Deal with np.int_ on Windows, handle missing __INDEX__

azure-pipelines.yml

@@ -92,46 +92,46 @@ jobs:
         codeCoverageTool: Cobertura
         summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/*coverage.xml'
 
-# - job: 'Windows'
-#   pool:
-#     vmImage: 'vs2017-win2016'
-
-#   strategy:
-#     matrix:
-#       Python37:
-#         python.version: '3.7'
-#         python_flag: ''
-
-#   steps:
-#     - task: UsePythonVersion@0
-#       inputs:
-#         versionSpec: '$(python.version)'
-#         architecture: 'x64'
-
-#     - task: NodeTool@0
-#       inputs:
-#         versionSpec: '12.x'
-
-#     - script: |
-#         which python > python.txt
-#         set /p PYTHON=<python.txt
-#         ln -s %PYTHON% %PYTHON%$(python.version)
-#         python --version
-#         which python$(python.version)
-#       displayName: "Which python"
-
-#     - script: |
-#         python -m pip install numpy pyarrow==0.15.1
-#       displayName: "Python deps"
-
-#     - script: npm install -g yarn
-#       displayName: "Install Yarn"
-
-#     - script: yarn
-#       displayName: 'Install Deps'
-
-#     - script: yarn build_python  --ci $(python_flag)
-#       displayName: 'build'
+- job: 'Windows'
+  pool:
+    vmImage: 'vs2017-win2016'
+
+  strategy:
+    matrix:
+      Python37:
+        python.version: '3.7'
+        python_flag: ''
+
+  steps:
+    - task: UsePythonVersion@0
+      inputs:
+        versionSpec: '$(python.version)'
+        architecture: 'x64'
+
+    - task: NodeTool@0
+      inputs:
+        versionSpec: '12.x'
+
+    - script: |
+        which python > python.txt
+        set /p PYTHON=<python.txt
+        ln -s %PYTHON% %PYTHON%$(python.version)
+        python --version
+        which python$(python.version)
+      displayName: "Which python"
+
+    - script: |
+        python -m pip install numpy pyarrow==0.15.1
+      displayName: "Python deps"
+
+    - script: npm install -g yarn
+      displayName: "Install Yarn"
+
+    - script: yarn
+      displayName: 'Install Deps'
+
+    - script: yarn build_python  --ci $(python_flag)
+      displayName: 'build'
 
 
 - job: 'Mac'

python/perspective/perspective/src/numpy.cpp

@@ -171,13 +171,15 @@ namespace numpy {
          /**
          * Catch common type mismatches and fill iteratively when a numpy dtype is of greater bit width than the Perspective t_dtype:
          * - when `np_dtype` is int64 and `t_dtype` is `DTYPE_INT32` or `DTYPE_FLOAT64`
+         * - when `np_dtype` is int32 and `t_dtype` is `DTYPE_INT64` or `DTYPE_FLOAT64`, which can happen on windows where np::int_ is int32
          * - when `np_dtype` is float64 and `t_dtype` is `DTYPE_INT32` or `DTYPE_INT64`
          * - when `type` is float64 and `np_dtype` is `DTYPE_FLOAT32` or `DTYPE_FLOAT64`
          * 
          * These errors occur frqeuently when a Table is created from non-numpy data or schema, then updated with a numpy array.
          * In these cases, the `t_dtype` of the Table supercedes the array dtype.
          */
         bool should_iter = (np_dtype == DTYPE_INT64 && (type == DTYPE_INT32 || type == DTYPE_FLOAT64)) || \
+            (np_dtype == DTYPE_INT32 && (type == DTYPE_INT64 || type == DTYPE_FLOAT64)) || \
             (np_dtype == DTYPE_FLOAT64 && (type == DTYPE_INT32 || type == DTYPE_INT64)) || \
             (type == DTYPE_INT64 && (np_dtype == DTYPE_FLOAT32 || np_dtype == DTYPE_FLOAT64));
 
@@ -238,13 +240,13 @@ namespace numpy {
             }
 
             double fval = item.cast<double>();
-            if (fval > 2147483647 || fval < -2147483648) {
+            if (!is_update && (fval > 2147483647 || fval < -2147483648)) {
                 binding::WARN("Promoting column `%s` to float from int32", name);
                 tbl.promote_column(name, DTYPE_FLOAT64, i, true);
                 col = tbl.get_column(name);
                 type = DTYPE_FLOAT64;
                 col->set_nth(i, fval);
-            } else if (isnan(fval)) {
+            } else if (!is_update && isnan(fval)) {
                 binding::WARN("Promoting column `%s` to string from int32", name);
                 tbl.promote_column(name, DTYPE_STR, i, false);
                 col = tbl.get_column(name);
@@ -423,6 +425,7 @@ namespace numpy {
 
         // We fill by object when `np_dtype`=object, or if there are type mismatches between `np_dtype` and `type`.
         bool types_mismatched = (np_dtype == DTYPE_INT64 && (type == DTYPE_INT32 || type == DTYPE_FLOAT64)) || \
+            (np_dtype == DTYPE_INT32 && (type == DTYPE_INT64 || type == DTYPE_FLOAT64)) || \
             (np_dtype == DTYPE_FLOAT64 && (type == DTYPE_INT32 || type == DTYPE_INT64)) || \
             (type == DTYPE_INT64 && (np_dtype == DTYPE_FLOAT32 || np_dtype == DTYPE_FLOAT64));
 

python/perspective/perspective/table/view.py

@@ -6,6 +6,7 @@
 # the Apache License 2.0.  The full license can be found in the LICENSE file.
 #
 
+import os
 import pandas
 from functools import partial, wraps
 from random import random
@@ -446,7 +447,7 @@ def to_csv(self, **options):
         Returns:
             :obj:`str`: A CSV-formatted string containing the serialized data.
         '''
-        return self.to_df(**options).to_csv(date_format=options.pop("date_format", "%Y/%m/%d %H:%M:%S"))
+        return self.to_df(**options).to_csv(date_format=options.pop("date_format", "%Y/%m/%d %H:%M:%S"), line_terminator='\r\n' if os.name == 'nt' else '\n')
 
     @wraps(to_records)
     def to_json(self, **options):