Welcome to pyreadstat’s documentation!¶
Metadata Object Description¶
Each parsing function returns a metadata object in addition to a pandas dataframe. That object contains the following fields:
notes: notes or documents (text annotations) attached to the file if any (spss and stata).
column_names : a list with the names of the columns.
column_labels : a list with the column labels, if any.
column_names_to_labels : a dictionary with column_names as keys and column_labels as values
file_encoding : a string with the file encoding, may be empty
number_columns : an int with the number of columns
number_rows : an int with the number of rows. If metadataonly option was used, it may be None if the number of rows could not be determined. If you need the number of rows in this case you need to parse the whole file. This happens for xport and por files.
variable_value_labels : a dict with keys being variable names, and values being a dict with values as keys and labels as values. It may be empty if the dataset did not contain such labels. For sas7bdat files it will be empty unless a sas7bcat was given. It is a combination of value_labels and variable_to_label.
value_labels : a dict with label name as key and a dict as value, with values as keys and labels as values. In the case of parsing a sas7bcat file this is where the formats are.
variable_to_label : A dict with variable name as key and label name as value. Label names are those described in value_labels. Sas7bdat files may have this member populated and its information can be used to match the information in the value_labels coming from the sas7bcat file.
original_variable_types : a dict of variable name to variable format in the original file. For debugging purposes.
readstat_variable_types : a dict of variable name to variable type in the original file as extracted by Readstat.i For debugging purposes. In SAS and SPSS variables will be either double (numeric in the original app) or string (character). Stata has in addition int8, int32 and float types.
table_name : table name (string)
file_label : file label (SAS) (string)
missing_ranges: a dict with keys being variable names. Values are a list of dicts. Each dict contains two keys, ‘lo’ and ‘hi’ being the lower boundary and higher boundary for the missing range. Even if the value in both lo and hi are the same, the two elements will always be present. This appears for SPSS (sav) files when using the option user_missing=True: user defined missing values appear not as nan but as their true value and this dictionary stores the information about which values are to be considered missing.
missing_user_values: a dict with keys being variable names. Values are a list of character values (A to Z and _ for SAS, a to z for SATA) representing user defined missing values in SAS and STATA. This appears when using user_missing=True in read_sas7bdat or read_dta if user defined missing values are present.
variable_alignment: a dict with keys being variable names and values being the display alignment: left, center, right or unknown
variable_storage_width: a dict with keys being variable names and values being the storage width
variable_display_width: a dict with keys being variable names and values being the display width
variable_measure: a dict with keys being variable names and values being the measure: nominal, ordinal, scale or unknown
There are two functions to deal with value labels: set_value_labels and set_catalog_to_sas. You can read about them in the next section.
Functions Documentation¶
- pyreadstat.pyreadstat.read_dta()¶
Read a STATA dta file
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. In Python 2.7 the string is assumed to be utf-8 encoded
metadataonly (bool, optional) – by default False. IF true, no data will be read but only metadata, so that you can get all elements in the metadata object. The data frame will be set with the correct column names but no data.
dates_as_pandas_datetime (bool, optional) – by default False. If true dates will be transformed to pandas datetime64 instead of date.
apply_value_formats (bool, optional) – by default False. If true it will change values in the dataframe for they value labels in the metadata, if any appropiate are found.
formats_as_category (bool, optional) – by default True. Takes effect only if apply_value_formats is True. If True, variables with values changed for their formatted version will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
usecols (list, optional) – a list with column names to read from the file. Only those columns will be imported. Case sensitive!
user_missing (bool, optional) – by default False, in this case user defined missing values are delivered as nan. If true, the missing values will be deliver as is, and an extra piece of information will be set in the metadata (missing_user_values) to be able to interpret those values as missing.
disable_datetime_conversion (bool, optional) – if True pyreadstat will not attempt to convert dates, datetimes and times to python objects but those columns will remain as numbers. In order to convert them later to an appropiate python object, the user can use the information about the original variable format stored in the metadata object in original_variable_types. Disabling datetime conversion speeds up reading files. In addition it helps to overcome situations where there are datetimes that are beyond the limits of python datetime (which is limited to year 10,000, dates beyond that will rise an Overflow error in pyreadstat).
row_limit (int, optional) – maximum number of rows to read. The default is 0 meaning unlimited.
row_offset (int, optional) – start reading rows after this offset. By default 0, meaning start with the first row not skipping anything.
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned, the user can then convert it to her preferred data format. Using dict is faster as the other types as the conversion to a pandas dataframe is avoided.
extra_datetime_formats (list of str, optional) – formats to be parsed as python datetime objects
extra_date_formats (list of str, optional) – formats to be parsed as python date objects
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data. If the output_format is other than ‘pandas’ the object type will change accordingly.
metadata – object with metadata. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_file_in_chunks()¶
Returns a generator that will allow to read a file in chunks.
If using multiprocessing, for Xport, Por and some defective sav files where the number of rows in the dataset canot be obtained from the metadata, the parameter num_rows must be set to a number equal or larger than the number of rows in the dataset. That information must be obtained by the user before running this function.
- Parameters
read_function (pyreadstat function) – a pyreadstat reading function
file_path (string) – path to the file to be read
chunksize (integer, optional) – size of the chunks to read
offset (integer, optional) – start reading the file after certain number of rows
limit (integer, optional) – stop reading the file after certain number of rows, will be added to offset
multiprocess (bool, optional) – use multiprocessing to read each chunk?
num_processes (integer, optional) – in case multiprocess is true, how many workers/processes to spawn?
num_rows (integer, optional) – number of rows in the dataset. If using multiprocessing it is obligatory for files where the number of rows cannot be obtained from the medatata, such as xport, por and some defective sav files. The user must obtain this value by reading the file without multiprocessing first or any other means. A number larger than the actual number of rows will work as well. Discarded if the number of rows can be obtained from the metadata or not using multiprocessing.
kwargs (dict, optional) – any other keyword argument to pass to the read_function. row_limit and row_offset will be discarded if present.
- Yields
data_frame (pandas dataframe) – a pandas data frame with the data
metadata – object with metadata. Look at the documentation for more information.
it (generator) – A generator that reads the file in chunks.
- pyreadstat.pyreadstat.read_file_multiprocessing()¶
Reads a file in parallel using multiprocessing. For Xport, Por and some defective sav files where the number of rows in the dataset canot be obtained from the metadata, the parameter num_rows must be set to a number equal or larger than the number of rows in the dataset. That information must be obtained by the user before running this function.
- Parameters
read_function (pyreadstat function) – a pyreadstat reading function
file_path (string) – path to the file to be read
num_processes (integer, optional) – number of processes to spawn, by default the min 4 and the max cores on the computer
num_rows (integer, optional) – number of rows in the dataset. Obligatory for files where the number of rows cannot be obtained from the medatata, such as xport, por and some defective sav files. The user must obtain this value by reading the file without multiprocessing first or any other means. A number larger than the actual number of rows will work as well. Discarded if the number of rows can be obtained from the metadata.
kwargs (dict, optional) – any other keyword argument to pass to the read_function.
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data
metadata – object with metadata. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_por()¶
Read a SPSS por file
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. In Python 2.7 the string is assumed to be utf-8 encoded
metadataonly (bool, optional) – by default False. IF true, no data will be read but only metadata, so that you can get all elements in the metadata object. The data frame will be set with the correct column names but no data. Notice that number_rows will be None as por files do not have the number of rows recorded in the file metadata.
dates_as_pandas_datetime (bool, optional) – by default False. If true dates will be transformed to pandas datetime64 instead of date.
apply_value_formats (bool, optional) – by default False. If true it will change values in the dataframe for they value labels in the metadata, if any appropiate are found.
formats_as_category (bool, optional) – by default True. Takes effect only if apply_value_formats is True. If True, variables with values changed for their formatted version will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
usecols (list, optional) – a list with column names to read from the file. Only those columns will be imported. Case sensitive!
disable_datetime_conversion (bool, optional) – if True pyreadstat will not attempt to convert dates, datetimes and times to python objects but those columns will remain as numbers. In order to convert them later to an appropiate python object, the user can use the information about the original variable format stored in the metadata object in original_variable_types. Disabling datetime conversion speeds up reading files. In addition it helps to overcome situations where there are datetimes that are beyond the limits of python datetime (which is limited to year 10,000, dates beyond that will rise an Overflow error in pyreadstat).
row_limit (int, optional) – maximum number of rows to read. The default is 0 meaning unlimited.
row_offset (int, optional) – start reading rows after this offset. By default 0, meaning start with the first row not skipping anything.
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned, the user can then convert it to her preferred data format. Using dict is faster as the other types as the conversion to a pandas dataframe is avoided.
extra_datetime_formats (list of str, optional) – formats to be parsed as python datetime objects
extra_date_formats (list of str, optional) – formats to be parsed as python date objects
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data. If the output_format is other than ‘pandas’ the object type will change accordingly.
metadata – object with metadata. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_sas7bcat()¶
Read a SAS sas7bcat file. The returning dataframe will be empty. The metadata object will contain a dictionary value_labels that contains the formats. When parsing the sas7bdat file, in the metadata, the dictionary variable_to_label contains a map from variable name to the formats. In order to apply the catalog to the sas7bdat file use set_catalog_to_sas or pass the catalog file as an argument to read_sas7bdat directly. SAS catalog files are difficult ones, some of them can be read only in specific SAS version, may contain strange encodings etc. Therefore it may be that many catalog files are not readable from this application.
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. The string is assumed to be utf-8 encoded
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned. Notice that for this function the resulting object is always empty, this is done for consistency with other functions but has no impact on performance.
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data (no data in this case, so will be empty). If the output_parameter is other than ‘pandas’ then the object type will change accordingly altough the object will always be empty
metadata – object with metadata. The member value_labels is the one that contains the formats. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_sas7bdat()¶
Read a SAS sas7bdat file. It accepts the path to a sas7bcat.
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. In python 2.7 the string is assumed to be utf-8 encoded.
metadataonly (bool, optional) – by default False. IF true, no data will be read but only metadata, so that you can get all elements in the metadata object. The data frame will be set with the correct column names but no data.
dates_as_pandas_datetime (bool, optional) – by default False. If true dates will be transformed to pandas datetime64 instead of date.
catalog_file (str, optional) – path to a sas7bcat file. By default is None. If not None, will parse the catalog file and replace the values by the formats in the catalog, if any appropiate is found. If this is not the behavior you are looking for, Use read_sas7bcat to parse the catalog independently of the sas7bdat and set_catalog_to_sas to apply the resulting format into sas7bdat files.
formats_as_category (bool, optional) – Will take effect only if the catalog_file was specified. If True the variables whose values were replaced by the formats will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
usecols (list, optional) – a list with column names to read from the file. Only those columns will be imported. Case sensitive!
user_missing (bool, optional) – by default False, in this case user defined missing values are delivered as nan. If true, the missing values will be deliver as is, and an extra piece of information will be set in the metadata (missing_user_values) to be able to interpret those values as missing.
disable_datetime_conversion (bool, optional) – if True pyreadstat will not attempt to convert dates, datetimes and times to python objects but those columns will remain as numbers. In order to convert them later to an appropiate python object, the user can use the information about the original variable format stored in the metadata object in original_variable_types. Disabling datetime conversion speeds up reading files. In addition it helps to overcome situations where there are datetimes that are beyond the limits of python datetime (which is limited to year 10,000, dates beyond that will rise an Overflow error in pyreadstat).
row_limit (int, optional) – maximum number of rows to read. The default is 0 meaning unlimited.
row_offset (int, optional) – start reading rows after this offset. By default 0, meaning start with the first row not skipping anything.
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned, the user can then convert it to her preferred data format. Using dict is faster as the other types as the conversion to a pandas dataframe is avoided.
extra_datetime_formats (list of str, optional) – formats to be parsed as python datetime objects
extra_date_formats (list of str, optional) – formats to be parsed as python date objects
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data. If the output_format is other than ‘pandas’ the object type will change accordingly.
metadata – object with metadata. The members variables_value_labels will be empty unless a valid catalog file is supplied. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_sav()¶
Read a SPSS sav or zsav (compressed) files
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. In Python 2.7 the string is assumed to be utf-8 encoded
metadataonly (bool, optional) – by default False. IF true, no data will be read but only metadata, so that you can get all elements in the metadata object. The data frame will be set with the correct column names but no data.
dates_as_pandas_datetime (bool, optional) – by default False. If true dates will be transformed to pandas datetime64 instead of date.
apply_value_formats (bool, optional) – by default False. If true it will change values in the dataframe for they value labels in the metadata, if any appropiate are found.
formats_as_category (bool, optional) – by default True. Takes effect only if apply_value_formats is True. If True, variables with values changed for their formatted version will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
usecols (list, optional) – a list with column names to read from the file. Only those columns will be imported. Case sensitive!
user_missing (bool, optional) – by default False, in this case user defined missing values are delivered as nan. If true, the missing values will be deliver as is, and an extra piece of information will be set in the metadata (missing_ranges) to be able to interpret those values as missing.
disable_datetime_conversion (bool, optional) – if True pyreadstat will not attempt to convert dates, datetimes and times to python objects but those columns will remain as numbers. In order to convert them later to an appropiate python object, the user can use the information about the original variable format stored in the metadata object in original_variable_types. Disabling datetime conversion speeds up reading files. In addition it helps to overcome situations where there are datetimes that are beyond the limits of python datetime (which is limited to year 10,000, dates beyond that will rise an Overflow error in pyreadstat).
row_limit (int, optional) – maximum number of rows to read. The default is 0 meaning unlimited.
row_offset (int, optional) – start reading rows after this offset. By default 0, meaning start with the first row not skipping anything.
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned, the user can then convert it to her preferred data format. Using dict is faster as the other types as the conversion to a pandas dataframe is avoided.
extra_datetime_formats (list of str, optional) – formats to be parsed as python datetime objects
extra_date_formats (list of str, optional) – formats to be parsed as python date objects
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data. If the output_format is other than ‘pandas’ the object type will change accordingly.
metadata – object with metadata. Look at the documentation for more information.
- pyreadstat.pyreadstat.read_xport()¶
Read a SAS xport file.
- Parameters
filename_path (str, bytes or Path-like object) – path to the file. In python 2.7 the string is assumed to be utf-8 encoded
metadataonly (bool, optional) – by default False. IF true, no data will be read but only metadata, so that you can get all elements in the metadata object. The data frame will be set with the correct column names but no data. Notice that number_rows will be None as xport files do not have the number of rows recorded in the file metadata.
dates_as_pandas_datetime (bool, optional) – by default False. If true dates will be transformed to pandas datetime64 instead of date.
encoding (str, optional) – Defaults to None. If set, the system will use the defined encoding instead of guessing it. It has to be an iconv-compatible name
usecols (list, optional) – a list with column names to read from the file. Only those columns will be imported. Case sensitive!
disable_datetime_conversion (bool, optional) – if True pyreadstat will not attempt to convert dates, datetimes and times to python objects but those columns will remain as numbers. In order to convert them later to an appropiate python object, the user can use the information about the original variable format stored in the metadata object in original_variable_types. Disabling datetime conversion speeds up reading files. In addition it helps to overcome situations where there are datetimes that are beyond the limits of python datetime (which is limited to year 10,000, dates beyond that will rise an Overflow error in pyreadstat).
row_limit (int, optional) – maximum number of rows to read. The default is 0 meaning unlimited.
row_offset (int, optional) – start reading rows after this offset. By default 0, meaning start with the first row not skipping anything.
output_format (str, optional) – one of ‘pandas’ (default) or ‘dict’. If ‘dict’ a dictionary with numpy arrays as values will be returned, the user can then convert it to her preferred data format. Using dict is faster as the other types as the conversion to a pandas dataframe is avoided.
extra_datetime_formats (list of str, optional) – formats to be parsed as python datetime objects
extra_date_formats (list of str, optional) – formats to be parsed as python date objects
- Returns
data_frame (pandas dataframe) – a pandas data frame with the data. If the output_format is other than ‘pandas’ the object type will change accordingly.
metadata – object with metadata. Look at the documentation for more information.
- pyreadstat.pyreadstat.set_catalog_to_sas()¶
Changes the values in the dataframe and sas_metadata according to the formats in the catalog. It will return a copy of the dataframe and metadata. If no appropriate formats were found, the result will be an unchanged copy of the original dataframe.
- Parameters
sas_dataframe (pandas dataframe) – resulting from parsing a sas7bdat file
sas_metadata (pyreadstat metadata object) – resulting from parsing a sas7bdat file
catalog_metadata (pyreadstat metadata object) – resulting from parsing a sas7bcat (catalog) file
formats_as_category (bool, optional) – defaults to True. If True the variables having formats will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
- Returns
df_copy (pandas dataframe) – a copy of the original dataframe with the values changed, if appropriate formats were found, unaltered otherwise
metadata (dict) – a copy of the original sas_metadata enriched with catalog information if found, otherwise unaltered
- pyreadstat.pyreadstat.set_value_labels()¶
Changes the values in the dataframe according to the value formats in the metadata. It will return a copy of the dataframe. If no appropiate formats were found, the result will be an unchanged copy of the original dataframe.
- Parameters
dataframe (pandas dataframe) – resulting from parsing a file
metadata (dictionary) – resulting from parsing a file
formats_as_category (bool, optional) – defaults to True. If True the variables having formats will be transformed into pandas categories.
formats_as_ordered_category (bool, optional) – defaults to False. If True the variables having formats will be transformed into pandas ordered categories. it has precedence over formats_as_category, meaning if this is True, it will take effect irrespective of the value of formats_as_category.
- Returns
df_copy – a copy of the original dataframe with the values changed, if appropiate formats were found, unaltered otherwise
- Return type
pandas dataframe
- pyreadstat.pyreadstat.write_dta()¶
Writes a pandas data frame to a STATA dta file
- Parameters
df (pandas data frame) – pandas data frame to write to sav or zsav
dst_path (str or pathlib.Path) – full path to the result dta file
file_label (str, optional) – a label for the file
column_labels (list or dict, optional) – labels for columns (variables), if list must be the same length as the number of columns. Variables with no labels must be represented by None. If dict values must be variable names and values variable labels. In such case there is no need to include all variables; labels for non existent variables will be ignored with no warning or error.
version (int, optional) – dta file version, supported from 8 to 15, default is 15
variable_value_labels (dict, optional) – value labels, a dictionary with key variable name and value a dictionary with key values and values labels. Variable names must match variable names in the dataframe otherwise will be ignored. Value types must match the type of the column in the dataframe.
missing_user_values (dict, optional) – user defined missing values for numeric variables. Must be a dictionary with keys being variable names and values being a list of missing values. Missing values must be a single character between a and z.
variable_format (dict, optional) – sets the format of a variable. Must be a dictionary with keys being the variable names and values being strings defining the format. See README, setting variable formats section, for more information.
- pyreadstat.pyreadstat.write_por()¶
Writes a pandas data frame to a SPSS POR file.
- Parameters
df (pandas data frame) – pandas data frame to write to por
dst_path (str or pathlib.Path) – full path to the result por file
file_label (str, optional) – a label for the file
column_labels (list or dict, optional) – labels for columns (variables), if list must be the same length as the number of columns. Variables with no labels must be represented by None. If dict values must be variable names and values variable labels. In such case there is no need to include all variables; labels for non existent variables will be ignored with no warning or error.
variable_format (dict, optional) – sets the format of a variable. Must be a dictionary with keys being the variable names and values being strings defining the format. See README, setting variable formats section, for more information.
- pyreadstat.pyreadstat.write_sav()¶
Writes a pandas data frame to a SPSS sav or zsav file.
- Parameters
df (pandas data frame) – pandas data frame to write to sav or zsav
dst_path (str or pathlib.Path) – full path to the result sav or zsav file
file_label (str, optional) – a label for the file
column_labels (list or dict, optional) – labels for columns (variables), if list must be the same length as the number of columns. Variables with no labels must be represented by None. If dict values must be variable names and values variable labels. In such case there is no need to include all variables; labels for non existent variables will be ignored with no warning or error.
compress (boolean, optional) – if true a zsav will be written, by default False, a sav is written
row_compress (boolean, optional) – if true it applies row compression, by default False, compress and row_compress cannot be both true at the same time
note (str, optional) – a note to add to the file
variable_value_labels (dict, optional) – value labels, a dictionary with key variable name and value a dictionary with key values and values labels. Variable names must match variable names in the dataframe otherwise will be ignored. Value types must match the type of the column in the dataframe.
missing_ranges (dict, optional) – user defined missing values. Must be a dictionary with keys as variable names matching variable names in the dataframe. The values must be a list. Each element in that list can either be either a discrete numeric or string value (max 3 per variable) or a dictionary with keys ‘hi’ and ‘lo’ to indicate the upper and lower range for numeric values (max 1 range value + 1 discrete value per variable). hi and lo may also be the same value in which case it will be interpreted as a discrete missing value. For this to be effective, values in the dataframe must be the same as reported here and not NaN.
variable_display_width (dict, optional) – set the display width for variables. Must be a dictonary with keys being variable names and values being integers.
variable_measure (dict, optional) – sets the measure type for a variable. Must be a dictionary with keys being variable names and values being strings one of “nominal”, “ordinal”, “scale” or “unknown” (default).
variable_format (dict, optional) – sets the format of a variable. Must be a dictionary with keys being the variable names and values being strings defining the format. See README, setting variable formats section, for more information.
- pyreadstat.pyreadstat.write_xport()¶
Writes a pandas data frame to a SAS Xport (xpt) file. If no table_name is specified the dataset has by default the name DATASET (take it into account if reading the file from SAS.) Versions 5 and 8 are supported, default is 8.
- Parameters
df (pandas data frame) – pandas data frame to write to xport
dst_path (str or pathlib.Path) – full path to the result xport file
file_label (str, optional) – a label for the file
column_labels (list or dict, optional) – labels for columns (variables), if list must be the same length as the number of columns. Variables with no labels must be represented by None. If dict values must be variable names and values variable labels. In such case there is no need to include all variables; labels for non existent variables will be ignored with no warning or error.
table_name (str, optional) – name of the dataset, by default DATASET
file_format_version (int, optional) – XPORT file version, either 8 or 5, default is 8
variable_format (dict, optional) – sets the format of a variable. Must be a dictionary with keys being the variable names and values being strings defining the format. See README, setting variable formats section, for more information.


XJB enables development and testing of Enterprise JavaBeans without having to keep assembling and deploying into a container. It is designed to allow a rapid code/test cycle, and to be simple to use. It keeps the container out of your way while you develop your EJBs.
Real applications grow. Many enterprise developers do not have the luxury of working on small, "green field" projects, and legacy J2EE applications in particular tend to be big, unwieldy beasts modelled on Sun's original PetStore showcase application.
XJB allows you to quickly test changes to a legacy code base - that is its primary purpose. If you are developing a new application from the ground up, we would hope you would have no need for XJB! Experience has shown that keeping the business logic separate from the container or lifecycle plumbing leads to a more testable system and a happier life.
Here are some things that XJB doesn't do:
On the other hand, here are some things it does do:
XJB is currently in heavy development while the team works towards a stable 1.0 release. We don't currently recommend its use in production projects but would be grateful if developers try out the latest development snapshot or a version straight from the repository and send us feedback. Announcements of alpha, beta and final releases of version 1.0 will be sent to the mailing lists.


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HIGH COURT OF CHHATTISGARH, BILASPUR Order Sheet Writ Petition (C) No.1509 of 2016 M/s E. Tech Projects Private Limited Versus The State of Chhattisgarh and others SB Hon'ble Shri Justice Sanjay K. Agrawal Motion 30/08/2017 Mr. Ajay Kumar Mishra, Senior Advocate with Mr. Ankit Singhal, Advocate for the petitioner. Mr. Arun Sao, Deputy Advocate General for the State/respondent
No.1.
Mr. Sudhir Kumar Bajpai, counsel for respondents No.2 and 3.
Mr. V.V.S. Murthy, Senior Advocate with Mr. Shantanu Kumar, Advocate for respondent No.4.
Mr. S.C. Mehadia, counsel for respondents No.5 and 6.
This writ petition has been filed calling in question legality and propriety of the order issued by respondent No.3 granting permission to respondents No.5 and 6 to establish Common Bio-Medical Waste Treatment and Disposal Facility for capacity two ton per day at Patwari Halka No.20, Khasra No.70/(1, 2, 5) at Village Siltara, Dharsiwa, Distt. Raipur subject to fulfillment of the conditions mentioned therein.
When the hearing commenced, Mr. Bajpai, learned counsel for respondents No.2 and 3 would take a preliminary objection that the writ petition is not maintainable as the reliefs claimed are covered under the provisions of the National Green Tribunal Act, 2010 (for short, 'the Act, 2010'). Further elaborating his submission, he would submit that the present writ petition is barred by Sections 14, 15 and 16 of the said Act as well as under the provisions of the Water (Prevention and Control of Pollution) Act, 1974 and the Air (Prevention and Control of Pollution) Act, 1981, as the order impugned is appealable and also barred by clause 13 of the Bio- Medical Wastes (Management and Handling) Rules, 1989. Therefore, this writ petition be dismissed primarily on this count.
Mr. Ajay Kumar Mishra, learned Senior Advocate appearing for the petitioner, would submit that in the writ petition no such fundamental question or questions relating to enforcement of any legal right relating to environment is involved and question also does not arise within the enactment in Schedule I and no such relief as claimed can be granted by the National Green Tribunal as such, this Court has jurisdiction to hear the writ petition by which the petitioner only claims to protect its commercial interest, as the work in question to establish Common Bio-Medical Waste Treatment and Disposal Facility has been granted to it by order dated 7-4-2003 for Raipur, Bhilai and Durg and now, contrary to that order, the impugned order has been passed on 31-3-2012 permitting respondents No.5 and 6 to establish the CBWTF at Raipur for District Raipur and just in order to protect its commercial tax and seeking quashment of order Annexure P-1, this writ petition has been filed by the petitioner.
Mr. Mehadia, learned counsel appearing for respondents No.5 and 6, would support the impugned order.
I have heard learned counsel for the parties and considered their rival submissions.
In order to consider the dispute raised by the parties, it would be appropriate to notice the definition of the term "substantial question relating to environment" as defined in clause (m) of Section 2 of the Act, 2010 which states as under:-
"substantial question relating to environment" shall include an instance where,--
(i) there is a direct violation of a specific statutory environmental obligation by a person by which,--
(A) the community at large other than an individual or group of individuals is affected or likely to be affected by the environmental consequences; or (B) the gravity of damage to the environment or property is substantial; or (C) the damage to public health is broadly measurable;
(ii) the environmental consequences relate to a specific activity or a point source of pollution;"
Sections 14, 15 and 16 of the Act, 2010 provide as under: -
"14. Tribunal to settle disputes.--(1) The Tribunal shall have the jurisdiction over all civil cases where a substantial question relating to environment (including enforcement of any legal right relating to environment), is involved and such question arises out of the implementation of the enactments specified in Schedule I.
(2) The Tribunal shall hear the disputes arising from the questions referred to in sub-section (1) and settle such disputes and pass order thereon.
(3) No application for adjudication of dispute under this section shall be entertained by the Tribunal unless it is made within a period of six months from the date on which the cause of action for such dispute first arose:
Provided that the Tribunal may, if it is satisfied that the applicant was prevented by sufficient cause from filing the application within the said period, allow it to be filed within a further period not exceeding sixty days.
15. Relief, compensation and restitution.--(1) The Tribunal may, by an order, provide,--
(a) relief and compensation to the victims of pollution and other environmental damage arising under the enactments specified in the Schedule I (including accident occurring while handling any hazardous substance);
(b) for restitution of property damaged;
(c) for restitution of the environment for such area or areas, as the Tribunal may think fit.
(2) The relief and compensation and restitution of property and environment referred to in clauses (a), (b) and (c) of sub-section (1) shall be in addition to the relief paid or payable under the Public Liability Insurance Act, 1991 (6 of 1991).
(3) No application for grant of any compensation or relief or restitution of property or environment under this section shall be entertained by the Tribunal unless it is made within a period of five years from the date on which the cause for such compensation or relief first arose:
Provided that the Tribunal may, if it is satisfied that the applicant was prevented by sufficient cause from filing the application within the said period, allow it to be filed within a further period not exceeding sixty days.
*** *** *** *** *** ***
16. Tribunal to have appellate jurisdiction.--Any person aggrieved by,--
(a) an order or decision, made, on or after the commencement of the National Green Tribunal Act, 2010, by the appellate authority under section 28 of the Water (Prevention and Control of Pollution) Act, 1974 (6 of 1974);
(b) an order passed, on or after the commencement of the National Green Tribunal Act, 2010, by the State Government under section 29 of the Water (Prevention and Control of Pollution) Act, 1974 (6 of 1974);
(c) directions issued, on or after the commencement of the National Green Tribunal Act, 2010, by a Board, under section 33A of the Water (Prevention and Control of Pollution) Act, 1974 (6 of 1974);
(d) an order or decision made, on or after the commencement of the National Green Tribunal Act, 2010, by the appellate authority under section 13 of the Water (Prevention and Control of Pollution) Cess Act, 1977 (36 of 1977);
(e) an order or decision made, on or after the commencement of the National Green Tribunal Act, 2010, by the State Government or other authority under section 2 of the Forest (Conservation) Act, 1980 (69 of 1980);
(f) an order or decision, made, on or after the commencement of the National Green Tribunal Act, 2010, by the Appellate Authority under section 31 of the Air (Prevention and Control of Pollution) Act, 1981 (14 of 1981);
(g) any direction issued, on or after the commencement of the National Green Tribunal Act, 2010, under section 5 of the Environment (Protection) Act, 1986 (29 of 1986);
(h) an order made, on or after the commencement of the National Green Tribunal Act, 2010, granting environmental clearance in the area in which any industries, operations or processes or class of industries, operations and processes shall not be carried out or shall be carried out subject to certain safeguards under the Environment (Protection) Act, 1986 (29 of 1986);
(i) an order made, on or after the commencement of the National Green Tribunal Act, 2010, refusing to grant environmental clearance for carrying out any activity or operation or process under the Environment (Protection) Act, 1986 (29 of 1986);
(j) any determination of benefit sharing or order made, on or after the commencement of the National Green Tribunal Act, 2010, by the National Biodiversity Authority or a State Biodiversity Board under the provisions of the Biological Diversity Act, 2002 (18 of 2003), may, within a period of thirty days from the date on which the order or decision or direction or determination is communicated to him, prefer an appeal to the Tribunal:
Provided that the Tribunal may, if it is satisfied that the appellant was prevented by sufficient cause from filing the appeal within the said period, allow it to be filed under this section within a further period not exceeding sixty days. "
A focused glance of the provisions contained in Section 14 of the Act, 2010, would show that the Tribunal shall have the jurisdiction over all civil cases where a substantial question relating to environment (including enforcement of any legal right relating to environment), is involved including the question arising out of the implementation of the enactments specified in Schedule I. Section 15 speaks about relief, compensation and restitution that can be granted.
The question would be whether the writ petition filed by the petitioner for enforcement of his commercial interest i.e. for quashing the order Annexure P-1 dated 31-3-2012 can be entertained, whereby the Chhattisgarh Environment Conservation Board after granting permission to the petitioner to establish such a CBWTF for Durg again granted such a permission to respondents No.5 and 6. The petitioner is not seeking any quashment relating to environment but only seeking quashment of the order relating to establishment of such a facility to respondents No.5 and 6 which the petitioner is legally entitled and which has been granted by bypassing his interest to respondents No.5 and 6.
In this connection, Mr. Ajay Kumar Mishra, learned Senior Counsel, would cite a judgment of the Bombay High Court (Nagpur Bench) delivered in W.P.No.6360/2015 (Central India AYUSH Drugs Manufacturers Association and others v. State of Maharashtra and others) decided on 28-9-2016 in which in paragraphs 16, 21, 24 and 27, Their Lordships of the Bombay High Court held thus, "16. Section 14 of the N.G.T. Act, empowers the Tribunal to settle disputes. All civil cases specified in sub-section (1) thereof are amenable to its jurisdiction and sub-section (2) specifically provides for all disputes arising from questions referred to in sub-section (1) are to be heard and settled by it. An application for adjudication of such disputes cannot be entertained by the Tribunal, if it is made beyond a period of six months from the date of cause of action. It has been given power to condone delay of period not exceeding 60 days. Section 15 points out relief, compensation and restitution which Tribunal is empowered to grant under Section 15 (3). Application for grant of any compensation or relief or restitution of property or environment under Section 15, cannot be entertained by the Tribunal, if it is not made within a period of 5 years from the date on which cause therefor first arose. Again it has been given power to condone delay for a period not exceeding 60 days. Under Section 16 Appellate jurisdiction of National Green Tribunal has been specified. It can entertain an appeal filed by a person aggrieved against 10 orders as specified therein, which include enactments mentioned in Schedule-I of the N.G.T. Act. Appeal is required to be filed by the aggrieved person within a period of 30 days, and the Tribunal can condone delay of a period not exceeding 60 days. Under Section 17, if death or injury to any person (other than a workman) or damage to any property or environment has resulted from an accident or adverse impact of any activity or operation or process, under any enactment specified in Schedule-I, the person responsible for it has to give such relief or pay compensation, as specified in Section 17 (1). Section 18 deals with the procedure for filing an application or appeal to the Tribunal.
21. Perusal of Section 14 of N.G.T. Act, reveals that civil cases covered under Section 14(1) are referred to as disputes in sub- section (2). These disputes therefore, must be civil in nature, must arise out of implementation of enactments specified in Schedule-I and therein substantial question relating to environment must be involved. If these three ingredients are satisfied, bar under section 14(1) gets attracted. Thus all civil cases are not cognizable by National Green Tribunal, tough they may arise out of implementation of Schedule-I enactments, if substantial question relating to environment does not arise therefrom. In section 14(1) words "including enforcement of any legal right relating to environment" are inserted after the word "environment". Thus, issue of enforcement of a legal right relating to environment or a substantial question relating to environment must surface and form subject-matter of a civil case arising out of implementation of Schedule-I enactments. Then only National Green Tribunal will have jurisdiction under Section 14.
24. When Section 14(c) is read along with and construed with Section 2(c) and (m), it is clear that civil cases which can be considered by the National Green Tribunal must affect environment. If it is not affecting environment, the Tribunal will not have jurisdiction. In present facts, relief sought for do not directly or indirectly affect environment.
27. Here, considering the jurisdiction given to the National Green Tribunal only to decide civil cases, where substantial question involved is in relation to environment, it is apparent that the NGT cannot be said to be conferred with the absolute jurisdiction to adjudicate all types of disputes or even all civil disputes. A limited jurisdiction to deal with specific type of civil disputes is only made available to it. Bare reading of Section 28 of the N.G.T. Act prescribing the bar of jurisdiction also substantiates this. Thus, power to pronounce upon the vires of any statutory provision or of any subordinate legislation can not be read into any of the provisions which confer either appellate or original jurisdiction upon National Green Tribunal. The Parliament which has deliberately employed wide or liberal words while laying down the compass or the scheme of N.G.T. Act, has not used such words while phrasing Section 14 of that Act or conferring jurisdiction upon National Green Tribunal. On the contrary, its intention to limit the power to decide certain specified nature of disputes is apparent. We find that the scheme of N.G.T. Act does not permit National Green Tribunal to decide upon the vires of any of the enactments which confer appellate or other jurisdiction upon it and find mention mention in Schedule-I of N.G.T. Act. It also does not empower it to examine validity of any Rules or Regulations made under these enactments."
In a judgment rendered by the Supreme Court in the matter of Bhopal Gas Peedith Mahila Udyog Sangathan and others v. Union of India and others1 Their Lordships have held that environment issues 1 (2012) 8 SCC 326 and matters covered under the National Green Tribunal Act, 2010 in Schedule-I should be understood and litigated before the National Green Tribunal only, and not before any High Court.
Following the provisions contained in Sections 14 and 15 of the Act, 2010 read with the decision rendered by the Bombay High Court in Central India AYUSH Drugs Manufacturers Association (supra), I am of the considered opinion that in the present petition, the petitioner has not raised any substantial question relating to environment within the meaning of Section 2(m) of the Act, 2010, but has only raised question relating to enforcement of his commercial interest with regard to establishment of said facility at Raipur which has already been granted to it and according to the petitioner, it has already made huge investment for establishment of such a plant. The question before this Court is whether respondent No.3 is justified in granting permission to respondents No.5 and 6 in ignorance or bypassing the permission granted to the petitioner herein. Therefore, no such question covered under Section 14 of the Act, 2010 is involved in the present petition and the Court is not called upon to consider any question or enforcement of any legal right relating to environment. Accordingly, I am of the considered opinion that this writ petition is not cognizable by the National Green Tribunal and this can be entertained by this Court under Article 226 / 227 of the Constitution of India. Thus, the preliminary objection raised on behalf of respondents No.2 and 3 is overruled and the matter is directed to be listed for final disposal at motion stage on 4-10-2017.
Interim order granted earlier shall continue till the next date of hearing.
Sd/-
(Sanjay K. Agrawal) Judge Soma


PrimeFaces Mobile theming is built on top of jQuery Mobile CSS Framework and the theme used on this page is created by the online Mobile ThemeRoller application. A custom theme is a jar file with a specific folder structure and configured for the mobile pages using primefaces.mobile.THEME context parameter.
Images, Links, Inset layout, formatting, header and footer.
#{player.position}
#{player.number}


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507 SumoBot
|
Go to the source code of this file.
Variables | |
| frt_text_queue | print_ser_queue |
| This queue allows tasks to send characters to the user interface task for display. More... | |
| shared_data< bool > * | commForward |
| Used in combination with the other comm__ variables to command the task_Motor task. More... | |
| shared_data< bool > * | commBackward |
| Used in combination with the other comm__ variables to command the task_Motor task. More... | |
| shared_data< bool > * | commLeft |
| Used in combination with the other comm__ variables to command the task_Motor task. More... | |
| shared_data< bool > * | commRight |
| Used in combination with the other comm__ variables to command the task_Motor task. More... | |
| shared_data< uint8_t > * | commEffort |
| Used in combination with the other comm__ variables to command the task_Motor task. More... | |
| shared_data< uint8_t > * | edge_out |
| Bitwise communication of which of the 6 edge sensors is currently seeing white. More... | |
This file contains extern declarations for queues and other inter-task data communication objects used in a ME405/507/FreeRTOS project.
Revisions:
License: This file is copyright 2012 by JR Ridgely and released under the Lesser GNU Public License, version 2. It intended for educational use only, but its use is not limited thereto.
| shared_data<bool>* commBackward |
Used in combination with the other comm__ variables to command the task_Motor task.
commBackward is used to indicate some type of backward motion - either pure translation, or a backward turn (in combination with commLeft or commRight).
| shared_data<uint8_t>* commEffort |
Used in combination with the other comm__ variables to command the task_Motor task.
commEffort is used to indicate the duty cycle requested of the motor driver.
| shared_data<bool>* commForward |
Used in combination with the other comm__ variables to command the task_Motor task.
commForward is used to indicate some type of forward motion - either pure translation, or a forward turn (in combination with commLeft or commRight).
| shared_data<bool>* commLeft |
Used in combination with the other comm__ variables to command the task_Motor task.
commLeft is used to indicate some type of left turn - either pure rotation, or a left-facing turn (in combination with commForward or commBackward).
| shared_data<bool>* commRight |
Used in combination with the other comm__ variables to command the task_Motor task.
commRight is used to indicate some type of right turn - either pure rotation, or a right-facing turn (in combination with commForward or commBackward).
| shared_data<uint8_t>* edge_out |
Bitwise communication of which of the 6 edge sensors is currently seeing white.
| print_ser_queue |
This queue allows tasks to send characters to the user interface task for display.


I’m currently reading Understanding Psychology as a Science by Zoltàn Dienes (Thanks to Marian Schneider for the recommendation!). The first couple chapters review the philosophy of science of Karl Popper, Thomas Kuhn, and Imre Lakatos. In the section on Kuhn, Dienes describes the “Kuhnian crisis”. Kuhn suggests that scientific revolutions occur when significant doubt is cast on an accepted paradigm. Such a crisis can lead to new paradigms that move the field forward. In Kuhn’s view, a paradigm defines the problems that need solving and consequently how to interpret experimental observations. Kuhn used the term ‘incommensurability’ to describe the possible mismatch between paradigms.
“If paradigms disagree over what needs solving, then claiming to have solved a non-existent problem by methods that do not count as providing a solution will not impress adherents of a different paradigm.” (p. 36).
In this post, I want to focus on the role of language and communication in the incommensurability of paradigms.
“Paradigms are also incommensurable because of disagreement over how to describe basic observations. Terms refer in different way in different paradigms: because of the change in theory of the solar system, before Copernicus the term ‘planet’ included the sun and moon; and only afterwards did it include the earth. If terms change meaning, then what counts as a basic description of the data changes too.” (p.36)
Kuhn eventually narrowed down his definition of incommensurability to exactly this: “the way terms can refer differently before and after theory change”.
In my research, I interface with the fields of machine learning, cognitive science and neuroscience. These fields have some shared history, but generally look at different questions. However, I would suggest that they are asking increasingly similar questions and that we may be moving towards a paradigm shift wherein these fields adopt overlapping paradigms—a shared set of problems to solve by working together. To this aim, I have decided to begin a series called Alphabet Soup for the purpose of disambiguating terms that I notice are used differently by different people in different times and places. First on my list are “representation” and “information processing”. Feel free to suggest others in the comments below. Stay tuned!


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Welcome to radio meteor. Click on a channel to listen, use left and right keys, or use Alt+letter to change channels


SocialGov platform will bring citizens, executives, legislatives, journalists and social activist together to communicate and engage each other to bring transparence governance.
Citizens can find executives and legislative hierarchical contacts and city financial transactions to bring transparency in the system.
We support Android platform at this time. Please stay tuned for other platforms.


Peer Programming and Feedback
What Has Pair Programming Been Like?
Pair programming has been a great experience mostly. For one, it is so great to meet my fellow boots not just from Chicago, but all over the country and elsewhere. I feel the networking component of it alone makes it all worth it. But also, it is incredibly humbling encountering someone who knows more than you, and can be a very rich experience when you are teaching someone that might no less about certain topics.
A frustrating part of the whole pairing process can be communicating to your pair what you are thinking or trying to do. Coding thus far is a relatively new skill for us, with tons of new concepts and even methods of thinking. It can be difficult to wrap you head around things in the comfort of your own head. But when you are forced to communicate it to another person who may have differing understandings of these working concepts, things can get complicated.
Feedback
I will be honest - I do not take criticism as well as I would like. My ego can be sensitive when learning something as new and as challenging as programming.  It tries to convince me that all I need is more time, and I will figure it out on my own and become an expert with just more practice. This can needlessly hamper progress.
Thus far, I feel like the feedback comments mostly tell me what I already suspect about myself and there has been nothing that has taught me something I do not know. A common criticism is that I can go quiet when researching a topic and there are periods of silence where my pair is unsure what I am doing. This can be remedied easily with just some more vocalization.
Writing feedback has been a little tough at times. Most of my pairs are very awesome people that are putting in the effort and are just struggling to make sense of all this. It can be difficult to nitpick when they do so much right and the only thing missing is experience.
Overall, I am loving pairing so far and there is no doubt in my mind that it helps accelerate my learning. Thanks DBC for encouraging us to learn this essential skillset!


A simple app for tracking the global covid-19 cases, developed by Ansley Rodrigues
Data as per covid-19-api Some data as per NOVELCovid/API News Api powered by NewsAPI.org


50
Applications
25
Assessors available
25th January 2016
Accept / reject deadline
30th January 2016
Asessor deadline
250
Assigned
125
Started
225
Accepted
25
Completed
9 Assessors
| Assessor | No. unaccepted | No. accepted | Select all / none |
|---|---|---|---|
| Dave Redford | 8 | 3 | |
| Phil Newman | 4 | 5 | |
| Pete Brown | 7 | 4 | |
| Tim Townsend | 6 | 4 | |
| Jamie Wilson | 5 | 4 | |
| Bill Knightly | 4 | 5 | |
| Shaun Murphy | 2 | 9 | |
| Terry Griffiths | 1 | 9 | |
| James Wattana | 1 | 9 |


| Class | Date | Time | My Registration | My Attendance |
| {{ registration.event.title }} | {{ registration.event.date | date:"D. j N Y" }} | {{ registration.event.date | date:"P" }} | {% if registration.cancelled %} cancelled {% elif registration.waitlist %} waitlist {% elif registration.late_promotion %} late promotion {% else %} registered {% endif %} | {% if registration.attended = True %} Attended {% elif registration.attended = False and not registration.waitlist and not registration.cancelled %} Absent {% endif %} |


Lego (Danish pronunciation: [lɑjˀɡ̊oːˀ]; stylized LEGO) is a line of plastic construction toys that are manufactured by The Lego Group, a privately held company based in Billund, Denmark.
This is like a block that connects the happiness of a child. (noun) Christian Pecson
This is like a block that connects the happiness of a child. (noun)


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SAC Effects
v1.0
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Single-object class from channels list. More...
#include <ChannelsList.h>
Public Member Functions | |
| Channel (QLayout *framework, std::string prefix, int index, bool isoutput) | |
| Channels constructor. More... | |
| ~Channel () | |
| Channels desctructor. | |
| int | getIndex () |
| It gets the channel index. More... | |
| void | setIndex (int index) |
| void | setLabel (std::string label) |
| It sets a label to the channel name, i.e., group box title and label text. More... | |
| void | setVolume (int volume) |
| It sets the channel volume level. More... | |
| void | mute (bool state) |
| It mutes channel. More... | |
| void | bypass (bool state) |
| It sets channel to bypass effects. More... | |
Public Attributes | |
| int | index |
| std::string | name |
| double | volume |
| bool | muted |
| bool | bypassed |
| AudioOutput * | audiooutput |
| QGroupBox * | groupbox |
| user interface elements More... | |
| QLineEdit * | label |
| QSlider * | volumeslider |
| QCheckBox * | mutecheckbox |
| QCheckBox * | bypasscheckbox |
| QComboBox * | deviceselector |
Definition at line 32 of file ChannelsList.h.
| Channel::Channel | ( | QLayout * | framework, |
| std::string | prefix, | ||
| int | index, | ||
| bool | isoutput | ||
| ) |
| framework | channel user interface framework |
| prefix | prefix of objects name of channel user interface |
| index | channel index |
| isoutput | true to create device selector to send audio to the system audio output devices |
Definition at line 213 of file ChannelsList.cpp.
| void Channel::bypass | ( | bool | state | ) |
| state | true to bypass effects and false to apply them |
Definition at line 318 of file ChannelsList.cpp.
| int Channel::getIndex | ( | ) |
Definition at line 271 of file ChannelsList.cpp.
| void Channel::mute | ( | bool | state | ) |
| state | true to mute the channel and false to unmuted |
Definition at line 309 of file ChannelsList.cpp.
| void Channel::setLabel | ( | std::string | label | ) |
| label | new label |
Definition at line 297 of file ChannelsList.cpp.
| void Channel::setVolume | ( | int | volume | ) |
| volume | integer number from 0 to 100 |
Definition at line 327 of file ChannelsList.cpp.
| AudioOutput* Channel::audiooutput |
system audio output devices object
Definition at line 39 of file ChannelsList.h.
| QCheckBox* Channel::bypasscheckbox |
checkbox object to bypass effect
Definition at line 48 of file ChannelsList.h.
| bool Channel::bypassed |
it tells channel to bypass effects or apply them
Definition at line 38 of file ChannelsList.h.
| QComboBox* Channel::deviceselector |
audio output device selector object
Definition at line 49 of file ChannelsList.h.
| QGroupBox* Channel::groupbox |
channel group box
Definition at line 44 of file ChannelsList.h.
| int Channel::index |
channel index
Definition at line 34 of file ChannelsList.h.
| QLineEdit* Channel::label |
field to change the channel label
Definition at line 45 of file ChannelsList.h.
| QCheckBox* Channel::mutecheckbox |
muted checkbox object
Definition at line 47 of file ChannelsList.h.
| bool Channel::muted |
it indicates if channel is muted
Definition at line 37 of file ChannelsList.h.
| std::string Channel::name |
channel name
Definition at line 35 of file ChannelsList.h.
| double Channel::volume |
current audio volume level
Definition at line 36 of file ChannelsList.h.
| QSlider* Channel::volumeslider |
volume level slider
Definition at line 46 of file ChannelsList.h.


Model Requests for World Editor
Posted: Sun Jan 06, 2008 1:34 am
If anyone has any requests for models they want for their maps lemme know and I'll create them.
A little artistic direction would be nice too (picture, description, and what textures you might want would be nice)
I actually have most of the xenimus terrain models made from scratch already for anyone that is interested.
(I should probably put together a pack with the skins and stuff in .X format)
A little artistic direction would be nice too (picture, description, and what textures you might want would be nice)
I actually have most of the xenimus terrain models made from scratch already for anyone that is interested.
(I should probably put together a pack with the skins and stuff in .X format)


Comments
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Greg Hurrell
Marking as ASSIGNED.
For reference, here is the original customer email which prompted me to open this bug report:
Furthermore, a substantial amount of the artwork retrieved from the Internet is plain wrong and duplicate, in various qualities, at that. I found an album for which I had 30 (sic) different covers!
If there is duplicate art, it's because the ID3 tags in the tracks lead Synergy to believe that the tracks are from a different album. There are two distinct problems here:
(1) Bad information in the ID3 tags.
If you have two tracks from the same album, but one has "The White Room" in the ID3 tag and the other has "Wihte RooM", for example, Synergy will think that it's dealing with two different albums. Depending on how bad the discrepancy is, it's possible that Amazon might provide different art for the two tracks.
(2) Synergy ignores the "compilation" flag (this is a design flaw in Synergy).
Just say you have an album called "Album Title" with the compilation flag set. Because Synergy ignores this flag, it will see "Track A" by "Art A" and "Track B" by "Artist B" as being from different albums, because Synergy will see "Artist A, Album" and "Artist A, Album Title" and "Artist B, Album Title" as being different identifiers, because they are not an exact match.
In order to fix this issue, Synergy will have to detect and pay attention to the compilation flag, and in those cases search Amazon using only the album title and not the combination of artist name plus album title. The fact that it includes the artist name in the search is what sometimes leads Amazon to supply different results, and that's why you have albums with multiple different covers.
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Greg Hurrell
Changing assignment to reflect my new email address.
https://wincent.com/a/news/archives/2006/05/change_of_email.php
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PAM GOLDING PROPERTIES EXCLUSIVE MANDATE |
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| 1. Title |
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Reg. No: VAT No: Name |
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| ID Number (SA Resident) |
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Phone no. ( |
) |
| Passport Number (Non-SA Resident) |
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| being the owner/s of the property known as |
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hereby irrevocably instruct PAM GOLDING PROPERTIES (”PGP”) to procure a willing and able Purchaser for the property described above for the sum of R ( |
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) |
| or such other price agreed upon by the Purchaser and myself/ourselves. |
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| 2. |
BROKERAGE: Brokerage calculated at 7.5% on the purchase price plus VAT thereon, shall be payable to PGP by me/us if they obtain a |
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binding offer from a willing and able Purchaser. |
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| 3. |
PGP shall have the sole and exclusive selling rights in respect of the property until 24h00 on |
and |
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they will be entitled to the brokerage plus VAT, as set out above, if the property is sold through any source whatsoever during the said |
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mandate period, or if any Purchaser introduced by PGP during this exclusive mandate purchases the property described above subsequent |
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to the expiry of this mandate. Accordingly, during the aforesaid period: |
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3.1 No agency other than PGP will be mandated to sell the property; |
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3.2 I/We will not personally market or sell the property; |
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3.3 I/We will not in any way hinder, frustrate or prevent PGP from effecting a sale in terms of this mandate. |
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| 4. |
PGP, its agents and the agent/s’ representative and any prospective Purchaser/s shall have access to view the property at all reasonable times. |
| 5. |
PGP shall have the right to affix boards or notices to the property advertising the property for sale, and once sold, that it has been sold. |
| 6. |
I/We hereby warrant that there are no latent defects of which I am / we are aware relating to the property. |
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| 7. |
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Benefits to the Seller - see accompanying brochure. |
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| SIGNED AT |
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on |
20 |
| WITNESS |
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OWNER/S |
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| WITNESS |
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SPOUSE (hereby consenting in terms of Section 15 of Act 88/84) |
| DATE |
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For PAM GOLDING PROPERTIES |
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| Listing No |
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| www.pamgolding.co.za |


The functions described below are used for calling a C function.
fn_pointer
is the function pointer returned by ffi_fn
.
Args
must be an array and argp
must be a pointer to a C object.
Please see the Foreign Function Interface Reference for examples.


public interface TriggerListener
Trigger
fires. In general, applications that use a
Scheduler
will not have use for this mechanism.ListenerManager.addTriggerListener(TriggerListener, Matcher)
,
Matcher
,
Trigger
,
JobListener
,
JobExecutionContext
| Modifier and Type | Method and Description |
|---|---|
String |
getName()
Get the name of the
TriggerListener . |
void |
triggerComplete(Trigger trigger,
JobExecutionContext context,
Trigger.CompletedExecutionInstruction triggerInstructionCode)
|
void |
triggerFired(Trigger trigger,
JobExecutionContext context)
|
void |
triggerMisfired(Trigger trigger)
|
boolean |
vetoJobExecution(Trigger trigger,
JobExecutionContext context)
|
String getName()
Get the name of the TriggerListener
.
void triggerFired(Trigger trigger, JobExecutionContext context)
Called by the
when a Scheduler
has fired, and it's associated Trigger
is about to be executed.
JobDetail
It is called before the vetoJobExecution(..)
method of this
interface.
trigger
- The Trigger
that has fired.context
- The JobExecutionContext
that will be passed to
the Job
'sexecute(xx)
method.boolean vetoJobExecution(Trigger trigger, JobExecutionContext context)
Called by the
when a Scheduler
has fired, and it's associated Trigger
is about to be executed. If the implementation vetos the execution (via
returning JobDetail
true
), the job's execute method will not be called.
It is called after the triggerFired(..)
method of this
interface.
trigger
- The Trigger
that has fired.context
- The JobExecutionContext
that will be passed to
the Job
'sexecute(xx)
method.void triggerMisfired(Trigger trigger)
Called by the
when a Scheduler
has misfired.
Trigger
Consideration should be given to how much time is spent in this method, as it will affect all triggers that are misfiring. If you have lots of triggers misfiring at once, it could be an issue it this method does a lot.
trigger
- The Trigger
that has misfired.void triggerComplete(Trigger trigger, JobExecutionContext context, Trigger.CompletedExecutionInstruction triggerInstructionCode)
Called by the
when a Scheduler
has fired, it's associated Trigger
has been executed, and it's JobDetail
triggered(xx)
method has been
called.
trigger
- The Trigger
that was fired.context
- The JobExecutionContext
that was passed to the
Job
'sexecute(xx)
method.triggerInstructionCode
- the result of the call on the Trigger
'striggered(xx)
method.Copyright 2001-2019, Terracotta, Inc.


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0427 222 5588
MAPS


CURL
A Ball Game with Physics
SinglePlayer
MultiPlayer
Options...
Credits
0
CURL
0
Select a Service
Host
Join
Enter peer id
Connect
Waiting for Connection
CURL
Boredom drove me to make this.
Mostly Pure HTML5
Sounds Made with
Bfxr
Electrolize
font.
Made by
Ragul Balaji
in 2014
Done
Options
RESET STATISTICS!
Done


A few things:
Complete the keyboard style harmonization of the passage we were working on in class. Remember, you are to do a realization for both the antecedent and the consequent phrases.
Work on the analytical sketching assignment that I gave you, which is due by the end of next week. Remember, if you give it to me early, I’ll hand it back with feedback so that you can make corrections.
Don’t forget to prepare for your tutorial, especially if you haven’t yet passed the keyboard stuff from this past week.
Have a good weekend!


This package contains R functions to gauge the impact of unpublished studies upon the meta-analytic summary effect of a set of published studies. (Credits: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282574.)
| Version: | 1.2.1 |
| Depends: | R (≥ 3.0.0), metafor |
| Published: | 2013-09-23 |
| Author: | Noory Y. Kim. Advisors: Shrikant I. Bangdiwala, Gerald Gartlehner. |
| Maintainer: | Noory Y. Kim <noory at live.unc.edu> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| In views: | MetaAnalysis |
| CRAN checks: | SAMURAI results |
| Reference manual: | SAMURAI.pdf |
| Package source: | SAMURAI_1.2.1.tar.gz |
| Windows binaries: | r-devel: SAMURAI_1.2.1.zip, r-release: SAMURAI_1.2.1.zip, r-oldrel: SAMURAI_1.2.1.zip |
| OS X binaries: | r-release: SAMURAI_1.2.1.tgz, r-oldrel: SAMURAI_1.2.1.tgz |
| Old sources: | SAMURAI archive |
Please use the canonical form https://CRAN.R-project.org/package=SAMURAI to link to this page.


The equation button allows you to input the differential, intial values, step size (possibly 2nd step size) and iterations (number of times to run the method).
The points button displays all the points found with Euler's method.
Point mode draws circles to indicate graphically all the points found with Euler's method.
This program supports all well known math functions and constants. Shown below are some example functions to input:
x^3+2x+3
e^x
sqrt(x)/cbrt(y)
tan(x)
atan(x)tanh(x)atanh(x)
abs(x/y+y/x)
log(x,e)
x > 0 ? x^2 : x mod 10


Last Name
First Name
Address
City
|
State
|
Zip/Postal code
Country
Bulgaria
Serbia
Romania
England
Phone(country code, area code, number
(+
)
-
E-mail
Birthday
Month
Day
Year(4 digit)
Gender
Male
Female
Starting Date
Spring 2006
Summer 2006
Comments/Questions


We are looking for great talents. If you are interested in the following positions,please send your CV to jobs@slamby.com


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.swing.text.html.HTML
public class HTML
Constants used in the HTMLDocument
. These
are basically tag and attribute definitions.
| Nested Class Summary | |
|---|---|
static class |
HTML.Attribute
Typesafe enumeration representing an HTML attribute. |
static class |
HTML.Tag
Typesafe enumeration for an HTML tag. |
static class |
HTML.UnknownTag
|
| Field Summary | |
|---|---|
static String |
NULL_ATTRIBUTE_VALUE
|
| Constructor Summary | |
|---|---|
HTML()
|
| Method Summary | |
|---|---|
static HTML.Attribute[] |
getAllAttributeKeys()
Returns the set of HTML attributes recognized. |
static HTML.Tag[] |
getAllTags()
Returns the set of actual HTML tags that are recognized by the default HTML reader. |
static HTML.Attribute |
getAttributeKey(String attName)
Fetches an attribute constant for a well-known attribute name (i.e. |
static int |
getIntegerAttributeValue(AttributeSet attr,
HTML.Attribute key,
int def)
Fetches an integer attribute value. |
static HTML.Tag |
getTag(String tagName)
Fetches a tag constant for a well-known tag name (i.e. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final String NULL_ATTRIBUTE_VALUE
| Constructor Detail |
|---|
public HTML()
| Method Detail |
|---|
public static HTML.Tag[] getAllTags()
public static HTML.Tag getTag(String tagName)
null
will be returned.
tagName
- the String
name requested
tagName
,
or null
if not foundpublic static int getIntegerAttributeValue(AttributeSet attr, HTML.Attribute key, int def)
attr
- the set of attributes to use to try to fetch a valuekey
- the key to use to fetch the valuedef
- the default value to use if the attribute isn't
defined or there is an error converting to an integerpublic static HTML.Attribute[] getAllAttributeKeys()
public static HTML.Attribute getAttributeKey(String attName)
null
will be returned.
attName
- the String
requested
Attribute
corresponding to attName
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


Phone : xxxxxxxxxx
<EMAIL_ADDRESS>Indian Institute of Technology Palakkad
UNNAT BHARAT ABHIYAN
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Bringing food, animals or plants into the UK
Pets and other animals
Dogs, cats, ferrets and horses
You can bring your dog, cat or ferret into the UK without quarantine as long as they meet the rules of the Pet Travel Scheme (PETS).
There’s a similar scheme for horses.
Other animals
There are no restrictions on bringing pet rodents, rabbits, birds, invertebrates, amphibians and reptiles to the UK from other EU countries.
Pet rabbits and rodents from countries outside the EU must spend 4 months in quarantine. They need a rabies import licence.
Contact the Centre for International Trade (Carlisle) for more information on the rules for travelling with these or other species of pet.
Non-native animals
There are rules you must follow when importing animals that aren’t native to the UK.


Simply drag the white circle to turn on the unit.
Once you have successfully turned on the device, you will be redirected to a screen that contains the home page, buttons representing common preset options, and a menu tab that can be clicked to open for cooking options beyond the commonly used presets.
If you have chosen the preset option, then you simply have to choose the desired circle indicated your desired darkness on your toast or bagel. You won't need to set the temperature or mode if you choose a preset; the toaster will automatically choose the temperature and time that works best for your desired food and darkness level.
The presets are also a good way to organize presets that are used more often. This allows for a quicker navigational system for commonly used functions and puts away the more intricate features unless they are truly needed. The presence of solid white icons against a darkened background offer a consistent, easy to read, elegant user interface that is functional and aesthetically pleasing.
If you have chosen to utilize the menu option, you will be directed to another screen that prompts you to choose your desired cooking mode. There are three cooking modes which include bake, broil, and toast.
Once you have chosen a cooking mode, you will be directed to a screen with a number pad that allows you to pad in your desired temperature level. Tap the checkmark in the bottom right hand corner to continue.
Once you have chosen a temperature, you will be redirected to a screen with another number pad that will allow you to choose your desired cooking time. Again, tap the bottom right hand corner to continue
Once you have entered your desired temperature, cooking time, and cook mode, the toaster will turn on, displaying the information that you have entered and the remaining cook time, along with a cancel button along the bottom of the screen.


Form
Document to help schools converting to academy status record any changes they've made to model documents as part of the conversion process.
MS Word Document , 38KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Schools converting to academy status should ask their solicitor or legal adviser to use this document to record any changes they want to make to the model documents they’ve completed as part of the academy conversion process.
The model documents you may want to change include:
Read more guidance on the process for converting to academy status .
Published: 12 July 2012


All Classes and Interfaces
Class
Description
Small helper methods for math tasks, as well as the configurable
Extra.DELTA
.grid_traverse* methods are based on "A Fast Voxel Traversal Algorithm for Ray Tracing",
by John Amanides and Andrew Woo - http://www.cse.yorku.ca/~amana/research/grid.pdf
It has been modified to include both cells when the ray "touches a grid corner",
and with a different exit condition
A map of primitive int keys to primitive int values.
A 2D point with integer x and y.
Wraps an
E
value in Item.userData
, so it is compared by the identity
of the Item, not the E value.An unordered set where the keys are objects.