Merge pull request #85 from ocefpaf/pre-commit

Author: ocefpaf

.appveyor.yml

@@ -1,42 +1,45 @@
+build: false
+
 environment:
-  CONDA_INSTALL_LOCN: C:\\Miniconda36-x64
+  PYTHON: "C:\\myminiconda3"
   matrix:
-    - TARGET_ARCH: x64
-      PY: 3.7
+    - PY: 3.6
+    - PY: 3.8
 
-platform:
-    - x64
+init:
+  - "ECHO %PYTHON_VERSION% %MINICONDA%"
 
 install:
-    # If there is a newer build queued for the same PR, cancel this one.
-    # The AppVeyor 'rollout builds' option is supposed to serve the same
-    # purpose but it is problematic because it tends to cancel builds pushed
-    # directly to master instead of just PR builds (or the converse).
-    # credits: JuliaLang developers.
-    - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod `
-         https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | `
-         Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
-           throw "There are newer queued builds for this pull request, failing early." }
-
-    # Add path, activate `conda` and update conda.
-    - cmd: call %CONDA_INSTALL_LOCN%\Scripts\activate.bat
-
-    - cmd: conda config --set always_yes yes --set changeps1 no --set show_channel_urls true
-    - cmd: conda update conda --quiet
-    - cmd: conda install pycryptosat --quiet
-    - cmd: conda config --add channels conda-forge --force
-    - cmd: conda config --set sat_solver pycryptosat
-    - cmd: conda config --set channel_priority strict
-    - cmd: conda config --set safety_checks disabled
-
-    - cmd: set PYTHONUNBUFFERED=1
-
-    - cmd: conda create --name TEST python=%PY% --file requirements.txt --file requirements-dev.txt
-    - cmd: conda activate TEST
-
-# Skip .NET project specific build phase.
-build: off
+  # If there is a newer build queued for the same PR, cancel this one.
+  # The AppVeyor 'rollout builds' option is supposed to serve the same
+  # purpose but it is problematic because it tends to cancel builds pushed
+  # directly to master instead of just PR builds (or the converse).
+  # credits: JuliaLang developers.
+  - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod `
+        https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | `
+        Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
+        throw "There are newer queued builds for this pull request, failing early." }
+
+  # Add path, activate `conda` and update conda.
+  - set URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe"
+  - curl -fsS -o miniconda3.exe %URL%
+  - start /wait "" miniconda3.exe /InstallationType=JustMe /RegisterPython=0 /S /D=%PYTHON%
+
+  - "set PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"
+  - call %PYTHON%\Scripts\activate
+
+  - conda config --set always_yes yes --set changeps1 no --set show_channel_urls true
+  - conda config --add channels conda-forge --force
+  - conda config --set channel_priority strict
+  - set PYTHONUNBUFFERED=1
+
+  - conda create --name TEST python=%PY% --file requirements.txt --file requirements-dev.txt
+
+  - conda activate TEST
+  - conda info --all
+  - conda list
 
 test_script:
-    - pytest -n 2 -rxs --cov=ctd tests
-    - pytest -s -rxs --doctest-modules -vv ctd
+  - "python -m pip install . --no-deps -vv"
+  - pytest -n 2 -rxs --cov=ctd tests
+  - pytest -s -rxs --doctest-modules -vv ctd

.travis.yml

@@ -64,15 +64,15 @@ script:
       check-manifest --verbose ;
       twine check dist/* ;
     fi
-  
+
   - |
     if [[ $TRAVIS_JOB_NAME == 'docs' ]]; then
       set -e
       cp notebooks/quick_intro.ipynb docs/source/
       pushd docs
       make clean html linkcheck
       popd
-      if [[ -z "$TRAVIS_TAG" ]]; then 
+      if [[ -z "$TRAVIS_TAG" ]]; then
         python -m doctr deploy --build-tags --key-path github_deploy_key_pyoceans_python_ctd.enc --built-docs docs/build/html/ dev
       else
         python -m doctr deploy --build-tags --key-path github_deploy_key_pyoceans_python_ctd.enc --built-docs docs/build/html/ "version-$TRAVIS_TAG"
@@ -92,4 +92,4 @@ deploy:
     repo: pyoceans/python-ctd
     tags: true
     all_branches: master
-    condition: '$TRAVIS_JOB_NAME == "tarball"'
+    condition: '$TRAVIS_JOB_NAME == "tarball"'

MANIFEST.in

@@ -1,4 +1,4 @@
 include *.txt
 include README.md
 recursive-include ctd *.py
-prune tests
+prune tests

ctd/extras.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.ma as ma
+
 from pandas import Series
 
 
@@ -17,9 +18,7 @@ def pointwise(x):
         if x < xs[0]:
             return ys[0] + (x - xs[0]) * (ys[1] - ys[0]) / (xs[1] - xs[0])
         elif x > xs[-1]:
-            return ys[-1] + (x - xs[-1]) * (ys[-1] - ys[-2]) / (
-                xs[-1] - xs[-2]
-            )
+            return ys[-1] + (x - xs[-1]) * (ys[-1] - ys[-2]) / (xs[-1] - xs[-2])
         else:
             return interpolator(x)
 
@@ -143,9 +142,7 @@ def gen_topomask(h, lon, lat, dx=1.0, kind="linear", plot=False):
 def plot_section(self, reverse=False, filled=False, **kw):
     import gsw
 
-    lon, lat, data = list(
-        map(np.asanyarray, (self.lon, self.lat, self.values))
-    )
+    lon, lat, data = list(map(np.asanyarray, (self.lon, self.lat, self.values)))
     data = ma.masked_invalid(data)
     h = self.get_maxdepth()
     if reverse:
@@ -167,8 +164,7 @@ def plot_section(self, reverse=False, filled=False, **kw):
     labelsize = kw.pop("labelsize", 11)
     cmap = kw.pop("cmap", plt.cm.rainbow)
     levels = kw.pop(
-        "levels",
-        np.arange(np.floor(data.min()), np.ceil(data.max()) + 0.5, 0.5),
+        "levels", np.arange(np.floor(data.min()), np.ceil(data.max()) + 0.5, 0.5),
     )
 
     # Colorbar key words.
@@ -218,14 +214,7 @@ def plot_section(self, reverse=False, filled=False, **kw):
 
     # Color version.
     cs = ax.contourf(
-        x,
-        z,
-        data,
-        cmap=cmap,
-        levels=levels,
-        alpha=1.0,
-        extend=extend,
-        zorder=2,
+        x, z, data, cmap=cmap, levels=levels, alpha=1.0, extend=extend, zorder=2,
     )  # manual=True
     # Colorbar.
     cb = fig.colorbar(

ctd/plotting.py

@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import pandas as pd
+
 from pandas_flavor import register_dataframe_method, register_series_method
 
 

ctd/processing.py

@@ -1,6 +1,7 @@
 import numpy as np
 import numpy.ma as ma
 import pandas as pd
+
 from pandas_flavor import register_dataframe_method, register_series_method
 
 
@@ -145,9 +146,7 @@ def _despike(series, n1, n2, block, keep):
     # Use the last value to fill-up.
     std = np.r_[std, np.tile(std[-1], block - 1)]
     mean = np.r_[mean, np.tile(mean[-1], block - 1)]
-    mask = np.abs(data - mean.filled(fill_value=np.NaN)) > std.filled(
-        fill_value=np.NaN
-    )
+    mask = np.abs(data - mean.filled(fill_value=np.NaN)) > std.filled(fill_value=np.NaN)
     data[mask] = np.NaN
 
     # Pass two recompute the mean and std without the flagged values from pass

docs/Makefile

@@ -17,4 +17,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/source/conf.py

@@ -61,6 +61,7 @@
 #
 from ctd import __version__ as VERSION
 
+
 version = VERSION
 # The full version, including alpha/beta/rc tags.
 release = VERSION

github_deploy_key_pyoceans_python_ctd.enc

@@ -1 +1 @@
-gAAAAABcksnrznRahez_EoXZFuM--QLw571JcTmBChG3FHtIT58M9kbyywC55snxWenuEYm2my2Z_9er_DXYRPcCcCJtQzx0jwM-4NkXgcwz5Pq-zolDbRQOK6mtAs7VAQgznj_R34EZLPwdELDbK2xZ0AhLdGCh5vuMkisHGLUrSCPQ3DcCkxuqWEIaADREj5-hVy88lzZ9atzRvCcKm-bdabHOZBCEUKSssQvC99w1zZgYlCfs6Ennqve6QE32rtRS962PiXGnVw1ez-QX2vIb7ZmhZUcOXIos6n_6fUFKbgU1q0ed6N9tY9gEwSoXtbTi1ANhgzneg5bxI1BaC3QgJ90F7VBl2dWwJF_-N1ezX_FwYMGTYrx_tLS0tybQd-M_EEBM_nwWH6MaOZMKV-37rP9ZCTjHBz6SAIadGdsDfHqAjHyXlh10YhWbz7hWHWYySmvca0TYJ6TwD5PiKRf6lgY0N3Ql1OHifd3_tG1f1Pi7BPOFzQYraslspV814QRGooEYF9jMB4OYkODv-OVsfAvBWPlAgjBcl-P5MC1NjAHmZqyIKuBTutZEhgl02b0LNmCgDUMW5DRRfsMf7-BL-FUEoyxRMC2cPr5gW7VYrGet9f7kAIK0St-_4DtZhOM-FXtHH9HK9i29oX7WvYWOatVcqzj722bVIaZ32OUByaSGPQlveWg2zIYClpSpwN5TGJZwLxMnpaK-ZkigC-xJcGDyBsYzDdHoKt_q1iH0pD9GYxT_7yselI2skVi6tKws4pltwsCv-NBEpwTY0deiCM4RWAn0ZQ7fegz3NZpam-Z9Z2v7iDKcnNcLlst7M237LUuo2EwRv695XTpS36CIjA0MlGujrjFKx6SWdMIZHmkxPdh97-cLwVIi6rLOej-MUKcIS2-aq1LSYrn_YwYHEKtDxVPU562Wgzxrwjo64MvvVw5zi6wEZwZjGDFPm5bCivfsF_bcVZqvBxVWSbmBGLgbtOncIt_SQf1jN7MmqQnVtSSEfcgHPV4lXGvqCsFf_3bUfxUjJvE9tfNeeO4h2IYiJcKb1m42Yz4OMNebsBYMRL5RTx8dsnCAUxNl-62fJcqiklx4poLqW4eN0Ky-aYperJbARWhPqPEb26T76KuXE-xXVUFCFUsl2orShoj7KnJCJvS1cHhQNoOISwO8N2iPlteHcOs_NrObyDJ6GkbvKwYHjtdZSfq-4UsIayC0mGHMIerwxQ5ZsIbnjW3_Jz3FBxkNIqU-U2NfZ6QEYGCxgYSu7dMeLM2DO0dcEpRTeVqbVqVsdv5O68KnOzkr_KtpcUcpBTGIRy2ziy6CqSQ2zIQZMpDbFGEob13AO90A8Zm3JPfAommo-zg8XnPv28qv-33hIDEseuaDJq0bLeFlj4IdBtaVzq_l9CUIFZHCsXu2PFQ0xfrPrcu0OQ84gNwFx_gR5GhFK1i46pwcvOCUqiqzMIC7FO-AhMJKqn5dCWoZYloHdpGa7kYz2-Ix4aCelXPVLaC1qQoPZwGO9E6oE7n53ZeinTj77sNY0qZyxGYdsL4GhLW-EO_BwOS8oMehb7sIXItw-dWjb-ohp5HDTOwH0DqEt3T-X6C3o6GeraMdAzGXnxKB0et0Kvpq65bPAcW0yA8jkrjxoYUIW-wnWqo5SMCzpHE4auEj5Ral7-4WID66jkHVhiz7i8qv5YW4foctwPEuDBxeXN_I-tMSNmmkwOCeDzNHA8wx4u9qVJTvhZC0c7MeIqGVC04jIhyjMabrusKCjEjokjtiDa5znICgC4N-lD6xORUhPSqEW5T-IfQg8loAYENpMoTD5V6LGbnu7aKwkbFCTI48w79iRSZjk0RMVhSFWZud1XbJpVB-F7Nr4ItGmNgmU4BSTJgU5bFmQa14YET2xaVBbDBpmqhhNpHvL0wnBKLFMbNJcUyi8hHE64X7rULLIycM6YQdEVlmhT_hJCfTDjg60QSsyY-om-N5H9yUeq0-RceNNdWvij81xghkCBkZnPoT7YxcynAAOdVRHb7HsUMkmit3NcWDum7CE7NNm5-8OrK-V3u4TpvJXORcec7NjHpe8ixUN1S2_ATme5lQz49-TQaDVDETS8EiLa2r2FleIrdKVTAFtF0CspA-_IKarPRFBrc2EUX6nUlxqNdmLeRbtnavVQYmT-nL-cFjtYea5cPSIIj5jB5UWW-gzGv2If45byj10-wxhsoqe6fADs9t16ianPC6BLAANWTyi5oW75FSI4DVmFvDQ98Znmhp0NTS7DzRFDXmrqs-Q1zd52wyOs3tA2zkDRZgcXg6lt0tfF9DAauGQEPNACrkRk-LRsBIP5vV8F0m0myYMB7dpPE_Agt8c0oBx07QAVjUhmWeqbfnd40YEuI91Xnru4AHVHhjRQqZeBcO1Dqz3h7000Wkr0xViAIE4QUxjkXW32fXKI0XqRI9HKLA9O75ISMcEWWBWD_swHDhVM_0P7o3jOgb1I6LpBitoAFopHRaeP0O--iFC__IhkGP_WVCmHjsuik7fFmUoVMFesaP03291NJ9EJviLL8fJ3WUKc4iOYrUqR6M_LE1ykErV3AAMznSjRApp-D4kx0zA_s3vbOyFu2M5P6FW7u9jJ4d2kz0OUUVZUDmlAyombeQSCdYqfnFiNketLFI3YGhaeyxc6V-ZoWJlDAUhVn4WxX2PPLib80iZPqIvKBbnZ1-vGpexcV7JQ9kukfC6gIAS6fnB-qbqDvz4FHvX1ZHLzUeFloS6-xL3KlzwOYug2M4R-JAP9--2eXVzJy3eStkgMUiWixYDIxmoaiSqVaU5kWSdttNs_dmDQK2SOqv_ljDQkwHAdwSJco4McXR_gOYMJnbEmBsTPAkPei1lmqwY8N53c9JgbtGDnD3qq624yeOpy9zZ4nCcjm6EK2_XYe_2Z6ptb_3YtDHzKOgt0DINlWvW4OZlsmJz1pEevOAD9h6c6_Ikro4pePyXVcGOtuP7KpzjzK3JnjWGRc_VfvTmh709PQxdq8wtUdILwWIXHombihk5YOGYlh8i3Xg5O1UOfNSWgsSKgf8VBPYZk3eosEnXKozG_aV2KTnSIc1VWy43kwMYsoRHEYXkIgSgac0iar0GBOFdPhUstu7abp7eBATkIy6bDVyE3Wvfr7zvTV2B6RwBP0o_Cr8HbWL5ZyXu3-FQ_3Bt4PiFKMNqTG1jF0I6xbGSRrXrQXUvwnZxnPQabgkrPReM3gm1BpM7vurX2LYH7eya7dCaKcb9DR9delcYmmLY0Qc_ZPvc1EaTDbGaU5huY_nP1ufCFCPKZA8LIdQygiD8Z37gt9wUVswieszRaiRWGcnRG558bvjk5HOW_vPCcq8KIHsdIGA9Twrv1DLtohTpHHKHaai--juIB94Wyjq3PSe5K6_e5vXUV8KdlB0gsVAHPBM4HnXxJ1rBSbUT6xPLbnHfhRn3aX-Q7VOXQjHCSi_sQN1dxiPf9WqNaub437XHMRlE-IuwWM1aVEJikEagIOJQvQybXmeD9YYOGmOpszLVjrm8R9x6os5JfuQu9PN18PoY9Avpat7Eqr19T4lWzwqVgUwDZZA3hOG8k0nq3fWhLbgkxF9O-PFT3V2eo8TcwQDIHq3IU_CqcjhSzMieAkIjsVdb4AMaf0Lq3XSo3BReNSyjEAjHpGbVb9AlCNKWoTK2xfUrlN8zAunccHz1BxlmlxiO8mZ9zxFeCRZXZxw-lluR1gw9HwypGnWZJLfS9_1Q_oHBquVLEj8zAu43MwYRV--G-nqD0pTY7a0BB3_xXTCc-PRk_UAVbpUVDuGzWmzJN6kXgUVrMCFxA_SX7Oma2NSkXXpzmOofLCtsf_ET2_lfkNYJlG7QTnfowbFbMpxebkSxAWIeUrS8BeGn7KabStxetdxyOVtpY4bUcUwFc9ItlUxyOmzeSOzLm_HOtrsnaH-T4wc3P9X5TFj_uCEw7WqSfTqssl5xhW2Db-9Up8u2rjjg_NwfERHS2eUPYbtT7qCszCV6jVUW5xZkPOMODx-ht7IyqKOyyagb6wbFY6CZhScX_hmMuqnqSY5EzSfF-AyZGlAdc9Kb8ogzoZxGox2coutJHD0VLsX3rDEOofUE1_OKO3T1y288FRd7Df-f4YSztjn8wFIoudq-VaUpmVC498svNmWkh9Q9cOZ3hBxMCI8FzzUdARpfC2aW84bfLaM7IErduNBIvUOVXj3iIxUGb4bJ5xBjKuvrhKowXtA216iSw_iB4ZdUzd26XEUXvoQX5LXQMaKA5hvtf22Sgc3oF0LceW4JAo-cxafLrH1UD_QxH3I8NGlTf2C0Xp8wEDQYvhWlQwHCqHNbIOvRCccpu-KtFqTvnoRddgCasAkH-pYgq94Ne0L028DlVy022H5mtsYr3v9XSGeSQfEXwcZSGXIgorsAjkgN8O8fySi2GzLr00b6lzdIjRXEppzwn0UR6RXKA==
+gAAAAABcksnrznRahez_EoXZFuM--QLw571JcTmBChG3FHtIT58M9kbyywC55snxWenuEYm2my2Z_9er_DXYRPcCcCJtQzx0jwM-4NkXgcwz5Pq-zolDbRQOK6mtAs7VAQgznj_R34EZLPwdELDbK2xZ0AhLdGCh5vuMkisHGLUrSCPQ3DcCkxuqWEIaADREj5-hVy88lzZ9atzRvCcKm-bdabHOZBCEUKSssQvC99w1zZgYlCfs6Ennqve6QE32rtRS962PiXGnVw1ez-QX2vIb7ZmhZUcOXIos6n_6fUFKbgU1q0ed6N9tY9gEwSoXtbTi1ANhgzneg5bxI1BaC3QgJ90F7VBl2dWwJF_-N1ezX_FwYMGTYrx_tLS0tybQd-M_EEBM_nwWH6MaOZMKV-37rP9ZCTjHBz6SAIadGdsDfHqAjHyXlh10YhWbz7hWHWYySmvca0TYJ6TwD5PiKRf6lgY0N3Ql1OHifd3_tG1f1Pi7BPOFzQYraslspV814QRGooEYF9jMB4OYkODv-OVsfAvBWPlAgjBcl-P5MC1NjAHmZqyIKuBTutZEhgl02b0LNmCgDUMW5DRRfsMf7-BL-FUEoyxRMC2cPr5gW7VYrGet9f7kAIK0St-_4DtZhOM-FXtHH9HK9i29oX7WvYWOatVcqzj722bVIaZ32OUByaSGPQlveWg2zIYClpSpwN5TGJZwLxMnpaK-ZkigC-xJcGDyBsYzDdHoKt_q1iH0pD9GYxT_7yselI2skVi6tKws4pltwsCv-NBEpwTY0deiCM4RWAn0ZQ7fegz3NZpam-Z9Z2v7iDKcnNcLlst7M237LUuo2EwRv695XTpS36CIjA0MlGujrjFKx6SWdMIZHmkxPdh97-cLwVIi6rLOej-MUKcIS2-aq1LSYrn_YwYHEKtDxVPU562Wgzxrwjo64MvvVw5zi6wEZwZjGDFPm5bCivfsF_bcVZqvBxVWSbmBGLgbtOncIt_SQf1jN7MmqQnVtSSEfcgHPV4lXGvqCsFf_3bUfxUjJvE9tfNeeO4h2IYiJcKb1m42Yz4OMNebsBYMRL5RTx8dsnCAUxNl-62fJcqiklx4poLqW4eN0Ky-aYperJbARWhPqPEb26T76KuXE-xXVUFCFUsl2orShoj7KnJCJvS1cHhQNoOISwO8N2iPlteHcOs_NrObyDJ6GkbvKwYHjtdZSfq-4UsIayC0mGHMIerwxQ5ZsIbnjW3_Jz3FBxkNIqU-U2NfZ6QEYGCxgYSu7dMeLM2DO0dcEpRTeVqbVqVsdv5O68KnOzkr_KtpcUcpBTGIRy2ziy6CqSQ2zIQZMpDbFGEob13AO90A8Zm3JPfAommo-zg8XnPv28qv-33hIDEseuaDJq0bLeFlj4IdBtaVzq_l9CUIFZHCsXu2PFQ0xfrPrcu0OQ84gNwFx_gR5GhFK1i46pwcvOCUqiqzMIC7FO-AhMJKqn5dCWoZYloHdpGa7kYz2-Ix4aCelXPVLaC1qQoPZwGO9E6oE7n53ZeinTj77sNY0qZyxGYdsL4GhLW-EO_BwOS8oMehb7sIXItw-dWjb-ohp5HDTOwH0DqEt3T-X6C3o6GeraMdAzGXnxKB0et0Kvpq65bPAcW0yA8jkrjxoYUIW-wnWqo5SMCzpHE4auEj5Ral7-4WID66jkHVhiz7i8qv5YW4foctwPEuDBxeXN_I-tMSNmmkwOCeDzNHA8wx4u9qVJTvhZC0c7MeIqGVC04jIhyjMabrusKCjEjokjtiDa5znICgC4N-lD6xORUhPSqEW5T-IfQg8loAYENpMoTD5V6LGbnu7aKwkbFCTI48w79iRSZjk0RMVhSFWZud1XbJpVB-F7Nr4ItGmNgmU4BSTJgU5bFmQa14YET2xaVBbDBpmqhhNpHvL0wnBKLFMbNJcUyi8hHE64X7rULLIycM6YQdEVlmhT_hJCfTDjg60QSsyY-om-N5H9yUeq0-RceNNdWvij81xghkCBkZnPoT7YxcynAAOdVRHb7HsUMkmit3NcWDum7CE7NNm5-8OrK-V3u4TpvJXORcec7NjHpe8ixUN1S2_ATme5lQz49-TQaDVDETS8EiLa2r2FleIrdKVTAFtF0CspA-_IKarPRFBrc2EUX6nUlxqNdmLeRbtnavVQYmT-nL-cFjtYea5cPSIIj5jB5UWW-gzGv2If45byj10-wxhsoqe6fADs9t16ianPC6BLAANWTyi5oW75FSI4DVmFvDQ98Znmhp0NTS7DzRFDXmrqs-Q1zd52wyOs3tA2zkDRZgcXg6lt0tfF9DAauGQEPNACrkRk-LRsBIP5vV8F0m0myYMB7dpPE_Agt8c0oBx07QAVjUhmWeqbfnd40YEuI91Xnru4AHVHhjRQqZeBcO1Dqz3h7000Wkr0xViAIE4QUxjkXW32fXKI0XqRI9HKLA9O75ISMcEWWBWD_swHDhVM_0P7o3jOgb1I6LpBitoAFopHRaeP0O--iFC__IhkGP_WVCmHjsuik7fFmUoVMFesaP03291NJ9EJviLL8fJ3WUKc4iOYrUqR6M_LE1ykErV3AAMznSjRApp-D4kx0zA_s3vbOyFu2M5P6FW7u9jJ4d2kz0OUUVZUDmlAyombeQSCdYqfnFiNketLFI3YGhaeyxc6V-ZoWJlDAUhVn4WxX2PPLib80iZPqIvKBbnZ1-vGpexcV7JQ9kukfC6gIAS6fnB-qbqDvz4FHvX1ZHLzUeFloS6-xL3KlzwOYug2M4R-JAP9--2eXVzJy3eStkgMUiWixYDIxmoaiSqVaU5kWSdttNs_dmDQK2SOqv_ljDQkwHAdwSJco4McXR_gOYMJnbEmBsTPAkPei1lmqwY8N53c9JgbtGDnD3qq624yeOpy9zZ4nCcjm6EK2_XYe_2Z6ptb_3YtDHzKOgt0DINlWvW4OZlsmJz1pEevOAD9h6c6_Ikro4pePyXVcGOtuP7KpzjzK3JnjWGRc_VfvTmh709PQxdq8wtUdILwWIXHombihk5YOGYlh8i3Xg5O1UOfNSWgsSKgf8VBPYZk3eosEnXKozG_aV2KTnSIc1VWy43kwMYsoRHEYXkIgSgac0iar0GBOFdPhUstu7abp7eBATkIy6bDVyE3Wvfr7zvTV2B6RwBP0o_Cr8HbWL5ZyXu3-FQ_3Bt4PiFKMNqTG1jF0I6xbGSRrXrQXUvwnZxnPQabgkrPReM3gm1BpM7vurX2LYH7eya7dCaKcb9DR9delcYmmLY0Qc_ZPvc1EaTDbGaU5huY_nP1ufCFCPKZA8LIdQygiD8Z37gt9wUVswieszRaiRWGcnRG558bvjk5HOW_vPCcq8KIHsdIGA9Twrv1DLtohTpHHKHaai--juIB94Wyjq3PSe5K6_e5vXUV8KdlB0gsVAHPBM4HnXxJ1rBSbUT6xPLbnHfhRn3aX-Q7VOXQjHCSi_sQN1dxiPf9WqNaub437XHMRlE-IuwWM1aVEJikEagIOJQvQybXmeD9YYOGmOpszLVjrm8R9x6os5JfuQu9PN18PoY9Avpat7Eqr19T4lWzwqVgUwDZZA3hOG8k0nq3fWhLbgkxF9O-PFT3V2eo8TcwQDIHq3IU_CqcjhSzMieAkIjsVdb4AMaf0Lq3XSo3BReNSyjEAjHpGbVb9AlCNKWoTK2xfUrlN8zAunccHz1BxlmlxiO8mZ9zxFeCRZXZxw-lluR1gw9HwypGnWZJLfS9_1Q_oHBquVLEj8zAu43MwYRV--G-nqD0pTY7a0BB3_xXTCc-PRk_UAVbpUVDuGzWmzJN6kXgUVrMCFxA_SX7Oma2NSkXXpzmOofLCtsf_ET2_lfkNYJlG7QTnfowbFbMpxebkSxAWIeUrS8BeGn7KabStxetdxyOVtpY4bUcUwFc9ItlUxyOmzeSOzLm_HOtrsnaH-T4wc3P9X5TFj_uCEw7WqSfTqssl5xhW2Db-9Up8u2rjjg_NwfERHS2eUPYbtT7qCszCV6jVUW5xZkPOMODx-ht7IyqKOyyagb6wbFY6CZhScX_hmMuqnqSY5EzSfF-AyZGlAdc9Kb8ogzoZxGox2coutJHD0VLsX3rDEOofUE1_OKO3T1y288FRd7Df-f4YSztjn8wFIoudq-VaUpmVC498svNmWkh9Q9cOZ3hBxMCI8FzzUdARpfC2aW84bfLaM7IErduNBIvUOVXj3iIxUGb4bJ5xBjKuvrhKowXtA216iSw_iB4ZdUzd26XEUXvoQX5LXQMaKA5hvtf22Sgc3oF0LceW4JAo-cxafLrH1UD_QxH3I8NGlTf2C0Xp8wEDQYvhWlQwHCqHNbIOvRCccpu-KtFqTvnoRddgCasAkH-pYgq94Ne0L028DlVy022H5mtsYr3v9XSGeSQfEXwcZSGXIgorsAjkgN8O8fySi2GzLr00b6lzdIjRXEppzwn0UR6RXKA==

requirements.txt

@@ -1,6 +1,6 @@
 gsw>=3.3.0
 matplotlib
 numpy
-pandas-flavor==0.1.2
 pandas>=0.24.0
+pandas-flavor==0.1.2
 scipy