave=False):
             #  Calculate the cross correlation function of node i to all other
             #  nodes which is not normalized yet.
             #  The real part has to be taken to get rid of small imaginary
@@ -210,9 +204,6 @@ def _calculate_correlation_strength(self, anomaly, max_delay, gamma=0.2):
             #  Store time delays at maximum cross correlation
             max_lag_matrix[i, :] = cc_one_to_all.argmax(axis=0) - max_delay
 
-        if self.silence_level <= 1:
-            progress.finish()
-
         return (correlation_strength, max_lag_matrix)
 
     def get_max_delay(self):

src/pyunicorn/climate/mutual_info.py

@@ -23,12 +23,12 @@
 # array object and fast numerics
 import numpy as np
 
+#  easy progress bar handling
+from tqdm import trange
+
 from ..core._ext.types import to_cy, FIELD
 from ._ext.numerics import mutual_information
 
-#  Import progress bar for easy progress bar handling
-from ..utils import progressbar
-
 #  Import cnNetwork for Network base class
 from .climate_network import ClimateNetwork
 
@@ -216,18 +216,12 @@
         #  Compute the information entropies of each time series
         H = - (p * log(p)).sum(axis=1)
 
-        # Initialize progress bar
-        if self.silence_level <= 1:
-            progress = progressbar.ProgressBar(maxval=self.N**2).start()
+        # Toggle progress bar
+        silence = self.silence_level > 1
 
         #  Calculate only the lower half of the MI matrix, since MI is
         #  symmetric with respect to X and Y.
-        for i in range(self.N):
-            # Update progress bar every 10 steps
-            if self.silence_level <= 1:
-                if (i % 10) == 0:
-                    progress.update(i**2)
-
+        for i in trange(self.N, disable=silence):
             for j in range(i):
                 #  Calculate the joint probability distribution
                 pxy = histogram2d(
@@ -246,9 +240,6 @@
                 mi.itemset((i, j), H.item(i) + H.item(j) - HXY)
                 mi.itemset((j, i), mi.item((i, j)))
 
-        if self.silence_level <= 1:
-            progress.finish()
-
         return mi
 
     def calculate_similarity_measure(self, anomaly):

src/pyunicorn/core/network.py

@@ -41,6 +41,7 @@
 from scipy.linalg import expm
 from scipy import sparse as sp      # fast sparse matrices
 from scipy.sparse.linalg import eigsh, inv, splu
+from tqdm import tqdm, trange       # easy progress bar handling
 
 import igraph                       # high performance graph theory tools
 
@@ -53,8 +54,6 @@
     _nsi_betweenness, _mpi_newman_betweenness, _mpi_nsi_newman_betweenness, \
     _do_nsi_clustering_I, _do_nsi_clustering_II, _do_nsi_hamming_clustering
 
-from ..utils import progressbar     # easy progress bar handling
-
 
 # =============================================================================
 
@@ -1153,8 +1152,7 @@
                     cum += link_prob[i]
                 return i
 
-            progress = progressbar.ProgressBar(maxval=N).start()
-            for j in range(n_initials, N):
+            for j in trange(n_initials, N):
                 # add node j with unit weight:
                 link_prob[j] = kstar[j] = w[j] = 1
                 total_link_prob += 1
@@ -1216,11 +1214,6 @@
                     total_link_prob += link_prob[i] + link_prob[j2]
                 # print(total_link_prob, link_prob.sum())
 
-                if j % 10:
-                    progress.update(j)
-
-            progress.finish()
-
         else:
             link_target = []
 
@@ -1321,30 +1314,28 @@
             return i
 
         this_N = n_initials
-        progress = progressbar.ProgressBar(maxval=N).start()
         it = 0
-        while this_N < N and it < n_increases:
-            it += 1
-            i = _inc_target()
-            total_inc_prob -= inc_prob[i]
-            w[i] += 1
-            inc_prob[i] = w[i]**exponent
-            total_inc_prob += inc_prob[i]
-            if (mode == "exp" and random.uniform() > hold_prob**w[i]) or \
-                    (mode == "rec" and random.uniform()
-                     < w[i]*1.0/(split_weight+w[i])):  # reciprocal
-                # split i into i,this_N:
+        with tqdm(total=N) as pbar:
+            while this_N < N and it < n_increases:
+                it += 1
+                i = _inc_target()
                 total_inc_prob -= inc_prob[i]
-                w[this_N] = w[i]*random.beta(beta, beta)
-                w[i] -= w[this_N]
-                inc_prob[this_N] = w[this_N]**exponent
+                w[i] += 1
                 inc_prob[i] = w[i]**exponent
-                total_inc_prob += inc_prob[this_N] + inc_prob[i]
-                this_N += 1
-            if this_N % 10:
-                progress.update(this_N)
+                total_inc_prob += inc_prob[i]
+                if (mode == "exp" and random.uniform() > hold_prob**w[i]) or \
+                        (mode == "rec" and random.uniform()
+                         < w[i]*1.0/(split_weight+w[i])):  # reciprocal
+                    # split i into i,this_N:
+                    total_inc_prob -= inc_prob[i]
+                    w[this_N] = w[i]*random.beta(beta, beta)
+                    w[i] -= w[this_N]
+                    inc_prob[this_N] = w[this_N]**exponent
+                    inc_prob[i] = w[i]**exponent
+                    total_inc_prob += inc_prob[this_N] + inc_prob[i]
+                    this_N += 1
+                    pbar.update()
 
-        progress.finish()
         return w
 
     def randomly_rewire(self, iterations):
@@ -4440,16 +4431,10 @@
         if self.silence_level <= 1:
             print("Calculating (weighted) node vulnerabilities...")
 
-        #  Initialize progress bar
-        if self.silence_level <= 1:
-            progress = progressbar.ProgressBar(maxval=self.N).start()
-
-        for i in range(self.N):
-            # Update progress bar every 10 steps
-            if self.silence_level <= 1:
-                if (i % 10) == 0:
-                    progress.update(i)
+        #  Toggle progress bar
+        silence = self.silence_level > 1
 
+        for i in trange(self.N, disable=silence):
             #  Remove vertex i from graph
             graph = self.graph - i
 
@@ -4467,10 +4452,6 @@
             #  Clean up
             del graph, network
 
-        #  Terminate progress bar
-        if self.silence_level <= 1:
-            progress.finish()
-
         return vulnerability
 
     #

src/pyunicorn/timeseries/surrogates.py

@@ -21,19 +21,19 @@
 import numpy as np
 from numpy import random
 
+# easy progress bar handling
+from tqdm import trange
+
 from ..core._ext.types import to_cy, ADJ, DEGREE, FIELD, DFIELD
 from ._ext.numerics import _embed_time_series_array, _recurrence_plot, \
     _twins_s, _twin_surrogates_s, _test_pearson_correlation, \
     _test_mutual_information
 
-# easy progress bar handling
-from ..utils import progressbar
-
-
 #
 #  Define class Surrogates
 #
 
+
 class Surrogates:
 
     """
@@ -704,15 +704,10 @@
         #  Initialize density estimate
         density_estimate = np.zeros(n_bins)
 
-        #  Initialize progress bar
-        if self.silence_level <= 2:
-            progress = progressbar.ProgressBar(maxval=realizations).start()
-
-        for i in range(realizations):
-            #  Update progress bar
-            if self.silence_level <= 2:
-                progress.update(i)
+        #  Toggle progress bar
+        silence = self.silence_level > 2
 
+        for _ in trange(realizations, disable=silence):
             #  Get the surrogate
             #  Mean and variance are conserved by all surrogates
             surrogates = surrogate_function(original_data)
@@ -738,9 +733,6 @@
             #  but you never know...)
             del surrogates, correlation_measure_test
 
-        if self.silence_level <= 2:
-            progress.finish()
-
         #  Normalize density estimate
         density_estimate /= density_estimate.sum()